Spatial Planning Under Data Paucity: Dasymetric Interpolation of Population, Validated by Google Earth, to Support Health Facility Location Modelling

International Journal of Geoinformatics and Geological Science

© 2017 by SSRG - IJGGS Journal

Volume 4 Issue 2

Year of Publication : 2017

Authors : Idris Mohammed Jega, Alexis J. Comber, Nicholas J. Tate

10.14445/23939206/IJGGS-V4I3P101

How to Cite?

Idris Mohammed Jega, Alexis J. Comber, Nicholas J. Tate, "Spatial Planning Under Data Paucity: Dasymetric Interpolation of Population, Validated by Google Earth, to Support Health Facility Location Modelling," SSRG International Journal of Geoinformatics and Geological Science, vol. 4,  no. 2, pp. 1-11, 2017. Crossref, https://doi.org/10.14445/23939206/IJGGS-V4I3P101

Abstract:

Small area population estimates are important for facility location-allocation analyses as they provide a spatial distribution of „demand‟ against which potential „supply‟ locations are evaluated. However, in many parts of the world small area estimates of census data are not available which makes it difficult to validate and constrain interpolated population or demand surfaces. For such cases a number of interpolation methods have been proposed to redistribute summary population census totals over small areas. Binary dasymetric interpolation has been shown to perform well across a range of spatial scales and resolutions supported by ancillary data. To date no published research describes the use of dasymetric approaches as methods for disaggregating population data over small areas in any part of West Africa. This study applies a binary dasymetric approach to generate population surface for Port-Harcourt, Nigeria, an area for which small area population estimates are unavailable. This was validated by visual inspection using Google Earth and found to be 87% correct. The demand surface was then used as input to a location-allocation analysis of health facility provision. The locations of current primary healthcare centres (PHCCs) were evaluated and then alternative, improved spatial arrangement for the facilities that optimised the spatial distribution of supply with that of demand, were suggested. The results show 13 alternative sites for PHCCs to be located would provide almost the same demand coverage as the 17 current locations of PHCCs. The analysis indicated that fewer PHCCs in a different spatial arrangement could satisfy the spatial distribution of demand. The results suggest that such methods can be used to support and inform decision making and spatial planning in countries where only limited socioeconomic data exist.

Keywords:

Small area population estimates, binary dasymetric mapping, health facility locationallocation, spatial planning.

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