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Open AccessArticle

A Machine Learning Approach to Study the Relationship between Features of the Urban Environment and Street Value

1
ESPACE, CNRS, Université Côte d’Azur, 06200 Nice, France
2
I3S, Inria, CNRS, Université Côte d’Azur, 06900 Sophia Antipolis, France
3
KCITYLABS (Kinaxia Group), 06560 Valbonne, France
*
Author to whom correspondence should be addressed.
Current address: 98, Bd Herriot, BP 3209, 06200 Nice, France.
Current address: 2000, route des Lucioles, 06900 Sophia Antipolis, France.
§
Current address: 80, route des Lucioles, 06560 Valbonne, France.
Urban Sci. 2019, 3(3), 100; https://doi.org/10.3390/urbansci3030100
Received: 17 July 2019 / Revised: 4 September 2019 / Accepted: 10 September 2019 / Published: 14 September 2019
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 R 2 = 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. View Full-Text
Keywords: urban environment; street value; machine learning; ensemble method; French Riviera urban environment; street value; machine learning; ensemble method; French Riviera
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Venerandi, A.; Fusco, G.; Tettamanzi, A.; Emsellem, D. A Machine Learning Approach to Study the Relationship between Features of the Urban Environment and Street Value. Urban Sci. 2019, 3, 100.

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