The relationship between multiple hazards and deprivation using open geospatial data and machine learning

Deprived settlements, usually referred to as slums, are often located in hazardous areas. However, there have been very few studies to examine this notion. In this study, we leverage the advancements in open geospatial data, earth observation (EO), and machine learning to create a multi-hazard susceptibility index and a transferrable disaster risk approach to be adapted in low- and middle-income country (LMIC) cities, with low-cost methods. Specifically, we identify multi-hazards in Nairobi's selected case study area and construct a susceptibility index. Then, we test the predictability of deprived settlements using the multi-hazard susceptibility index in comparison with EO texture-based methods. Lastly, we survey 100 households in two deprived settlements (typical and atypical slums) in Nairobi and use the survey outcomes to validate the multi-hazard susceptibility index. To test the assumption that deprived areas are dominantly located in areas with higher susceptibility to multiple hazards, we contrast morphologically identified deprived settlements with non-deprived settlements. We find that deprived settlements are generally more exposed to hazards. However, there are variations between central and peripheral settlements. In testing the predictability of deprivation using multi-hazards, the multi-hazard-based model performs better for deprived settlements than for other classes. In contrast, the texture-based model is better at classifying all types of morphological settlements. Lastly, by contrasting the survey outcomes to the household interviews, we conclude that proxies used for the multi-hazard susceptibility index adequately capture the hazards. However, more localized proxies can be used to improve the index performance.