Detecting residential reconversion within cities: how can ‘big data’ be mobilized to better understand what is going on?

Jean Dubé, Katarzyna Kopczewska and Sarah Desaulniers

Chapter 5 in Handbook on Big Data, Artificial Intelligence and Cities, 2025, pp 73-91 from Edward Elgar Publishing

Abstract: Urban growth has contributed to many changes within cities over time. One of the recent trends has been the return to the center. Gentrification and residential reconversion have contributed to reshaping many central neighborhoods. However, the two phenomena are different: gentrification is largely based on the renovation of existing housing stock, while residential reconversion is a more drastic change: existing housing and commercial stock are destroyed to make room for new buildings. While this change remains relatively sparse over space and time, it has nevertheless gained momentum since 2000. One of the main challenges is to identify where and when residential reconversion occurs. The use of machine learning (ML) algorithms might help to identify such changes at a city scale. The aim of this chapter is to present what residential reconversion is, why it occurs, and how to detect it using disaggregated spatial microdata.

Keywords: Residential reconversion; Spatial analysis; Big data; Statistical analysis; Machine Learning; Spatial microdata (search for similar items in EconPapers)
Date: 2025
ISBN: 9781803928043
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