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Poverty Reduction: Fighting Poverty with Maps.

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MAPS HELP IN FIGHT ON AFRICAN POVERTY
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www.geoconnexion.com 35
MAPS HELP IN FIGHT ON
AFRICAN POVERTY
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
FELICIA AKINYEMI LOOKS AT THE USE OF MAPS FOR POVERTY REDUCTION ACTIONS IN AFRICA
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
indicator.
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
respectively.
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.
Conclusion
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.
References
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 $1 purchasing power parity (PPP) per day
* High-income economies, as defined by the World Bank, are excluded (source: UN, 2004a)
Region 1990 1999 2001
Northern Africa 2.6 2.0 1.9
Sub-Saharan Africa 46.9 42.7 46.6
Latin America and the Caribbean 10.6 10.6 10.0
Eastern Asia 33.0 17.8 16.6
Southern Asia 39.7 30.5 30.4
South-Eastern Asia 18.4 10.8 10.2
Western Asia 1.6 4.2 3.7
Commonwealth of Independent States 0.5 10.3 5.0
Transition countries of Southeastern Europe 0.4 1.7 2.1
PERCENTAGE OF POPULATION LIVING BELOW US$ 1 PER DAY[*]
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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www.geoconnexion.com 37
Council for Science (ICSU), September 14-16,
2005, Berlin, Germany, pp. 1-13.
Brooner, W. G. 2002. Promoting Sustainable
Development with Advanced Geospatial
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198-205.
Gauci, A. 2005. Spatial maps: Targeting and
Mapping Poverty. Working Paper, Poverty
and Social Policy Team, ESPD, UN Economic
Commission for Africa, 2005,
http://www.uneca.org/espd/publications/sp
atial_maps_targeting_mapping_poverty.pdf
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,
http://www.uneca.org/espd/publications/p
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gn_to_reduce.pdf
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
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Agriculture Organization of the United
Nations.
United Nations (UN, 2004a) Implementation
of the United Nations Millennium
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Useful sites on poverty mapping
CGIAR-CSI - http://www.csi.cgiar.org/
CIESIN –
http://www.ciesin.columbia.edu/povmap
IFPRI – http://www.ifpri.org
MDGs websites - http://ddp-
ext.worldbank.org/ext/GMIS/home.do?siteId=2
MDG progress in Africa:
http://geoinfo.uneca.org/mdg/
MDG database: http:/mdgs.un.org/unsd/mdg/
Povertymap website -
http://www.povertymap.net
World Bank – http://www.worldbank.org
Article by Felicia O.Akinyemi Ph.D, Department
Surveying and Geoinformatics, University of Lagos,
Akoka, Lagos State, Nigeria. e-mail:
felicia.akinyemi@gmail.com
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). ...
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