Abstract
There is a growing concern regarding the use of relatively coarse units for the aggregation of various spatial information. Researchers thus suggest that the street segment might be better suited than areal units for carrying out such a task. Furthermore, the street segment has recently become one of the most prominent spatial units, for example, to study street network centrality, retail density, and urban form. In this paper, we thus propose to use the street segment as unit of analysis for calculating the residential valorisation of urban space. To be more specific, we define a protocol that characterises street segments through a measure of central tendency and one of dispersion of prices. Moreover, through Bayesian clustering, it classifies street segments according to the most probable combination house type-valuation to provide a picture of local submarkets. We apply this methodology to the housing transactions exchanged in the French Riviera, in the period 2008–2017, and observe that outputs seem to align with local specificities of the housing market of that region. We suggest that the proposed protocol can be useful as an explorative tool to question and interpret the housing market, in any metropolitan region, at a fine level of spatial granularity.
Original language | English |
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Title of host publication | Computational Science and Its Applications – ICCSA 2020 |
Editors | Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blecic, David Taniar, Bernady O. Apduhan, Ana Maria A.C. Rocha, Eufemia Tarantino, Carmelo Maria Torre, Yeliz Karaca |
Place of Publication | Cham, Switzerland |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 505-520 |
Number of pages | 16 |
ISBN (Print) | 9783030588106 |
DOIs | |
Publication status | Published - 29 Sept 2020 |
Event | 20th International Conference on Computational Science and Its Applications - Cagliari, Italy Duration: 1 Jul 2020 → 4 Jul 2020 https://2020.iccsa.org/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12252 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 20th International Conference on Computational Science and Its Applications |
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Abbreviated title | ICCSA 2020 |
Country/Territory | Italy |
City | Cagliari |
Period | 1/07/20 → 4/07/20 |
Internet address |
Funding
Acknowledgements. This research was funded by the French Government, through the National Research Agency, under the Investissements d’Avenir IDEX UCA JEDI, with reference number ANR-15-IDEX-01. The authors would like to thank Pr. Andrea Tettamanzi, at I3S Laboratory in Université Côte d’Azur, and Dr. Denis Overal, director of the R&D at Kinaxia, for their support and insightful suggestions. This research was funded by the French Government, through the National Research Agency, under the Investissements d?Avenir IDEX UCA JEDI, with reference number ANR-15-IDEX-01. The authors would like to thank Pr. Andrea Tettamanzi, at I3S Laboratory in Universit? C?te d?Azur, and Dr. Denis Overal, director of the R&D at Kinaxia, for their support and insightful suggestions.
Keywords
- Bayesian clustering
- French Riviera
- house prices
- street network
- summary statistics