  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 predicting
informal settlements using only street intersections data, regardless of the
variation of urban form, number of floors, materials used for construction or
street width. 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 a
machine learning approach, using Multinomial Logistic Regression (MNL) and
Artificial Neural Networks (ANN). The proposed model relies on defining
informal settlements based on two ubiquitous characteristics that these regions
tend to be filled in with smaller subdivided lots of housing relative to the
formal areas within the local context, and the paucity of services and
infrastructure within the boundary of these settlements that require relatively
bigger lots. 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.
