Spatial Poverty Assessments

Alex de SherbininSenior Staff Associate

Center for International Earth Science Information Network (CIESIN) The Earth Institute at Columbia University

Deputy ManagerNASA Socioeconomic Data and Applications Center (SEDAC)

GEOSS Science & Technology Stakeholder Workshop30 August 2012

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NASA Socioeconomic Data & Applications Center (SEDAC)

• Focus on human dimensions of environmental change

• Integration of social and Earth science data, especially with remote sensing

• Direct support to scientists, applied and operational users, decision makers, and policy communities

• Strong links to geospatial data community

Outline

• Spatial poverty data• Remote sensing for poverty research• Creating a human “observing system”• Concluding Thoughts

Measures of Well Being

• Household income/consumption expenditures • Non-monetary indicators of well being

– Malnutrition – Unsatisfied Basic Needs– Infant Morality Rates

• Foster, Greer, Thorbecke measures: – Percent of population below the poverty line (Head Count Index;

FGT_0)– Average shortfall between welfare levels and the poverty line

measured as a percent of the poverty line (Poverty Gap Index; FGT_1)

Spatial Poverty Data

Why Map Poverty?

• Understand spatial patterns and how poverty varies subnationally and across countries

• Identify hotspots in need of intervention• Understand the spatial correlates of poverty

– Biophysical correlates– Socioeconomic correlates– Spatial isolation or “poverty traps”

In South Africa, the urban-rural poverty differential that applies to the country as whole is not necessarily reflected uniformly across all urban-rural gradients within the country

In Bangladesh, the pattern of poverty rates is primarily shaped by proximity to the capital Dhaka. Poverty rates rise with distance from Dhaka. Coastal areas are less disadvantaged than the inland remote areas.

In Malawi, Llongwe has less poverty within its limits, but is surrounded by regions of very high poverty. Blantyre, by contrast, has very high poverty within its limits, but is surrounded by regions of only moderate poverty.

Spatializing Demographic and Health Survey Data

Analysis of infant and child mortality

For both infant and children, the chances of survival decrease monotonically the further one resides from a city (of 50,000 persons or more), in a 10-country West Africa study

Balk, D., T. Pullum, A. Storeygard, F. Greenwell, and M. Neuman. 2004. A Spatial Analysis of Childhood Mortality in West Africa. Population, Space and Place, Vol. 10, No. 3.

Global Hunger Map

Identification of Hunger Hotspots• Defined by the

Millennium Development Project Hunger TF as those sub-national units with rates of childhood malnutrition >20% and >100,000 children who are underweight

• 75 sub-national units met this criteria

Hunger by Farming Systems

1

2

3

Farming Systems Data Source: Dixon, J., A. Gulliver with D. Gibbon. 2001. Farming Systems and Poverty: Improving Farmers’ Livelihoods in a Changing World. United Nations Food and Agriculture Organization. (Available at http://www.fao.org/farmingsystems/).

What are the biophysical correlates

of malnutrition?

de Sherbinin. 2009. “The Biophysical and Geographical Correlates of Child Malnutrition in Africa” Population, Space and Place Vol.15

Spatial Error Model Results

(pseudo-r square)

IMR Map

High : 208

Low : 2.0

IMR (2000)

• Sources – Demographic and Health Surveys (41 countries)– Multiple Indicator Cluster Surveys (5 countries)– National Human Development Reports (14 countries)– National Statistical Offices (16 countries)– UNICEF Childinfo – (115 countries)

• Subnational representation– 8,029 units (6,886 in Brazil and Mexico alone)– 77 countries have subnational data; 115 national only– 80% of world population has subnational data– Average 14 units per country (outside Brazil and Mexico)

• Converting rates to counts– For each subnational unit,

estimates of live births, infant deaths calculated based on gridded population, national fertility data, and subnational IMR.

• Calibration– Subnational IMR values

adjusted to be consistent with national UNICEF 2000 IMR values

Source: de Sherbinin et al. AGU 2004.

IMR

Growing Season

Growing Season (days)

High : 365

Low : 0

Cumulative Population by Growing Season (days)

0.00.51.01.52.02.53.03.54.04.55.0

IMR by Growing Season (days)

0102030405060708090

100

Analysis for non-wealthy countries only

Elevation

IMR

Elevation

IMR by Elevation (meters)

0102030405060708090

100

unknown 0-50 50-100 100-500 500-1000 1000-

Cumulative Population by Elevation (meters)

0.00.51.01.52.02.53.03.54.04.55.0

unknown 0-50 50-100 100-500 500-1000 1000-

High

Low

Analysis for non-wealthy countries only

Malaria

IMRCumulative Population (billions) by Malaria Risk Score

0.00.51.01.52.02.53.03.54.04.55.0

0 0-.08 .08-3.4 3.4-8.4 8.4-37

IMR by Malaria Risk Score

0102030405060708090

100

0 0-.08 .08-3.4 3.4-8.4 8.4-37

Malaria Transmission Index

High : 37

Low : 0

Analysis for non-wealthy countries only

Soils

Cumulative Population by Soil Constraint Class(1=few constraints; 7=severe constraints)

0.00.51.01.52.02.53.03.54.04.55.0

Unknown 1-3 4 5 6 7

IMR by Soil Constraint Class

01020

3040506070

8090

100

Unknown 1-3 4 5 6 7

Soil Constraints On Agricultural Production

Undefined

1. No constraints

2. 1-20 Slight

3. 20-40 Moderate

4. 40-60 Constraints

5. 60-80 Severe

6. 80-99 Very Severe

7. 100 % severe constraints

Analysis for non-wealthy countries only

Drought

IMR by Drought Index

0102030405060708090

100

1 2 3 4 5 6 7 8 9 10 11

High :

Low :

Drought Index

IMRCumulative Population by Drought Index

0.00.51.01.52.02.53.03.54.04.55.0

1 2 3 4 5 6 7 8 9 10 11

Analysis for non-wealthy countries only

Rails

High : 99

Low : 0

% of Grid within 2km of Railroad

Cumulative Population (billions) by Rail Density

0.00.51.01.52.02.53.03.54.04.55.0

0 0-5 5-15 15-23 23-100

IMR by Rail Density

0102030405060708090

100

0 0-5 5-15 15-23 23-100

IMRAnalysis for non-wealthy countries only

Ports

IMR

Distance to nearest port

High

Low

Cumulative Population by Nearest Port (decimal degrees)

0.00.51.01.52.02.53.03.54.04.55.0

0-.5 .5-1 1-2 2-3 3-4 4-5 5-7 7-10 10-15 15-24

IMR by Nearest Port (decimal degrees)

0102030405060708090

100

0-.5 .5-1 1-2 2-3 3-4 4-5 5-7 7-10 10-15 15-24

Analysis for non-wealthy countries only

Compared with the non-poor, poor people are more likely to be found in drought-prone areas with shorter growing seasons

Non-poor Poor

Millennium Ecosystem Assessment, 2005

For the Millennium Ecosystem Assessment CIESIN calculated average IMR within each MA ecosystem. We also calculated another measure of well-being, the ratio of the share of world population to share of world GDP. The drylands are the most disadvantaged. We further calculated rates of population growth within each ecosystem unit, and noted that the drylands had the highest rate of growth. To have fragile ecosystems with low levels of well-being experience the highest population growth is bound to make challenges more difficult in these regions.

The poor are at much greater risk of experiencing a drought

Not Poor Somewhat Poor Moderately Poor Poor Extremely Poor

Delhi, India: Multiple Deprivation Index and ASTER Nighttime Thermal Infrared

Nighttime Temp

Pov

ert

y

Nighttime TempMDI / Poverty

Houston, Texas: Income Level and MODIS Nighttime Thermal Infrared

Income PC

Te

mp

era

ture

Income PC

Nighttime Temp

Remote Sensing Applications for Poverty Research

Night-time Lights Estimates of GDP / Population

Comparison of HH Assets Index and Wealth Based on Mean Brightness of NTL

Source: Noor et al., Population Health Metrics, 2008

http://www.ciesin.columbia.edu/confluence/display/slummap/Global+Slum+Mapping

Dar Es Salaam, Tanzania, 1982 and 2002

Source: Data courtesy of Richard Sliuzas, ITC

Damascus, Syria

•Very loosely structured•Historical ethnic quarters/neighborhoods•Poor residents currently being displaced in some areas with urban development/tourism

•Formal Urban Planning•Typical Urban Services•Middle to Upper Income

•Unstructured Settlements•Lowest to lower middle income•Rural migrants

Neighborhood Mapping

Source: Slide courtesy Eddie Bright, ORNL

Settlement characterization tool

Source: Slide courtesy Eddie Bright, ORNL

Source: Lela Prashad, www.nijel.org

Creating a Human “Observing System”

Source: www.benwilhelmi.com

Frequency of Demographic and Health Surveys

Subnational Poverty and Extreme Poverty Prevalence

Source: Harvest Choice, http://harvestchoice.org/maps/sub-national-poverty-and-extreme-poverty-prevalence

Mean Number of Censuses 1970-2010

Migration Data

Migration is one of the main demographic drivers of environmental change, yet there are very few data on human movements

Source: Adamo, CODATA side event, Rio+20, June 2012

Source: Adamo, CODATA side event, Rio+20, June 2012

Concluding Thoughts

• There is a growing availability of spatial poverty data, but– gaps remain

• Integration of “bottom up” with “top down” data is possible, but – Development of globally integrated and harmonized subnational SE data is costly – It needs to be driven by specific research or decision making needs– One size fits all approaches for web services are unlikely to work

• Growth in novel data sources – anonymized mobile phone records for human movement, crowd sourced data, etc. – are exciting developments, but

– as yet have not provided a globally consistent view– Data quality issues may exist