Measuring the contribution of built-settlement data to global population mapping

Abstract

Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. These gaps have prompted the interpolation of the built-environment and settlements, but the utility of such interpolated data in further population modelling applications has garnered little research. Thus, our objective was to determine the utility of modelled built-settlement extents in a top-down population modelling application. Here we take modelled global built-settlement extents between 2000 and 2012, created using a spatio-temporal disaggregation of observed settlement growth. We then demonstrate the applied utility of such annually modelled settlement data within the application of annually modelling population, using random forest informed dasymetric disaggregations, across 172 countries and a 13-year period. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents.

Spatiotemporal Change of Eco-Environmental Quality in the Oasis City and Its Correlation with Urbanization Based on RSEI: A Case Study of Urumqi, China

“…However, the results of this study are different from those in coastal areas, mainly in that the natural background of coastal areas is better than that of arid areas [55]. Night light data have been confirmed to be highly correlated with human social activities and are widely used in urbanization research [19][20][21]. The growth of CNLI reflects the rapid urban and economic development of Urumqi over the past 25 years, which is mainly due to the state's western development plan put forward in 2000, which mainly focused on economic development in the early stage.…”

“…With the continuous development of remote sensing technology, remote sensing nighttime light has been proved to be highly correlated with social economies, such as population [19] and economic activities [20,21]. Considering the respective advantages of daytime optical remote sensing and nighttime light remote sensing in revealing the status of the ecological environment and characteristics of urbanization, scholars began to try to integrate the two types of remote sensing data to carry out the coupling study of urbanization and ecological environment [22].…”

Assessing LISFLOOD-FP with the next-generation digital elevation model FABDEM using household survey and remote sensing data in the Central Highlands of Vietnam

“…We avoided using such an unconstrained gridded population data set as the unconstrained versions tend to distribute population to uninhabited land (i.e. a frequently flooded floodplain) (Stevens et al, 2019;Niveves et al, 2021), which typically leads to overestimation of flood risk (Smith et al, 2019).…”

Integrating social vulnerability into global flood risk mapping

“…This has important implications when considering flood risk as unconstrained population datasets tend to distribute people across uninhabited land (e.g. frequently flooded floodplains) 39,41 , whereas in reality people (often) live on the margins of floodplains, out of reach of all but the most extreme floods. A result of this is that unconstrained population datasets overestimate flood exposure 42,43 .…”