RESEARCH ARTICLE

Vulnerability assessment in a volcanic risk evaluationin Central Mexico through a multi-criteria-GISapproach

Jose Fernando Aceves-Quesada Æ Jesus Dıaz-Salgado ÆJorge Lopez-Blanco

Received: 12 May 2005 / Accepted: 7 March 2006 /Published online: 30 October 2006� Springer Science+Business Media B.V. 2006

Abstract The Valley of Toluca is a major industrial and agricultural area in Central Mexico,

especially the City of Toluca, the capital of The State of Mexico. The Nevado de Toluca volcano

is located to the southwest of The Toluca Basin. Results obtained from the vulnerability

assessment phase of the study area (5,040 km2 and 42 municipalities) are presented here as a

part of a comprehensive volcanic risk assessment of The Toluca Basin. Information has been

gathered and processed at a municipal level including thematic maps at 1:250,000 scale. A

database has been built, classified and analyzed within a GIS environment; additionally, a

Multi-Criteria Evaluation (MCE) approach was applied as an aid for the decision-making

process. Cartographic results were five vulnerability maps: (1) Total Population, (2) Land Use/

Cover, (3) Infrastructure, (4) Economic Units and (5) Total Vulnerability. Our main results

suggest that the Toluca and Tianguistenco urban and industrial areas, to the north and northeast

of The Valley of Toluca, are the most vulnerable areas, for their high concentration of popu-

lation, infrastructure, economic activity, and exposure to volcanic events.

Keywords Vulnerability Æ Volcanic risk Æ Multi-criteria evaluation ÆNevado de Toluca Volcano Æ Central Mexico

Introduction

The Valley of Toluca is located in Central Mexico at an altitude of 2,600 m above sea level

(masl), and approximately 70 km west of Mexico City. The City of Toluca is located in the

J. F. Aceves-Quesada Æ J. Dıaz-Salgado Æ J. Lopez-Blanco (&)Institute of Geography, National University of Mexico, Circuito Exterior, Cd. Universitaria,CP 04510, Mexico DF, Mexicoe-mail: jlopezblanco@hotmail.com

J. F. Aceves-Quesadae-mail: faceves@geofisica.unam.mx

123

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valley and is the capital of The State of Mexico. In the conterminous of Toluca City a large

industrial complex has developed, which in turn has fostered the city’s expansion, leading

to the incorporation of several municipalities to the urban area, including Toluca, Lerma,

Metepec, Zinacantepec and San Mateo Atenco. This has resulted in a large urban area of

about two million inhabitants (INEGI 2001b) (Fig. 1). Another industrial complex has

grown to the east of The Valley of Toluca, including the municipalities of Tianguistenco,

Ocoyoacac, Calpulhuac and Almoloya del Rıo. The Nevado de Toluca Volcano (Zina-

cantecatl in the Nahuatl language) is located to the southwest of the valley; it is a large

volcanic structure rising to 4,636 m asl that has undergone violent eruptions during its

recent geologic history (Aceves 1997), and in the event of being reactivated it would

represent a significant hazard for the valley of Toluca. This study is part of a broader

investigation on volcanic risk in the Nevado de Toluca area (Aceves et al. 2006).

Conceptual framework

Many scientists have developed the concept of volcanic hazard as the set of events taking

place in a volcano that may cause damages to people and properties exposed to them

(Arana and Ortiz 1996). Vulnerability is the expectation of damage or loss that may be

inflicted to an element exposed and conditioned to a potential volcanic event of varying

severity. It is measured as the percentage of total damages or losses associated to the

potential event (Arana and Ortiz 1996; Tilling 1989). The total volcanic risk has been

defined as the predictable consequences of a volcanic event in terms of loss of life and

injuries, and destruction of specific types of properties or other kind of economic loss

(Crandell et al. 1984). In order to mitigate these effects, vulnerability, hazard, and risk

maps have been derived from a number of assessment works over the past decades to

predict the path and impact of the different volcanic materials which may potentially be

ejected.

In several places some works have been conducted with disaster-mitigation aims.

Fournier D’Albe (1979) conducted a study on the prediction and mitigation of volcanic

eruptions to establish risk levels based on three factors: (1) Population and the threatened

material goods; (2) The proportion of those possibly affected and (3) The probability of

occurrence of a volcanic hazard.

During the 1980’s decade, UNESCO prompted the fulfillment of several works aimed at

mitigating natural disasters, dedicating a series of works to volcanic hazard and risk-

assessment, recommending the identification of high-risk volcanoes and the development

of stratigraphic and volcanologic studies to identify hazards from past events (emitted

products, cyclicity, magnitude of events, etc.; Westercamp 1982; Crandell et al. 1984;

Yokoyama et al. 1984; Rosi 1996; National Land Agency Government of Japan 1992;

Gomez-Fernandez 1998, 2000; Lirier and Veteli 1998; Stieltjes and Mirgon 1998; Pareschi

et al. 2000; Torrieri et al. 2002).

Stieltjes and Mirgon (1998) developed a method to assess the vulnerability of

Martinique Island, in the event of a new eruption by Mount Pelee. They considered that the

vulnerability of communities is linked with many factors, namely social, demographic,

economic, cultural, physical, technical, functional and institutional, which could be

grouped into two broad sets of factors, permanent (relief, constructions, and existing

infrastructure) and conjectural factors (seasonal variations in population size, eruption

type, meteorological conditions, etc.).

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Fig. 1 Location map of the study area including the 40 municipalities

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Vulnerability is a very complex concept, and whatever the origin of the natural hazard,

its approximation could be considered as a two-level approach: the preliminary qualitative

analysis of vulnerability factors makes it possible to estimate the response capability of a

community against a threatening event. The quantitative vulnerability analysis makes it

possible to measure the direct impact of a phenomenon on a community and its envi-

ronment (Van Westen 1997; Stieltjes and Mirgon 1998).

In Mexico, the most important works about volcanic hazards have been the maps of the

Popocatepetl (Macias et al. 1995), Colima (Martin del Pozo et al. 1995), Nevado de

Toluca (Aceves et al. in press) and Pico de Orizaba (Sheridan et al. 2001) volcanoes. The

history of activity of these volcanoes shows very explosive eruptions (mainly of the plinian

and vulcanian types) accompanied by ash fall, pyroclastic flows, lahars and debris ava-

lanches. Those hazard maps were important in evaluating the distance, direction and

frequencies of those deposits to establish the potential hazard areas in case of a new

eruption.

The Nevado de Toluca (NDT) is a large composite volcano, whose geological history

has undergone two major active phases. The first one occurred 1.2 million years ago, when

the formation of the volcanic building began. The emitted products were lava-flows

(andesitic composition); lahars and some pyroclastic flows (Aceves et al. 2006; Bloomfield

et al. 1977; Macias et al. 1997). The second active phase began some 100,000 years ago

(Cantagrel et al. 1981), although most of the activity concentrated on the past 50,000 years

(Aceves 1998; Aceves et al. 2006). This phase is characterized by the presence of large

eruptions every 12,000 years, approximately. In this phase materials of dacitic composition

prevailed (Bloomfield et al. 1977).

A reconstruction of the Nevado de Toluca volcano’s eruptive history has been carried

out by our team based on detailed fieldwork, using information derived from more than 150

stratigraphic sections, along with photointerpretation techniques and cartographic analyses,

and supplemented with literature surveys about the volcano’s geological characteristics

(Bloomfield et al. 1977; Cantagrel et al. 1981; Macias et al. 1997; Solleiro et al. 2004).

This reconstruction shows that, in the past 50,000 years, the Nevado de Toluca has

experienced eight phreatomagmatic, four plinian, three subplinian and one-ultraplinian

eruptions. In addition, the NDT has suffered two structural collapses that generated two

debris avalanches in the past 100,000 years (Aceves et al. 2006).

Materials and methods

The database search, transformation and integration (in a digital format) were done from

hardcopies of thematic maps at 1:250,000 scale from the Instituto Nacional de Estadıstica,

Geografıa e Informatica agency (INEGI), as well as gathering information from a census at

a municipality level published by INEGI (2001a, b, c). From this information, a carto-

graphic-statistical database was elaborated, composed by thematic maps and attribute

tables. The GIS used for information processing activities were ILWIS (ITC 1998) and IDRISI

(Eastman 1997) software-packages. The first one was used considering its capability for

handling vector geographic database and for its user-friendly digitizing procedure of

geographic features. The second, which allows the processing of geographic information in

raster format (pixels) was used for further potential handling of thematic information; it

also contains powerful overlaying and interpolation tools that allow handling complex

methods in the decision-making process approach, like the Multi-Criteria analysis applied

in this work (Bosque et al. 1994). The high quality of information generated by the INEGI

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agency is worth noting, which allowed us to complement the fieldwork carried out to

determine the volcano’s geologic history and the areas covered with pyroclastic materials

from previous eruptions, where many settlements and infrastructure are now settled.

Once the maps were digitized in the vector format they were rasterized using a

framework of row–column (713 rows, 418 columns, 125-m pixel size) array previously

built in the IDRISI GIS software-package (Eastman 1997). A rasterizing process was applied

to each one of the thematic maps in order to assign a numerical attribute to each pixel,

according to the thematic fact that it represents.

Parallel to the above process, we identified the main factors or criteria that impact the

valley’s vulnerability to a greater or lesser extent. In this way, four factors (criteria) were

identified and assessed in this study: (1) Total population, (2) Infrastructure, (3) Economic

units and (4) Land-use and cover distribution. For this purpose we used environmental and

socioeconomic information from maps and statistics at a municipality level, as follows:

population amount, main urban and rural areas, length and type of highways, number of

schools, number of rural clinics and hospitals, number and extension of agricultural units

(in ha), number of economic units and total economic production (in thousands of pesos),

and the land use/cover types as well.

Criteria used for taking these factors into consideration are based on results presented in

Yokoyama et al. (1984), Aguilar and Sanchez (1993), Scott (1993), Stieltjes and Mirgon

(1998) and Torrier et al. (2002). These authors consider that human population represents

the most vulnerable element, hence it has been considered as the most important factor in

this assessment. Next in importance is the infrastructure factor, which according to its

development, magnitude and extent, could affect the population vulnerability (its loss),

however, would also represent a higher cost depending of their losses. The third factor, in

order of importance, is the economic production (agriculture, industry, services, etc.). The

assessment and combination of factors were carried out using a decision-making approach

known as Multi-Criteria Evaluation (MCE) integrated in a GIS environment. This

approach has recently been used in other fields of science, but references for volcanic

hazard and vulnerability assessment are scarce (Torrieri et al. 2002). In the past decade,

MCE has received renewed attention in the context of GIS-based decision-making (Pereira

and Duckstein 1993; Heywood et al. 1995; Malczewsky 1996; Tkach and Simonovic 1997;

Simonovic and Nirupama 2005). This combination has proved to be useful in solving

conflicting situations for individuals or groups interested in the spatial context (Malczewski

1996; Janssen and Rietved 1990) and it is also a powerful approach for land-suitability

assessments (Joerin et al. 2001).

This approach has been used to integrate and simultaneously assess a series of elements

oriented towards a specific objective, applying decision rules, based on analysis, discussion

and hierarchies of alternatives in order to make decisions on land-suitability problems

(Dıaz Salgado and Lopez Blanco 2000, 2001; Ceballos-Silva and Lopez-Blanco 2003a, b;

Torrieri et al. 2002).

Based on the objective of evaluating the vulnerability associated with volcanic hazards,

a decision rule set was chosen and structured, which integrates the assessment and ranking

criteria (in this case four) established from the outlined objective, and the selection of

alternatives, represented by the spatial objects (pixels) contained in the thematic layers

(digital maps). Thus, each criterion constitutes a thematic map in the GIS database, and in

this phase, we understood the major importance for the total evaluation, of the factor

selection process (criteria) in a consistent and objective way. The MCE is based on

integrating all criteria and alternatives in a pair-comparison matrix (PCM), named as of

decision or evaluation as well, where criteria are in the main column, and alternatives in

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the main row, and in the inner cells punctuations derived from the assessment resulted

from the experts evaluation. These punctuations represent the value, preference level,

degree of attraction or significance that each alternative has obtained in each criterion

(Barredo-Cano 1996). In this way, categories or classification levels will be assigned

considering the quantitative values corresponding to criteria in the pair-comparison matrix

process (Table 1). Since they are generally measured in nominal or qualitative scale, in

published printed maps or from bibliographic sources, it was necessary to convert them to a

common scale of intervals or ranks. These intervals allowed us to make an assessment and

interpretation of thematic-map information of criteria for each one of alternatives with the

purpose of representing the different vulnerability values, and, finally leading to the

alternative classification from low to high vulnerability levels (0 to 4 in this case),

according to the punctuation scale established.

Once the assessment matrix and the thematic vulnerability maps were established, we

set the relative importance among criteria, because not all of them have the same influence

or preference of intensity according to the type of evaluation projected, and they were

assigned to a specific weight value. This assign was strongly based on previous references,

points of view and experience of specialists (researchers, decision makers and land

management workers), consultation and opinion polls with experts on each criterion or

topic, bibliographic references, and taking into account the characteristics of the study area

that is at volcanic risk.

There are different approaches for weighting criteria. One of the most extensively used

in the MCE-GIS spatial research is known as the Hierarchical Analytical Process (HAP).

Table 1 Decision matrix to establish vulnerability levels considering criteria categories

Criteria Vulnerability level

Value 1 lowvulnerability

Value 2 mediumvulnerability

Value 3 highvulnerability

Value 4 veryhigh vulnerability

Totalpopulation

Total populationper municipality

<10,000 inhabitants

Total populationper municipality

10,001–50,000inhabitants

Total populationper municipality

50,001–400,000inhabitants

Total populationper municipality

>400,000 inhabitants

Landuse/cover

No vegetated area Juniper forest Rain-fed agriculture Urban areasAlpine grassland Grassland Quercus forest Irrigating

agricultureCattail and sedge

wetlandCloud mountain

forestPinus forest

Cattle use grassland

Infrastructure Medical units permunicipality <5

Medical units permunicipality5–15

Medical units permunicipality16–50

Medical units permunicipality >50

Schools permunicipality <10

Schools permunicipality10–50

Schools permunicipality

51–250

Schools permunicipality >250

Highway length permunicipality<50 km

Highway lengthper municipality50–100 km

Highway lengthper municipality101–200 km

Highway length permunicipality >200 km

Economicunits

Gross total product(thousands of pesos)

per municipality<10,000

Gross total product(thousands of pesos)

per municipality10,000–100,000

Gross total product(thousands of pesos)

per municipality100,001–500,000

Gross total product(thousands of pesos)

per municipality>500,000

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Saaty (1980) developed this technique and in more recent years Eastman (1997) imple-

mented it in the GIS IDRISI as the weighted linear combination module. This method is

based on the development of a square matrix, known as the pairwise comparison matrix, in

which the number of criteria to weight will determine the number of rows and columns to

be considered.

Each column and row in the matrix is labeled with the name of one of the criterion

(following the same order in both axes, from left to right in columns and from top to

bottom in rows). Only the bottom-left triangle in the matrix will be evaluated since the top-

right portion is symmetrically identical. Next, cells are filled, comparing the relative

importance of the criterion of each row in relation to the criterion of the corresponding

column, advancing from column to column, from left to right. The comparison allows

establishing hierarchies or weights for the various criteria, thereby assigning a relative

value of weight to each of them against the other ones, based on a scale of trials of value or

levels of importance established by the same procedure.

The scale used for weight assign is a numeric scale including 17 values or hierarchies

that go from a minimum value of 1/9 (the less important), up to nine (the most important).

Obviously, in the matrix diagonal, values of 1 are assigned only to those factors that denote

equality; if two factors have the same importance they will be given a value of 1 (see

Fig. 2). The GIS IDRISI contains modules that allow to carry out the automated procedure of

matrix addition by means of overlaying and multiplying each map by a constant (criteria

weight), producing a new map, of vulnerability level in this case, with values ranging from

1 to 4 per pixel, being 4 the value with the highest vulnerability level.

Besides establishing the weighting criteria, the MCE procedure used in this work also

offers a quantitative measure of consistency among the relationships obtained from simul-

taneous criteria compared. A consistency index indicates the probability of have been as-

signed values in a randomly way. Values below 0.1 indicate good consistency; when they

exceed that limit recalculating the matrix is necessary. Figure 3 illustrates the methodological

diagram followed in this study, including the two more important stages in this study: (1)

Vulnerability per criterion determination and (2) Multi-Criteria evaluation (MCE) procedure.

Results

The study area comprises 40 municipalities, 37 belonging to The State of Mexico, two to

The State of Guerrero and one to The State of Morelos (Fig. 1). The processed information

was reviewed and transformed at a municipality level. The City of Toluca, which is the

State of Mexico’s capital, concentrates most of the public services, fostering its growth and

making of it an attraction pole for immigration, this causes a disproportionate spatial and

population growth compared to all other municipalities (INEGI 2001a, b, c). The growth of

Fig. 2 The 17 hierarchies scale of relative importance to construct the pair comparison matrix

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Toluca city has incorporated some municipalities to its urban area (such as Lerma, Zin-

acantepec, Metepec, San Mateo Atenco), resulting in a large urban zone of approximately

two million inhabitants, plus an important industrial region. Second in importance are the

municipalities of Tenancingo and Tenango, with a little more than 50,000 inhabitants each.

Next, with a population between 25,000 and 50,000 inhabitants each, are the municipalities

of Calimaya, Calpulhuac, Coatepec, Ixtapan de la Sal and Villa Guerrero. The remaining

municipalities have less than 25,000 inhabitants. The municipalities of Ocoyoacac, Cal-

pulhuac and Santiago Tianguistenco also host an important industrial park, with a popu-

lation of nearly 120,000 inhabitants together (Fig. 4).

With respect to the total highway length (Primary and Secondary Roads), the munici-

pality of Toluca has the highest value with over 400 km. Next, Zinacantepec, Villa

Guerrero, Tenango, Tenancingo, Malinalco, Ixtapan de la Sal and Coatepec with a rank

from 100 km to 200 km. The remaining municipalities have less than 100 km of highways

(Fig. 5). Regarding the number of schools (Fig. 6), Toluca concentrates the largest number

of education centers with a little more than 600, followed by Zinacantepec, Villa Guerrero,

Tenancingo and Coatepec, which comprise in the rank of 100 to 200 education centers. All

other municipalities are in the rank of lesser than 100 schools.

Figure 7 shows health-care services delivered in health-care units, including hospitals

and rural clinics. Again, the municipality of Toluca is the area with the most important

health-care infrastructure, with 93 health-care units, of which ten are hospitals. Next, with

a much smaller number of medical units, is Zinacantepec, with 14 units and none hospital.

Besides the Toluca City, only Ixtapan de la Sal, Metepec, Tenancingo and Coatepec have

hospitals. The remaining municipalities have less than ten rural clinics, and none of them

has hospitals.

Fig. 3 Methodological diagram used to obtain vulnerability maps of the Toluca Basin, Central Mexico

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Vulnerability maps

For the vulnerability analysis, many socioeconomic variables that can be taken into

account. These variables depend on physical, social, economical and cultural charac-

teristics of each region. Table 1 shows the evaluation matrix for the four criteria chosen.

Vulnerability level intervals were established based on the number of estimated losses

and damages should a volcanic hazard event occur. The first variable established,

according to the recommendations of works on evaluation of natural risks revised for this

research (Westercamp 1982), and which should be considered as the most important

variable, is the population. The vulnerability map for the total population (Fig. 8A) was

delineated based on the population living in each municipality, resulting in a classifi-

cation of municipalities into four categories, according to the highest and the lowest

values, and according to the following criteria: municipalities with more than 400,000

inhabitants represent areas of very high vulnerability (value 4), municipalities with

population ranging from 50,001 to 400,000 have a high vulnerability (value 3);

municipalities with population from 10,000 to 50,000 have moderate vulnerability (value

2); and municipalities with less than 10,000 inhabitants represent areas with low vul-

nerability level (value 1).

In the making of the vulnerability map according to the land use/cover criterion

(Fig. 8B), we considered the urban land use, as well as irrigated agricultural areas (whole-

year crops) and pine forest, to be the areas of highest vulnerability level (value 4). Areas

Fig. 4 Number of inhabitants per municipality

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with rain-feed agriculture, oak forest, mountain-cloud forest, and grassland with cattle use,

were considered as class 3, or high-vulnerability areas. Fir and Juniper forest, as well as

grasslands (without evidence of cattle use) were regarded as areas with moderate vul-

nerability (class 2). Last, we defined areas of low cattle-raising or agricultural value,

including areas devoid of vegetation, alpine or high-mountain grasslands and cattail-sedge

wetland areas as the less vulnerable areas, corresponding to class 1.

The infrastructure vulnerability map (Fig. 8C) was built by integrating the information

on the number of health-care units, the number of schools and highway length per

municipality; then assigning a weight with respect to interval found in each case, based on

the minimum and maximum values. The weight of each variable was added for each

municipality, resulting in a new reclassification into four categories. In this way we

obtained a vulnerability level per municipality.

As for the vulnerability of municipalities based on health-care units, we found that most

of the municipalities have between 15 and 50 health-care units; so we assigned the highest

vulnerability (value 4) to those municipalities with more than 50 health-care units; a high

vulnerability (value 3) to municipalities with between 16 and 50 health-care units; a

moderate vulnerability (value 2) to municipalities with between 5 and 15 health-care units;

and a low vulnerability (value 1) to those with less than five units.

Vulnerability values based on the number of schools were assigned as follows: the most

vulnerable municipalities (class 4) are those with more than 250 schools; high vulnerability

(class 3) include those with between 51 and 250 schools; class 2, or moderate vulnerability,

Fig. 5 Highway length per municipality

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include municipalities with ten to 50 schools; and the less vulnerable municipalities (class 1)

are those with less than ten schools.

Values derived according to the highway length were established giving a value of 4 to

the most vulnerable municipalities with more than 200 km of highways, a high vulnera-

bility (class 3) to those with lengths between 101 km and 200 km; moderate vulnerability,

to those with between 50 km and 100 km (class 2); and the less vulnerable ones (class 1)

are those municipalities with less than 50 km of highways.

Vulnerability values based on infrastructure were obtained adding the weights of the

three variables and classifying them in the following way: municipalities with a very

high vulnerability, those with values above 10; high vulnerability, municipalities with

values between 8 and 9; moderate vulnerability, those with values between 5 and 7;

and with the lowest vulnerability, those municipalities with values between 3 and 4.

The map of vulnerability by economic units (Fig. 8D) was made based on the gross

total product for each municipality, in thousands of pesos. The following were regarded

as the most suitable values to establish vulnerability: class 4, municipalities with a

gross production exceeding 500 million pesos per year; municipalities with a high

vulnerability, those with a production between 101 and 500 million pesos; with a

moderate vulnerability, if the gross production lies between 10 and 100 million pesos;

and class 1, or low vulnerability, municipalities with a product lower than 10 million

pesos.

Fig. 6 Number of schools per municipality

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The vulnerability maps and the total vulnerability map (Fig. 9) show that the Toluca

municipality always has the highest vulnerability levels, demonstrating that from the

population, infrastructure and economic production perspectives, it represents the most

vulnerable area for concentrating a high population density, the major part of services

and an important industrial plant. After Toluca, the municipalities of Zinacantepec,

Tianguistenco, Lerma, San Mateo Atenco and Calpulhuac are classified as vulnerable, for

being industrial centers that have been growing steadily around Toluca. Last, the total

vulnerability map was completed following the Multi-Criteria method in IDRISI and

applying the pairwise comparison matrix, where the four criteria-maps were compared and

weighed with each other (Table 2).

By using ‘‘semi-subjective’’ judgments of value, land use and vegetation was con-

sidered as slightly less important than total population, and was given a weight of 1/3

according to the scale previously established (Fig. 2). Infrastructure was deemed mod-

erately less important than population, assigning a weight of 1/5 to it, while in the

comparison between economic units and population the former were far less important,

with a weight of 1/7. Then, infrastructure was compared to land use/cover, and the

former was defined as slightly less important, with a weight of 1/3, whereas a value of 1/

5 was assigned for the comparison between economic units and land use, denoting that

the former are moderately less important. In the last comparison between economic units

and infrastructure, the former were regarded as slightly less important, assigning value of

1/3 to them.

Fig. 7 Number of medical units per municipality

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Fig. 8 Vulnerability maps. (A) Vulnerability of total population; (B) Vulnerability of land use/cover;(C) Vulnerability of infrastructure; (D) Vulnerability of economic units

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Fig. 9 Map of total vulnerability

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Conclusions

This study is a first step towards a more comprehensive research about vulnerability

assessment as input to determine volcanic risk evaluation in one of the highest volcanoes in

Mexico. The methodology applied here is a real alternative to a scarce and of low spatial

resolution information and for a rapid and concise short-, mid- and long-term analysis, in

the decision-making process and in the construction of risk-prevention maps.

One of the most important value in the use and application of the GIS tools lies in that

they constitute a distinctive information integration and management system that allows to

gather, concentrate, analyze, represent and facilitate the management and interpretation

(qualitative and quantitative) of spatial and attribute information in a far more effective,

rapid and integrated way. Additionally, it makes good use of the enormous capacity of

spatial technology in a single work environment for the fusion and review of various

information layers, extraction of relevant data and ongoing information updating to enrich

the system, compared to other types of manual methodologies and traditional map-

interpretation techniques used before.

Such advantages allow us to handle several scenarios and generate cartography during

the planning stage, before any decision is made on actions to take. In the short-term, in the

event of an imminent eruption, the most vulnerable areas requiring immediate support are

identified. In the long-term, when information and awareness programs are established

among the population, it allows the implementation of simulated contingencies, land-use

planning and appropriate resource management.

Regarding the application of the Multi-Criteria assessment techniques, the advantage of

this methodology integrated in GIS as relatively common tools for a number of investi-

gations like the reported here is worth mentioning, where there are several factors and

variables influencing in the occurrence of a given fact, phenomenon or objective, and there

are several points of view in the decision-making process. Furthermore, these allow us to

handle the assessment in a quantitative way, providing us a greater real-world approxi-

mation validity and less subjectivity in the analysis and selection of criteria.

As regards, selection and assessment of criteria and their weights, it can be stated that

eliminating subjectivity of persons in charge of these aspects is highly difficult; however,

the application of these kinds of methodologies involves a high degree of semi-subjectivity,

that is, they include criteria and ideas based on experience, as well as applications developed

by experts in volcanology and in the study and management of volcanic risks and disasters,

and that are published in thesis, journal papers, books and reports, or that may be obtained

from a personal or impersonal opinion poll among those experts, which provide weight

assign and results with a certainly and veracity. The methodology might be strongly rein-

forced through the formation of a discussion group including experts, decision-makers

Table 2 Pairwise comparison matrix and relative weights criteria to estimate vulnerability in the volcanichazard evaluation

Criteria Total population Land use/cover Infrastructure Economic units Criterion weight

Total population 1 0.5604Land use/cover 1/3 1 0.2605Infrastructure 1/5 1/3 1 0.1276Economic units 1/7 1/5 1/3 1 0.0516Total – – – – 1.0000

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(government agencies) to analyze weight assign and gather their opinion to make final

adjustments to these weights.

Another important reason for using Multi-Criteria techniques is that they make possible

to assess all criteria simultaneously, with no need to carry out several map-overlaying

operations, modifying value attributes using a constant value, and making a final map

reclassification resulting from the combination of all criteria layers.

In the Valley of Toluca, the highest-vulnerability areas are concentrated in those

areas that have been affected by all previous eruptions, coinciding with areas that have

a great concentration of population, industry and high-value agricultural lands (irrigated,

in this case). The Municipality of Toluca stands out over the other municipalities,

indicating an important bias in terms of vulnerability, representing an anomaly found

in this study derived from handling information at a municipality level. This anomaly

will be corroborated further in a subsequent phase of this study where information will

be handled at a finer scale, that is, at a locality level, both in urban and rural areas, and

by obtaining a more complete and detailed cartographic database and using aerial

photographs.

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