A machine learning approach to study the relationship between features of the urban environment and street value

Alessandro Venerandi, Giovanni Fusco, Andrea Tettamanzi, David Emsellem

Research output: Contribution to journalArticlepeer-review

Abstract

Understanding what aspects of the urban environment are associated with better socioeconomic/liveability outcomes is a long standing research topic. Several quantitative studies have investigated such relationships. However, most of such works analysed single correlations, thus failing to obtain a more complete picture of how the urban environment can contribute to explain the observed phenomena. More recently, multivariate models have been suggested. However, they use a limited set of metrics, propose a coarse spatial unit of analysis, and assume linearity and independence among regressors. In this paper, we propose a quantitative methodology to study the relationship between a more comprehensive set of metrics of the urban environment and the valorisation of street segments that handles non-linearity and possible interactions among variables, through the use of Machine Learning (ML). The proposed methodology was tested on the French Riviera and outputs show a moderate predictive capacity (i.e., adjusted R2=0.75
) and insightful explanations on the nuanced relationships between selected features of the urban environment and street values. These findings are clearly location specific; however, the methodology is replicable and can thus inspire future research of this kind in different geographic contexts.
Original languageEnglish
Article number100
Number of pages25
JournalUrban Science
Volume3
Issue number3
DOIs
Publication statusPublished - 14 Sep 2019

Keywords

  • urban environment
  • street value
  • machine learning
  • ensemble method
  • French Riviera

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