Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19

Hidden Costs of the COVID-19 Pandemic Response

“…Estimates rely largely on counterfactuals—estimating the number of deaths avoided in the “what if” scenario that lockdowns were not implemented [ 12 ]. These benefit estimates are often based on SIR compartmental epidemiological models, although simpler curve-fitting models have also been used, such as the IHME, which was particularly well publicized and influential in early pandemic planning [ 15 , 16 ]. These and other approaches to model and predict COVID-19 mortality under different policy scenarios show a wide range of predictions and substantial uncertainty, demonstrating the difficulty in modeling counterfactual scenarios [ 17 , 18 , 19 , 20 ].…”

A Framework to Probe Uncertainties in Urban Cellular Automata Modelling Using a Novel Framework of Multilevel Density Approach: A Case Study for Wallonia Region, Belgium

“…The scale problem has been addressed using a variety of methods. It runs the gamut of from straightforward scale analysis methods like geographic variance, local variation, and texture analysis, to more intricate methods like semi-variograms and fractals (Chen and Henebry 2009;García-Álvarez et al 2019a, b;Young et al 2021;Zhang et al 2019). According to earlier research by Kok and Veldkamp (2001) on the impact of modifying scale in a LUCC model for Central America, increasing the resolution from 15*15 km to 75*75 km enhanced the model's explanatory power (r 2 ) but had no discernible impact on the explanatory factors.…”