The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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2. POVERTY MAPPING 
Poverty mapping - the spatial representation and analysis of 
indicators of human wellbeing and poverty - is becoming an 
increasingly important instrument for investigating and 
discussing social, economic, and environmental problems 
(Henninger and Snel 2002). One of the main problems in 
poverty mapping is to combine socio-economic data aggregated 
by administrative boundaries with environmental data based on 
natural boundaries. But despite these difficulties, combining the 
data is important, because environmental data such as agro- 
ecological zones, are often important in terms of food 
production potential, market accessibility and vulnerability. 
Geo-referenced measures of child nutritional status can also be 
aggregated to aridity zones to examine the relationship between 
child nutritional status and aridity (Henninger 1998). 
2.1 Spatial Datasets for Poverty Mapping 
The types of spatial datasets required for use in poverty 
mapping depend on the way poverty is defined within context of 
the application. Akinyemi (2007b), in a poverty mapping 
measures in use, identified the most common spatial datasets as 
land cover, normalized differential vegetation index (NDVI), 
rainfall data, and soil fertility and quality. This finding is 
confirmed by Hyman et al. (2005), who noted that soil 
characteristics, topography, rainfall, evapotranspiration, and 
vegetative vigor proved to be important explanatory factors in 
describing poverty in several poverty-mapping studies. 
Datasets on travel times to markets and distances to towns and 
facilities are also important explanatory factors in poverty and 
food security outcomes in several studies. A search of the 
literature reveals that either a bottom-up or a top-down 
approach to poverty mapping is used. The former uses socio 
economic data aggregated at the subnational level such as 
survey and census data. Whereas the latter approach uses 
satellite imagery, existing global environmental maps and GIS 
models (see FAO 2002). The examination of GIS use for 
poverty mapping in this study includes both approaches. 
3. POVERTY MAPPING WITH GIS 
The use of GIS to provide a spatial framework for poverty 
mapping allows the use of new units of analysis, for example, 
switching from administrative to ecological boundaries) and 
access to new variables like community characteristics, not 
collected in the original survey (see Henninger 1998). To 
derive greater benefits in poverty mapping with GIS, it is proper 
to identify available GIS functions. It is equally important to 
identify those functions that are required but are not 
traditionally available in a GIS. To successfully do these, the 
types of analysis for which the GIS is needed must be known. 
This involves identifying the types of analysis required for 
poverty management (e.g. poverty assessment) and the 
functions required of GIS to carry out the analysis involved. As 
a prerequisite, to knowing the analysis to be carried out, the 
information needed to be produced for poverty assessment must 
be known. 
Agro-Ecoiogical 
Soil and Land 
denradation 
Landlessness or no 
access to land 
Lacking agro-ecological 
technoloaies 
Low agricultural 
Droductivitv 
ICT & Internet 
access ineaualitv 
Inability to keep 
abreast of information 
Lacking knowledge 
of institutions 
Xnf0t7nation (Knowledge) 
x 
"Poverty' 
T füHl HT'33lf’<*llWÇ 
Political & Social Exclusion 
Unemployment 
Insecurity Lack of 
fair trial 
Low participation - 
social, economic. Dolitical 
Low mutual aid, 
Low asset solidarity networks 
hoop 
Rural & Youth 
underdevelooment 
Low international 
trade/indehtedness 
xsxr 
Low-Income/ 
consumotion 
High-Illiteracy 
rate 
Gender 
ineaualitv 
Ill-health/ Maternal & 
Diseases Under-5 mortality 
Inadequate 
infrastructure 
Monetary & Basic 
(essential) Needs 
Figure 1 : The many dimensions of poverty and indicators (Source: Akinyemi 2005)