| 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. |
- Abascal, Angela, et al.
(författare)
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Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas
- 2022
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Ingår i: Computers, Environment and Urban Systems. - : Elsevier BV. - 0198-9715 .- 1873-7587. ; 95
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Tidskriftsartikel (refereegranskat)abstract
- Many cities in low- and medium-income countries (LMICs) are facing rapid unplanned growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is lacking. There exist visible differences in housing conditions and urban spaces, and these differences are linked to urban deprivation. However, the appropriate geospatial information for unravelling urban deprivation is typically not available for DUAs in LMICs, constituting an urgent knowledge gap. The objective of this study is to apply deep learning techniques and morphological analysis to identify degrees of deprivation in DUAs. To this end, we first generate a reference dataset of building footprints using a participatory community-based crowd-sourcing approach. Secondly, we adapt a deep learning model based on the U-Net architecture for the semantic segmentation of satellite imagery (WorldView 3) to generate building footprints. Lastly, we compute multi-level morphological features from building footprints for identifying the deprivation variation within DUAs. Our results show that deep learning techniques perform satisfactorily for predicting building footprints in DUAs, yielding an accuracy of F1 score = 0.84 and Jaccard Index = 0.73. The resulting building footprints (predicted buildings) are useful for the computation of morphology metrics at the grid cell level, as, in high-density areas, buildings cannot be detected individually but in clumps. Morphological features capture physical differences of deprivation within DUAs. Four indicators are used to define the morphology in DUAs, i.e., two related to building form (building size and inner irregularity) and two covering the form of open spaces (proximity and directionality). The degree of deprivation can be evaluated from the analysis of morphological features extracted from the predicted buildings, resulting in three categories: high, medium, and low deprivation. The outcome of this study contributes to the advancement of methods for producing up-to-date and disaggregated morphological spatial data on urban DUAs (often referred to as 'slums') which are essential for understanding the physical dimensions of deprivation, and hence planning targeted interventions accordingly.
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| 3. |
- Abascal, Angela, et al.
(författare)
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Making Urban Slum Population Visible : Citizens and Satellites to Reinforce Slum Censuses
- 2024
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Ingår i: Urban Inequalities from Space. - : Springer. - 9783031491856 - 9783031491832 ; , s. 287-302
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Bokkapitel (refereegranskat)abstract
- In response to the “Leave No One Behind” principle (the central promise of the 2030 Agenda for Sustainable Development), reliable estimate of the total number of citizens living in slums is urgently needed but not available for some of the most vulnerable communities. Not having a reliable estimate of the number of poor urban dwellers limits evidence-based decision-making for proper resource allocation in the fight against urban inequalities. From a geographical perspective, urban population distribution maps in many low- and middle-income cities are most often derived from outdated or unreliable census data disaggregated by coarse administrative units. Moreover, slum populations are presented as aggregated within bigger administrative areas, leading to a large diffuse in the estimates. Existing global and open population databases provide homogeneously disaggregated information (i.e. in a spatial grid), but they mostly rely on census data to generate their estimates, so they do not provide additional information on the slum population. While a few studies have focused on bottom-up geospatial models for slum population mapping using survey data, geospatial covariates, and earth observation imagery, there is still a significant gap in methodological approaches for producing precise estimates within slums. To address this issue, we designed a pilot experiment to explore new avenues. We conducted this study in the slums of Nairobi, where we collected in situ data together with slum dwellers using a novel data collection protocol. Our results show that the combination of satellite imagery with in situ data collected by citizen science paves the way for generalisable, gridded estimates of slum populations. Furthermore, we find that the urban physiognomy of slums and population distribution patterns are related, which allows for highlighting the diversity of such patterns using earth observation within and between slums of the same city.
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| 4. |
- Georganos, Stefanos, et al.
(författare)
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Is It All the Same? : Mapping and Characterizing Deprived Urban Areas Using WorldView-3 Superspectral Imagery. A Case Study in Nairobi, Kenya
- 2021
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 13:24
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Tidskriftsartikel (refereegranskat)abstract
- In the past two decades, Earth observation (EO) data have been utilized for studying the spatial patterns of urban deprivation. Given the scope of many existing studies, it is still unclear how very-high-resolution EO data can help to improve our understanding of the multidimensionality of deprivation within settlements on a city-wide scale. In this work, we assumed that multiple facets of deprivation are reflected by varying morphological structures within deprived urban areas and can be captured by EO information. We set out by staying on the scale of an entire city, while zooming into each of the deprived areas to investigate deprivation through land cover (LC) variations. To test the generalizability of our workflow, we assembled multiple WorldView-3 datasets (multispectral and shortwave infrared) with varying numbers of bands and image features, allowing us to explore computational efficiency, complexity, and scalability while keeping the model architecture consistent. Our workflow was implemented in the city of Nairobi, Kenya, where more than sixty percent of the city population lives in deprived areas. Our results indicate that detailed LC information that characterizes deprivation can be mapped with an accuracy of over seventy percent by only using RGB-based image features. Including the near-infrared (NIR) band appears to bring significant improvements in the accuracy of all classes. Equally important, we were able to categorize deprived areas into varying profiles manifested through LC variability using a gridded mapping approach. The types of deprivation profiles varied significantly both within and between deprived areas. The results could be informative for practical interventions such as land-use planning policies for urban upgrading programs.
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| 5. |
- Morlighem, Camille, et al.
(författare)
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Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys
- 2023
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Ingår i: GeoHealth. - : American Geophysical Union (AGU). - 2471-1403. ; 7:10
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Tidskriftsartikel (refereegranskat)abstract
- Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale. Global climate change and rapid urbanization in sub-Saharan Africa (SSA) are likely to affect the epidemiology of vector-borne diseases such as malaria in urban and peri-urban areas. In this context, a better understanding of intra-urban malaria risk and its determinants has become even more urgent. Malaria risk has often been modeled at the national scale from Demographic and Health Surveys (DHS), which are periodically conducted in more than 90 developing countries. However, survey cluster coordinates in DHS are randomly displaced by up to 2 km in urban areas to protect respondent privacy, which reduces the accuracy of malaria models and risk maps at the intra-urban scale. In this study, we tested the potential of spatial optimization methods to overcome the effect of DHS displacement. We found that spatial optimization methods improved the performance of malaria models, but the improvement in performance is small for a higher computational cost. With these methods, we predicted malaria risk in several SSA cities (Dakar, Dar es Salaam, Kampala and Ouagadougou). We expect the quality and quantity of available data on malaria and other vector-borne diseases to improve in the future, which will certainly make these methods extremely useful in the fight against these diseases. We tested spatial optimization approaches to overcome the effect of cluster spatial displacement in Demographic and Health Surveys (DHS)Spatial optimization reduced the effect of displacement, but the percentage of variance explained in malaria models remained lowWe proposed potential adaptations to the DHS sampling strategy to better support the study of malaria risk at the intra-urban scale.
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| 6. |
- Morlighem, Camille, et al.
(författare)
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The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities : Malaria as an Example
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:21
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Tidskriftsartikel (refereegranskat)abstract
- Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements.
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| 7. |
- 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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| 8. |
- 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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