Exploring nationally and regionally defined models for large area population mapping

Abstract

Interactions between humans, diseases, and the environment take place across a range of temporal and spatial scales, making accurate, contemporary data on human population distributions critical for a variety of disciplines. Methods for disaggregating census data to finer-scale, gridded population density estimates continue to be refined as computational power increases and more detailed census, input, and validation datasets become available. However, the availability of spatially detailed census data still varies widely by country. In this study, we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models. We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts. Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia. Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country. This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.

Incorporating spatial non-stationarity to improve dasymetric mapping of population

“…Despite the aspiration to arrive at a finer approximation of the actual spatial distribution of the population through ancillary data with ever-increasing detail, a frequently recurring problem in dasymetric mapping remains the overestimation of lowpopulation-density areas and the underestimation of highpopulation-density areas (Eicher & Brewer, 2001;Gallego, 2010;Gaughan, Stevens, Linard, Patel, & Tatem, 2014;Harvey, 2002;Li & Corcoran, 2011;Li & Weng, 2005;Lu et al, 2006;Mennis & Hultgren, 2006;Su et al, 2010;Upegui & Viel, 2012;Ural et al, 2011;Yang, Yue, & Gao, 2013). This can be attributed to a lack of accounting (sufficiently) for spatial nonstationarity: the derivation of global parameters (be they interpolation weights or regression coefficients) imposes an averaging effect on the disaggregation that masks intrinsic variation in population distribution characteristics, which manifests itself particularly at the population density extremes.…”

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

“…Weather, ecological and land cover datasets come from various sources and would need to be mapped on a common coordinate system. The World-pop and Malaria-Map projects [100][101][102] have begun important work in this direction. Coupling these efforts with modeling environments and analytics such as SIBEL will be useful in the design and analysis of public policies.…”

Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors

“…Secondly, precise location information allows accurate matching of the data to a wide range of environmental variables, such as climatic, socio-economic and intervention variables, to exploit relationships between the biological data and these environmental variables 29,30 . The use of data linked to wider areas is a current area of research aimed at improving model predictions in circumstances where data linked to precise locations are particularly sparse 31,32 . For any kind of spatial analysis, it is essential to know whether the geographical coordinates provided represent a precise location or wider area, what the definition of a precise location is, and where the boundaries of the wider areas lie.…”