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Analysing the relationship between hazards and deprivation using machine learning

Kabiru, Priscilla Ngima (2021) Analysing the relationship between hazards and deprivation using machine learning.

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Abstract:According to literature, slums, herein referred to as deprived settlements, are located in hazardous areas. However, there have been very few studies that examine this notion. Studies that have analyzed this relationship (between hazards and deprived settlements) have primarily focused on single-hazards. In contrast, the analyses of multi-hazards have been hindered by a lack of sufficient methods and data. However, technological advancements in geospatial data and techniques present an opportunity to empirically investigate the relationship between hazards and deprivation. This study identifies multi-hazards in the select case study area of Nairobi through literature review and expert interviews. Using geospatial data, we identify proxies used to construct a city-wide index to investigate the location of deprived settlements and multi-hazards. We contrast morphologically identified deprived settlements to non-deprived settlements. We find that settlements in the inner city are more exposed to hazards than those located in the periphery. Further, physical traits determine the degree of susceptibility to hazards that a neighbourhood faces. Therefore, in partial agreement to literature, deprived settlements in the inner city are highly exposed to hazards, but so are formal planned high to mid-density settlements. On the other hand, deprived settlements in the urban periphery are less exposed except to hazards influenced by the neighbourhood characteristics, such as fire. Additionally, we test the predictability of deprivation using multi-hazards. We find that despite obtaining a high OA of 74%, the classification results by multi-hazards appear generalized. In contrast, though obtaining a lower OA by 2%, texture features result in more realistic land use classification. Lastly, we conduct household interviews in two deprived settlements to contrast the findings of the index. The index proxies used adequately capture the hazards. However, more localized data can improve multi-hazard index performance. Moreover, the cross-cutting approach of hazard assessment from the city to the household level lead to the detection of hidden patterns of deprivation – intra-settlement socio-spatial marginalization.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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