Disaster-related Statistics Frameworkcommunities.unescap.org/system/files/merged_rev1_final.pdf ·...

DRAFT FOR CONSULTATION – please do not quote or reference

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Asia-Pacific Expert Group on Disaster-related Statistics

DRSF DRAFT 2.0 (2nd consultation draft)

DRAFT FOR CONSULTATION – Please Do Not Reference or Quote

Disaster-related Statistics Framework:

Handbook for National Compilation of Statistic for Disaster Risk Reduction

Contents Chapter 1: Introduction .......................................................................................................................... 3

Background ......................................................................................................................................... 3

The Need for a Statistical Framework ................................................................................................. 5

Components of A Basic Range of Disaster Related Statistics.............................................................. 7

International Indicators for Sendai Framework and Sustainable Development Goals ...................... 9

Chapter 2: Main Concepts for Measurement ....................................................................................... 11

2a) Identifying and counting disaster occurrences and magnitude ....................................................... 11

Hazards types ................................................................................................................................ 14

Magnitude ..................................................................................................................................... 15

Disaster Occurrences Time Series ................................................................................................. 16

2b) Disaster risk ..................................................................................................................................... 16

Background ................................................................................................................................... 16

Scope of measurement ................................................................................................................. 18

Estimating exposure to hazards .................................................................................................... 18

Hazard Mapping ............................................................................................................................ 19

Exposure Statistics ........................................................................................................................ 20

Vulnerability .................................................................................................................................. 24

Coping capacity ............................................................................................................................. 25

Geographic disaggregation ........................................................................................................... 27

2c) Material Impacts and Economic Loss ......................................................................................... 28


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Material Impacts ........................................................................................................................... 28

Economic Costs from Material Impacts ........................................................................................ 32

Direct Economic Impacts to Agriculture ....................................................................................... 35

Economic Loss and Poverty ........................................................................................................... 36

2d) Human Impacts ........................................................................................................................... 36

Disaggregation of human impacts statistics ................................................................................. 38

Deaths or Missing ......................................................................................................................... 38

Injured and ill ................................................................................................................................ 39

Displaced Populations ................................................................................................................... 40

Impacts to livelihood ..................................................................................................................... 44

Disruptions to basic services ......................................................................................................... 45

Aggregated statistics on human impacts ...................................................................................... 46

2e) Disaster Risk Reduction Activities ............................................................................................... 48

Part 2: Guidelines for Implementation ................................................................................................. 51

Chapter 3: Statistical Classifications and Definitions ............................................................................ 51

Direct Material Impacts Classification .............................................................................................. 51

Critical infrastructures : elaboration for items [2.1.2], [2.1.3.1], [2.1.4] .................................... 57

Disaster Risk Reduction Characteristic Activities (DRRCA) Classification ............................................. 58

Disaster Risk Reduction Characteristic Activities (DRRCA) and Transfers .................................... 58

Chapter 4: Principles for Implementation ............................................................................................ 61

Statistical Coordination ................................................................................................................. 61

Roles & Responsibilities ................................................................................................................ 62

Legal framework ........................................................................................................................... 65

Confidentiality ............................................................................................................................... 66

Transparency and Accessibility to Data and Metadata................................................................ 66

Chapter 5: Basic Range of Disaster-related Statistics Tables & Data Sources ..................................... 69

Time period ................................................................................................................................... 70

Geographic regions ....................................................................................................................... 70

Prioritization ................................................................................................................................. 71

Summary tables of disaster occurrences (A) ................................................................................ 72

Selected Background Statistics and Exposure to hazards (B tables) ............................................ 72

Summary tables of human impacts (C Tables) .............................................................................. 73

Summary tables of direct material impacts (D Tables) ................................................................. 74


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Summary tables of direct material impacts in monetary terms (E Tables) .................................. 74

Summary Tables of Direct Environmental Impacts (F Tables) ...................................................... 74

Disaster Risk Reduction Expenditure and Transfers (DRRE Tables) .............................................. 75

Chapter 6: Data Sources and Measurement Units ............................................................................... 75

Data Sources ..................................................................................................................................... 75

Demographic and Social Statistics ................................................................................................ 76

Employment statistics and national accounts .............................................................................. 77

Data Collection during a Disaster Occurrence ............................................................................. 77

Population and Health Administrative Data ................................................................................. 78

Mapping and Environmental Monitoring ..................................................................................... 79

Measurement Units .......................................................................................................................... 82

Dwellings ....................................................................................................................................... 82

Critical Infrastructure .................................................................................................................... 82

Disruptions to Basic services from a Disaster ............................................................................... 84

Impacts to Livelihood ................................................................................................................... 85

Bibliography .......................................................................................................................................... 85

Chapter 1: Introduction

Background

1. ESCAP Resolution E/ESCAP/RES/70/2 on “Disaster-related Statistics in Asia and the Pacific”,

established the Asia and Pacific Expert Group on Disaster-related Statistics and requested it to

develop a basic range of disaster-related statistics along with guidance for implementation.

2. The Framework for the Development of Environment Statistics (FDES) defined “extreme events

and disasters” as one of its six core components with a reference to the Emergency Events

Database (EM-DAT). EMDAT is managed by the Centre for Research on the Epidemiology of

Disasters (CRED) at the Université catholique de Louvain, with support from the World Health

Organisation (WHO) and the Belgian Government, and is currently one of the most extensively

utilized international databases of disaster-related statistics. CRED defines a disaster as a rare and

“unforeseen and often sudden event that causes great damage, destruction and human suffering”

and focusses mainly on statistics on impacts from relatively large disasters, pulling data from a

combination of official and non-official sources.

3. The demand for internationally comparable methods for producing statistical evidence for disaster

risk reduction received renewed and increased attention internationally with the adoption by the


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UN General Assembly of the Sendai Framework for Disaster Risk Reduction and with prominent

inclusion of disaster risk reduction targets within the UN Sustainable Development Goals (SDGs).

4. According to the Sendai Framework, as adopted by the UN General Assembly in December,

2016, a disaster is “a serious disruption of the functioning of a community or a society at any scale

due to hazardous events interacting with conditions of exposure, vulnerability and capacity,

leading to one or more of the following: human, material, economic and environmental losses and

impacts.” (UNGA, 2015).

5. Presently, countries have different practices with regards to applying this definition for compiling

data and preparing statistical tables, which makes it difficult to make comparisons or conduct time

series analyses covering multiple disasters. This handbook can be utilized to address challenges

for creating coherence across data sources and to incorporate statistics related to all types of

disasters (regardless of scale) in alignment with the UN General Assembly definition, towards a

nationally centralized and internationally-coherent basic range of disaster-related statistics.

6. Recommendations in this handbook are aligned with a wide range of existing guidelines and

international standards adopted by the UN Statistical Commission, including recommendations for

population censuses, a classifications and other standards for economic statistics, including the

SNA and the System for Environmental-Economic accounts (SEEA). For development of this

handbook, the Asia Pacific Expert Group on Disaster-related statistics consulted with a broad

spectrum of disaster risk reduction and statistical expertise and with established groups and

forums focussing on related topics, including: the UNECE Task Force on Extreme Events and

Disasters, UN Expert Group on Statistical Classifications, the Advisory Expert Group on

National Accounts, UN Expert Group on Environment Statistics, and the UN Committee of

Experts on Global Geospatial Information Management (UN-GGIM).

7. Key applications for disaster-related statistics are risk assessment and post-disaster impacts

assessments. Risk assessment is a continuous process because risks are dynamic. Moreover,

outcomes of disaster impacts assessments often will be important new information for future risk

assessment. Therefore, variables used for assessing risk and for disaggregation of impacts

statistics will often mirror each other.

8. Usually, disaster risk assessment is primarily a responsibility of disaster management agencies (or

other related institutions). However, a lot of the data used for describing core drivers of disaster

risk (particularly exposure and vulnerability) are derived from established social and economic

statistics systems managed by national statistics offices. Also, data inputs used to describe and

predict hazards are derived from various other ministries and by the meteorological, geological,

and other geographic authorities.

9. Post-Disaster Needs Assessments (PDNAs) are conducted by the governments of affected

countries in collaboration with international agencies, particularly the World Bank. Guidelines for

conducting post disaster assessments and for using these assessments for developing disaster

recovery plans have been developed and published by the World Bank’s Global Facility for

Disaster Risk Reduction (GFDRR), in collaboration with the European Commission and the UN

Development Programme. The basic framework for PDNA studies derived the Damage and Loss

Assessment (DALA) Handbook (ECLAC, 2003).


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10. The DALA Handbook provides a globally recognized conceptual framework for impact

assessment studies, organized according to the different components or sectors in the economy.

However, PDNAs, following DALA methodology, are usually only conducted after very large

scale disaster events such as hurricane Yolanda in the Philippines, Thailand’s 2011 floods, and

Cyclone Evan that caused major economic destruction in Fiji and Samoa. The World Bank’s

GFDRRR website currently hosts post-disaster assessment reports for 49 disasters in 40 countries,

including 15 cyclones and multiple droughts, floods, earthquakes, tropical storms, and 1 volcanic

eruption (Cape Verde 2014-15). The DALA methodology “focuses on the conceptual and

methodological aspects of measuring or estimating the damage caused by disasters to capital

stocks and losses in the production flows of goods and services, as well as any temporary effects

on the main macroeconomic variables.” (UNECLAC, 2003).

11. As a complement to these assessment guidelines, DRSF provides a comprehensive framework for

producing the underlying statistics used in assessments and other applications, including for the

relatively smaller scale but more frequently occurring forms of disasters. Implementation of

DRSF can lead to increased availability and comparability of statistical inputs for use in the

assessments and an improved alignment between PDNAs and the regular outputs of official

statistical systems, such as the System of National Accounts (SNA).

12. For development of this framework, the Expert Group studied the current practices in national

disaster-related databases (or results of assessment studies) from across the region and conducted

pilot studies in four countries (Bangladesh, Fiji, Indonesia, and the Philippines) as a way of

evaluating the feasibility of the recommendations under discussion by the Expert Group. In

addition, several international compilations of statistics or reporting tools available for public

access were utilized by the expert group as important references to define the scope for the basic

range of statistics and develop methodological guidance, including: EMDAT, UNISDR Global

Assessment Report (GAR) Risk Data Platform, DesInventar (Disaster Information Management

System), the UN Environment Global Resource Information Database (GRID) network, and

Munich Re Natural catastrophe statistics online (NatCatSERVICE).

13. DRSF complements these international reporting tools and databases by supporting improved

comparability of official statistics at the national (or regional) levels through application of

harmonized approaches to measurement.

The Need for a Statistical Framework

14. The purpose of the DRSF is to help national statistical systems, particularly the national disaster

management agencies (NDMAs) and national statistics offices (NSOs), provide statistical

information for informed disaster risk reduction policies to achieve the goals and targets in the

Sendai Framework on Disaster Risk Reduction and the 2030 Agenda for Sustainable

Development. Disasters pose direct threats to sustainable development and while many hazards,

like earthquakes and floods, are, to some extent, unavoidable, many lives can be saved and huge

damages can be avoided through evidence-based disaster risk reduction, response, and recovery.

15. The ESCAP Resolution 70/2, establishing this Expert Group, recognized better use of

disaggregated data as a challenge for evidence-based disaster risk management policy, stressing

the importance of disaggregated data related to disasters in enabling a comprehensive assessment


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of the socioeconomic effects of disasters and strengthening evidence-based policymaking at all

levels for disaster risk reduction and climate change adaptation,

16. The demand for improvements to the quality and accessibility of basic statistics on disasters has

been acknowledged extensively elsewhere as well, for example in many reports on disaster risk

surveys of current data availability and national statistical capacities. Research (e.g. World Bank,

2017) has suggested that effects of disasters have been underestimated in the past. The Report of

the OECD Joint Expert Meeting on Disaster Loss Data titled “Improving the Evidence Base on

the Costs of Disasters: Key Findings from an OECD Survey” (OECD, 2016) outlined some

critical problems and limited availability of internationally comparable statistics for many types

of analyses on disasters, including for measuring economic loss and for monitoring disaster

response and risk reduction activities. The introductory paragraph of this report states:

“The rationale for the work on improving the evidence base on the cost of disasters

is grounded in the evidence that recent shocks from natural and man-made disasters

continue to cause significant social and economic losses across OECD countries. The

increase in damages is widely considered to outpace national investments in disaster

risk reduction, but this claim is more intuitive than supported by evidence. Indeed, there

is hardly any comparable data available on national expenditure for disaster risk

management and data on disaster losses is generally incomplete and thought to be

underestimated. Such estimates of the comprehensive costs of disasters are necessary to

analyse the benefits of past and future risk management policies. In particular, this

information is helpful to inform decision making and to develop cost effective strategies

and measures to prevent or reduce the negative impacts of disasters and threats. Policy

makers, at present, possess usually scattered and incomplete data resources, which are

not comparable across countries. To design policies to reduce losses from disasters we

need to know how such economic losses are counted.”

17. The Hyogo Framework for Action 2005-2015, predecessor to the Sendai Framework, emphasized

the importance to: “Develop systems of indicators of disaster risk and vulnerability at national and

sub-national scales that will enable decision-makers to assess the impact of disasters on social,

economic and environmental conditions and disseminate the results to decision-makers, the public

and population at risk.” (UN, 2005, p.9). Demands for comparable statistics for international

analyses of disaster risk has been updated and given increased attention with the adoption of the

Sendai Framework and SDG indicators. UNISDR, as the main coordinating agency for

international monitoring of the Sendai Framework indicators, launched an international

monitoring process and online tool1 for collecting figures for the agreed international indicators

from official national sources, particularly the NDMAs and NSOs.

18. Basic requirements for the international indicator monitoring systems include comparability of

concepts and methods for measurement across disaster occurrences. Thus, these systems depend

heavily on coordination and consistency at the national and local levels, which is accomplished

via the adoption and application of a commonly agreed measurement framework.

1 https://www.unisdr.org/we/inform/events/55594


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19. As development of centralized databases for a basic range of disaster-related statistics is a new

endeavour in nearly all countries, there is a strong demand for technical guidance and sharing of

tools and good practices internationally.

20. There are growing challenges to predicting disaster risk due to climate change and other factors of

the modern globalized world. However, from a technical perspective, there are also many

enhanced opportunities, like free availability of software and methodologies for making use of

new data sources, such as remote sensing, mobile phone datasets, and especially the use of GIS

for assimilation of data and production of new statistics. The World Bank’s Global Facility for

Disaster Reduction and Recovery (GFDRR) stressed that “these advances and innovations create a

need for better standards and transparency, which would enable replicating risk results by other

actors, reporting on modelling assumptions and uncertainty, and so forth.”

21. Geographic referencing is a crucial element for compilation of nearly all the components of the

basic range of disaster-related statistics. One of the advantages of working with data in geographic

information system (GIS) software is flexibility to present statistics at different scales, and with

combinations of layers of variables. Agencies responsible for the underlying statistics should

develop a common framework for utilizing this flexibility while also maintaining standards to

ensure correct interpretation of these statistics and to maintain a minimum quality assurance in

terms of reliability, accuracy, and relevant protections of confidentiality of the statistics at

different scales, depending on their use in disaster risk reduction.

22. Disaster statistics is a unique domain in several ways. Each hazard or disaster is different,

unpredictable, and creates significant changes to the social and economic context for affected

regions. Disaster risk is unevenly dispersed within countries, across the world and over time. To

identify authentic trends, rather than random fluctuations or effects of extreme values, much of the

analyses of disaster related statistics requires a very long time series. This puts an exceptionally

high value for longitudinal coherence of measurement for disaster statistics.

23. The main users of this framework are expected to be national disaster management agencies and

national statistics offices, but there are a diverse range of other national stakeholders involved in

collections of relevant data , such as ministries of environment, mapping agencies and land

management authorities, ministries of finance, ministries of health, economic and social

development policy makers, meteorological organizations, and so on

24. Statistics provide the context and a broad vision for comparisons and for a deeper understanding

of risk across individual and multiple hazards. Harmonized statistics are used to inform

international support and boost solidarity, not only for responding to major disasters but also for

addressing risks on a continuous basis and with support from international cooperation.

Components of A Basic Range of Disaster Related Statistics

25. The DRSF provides recommendations on methodologies for how to apply internationally agreed

concepts and terminologies to production of official statistics. This includes technical

recommendations on estimation for a basic range of disaster-related statistics used for multiple

purposes, including calculation of indicators used for national and international monitoring.


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Fig.1: Components of the DRSF

26. Figure 1 shows the main components for the basic range of disaster-related statistics. Indirect

economic impacts are estimated by using statistics from the basic range in modelled scenarios or

other types of estimation for long-term impacts to economies. Thus indirect impacts estimation is

one of the many applications (rather than a core component) of the basic range of disaster-related

statistics. All other elements of Figure 4 can potentially be measured or estimated from direct

observations and incorporated into a centralized database of disaster-related statistics.

27. Figure 1 can be read like a timeline from left to right. First, there are statistics on disaster risk,

before the hazard occurrence. A threshold is passed at the moment of a call for emergency, at

which point data begin to be collected on the disaster occurrence and its impacts on people,

infrastructure, and the economy. Disaster risk reduction activities occur on a continuous basis

(like other activities of an economy). Indirect impacts, generally are experienced and estimated

during a period of time after the emergency response needs have already been met.

28. This handbook describes conventions and technical guidance for applying the agreed international

concepts and definitions of disaster risk reduction into the practice of statistics collection and

reporting for this basic range of statistics. This includes, for example, guidance on measurement

units, classifications, and other conventions for producing coherent statistics on disasters over

time and across countries.

29. Case studies showing development of compilations of summary statistics, aggregated across

multiple disaster occurrences are presented as sample outputs and to share experiences with an

aim towards illustrating the rationale for recommendations provided in the text.

30. Statistics in this framework must be derived from a wide variety of sources. Important data

sources for compiling the basic range of disaster-related statistics are: population and housing

census, household surveys, monitoring data from geophysical, meteorological and geographic

Emergency


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organizations, the national accounts and its sources, disaster management agency assessments and

monitoring, ministry of environment assessments and monitoring, administrative records of health

and safety institutions, administrative records from emergency response and recovery operations,

administrative records on population, health and other demographic issues (e.g civil registration

systems) and, where possible, specialized surveys targeting disaster-affected households and

businesses.

31. Background statistics, such as GDP, basic demographic statistics, indicators of poverty, and

environmental condition, are essential information for providing context to statistics on disaster

impacts, or the risk of impacts, in order to develop meaningful indicators for tracking progress,

making comparisons, and developing policies.

International Indicators for Sendai Framework and Sustainable

Development Goals

32. In 2015, global leaders adopted landmark agreements, establishing new international goals and

targets, in the forms of the Sendai Framework for Disaster Risk Reduction 2015-2030 and the

Sustainable Development Goals (SDGs).

33. The 2030 Agenda for Sustainable Development established 17 Goals and 169 targets for the

eradication of poverty and the achievement of sustainable development. In March 2016, the 47th

Session of the United Nations Statistical Commission (UNSC) agreed to a Global Indicator

Framework, specifying 230 indicators for measuring progress towards the Sustainable

Development Goals. In the SDGs, there are 11 disaster-related targets, spanning several of the 17

goals, and covered by 5 indicators (see Annex). By decision of the inter-agency expert group

(IAEG) on SDG indicators, the definitions for these indicators are aligned with indicators adopted

for international monitoring of the Sendai Framework.

34. The Sendai Framework for Disaster Risk Reduction was adopted at the Third UN World

Conference in Sendai, Japan, in March 2015. It is the outcome of stakeholder consultations

initiated in March 2012 and inter-governmental negotiations from July 2014 to March 2015,

supported by the United Nations Office for Disaster Risk Reduction (UNISDR) at the request of

the UN General Assembly. Furthermore, after adoption of the Sendai Framework, an

intergovernmental process was established to reach agreement on terminologies and indicators for

monitoring the targets of the Sendai Framework. This intergovernmental process completed in

December, 2016 with a report2 endorsed by the UN General Assembly. In order to help ensure

cohesion between national compilations of official statistics with demands for global indicators,

the terminologies used in the DRSF are aligned with this Sendai Framework Report.

35. The Sendai Framework establishes four priorities for action: (1) Understanding disaster risk, (2)

Strengthening disaster risk governance to manage disaster risk, (3) Investing in disaster risk

reduction for resilience, and (4) Enhancing disaster preparedness for effective response and to

“Build Back Better” in recovery, rehabilitation and reconstruction. The Sendai framework

contains a statement of outcome, for the next 15 years, which is to achieve a substantial reduction

of disaster risk and losses, to lives, livelihoods and health and to the economic, physical, social,

cultural, environmental assets of persons, businesses, communities and countries. The proposed

targets for monitoring progress in the framework are:

2 A/71/644: “Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction”


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1. Reduce global disaster mortality

2. Reduce the number of affected people

3. Reduce direct disaster economic loss

4. Reduce disaster damage to critical infrastructure and disruption of basic services, among them

health and educational facilities

5. Increase the number of countries with national and local disaster risk reduction strategies

6. Enhance international cooperation

7. Increase the availability of and access to multi-hazard early warning systems and disaster risk

information

36. A collection of 27 independent (excluding composite) indicators were adopted for international

monitoring of all seven Sendai Framework targets. Monitoring the 7 targets in the Sendai

Framework requires, as a minimum, a system of compilation of good quality basic statistics on

disaster risk, disaster occurrences, direct impacts and commitments to interventions for reducing

risks. This basic requirement draws from multiple data sources and across multiple governmental

agencies and should cover, in principle, the complete range of different types of disasters relevant

to the country.

37. The specifications for the Sendai Framework and SDG Indicators provide the common baseline

reference for the scope and prioritization for the high-level international demands for statistics.

However, all countries are starting from very different contexts in terms of the nature (e.g. extent

and intensity) of their baseline disaster risk factors. Thus, implementation of DRSF is a tool to

support national agencies with development of statistical compilations with a broader scope and

broader range of applications, including nationally designed indicators and various forms of

statistical analyses as required for decision-making at the national and local levels.

38. Diversity in current practices combined with the demand for international comparisons and time

series indicators creates the need for clear guidance on practical measures and, in some cases,

conventions for simplifying and harmonizing measurement. Improved coherence and

transparency of approaches to measurement of basic disaster statistics is necessary for analyses of

the critical drivers of differences in trends for the internationally-adopted indicators.

Harmonization in the underlying statistics used in indicators is also needed to distinguish between

authentic examples of progress from random variations in the time series or differences caused

mainly by discrepancies in scope of measurement.

39. Statistical databases are summaries of broader collections of raw data gathered from a number of

sources, including the operational databases used for emergencies, surveys, censuses, monitoring

systems, and administrative records. Indicators draw from these databases to provide targeted

information to policy-makers and to the general public to help inform disaster risk reduction and

to identify if and where progress is being made. Where possible, indicators should also be used to

identify and encourage actions to reduce risk and create sustainable development before

disasters.

40. DRSF rests in the middle of the theoretical information pyramid. The production of statistical

tables inevitably involves some degree of aggregation and summary of basic microdata, but the

statistics framework also needs to be relatively complete and flexible for calculating a broad range

of indicators, as well as for facilitating other types of analyses.

Figure 2 : Information pyramid for disaster risk reduction


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Chapter 2: Main Concepts for Measurement

2a) Identifying and counting disaster occurrences and magnitude

41. A disaster is:

“A serious disruption of the functioning of a community or a society due to hazardous events

interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the

following: human, material, economic and environmental losses and impacts.” -The United Nations International Strategy for Disaster Reduction (UNISDR), adopted by the UN General

Assembly (December, 2016)

42. DRSF applies the above definition but elaborates some criteria for producing harmonized statistics

on occurrences and direct impacts of disasters. For each disaster occurrence, there are at least four

characteristics of the event that should be recorded. These characteristics of disasters are used for

making connecting with other variables, including the statistics on disaster impacts. The four

characteristics are:

a. Timing (date, year, time and duration of emergency period)

b. Location and scale(region(s)/province(s)/country(ies) and affected area raster or shapefile)

c. Hazard type (e.g. geological, meteorological, etc.)

d. Magnitude of impacts (large, moderate, small)

43. In addition, each disaster occurrence is given a unique identifier code (e.g. a numeric code) for

ease of reference and querying within a multi-disaster occurrence database.

44. There are international initiatives for unique naming and coding of hazards, which can be

utilized, where applicable, by the national agencies, such as (e.g.) the GLobal IDEntifier number

(GLIDE) initiative promoted by CRED, OCHA/ReliefWeb, OCHA/FSCC, ISDR, UNDP, WMO,

IFRC, OFDA-USAID, FAO, La Red and the World Bank.3

3 http://www.glidenumber.net/glide/public/about.jsp

Indicators

Summary statistics (DRSF)

Sources of basic data (censuses, surveys, admin. records, data

used for operartional response)


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Figure 3: Criteria and Statistical Requirements for Disaster Occurrences

45. From the international definition of a disaster, two basic criteria can be derived for measurement

purposes (figure 3): observation of significant impacts (“human, material, economic and

environmental losses and impacts”) and an emergency declaration (“A serious disruption of the

functioning of a community or a society”). These criteria are meant to help compilers of the

statistics to establish a clear scope for applying the agreed international definition for a disaster into

the practice of measurement with an acceptable degree of comparability. Although each disaster

occurrence is unique and they are happening in different social, economic and political contexts,

application of these two basic criteria can help to improve the consistency in coverage for a basic

range of disaster-related statistics across countries.

46. An emergency declaration (at local, regional or national level) is the signal of an abnormal

disruption. Emergency declarations are called by officially responsible agencies and are the

catalysts that spur collection of data. Emergency declarations can take various forms depending on

the type of hazard and laws and administrative policies of the responsible government. The

differences in laws and administrative polices across countries are prerogatives of the governing

authorities and, generally, do not significantly affect the statistics.

47. Sometimes, e.g. for slowly evolving risks leading to disaster, the emergency response may take

the form of initiating collection of data for monitoring the situation, followed by implementation

of a series of preventative measures (such as evacuations or other responses to boost coping

capacity and minimize impacts).


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48. Other emergencies, especially sudden or unexpected hazards, are more explicitly represented by a

formal and public declaration and request to mobilize resources for response. The scale of

emergency declaration (local, regional, national or international) is a useful indication for

assessing and categorizing the scale of the disaster occurrence.

49. The statistical requirements at the bottom of Figure 3 should be developed and maintained in

centralized databases for each country and for each identified disaster occurrence. While the more

common statistical demands in relation to individual occurrences of disasters are information on

impacts, counting and describing disaster occurrences according to their basic characteristics has

some important analytical applications as well. For example, for reviewing the trends in

occurrences over a very long time period (e.g. 50-100 year trends), which can be used as inputs

for risk assessment. Counts of disaster occurrences also provide the basis for calculating statistics

on intensity of impacts from disaster occurrences over time (e.g. average number of fatalities or

costs of damages per occurrence).

50. It is of central importance that the counts and descriptive characteristics of disaster occurrences

are produced consistently over time (i.e. across occurrences). If the scope for incorporating

disaster occurrences into outputs of official statistics is variable over time, than there will be

fundamental inconsistencies in the scope of aggregated statistics on impacts.

51. Such inconsistencies are common in the current national and international compilations for

disaster occurrences. A comparison of simple counts of disaster occurrences by hazard types for

any given country from different databases (e.g. a comparison between international sources and

the records of an official national agency) reveals large differences in the numbers of events that

are recognized as the basis for statistics like number of deaths or economic impacts. Sometimes

inconsistencies are caused by errors but there can also be valid conceptual differences in scope of

measurement between databases, which will be improved through implementation of a common

framework.

52. There will be borderline cases and small differences in interpretations for special cases, which can

be accepted, but an overall goal for disaster occurrence statistics is to minimize the

inconsistencies. There are two primary sources for conceptual inconsistencies for counting

disasters (and their impacts) in the current national and international practices. The first source is a

different scope of the hazards that are accounted as a disaster. The second source is use of a

minimum scale of impact threshold.

53. Impact thresholds are an application of basic statistics for analysis and comparisons. Thresholds

are used as a tool to put practical limits on the scope for disaster impacts statistics and for time

series or multi-country analyses. For example, within EMDAT, minimum threshold criteria were

defined so that the compilations focus primarily on moderate to large-scale emergencies. For the

primary sources and in the official national databases, there is no need to define a minimum scale

of impact threshold prior to analysis. For databases, compilations need only to apply the criteria

for a disaster occurrence in diagram 1, i.e. at least some impact was recorded regardless of how

minor.4 In this way, the relatively small-magnitude disaster events are included within the

statistical databases and it is up to users of these statistics (including CRED and others) to define

thresholds or other criteria, as needed, to match their own needs. In general, producers of official

statistics should avoid introducing threshold criteria or other potential biases into the primary

database so that a broader range of potential applications of the statistics will be possible, through

the subsequent grouping, threshold filtering, or other analyses of the basic data.

4 Ground-shaking from earthquake with no impacts is a hazard, but it becomes a disaster occurrence at the

moment that impacts, however large or small, could be identified.


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54. In principle, collection of statistics related to disasters is applicable for disasters of any magnitude

and there is a clear demand for a nationally coherent measurement framework for application at

different scales. Paragraph 15 of the Sendai Framework states that it applies “to the risk of small-

scale and large-scale, frequent and infrequent, sudden and slow-onset disasters caused by natural

or man-made hazards, as well as related environmental, technological and biological hazards and

risks. It aims to guide the multi-hazard management of disaster risk in development at all levels as

well as within and across all sectors.”

Hazards types

55. Current practices for scope of coverage of hazard types are extremely variable. Many countries

have an officially adopted list of hazard types and definitions inscribed into the national laws for

disaster response. In these cases, the scope of official data collections (and metadata) usually

should be aligned with the scope and terminologies from the legal text. For all cases, a formal list

and glossary of the hazards should be published as part of the core metadata alongside the

statistics.

56. As with the case of the impacts threshold, it is only at the stage of analysis and production of

indicators from the databases that filtering or limiting the selection of hazard types becomes

applicable, and this filtering depends on the particular requirements of users. In principle,

statistics for all hazard types recognized within the country could be compiled in accordance with

DRSF.

57. However, as a general recommendation towards increased consistency in scope of disaster-related

statistics, national agencies are encouraged to follow the scope of hazards defined for monitoring

of international indicators in UN General Assembly (2016) and in the subsequent UNISDR

Methodological Guidance. This recommendation is to report nationally aggregated statistics

according to the overall scope of coverage of the IRDR Peril Classification and Hazard Glossary

(IRDR, 2014) and for two additional categories of hazards defined for the Sendai Framework:

environmental hazards and technological hazards.

58. For organization of the presentation of statistics on disaster occurrences into categories of hazard

types, the main perspective is time series analysis. One of the important examples of an

aggregated category of hazards is climate-related disasters. These are hazards in the

meteorological and hydrological hazard families as defined by IRDR (2014).5

59. Climate is “the synthesis of weather conditions in a given area, characterized by long-term

statistics (mean values, variances, probabilities of extreme values, etc.) of the meteorological

elements in that area.” (WMO, 2017)

60. The Intergovernmental Panel on Climate Change (IPCC) has indicated a strong likelihood that

climate change will lead to increases in frequency and severity of related hazards, and reduce

overall predictability of such hazards based on historical records (see, e.g., IPCC, 2012 and

Birkman, 2013). Trends will be different and unevenly distributed across the globe. Statistics are

needed for assessing how climate change may be impacting disaster risk for different countries or

different regions over time.

5 Allignment with meteorological and hydrological families of IRDR can be used as the broad scope for measurement of climate-related disasters. However, some special distinctions may be needed in the details, for example to distinguish between fires that are accidents caused directly by human activities in urban area as compared to wildfires that are consequences of extreme climate conditions (dry heat).


15

61. Another aggregated category of hazards mentioned in the Sendai Framework are “man-made

disasters”. Although the term “natural disasters” is no longer used, man-made hazards refers

especially to environmental and technological hazards, which are not covered by IRDR (2014).

62. In UNGA (2016), technological hazards “originate from technological or industrial conditions,

dangerous procedures, infrastructure failures or specific human activities. Examples include

industrial pollution, nuclear radiation, toxic wastes, dam failures, transport accidents, factory

explosions, fires and chemical spills. Technological hazards also may arise directly as a result of

the impacts of a natural hazard event.”

63. Also from UNGA (2016), environmental hazards: “may include chemical, natural and

biological hazards. They can be created by environmental degradation or physical or chemical

pollution in the air, water and soil. However, many of the processes and phenomena that fall into

this category may be termed drivers of hazard and risk rather than hazards in themselves, such as

soil degradation, deforestation, loss of biodiversity, salinization and sea-level rise.”

64. Other hazards not covered in the scope IRDR (2014) are violent conflicts, including civil war and

the associated human crises, e.g. refugee crises. The OECD estimates that approximately 80% of

international transfers of humanitarian aid goes to conflict-related settings.6. UNGA (2016)

excludes "the occurrence or risk of armed conflicts and other situations of social instability or

tension which are subject to international humanitarian law and national legislation" from its

definition of a hazard for the purpose of Sendai Framework monitoring.

65. A cascading multiple-hazard disaster occurrence is a disaster in which one type of hazard

(such as a strong storm or a tropical cyclone) causes one or more additional hazards (e.g. flooding

or landslides), that create combined impacts to the population, infrastructure and the environment.

In some cases (e.g. Indonesia), cascading multi-hazard disasters can be reported as their own

specialized category of hazard types, noting also the original trigger hazard (e.g. storm), as well as

the connected hazards (e.g. floods, landslide). Cascading multiple-hazard are not simply events

with proximate timing or locations by coincidence. They are events that are explicitly linked to the

same original trigger hazard, and thus are part of a single disaster occurrence.

66. “A slow-onset disaster emerges gradually over time. Slow-onset disasters could be associated

with, e.g., drought, desertification, sea level rise, epidemic disease.” (UNGA, 2016). Slow-onset

disasters emerge after a period of slowly evolving catastrophic risk, which, given available data

and the right monitoring conditions, can be identified early in order to develop preventative and

mitigation measures for minimizing impacts.

67. “A sudden-onset disaster is one triggered by a hazardous event that emerges quickly or

unexpectedly. Sudden-onset disasters could be associated with, e.g., earthquake, volcanic

eruption, flash flood, chemical explosion, critical infrastructure failure, and transport accident.”

(UNGA, 2016).

Magnitude

68. Scale of impacts is another important characteristic for presentation of statistics and analyses.

Usually, large scale disasters are less frequent but also attract international attention and solidarity

for response and assistance. Smaller scale disasters have less extensive impacts, but may be more

frequent and the cumulative effect can be very significant but also more likely underrepresented

by current databases.

6 See statistics on humanitarian aid at stats.oecd.org


16

69. It is a common practice of disaster management agencies to categorize disaster occurrences

according to a 3-category scale (minor, moderate, and large scale occurrences). There are various

ways for classifying magnitude. The recommended approach is to refer to the geographic scale of

the call for emergency and support, i.e.: national scale, regional, or local scale disasters. The use

of the geography of the call for emergency is useful as a generic proxy measure for the scale of

the impacts to society.

70. Large disasters are disasters in which the emergency is at a national (or higher) scale and have

special characteristics of interest for analysis because they are relatively rare but have extensive

and long-term effects on sustainable development. Large disasters are often also covered by post-

disaster assessment studies, creating opportunities for more comprehensive and more detailed

compilations of statistics on direct and indirect impacts. The impacts of large disasters often cross

administrative boundaries, including international borders, and therefore recordings of statistics

for large scale events are usually applicable to multiple reporting regions. An example was

Cyclone Evan (2012), which caused major damages in Fiji and Samoa, spurring separate

internationally-funded post disaster assessment studies in both countries.

71. Medium and small scale disasters refer to emergencies at smaller than national geographic

scales, which usually result in relatively smaller values of impacts per occurrence but with large

shares of the total number of disaster occurrences for a country or region. This distinction is

related to the concept of intensive and extensive risk from disasters developed by UNISDR

(2015). “Extensive risk is used to describe the risk associated with low-severity, high-frequency

events, mainly associated with highly localized hazards. Intensive risk is used to describe the risk

associated to high-severity, mid to low-frequency events, mainly associated with major hazards.”

Disaster Occurrences Time Series

72. Disasters occur randomly in space and over time, which makes analysis of their impacts also

highly sensitive to the selected time period. The current international standard for a baseline time

series analysis of disaster impacts statistics from the Sendai Framework and SDGs is the 16-year

period from 2015-2030. For some other analytical purposes, such as for risk assessments by

hazard type, a much longer time period is needed.

73. Trends are easier to identify over a relatively longer time period. Although year to year variations

in disaster impacts are highly susceptible to randomness of disaster occurrences, compilations of

annual statistics within the databases is recommended to allow for flexibility by users to modify

their own selections of time periods for their analysis (e.g. into 3-5 year period smoothed

averages). Flexibility is important because, in some extreme cases, inclusion (or exclusion) within

the time period of one particular abnormal occurrence could dramatically change the analysis.

Choices in relation to time periods for dissemination and analyses of statistics vary depending on

the special characteristics of hazard types. For example, the time scale for occurrences of

earthquakes and tsunamis is typically much longer than certain types of floods or meteorological

hazards. Biased or incorrect interpretations of the time series can be avoided if decisions related to

the scope of time series is determined solely according to statistical considerations (see annex for

examples at the global scale).

2b) Disaster risk

Background


17

74. Improved utilization of official statistics for understanding disaster risk is a basic motivation for

development of DRSF and its implementation in national statistical systems. Improved

understanding of risk is also priority number one of the Sendai Framework.

75. Disaster risk “is the potential loss of life, injury, or destroyed or damaged assets which could

occur to a system, society or a community in a specific period of time, determined

probabilistically as a function of hazard, exposure, vulnerability and capacity.” (UNGA, 2016)

76. Disasters are the outcome of present conditions of risk, including exposure to a hazard and the

related patterns of population and socioeconomic development. (UNGA, 2016) “Disaster risk is

geographically highly concentrated and very unevenly distributed” (Pelling, in UNU 2013).

Measurement must account for extreme variability of risk with a broad coverage of the land and

population while also targeting relatively high-risk hotspots with disaggregated statistics.

77. Statistics on the underlying risk are the contextual information for analysing statistics on disaster

impacts and for understanding how impacts from disasters can be reduced for the future.

78. Paragraph 6 of the Sendai Framework, states:

“More dedicated action needs to be focused on tackling underlying disaster risk drivers,

such as the consequences of poverty and inequality, climate change and variability,

unplanned and rapid urbanization, poor land management and compounding factors such as

demographic change, weak institutional arrangements, non-risk-informed policies, lack of

regulation and incentives for private disaster risk reduction investment, complex supply

chains, limited availability of technology, unsustainable uses of natural resources, declining

ecosystems, pandemics and epidemics. Moreover, it is necessary to continue strengthening

good governance in disaster risk reduction strategies at the national, regional and global

levels and improving preparedness and national coordination for disaster response,

rehabilitation and reconstruction, and to use post-disaster recovery and reconstruction to

‘Build Back Better’, supported by strengthened modalities of international cooperation.”

79. Disaster risk is dynamic and its measurement is captured, in part, by common work of national

statistics offices and other providers of official statistics at the national level, such as:

demographic changes, poverty and inequality, structure of the economy, expenditure, economic

production, conditions of ecosystems, and land management.

80. The focus in DRSF is to clarify the role of official statistics as inputs, which should be made as

accessible as possible, for risk assessments. In Birkman (2013), Mark Pelling describes two

complementary types of risk assessment internationally: risk indices and hotspots. UNDP and

UNEP-GRID have been among the leading international agencies developing global disaster risk

indices (or DRIs). DRIs can be developed for individual hazard types (e.g. for floods or cyclones)

or multi-hazard risk, i.e an index covering multiple hazard types.

81. The hotspots approach follows a similar model that has been used in the domain of biodiversity,

and focuses on applying analyses at a more geographically detailed scale, utilizing key data that

can indicate relatively high levels of likelihood for hazards combined with exposure and

vulnerabilities of the population. Many interesting examples are emerging, for example in the

disaster management agency of Indonesia (BNPB), which is tracking statistical information on

economic activities (derived, e.g., from local tax revenue records) and on children (from

administrative records on enrolment in schools) in relation to the hazard areas of the country.


18

82. Modern versions of DRIs and other models that can be found in the literature now incorporate

both approaches through geographically disaggregated statistics and analysis using geographic

information systems (GIS) . An advantage of the GIS-based risk production of statistics for

assessment is the potential to apply the methods to produce summary statistics at different

geographic scales, i.e. at the global, national or regional scales, and for hotspots.

Scope of measurement

83. In the literature and current practice of many disaster management agencies (e.g. the national

disaster management agency of Indonesia, BNPB), disaster risk is defined and measured

according to three core elements: exposure to hazards, vulnerability and coping capacity.

𝑅𝑖𝑠𝑘 = 𝑓(𝐻𝑎𝑧𝑎𝑟𝑑, 𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦, 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦)

84. This basic definition for measurement of risk appears in many sources in the disaster risk

reduction literature, and has also been known as the PAR model (Birkman, 2013). Disasters occur

at the intersection of the hazard (e.g. an earthquake) and the human processes generating

exposure, vulnerability and coping capacity. Risk of impacts from a disaster is not driven only by

the scale of the hazard itself (e.g. force of energy of the earthquake or category of storm) but

equally so by social factors that create exposure, vulnerability and coping capacity (UNISDR,

2015).

85. Exposure to hazards, vulnerability and coping capacity are not independent factors of risk. This

basic formula is useful as the conceptual basis for defining the scope and organizing statistics on

risk in DRSF. It should not to be taken literally as a mathematical formula for econometrics.

Estimating exposure to hazards

86. There are two main elements to measuring exposure to hazards; there is a probabilistic mapping of

the hazard on the one side and a complement mapping of the population, critical infrastructure

(and other objects of interest such as high nature value ecosystems) for the exposure side.

87. The mapped area meeting in the middle is the hazard exposure measurement. Producing statistics

that can be used for estimating the exposure element is one of primary responsibilities of national

statistics offices and census organizations (e.g. through the regular population and housing

census).

Figure 4: Population exposed to hazards measurement


19

(Sources: Right Map: UN Environment-GRID’S frequency of flood hazard map. Left map: Population census

2015 from KOSTAT, resampled by UNESCAP to the DLR’s Global Urban Footprint.)

Hazard Mapping

88. For hazard mapping, many variables can be relevant, most of which are not normally a domain

for national statistics offices, but are often available from the official sources of disaster

management, meteorological and geographic information for a country (or region).

89. The BNPB Indonesia example (see annex) provides a good practice example of the types of data

inputs needed for hazard mapping, among which include:

a. knowledge of the distribution of soil-type to model the spatial variation of ground

acceleration from an earthquake,

b. water supply and use balances and other statistical information used for tracking the

hydrological cycle and use of water in the economy

c. values for surface roughness to define the distribution of wind speed from a tropical

cyclone;

d. a digital elevation model (DEM) to determine potential flood height or other hazard

features.

90. There are also software tools and other resources available for probabilistic hazard modelling

software, e.g.:

e. The Austalian Government’s Earthquake Risk Model

(http://www.ga.gov.au/scientific-

topics/hazards/earthquake/capabilties/modelling/eqrm)

f. BNPB Indonesia’s InARisk (http://inarisk.bnpb.go.id/)

g. CAPRA (http://www.ecapra.org/)

h. U.S. Environmental Protection Agency’s CAMEO (https://www.epa.gov/cameo)

91. A collection of the spatial, intensity, and temporal characteristics for a set of potential hazards is

known as a hazard catalogue. Hazard catalogues and statistics on impacts from historical events

together with risk models can be used in a deterministic or probabilistic manner. Deterministic

risk models are used to assess the impact of specific events on exposure. Typical scenarios for a

deterministic analysis include renditions of past historical events, worst-case scenarios, or possible

events at different return periods. A probabilistic risk model contains a compilation of all possible

“impact scenarios” for a specific hazard and geographical area. A goal for probabilistic hazard

http://www.ga.gov.au/scientific-topics/hazards/earthquake/capabilties/modelling/eqrm

http://www.ga.gov.au/scientific-topics/hazards/earthquake/capabilties/modelling/eqrm

http://www.ecapra.org/

https://www.epa.gov/cameo


20

modelling is convergence of results and a long time series of input data is usually necessary. For

example, a simulation of 100 years of hazard events is too short to determine the return period and

random samples over a period of 100 years of events could easily omit events, or include multiple

events.

92. According to IPCC, three changes are likely to be observed for climate-related hazards for some

geographic regions as a result of rising global temperatures: increases in frequency, severity, and

decreased predictability of hazards. Thus, climate change has contributed to the dynamic nature of

hazards, as an input into the formula for assessing risk. The other risk factors discussed below

(exposure, vulnerability, capacity) are, for different reasons, also highly dynamic.

Exposure Statistics

93. For the exposure side, the objective is to measure people, infrastructure, housing, production

capacities and other assets located in hazard-prone areas.

94. Exposure statistics have dual purposes in disaster statistics. In addition to one of the three basic

metrics for disaster risk, exposure statistics are also useful as baseline statistics for assessing

impacts after a disaster.

95. An approach has been developed for DRSF (see annex), applying the available population census

data using GIS. A method was developed and pilot tested among countries in Asia and the Pacific

to demonstrate the possibilities for applying census statistics for estimating population exposure to

hazards at different scales, based on the available public access population census counts by

administrative region (which can be accessed from national statistics offices at different scales,

depending on the country). The methodology7 was developed and tested among Expert Group

countries during 2016 and 2017 and a complete step-by-step manual describing the steps to

replicate the output statistics for any country (or region) using the available population data from

census authorities.

96. The basic objective for this methodology is to provide national agencies with a simple,

reproducible and scalable approach to producing statistics on population exposure, i.e.

estimations of population density in areas exposed to natural hazards or disasters from publically-

accessible data sources.

97. The difference in geographic distribution of hazard areas as compared to the normal dissemination

of population data (i.e. administrative areas at sub-regional or district levels ) creates the

requirement to re-allocate distribution (down-scale) population data so that it can be overlaid with

a reasonable degree of accuracy to the actual geographic areas of a hazard or disaster. The basic

requirement is assimilation of population statistics or other exposure elements (e.g. critical

infrastructure) with the hazard maps. The methodology developed for DRSF uses a grid-based

assimilation of census data in GIS.

Figure 5: Grid-based data assimilation

7 See full methodology descriptions at the Expert Group website (http://communities.unescap.org/asia-pacific-expert-group-disaster-related-statistics)


21

source: Jean-Louis Weber, CBD Technical Series 77, 2014

98. Generally, the lower the level of geographic detail of the population aggregates (e.g.

administrative regions 01, 02, 03), the more accurate the gridded estimates of population density

should be for producing statistics on hazard exposure.

99. So, for example, in cases such as Tonga, in the Pacific, where census data are accessible by GPS

coordinates, no modelled estimation is required as the census records effectively reveal point

locations for households and the number of people living there (among other relevant data from

the census). These statistics can be used for highly accurate and high-resolution analyses of

location of population with respect to other geographic elements8, including in relation to hazard

area. More commonly in current practice, the most detailed level of geographic area for data

collected by the census organisations are geographic areas called census blocks, which are

instruments for organizing census collection operations and usually contain somewhere between

50-200 households, depending on the country and region. The level of geographic aggregation for

census data that are available to most users is usually at the level of administrative region (e.g.

provinces, municipalities or administrative level 01).

100. Pilot studies for the population exposed to hazards estimation methodology revealed that, with

high quality data of built-up areas such as the DLR Global Urban Footprint (GUF) produced from

radar satellite images (accessible at https://urban-tep.eo.esa.int/#), it is possible to estimate

location of population using a simple model with results that are at least comparable with other

existing international estimations (such as, e.g., by Worldpop.org (http://maps.worldpop.org.uk/#/

or by Global Human Settlement Layer by JRC http://ghslsys.jrc.ec.europa.eu/) based on census

results produced for public use by national statistics offices. Due to the method’s simplicity,

transparency and the opportunity for free access to high resolution GUF data, reproducing

estimations for population to hazard exposure is feasible at different scales according to the detail

of population data available and for varying policy requirements.

101. Hazard exposure statistics come in the form of maps that are also very simply converted into

standardized statistical tables. The figure below summarizes the basic inputs from the hazard and

8 See the Pacific Community’s POPGIS tool (prism.spc.int)

Satellite

images

Hotspots,

Occurences

,

Monitoring

data, samples

Socio-

economic

statistics

Classify,

aggregate

& map

Extrapolate

Overlay

Data inputData assimilation

(e.g. within

1 ha or 1 km2 grids)

Statistics integration,

analysis & reporting

Ref. Geo-

DataCode, Name

Disaggregate

& map

Data QA/QC,

analysis &

processing

e.g. by

administrative

unitse.g. by river

catchments or risk

perimeters…

https://urban-tep.eo.esa.int/

http://maps.worldpop.org.uk/#/


22

the exposure side, which will have close relationships to the measurement of vulnerability and

could be tabulated into standard summary statistics tables, such as in the DRSF Table B1b, which

is organized according to relevant social groups.

Figure 6: Hazard Exposure Model


23

0-45-60

60+M

aleFem

aleUrban

RuralDisabled

Poor

1Population

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

2Population in Hazard Areas

2.1Geophysical

2.1.1H

igh exposure

2.1.2M

oderate exposure

2.1.3Low

exposure

2.2Hydrological

2.2.1H

igh exposure

2.2.2M

oderate exposure

2.2.3Low

exposure

2.3Biological

2.3.1H

igh exposure

2.3.2M

oderate exposure

2.3.3Low

exposure

2.4M

eteorological & Clim

atalogical2.4.1

High exposure

2.4.2M

oderate exposure

2.4.3Low

exposure

2.5O

ther [specify]

2.5.1H

igh exposure

2.5.2M

oderate exposure

2.5.3Low

exposure

TOTAL

C2a4 - Specific

vulnerability groupsN

O

TOTAL

C2a1 - Age groupsTO

TALC2a2 - Sex

TOTAL

C2a3 - Urban/Rural

population

Data so

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and

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ter 2). H

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High

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: Po

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by So

cial


24

Vulnerability

102. The Sendai Framework recommendations adopted by the UN General Assembly in 2016 defined

vulnerability as “the conditions determined by physical, social, economic and environmental

factors or processes which increase the susceptibility of an individual, a community, assets or

systems to the impacts of hazards.”

103. In some reports, terminologies such as susceptibility, exposure, sensitivity, fragility, and coping

capacity have been used interchangeably with vulnerability. Also the variables for describing

different types of risk factors are not always independent. However, from a measurement

perspective, vulnerability is a distinct and useful concept for organizing statistics on the baseline

conditions, as descriptions of the population and infrastructure, a step beyond the simple

overlapping of location with hazards (i.e. exposure).

104. Previous studies can suggest a potential short list for geographically disaggregated variables for

compilation to improve the availability of reference statistics for identifying potentially

vulnerable segments of the population, such as:

Median household disposable income

Education enrolment, by age group and level of achievement and by male and

female heads of households

Information on assets of households, such as type of dwelling

Other human development statistics, by age group, including evidence related to

nutrition and childhood health,

Type of employment, e.g. identifying households engaged in agriculture of fishing

Urban versus rural distribution of affected or exposed areas

105. All of the above are items for potential disaggregation of the exposed populations, where

available, and could be compiled into basic summary statistics on disaster risk at different scales,

similar to DRSF table B1b.

106. Vulnerability arises from a wide variety of causes. Children are more vulnerable than adults for

physiological reasons. Women could be more vulnerable as a result of social factors, related to

(e.g.) type of employment or economic status. Studies of vulnerabilities for ageing populations

have revealed location and type of residence can be a good reference for assessing vulnerability

for the elderly, especially in cities.

107. If the statistics used in vulnerability assessments are gathered and updated on a regular basis by

geographic regions, and specifically for hazard areas within countries, than disaster management

agencies would have a priori information on extent and specific locations (among other

characteristics) of vulnerability for developing targeted disaster risk reduction or response

strategies at local and national levels, in alignment with the overarching objective of Sustainable

Development Goals and of not leaving anyone behind.

108. Vulnerability assessments for disasters cut across the three traditional sustainable development

pillars (economic, social, and environmental) and measurement goes beyond people or

households. For example, although pollution in water bodies is generally considered as an

environmental problem, in the context of disaster risk, pollution is also a social and economic

liability because it can lead to significantly worse impacts to human lives and health and to the

economic costs of recovery.


25

109. The 2010 World Development Report (World Bank, 2010) stated that “natural systems, when

well-managed, can reduce human vulnerability”. Examining and supporting cases of positive

synergies between environmental protections, also called ‘pro poor environmental policies’ is one

of the objectives for the United Nations Poverty and Environment Initiative (PEI). Wherever

environments are heavily polluted or degraded, often it is the relatively poor populations that are

more likely to be disproportionately affected and, by extension, more vulnerable in the event of a

disaster.

110. Another example is vulnerability of infrastructure, which is sometimes called “physical

vulnerability”. The response of existing structures to potential hazards is not only an engineering

problem. In most cases, physical vulnerability also stems from other social-economic or

environmental problems. Relatively poor households often have little choice but to accept

relatively less resilient shelters in their dwellings or work places. Poorer communities, such as

slums or lower income areas of urban sprawl, often are the most likely to be situated in areas with

high exposure to hazards.

111. Population density and geographic location are the basic dimensions of exposure measurement,

but they also can be factors for vulnerability. Many rural communities face marginally higher

vulnerabilities due to the generally poorer access to transportation, health facilities, and other

types of critical infrastructure or support services. The largest share of people living in poverty

also tends to be in rural areas in developing countries. On the other hand, other facets of rural

communities, such as informal community support systems, could be notable sources of

resilience.

112. The defining characteristic of the urban centres, particularly the megacities, many of which are

located in coastal zones or otherwise hazardous locations in Asia and Pacific, is extreme

population density. While there are social benefits to having large groups of people concentrated

within relatively small geographic areas, such conglomerations can be inherently vulnerable to

impacts from hazards. Also, the characteristics of urban slums9, as defined by the United

Nations Human Settlements Programme (UN-Habitat) are likely to be key factors for vulnerability

in those communities.

113. Economic-related vulnerabilities include structural factors that are specific to geographic regions

within countries. For example, tourism and agriculture both have characteristics that can lead to

increased vulnerability to impacts from a disaster as compared to other types of economic activity.

So, economies based on agriculture and other kinds of productive activities that are space

intensive and/or heavily dependent on meteorological and other environmental conditions will, in

most cases, be relatively more vulnerable to natural hazards as compared to, for example,

services-based economies. Thus, some of the economic vulnerabilities to disasters are assessed

through macroeconomic analysis on the structure of economies for specific geographic areas

exposed to hazards.

Coping capacity

114. The term “resilience” has been applied in reports on disaster risk reduction with various

meanings or descriptions. Commonly, resilience is mentioned almost interchangeably with the

concept of coping capacity. This is the ability for households or businesses or infrastructure to

withstand external shocks without sustaining major permanent negative impacts, and instead

guiding towards opportunities for improvements in the future (e.g.. “building back better”).

9 A slum household suffers: lack of access to improved water source, lack of access to improved sanitation

facilities, lack of sufficient living area, lack of housing durability or lack of security of tenure (UN-Habitat,2016)


26

115. Birkman (2013) writes: “In contrast to vulnerability, resilience emphasizes that stressors and

crises in social-ecological systems also provide windows of opportunity for change and

innovation. Hence crises and destabilization processes are seen as important triggers for renewal

and learning.”

116. Many strategies for coping with disasters are informal and not managed by governments or

through regulations, and therefore their significance to understanding risk is difficult to measure

with statistics. For example, one of the coping mechanisms in the case of drought or other types of

climate or hydrological-related hazards is simply migration, either permanently or temporarily, in

search of a livelihood outside the worst affected areas. Population displacement and other

movements of the population that correspond in timing with a disaster can sometimes be captured

via statistics from population censuses or population administrative records. More difficult is to

attribute movements specifically to hazards or a past disaster.

117. There also are coping mechanisms which are organized efforts that can be captured by statistics

for disaster risk assessments. Disaster preparedness is a good example. After major earthquakes

struck the Canterbury province of New Zealand, population and housing census results revealed

significant increases in disaster preparedness of households (e.g. rationing emergency food and

water storage). Such information reveals a decrease in overall risk, via increased coping

capacities, and also direct benefits from learning and from educational programmes enacted after

the experience of previous disasters.

118. Basic statistics on coping capacities are an important input for understanding risk, but an

additional use for statistics on coping capacity is to show direct results from investments in

increased preparedness. Disaster management agencies utilize the best available risk information

to design and implement activities to reduce the impacts of disasters. The aim of these activities is

that they improve preparedness and strengthen the overall resilience of a community before a

hazard or disaster.

119. Disaster risk reduction-related activities (see Section 2e) are activities that boost the coping

capacities of society. In order to assess the direct results of these investments, governments should

also collect statistics for assessing how these investments affect coping capacities, e.g. coverage of

early warning systems and the basic knowledge and preparedness of households.

120. People are not equally able to access the resources and opportunities (or knowledge and

information about hazards). The same social processes involved in the disadvantages of poverty

also can have a significant role in determining their level of preparedness and access to

information and knowledge. (Wisner et al., 2003).

Summary Statistics Table B3: Coping Capacity Background Statistics


27

Geographic disaggregation

121. For producing, and utilizing statistics for risk assessment, a key requirement is geographic

disaggregation. Data assimilation in GIS creates possibilities to apply the available data to

produce and communicate statistics at multiple scales. At a minimum, variables identified for

vulnerability to disasters should be compiled to the lowest available sub-national administrative

regions (e.g. Administrative region 02 or 03). In DRSF background statistic tables, all variables

are organized according to geographic regions used for statistics within the country. In reporting

tables, geographic disaggregation is predetermined by existing practices and requirements of

users. However, within GIS, geographic regions can be defined or adapted to the specific analysis.

Geo Region

1

Geo Region

2

Geo Region

3… National

Measuremen

t Unit

Coping Capacity Table1 GDP SDG 1.5.2 SDG 1.5.2 SDG 1.5.2 SDG 1.5.2 SDG 1.5.2 Currency

2 GDP per capita Currency

3 Median Households disposable income Currency

3.1 Local currency (NAME...)Currency

3.2 US$ PPP US$ PPP

4 Number of dwellings with slum

designation

no. of units

5 Population living in areas with slum

designation

no. of people

No. of systems

6.1 Population covered Sendai G-3 Sendai G-3 Sendai G-3 Sendai G-3 Sendai G-3 %

6.2 Share of population in exposure areas covered %

6.3InvestmentExpenditure (also DRRE_A,

3.2) Currency

7.1 Share of households with emergency plan %

7.2 Share of households with backup storage of food and water %

7.3 Share of households with improved access to water and sanitation %

7.4 Other Preparedness (houehold level) %

8.1 Forest area sq km

8.2 Share of water bodies in good condition %

8.3 Other ecosystem condition measures

Currency

9.1 Disaster risk reduction characteristic transfers received Currency

9.2 Disaster Risk Prevention Currency

9.3 Disaster Risk Mitigation Currency

9.4 Disaster Management Currency

9.5 Disaster Recovery Currency

9.6 General Government, Research & Development, Education Expenditure Currency

Currency

6 Early Warning Systems

9 Risk Reduction Activity

10 DRRCA Transfers fom Central to local

government

7 Household Preparedness

8 Environmental Resilience

Data Sources: Household Preparedness from Population and Housing Census and/or household surveys

Forest Area: national land cover statistics

Water and ecosystem assetments from national water enviroment protection authorities

Risk Reduction Activity: Finance Ministry and National Accounts

Definition of Slums: UN-HABITAT defines a slum household as a group of individuals living under the same roof in an urban area who lack one or

more of the following: (1) Durable housing of a permanent nature that protects against extreme climate conditions; (2) Sufficient living space which

means not more than three people sharing the same room. (3) Easy access to safe water in sufficient amounts at an affordable price. (4) Access to

adequate sanitation in the form of a private or public toilet shared by a reasonable number of people and (5) Security of tenure that prevents

forced evictions.

Highlighted areas show links to global indicators


28

122. Often it is useful to define homogenous regions --- e.g. urban and rural, residential and non-

residential, agricultural land, etc. One of the basic inputs for developing exposure statistics are

land cover and land use maps and, where available, the cadastres of municipalities. Land cover

and land use maps, among other kinds of geospatial information, provide the necessary baseline

information for defining specific geographic objects of interest in risk assessment.

123. Risk statistics differ from impacts statistics in that they are baseline information about the

population or infrastructure compiled prior to a disaster for risk areas whereas impacts statistics

are information for describing a population affected by a specific and unique disaster occurrence

(and affected area). However, the disaggregation of statistics on the human impacts (see sections

2d) should, in many cases, mirror the groups that were identified in the vulnerability assessments

– e.g. children, the elderly and the income poor – and eventually the two measurement systems

should become mutually reinforcing to improve one another, built upon the same basic initial data

collections used for disaster risk measurement. For example, baseline statistics on economic

activity for areas exposed to hazards can be reused for estimating costs of damages in impacts

assessments.

124. Increasingly, traditional data sources of the national statistical system like household and

business registers, household and business surveys, population and housing censuses are

conducted with use of detailed geographic referencing. The geographic referencing may be

confidential at the level of individual records, but summary statistics can be disseminated for use

for comparisons at different scales or specifically defined areas (e.g. Hazard areas). The quality

and level of detail of available data with geographic referencing of households, businesses, and

other land uses, varies greatly between countries, and sometimes within countries (e.g. between

rural areas and urban centres). But the broad trend for official statistics has been a rapid expansion

in the possibilities, using affordable tools and the existing data, to greater level of flexibility and

level of detail for geographic disaggregation of statistics on risk.

2c) Material Impacts and Economic Loss

Material Impacts

125. Direct material impacts encompass physical damages from a hazard to assets and other objects

valuable to society, such as critical infrastructure. Direct material impacts also constitute the

source of direct economic loss measurement, as defined for the Sendai Framework.

126. Initially, statistics on direct material impacts are produced by disaster management agencies

based on assessments conducted immediately after an emergency (UNGA, 2016). These statistics

are complemented by statistical information on the location and basic characteristics of

infrastructure known prior to the hazard, e.g. estimates of exposed infrastructure. Thus,

background statistics on infrastructure serve an additional purpose as baseline or contextual

information for impacts statistics.

127. In addition, complementing assessments of material impacts by the disaster management

agencies are analyses of regular sources of time series statistics within the national statistical

system, such as the population and housing census, business surveys, and compilations of other

records of economic activity that are used to evaluate trends on a continuous basis, i.e. before,

during, and after disasters.


29

128. Direct observations of material impacts from a disaster are compiled, initially, in physical (or

volume) terms, i.e. in terms of area (sq. m), number of people affected, or counts of units (e.g.

buildings), by type, that are damaged or destroyed. Defining measurement units (see Chapter 5)

is a crucial step for designing the collection and dissemination of a robust and consistent

compilation of material impacts statistics. The scope of measurement is defined according to the

stocks of physical objects (e.g. assets) exposed to hazards (see classification of material impacts in

Chapter 3). Prioritization is given to especially important groups of assets, particularly critical

infrastructure and agricultural crops.

129. There are multiple possibilities for measurement units and for compiling direct material impacts

from disasters and national statistical systems should aim to converge toward consistency across

disaster occurrences so that of summary tables of impacts can be compiled over time, such as in

the examples below. Collection of basic data on numbers of units (e.g. number of buildings) of

the different categories of critical infrastructure, see example below from the Philippines, is a

good starting point. On this basis, additional data – e.g. classes of hospitals damaged, length of

roads, numbers of people affected by disruptions, and so on - can be integrated, gradually

building more sophisticated compilations for assessing the scale of the material impacts and the

recovery needs.

Sample Table 1: Damages to Critical Infrastructure in the Philippines, 2013-15

Source: report from Philippines for DRSF Pilot Studies (2016); units: no. of buildings

130. Critical Infrastructure is “the physical structures, facilities, networks and other assets which

provide services that are essential to the social and economic functioning of a community or

society. ” (UNGA, 2016). A list of critical infrastructure is presented as a sub-group of the

broader classification of direct material impacts in Chapter 5.

Region I (Ilocos) 3 33 5

Region II (Cagayan Valley) 30 0 0 19 8

Region III (Central Luzon) 64 140 12

Region IV-A (Calabarzon) 12 0 0 5

Region IV-B (Mimaropa) 123 0 0

Region V (Bicol) 66 0 0 10 1

Region VI (Western Visayas) 36 0 1

Region VII (Central Visayas) 286 82 37 55 26 18

Region VIII (Negros Island Region) 347 0 0 24 4 0

Region IX (Zamboanga Peninsula) 0 0

Region X (Northern Mindanao) 0 0 0 3 3

Region XI (Davao Region) 0 0 18 3

Region XII (Soccsksargen) 0

Region XIII (Caraga) 0 0 39 6

National Capital Region (NCR) 8

Cordillera Administrative Region

(CAR) 1 20

Autonomous Region of Muslim

Mindanao (ARMM)

Nattional total

PH

ILIP

PIN

ES

Region

DAMAGES TO CRITICAL INFRASTRUCTURE

Hospitals/

Health

facil ities

Education

facil ities

Other critical

public

administration

buildings

Roads BridgesOther critical

infrastructures


30

131. Damages to dwellings create an explicit link between human and material impacts tables. In the

example below, impacts are measured again in terms of numbers of units, this time for the case of

Indonesia. Number of dwellings will be roughly equivalent to number of households affected. In

principle, the same source of this information could also be used to calculate the number of

persons affected by damaged or destroyed dwellings, which is also part of the displaced

population, as long as the dwellings affected can be linked to (or estimated for) the population per

household in those units. There is also an opportunity, having identified and counted specific

dwellings affected, to collect data on characteristics (age, sex, poverty status, etc.) of those

affected.

Sample Table 2: Damages to Dwellings in Indonesia

Damaged Dwellings (#of units)

geophysical hydrological meteorological Climatological Other total

Aceh 9307 2026 201 0 11534

Bali 3 148 46 197

Bangka-Belitung 0 103 103

Banten 55 403 173 631

Bengkulu 321 178 112 611

Gorontalo 3 3 6

Irian Jaya Barat 0

Jakarta Raya 3 0 250 253

Jambi 47 148 162 0 357

Jawa Barat 1345 6969 1547 9861

Jawa Tengah 830 1285 4768 0 6883

Jawa Timur 612 576 3218 0 4406

Kalimantan Barat 90 158 248

Kalimantan Selatan 334 129 0 463

Kalimantan Tengah 1 0 1

Kalimantan Timur 47 1 39 0 87

Kalimantan Utara 1 0 1

Kepulauan Riau 4 49 111 0 164

Lampung 0 0 1023 1023

Maluku 620 83 703

Maluku Utara 146 23 169 Nusa Tenggara Barat 735 1454 129 2318 Nusa Tenggara Timur 11 151 32 194

Papua 1 0 1

Riau 9 30 343 0 382

Sulawesi Barat 76 31 107

Sulawesi Selatan 66 23 697 786

Sulawesi Tengah 27 3 0 30

Sulawesi Tenggara 8 114 122

Sulawesi Utara 37 145 6 188


31

Sumatera Barat 2688 504 281 0 3473

Sumatera Selatan 78 20 244 342

Sumatera Utara 27 30 2249 0 2306

Yogyakarta 22 18 61 101

Papua Barat 9 305 314

National Total 17023 15107 16235 0 0 48365

Damaged Dwellings, # of units (1900-present), accessed from Data Informasi Bencana Indonesia

(DIBI) http://dibi.bnpb.go.id, 2017

132. There are several purposes for accounting for damages to assets after a disaster, including

improving knowledge of physical vulnerabilities to hazards, estimating the value of economic loss

from a disaster, and also for identifying disruptions of basic services from a disaster, which is the

focus of Sendai Framework Target D: “substantially reduce disaster damage to critical

infrastructure and disruption of basic services, among them health and educational facilities,

including through developing their resilience by 2030.”

133. Statistics on disruptions to services from material impacts can be presented according to hazard

types and according to geographic regions within the country. There are two measurement units

used for statistics on disruptions to basic services: numbers of persons affected and length of

time (e.g. number of days) for the disruptions.

Table D2a Disruptions to Basic Services from a Disaster by Hazard Type

134. In addition to damages to critical infrastructure and other buildings, another important component

of direct material impacts is damages to the land and other natural resources, especially to

agricultural land, destruction of trees, and damages to the conditions of important ecosystems such

as forests and water bodies.

135. There are also material impacts to other forms of capital besides produced assets, such as

household consumer durables or other various types of environmental assets. The System of

Environmental-Economic Accounting (SEEA) 2012 – Central Framework is an internationally

agreed standard for producing comparable statistics on the environment and its relationship with

the economy, following a similar accounting structure as the SNA. According to SEEA,

Disruptions to Basic services from a Disaster1 Health services Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7

1.1 No. of people

1.2 Length of time

2 Educational services Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6

2.1 No. of people

2.1 Length of time

3 Public administration services Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8

3.1 No. of people

3.2 Length of time

4 Water services

4.1 No. of people

4.2 Length of time

5 Other Basic Services

5.1 No. of people

5.2 Length of time

6 Total Disruptions Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5

6.1 No. of people

6.2 Length of time

http://dibi.bnpb.go.id/


32

environmental assets are “the naturally occurring living and non-living components of the Earth,

together constituting the biophysical environment, which may provide benefits to humanity.”

Some environmental assets are also economic assets and included within the SNA assets

boundary, particularly land and natural resources. However, some other benefits from ecological

processes recognized in SEEA are beyond the scope of SNA and are not currently valued in

monetary terms. An example is the natural protections against hazards provided by vegetation as a

natural barrier on coastlines against storms or for upstream absorption of water during a period of

heavy rainfall. Such processes are recognized as benefits of ecosystem services in the SEEA

Experimental Ecosystem Accounting framework, but beyond the scope of current practice for

monetary valuations in national accounts.

136. Human capital is also affected in complex ways as a direct consequence of material impacts from

a disaster. For example, employment can be disrupted by damaged or destroyed economic assets

or due to displacement or migration away from an affected area. Impacts to human and social

capital are incorporated into the basic range of disaster related statistics via human impacts (see

next section).

137. In economic terms, impacts to agriculture are often among the most significant impacts from

disasters. In part this is because, as a land intensive activity, agriculture faces a relatively large

exposure to hazards. Another reason is because there are many forms of material impacts to

agricultural establishments. They are manifested as damages to the land itself, including the soil

(e.g. accelerated erosion, landslide impacts, salinization), to land improvements (e.g. irrigation

systems), to constructed assets (building and equipment) as well as direct losses to the growing

(non-harvested) crops. Each of these components of damages can be measured separately and in

both physical and monetary terms.

Economic Costs from Material Impacts

138. According to the currently available statistics, economic impacts from disasters are on the rise in

many countries, creating fundamental challenges to the achievement of sustainable development.

139. Addressing increased exposure to economic impacts of disasters through policies is aided by

clear concepts for measuring economic loss from disasters that are harmonized, as much as

possible, across countries and across disasters.

140. Direct economic loss is one of the core disaster-related indicators for monitoring progress in the

SDGS and in the Sendai Framework for Disaster Risk Reductions. In that context, direct

economic loss is defined for the monitoring of Target C in the Sendai Framework for Disaster

Risk Reduction as "the monetary value of total or partial destruction of physical assets existing in

the affected area." Methodological guidance developed for monitoring international indicators for

economic loss (target C) of the Sendai Framework (UNIDSR, 2017) acknowledged significant

differences in current practices for economic loss measurement around the world and that,

currently, “in many disaster loss databases and disaster situation reports, it is often difficult to

determine which methodology, criteria and parameters have been used for estimation of the

economic value of losses.”

141. UNISDR guidance recommends calculating the direct economic loss indicator based on

assessments of damages by disaster management agencies. For these assessments, valuation of

the physical impacts should be aligned, as much as feasible, with established principles and

standard practices in economic statistics, such as the System of National Accounts (SNA).

Although, it is not an objective in the SNA to measure economic impacts of disasters, it addresses

the general equilibrium of economic flows and the changes in assets and liabilities and there are


33

important relationships between direct or indirect impacts of disasters with outputs of the national

accounts.

142. In principle, losses of assets from a disaster has a special recording in the SNA, an item called

catastrophic losses10

, which are represented as a special type of change (“other changes in

volume”) to the national balance sheet for physical assets. This is a change in the stock of assets,

which has no direct or explicit effect on the flows portion of the accounting framework, such as

production and income. However, there are also indirect economic effects of disasters on

production, consumption, and employment, but these values are implicit in the accounts and thus

are not directly observed but estimated via some form of modelled scenario analysis.

143. There is a strong interest for measures of indirect economic losses from disasters, for example to

produce estimates of the effect of disasters on GDP growth. Assessment studies to estimate

indirect economic losses from disasters are sometimes conducted, at least for key sectors of the

economy, after the large scale disasters but less commonly for smaller occurrences, like minor

floods, localized storms, or wild fires.

144. For many disasters, the indirect economic impacts are likely to be much larger in value as

compared to direct impacts (i.e. the cost of reconstructing damaged assets). However, an initial

focus on reliable measurement of direct economic costs is a sensible first priority because the

direct economic loss values are also basic building blocks upon which assessments and

modelling of indirect economic impacts can be developed over time. Included as part of

assessments of indirect economic losses from disasters are reduced employment and income to

households (also called “impacts to livelihood” –see next section), including for households or

businesses that did not sustain any direct losses to their assets or other durables. These negative

effects will be incorporated implicitly into GDP along with the post-disaster restoration activities

(as positive contributions to GDP, rather than as losses). So, aggregate indirect impacts (or

‘losses’) to economies are ambiguous from the national accounts and requires some assumptions

and modelling.

145. The direct impacts from disasters recorded in the SNA Other Changes in Volume to Asset

account, where available, cover losses in asset values from relatively large scale occurrences (see

definition above), and therefore should be appended with estimates of the costs of damages from

smaller scale events as well. Compilation of values for direct economic loss should, initially, be

calculated by disaster management agencies based on available data from the insurance industry,

from the usual sources of statistics of economic activity before and after the disaster (e.g.

government finance statistics, tax records, enterprise surveys), and, where possible, from specially

designed post-disaster assessment surveys of households and businesses in affected areas.

146. According to the System of National Accounts, an asset is “a store of value representing a

benefit or series of benefits accruing to the economic owner by holding or using the

entity over a period of time. It is a means of transferring value from one accounting period to

another. “ (SNA 2008, para 3.30). Assets have an intrinsic value represented by their expected

benefits to owners. This value can be lost or reduced directly by a disaster. Also for consideration

10 “The volume changes recorded as catastrophic losses in the other changes in the volume of assets account are

the result of large scale, discrete and recognizable events that may destroy a significantly large number of assets

within any of the asset categories. Such events will generally be easy to identify. They include major

earthquakes, volcanic eruptions, tidal waves, exceptionally severe hurricanes, drought and other natural

disasters; acts of war, riots and other political events; and technological accidents such as major toxic spills or

release of radioactive particles into the air. Included here are such major losses as deterioration in the quality

of land caused by abnormal flooding or wind damage; destruction of cultivated assets by drought or outbreaks

of disease; destruction of buildings, equipment or valuables in forest fires or earthquakes.” [SNA 12.46]


34

for measurement of direct economic loss are “household consumer durables”, a class of product,

such as privately owned cars, which have a value in their own final use by households.

147. According to the SNA concept of value for assets, their destruction is a loss from the perspective

of owners and a direct reduction in the overall balance of savings for an economy. When it comes

to monetary valuation of damages to these assets, the approach described in UNISDR (2017)

offers a different perspective, which is to identify estimated replacement costs – effectively the

costs of post-disaster reconstruction – which are akin to opportunity costs to the broader economy

created by the need to rebuild the asset base.

148. In the SNA, production is an activity carried out under the responsibility, control and

management of an institutional unit (households, government, businesses), that uses inputs of

labour, capital, and goods and services to produce outputs of goods and services (SNA, 2008).

The balancing item from production accounts is Value Added (VA), which summed at the country

level is GDP. VA and GDP are firstly computed Gross, which means with no deduction of the

regular depreciation (consumption of fixed capital in the SNA terminology) resulting from assets

use and obsolescence. When deduction is done, VA and production are called “Net” (NVA, NDP).

149. The reconstruction and replacement costs for destroyed or damaged assets are are included

implicitly on the right side of Figure 7. The values of these reconstruction and replacement

activities are implicitly a part of GDP, and they also represent the actual costs to the resident

economic entities for recovering the losses to assets.

150. Whereas, in principle, catastrophic losses to the baseline value of national assets is conceptually

well aligned with the concept of direct economic loss from the Sendai Framework and SDGs, the

approach for valuation actually depends on data on estimated costs for reconstruction of the

assets, which is an activity valued as part of productive and income-earning activities.

Figure 7: Catastrophic Losses in the SNA

151. Sources of data on reconstruction costs are records from insurance claims or payments, e.g. from

government, for repairs to infrastructure. On aggregate, these values are the costs for repairing or

replacing the lost assets or consumer durables.


35

152. Expenditures on disaster reconstruction and recovery may be difficult to identify and isolate from

the current sources of statistics on construction and other related activities because they are

implicitly recorded as part of a broader classification of transactions. However, in principle, a

functional selection of transactions, known as “satellites” to the SNA, can be compiled for the

relevant flows that are already included, but not explicitly, in the national accounts as

reconstruction after a disaster.

153. The value for these implicit transactions within the broader expenditure accounting could be

estimated, especially for the government expenditure. The purpose of satellite accounts is to

present, in a systematic and comprehensive way, key aggregate economic indicators (called

balancing items in the national accounts) for analysis of a particular domain such as education,

health, research and development, environmental protection or on a multi-dimensional activities

such as tourism. A main aggregate of interest from disaster risk reduction satellite account is

national DRR expenditure. Its magnitude can be compared with other activities and with the total

GDP. Within the development of a disaster risk reduction satellite account (see Section 2e) is an

item for reconstruction expenditures and also the post-disaster “structural measures” for future

disaster prevention (i.e. “building back better”).

154. Theoretically, an expenditure accounting approach to estimating direct economic costs from

disaster has an advantage of being based on observed transactions and thus aligned with actual

expenses and real costs to society for restoring the stock of infrastructure as the basic building

blocks for economic activity. However, in reality, not all damaged or destroyed assets are

recovered through reconstruction at all or at least not precisely with a replacement of the assets

that were there before. Some assets are replaced by qualitatively different new assets. The costs

of “building back better”, for example, are different from the losses to assets. However, these

additional costs are also useful statistics, as important component of the overall economic

investment in achieving disaster risk reduction targets.

155. In summary, while there is a strong international demand for internationally comparable

indicators for direct economic loss, there is also an interest to produce multiple related figures,

where possible, in order to meet different purposes of economic analysis, including, in particular:

assessments of the indirect economic impacts of disasters and accounting for costs of

expenditure for the post- disaster rebuilding and the broader measurement of disaster risk

reduction expenditures, including costs for “building back better”.

Direct Economic Impacts to Agriculture

156. A very important case for understanding the scope for valuing direct material impacts to from

disasters is agriculture, forestry and fisheries.

157. Economic assets include machinery and equipment used in production and also land (or

improvements to land, following the SNA definition) and other resources like livestock and

plantations. Sometimes improvements (e.g. irrigation) that were made to the land are undone as

a direct impact of a disaster, making continued use of the land impractical without restoration and

restarting the production process. Also, if crops, livestock or trees are killed by a disaster, the

only option is to purchase replacement inputs from the market and, effectively, restart the

production process over again.


36

158. In cases of damaged or destroyed crops, the market value of the finished product provides an

estimate of the value of losses in foregone revenue from the owner’s perspective. For practical

reasons, this price is the recommended value used to estimate the value of impacts from a disaster

to work-in-progress, unharvested crops, and also to livestock, fisheries and to forest cover (both

cultivated and non-cultivated forests are recognized as assets in the SNA).

159. Thus, for the case of agriculture, there are at least 3 distinct types of direct impacts and valuation.

These different values can be aggregated for direct impacts without double-counting: (i)

estimated market price value of destroyed crops, livestock, and trees, (ii) replacement costs for

damaged or destroyed buildings and equipment, and (iii) recovery costs for damages to restore

improvements to the land.

Economic Loss and Poverty

160. The demand for direct economic loss from disasters indicator in the Sendai Framework and

SDGs goes beyond the aggregate analysis, but also to provide for focused analyses for risk

reduction for the poor and other people in vulnerable situations. This can be accomplished via the

linkages to human impact statistics, in particular households affected by damages to their

dwellings or other assets. In the indirect economic costs assessments, considering the social costs

of damages to assets (effects on employment, changes in the structure of the economy, etc.) is

also crucial for assseing economic impacts and vulnerable groups. Another important link for

understanding this relationships with poverty reduction is to review statistics on financial support

during and after a disaster.

161. In the Impacts of Climate Change on Human Life Survey (2015) of the Bangladesh Bureau of

Statistics, data were collected from households on financial support after a disaster by source and

purpose of the assistance. This type of data could also be used to produce statistics categorized

according to recipients, including relevant population groups and geographic regions.

2d) Human Impacts

162. In DRSF, there are two basic categories of statistics on impacts from disasters: the material

impacts (previous section) and human impacts. Some of the statistics relate to both categories and

therefore provide a bridge between the material and human impacts tables. For example, in

principle, the same data sources are used for accounting for damaged or destroyed dwellings (an

indicator in the Sendai Framework Target C for economic loss) should also be applicable for

estimating the number of people whose houses were damaged due to hazardous events (also an

indicator for monitoring the Sendai Framework, under Target B for affected population) and a

potential cause of temporary or permanent displacement. Also, often there are medical costs and

impact to economic livelihoods of inviduals, which are human impacts that should also be

incorporated, where feasible, into the broader assessments of economic losses from disasters.


37

163. The rows in the summary statistics tables on human impacts (see Table C2 below) provides a

summary of a basic range of statistical outputs compiled from various sources for describing the

human impacts of disasters, with links to Sendai framework Target B indicators highlighted.

DRSF Table C2: Summary of human impacts by hazards types and geographic regions

Geo-physical

Hydrological

Meteorological & Climatalogical

Biological

Other

Adjustment for multiple counting of occurneces

by types

TOTAL Region 1

1 - Su

mm

ary of H

um

an Im

pacts

Hu

man

, affected p

op

ulatio

n

1.1D

eaths o

r missin

gSD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1

1.1

.1D

eath

sSe

nd

ai A-2

Sen

dai A

-2Se

nd

ai A-2

Sen

dai A

-2Se

nd

ai A-2

Sen

dai A

-2

1.1

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38

Disaggregation of human impacts statistics

164. When estimates for human impacts are initially recorded by disaster management agencies, basic

demographic and social information about the affected people (such as age and sex) may not yet

be known because this information about the population is not necessarily a priority during the

emergency period. Therefore, disaggregation of impacts may involve a secondary step of

estimation and linking between multiple data sources.

165. Sometimes there are challenges in producing disaggregated demographic and social information

for describing affected populations. The sample table extracted from the Philippines pilot study

reporting shows an example of how available disaggregated statistics can be utilized, even when

the information is incomplete, by including a category for “unidentified”.

166. For future disaster occurrences and through increased experience with compiling summary

statistics after disasters over time, it becomes possible, via linking datasets, to produce social

and demographically disaggregated statistics more completely for a basic range of human impact

statistics for specific disaster or for multiple occurrences over a period of time and for regional

and national levels.

Sample Table: demographic disaggregation of affected population statistics, extract from

Philippines

Dea

th

Year Age groups

TOTAL

Sex

TOTAL

0-4

5-60 60+ Unidentified

Male Female Unidentified

2013

46

423

246 5,899 6,614

887 864 4,863 6,614

2014

22

202 45 25 294

200 87 7 294

2015 12 95 18 10 135 94 41

135

Mis

sin

g

Year Age groups

TOTAL

Sex

TOTAL

0-4

5-60 60+ Unidentified

Male Female Unidentified

2013 4 42 1 1,038 1,085 91 28 966 1,085

2014 2 19 0 11 32 25 7 0 32

2015 0 13 0 13 26 20 2 4 26

Source: Philippines Department of National Defense and Philippines Statistics Authority, via DRSF Pilot Study, 2016

Deaths or Missing

167. Deaths or missing is a combined category of statistics because missing people are either found or,

unfortunately, eventually declared dead. The transition from missing to dead follows a procedure

and period of time, which varies according to national laws. The differences in laws and practices

in terms of the time period for missing persons do not affect the statistics in the long-term


39

because, eventually, in all cases the total amount of fatalities includes the missing and later

declared dead.

168. However, rules for attribution for deaths or missing population to a disaster currently vary

internationally, which affects the comparability for scope of human impacts measurements. Rules

for attribution of deaths to a disaster cannot be standardized across all cases, but the general

framework for attribution is:

a. deaths occurring during an emergency period (or deaths caused by an injury or illness sustained

during an emergency) and believed to be caused by a hazard, and

b. indirect fatalities associated with a hazard, e.g. deaths from illnesses caused by consequences

(poor access to water and sanitation, exposure to unsanitary or unsafe conditions) resulting from a

hazard.

169. The usual source of official records for deaths and causes of death, where it could be determined,

are via civil registration authorities and the Ministry of Health, which is responsible for

maintaining and monitoring health information systems. However, in the event of a disaster,

records for deaths or missing are, in the short-term, more commonly a responsibility of the

national disaster management agency in partnership with the Ministry of Health and others as part

of the disaster response and compilation of impacts from the disaster. These figures are reported

by different levels of local and national government and usually at some stage are shared in

official reports to the general public via the press. Commonly there is a need to revise original

reported counts on deaths (and other human impacts) following the emergency and after sufficient

time to assess the sources of data and account for all of the cases. The revised figures must be

stored in the centralized compilations of disaster impacts statistics across occurrences and utilized

for calculating indicators.

170. A key consideration at this stage of compilation of revised figures is ensuring that the final

official counts of deaths after a disaster are also incorporated into the broader official system of

administrative records (i.e. the civil registration system) and statistics, which is also the source

used for the long-term and comprehensive official statistics on mortality and health of the

population. These administrative sources have many important uses, including for estimating the

rate of growth of populations and for investigating public health issues, such as trends in mortality

from different types of health challenges. Civil and health administrative records contain

confidential information, but can be utilized to produce broad summary statistics for describing

trends in the population without revealing private information about individuals.

Injured and ill

171. Besides deaths, the other two main physical impacts from disasters to humans are injuries and

illness. The relative importance of injuries or illnesses will vary depending on the characteristics

of the underlying hazard as well as on social factors, especially the vulnerability factors of the

population in an affected area.

172. In Bangladesh, for example, illness is a more frequently occurring impact from disasters

compared to injuries, overall. But, the frequencies for injuries or illnesses vary by hazard type and

also depending on the age and gender of the exposed population.


40

Sample Table: Illness/ Injuries in Bangladesh with demographic disaggregation, 2006-2015

Bangladesh

C2a1 - Age groups TOTAL

C2a2 - Sex TOTAL

0-4 5-60 60+ Male Female

Illness 330378 1472750 87605 1890733 990769 899966 1890735

Injuries 2324 25273 5309 32906 19126 13782 32908

Source: Bangladesh Disaster-related Statistics, Bangladesh Bureau of Statistics 201

173. Injuries and illness causes economic costs to household, both directly and indirectly (via

insurance premiums). Since direct economic loss measurement (see previous section) is limited to

the costs associated with material impacts to assets, medical costs (and other related logistical

costs) associated with responding to the emerging and the physiological (including psychological)

impacts to people should compiled as additional measures for accounting for the overall

economic costs of disasters.

Displaced Populations

174. One of the immediate and conspicuous ways in which lives and livelihoods are impacted after a

disaster is though temporary or permanent displacement. Displacement statistics are organized

according to two characteristics: length of time and whether or not displacement was arranged (or

ordered or financed) by governing agencies.

175. For all types of movement of the population after a hazard, including evacuations and permanent

relocations of people due to a disaster, the suggested term is displacement.

176. In the adopted terminology for the Sendai Framework (UNGA, 2016), evacuation is defined as:

“moving people and assets temporarily to safer places before, during or after the occurrence of a

hazardous event in order to protect them.” Evacuations are not considered part of “affected

population” according to the conception of Sendai Framework indicators. This is because

evacuation is also a method of disaster risk reduction. However, at the level of the basic statistics,

both analytical perspectives can be accommodated in output tables from the database.

177. Sometimes, there are also voluntary evacuations, in which households temporarily relocate from

a hazard area on their own expense (e.g. temporarily residing with family in another part of the

country). In this case, use of household surveys, and/or estimation is required for producing

counts of individuals and households.

178. The other common cause of displacement after a disaster is displacement caused by a damaged or

destroyed dwellings. In the extreme cases, dwellings are completely destroyed, effectively leaving

households homeless and in need of immediate relocation to another site. Another possibility

includes minor damages that could be repaired but require a temporary relocation of the household

for safety reasons. There are also cases where the dwelling structure may have received negligible

damages but due to the changes of circumstances regarding the location of the dwelling, the area


41

is deemed unsafe for continued residential occupation. For all cases, the statistics can be

summarized most broadly according to permanent or temporary displacement.

179. The tables below show some sample statistics on evacuations for Philippines and Indonesia

collected from national official sources. In the case of the Philippines, the term “displaced” was

used for numbers of people evacuated as result of a disaster.

Sample Table 3: Evacuations in the Philippines by Hazard Type and Geographic Region,

2013-15

Source: Philippines Department of National Defense and Philippines Statistics Authority, via DRSF Pilot Study, 2016 Sample Table: Number of people evacuated by region and hazard type in Indonesia (2015)

IND

ON

ESIA

Province

EVACUATED

Drought Earthquake Flood Flood and

Landslide Landslide

Tidal Wave/ Abrasion

Tornado

Aceh 0 0 36522 68 456 336 29491

Bali

0

0

Bangka Belitung

0 0 0

0

Banten

0 0

0

0

Bengkulu

0 0 0 0 0

Central Java 0 0 2833 25 1166

700

Central Kalimantan

0

0

0

Central Sulawesi

0 200 375 4 East Java 0 0 1040 0 760 0 5

geophysicalmeteorologi

caltotal

Region I (Ilocos) 567,177 567,177

Region II (Cagayan Valley) 724,559 724,559

Region III (Central Luzon) 2,227,691 2,227,691

Region IV-A (Calabarzon) 561,932 561,932

Region IV-B (Mimaropa) 44,183 44,183

Region V (Bicol) 2,131,495 2,131,495

Region VI (Western Visayas) 99 2,471,882 2,471,981

Region VII (Central Visayas) 465047 870,617 1,335,664

Region VIII (Negros Island Region) 1,949,110 1,949,110

Region IX (Zamboanga Peninsula) 3,600 3,600

Region X (Northern Mindanao) 73,003 73,003

Region XI (Davao Region) 207,057 207,057

Region XII (Soccsksargen) 129,368 129,368

Region XIII (Caraga) 536,806 536,806

National Capital Region (NCR) 264,323 264,323

Cordillera Administrative Region (CAR) 239,936 239,936

Autonomous Region of Muslim Mindanao (ARMM) 27,116 27,116

National total (unadjusted) 465146 13029855 13495001

DISPLACED

PH

ILIP

PIN

ES


42

East Kalimantan

0 10 0 5 0 12165

East Nusa Tenggara 0

85

1190

5439

Gorontalo

406

0

522

Jakarta

1762

5997

7419

Jambi

150

0

0

Lampung

0

0

0

Maluku 4 0 8 423 12

1069

North Kalimantan

2238

0

11

North Maluku

11796 0

0

North Sulawesi

4031

3672

583

North Sumatra

0 75 77 500

11113

Papua

0

0

0

Riau

0 55 0 0

86

Riau Islands

0

0

792

South Kalimantan

0 0 0

0

South Sulawesi

30 103

211 0 40

South Sumatra

0 0 0 0

0

Southeast Sulawesi

0 65

0

West Java

0 1577 65 11825 0 4154

West Kalimantan

51 0 1740

8

West Nusa Tenggara

0 600 0 2500

0

West Papua

0 West Sulawesi

0

0

0

West Sumatra

0 1854 0 8382

75

Yogyakarta

0

22

3

National Total 4 30 65461 1033 38442 336 73675

Source: Informasi Bencana Indonesia (DIBI): http://dibi.bnpb.go.id

180. An interesting example relating to the displacement associated with impacts to dwelling comes

from New Zealand, where the Population and Housing Census was used to asses changes in the

stocks of dwellings after a series of major earthquakes in Canterbury province. The National

Statistics Office of New Zealand also studied other types of impacts of the earthquake on housing

(see Goodyear, 2014), such as investigating the pre and post-disaster trends in numbers of

occupied dwellings, household deprivation and crowding, and numbers of people living in ‘other

private dwellings’ (e.g. mobile dwelling or motor camps). As the displaced population obviously

needed to resettle (either temporarily or permanently) elsewhere, some districts within the

province received a net upsurge in occupied dwellings and these changes could be observed in the

statistics as well.

Sample Table: Estimated changes in occupied private dwelling stocks, Christchurch, New

Zealand , 2001-2013

http://dibi.bnpb.go.id/


43


44

Impacts to livelihood

181. Impacts (or disruptions) to livelihoods is a concept from the internationally adopted

recommendation for the Sendai Framework monitoring (UNGA, 2016). The concept is broad and

measurement for Sendai Framework indicators is deferred to national practices. UNISDR

guidance defines livelihoods as: “the capacities, productive assets (both living and material) and

activities required for securing a means of living, on a sustainable basis, with dignity.”

182. A core factor for sustainable livelihood of most households, not already covered elsewhere in the

framework, is employment. Impacts to employment are measured similarly with disruptions to

basic services, i.e. in terms of number of people affected and length of time.

183. Utilizing a household survey specially designed for evaluating impacts from disasters,

Bangladesh Bureau of Statistics reported statistics on impacts to employment (and other basic

factors of livelihood like access to water and sanitation) across the affected population, according

to numbers of individuals affected by geographic regions and in terms distribution in ranges of

number of losses of days.

Sample Table: Number of Households experiencing disruptions to employment or in access to

water and sanitation due to disasters, Bangladesh 2009-2014

Division Disruptions to Employment

Disrupted access to water and sanitation

Barisal Division 4361261 108501

Chittagong Division 818137 77650

Dhaka Division 430540 139357

Khulna Division 931668 120061

Rajshahi Division 668873 56920

Rangpur Division 613704 55125

Sylhet Division 488564 55859

Bangladesh 409776 613474 Source: Bangladesh Disaster-related Statistics 2015


45

Sample Table: Number of Households missing work due to disasters,distribution by number of

days missed, Bangladesh, 2009-2014

Division/District

Working days

Total Number of Days

1-7 8-15 16-30 31+ Total

Bangladesh 395088 400737 230251 52042 1078118

Barisal Division 170240 81965 7721 4888 264814

Chittagong Divition 69765 35149 10533 696 116143

Dhaka Division 54301 86973 57418 1639 200332

Khulna Division 19748 15277 28073 14027 77125

Rashahi Division 12524 25427 29323 24978 92251

Rangpur Division 44140 51930 30453 3217 129740

Sylhet Division 24370 104015 66731 2598 197713 Source: Bangladesh Disaster-related Statistics 2015

Disruptions to basic services

184. Disasters are defined as disruptions to the functioning of a community or a society (UNGA,

2016), and some particular types of disruptions can be estimated based on the available data on

material impacts from disasters.

185. Disruptions to services from material impacts, like all other impacts tables, can be presented

according to hazard types (as in Table D2a below) and/or according to geographic regions within

the country. These statistics are an extension of direct impacts to critical infrastructure (Table

D2).

Table D2a Disruptions to Basic Services from a Disaster by Hazard Type


46

Measurement units: number of people and period of time

Definitions of Services: see Material Impacts Classification in Chapter 3

Aggregated statistics on human impacts

186. There are many ways that human impact statistics can be presented or aggregated in summary

tables. This is a choice of presentation for dissemination of statistics, rather than a conceptual

decision, but the scope of compilations of various types of impacts also substantively affects the

aggregations of combined counts of multiples types of human impacts from disasters (e.g. “total

affected population”). Databases can always be queried in multiple ways for multiple purposes.

The presentation and organized structure of human impacts tabulation will vary depending on the

requests of users. The figure below demonstrates one way (among several possibilities) of

structuring human impact variables.

Figure 8: Sample structure of basic range of human impacts statistics

Geo

-ph

ysic

al

Hyd

rolo

gica

l

Met

eoro

logi

cal &

Clim

atal

ogi

cal

Bio

logi

cal

Oth

er

TOTA

L

Disruptions to Basic services from a Disaster1 Health services Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7

1.1 No. of people

1.2 Length of time

2 Educational services Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6

2.1 No. of people

2.2 Length of time

3 Public administration services Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8

3.1 No. of people

3.2 Length of time

4 Transport services

4.1 No. of people

4.2 Length of time

5 Electricity and energy services

5.1 No. of people

5.2 Length of time

6 Water services

6.1 No. of people

6.2 Length of time

7 ICT services

7.1 No. of people

7.2 Length of time

8 Other basic services

8.1 No. of people

8.2 Length of time

9 Total disruptions Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5

Hazard types


47

187. There is a broad demand for aggregated counts of “affected population” after a disaster, for

example as an indicator for international monitoring of the Sendai Framework (UNGA, 2015).

UNISDR Guidance has ruled out, for purposes of international monitoring of the Sendai

Framework, adjustments for multiple counts of the same individual, which may be affected by the

same disaster in several ways. For example an unfortunate individual could experience an injury, a

damaged dwelling, and a temporary loss of employment all from the same disaster. This means

the Sendai Framework “affected population” indicator is actually a count of number of impacts,

rather than number of people.

188. For other purposes or compilations of statistics, another possibility is to estimate an adjustment

for multiple counts as an additional aggregation (variables 1.9 and 1.10 in C tables) measured in

terms of numbers of people. Both aggregations are relevant for users and should be possible to

estimate utilizing the same basic underlying sources of data.

189. A similar situation can be observed for several other areas of social statistics, such as (e.g.)

statistics on domestic violence or abuse. For statistics on domestic violence, usually there are

multiple categories of abuse reported (e.g. physical, verbal/psychological, sexual, other) and

sometimes, the same individuals may be affected by multiple categories of abuse. Thus, there are

two potential aggregated statistics among the relevant populations: total number of people affected

by abuse and total number of individual cases of abuse across all categories.

190. Another example arose with the demand for measurement of population living in slums. Slum-

dwelling households are defined according to a list of either/or categories. The aggregated

Hu

man

Imp

acts

Physically Affected

Dead

Missing

Injured or Ill Displaced

Other Impacts to Livelihood

Damged or Destroyed Dwelling (1.1.4)

Loss of employment

Otherwise Affected

Received humanitarian assistance

(e.g. food or assistance with evacutionn)


48

number, which is also an international SDG indicator, is calculated as the number of households

experiencing at least one or more of the defining characteristics of slums:

(a)Durable housing of a permanent nature that protects against extreme climate conditions;

(b)Sufficient living space which means not more than three people sharing the same room;

(c)Durable housing of a permanent nature that protects against extreme climate conditions;

(d) Sufficient living space which means not more than three people sharing the same room;

(e)Easy access to safe water in sufficient amounts at an affordable price;

(f) Access to adequate sanitation in the form of a private or public toilet shared by a

reasonable number of people.

(g) Security of tenure that prevents forced evictions.

(h) Easy access to safe water in sufficient amounts at an affordable price.

191. Since there are many categories of human impacts included in the basic range of disaster-related

statistics, there are multiple sets of double-counting adjustments for consideration in each

aggregation, multiplied by the number of categories that are non-exclusive, e.g.: injured/ill,

displaced and otherwise affected.

192. The Venn diagram below is a visualization of the different types of multiple counts (a,b,c,d)

from a hypothetical example. In practice, measurement of counts for each individual case of

multiple counts may not be feasible because it requires matching identification of individuals for

different impacts (potentially recorded from different data sources). However, a general estimate

(N) for counts for situations ‘a’, ‘b’, ‘c’, an ‘d’ is sufficient for making an estimated adjustment

from the number of impacts to counts of individuals. In this case, the adjustment is equal to N-1.

Figure 9: Venn Diagram of cases of multiple counts for individuals impacted by a disaster.

2e) Disaster Risk Reduction Activities

h f e a

b c d

g


49

193. The Sendai Framework describes disaster risk reduction (DRR) as a scope of work “aimed at

preventing new and reducing existing disaster risk and managing residual risk, all of which

contributes to strengthening resilience. DRR encompasses all aspects of work including the

management of residual risk, i.e. managing risks that cannot be prevented nor reduced, and are

known to give rise to, or already, materialize into a disaster event.” (United Nations, 2015)

194. In order to make a case for increases or improvements in DRR, a sufficiently accurate

quantification of the existing activities is needed. Government and other entities allocate budgets

to DRR and information on these activities is needed to determine effective means, within the

different contexts of disaster risk, to identify new projects or investment opportunities that could

significantly reduce risk or prevent unacceptable risks of impacts from a disaster.

195. Another important purpose for measuring and monitoring DRR activities and expenditures is they

can be critical inputs for estimating the economic costs from disasters, since a large part of post-

disaster recovery is support for basic needs of affected communities and the reconstruction effort.

196. Often the publically-financed disaster risk reduction activities, particularly disaster recovery, are

transfers from budget from central government to local authorities, and/or international transfers

(e.g. ODA). These transfers can be tracked through balance of payments and national accounts

statistics, just as with other types of transfers and activities (production, investment, employment)

in the economy as long as the activities with a DRR purpose can be specifically identified and

isolated from the broader national figures.

197. There are two complementary approaches that can be applied for isolating the relevant values and

producing statistics on DRR activities, particularly the quantifications, in monetary terms, of DRR

transfers and expenditures.

198. The first approach is to produce a focused analysis of transfers from relevant institutions and to

analyse transfers and expenditures on a particular geographic region and time period where there

is a large-scale disaster recovery underway. This is an application of the existing statistics on

government finance and statistics derived from administrative records or outcomes of surveys or

censuses on the activities of businesses and households for analysis of trends for estimation of

approximate shares of DRR-related activities.

199. A second approach is to develop a series of functional accounts and indicators that track all types

of transfers and expenditures in the economy with a specific DRR purpose. The tool that

statisticians use to produce the economic statistics in the latter approach is to develop specific

functional classification in order to define the domain of interest. DRR-characteristic activities

(DRRCA) are defined (in order to objectively identify shares of expenditures or transfers with a

DRR purpose).

200. The provisional classification of DRRCA is developed (see detail in Chapter 3), starting from the

Sendai Framework and the recently adopted terminology adopted by the UN General Assembly.

(UNGA, 2016). Following the Sendai Framework definition for disaster risk reduction, the

scope of DRRCA. activities is:


50

a) Disaster Risk Prevention

b) Disaster Risk Mitigation

c) Disaster Management

d) Disaster Recovery

e) General Government, Research & Development, Education Expenditure

Disaster risk reduction characteristic transfers include:

a) Internal transfers between public government services

b) Risk transfers, insurance premiums and indemnities

c) Disaster related international transfers

d) Other transfers

201. The same approach is also utilized for several other important cross-cutting domains of

economies (e.g. health, tourism, education), often designed as “satellite accounts”, which refers to

their nature as specially designed extracts (or “satellites”) of the system of national accounts

(SNA).

202. Typical outputs from accounts of expenditures or transfers of DRR activity, following the basic

framework of the SNA, will include:

a. Total national expenditure with a DRR purpose

b. DRR expenditure by source of financing (e.g. central government, local government,

private sector)

c. DRR expenditures and transfer by beneficiaries

d. DRR expenditure by type of DRR activity (e.g. disaster preparedness, recovery and

reconstruction, early warning systems, etc.)

e. Values of transfers from central government to local authorities

f. Values of transfers from international donors – i.e. DRR-related overseas

development assistance (ODA).

203. While hazards and disasters are events happening randomly in terms of timing and in relation to

the society, DRR is a continuous activity needed to strengthen society’s resistance and resilience

and thus DRR statistics should be compiled on a continuous and periodic basis (e.g. as annual

accounts). In this way, DRR statistics become an integrated and relatively conventional domain of

statistics, as an extension to the existing national accounts.

204. However, as there may be special demands for analysis of DRR activities at certain periods, such

as after a large-scale disaster, regular compilations of accounts of DRR expenditures and transfers

are complemented by specially designed studies and statistics for analyses of specific events or

to improve the understanding of the effectiveness of DRR investments made before or after a

disaster.


51

Part 2: Guidelines for Implementation

Chapter 3: Statistical Classifications and Definitions

205. This chapter elaborates classifications for two concepts introduced in Chapter 2, developed in

detail with an aim to improve comparability of disaster-related statistics: (i) direct material

impacts, and (ii) disaster risk reduction characteristics activities (DRRCA).

206. Most of the groupings and definitions utilized in this draft section come from one of two sources

referenced as [UNGA, 2016], which refers to the “Report of the open-ended intergovernmental

expert working group on indicators and terminology relating to disaster risk reduction” or [SNA,

2008], which refers to the 2008 edition of the System of National Accounts. Also utilized for

definitional reference is the UN Central Product Classification (CPC ver. 2.1) and the System of

Environmental-Economic Accounts –Central Framework (SEEA, 2012).

Direct Material Impacts Classification

207. An indicator for international monitoring of direct economic loss for the Sendai Framework and

SDG indicators is defined as measurement, at first in physical terms, and then valuation in

monetary terms of physical damages to assets from a disaster.

208. Comparable and consistent for measurement of material impacts (and, subsequently, economic

loss valuation) from a disaster across disaster occurrences needs to be built upon a clear and

precise delineation of the scope of measurement – i.e. the objects of material impact. A

harmonized scope of measurement and categorization can be defined with reference to a standard

classification, building upon the definition of assets from the SNA, among other references,

presented below.

209. In addition, a specialized functional sub-category for critical infrastructure is required to

measure direct impacts specifically to critical infrastructure. The scope of measurement for

material impacts can be defined at the national level with reference to this classification, and with

a recommended prioritization for critical infrastructure.

210. Critical infrastructure is the physical structures, facilities, networks and other assets which

provide services that are essential to the social and economic functioning of a community or

society. [UNGA p.13, 2016] The detail classification for critical infrastructure is presented below

as an elaboration, and not as a duplication, of the broader classification. given its special

importance,. Please note that one of the basic principles of statistical classifications is that they

are mutually exclusive, i.e. the same item is not replicated in multiple locations within the

classification.

211. Each fixed asset can be damaged or destroyed (illustrated below for the case of dwellings only).

Destroyed assets are asset with total damages, which are beyond repair. A destroyed asset is a

total loss in terms of asset value and replacement would require a complete reconstruction.


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2.1 Direct impacts on fixed assets (SNA asset definition)

2.1.1 Dwellings:

Dwellings are buildings, or designated parts of buildings, that are used entirely or primarily as

residences, including any associated structures, such as garages, and all permanent fixtures

customarily installed in residences. Houseboats, barges, mobile homes and caravans used as principal

residences of households are also included, as are public monuments identified primarily as dwellings.

[SNA 10.68]

2.1.1.1 Dwellings destroyed

2.1.1.2 Dwellings damaged

2.1.2 Buildings and structures

Buildings: Buildings other than dwellings include whole buildings or parts of buildings not designated

as dwellings. Fixtures, facilities and equipment that are integral parts of the structures are included.

Public monuments identified primarily as non-residential buildings are also included. [SNA 10.74]

Examples include products included in CPC 2.0 class 5312, non-residential buildings, such as

warehouses and industrial buildings, commercial buildings, buildings for public entertainment, hotels,

restaurants, schools, hospitals, prisons etc. Prisons, schools and hospitals are regarded as buildings

other than dwellings despite the fact that they may shelter institutional households. [SNA 10.75]

Structures: Other structures include structures other than buildings, including the cost of the streets,

sewer, etc. The costs of site clearance and preparation are also included. Public monuments for which

identification as dwellings or non-residential buildings is not possible are included as are shafts,

tunnels and other structures associated with mining mineral and energy resources, and the construction

of sea walls, dykes, flood barriers etc. intended to improve the quality and quantity of land adjacent to

them. The infrastructure necessary for aquaculture such as fish farms and shellfish beds is also

included. [SNA 10.76]

Examples also include products included in CPC 2.0 group 532, civil engineering works, such as

highways, streets, roads, railways and airfield runways; bridges, elevated highways, tunnels and

subways; waterways, harbours, dams and other waterworks; long-distance pipelines, communication

and power lines; local pipelines and cables, ancillary works; constructions for mining and

manufacture; and constructions for sport and recreation. [SNA 10.77]

2.1.2.1 Critical buildings and structures: Critical buildings and structures are defined as a sub-

category of assets for disaster statistics (see elaboration below)

2.1.2.2 Other buildings and structures: Buildings and structures not designated as critical

2.1.3 Machinery and equipment:

Machinery and equipment cover transport equipment, machinery for information, communication and

telecommunications (ICT) equipment, and other machinery and equipment. Tools that are relatively


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inexpensive and purchased at a relatively steady rate, such as hand tools, are excluded. Also excluded

are machinery and equipment integral to buildings that are included in dwellings and non-residential

buildings. Machinery and equipment acquired for military purposes are included, except for weapons

systems, which form another category. [SNA 10.82]

2.1.3.1 Critical machinery and equipment:

Critical machinery and equipment are defined as a sub-category of assets for disaster statistics (see

below)

2.1.3.2 Other machinery and equipment:

Other machinery and equipment consists of machinery and equipment not elsewhere classified.

Examples include products other than parts and items identified in other categories of fixed capital

formation included in the International Central Product Classification (CPC), Ver.2.0 divisions 43,

general purpose machinery; 44, special purpose machinery; 45, office, accounting and computing

equipment; 46, electrical machinery and apparatus; 47, radio, television and communication

equipment and apparatus; and 48, medical appliances, precision and optical instruments, watches and

clocks.

2.1.4 Environmental Resources

Not all environmental resources qualify as economic assets. It is useful to delineate the naturally

occurring resources that fall within the asset boundary of the SNA from those that do not and cannot

be assessed as economic loss in monetary terms consistently with the SNA. To qualify as an economic

asset, the asset must be capable of bringing economic benefit to an institutional unit [SNA 10.168].

The SNA further distinguishes between cultivated and non-cultivated natural resources. Cultivated

biological resources cover animal resources yielding repeat products and tree, crop and plant resources

yielding repeat products whose natural growth and regeneration are under the direct control,

responsibility and management of institutional units. [SEEA 5.24 and SNA10.88].

2.1.4.1 Agriculture land, livestock, fish stocks, and managed forests

2.1.4.1.1 Land

Land consists of the ground, including the soil covering and any associated surface waters, over which

ownership rights are enforced and from which economic benefits can be derived by their owners by

holding or using them. The value of land excludes any buildings or other structures situated on it or

running through it; cultivated crops, trees and animals; mineral and energy resources; non-cultivated

biological resources and water resources below the ground. The associated surface water includes any

inland waters (reservoirs, lakes, rivers, etc.) over which ownership rights can be exercised and that

can, therefore, be the subject of transactions between institutional units. However, water bodies from

which water is regularly extracted, against payment, for use in production (including for irrigation) are

included not in water associated with land but in water resources. [SNA 10.175]

Land improvements are the result of actions that lead to major improvements in the quantity, quality

or productivity of land, or prevent its deterioration. [SNA 10.79]


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Managed Forest (area): Tree, crop and plant resources yielding repeat products cover plants whose

natural growth and regeneration are under the direct control, responsibility and management of

institutional units. They include trees (including vines and shrubs) cultivated for fruits and nuts, for

sap and resin and for bark and leaf products. [SNA 10.95]

2.1.4.1.2 Livestock & Fisheries

Animal resources yielding repeat products cover animals whose natural growth and regeneration are

under the direct control, responsibility and management of institutional units. They include breeding

stocks, dairy cattle, draft animals, sheep or other animals used for wool production and animals used

for transportation, racing or entertainment. Animals raised for slaughter, including poultry, are not

fixed assets but inventories. Immature cultivated assets are excluded unless produced for own use.

This includes aquatic resources yielding repeat products, consisting of aquatic resources maintained

for controlled reproduction.

Although the SNA recommends to exclude immature animals in valuation, immature animals should

be included for calculating direct impacts from disasters. Immature animals are particularly vulnerable

to hazards and accumulate value in the future, post-disaster period, though natural growth (recorded as

capital accumulation in the SNA). However, according, to the SNA, mature animals should be valued

at their market value in exchange at the time of the disaster.

2.1.4.1.3 Agricultural Crops

Work-in-progress consists of output produced by an enterprise that is not yet sufficiently processed to

be in a state in which it is normally supplied to other institutional units. Work-in-progress occurs in all

industries, but is especially important in those in which some time is needed to produce a unit of

finished output, for example, in agriculture, or in industries producing complex fixed assets such as

ships, dwellings, software or films. Although work-in progress is output that has not reached the state

in which it is normally supplied to others, its ownership is nevertheless transferable, if necessary. For

example, it may be sold under exceptional circumstances such as the liquidation of the enterprise.

[SNA 10.134]

2.1.4.1.4 Primary forests and other uncultivated resources

Uncultivated natural resources cover fish stocks and unmanaged forests. Non-cultivated biological

resources consist of animals, birds, fish and plants that yield both once-only and repeat products over

which ownership rights are enforced but for which natural growth or regeneration is not under the

direct control, responsibility and management of institutional units. Examples are virgin forests and

fisheries within the territory of the country. Only those resources that are currently, or are likely soon

to be, exploitable for economic purposes should be included. [SNA 10.182]

Naturally regenerated forest of native species, where there are no clearly visible indications of human

activities and the ecological processes are not significantly disturbed. Key characteristics of primary

forests are that: (a) they show natural forest dynamics, such as natural tree species composition,

occurrence of dead wood, natural age structure and natural regeneration processes; (b) the area is large

enough to maintain its natural characteristics; and (c) there has been no known significant human

intervention or the last significant human intervention occurred long enough in the past to have

allowed the natural species composition and processes to have become re-established. [SEEA 5.286]

2.1.4.2 Water resources


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Water resources consist of surface and groundwater resources used for extraction to the extent that

their scarcity leads to the enforcement of ownership or use rights, market valuation and some measure

of economic control. [SNA 10.184]

2.2. Direct impacts on household consumer durables

A consumer durable is a good that may be used for purposes of consumption repeatedly or

continuously over a period of a year or more (excluding dwellings and valuables). A key example

relevant for disaster impacts is cars used for household own-use purposes.

2.3 Direct impacts on valuables (SNA asset definition)

Valuables are produced goods of considerable value that are not used primarily for purposes of

production or consumption but are held as stores of value over time. Valuables are expected to

appreciate or at least not to decline in real value, nor to deteriorate over time under normal conditions.

They consist of precious metals and stones, jewellery, works of art, etc. Valuables may be held by all

sectors of the economy. [SNA 10.13]

2.3.1 Art objects, music instruments:

Paintings, sculptures, etc., recognized as works of art and antiques are treated as valuables when they

are not held by enterprises for sale. In principle, museum exhibits are included under valuables. [SNA

10.153]

2.3.2 Other valuables:

Other valuables not elsewhere classified include such items as collections of stamps, coins, china,

books etc. that have a recognized market value and fine jewellery, fashioned out of precious stones,

and metals of significant and realizable value. [SNA 10.154]

2.4 Critical goods & services

Goods: Goods are physical, produced objects for which a demand exists, over which ownership rights

can be established and whose ownership can be transferred from one institutional unit to another by

engaging in transactions on markets. The production and exchange of goods are quite separate

activities. Some goods may never be exchanged while others may be bought and sold numerous times.

The production of a good can always be separated from its subsequent sale or resale.[SNA 6.15]

Services: Services are the result of a production activity that changes the conditions of the consuming

units, or facilitates the exchange of products or financial assets. These types of service may be

described as change-effecting services and margin services respectively. Change-effecting services are

outputs produced to order and typically consist of changes in the conditions of the consuming units

realized by the activities of producers at the demand of the consumers. Change-effecting services are

not separate entities over which ownership rights can be established. They cannot be traded separately

from their production. By the time their production is completed, they must have been provided to the

consumers. [SNA 6.17]


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Disruptions to critical Services (or “basic services”) is one of the defining impacts of disasters and the

focus for Sendai Framework Target D: “Substantially reduce disaster damage to critical infrastructure

and disruption of basic services”. In this context, the critical services are:

Health Services (CPC 86: “Human health services”)

Educational Services (ISIC 85)

Public Administration Services (CPC 91 “Administrative services of the government”)

Transport Services (ISIC 49: “Land transport and transport via pipelines”, ISIC 50 “Water

transport” , ISIC 51: “Air transport”)

Electricity and Energy Services (ISIC 35: “Electricity, gas, steam and air conditioning

supply”)

Water Services (ISIC 36: “ Water collection, treatment and supply”)

ICT Services (CPC 4 “Telecommunications, broadcasting and information supply

services”)

The portions of these services provided by government are included as part of the UN Central Product

Classification (CPC rev 2.1)11, within Section 9: “Community, social and personal services” and in

the International Standard Industrial Classification of All Economic Activities(ISIC Rev 4) Sections

O, P, or Q.

2.4.1 Inventories (SNA asset definition)

Inventories are produced assets that consist of goods and services, which came into existence in the

current period or in an earlier period, and that are held for sale, used in production or other use at a

later date. Inventories consist of stocks of outputs that are still held by the units that produced them

prior to their being further processed, sold, delivered to other units or used in other ways and stocks of

products acquired from other units that are intended to be used for intermediate consumption or for

resale without further processing. Inventories of services consist of work-in-progress or finished

products, for example architectural drawings, which are in the process of completion or are completed

and waiting for the building to which they relate to be started. Inventories held by government

include, but are not limited to, inventories of strategic materials, and grain and other commodities of

special importance to the nation.

Agricultural inventories may be considered as critical goods and services, in selected cases.

Finished goods consist of goods produced as outputs that their producer does not intend to process

further before supplying them to other institutional units. A good is finished when its producer has

completed his intended production process, even though it may subsequently be used as an

intermediate input into other processes of production. Thus, inventories of coal produced by a mining

enterprise are classified as finished products, although inventories of coal held by a power station are

classified under materials and supplies. Inventories of batteries produced by a manufacturer of

batteries are finished goods, although inventories of the same batteries held by manufacturers of

vehicles and aircraft are classified under materials and supplies. [SNA 10.142]

11 https://unstats.un.org/unsd/cr/registry/cpc-21.asp


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Critical infrastructures : elaboration for items [2.1.2], [2.1.3.1], [2.1.4]

Critical infrastructure: The physical structures, facilities, networks and other assets which provide

services that are essential to the social and economic functioning of a community or society. [UNGA

p.13]. Most critical infrastructures are assets involved in providing non-profit services, according to

the SNA.

2.1.2.1.1 Hospitals, health facilities: Defined as building in CPC under 5312 “non-residential

buildings”

2.1.2.1.2 Education facilities: Defined in CPC under 5312 “non-residential buildings”

2.1.2.1.3 Other critical public administration buildings

2.1.2.1.4 Public monuments: Public monuments are identifiable because of particular historical,

national, regional, local, religious or symbolic significance [SNA 10.78]

2.1.2.1.5 Roads: Defined in CPC under 532 “Civil engineering works”

2.1.2.1.6 Bridges: Defined in CPC under 532 “Civil engineering works”

2.1.2.1.7 Airports: “Passenger Transport Services (CPC 64); Freight Transport Services (CPC 65)

2.1.2.1.8 Piers: “Passenger Transport Services “(CPC 64); “Freight Transport Services” (CPC 65)

2.1.2.1.9 Railway Stations: “Passenger Transport Services “(CPC 64); “Freight Transport Services”

(CPC 65)

2.1.3.1.1 Transport equipment: Transport equipment consists of equipment for moving people and

objects. Examples include products other than parts included in CPC 2.0 division 49, transport

equipment, such as motor vehicles, trailers and semi-trailers; ships; railway and tramway locomotives

and rolling stock; aircraft and spacecraft; and motorcycles, bicycles, etc. [SNA 10.84]

2.1.3.1.2 Electricity generation facilities: Defined as structures and in CPC under 532 “Civil

engineering works”

2.1.3.1.3 Electricity grids: Defined as structures and in CPC under 532 “Civil engineering works”

2.1.3..1.4 ICT equipment: Information, computer and telecommunications (ICT) equipment consists

of devices using electronic controls and also the electronic components forming part of these devices.

Examples are products within CPC 2.0 categories 452 and 472. In practice, this narrows the coverage

of ICT equipment mostly to computer hardware and telecommunications equipment. [SNA 10.85]

2.1.3.1.5 Dams: Defined in CPC under 532 “Civil engineering works”

2.1.3.1.6 Water supply infrastructure: Defined in CPC under 532 “Civil engineering works”

2.1.3.1. 7 Water sewage & treatment systems: Defined in CPC under 532 “Civil engineering works”


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2.2.1 Agriculture land, livestock, fish stocks, and managed forests

2.5.15 Other non-public critical infrastructures

Disaster Risk Reduction Characteristic Activities (DRRCA)

Classification

212. The Sendai Framework describes disaster risk reduction (DRR) as a scope of work “aimed at

preventing new and reducing existing disaster risk and managing residual risk, all of which

contributes to strengthening resilience. DRR encompasses all aspects of work including the

management of residual risk, i.e. managing risks that cannot be prevented nor reduced, and are

known to give raise to, or already, materialize into a disaster event.”

213. A tool that statisticians use to produce these economic statistics is to develop specific functional

classifications in order to define the domain of interest. In this case, DRR-characteristic activities

(DRRCA) are defined in order to objectively identify shares of expenditures or transfers with a

DRR purpose.

214. The terms, definitions and annotations of the DRRCA displayed below are extracted, as much as

possible, from UNGA (2016). Definitions are provided to set the scope for organizing the data

and metadata captured from relevant sources (e.g. government finance statistics) and should be

applied and adapted with more detailed activities and metadata descriptions, as available, from

national sources.

Disaster Risk Reduction Characteristic Activities (DRRCA) and Transfers

1. Disaster risk prevention Activities and measures to avoid existing and new disaster risks

a. Risk prevention in advance of hazardous event

The concept and intention to completely avoid potential adverse impacts of hazardous

events. While certain disaster risks cannot be eliminated, prevention aims at reducing

vulnerability and exposure in such contexts where, as a result, the risk of disaster is

removed. Examples include dams or embankments that eliminate flood risks, land-use

regulations that do not permit any settlement in high-risk zones, seismic engineering

designs that ensure the survival and function of a critical building in any likely earthquake

and immunization against vaccine-preventable diseases.

b. Risk prevention in or after hazardous event Prevention measures taken to prevent secondary hazards or their consequences such as

measures to prevent contamination of water supplies or measures to eliminate natural

dams resulting of earthquake induced landslides and/or rock falls.

2. Disaster risk mitigation Activities and measures to reduce or lessen existing disaster risk or to limit the adverse

impacts of a hazardous event


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a. Structural measures, constructions

Structural measures: Any physical construction to reduce or avoid possible impacts of

hazards, or application of engineering techniques to achieve hazard resistance and

resilience in structures or systems. Common structural measures for disaster risk reduction

include constructed dams, flood levies, ocean wave barriers, earthquake-resistant

construction, and evacuation shelters. Structural measures will include “building back

better” immediately after a disaster.

b. Non-structural measures

Any measure not involving physical construction that uses knowledge, practice or

agreement to reduce risks and impacts through their integration in sustainable

development plans and programmes, in particular through policies and laws, public

awareness raising, training and education typically to reduce vulnerability and exposure.

Non-structural measures may include risk transfers paid/received (e.g. insurance

purchases).

c. Land-use planning

Land- use planning can help to mitigate disasters and reduce risks by discouraging

settlements and construction of key installations in hazard-prone areas, including

consideration of service routes for transport, power, water, sewage and other critical

facilities.

d. Early warning systems management

Inter-related sets of hazard warning, risk assessment, communication and preparedness

activities that enable individuals, communities, businesses and others to take timely action

to reduce their risks.

3. Disaster risk management The organization and management of resources and responsibilities for creating and

implementing preparedness and addressing all aspects of emergencies and others plans to

respond to, and to decrease the impact of disasters. The plans set out the goals and specific

objectives for reducing disaster risks together with related actions to accomplish these

objectives.

a. Preparedness The knowledge and capacities developed by governments, professional response and

recovery organizations, communities and individuals to effectively anticipate, respond to,

and recover from, the impacts of likely, imminent or current disasters.

b. Emergency management National-level plans need to be specific to each level of administrative responsibility and

adapted to the different social and geographical circumstances that are present. The time

frame and responsibilities for implementation and the sources of funding should be

specified in the plan. Linkages to sustainable development and climate change adaptation

plans should be made where possible.

c. Other disaster responses

Includes provision of emergency services and public assistance by private and community

sectors, as well as community and volunteer participation.

Disaster responses includes medical costs for people injured or ill during the disaster.

According to SNA, these costs will include total expenditure on health measures the final

use by resident units of health care goods and services plus gross capital formation in


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health care provider industries (institutions where health care is the predominant activity).

[SNA 29.135]

d. Emergency supply of commodities

Resources and responsibilities for providing emergency support of commodities during a

disaster.

4. Disaster Recovery The restoring or improving of livelihoods and health, as well as economic, physical,

social, cultural and environmental assets, systems and activities, of a disaster-affected

community or society, aligning with the principles of sustainable development and “build

back better”, to avoid or reduce future disaster risk.

a. Relocation Of people who, for different reasons or circumstances because of risk or disaster, have

moved permanently from their places of residence to new sites.

b. Rehabilitation

The rapid and basic restoration of services and facilities for the return to normal

functioning of a community or a society affected by a disaster.

c. Reconstruction

The medium and longer-term repair and sustainable restoration of critical infrastructures,

services, housing, facilities and livelihoods required for full functioning of a community

or a society affected by a disaster.

5. General Government, Research & Development, Education Expenditure

a. General Government Expenditure for Disaster Risk Reduction

Expenditure, whose value must be estimated indirectly, incurred by general government

on both individual consumption goods and services and collective consumption services,

with an explicit disaster risk reduction purpose.

b. Research & Development, Risk assessment, and Information

Risk assessments (and associated risk mapping) include: a review of the technical

characteristics of hazards such as their location, intensity, frequency and probability; the

analysis of exposure and vulnerability including the physical social, health, economic and

environmental dimensions; and the evaluation of the effectiveness of prevailing and

alternative coping capacities in respect to likely risk scenarios.

ISO 31000 defines risk assessment as a process made up of three processes: risk

identification, risk analysis, and risk evaluation.

Risk information includes all studies, information and mapping required to understand the

risk drivers and underlying risk factors.

c. Education to Disaster Risk Reduction

Includes natural and engineering science, training of risks professional, risks specialized

medicine professionals


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Chapter 4: Principles for Implementation

215. The Fundamental Principles of Official Statistics were adopted by the UN General Assembly at

its 68th Session in 2014. (A/RES/68/261)12. Guidelines for Implementation for the Fundamental

Principles of Official Statistics were developed and finalized by the UN Statistics Commission in

2015.13

216. Disaster-related statistics is an emerging area of official statistics, with special characteristics that

are unusual compared to some other more traditional domains of official statistics. However,

disaster-related statistics should be viewed as integrated extension of the broader national statistics

system, and thus its development should be in full alignment with the Fundamental Principles as

adopted by the UN General Assembly.

Statistical Coordination

217. Statistical coordination is an especially important factor for implementing DRSF because most of

the compilations of statistic involve a close collaboration between statistics offices, disaster

management agencies, and (in most cases) several other producers of official data.

218. The Fundamental Principles Implementation Guideline describes the scope of good practices in

Statistical Coordination as follows:

“The issue of statistical coordination is based on the conceptualization of coordination as the set

of processes and procedures for consolidating and achieving official statistics within an

institution or between institutions. Coordination usually involves two fields, conceptual

harmonization and institutional management.

The conceptual harmonization implies that, for all participants institutions in the management

of official statistics, the variables have the same definition, are known and shared by national or

international classifications of the subject, are encoded in the same way, the methodology is

shared in all phases of the life cycle of the statistical operation, and in the best scenario, the

databases are shared.

Interagency coordination and management aims at the efficient management process within or

between institutions, i.e., mechanisms of communication, monitoring and control, and

processes and procedures of articulation.”

219. Disaster-related statistics databases, usually managed by disaster management agencies with

extensive utilization of statistics shared by national statistics offices and other agencies, must be

built upon a common, nationally-adopted set of terminologies and basic concepts for

measurement. Datasets, including complete metadata, should be shared between institutions,

where possible according to a regular update schedule.

12 https://unstats.un.org/unsd/dnss/gp/FP-New-E.pdf 13 https://unstats.un.org/unsd/dnss/gp/impguide.aspx


62

220. An important coordination mechanism, particularly during early stages of development or

expansion in scope for a database, is to establish a multi-agency technical working group,

involving all key data providers across government and chaired by a senior official in the

institution managing the database.

221. The purpose and various analytical uses and principles objectives in terms of quality of the

database should be made explicit and utilized by the national working group as the terms of

reference for their coordination activities. The ultimate goal for statistical coordination and other

activities for statistics development should be to make official statistics as accessible as possible

for use in disaster risk reduction policy and related research.

Roles & Responsibilities

222. Implementation of a statistical framework should help national agencies to define and implement

clear requirements, roles and responsibilities across government regarding collection and

application of data, and how it is made accessible for policy-relevant research and monitoring

purposes.

223. The framework should also help to identify opportunities to utilize existing data sources within

the national statistical system (NSS). In some cases, adaptions to the sources or to the way that

data are shared between agencies are needed to fit the purposes for disaster risk reduction

statistical analysis. It is usually more efficient to adapt and reuse existing streams of data than to

establish new ones in response to each new policy question or indicator.

224. Through implementation of DRSF it will be possible to: (i) improve production of statistics from

existing databases and (ii) bridge the representations of the realm of disasters and risk reduction

on the one hand, with the socio-economic statistics on the other. The bridge between the two

domains of statistical information is essential for producing indicators. This bridge requires

strong partnership between disaster management agencies, national statistical offices, and other

official sources of relevant data and a strong mutual understanding of core concepts and the

methods for applying these concepts to practice for producing coherent statistics.

Figure 10: Statisics and Policy Planning for Disaster Risk Reduction


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225. The scope for demands for a basic range of disaster-related statistics and indicators rests within a

broader context, which includes operational databases that are used for emergency response.

Implementation of DRSF will allow governments to produce coherent information and to make

use of the same instruments and collections of data for multiple purposes.

Figure 11: Uses of disaster-related data collections

226. The ideal scenario for disaster-related statistics, as described within the Sendai Framework, is

that, with improved availability of statistics, disaster risk reduction becomes an integrated part of

the broader sustainable development planning of the country at national and local levels. Some

examples are integrating disaster risk assessments into land use planning and building resilience to

disasters as a part of the broader strategy against multi-dimensional poverty.

227. The risk management cycle is a useful concept for understanding the demands for statistics in

relationship to various perspectives of decision-makers. While there are some overlapping

Data Collection

Infrastructure Development Risk Assement Exposure

Resilience of Communities Post Disaster Assesment Hazard

Land use planning Indicators/Monitoring Vulnerability

Poverty Reduction Empirical Research Coping Capacity

Economic Development Planning Disaster Impact

DRR Activity

Operational Uses

Emergency Response

Evacuations

Early Warning Systems

Disaster Risk Management Planning

Summary & Time Series Statistics

Integrated Sustainable

Development Policy


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statistical requirements to support decision-making across the different phases of the cycle of

disaster risk management, there are also important differences.

228. During an emergency, responding agencies have special and relatively extreme requirements in

terms of timeliness and level of geographic detail required for the information to serve operational

purposes and inform an efficient and well-coordinated emergency response. The priority is to save

lives and minimize other damaging effects on the population, rather than on accuracy,

comparability between sources, or other qualitative characteristics of the figures.

229. In contrast, the reliability and comparability of statistics becomes crucial for risk assessment and

for designing prevention and preparedness programmes after disasters, especially when there is a

need for comparisons over time. Table 1 provides an overview of issues faced by decision-makers

and a sample of the demand for statistics. in each phase of the risk management cycle.

Figure 12: Cycle of Disaster Risk Management

Reference: this diagram adapted from Thailand Department of Disaster Prevention and Mitigation (DDPM)

Table 1: Statistics in Disaster-risk reduction decision making

Typical issues in the different phases of disaster risk

management

Typical decisions and plans to be made

Sample of use of statistics

Peace time: Risk Assessment Disaster risks can be estimated but

Prioritizing investments in

risk reduction

Dynamic hazard profiles (magnitude,

temporal and spatial distribution)


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are not known

Development investments should be

informed by risk profiles

Use of best available knowledge so

that development does not

exacerbate existing ( and or create

new) disaster risks

How to invest in development

while avoiding new risks

Vulnerability and baseline of

exposure: (demographic and,

socioeconomic statistics) and

baseline of exposure in areas prone

to hazards Learning from experience of past

disasters, e.g. effectiveness of early

warning systems

Integrating historical disaster

impacts statistics to update hazard

profiles and vulnerability

assessments

Peace time: risk reduction, mitigation and

preparedness Risk Profiles are changing as new

information becomes available and

development in potentially

vulnerable areas takes place

Early warning systems and other

monitoring systems, where

available, are continuous delivering

information on risks and possibilities

for mitigating impacts

Introduction of new measures

to reduce disaster risk

Introduction of mechanisms to

improve or ensure sufficient

early warning and adequate

preparedness

How to invest in risk

reduction measures as an

integrated part of the broader

poverty reduction and

sustainable development

initiatives

Whether and how to

discourage development in

hazardous areas

Scale, locations and other

characteristic of investment in

disaster risk reduction

Signals of slowly developing risks

approaching thresholds to a

potential disaster

Level of awareness, preparedness,

and investment against disasters by

households, businesses, and

communities

Identifying factors that cause and or

exacerbate disaster risks

Response Imperative is to act quickly and

efficiently to save lives and mitigate

unnecessary suffering

Sufficient resources to put crisis

under control

Urgent demand to meet

overwhelming needs for places

where vital systems and delivery of

basic resources were affected

Determine the magnitude of

the disaster and prioritize

needs for emergency relief

How to make the response the

most efficient

How to manage needs given

impacts to local supplies of

goods and services (how to

address temporary

interference to local services

supply)

How to mount emergency

response while also putting in

place requirements for

medium and long term

recovery

Disaster occurrence, including

temporal, and spatial spread of the

event

Disaster type and characteristics of

impacts, e.g. rapid or slow onset,

intensive or extensive impacts, etc.

Immediate indication of impacts on

population, damage, losses, and

disruption of basic services

Recovery needs, which potentially

could be increasing

Disaster response: who, what,

where, when, and how much

Medium and long term recovery Unaddressed humanitarian needs

Risk that fragile communities could

regress into a new emergency crisis

if recovery needs are not met

Less spotlight on initial response

may translate to less resources for

recovery

Often a normal development policy-

planning cycle resumes with many

requirements but, due to disaster,

less available resources

How to prioritize recovery of

economic sectors and

determination of appropriate

scale of re-building effort in

affected location

How to determine appropriate

level of investment required

for complete to recovery from

impacts for disasters:

Returning to consideration of

future risk identification and

mitigation (see above)

Comprehensive and credible post-

disaster accounting for damage,

losses, and disruption of functions /

services

Magnitude of requirements for

economic recovery (e.g. scope of

direct and economic impacts)

Assessing effectiveness of coping

mechanisms of communities,

localities and sectors

Identification of new vulnerabilities

created by the disaster

Legal framework


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230. A legal framework is used to enscribe into national law the principles and responsibilities for

producing and sharing data and statistics for meeting demands for policy. Legal frameworks also

help data producers to identify and consult with users.The Fundamental Principles Implementation

guidelines provided links to examples of good practices of national legal frameworks and related

codes of practice for organizing and implementing statistical work programmes.14

231. For some components in the DRSF, disaster management agencies are involved in both the

production and use of official statistics in the analyses used to formulate policies. The

Fundamental principles of independence and impartiality of the statistics programmes should be

emphasized and statistics offices may be in a position to advise its partners on how to implement

impartiality and establish a legal framework for producing and sharing statistics related to

disasters. “A strong position of independence is essential for a statistical agency in order to

establish credibility among its users.” (see UN Handbook of Statistical Organization 2003, page 5)

232. “Choices of sources and statistical methods as well as decisions about the dissemination of

statistics are only made by statistical considerations.” (See Fundamental Principle 2)

Confidentiality

233. “In order to maintain the trust of respondents it is the utmost concern of official statistics, to

secure the privacy of data providers (like households or enterprises) by assuring that no data is

published that might be related to an identifiable person or business.” (United Nations, 2015)

National Statistics offices are well experienced with protecting confidentiality of respondents, as a

fundamental principle for the practice of official statistics. Statistics offices rely on public

goodwill and cooperation and trust of respondents as a basic factor for producing timely and

accurate statistics.

234. Disaster statistics pose somewhat of a special case given that disasters are unusual and extreme

events. There is a need for fairly detailed geographic disaggregation of statistical information,

especially for use in emergency response. However, the focus of DRSF tables is on summary

statistics, i.e aggregations and statistical summaries of information, which do not include

references to identification of individual households or businesses. In cases where access to

microdata (i.e. raw data sets) are required for research purposes, methods are available for

anonymizing microdata prior to release to researchers. The IHSN has developed guidance on

anonymization procedures for household surveys15, including links to software tools and statistical

programming codes that have been tested for anonymization of various survey datasets.

Transparency and Accessibility to Data and Metadata

235. Principle 1 of the Fundamental Principles for Official Statistics states:

“Official statistics provide an indispensable element in the information system of a democratic

society, serving the Government, the economy and the public with data about the economic,

demographic, social and environmental situation. To this end, official statistics that meet the test

14 https://unstats.un.org/unsd/dnss/gp/Implementation_Guidelines_FINAL_without_edit.pdf 15 http://www.ihsn.org/anonymization


67

of practical utility are to be compiled and made available on an impartial basis by official

statistical agencies to honour citizens’ entitlement to public information.”

236. The resolution A/RES/68/261 of UN General Assembly stated that countries need to “facilitate a

correct interpretation of the data, the statistical agencies are to present information according to

scientific standards on the sources, methods and procedures of the statistics.”

237. Also ,the Sendai Framework Priority for Action 1states that countries need to “promote and

enhance, through international cooperation, including technology transfer, access to and the

sharing and use of non-sensitive data and information, as appropriate, communications and

geospatial and space-based technologies and related services.”

238. The documentation of methods and core descriptions of data is a standard and indispensable

component of any dataset used in official statistics and as part of the statistical dissemination

procedure.

239. “For the qualified users it is necessary not only to read the pure statistical results but also to have

a professional understanding of how the statistics have been produced. The qualified user will

reach the necessary understanding on how to use the statistical results only after knowledge about

data sources methods and procedures. This is why it is important that every statistic includes

relevant and scientific documentation.” (UN Statistics Division, 2015)

240. Many of the currently available data sources of disaster-related statistics (nationally and

internationally) include insufficient metadata documentation, which limits their practical utility.

For cases where statistics were accompanied with metadata describing methods, many differences

were uncovered between data sources in terms of fundamentals like use of terminology, scope of

measurement, method of valuation, or type of data source. This situation exemplifies the

importance of metadata as a crucial component of dissemination of statistics to avoid misleading

comparisons or mixing of incoherent statistics.

241. Wherever applicable, text from this handbook may be applied directly as metadata attached to

statistical tables. In other cases, this handbook can be referenced as a baseline along with a

description of divergences in methods or scope of measurement as applied in practice. Thus, thee

descriptions of concepts and methods in this handbook can be used as a reference towards

improved harmonization or, at minimum, expanded metadata documentation for the basic range of

disaster-related statistics produced by official national agencies.

242. The International Household Survey Network (IHSN) has published comprehensive guidance on

metadata documentation of datasets. Although the founding focus for IHSN is survey datasets,

the IHSN recommendations on scope16 for metadata are broadly applicable for all types of

datasets in DRSF. UN Statistics Division (2015) also provides the generally applicable guidelines

for dissemination of micro data (see pgs. 55-56).

243. A centralized database on disaster-related statistics must include a strategically designed system

of unique identifiers and coding for individual disaster occurrences and their main characteristics.

16 http://www.ihsn.org/documentation-scope


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Identifier codes within the datasets are an efficient method for linking data with metadata and to

establish explicit links between related variables within the database. Each disaster occurrence

may have many sources of data that are compiled and utilized to describe the relevant variables

attached to that particular occurrence. Usually, each data collection, when integrated, is

accompanied by a standard package of metadata explanations plus notes on revisions or other

pertinent details. Another requirement particular for disaster statistics is the need to clearly define

the scope of hazards incorporated in the statistics, ideally in the form of a publically accessible

national hazards glossary.

244. In the current practice, disaster occurrences and impacts information is typically stored as a set of

records (see, for example, Desinventar.org). Given a standardized use of reference to time (“Start

date”) and location (“geographic name”), basic statistics on disaster occurrences and impacts

could be queried from these underlying records for multiple disaster occurrences over a specified

period of time (e.g. 2015-2030). However, as documentation of the measurement and data source

is needed for each variable and each data collection is accompanied by, usually a more functional

database structure would involve separate collections of data according to the different variables

or sources of data, which are then linked together for production of statistical tables using event

identifiers or other linking variables (such as the year, location, and hazard type).

245. As an example, OECD produced a “data model” on impacts from disaster, adapted slightly

below. In this presentation, each box constitutes an individual compilation of data and metadata,

which are linked back to the unique event ID and may be queried according to any of the basic

characteristics of disasters.

Figure 11: Database Model for Disaster Impacts Statistics


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246. Collection of data is an investment of resources. Comprehensive documentation of the outputs

from a data source is a vital protection for the value of the investment in statistics production for

future use. Metadata is a cornerstone for creating coherence across occurrences or across datasets.

For this purpose, units of measurement, scope of measurement, definitions for key technical

terms, and methods used for monetary valuation, are all key examples of methodological choices,

for which multiple options are always possible. So, documentation of these choices, in a

comprehensive manual, glossary, and/ or technical annex attached to statistical releases is a

fundamentally important standard practice.

Chapter 5: Basic Range of Disaster-related Statistics Tables & Data

Sources

Accompanying this document are spreadsheets containing detailed summary tables for the basic

range of summary statistics. These are sample output queries that could be derived from a

centralized disaster-related statistics database following DRSF. The sample tables, including the

links to global indicators, are available for review from the Expert Group Website here:

http://communities.unescap.org/asia-pacific-expert-group-disaster-related-statistics/content/drsf

247. The basic range of disaster related statistics is described as a set of sample generic tables see link

above, organized according to the following categories:

o A: Summary tables of disaster occurrences

o B: Selected Background Statistics and Exposure to hazards

o C: Summary tables of affected population

o D: Summary tables of direct material impacts in physical terms

o E: Summary tables of direct material impacts in monetary terms

o F: Summary table of direct environmental impacts

o DRRE: Disaster risk reduction expenditure accounting

248. A collection of theses 7 types of DRSF tables have been developed to demonstrate the scope for

a basic range of disaster related statistics for prioritization and harmonization for evidence-based

disaster risk reduction policy development. The basic range of disaster related statistics is a

collection of potential output tables or queries from centralized national disaster statistics

databases. The tables could be used as sample templates to help ensure completeness of

compilations in disaster-related databases and referencing among variables drawing from multiple

data sources.

249. In practice, the tables will be adapted for each national context and the demands for reporting.

Not all elements within tables, will be available or relevant in all situations and national contexts.

Also, in many cases more detailed information than what is presented in the basic range of

disaster-related statistics will be available, or the data will be available in other formats, levels of

aggregation or other measurement units than what is presented. These tables, therefore, represent

a generic example presentation to help national agencies to identify and develop their own

http://communities.unescap.org/asia-pacific-expert-group-disaster-related-statistics/content/drsf


70

systems for disseminating the relevant data into summary statistic tables to meet the needs of

national policy development, as well as for international reporting purposes.

250. A centralized database does not require that all related basic data be stored physically in the same

place or on the same server. Rather, the true objective should be for a centralized portal for which

the basic range of disaster-related statistics can be queried and reported seamlessly for more

efficient use in calculating indicators, conducting risk and post-disaster assessments, and other

purposes.

251. There are three main types of background statistics in the basic range of disaster-related statistics:

(i) counts and basic characteristics of disaster occurrences, (ii) background statistics related to

vulnerability and coping capacity, and (iii) statistics on exposure to hazards.

252. There are three main categories of direct impacts: (i) human impacts (affected population), (ii)

material impacts (includes critical infrastructure), and (iii) environmental impacts (F tables).

Material impacts are estimated at first in physical terms (D tables) and then, where possible, are

calculated in monetary terms (E tables).

253. The DRRE tables are sample accounting tables, to be developed, as special functional accounts

(or “satellite accounts”) of the national accounts, following standard practices of the System of

National Accounts (SNA).

Time period

254. The relevant time period for reporting the basic range of disaster-related statistics varies by table

and according to the analysis. From a well-structured and well-documented database, the impacts

tables (C,D, E, & F tables) could be reported for virtually any time period, as needed for the

analysis. However, for most purposes, a minimum aggregated time period of at least 3-5 years is

the most relevant given randomness and large year-to-year fluctuations in disaster occurrences and

their impacts. For the Sendai Framework monitoring, for example, governments specified a

monitoring period of 2020-2030, as compared with 2005-2015 for the affected population

indicators.

255. Other components of the framework, i.e. Background statistics and the DRR activity statistics

(DRRE tables), represent activities that are continuous and based on data that compiled for

reporting on a regular basis – usually annually.

Geographic regions

256. Geographic referencing is a crucial element for compilation of nearly all the components of the

basic range of disaster-related statistics. One of the advantages of working with data in geographic

information system (GIS) software is that statistics can be calculated and then reported at

virtually any geographic scale or for any type of specially designed functional classifications of

geographic areas (e.g. river basins, hazard areas). In many cases, results of analyses will be

sensitive to the geographic scale, and therefore flexibility for scale of analysis (ability to zoom in

or zoom out for specific areas of interest) is very important for users and one of the key functions


71

of organizing statistics in GIS. The sample DRSF tables provide a generic presentation (“Geo

Region 1”, “…”), adaptable to the different needs or availability of geographically disaggregated

data. GIS tools allow that virtually any statistical tables with geographic referencing can also be

presented for communication purposes as maps, and vice versa.

257. Some key examples of categories for geographic disaggregation in the tables are: municipalities

(e.g. Admin level 03), regions or provinces (Admin level 01 or 02), hazard areas, river catchment

areas as specified by the relevant authorizes, coastal zones, or other environment-related

geographic regions that could be of relevance for analysis of the statistics.

Prioritization

258. One of the purposes for developing a basic range of disaster-related statistics is to help national

statistical systems to identify and adopt priorities for statistical development related to disasters.

For statistical systems at an early stage in development of nationally harmonized disasters

statistics, a limited core, first-tier compilation of statistics can be useful as a basis for expanding

into use of a broader collection of data sources in the future.

259. In part, prioritization depends on the current priority policy questions for decision-makers in the

country. These priorities will vary but some common priorities can be identified for each of the

main types of disaster risk reduction decision-making.

260. Sendai Framework and SDG targets provide the broad macro-scale priorities for goals of policy

and a common international approach to monitoring progress. Within the tables, the input

variables (both numerators and denominators) used in internationally adopted indicators (SDGs

and Sendai Framework) have been highlighted and indicate references to the goals and target

codes. These and other nationally-identified indicators could help compilers of these statistics to

prioritize elements within the tables, if necessary.

261. Identifying a short-list of 1st-tier statistics should not lead to prioritization of coverage for these

statistics over their quality. Prioritization of qualitative aspects of the statistics is a function of the

expected uses. For example, if the statistics will be utilized in time series analysis (e.g. indicator

reporting and monitoring of progress over time), than consistency, metadata transparency and

international comparability are priority considerations for making the data accessible for their

intended uses. In contrast, during an emergency, urgent accessibility to data on exposure at

flexible scales is the first priority.

262. Another factor for prioritization for implementation of DRSF is building a strengthened basis

upon which to expand compilations of disaster-related information in the future. A systematic and

strategic approach to prioritization should prioritize data inputs that are utilized for multiple

analytical purposes and that could provide basic building blocks for more detailed analysis or

disaggregated statistics. For example, referring to Figure 3 in chapter 2, identification of a direct

four primary characteristics of the disaster: timing, location, hazard type and magnitude, are

minimum requirements to identify disasters and describe their basic characteristics. Thus, these

are core systems components for developing databases or series statistics on disaster impacts.


72

263. Another example are the exposure statistics, i.e. population, land and infrastructure in hazard.

These statistics are used for risk assessment, emergency response and also can be useful as

baseline statistics for assessing the scale of impacts after a disaster.

Summary tables of disaster occurrences (A)

264. Identifying a disaster occurrence is an essential first step for centralized compilation of disaster-

related statistics because of the need to attribute impacts specifically to disasters.

265. Based on the decisions by disaster management agencies, a simple register of disaster

occurrences, including with unique reference codes and the basic characteristics (e.g. geographic

scope of emergency, timing of emergency) is prepared. From these registers, basic counts of

disaster occurrences can be produced and reported for various purposes, particularly trends overt

time.

266. These counts of disaster occurrences are background statistics, useful for computing indicators

related to disaster impacts (e.g. indicators of relative disaster impact intensity). Counts of

occurrences also are used for tracking the long-term trends in numbers of occurrences of different

scales by regions within countries and internationally (e.g. for tracking potential impacts of

climate change). The latter analysis usually requires an exceptionally long time series.

267. The timing reference included in the basic compilations of disaster occurrences is usually a date,

or at least month and year. This type of time references is sufficient for most purposes. However,

for some types of hazards there may also be interest in measuring and reporting the length of time

for a disaster or an emergency as an additional descriptive characteristic of disasters. There are

several challenges for producing a consistent or generic measurement for length of time for a

disaster. Disaster occurrences are processes, which, from the perspective of recovery, may last a

very long time. Underlying hazards are also processes without a necessarily clear and discrete

time reference. For example, earthquakes are involving relatively strong earth shaking are

accompanied by aftershocks or other linked hazards that can take place over a long period of

time. In the case of earthquakes of Canterbury, New Zealand, earth shaking initiating in 2010

continued for over 2 years with some of the aftershocks creating far more significant impacts

than the initial shaking. Therefore, as a simple convention for time referencing of disaster

occurrences across the wide diversity of types of occurrences, the emergency period is

recommended as a main reference (see 2a).

Selected Background Statistics and Exposure to hazards (B tables)

268. Exposure to hazards is generally calculated by disaster management agencies, utilizing statistics

on population, land and infrastructure derived from the existing official sources. Exposure is

measured according to hazard area maps, produced using a variety of physical data inputs (see

2b).

269. Vulnerability is one of the critical factors of disaster risk. Therefore, measures of vulnerability

are potentially vital background statistics, but difficult to define a priori. To develop an empirical

approach to measuring vulnerability prior to a disaster, there is a need to first develop a basic


73

minimum selection of categories for disaggregated statistics describing the population and

infrastructure, especially for parts of the countries within hazard areas.

270. A simple basic minimum set of statistics to derive from the population exposure data was

developed for DRSF and included in table B1b: Population Exposure by social groups , as

follows:

Age groups

Sex

Urban vs. rural populations

Persons with disabilities

Economic poor (income below national or international poverty)

271. By agreement in the Sendai Framework, governments report on number of people per 100,000

that are covered by early warning information through local governments or through national

dissemination mechanisms (indicator G3). The underlying statistics on population and early

warning coverage should be a part of the design and regular monitoring of these systems.

Additionally, some institutions have also produced statistics for monitoring on the effectiveness of

early warning systems in real-life situations.

272. Other key variables for describing coping capacity, or resilience, to disasters could include:

household preparedness, environmental resilience, and relevant summary extracts from the

accounting of other disaster risk reduction activities. Statistics for the Coping Capacity

Background Statistics table (table B3) should be compiled and reported by geographic regions,

according to the needs of users, which may be administrative regions of the country or hazards

areas, or both.

Summary tables of human impacts (C Tables)

273. There are many possible ways to organize a list of variables on human impacts from disasters.

The DRSF C table is a template, not necessarily comprehensive for all cases, but it is an option

from which the Sendai Framework SDG indicators can be derived. In many countries, statistics

for further detailed categorizations of variables are available and could be reported in summary

tables, according to the demand.

274. As discussed in section 2d, a simple aggregation across impacts creates multiple possibilities for

double-counting of the same individuals. This issue is potentially managed by estimation of

numbers of multiple impacts to the same individuals for an adjustment at the bottom of each C

table.

275. For disaggregation by social groups, references to national definitions should be applied, and

documented clearly in metadata for: urban and rural, poor (i.e. national poverty line), and

disabled persons.

276. Data sources for measurement of human impacts is a combination of the initial observations and

administrative records during disaster response and recovery by disaster management agencies

combined with descriptions of the population in affected areas with specially designed surveys


74

(where available) and the existing sources of official population and social statistics, such as

censuses and surveys.

Summary tables of direct material impacts (D Tables)

277. Direct material impact (D) tables are for recording direct material impacts in "physical" terms,

such as area (sq. m) of damages or number of buildings, by categories. Recommendations for

physical measurement units are presented later in this Chapter.

278. Direct impacts to cultural heritage and to the environment are identified separately due to special

characteristics regarding measurement units and monetary valuation. Cultural heritage are

unowned (or part of public owned infrastructure) with special values to the population, which are

sometimes irreplaceable.

279. Disruptions of basic services from a Disaster (D2 tables) are an extension of direct material

impacts tables (especially impacts to critical infrastructure), because usually disruptions to

services are a consequence of damaged or destroyed infrastructure. Measurement units for

disruptions of basic services are number of persons and length of time. Statistics on disruptions to

basic services and on material impacts from disasters are observations, usually collected and

compiled by disaster management agencies in the period shortly after an emergency.

Summary tables of direct material impacts in monetary terms (E Tables)

280. The direct material impacts in monetary terms tables mimic the direct material impacts (D

tables). Monetary valuations of material impacts needed for calculating direct economic loss

(SDG 1.5.2 and Sendai Framework Target C indicators) are based on the costs of the physical

damages, in most cases, according to the costs of reconstruction or replacement.

281. This monetary valuation of material impacts normally requires a combination of data sources,

including insurance claims assessments or assessments for cost of reconstruction, the recorded

values of assets prior to a disaster (where available), records of actual transactions for recovery of

damages, i.e. expenditure on post-disaster reconstruction, and average costs of crops or other

exposed assets for estimating costs of damages based on average per unit values.

Summary Tables of Direct Environmental Impacts (F Tables)

282. The final component of direct impacts statistics are the environmental impacts from disaster (F

tables). Environmental impacts variables are built upon a nationally standardized classification of

land cover types (such as the 14-class example presented in the F tables).

283. Land cover statistics is typically the responsibility of national mapping or national environmental

agencies. Integration of land cover statistics is made with reference to (i) the location of specific

direct impacts and (ii) hazards and exposure (B tables). There are also functional categories of

land cover that could be of special interest for assessing direct impacts, i.e: designated biological

reserves and World Heritage sites.


75

284. Monitoring impacts to water resources, ideally, should be an extension of data collection and

monitoring programmes of national and regional water authorities.

285. Other data sources for environmental impacts are similar as with other material impacts, i.e. the

observations of disaster management agencies after an emergency, measured in terms of land area.

286. Emissions of sulphur associated with volcanic eruptions and carbon emissions from wildfires are

typically estimated by institutions responsible for official scientific monitoring of atmospheric

conditions. Some national space agencies or other international scientific organizations are

monitoring these emissions globally.

Disaster Risk Reduction Expenditure and Transfers (DRRE Tables)

287. While disasters, and their impacts, are occurring randomly, disaster risk reduction is a continuous

activity (although certain activities boosted in the recovery period after a major disaster), related

to disaster response and informed by the gradual improvements in knowledge on disaster risks and

how to minimize them.

288. The disaster risk reduction activity accounting tables have been developed in alignment with the

standards and formats of the System of National Accounts (SNA) because the information in these

tables are extractions from the broader aggregated accounting framework for the economy as a

whole. In principle, the statistics could be derived from the same data sources that are used in

national accounts. Disaster risk reduction activity (expenditure and transfers) accounts tables.

289. Statistics on DRR activity serve many purposes, including to track the response of government

and non-government institutions to disaster risk over time, to improve understanding of the types

of effectiveness of different types of programs, and as inputs for estimating economic loss of

disasters.

Chapter 6: Data Sources and Measurement Units

Data Sources

290. There are many important opportunities within the basic range of disaster-related statistics for

making extensive use of existing sources of official statistics, as briefly introduced above for each

of the categories of summary statistics tables. This section prevents a further elaboration on data

sources and some other important considerations for each of the main data sources that are

important for compiling these statistics.

291. A crucial first step for implementation of DRSF by national agencies is a detailed mapping of

existing sources of data sources accessible at national level for calculating variables in the basic

range of disaster-related statistics tables.


76

292. Statistics for relatively large disasters benefit from greater attention from post-disaster

assessments and specially targeted data collections after the occurrences. Smaller and more

frequent disaster occurrences and their impacts rely more on the regular and continuous sources of

official statistics, such as including questions in household surveys or extracting information from

monitoring systems operating in areas of the county exposed to hazards.

Demographic and Social Statistics

293. Population censuses or household surveys have several roles in the basic range of disaster-related

statistics, in particular for measuring risk, especially exposure and vulnerability of the population

to hazards. Population censuses can also be used for producing measures of household

preparedness and for disaster impacts, to the extent that these elements were incorporated into

census questionnaires.

294. In addition, population and housing censuses are used to contribute data for estimation of disaster

impacts through comparisons of results from periods before and after a disaster (for example

changes in housing stock and descriptions of movements in the population).

295. Estimation of exposure of population to hazards requires statistics on population and on land and

infrastructure at the most detailed geographic scale (highest geographic resolution) as available in

order to overlay this information in GIS with the maps of hazard areas to calculate the numbers

for the overlapping areas (see Section 2b). For population, this is the results of the population

census, conducted for most countries on an approximate 10-year cycle. Other potential sources of

population statistics, or sources for intra-census projected updates of the population estimates,

include population or housing registries used for administrative purposes by government, or

household surveys.

296. At the most detailed level, location of residencies and the population distribution can be gathered

from a cadastre and from point location (i.e. GPS coordinates) of a household. GPS locations for

residencies, business or other respondent are increasingly available due to the emergence of

handheld GPS technologies and the use of tablet to collect census or survey data digitally. The

next level of aggregation upward are the primary sampling units (PSU), which are used by census

organisations for planning their enumerations. PSUs are typically smaller than the smallest

administrative units, but contain many households and may contain areas both within and outside

of hazard areas. Population figures by PSU are usually not made available publically; rather the

statistics are disseminated by administrative region (e.g. administrative level 1, 2, 3). Results of

pilot studies for the DRSF conducted during 2016 and 2017 (see annex) showed that a simple yet

adaptable model, is potentially applicable and sufficient to deliver reliable statistics on population

exposed to hazard based on publically available releases from population and housing censuses.

297. Statistics on household preparedness can be derived from household surveys and population and

housing censuses via inclusion of questions about basic preparedness, such as storage of water and

other provisions for an emergency and knowledge and awareness of emergency plans (column 7

in table B3). Sample surveys on household preparedness could target analysis in particular hazard

areas or for the national population as a whole.


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298. Statistics for categories of vulnerability in relation to population exposure to hazards (table B1b)

are also derived from household surveys, population and housing censuses, and other sources of

social statistics, usually managed by national statistics offices. These statistics may not be

currently accessible according to the current hazard maps, therefore an active collaboration for

producing and sharing of these statistics is needed between national statistics offices and disaster

management agencies in order to derive (and update regularly) the overlaid relationships between

the basic demographic and social descriptions of the population with hazard maps.

Employment statistics and national accounts

299. A potentially important source of vulnerability information that could be considered as

additional compilations for assessing vulnerability are summary statistics on structure of

employment (e.g. shares of employment by main categories of activity – agriculture,

manufacturing, services), diversity of the population, metrics for levels of inequality, and so on.

300. Macroeconomic background statistics are relevant for describing potential structural economic

vulnerabilities, derived from national accounts main aggregates and employment statistics. Most

official national sources produce national accounts main aggregates and employment statistics at

national and regional levels. Potential for producing such aggregated economic statistics for an

affected region or hazard area should also be investigated. Specific types of activities of special

importance for assessing risk or assessing impacts, such as tourism or agriculture, need to be

identified in advance as potential extractions of the broader national accounts compilations for use

in risk or post disaster assessments.

301. For the economic valuation of the material impacts to assets from disasters (direct economic

loss), many sources need consideration, especially values for insurance claims, or from other

estimations based on establishment surveys for construction activities. Economic statistics related

to material impacts from disasters are also important for building core assumptions for estimating

indirect economic losses, such as the approximated effects to production and employment, on

aggregate or for specific segments of the economy.

302. Costs of disaster risk reduction activities (DRRE accounts) may be derived from reports of

activities of the relevant institutional units and reports government finance statistics (filtered

according to primary purpose), and other compilations of statistics from administrative sources,

which are commonly used in national accounts, such as tax records.

303. For most countries in Asia and Pacific, Africa and in Europe, producing national accounts is a

responsibility of national statistics offices. However in some countries, including in most of the

countries in Latin American and the Caribbean, national accounts are compiled within Central

Banks. Another common arrangement (e.g. in the United States and in Thailand) is production of

national accounts by a specialized economic advisory council within the government. Regardless

of the institutional arrangements of a particular country, a close exchange of statistics and

metadata between national accounts and the centralized disaster-related statistics database is one

of the crucial interactions for building the basic range of disaster-related statistics.

Data Collection during a Disaster Occurrence


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304. Data collected as part of the emergency response from a disaster can be crucial for producing

statistics on the impacts of a disaster, particularly in terms of providing disaggregated statistics on

the human and economic impacts. These are initial figures appearing in reports about disasters,

and for which there is always the possibility for amendment, subsequently, if new information

becomes available and by linking with other data sources after the emergency period.

305. Statistics on and human and material impacts are usually estimated, at least initially, from data

collected as part of the disaster response by disaster management agencies. This data can be

reused for compilations of statistics on the human and material impacts after the disaster.

306. A part of response to disasters is providing various types of support to households or enterprises,

whether that be support for evacuation or relocation or transfers of other basic needs like food and

other supplies or financial resources to help with recovery. This support is usually accompanied

by a system of registration and/or collection of basic information of the households or other

entities receiving support. These records can be stored as non-public databases, from which

statistics can be derived. The records should be designed to collect information important for

producing statistics for disaggregated analysis of the impacts, such as identification of basic

characteristic like age, sex, disability, employment, and income.

307. Since not all people or businesses in an affected area can always be reached by an emergency

response and recovery efforts of a government agency, administrative records from the emergency

response need to be complemented by identification of other data sources or methods for

estimation of impacts of population not covered, for example displaced populations who relocated

as result of a disaster voluntarily, without government support.

308. Sometimes a specialized follow-up survey (or amendments to an existing census or survey)

targeting impacted areas, is a good option, building upon data that could be collected as part of the

emergency response.

Population and Health Administrative Data

309. The usual source of official records for deaths and causes of death, where it could be determined,

are civil registration authorities and the Ministry of Health, which is responsible for maintaining

and monitoring health information systems. In the event of a disaster, particularly for large scale

disasters, records for deaths or missing is, in the short-term, more commonly tabulated as part of

the emergency response and broader compilation and initial assessment of human and material

impacts from disasters. These figures are reported by and to the different levels of local and

national government and usually at some stage are shared in official reports to the press and the

general public.

310. Commonly, there is a need to revise original reported counts on deaths (and other human

impacts) following the emergency and after sufficient time to assess the sources of data and

account for all of the cases. The revised figures, which may be different than initial reports to the

public, must be stored in the centralized compilations of disaster impacts statistics and utilized in

the indicators.


79

311. A key consideration for the broader statistical system is ensuring that the final official counts of

deaths after a disaster are incorporated into the broader official system of administrative statistics

(i.e. the civil registration system), which is also the source used for the long-term and

comprehensive official statistics on mortality and health of the population. These administrative

sources have many important uses for the broader statistical system, including for estimating the

rate of growth of populations and for investigating public health issues, such as trends in mortality

from different types of health challenges. Civil and health administrative records contain

confidential information, but can be utilized to produce broad summary statistics for describing

trends in the population without revealing private information about individuals.

312. In principle, deaths are recorded in civil registers and/or in health information systems according

to a standard classification for causes of death. The current international cause of death

classification, called ICD 10 (2016)17, is managed by the World Health Organisation (WHO).

ICD10 does not include specific coding for deaths from a disaster, and therefore civil registration

systems based on ICD10 cannot be used directly as a data source for calculating deaths or missing

from disasters (thus, there is a need to provide users access to summary statistics from the impacts

reports from disaster management agencies). But, ICD10 includes a general category for

“External Causes for Morbidity and Mortality” (code XX).

313. Medical professionals responsible for cause of death registrations might not be trained (or

authorized) to attribute cause of death to a specific external event, like a disaster. On the other

hand, there is a lot of experience with international reporting on deaths associated with other types

of external causes of deaths. An example is the international compilation of statistics on traffic

accident mortality compilation by the World Health Organisation from official national sources.18

314. Protection of confidentiality is an important point for emphasis in the use of the administrative

records, because these records need to be protected against use for identifying individuals.

Linking records of individuals from various administrative sources (including CRVS, but also

other sources related to, e.g. education enrolment, tax enrolment, etc.) with data collected on

disaster impacts is a potential method for describing the population affected in terms of relevant

disaggregation categories – e.g. by sex, age, disability, income, etc. These are estimated

calculations for each category, based on linking records between microdata on human impacts

with the relevant administrative sources.

Mapping and Environmental Monitoring

315. Hazard maps are developed utilizing specialized expertise on hazards, e.g. earthquakes, extreme

meteorological events, floods, tsunami, etc. Hazard data typically are produced as official

products by national meteorological, geological, hydrological, disaster management, or other

scientific organizations working within or in collaboration with governments. Most of the data

inputs used for producing hazard maps are outputs from geographic and environmental monitoring

systems related to the types of hazards (water balances, meteorological data) or other basic

underlying environmental characteristics, such as elevation models, soil characteristics, land cover

and so on.

17 http://apps.who.int/classifications/icd10/browse/2016/en 18 http://www.who.int/gho/road_safety/mortality/en/


80

316. Estimating the geographic area of hazards is based on probabilistic modelling, utilizing the

available data and in relation to the length of the relative time period (extreme events are more

probable the longer the timespan under study) and a confidence interval chosen by the experts

producing the maps. Different degrees of exposure or probabilities of a hazard are used to produce

multiple mapped layers according to different expected degrees of risk (high, medium and low

exposure). Internationally, sources of hazard maps available for public use include UNEP-GRID

and the Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI).

317. The prediction of probabilities of future events can be improved by information from historical

disasters, but probability of hazards is dynamic (for example due to climate change). Therefore

the probabilistic models need to be updated at regular intervals, integrating new information as it

becomes available.

318. In addition to hazard maps produced by experts for measuring risk prior to a disaster, sometimes

maps of impacts of disasters (‘disaster footprints’) are produced as GIS vector or raster files,

which can be useful for making improvements to hazard mapping and for analysis of impacts or

producing statistics on affected areas, (e.g. baseline population in an affected area).

319. Land cover and land use maps, as well as other sources of related information, such as cadastres,

maps of critical ecosystems or hotspots, maps of critical infrastructure, and a broad range of other

geographic information can be important inputs for analyses of exposure to hazards. In addition,

statistics describing environmental condition , for example related to the quality of water bodies or

characteristics of forests, are also important inputs for use in measurement of factors of

vulnerabilities to disasters. GIS can be used for integration of a comprehensive repository of

relevant geographic information, as layers that be integrated to produce statistics relatively simply

as long as official maps can be gathered in consolidated into a centralized database for disaster

risk reduction purposes.

320. Figure 12 (belowe) is a visual overview of the basic range of disaster related statistics with a

generic referencing to the main expected sources (see key in left hand corner).


81

Timing of

Data Collection

ExposureV

ulnerability H

uman Im

pactD

wellings

Population Density

Age structure

Death/M

issingBuldings and strutres

Land cover/usePoverty

Injured/IllM

achinary

GD

P per capitaD

isabled Evaculated/Relocted

Valuables

Household Incom

eG

enderN

atural Resources

Hazard

Slums

Critical goods and services

Elevation map

Median Incom

e Loss of Jobs

critical infrastructure

Meteorological data

Coping Capacity People received aid

Cultural heritage

Data from

past hazardsPreparedness (household level)

Otherw

ise affectedEconom

ic Impacts

Distribution of soil type

Early warning system

s

value for surface roughnessInvestm

esnt in DRR

slope and river flow variables

(flood)Transfers fom

Central to local government

EvacuatuationsReconsruction and recovery

annual precipitation index

(drought)Interantional Transfers (O

DA

) into countryRelocations

annual precipitation and

geological data (landslide)International Transfers (O

DA

) outflows

People receiving aid

Total Expenditure by DRRCA

category

Total Expeedniture on DRRCA

by government

Total Expeedniture on DRRCA

by non-government

Beneficiaries of Total Expenditure

Beneficiaris of Total Transfers Received

Risk Assessm

ent

DRR A

ctivity

People affected by dwellings

demaged/destoried D

isaster Impact

BEFORE

DU

RING

AN

D A

FTER

Data sources

Geophysical/M

eteorological organisations

Ministry of Environm

ent/ Mapping A

gency

Population Census/Household surveys

Household Survey

National A

ccounts

Disaster M

anagement A

gency Assesm

ent

Fig

ure 1

2 : D

RS

F B

asic R

an

ge V

aria

bles &

Data

So

urces


82

Measurement Units

321. The remainder of this chapter specifies recommendations for measurement units for impacts

statistic for each of the core objects of measurement. Measurement units are a basic and vital

consideration for the design of basic data collection and, subsequently, compilation into nationally

centralized databases.

Dwellings

322. Dwellings are a special case where the number of units typically is aligned with number of

households impacted. Individual buildings may have multiple units (e.g. apartment buildings)

affected by a disaster and the number of units is a good approximation for the number of

households affected by damaged or destroyed dwellings. Some material impacts variables are

reported as numbers of buildings, but for dwellings it’s important to also specify number of units

(i.e. approximate number of households affected).

323. There is also an interest in number of individual people affected (e.g. for calculating an

aggregated affected population indicator). If basic data on number of individuals residing within

each affected dwelling are not available from the impacts assessment, this can be estimated based

on statistics on average household size within the affected area.

324. For economic loss valuation, sometimes direct observations of costs are unavailable and thus

there may be a need to also collect data on estimated size or area of damages (i.e. in terms of

square meters of damage) as an input for estimating expected costs of the damages.

325. Therefore, for the case of dwellings we have the non-exclusive options shown below. If planned

in advance of data collection, than, theoretically, all of these measurements could be derived for

dwellings from the same primary data sources.

Dwellings Measurement Unit Menu

Dwellings No. of units (households)

No. of people

Area in sq. m

Cost of impacts in local currency

Damaged

Destroyed

Critical Infrastructure

326. Critical infrastructure is heterogeneous by nature. They are assets or consumer durables of varied

forms, including buildings, equipment, land, and inventories. There is no possibility to produce an

aggregated count of total damages to critical infrastructure without a common unit of

measurement across all the relevant types of assets, i.e. monetary valuation of impacts to assets.

327. Initially, impacts to critical infrastructure must be observed in physical terms, which could vary

depending on the type of infrastructure or type of damages. For each category of infrastructure,

there are multiple options for measurement units in physical terms. The below table offers options

for prioritization, noting that multiple measures is always a possibility.


83

328. For many of the types of critical infrastructure, a simple option is to count number of units

according to relevant categories already used in statistics or in their management by governments.

For example, many countries use a tiered system (tier 1, 2, 3) to classify the different types of

health facilities (from large hospitals down to small clinics) available to the population.

Critical Infrastructure Physical Measurement Units Menu

Measurement unit

Critical infrastructures

Hospitals, health facilities

no. of buildings by official category (tier

1, tier 2,..) capacity (no. of beds) sq m.

Education facilities no. of buildings by official category (tier

1, tier 2,..) capacity (no. of students) sq m.

Other critical public administration buildings

no. of units Public monuments

no. of units Religious buildings

no. of buildings by official category

Roads

km

capacity (avg. daily traffic affected)

no. of roads by official category

Bridges

km


no. of bridges by official category


84

Railway

no. of units Airports

no. of buildings by official category


Ports

no. of units by official category


Transport equipment

no. of units Electricity generation facilities

no. of units

capacity (no. of people affected)

Electricity grids

no. of units


ICT Equipment capacity (no. of

people affected) no. of units Dams no. of units by official

category

no. of units


Water supply infrastructure

no. of units


Water sewage & treatment systems

no. of units


Agriculture land, livestock, fish stocks, and managed forests sq. km

capacity (food production affected)

Disruptions to Basic services from a Disaster

329. Often material impacts, particularly damages to critical infrastructure, result in disruptions to

basic services after a disaster.

330. Statistics on disruptions to basic series are measured in terms of number of people and length of

time. Material impacts from a hazard are the triggers that cause disruptions to services. However,


85

from a measurement perspective, these disruptions are observed as impacts to the population and

should be measured in terms of number of people affected

331. Additionally, the time element of the disruptions is critical for understanding the nature and

magnitude of these impacts, the underlying risks for the affected area and the challenges for

recovery. Thus, all disruptions to basic services are recorded with 2 dimensions: no. of people

affected and length of time (See DRSF D2 tables).

Impacts to Livelihood

332. Measurement units for impacts to from disasters should follow the example of disruptions to

basic services, i.e. the length of time (e.g number of days lost) alongside with the number of

people affected. Numbers of jobs by sector could also be of importance, especially for economies

with a high dependence on specific critical sectors, such as agriculture.

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