In order to reduce poverty we must first describe, explain and predict
its spatial distribution over large areas with as high a level of local
accuracy as possible. Poverty maps are traditionally produced by
exploiting links between census (wide area) and survey (smaller area
coverage) data. The detailed relationships found within the survey data
are extended to the census data that must share some predictor variables
in common with the survey data. Both census and survey data tend to be
socio-economic in nature; the mapping thus exploits the internal
correlations within potentially strongly correlated data sets - one
'measure' of poverty is often correlated with another.
Rather than look at the correlates of poverty, we should like to
identify its causes. We suggest that poverty is multi-dimensional and
that many of its dimensions are environmentally related; people are poor
because they are unhealthy, or under-fed, or without access to fuel and
water etc. Each of these is environmental in some way or other, and a
correct approach to reducing poverty might be first to identify its
(environmental) causes. We have attempted to do this with survey data
from Uganda and environmental data derived from multi-temporal satellite
imagery that measures land-surface conditions and processes
(temperature, rainfall, vegetation growth etc.). The same satellite data
have already been used to understand the distribution of farming systems
throughout Africa and to predict the distribution and intensity of
insect and tick carriers of a variety of diseases, and the incidence and
prevalence of the diseases they transmit.
In this analysis therefore we examined to what extent satellite data (as
a proxy for environmental conditions) are correlated with household
survey data. Whilst correlation obviously does not automatically imply
causation, we suggest an environmental approach is more likely to reveal
causes than will the traditional approach of small area mapping using
census and survey data.
However, it is first necessary to establish the relative predictive
accuracies of the traditional and environmental approaches. The initial
results from the environmental approach, described here, are promising,
though we have not yet compared them directly to small area methods.
PPLPI, FAO, Rome, Italy, vi+60 pp.