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

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.

Reliability Analysis of LandScan Gridded Population Data. The Case Study of Poland

“…At the same time, the deviation rate index (DRI) definition is similar to the difference dataset index computed by Hall et al [26] and difference rate implemented by Oyabu et al [41]. Furthermore, similar to [17,25,26,41], we attributed accuracy to the grid cell, whereas [10,13,18,23,24] accuracy results are more vague and refer to administrative units. The green region reflects districts with the highest value of reasonably reliable data (68.14%), as well as a relatively low share of the most and poorly reliable data: 37.3 and 39.6%, respectively, that fall in the lower global quartile.…”

New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products

“…Global or large scale gridded population datasets considered state-of-the-art in terms of open access archives of population distribution data include: the Rural-Urban Mapping Project (GRUMP) [5], the Gridded Population of the World, Version 4 (GPWv4) [6,7], the LandScan Global Population database [8,9], the Global Human Settlement Layer-Population grid (GHS-POP) [10,11], the WorldPop datasets [12][13][14][15][16] and the recently developed High Resolution Settlement Layer (HRSL) population grids [17]. Current and previous versions of these products have proved to be an important source of information and essential input for a wide range of cross-disciplinary applications including: poverty mapping [18][19][20], epidemiological modelling and disease burden estimation [21][22][23], interconnectivity and accessibility analyses [24][25][26], deriving past and future population estimates [15,27,28], disaster management [29][30][31] and human settlement characterisation [32] among others.…”

Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice

“…More recently, independent spatio-temporal quality assessment of the GHSL built-up time series was performed for the USA showing very encouraging accuracy that generally increases over time (Leyk et al 2018). Most relevant for the present work, these built-up areas exhibited strong correlation with population distribution and density, and suitability for population disaggregation and modelling (Freire et al 2015(Freire et al , 2016Linard et al 2017;Nieves et al 2017).…”

“…Making these datasets available open and free helps to increase access, promotes transparency, and ensures accountability of the information produced. Global, consistent and updated geospatial data such as that made openly available in the framework of the Global Human Settlement Layer (GHSL) (Pesaresi and Ehrlich 2009) are already providing an effective contribution and improving the disaggregation of census data into derived population grids (Freire et al 2015;Linard et al 2017;Nieves et al 2017). However, it remains to be tested in large scale if such remotely sensed data can also assist in assessing and mitigating major deficiencies present in geospatial population statistics.…”