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Poverty Reduction: Fighting Poverty with Maps.


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Poverty is multidimensional and goes beyond mere insufficient income
to encompass deprivation of basic capabilities such as education and
health (Sen, 2001). It includes low income, low food consumption, ill-
health, reduced life expectation, poor education, lack of assets, limited
access to natural resources, low social status, lack of political voice, poor
access to ICT (Information and Communication Technology), social servic-
es and welfare facilities (See Figure 1).
Figure 1 reveals that poverty is multidimensional and the indicators
are numerous. With these dimensions closely related one to another,
indicators rarely occur alone as the presence of one form of poverty
appreciably increases the probability of occurrence of all others. Many
approaches are in use in measuring poverty depending on the objectives
that have to be achieved. Usually, an acceptable minimum is defined as a
requirement to live a decent life. This acceptable minimum is referred to
as the poverty line which for any poverty indicator is a threshold below
which an entity (person or community) is said to be poor. The poverty
line is society specific as what is considered an acceptable minimum in a
developing country context would differ significantly from that of a
developed country context. It is in recognition of this fact that apart from
the poverty lines of US$1 and US$2 per day (extreme and moderate
poverty lines respectively) used for international comparison; different
poverty lines are used in different countries or between urban and rural
areas to reflect local conditions.
In developing strategies to reduce poverty and solve other related
problems, poverty levels are better depicted on maps to delineate the
spatial patterns of poverty. These maps are referred to as poverty maps
which are useful tools in decision making for poverty reduction.
Sometimes poverty is mapped in conjunction with variables like agricul-
tural yields, agroclimatic zones, infrastructure e.g. roads, in order to
compare and infer relationships between poverty and these variables.
A Global Challenge
Halving world extreme poverty (people living below one dollar a day) is
the first and most prominent of the eight Millennium Development Goals
(MDGs), thus making the issue of poverty reduction a global challenge
(see MDGs websites, also see Table 1). Table 1 shows the poverty
situation in the regions of the world from the early 1990s to the
beginning of this century.
Beyond global initiatives, analysis of poverty at local levels is impera-
tive as various central government activities in many developing
countries are being decentralized to the local councils. In developed
countries poverty is fast becoming an issue as well. Information on
micro-level poverty is needed by local administrations and communities
to aid policy-making and empowerment of marginalized groups
(Akinyemi, 2005).
Currently, many organizations and research institutions are actively
engaged in promoting the use of poverty maps for handling poverty and
food insecurity in different parts of the world. Some examples are The
G. Keith Douce, Univ. of Georgia, USA coutesy of Forestry Images
36 November 2007 | GEOconnexion International Magazine
World Bank, CGIAR-CSI (Consultative Group for
International Agriculture Research - Consortium
for Spatial Information), CIESIN (Center for
International Earth Science Information
Network of Columbia University), World
Resources Institute, IFPRI (International Food
Policy Research Institute), UN Food and
Agricultural Organization.
Spatial Data
Spatial datasets of administrative boundaries,
road network, rivers are needed for referencing
poverty maps. It is to these reference datasets
that socio-economic and demographic
variables derived, for example, from a census
and cultural data are linked, which makes the
data available for spatial analysis. Other spatial
datasets often required are the distance to
nearest road and facilities, distance to larger,
urban centers (i.e. travel time to markets),
availability and quality of roads (density of road
network). These datasets are very useful as they
are important determinants of poverty in
developing countries. For example, distances
to markets and associated transport costs
largely determine the amount of income
generated by small-scale farmers.
Spatial data are increasingly needed for use
in poverty reduction programs, both for direct
input into poverty assessment, visualization
purposes and presentation. According to
Brooner (2002), the collection, analysis, and use
of geographic information offer a starting point
on the path to sustainable development.
Seeing the importance of spatial variables as
determinants of poverty, the potentials of
Geographic Information Technology (GIT) are
being harnessed for poverty reduction.
Poverty maps as policy tools
The development of appropriate policies for
reduction and eventual eradication of poverty
and hunger hinges on the extent to which we
can delineate the spatial patterns of hunger
and poverty (Sui, 2002). Poverty maps are
useful tools in focusing on pockets of poverty
spatially distributed within countries. A simple
GIS application could be to overlay the poverty
levels with road or rail infrastructure datasets.
From close inspection, areas of high poverty
may occur in areas of sparse road network. This
leads to generating new hypothesis, for
example, ‘the greater the degree of isolation,
the higher the level of poverty experienced in a
locality’. Isolation can be measured by distance
to nearest road or urban center while poverty is
measured using a particular poverty measure or
The results from a study conducted by
Gauci and Steinmayer (2005) on different
African countries (e.g. West Africa - Nigeria,
Benin; East Africa - Kenya, Rwanda; Central
Africa – Cameroon, Chad; Southern Africa –
Zambia, Angola) confirm that in Africa, poverty
increases with distance from major cities. See
Figures 1 and 2 for examples of poverty maps
showing the Nigerian and Rwandan cases
The poverty situation in Nigeria reveals that
the vicinity to urban centres is also vicinity to
market places as shown in Figure 2. Also,
Rwandan poverty incidence is high, particularly
outside Kigali (the national capital) and seems
to increase with distance from the capital. One
major conclusion reached on the basis of these
maps is that improving infrastructure to
connect remote districts to the market centers
would contribute to reducing poverty in Africa.
Poverty maps show the spatial distribution of
poverty and are particularly useful in geographi-
cally targeting poverty reduction programs. For
example, if a specific poor area exhibits a lack of
public endowments that stifle higher economic
growth, poverty maps can help pinpoint invest-
ment areas that need attention to accelerate
economic growth and focus poverty reduction
spending. Besides, private expenditure can be
heightened in targeted areas for poverty
reduction (Gauci, 2005). Countries like Brazil
and Kenya have experienced accountability and
more transparency in the allocation of funds for
developmental project, which are facilitated
with the use of poverty maps.
Akinyemi, F.O. (2005) Modelling Poverty
Reduction from a System Perspective. In
Horst Kremers (ed.) Proceedings,
International Symposium on Generalization
of Information, Committee on Data for
Science and Technology (CODATA), Inter.
Table 1: Population below $1 purchasing power parity (PPP) per day
* High-income economies, as defined by the World Bank, are excluded (source: UN, 2004a)
Region 1990 1999 2001
Northern Africa 2.6 2.0 1.9
Sub-Saharan Africa 46.9 42.7 46.6
Latin America and the Caribbean 10.6 10.6 10.0
Eastern Asia 33.0 17.8 16.6
Southern Asia 39.7 30.5 30.4
South-Eastern Asia 18.4 10.8 10.2
Western Asia 1.6 4.2 3.7
Commonwealth of Independent States 0.5 10.3 5.0
Transition countries of Southeastern Europe 0.4 1.7 2.1
The many dimensions of poverty and indica-
tors (Source: Akinyemi 2005)
The poverty situation in Nigeria as it relates to distance to major towns
(Source: Gauci and Steinmayer 2005)
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Council for Science (ICSU), September 14-16,
2005, Berlin, Germany, pp. 1-13.
Brooner, W. G. 2002. Promoting Sustainable
Development with Advanced Geospatial
Technologies. Photogrammetric
Engineering and Remote Sensing, 68(3):
Gauci, A. 2005. Spatial maps: Targeting and
Mapping Poverty. Working Paper, Poverty
and Social Policy Team, ESPD, UN Economic
Commission for Africa, 2005,
Gauci, A. and V. Steinmayer. 2005. Poverty
Maps: A Useful Tool for Policy Design to
Reduce Poverty. Working Paper, Poverty
and Social Policy Team, ESPD, UN Economic
Commission for Africa, March 2006,
Sen, A (2001) capabilities: the Concept and
its Operationalization, Working Paper no 66,
Queen Elizabeth House, U.K.
Sui, D. Z. 2002. GIS and Spatial Analysis Tools
for Poverty and Food Insecurity Mapping.
Working Paper No. 7, Environment and
Natural Resources, Rome: Food and
Agriculture Organization of the United
United Nations (UN, 2004a) Implementation
of the United Nations Millennium
Declaration. Report of the Secretary-
General, A/59/282 59th Session General
Useful sites on poverty mapping
MDGs websites - http://ddp-
MDG progress in Africa:
MDG database: http:/
Povertymap website -
World Bank –
Article by Felicia O.Akinyemi Ph.D, Department
Surveying and Geoinformatics, University of Lagos,
Akoka, Lagos State, Nigeria. e-mail:
Poverty incidence in Rwanda (Source: Gauci and Steinmayer 2005)
... Figure 1 reveals that poverty is multidimensional and the indicators are numerous. With these dimensions closely related one to another, indicators rarely occur alone as the presence of one form of poverty appreciably increases the probability of occurrence of all others (Akinyemi 2007a). ...
Full-text available
To achieve the goal of poverty reduction, as encapsulated within the Millennium Development Goals, the collection, analysis, and use of geographic information as it relates to the multidimensionality of poverty offers a starting point. The spatial handling of poverty is an emerging paradigm for which researches on the spatial modelling of poverty are required. Attempting to contribute to a better understanding of poverty mapping, this paper examines GIS suitability for use in poverty application areas. GIS analysis functions most appropriate for use in specific poverty mapping tasks are examined. The uses are identified as data integration of socio-economic, environmental, cultural data, etc.; 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; deriving straight-line and network distances; visualisation and presentation of the results of poverty analysis. Special emphasis is placed on ways in which GIS is being used and its suitability for poverty reduction tasks to help draw out some relevant methodological and policy lessons.
Full-text available
Concern for poverty is not new and has been the focus for centuries by historians, sociologists, and economists. The cause has been identified, ranging from shortcomings in the administration of income support, until the injustice of the social and economic system. Various attempts have been proposed, from the reform of social security system for changes in the form of the socio-economic system. Because poverty is a multidimensional problem, solutions to poverty require a set of coordinated action, particularly through charity. Indonesia, which has a population with a large population, of course, the problem of poverty continues to be a problem in economic development. Nevertheless, the potential zakat Indonesia larger community and cooperation among stakeholders and government regulation is a solution to reduce the level of poverty in Indonesia. It is certainly different from the Brunei Darussalam to the level of a small population and large government revenues, management of zakat by MUIB in the form of cash grants, the capital of commerce, and others are implementable can solve the problem of poverty in this country.
Conference Paper
Full-text available
Poverty reduction has become the focus of historians, sociologists and economists throughout its history. The causative factors of poverty have been identified in the form of income ineligibility to social injustice and economic system. The efforts of poverty reduction into government policy to reform the social security system for socio-economic system reform. Because poverty is a multidimensional problem, solutions that do require a set of co-ordinated action, particularly through charity. Indonesia has a large population with the problem of poverty is the target completion in economic development. However, optimizing the potential of zakat which is high on the people of Indonesia and the cooperation among stakeholders and government regulation to be a solution in reducing poverty. It is certainly different from Brunei Darussalam to the level of a small population and government revenues are high, so the management of zakat by MUIB in the form of cash grants, capital of commerce, and others are implementable can resolve the problem of poverty in this country
Urban poverty in Iraq, which is rapidly increasing, the number of Iraqis living below the poverty line has increased since the US-led invasion of Iraq in March 2003 to one-fifth of the population. This study aimed at showing how GIS tools and geo-statistical techniques can be useful to develop structured databases for resource use modeling, planning and management geared to address poverty reduction. This information is applied to reconstruct the routes of poverty alleviation and to model the poverty process, coupled by the GIS (Geographical Information System) and statistical analyses. The use of GIS for poverty-related data handling is superior not only to manual (traditional) data handling methods, but advantageous over other information systems as it admits data coming from different sources.