Empiric recommendations for population disaggregation under different data scenarios

Abstract

High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping.

Cost estimation for the monitoring instrumentalization of Landslide Early Warning Systems

“…Besides, a considerable amount of the population are exposed to landslide risks in Medellín, which has been increasing in the last 25 years (Kühnl et al, 2022). The city itself is shaped by different types of urban structural configurations and socioeconomic levels, from high-rise buildings in the business district and the wealthier neighbourhoods to light-weight and low-rise buildings in precarious neighbourhoods.…”

“…When it comes to decisive factors for the suitability and prioritization of possible locations for implementing an EWS, we use socio-demographic and infrastructure variables. First, we use a high resolution population map with the estimated number of people per building and grid of 100 meters (Sapena et al, 2022). The population counts are used for identifying exposed sites with high-density population and high landslide susceptibility.…”

“…In this context, Earth Observation (EO) from in situ or remote sensors plays an important role in early warning, as well as in mapping and monitoring natural hazards (Grasso, 2014;Casagli et al, 2017). EO provides information that can be used, for example, to model the Earth's surface (Geiß et al, 2020), identify populations exposed and vulnerable to different natural hazards (Geiß et al, 2017;Müller et al, 2020;Kühnl et al, 2022), characterize building structural types for seismic risk analyses (Taubenböck et al, 2009;Aravena Pelizari et al, 2021), or develop a global flood monitoring system (Chini and the Global Flood Monitoring team, 2022). EO-derived data capture the shape of the terrain and the natural and built elements on it.…”

Fine-grained population mapping from coarse census counts and open geodata

“…Top-down approaches 6 , 11 commonly use dasymetric disaggregation to redistribute the known, spatially coarse population counts for census areas on the order of many km 2 across smaller spatial units 12 —for instance square blocks of size 100 × 100 m—with the help of auxiliary variables that covary with population density. Examples of such covariates for population disaggregation are the presence of buildings 13 , building counts 10 , building volumes 14 , and cumulative road lengths 15 .…”

Fine-grained Population Mapping from Coarse Census Counts and Open Geodata

“…Top-down approaches 6,11 commonly use dasymetric disaggregation to redistribute the known, spatially coarse population counts for census areas on the order of many km 2 across smaller spatial units 12 -for instance square blocks of size 100×100 meters -with the help of auxiliary variables that covary with population density. Examples of such covariates for population disaggregation are the presence of buildings 13 , building counts 10 , building volumes 14 , and cumulative road lengths 15 .…”