The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
4.1 Data Integration 
A GIS allows a wide variety of data integration forms. One 
layer of data (such as districts) can be presented on top of 
another (such as climate zones), not only to create a visual 
display but also to generate a new data set in which each point 
has attributes from the two original data sets. The points in the 
integrated data set can then be used to analyze social, economic, 
and spatial relationships using either cross tabulations or formal 
statistical and econometric methods. Thus, for example, 
information on the distance from a village to an urban centre 
can be combined with area-based soil data in order to assess the 
agricultural potential of rural communities; information on the 
road network can be combined with information on population 
density to generate indicators of transportation density for each 
district (Bigman and Fofack 2000). 
4.2 Overlay 
Overlay is of exceptional use in poverty mapping, such as the 
overlay of different datasets in a health related poverty 
application, for example, rivers, treatment areas, 
settlement/village are needed for identifying high risk 
population. Overlaying poverty maps atop maps of local 
infrastructure (schools, health clinics, hospitals, water supply 
facilities, and roads) may improve the understanding of poverty 
dynamics, shed more light on the possible constraints to growth 
and poverty reduction, and improve priority setting, impact 
assessment, and policymaking (Fofack 2000). 
Overlaying data on stunting rates in under-age 5 children (a 
proxy for poverty) and that of amphibian species and endemic 
bird areas helps to highlight spatial correlations and disparities 
between the datasets. With these areas where high percentage of 
underweight children coincide with a high occurrence of 
amphibian species and endemic bird areas may indicate areas in 
which poor people likely have no other choice than the 
unsustainable extraction of resources, which in turn threatens 
biodiversity. The highlighted areas can then be given the 
highest priorities for poverty alleviation and conservation (Snel 
2004). Henninger and Snel (2002) analysed the correlation of 
some Human Development Indicators (HDI) surrogates with the 
marginal condition factors using point and polygon overlay 
analysis functions in Arc/Info and Arc/View software. For each 
HDI sample point, a geographically referenced value was 
extracted from each thematic layer. 
4.3 Buffer 
This involves delineating the area that lies within a specified 
threshold distance from selected features or places. Buffers can 
then be created around selected features (e.g. health facilities, 
school, village, water point) for specific rates to be computed 
(e.g. calculate population, number of disease cases, or 
prevalence within a radius, see WHO 2003). 
4.4 Query 
The use of GIS query tool is also very important. GIS allows 
querying based on attribute or by location and both selection 
methods are appropriate for use in poverty mapping. Since 
most data in use are socioeconomic or demographic variables 
derived, for example, from a census, these data are linked to 
geographic units appropriate to the levels of data publication. 
Linking of socioeconomic and/or cultural data to a specific 
location makes the data available for spatial analysis, which in a 
sense makes such data spatial (Akinyemi 2007b). 
Queries can be applied that use the features of one layer to 
choose features in another layer such as distance from village to 
urban centres, travel times to markets and distances to facilities. 
Utilizing such measures of distance and physical accessibility is 
increasingly important in poverty mapping studies, since 
income generation for small-scale farmers, for example, often 
depends on distances to markets and associated transport costs 
(Van De Walle 2002, Jacoby 2000, both cited in Hyman et al. 
2005). 
4.5 Visualization and Representation 
The Geostatistical tool in ESRI ArcGIS is useful in 
visualization for the better understanding of data used for 
mapping poverty. Spatial units of use in poverty mapping could 
be represented as dots or represented as polygons e.g. census 
tracks, enumeration areas. Graphical representation of point 
data can be used to convey information about other data 
dimensions by plotting them as symbols that may vary in size, 
shape, colour, hue or saturation. 
GIS also provides a function that let you construct histograms 
of the classification scheme with which the data is represented 
on a map. The classification histogram aids the visualization of 
how attribute values of features are distributed across the 
overall range of values. If the data is multidimensional, this 
technique can not only improve the clarity of the graphical 
display but also portray information about the data in ways that 
may induce viewers to discover patterns or trends. When the 
number of data points is large, representing aggregate numbers 
of a variable in the same area using graduated symbol mapping 
for example, is not appropriate as it is difficult to identify a 
coherent pattern. In the alternative, such data representing 
discrete objects can be treated as continuous. Bracken and 
Martin (1989) cited in (Longley and Batty 2003) have suggested 
that a field or surface approach provides a useful way of 
handling socio-economic data. 
Poverty maps are means of visually communicating the results 
of poverty assessment. Most poverty maps are meant for 
printing on paper such as hardcopy maps produced in reports 
and atlases etc. which can be made available on the internet. 
5. CONCLUSION 
The immense value of GIS as the singular tool for 
understanding human-environment interaction is resulting in its 
increasing use. Consequent upon these increasing uses of GIS in 
handling poverty and other social problems, it became 
necessary to examine GIS suitability, for example, in poverty 
assessment and to identify where enhancement of GIS 
functionalities is required. Most common GIS uses were 
identified as data integration, delineation of areas lying within a 
specified threshold distance from selected features or places, 
deriving further data from spatial analysis for multivariate 
analysis of poverty, visualisation and presentation of the results 
of poverty analysis in the form of maps. 
The uses we just identified help to demonstrate GIS suitability 
for poverty mapping applications. Some of the functions 
required for poverty mapping, although absent in today’s GIS, 
can be added by implementing some econometric (income 
poverty) and anthropometric measures (human poverty) within 
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