Modelling changing population distributions: an example of the Kenyan Coast, 1979-2009

• Published in: International journal of digital earth Vol. 10; no. 10; pp. 1017 - 1029
• Main 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.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 03.10.2017; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Biological settlement; Census; Censuses; Coastal environments; Distribution; distribution modelling; gridded population datasets; Growth rate; High resolution; Historic sites; Human population; Kenya; Land cover; Modelling; Population; Population distribution; Spatial distribution; temporal change; Time series; Human population; gridded population datasets; Kenya; temporal change; distribution modelling
• ISSN: 1753-8947; 1753-8955
• DOI: 10.1080/17538947.2016.1275829

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.