Citation

da Fonseca Feitosa F, Vieira Vasconcelos V, Moutinho Duque de Pinho C, Frizzi Galdino da Silva G, da Silva Gonçalves G, Correa Danna LC, Seixas Lisboa F. Appl. Geogr. 2021; 133: e102494.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.apgeog.2021.102494

PMID

unavailable

Abstract

The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes.

Language: en

Keywords

Classification modelling; Co-production of knowledge; Informal settlements; Precarious settlements mapping; Slums; Typologies of precarious settlements