Spatially Constrained Clustering of Nigerian States: Perspective from Social, Economic and Demographic Attributes

Year 2020, , 68 - 79, 26.04.2020

Olanrewaju Lawal

https://doi.org/10.30897/ijegeo.588032

Cited By: 4

Abstract

Creation
and differentiation of regions are some of the basic tasks in geographic
analysis. Regionalisation attempts to create a generalised representation of
the processes which is taking place at the level of the amalgamated geographic
units. To this end, this study examined the combined use of demographic,
economic and poverty characteristics of States across Nigeria to create regions
relevant for economic and development planning. The study utilised dependency
ratios derived from gridded age structure data, Gross domestic product (GDP),
poverty index. K-Means and Max-p algorithm were used for identification of
regions. Correlation analysis showed that
Youth dependency and total dependency have a strong statistically significant
positive relationship (r=0.998, p<0.01) indicating that dependency in the
country is driven by youth. The best K-Mean clustering implementation without
considering contiguity identified 12 regions with a ratio of between and total
sum of squares (RBTSS) of 0.789. The Max-p algorithm was tested with population
constrain, the best result identified 9 regions with RBTSS of 0.611 constrained
by a minimum population of 8% and implemented with the greedy local search
algorithm, this was the same for the simulated annealing approach (SA). With
high dissimilarity still common across a handful of the regions identified, a
further test was carried out using a minimum bound of 3 States and the SA local
search approach. The best result identified 11 contiguous regions with only one
region having a relatively high within region dissimilarity and a RBTSS of
0.626. The results confirmed that there are more than 6 regions as currently
defined for the country. The analyses showcased an example of knowledge
discovery from a spatial dataset which could support regional development
planning. From the results, there is a clear need for re-examination of current
regions and designing of better-defined regions to ensure that development is
guided by evidence.

Keywords

Spatially constrained clustering, Geoinformatics, Regionalisation, K-Means, Max-p

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