Mapping is defined for the purposes of this paper as spatial analysis of poverty and food security, in visual and econometric terms. Spatial determinants are important for understanding the distribution of assets that are fundamental for alleviating poverty and combating food insecurity; these include human capital such as health, education and technology, and social capital such as the ability to cooperate and social networks. Spatial analysis has most promise in the area of natural resources, because natural capital-asset holdings such as natural resource stocks, land quality and environmental quality are difficult to characterize with conventional variables, and are spatially distributed by definition. Infrastructure variables such as road density and quality, and access to labour, product and input markets also have an important spatial dimension.
Poverty mapping has two primary uses. The first is spatial identification of the poor, on which this paper concentrates. Poverty mapping has in many instances served to target social, agricultural, emergency, environmental and anti-poverty programmes. Poverty maps have been crossed with environmental and agricultural-system maps in order to use visual spatial analysis to discern correlations. Numerous examples will be provided in the paper; for further reference, Snel and Henninger (2002) provide detailed case studies of poverty-mapping applications.
The second use is to create, as a by-product, explanatory and dependent spatial variables for use in multivariate analysis in combination with recently developed tools that permit the spatial dimension to be incorporated in multivariate examination of poverty issues.
Different methodologies are used for locating food-insecure or poor people, and for evaluating determinants of poverty and food insecurity; these include econometric models, livelihood-systems analysis and participatory appraisals. In each case, poverty mapping is used to reveal the location of poor people and the location-related aspects of the identified determinants of poverty and food insecurity. In econometrics-based methodologies, this assessment generally takes place within a multivariate regression framework, though it can and should be complemented with other types of information. The livelihood approach uses in-country expert opinions to categorize households by asset structures and livelihood strategies, thus revealing the location and determinants of poverty. The participatory approach elicits self-generated definitions of poverty, and with it the location and determinants, from respondents in the population under study. These methodologies are likely to lead to different outcomes with regard to locations - that is, maps - and policy implications; few comparisons of the practical differences have been made, however.
Spatial analysis of poverty has been utilized in a number of policy and research applications ranging from targeting emergency food aid and anti-poverty programmes to assessments of the determinants of poverty and food insecurity, in addition to providing visual representations of spatial relationships between variables. These applications have been used by organizations ranging from national governments to non-governmental organizations (NGOs) and multilateral development organizations (see Henninger, 1998, and Snel and Henninger, 2002, for a review of many of these applications).1 The methodologies utilized are diverse, ranging from participatory poverty profiles to sophisticated econometric techniques; most are under continuing development. Each has different data requirements and implementation costs, and different advantages and disadvantages.
Poverty mapping is essentially a tool; its functionality must therefore be seen and evaluated in light of the objectives for which it is put to use - the research and policy questions and hypotheses upon which it can shed light. Poverty mapping should be initiated with clear objectives in mind that will help to guide interpretation of the output and determine the appropriate methodology. Although poverty mapping can serve as a useful exploratory or directed tool in establishing and presenting the spatial relationship between a pair or series of indicators, it can lead to serious misinterpretation of causal relationships between variables. Poverty maps do not as a rule represent causal linkages so much as visual correlations; interpreting causality can thus lead to serious policy and analytical mistakes. In a multivariate regression framework, however, using appropriate econometric analysis techniques, variables derived from poverty mapping exercises can serve as determinants - or outcomes - of causal relationships. Some livelihood approaches also attempt to understand causal relationships.
Most types of poverty mapping increasingly depend on data generated by geographical information systems (GIS), where values are fixed to specific locations on a grid. The spatial location of poor people facilitates integration of data from sources such as satellites, censuses, household surveys, sectoral surveys, models and simulations for the analysis of the determinants and impacts of poverty. GIS techniques provide four functions in poverty mapping (see Bigman and Deichmann, 2000a):
Recent studies have stressed the importance of geography and spatial variables as determinants of poverty. Most of the recent voluminous research on poverty and food insecurity, however, has been surprisingly limited to rudimentary and one-dimensional characterizations of the roles of regions and access to different types of infrastructure, public services and product and labour markets. Many poverty-mapping exercises involve simply a ranking of areas by some poverty, food security or marginality indicator and have no need for maps except as communication tools. GIS techniques can be used to incorporate spatial analysis into the determinants of rural poverty or food insecurity, or into issues that are important for alleviating poverty and food insecurity. This could include the determinants of migration, participation in off-farm labour activities, product market participation, crop choice or technology adoption. One of the most common applications is to the analysis of the causal relationship between poverty and the environment, where few links have been found, often because of technical, estimation or data limitations (see Lipper, 2001).
1 Mapping efforts that are not directly tied to poverty or food security are not included, such as the FAO farming system and agro-ecological typologies, or the many environmental and production applications among the Consultative Group on International Agricultural Research (CGIAR) centres, though these may be relevant to combining with poverty-mapping exercises.