Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009. Issue 10 (3rd October 2017)

• Record Type: Journal Article
• Authors: Linard, Catherine; Kabaria, Caroline W.; Gilbert, Marius; Tatem, Andrew J.; Gaughan, Andrea E.; Stevens, Forrest R.; Sorichetta, Alessandro; Noor, Abdisalan M.; Snow, Robert W.
• Is Part Of: International journal of digital earth. Volume 10:Issue 10(2017)
• Page Start: 1017
• Page End: 1029
• Publication Date: 2017-10-03
• Subjects: Human population -- distribution modelling -- gridded population datasets -- temporal change -- Kenya; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2016.1275829
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit. Here we make use of recently released multi-temporal high-resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.