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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