Despite being an indicator of modernization and macro-economic growth, urbanization in
regions such as Sub-Saharan Africa is tightly interwoven with poverty and deprivation. This has
manifested physically as slums, which represent the worst residential urban areas, marked by lack
of access to good quality housing and basic services. To effectively combat the slum phenomenon,
local slum conditions must be captured in quantitative and spatial terms. However, there are
significant hurdles to this. Slum detection and mapping requires readily available and reliable data,
as well as a proper conceptualization of measurement and scale. Using Bamenda, Cameroon, as a
test case, this dissertation research was designed as a three-pronged attack on the slum mapping
problematic. The overall goal was to investigate locally optimized slum mapping strategies and
methods that utilize high resolution satellite image data, household survey data, simple machine
learning and regionalization theory.
The first major objective of the study was to tackle a "measurement" problem. The aim was
to explore a multi-index approach to measure and map local slum conditions. The rationale behind
this was that prior sub-Saharan slum research too often used simplified measurement techniques
such as a single unweighted composite index to represent diverse local slum conditions. In this
study six household indicators relevant to the United Nations criteria for defining slums were
extracted from a 2013 Bamenda household survey data set and aggregated for 63 local statistical
areas. The extracted variables were the percent of households having the following attributes: more
than two residents per room, non-owner, occupying a single room or studio, having no flush toilet,