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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