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

Venerandi, Alessandro and Fusco, Giovanni and Tettamanzi, Andrea and Emsellem, David (2019) A machine learning approach to study the relationship between features of the urban environment and street value. Urban Science, 3 (3). 100. ISSN 2413-8851 (https://doi.org/10.3390/urbansci3030100)

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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.

  • Item type:Article
    ID code:76709
    Dates:
    Date
    Event
    14 September 2019
    Published
    10 September 2019
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:08 Jun 2021 12:00
    Last modified:09 Apr 2024 02:27
    URI:https://strathprints.strath.ac.uk/id/eprint/76709
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