Abstract: This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Within the framework of the World Bank’s Central American Country Disaster Risk Profiles (CDRP) initiative a disaggregated property stock exposure model has been developed as one of the key elements for subsequent disaster risk and loss estimation. Global spatial datasets are thereby used consistently to ensure wide-scale applicability and transferability. Residential and mixed use areas need to be identified in order to spatially link accordingly compiled property stock information. Impervious Surface Area (ISA) data based on remotely sensed nighttime lights are used as proxy to identify areas of peak human activity. Intense lighting in that context is associated with a high likelihood of commercial and/or industrial presence. Areas of low light intensity, in turn, can be considered more likely residential. Several ISA intensity thresholds are tested for Cuenca City, Ecuador, in order to best match a given reference situation on the ground based on cadastral land use data. Results are considered highly relevant not only for the CDRP initiative but more general underline the relevance of remote sensing data for top-down modeling approaches at wide spatial scale.
ECRS-1_paper_final.pdf (339.2 K)