| 1. |
- Abascal, Angela, et al.
(författare)
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AI perceives like a local : predicting citizen deprivation perception using satellite imagery
- 2024
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Ingår i: npj Urban Sustainability. - : Springer Nature. - 2661-8001. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Deprived urban areas, commonly referred to as ‘slums,’ are the consequence of unprecedented urbanisation. Previous studies have highlighted the potential of Artificial Intelligence (AI) and Earth Observation (EO) in capturing physical aspects of urban deprivation. However, little research has explored AI’s ability to predict how locals perceive deprivation. This research aims to develop a method to predict citizens’ perception of deprivation using satellite imagery, citizen science, and AI. A deprivation perception score was computed from slum-citizens’ votes. Then, AI was used to model this score, and results indicate that it can effectively predict perception, with deep learning outperforming conventional machine learning. By leveraging AI and EO, policymakers can comprehend the underlying patterns of urban deprivation, enabling targeted interventions based on citizens’ needs. As over a quarter of the global urban population resides in slums, this tool can help prioritise citizens’ requirements, providing evidence for implementing urban upgrading policies aligned with SDG-11.
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| 2. |
- Vanhuysse, Sabine, et al.
(författare)
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Fine-scale mapping of urban malaria exposure under data scarcity : an approach centred on vector ecology
- 2023
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Ingår i: Malaria Journal. - : BioMed Central (BMC). - 1475-2875. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Although malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts. METHODS: A workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m. RESULTS: The identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation. CONCLUSIONS: This study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.
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| 3. |
- Vanhuysse, Sabine, et al.
(författare)
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Putting the Invisible on the Map : Low-Cost Earth Observation for Mapping and Characterizing Deprived Urban Areas (Slums)
- 2024
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Ingår i: Urban Inequalities from Space. - : Springer. - 9783031491825 - 9783031491832 ; , s. 119-137
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Bokkapitel (refereegranskat)abstract
- It is estimated that more than half of city dwellers in sub-Saharan Africa currently live in deprived urban areas, often called slums or informal settlements, although these terms cover different urban realities. While the first target of Sustainable Development Goal (SDG) 11 is “to ensure access for all to adequate, safe and affordable housing and basic services and upgrade slums,” there is a huge gap in timely spatial data to support evidence-based policies and monitor progress toward that objective. In this study, we document the potential of Earth Observation (EO) for mapping and characterizing deprived urban areas (DUAs) to narrow this gap. First, we provide a synthesis of user requirements that can be met without resorting to ancillary sources such as censuses and socioeconomic surveys, and we propose a list of cost criteria that should be minimized in EO workflows. Next, we present the city-scale and DUA-scale workflows that we developed based on three case studies and an assessment of their suitability for supporting pro-poor policies, in light of the cost criteria. We also share the main lessons learned and propose some avenues for future research.
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