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

Abstract

The recent growth of high-resolution spatial data, especially in developing urban en- vironments, is enabling new approaches to civic activism, urban planning and the provi- sion 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 so- cioeconomic, 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 (LMIC) 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 coordi- nate local community knowledge with political agency, technical capability, and further research.

Keywords: OpenStreetMap, Cities, Slums, Network Analysis, Remote Sensing, Human Development, Urban Planning, GIS, Cloud Computing