The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
Defining the terms functions and analysis within the context of 
GIS, functions are the operations that a GIS can perform. For 
example, most systems contain functions for digitizing, 
querying, and overlaying spatial data. Analysis is the process 
used to explore the details of some phenomenon using the 
system's functions. The types of functions a GIS system can 
perform determine the possibilities for analysis (ESRI and TAL 
2001,2002). 
Table 1 shows the objectives in most poverty assessment 
activities, the GIS functions that are available and those 
required in addition (the listed poverty assessment objectives 
and GIS functions shown in Table 1 are by no means 
exhaustive. The table is still under construction). 
It is evident that the non-availability of some required 
functions for poverty mapping, for example, poverty measures 
is limiting GIS use. Core poverty assessment activities 
presently have to be carried out outside the GIS environment. 
Consequently, poverty analysis has to be done in other 
software packages. The 
poverty results are then brought into GIS afterwards for further 
analysis and/or visualization. Where required functions for 
specific poverty reduction analysis are not available, there is 
the possibility to add these functions by customizing GIS 
packages, for example, with ESRI ArcObjects. An example of 
poverty related customizations of GIS is the SAS Bridge to 
ESRI which adds the analytic intelligence of SAS to the 
mapping capabilities of ArcGIS (see Tesfamicael 2005). For 
other studies, see Manansala (1999), Hall and Conning 1991). 
In our quest to identify GIS use and suitability for poverty 
management tasks, we will look at some core GIS functions 
and their uses in poverty mapping. 
4. USING AVAILABLE GIS FUNCTIONS IN 
POVERTY MAPPING 
GIS use is important in poverty mapping for its data integration 
capability. Data for poverty analysis come from various 
sources such as census (with wider coverage of a country’s 
population), household surveys, and agricultural surveys. 
Poverty 
assessment 
objectives 
Poverty mapping measures and indicators 
GIS functions and analysis 
Available 
Not available 
Assess poverty 
level using a 
particular poverty 
measure and 
indicators 
Econometrics - Small area estimation (current 
consumption expenditures, income, and wealth); 
Social - Unsatisfied basic needs (nutrition, water, 
health, and education); Demographic (gender and 
age structure of households, child nutritional status - 
calorie intake, low height for age, low weight for 
age, low weight for height, body mass index, low 
birth weight, and household size and age structure); 
Vulnerability (level of household exposure to 
shocks, environmental endowment and hazard, 
physical insecurity, empowerment, governance, 
diversification and risk of alternative livelihood 
strategies, structural inequities) 
Distance measurement 
with proximity analysis 
e.g. distance from villages 
to main roads, other 
towns, health facilities; 
multi-criteria evaluation 
tools 
Econometrics e.g. small 
area estimation routines, 
F oster-Greer-Thorbecke 
poverty index; principal 
components analysis, 
factor analysis human 
development indices - 
human development 
index, human 
poverty indices 
Relate poverty 
patterns with socio 
economic, 
environmental 
variables, etc. 
As in Poverty-biodiversity which relate poverty 
with, for example, major tropical wilderness and 
biodiversity hot spots; examine people’s 
susceptibility to poverty 
Overlay analysis for 
understanding spatial 
association between 
variables; Ranking of 
units of interest based on 
a poverty indicator 
Examine spatial 
and temporal 
variations in 
poverty 
Poverty dynamics - movement in and out of poverty 
Poverty time series maps, 
dynamic mapping 
Poverty dynamics 
indices 
Table 1: Poverty Assessment with GIS 
The increasing number of variables from these different sources 
used in poverty mapping applications shows the usefulness of 
GIS for data integration. GIS use also includes the generation of 
spatial variables such as distance measurement with proximity 
analysis, for example, distance to nearest urban centre or health 
facility; overlay analysis, for example, in seeking to understand 
the association between land use type (change) and population 
density or race (see Mennis and Liu 2005). 
Below are some standard GIS functions used for poverty 
mapping applications: 
1. data integration - Integration of multiple databases from 
different sources such as socio-economic, environmental, 
cultural data, etc. 
2. overlay - analysis of spatial association between variables 
3. buffer - delineating the area that lies within a specified 
threshold distance from selected features or places 
4. query - deriving further data from spatial analysis such as 
spatially generated explanatory variables as input for 
multivariate analysis of poverty (such as distance to markets, 
urban centres and facilities) 
5. visualisation and data presentation 
The uses of these GIS functions in poverty mapping are treated 
in details in the next subsection. 
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