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Article

Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps

1
Mansueto Institute for Urban Innovation, University of Chicago, Chicago, IL 60637, USA
2
Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
3
Department of Sociology, University of Chicago, Chicago, IL 60637, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 685; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110685
Received: 16 September 2020 / Revised: 30 October 2020 / Accepted: 13 November 2020 / Published: 16 November 2020
The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion through informal settlements (slums). These neighborhoods are too often characterized by a lack of connections, both physical and socioeconomic, with detrimental effects to residents and their cities. Here, we show how a scalable computational approach based on the topological properties of digital maps can identify local infrastructural deficits and propose context-appropriate minimal solutions. We analyze 1 terabyte of OpenStreetMap (OSM) crowdsourced data to create worldwide indices of street block accessibility and local cadastral maps and propose infrastructure extensions with a focus on 120 Low and Middle Income Countries (LMICs) in the Global South. We illustrate how the lack of physical accessibility can be identified in detail, how the complexity and costs of solutions can be assessed and how detailed spatial proposals are generated. We discuss how these diagnostics and solutions provide a multiscalar set of new capabilities—from individual neighborhoods to global regions—that can coordinate local community knowledge with political agency, technical capability, and further research. View Full-Text
Keywords: OpenStreetMap; cities; slums; network analysis; remote sensing; human development; urban planning; GIS; cloud computing OpenStreetMap; cities; slums; network analysis; remote sensing; human development; urban planning; GIS; cloud computing
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MDPI and ACS Style

Soman, S.; Beukes, A.; Nederhood, C.; Marchio, N.; Bettencourt, L.M.A. Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps. ISPRS Int. J. Geo-Inf. 2020, 9, 685. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110685

AMA Style

Soman S, Beukes A, Nederhood C, Marchio N, Bettencourt LMA. Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps. ISPRS International Journal of Geo-Information. 2020; 9(11):685. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110685

Chicago/Turabian Style

Soman, Satej, Anni Beukes, Cooper Nederhood, Nicholas Marchio, and Luís M.A. Bettencourt 2020. "Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps" ISPRS International Journal of Geo-Information 9, no. 11: 685. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110685

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