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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004 
TPoveriy inventory map ping 
  
Figure I: Integrated poverty management concept of the 
GTGIS (Source: Akinyemi, 2003) 
2.2 Poverty Alleviation Module 
It involves the use of geographic targeting method in 
transferring benefits to poor households or neighbourhoods. 
Based on the concept of Poverty Vulnerability Mapping (PVM), 
the balance of factors (of strengths and weaknesses) of 
households, communities and states which render them poverty 
prone to varying degrees are mapped. With those vulnerability 
maps, interventions can be transferred to the poor based on 
where they are located using geographic targeting. Furthermore, 
the household socio-economic indicators stored in the GIS 
database enables the intelligent transfer of interventions based 
on what their needs are, as opposed to what decision makers 
think the poor need. In analysing for the PVM, households are 
divided into poverty classes based on their vulnerability levels. 
2.3 Poverty Monitoring Module 
It is concerned with the regular monitoring of poverty 
performance indicators using analysis of poverty trend between 
different time periods. It evaluates the impacts of interventions 
on reducing poverty over a period of time. 
Tools in the three modules culminate in the ability of the system 
fo manage poverty in a comprehensive manner as against the 
piecemeal manner in which most systems handle the poverty 
problem (Akinyemi, 2003). 
3. DATA REQUIREMENT OF THE GTGIS 
The GTGIS model requires data about HOUSEHOLD HEAD, 
BUILDING, EMPLOYMENT, SPOUSE, STREET AND 
NEIGHBOURHOOD as superclasses (regular entities). While 
HEALTH, DEPENDANT (children), INFORMAL 
OCCUPATION, FORMAL OCCUPATION and PENSION are 
subclasses (weak entities) for the Poverty Assessment Module. 
These datasets are processed as poverty levels for input into the 
Poverty Alleviation Module to generate poverty maps for 
households and neighbourhoods. Constructing poverty maps at 
such fine level of geographic disaggregation enhances the 
usefulness of poverty maps. This 1s due to the fact that 
> 
3 
aggregated poverty data at state and local government levels 
hide much variation in poverty. 
These datasets are all socio-economic and demographic in 
nature and are to be derived principally from census or 
specialized sample surveys. Non — release of disaggregated 
household level census information diminishes the usefulness of 
census data (detailed household census figures are withheld for 
years before release for the purpose of confidentiality). We can 
resolve this problem by developing poverty maps on the basis 
of welfare using indicators like access to water, education, 
health etc. 
Poverty at the household (micro-economic) level is the main 
focus of the GTGIS system because it is at this level that the 
primary manifestation of poverty occurs. Moreover, poverty at 
neighbourhood, regional and national levels are but aggregates 
of poverty occurrences in households. Since household poverty 
is an intangible phenomenon, how do we formalize its 
geometric component as a spatial object? 
We opted for a vector mode of geometric representation in 
which the outline of buildings housing each household is 
captured as the basic unit of space to which household 
information is explicitly recorded in the database (figure 2). 
  
  
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Figure 2: Buildings housing each household as the basic unit of 
space