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predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning

Ibrahim, MR; Titheridge, H; Cheng, T; Haworth, J; (2019) predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning. Computers, Environment and Urban Systems , 76 pp. 31-56. 10.1016/j.compenvurbsys.2019.03.005. Green open access

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Abstract

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and informal settlements using street intersection data. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics of informal settlements. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context. To enable this model to be used as a pragmatic tool for future prediction or further research concerning informal settlements and slums, we have made the python code for the pre-trained ANN models of the five studied cities.

Type: Article
Title: predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.compenvurbsys.2019.03.005
Publisher version: https://doi.org/10.1016/j.compenvurbsys.2019.03.00...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Machine Learning; Slums; Informal Settlements; Complexity; Spatial network; Spatial statistics; Neural Networks; Egyptian cities
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10071808
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