Accounting for spatial non - stationairty to estimate population distribution using land use / cover : case study : the Lake Naivasha Basin, Kenya

Dawit Woubishet Mulatu, A. van der Veen, R. Becht, P.R. van Oel, D.J. Bekalo

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Remotely-sensed data can be used to overcome deficiencies in data availability in poorly monitored regions. Reliable estimates of human population densities at different spatial levels are often lacking in developing countries. This study explores the applicability of a geographically-weighted regression (GWR) model for estimating population densities in rural Africa using land use/cover data that have been derived from remote-sensing while accounting for spatial non-stationarity. This study was conducted for the Lake Naivasha basin in Kenya where population pressure, intense land utilization in the catchment and informal settlements in Naivasha town due to lucrative economic activities are the major challenges of the basin socio-ecological system. The results of this study show that using a GWR model for taking into account the spatially-varying relationship between specific land use/cover c
lasses and population significantly improves population estimates and handles the spatial non-stationarity that could not be addressed by global ordinary least squares (OLS) model. The result revealed that the parameter estimates (coefficients) for grassland and cropland use/cover have a significant spatially varying relationship with population and exhibit locally different signs, which would have gone undetected by a global model. Consequently, this study indicates that incorporating spatial non-stationarity can significantly improve population density estimates for rural Africa based on remotely-sensed data.
Original languageEnglish
Pages (from-to)33-44
JournalJournal of settlements and spatial planning
Volume4
Issue number1
Publication statusPublished - 2013

Fingerprint

Dive into the research topics of 'Accounting for spatial non - stationairty to estimate population distribution using land use / cover : case study : the Lake Naivasha Basin, Kenya'. Together they form a unique fingerprint.

Cite this