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.
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Notes
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A street segment is the line connecting two intersections in a street network.
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References
Jaillet, M.C., Perrin, E., et Ménard, F.: Diversité sociale, ségrégation urbaine, mixité. Research report Plan Urbanisme Construction Architecture (PUCA) (2008)
Carpenter, A., Peponis, J.: Poverty and connectivity. J. Space Syntax 1(1), 108–120 (2010)
Conzen, M.R.G.: Alnwick, Northumberland: a study in town-plan analysis. Trans. Papers (Inst. Brit. Geogr.) 27, iii–122 (1960)
Jacobs, J.: The Death and Life of Great American Cities. Random House, New York (1961)
Porta, S., Crucitti, P., Latora, V.: The network analysis of urban streets: a primal approach. Environ. Plan. B Plan. Des. 33(5), 705–725 (2006)
Hillier, B.: Space is the Machine: a Configurational Theory of Architecture. Space Syntax, London (2007)
Araldi, A., Fusco, G.: Decomposing and recomposing urban fabric: The city from the pedestrian point of view. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10407, pp. 365–376. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62401-3_27
Araldi, A., Fusco, G.: Retail fabric assessment: describing retail patterns within urban space. Cities 85, 51–62 (2018)
McMillen, D.P.: Changes in the distribution of house prices over time: structural characteristics, neighborhood, or coefficients? J. Urban Econ. 64(3), 573–589 (2008)
Chiaradia, A., Hillier, B., Barnes, Y., Schwander, C.: Residential property value patterns in London: space syntax spatial analysis. In: Proceedings to the 7th International Space Syntax Symposium, Stockholm, Sweden (2009)
Narvaez, L., Penn, A., Griffiths, S.: Space syntax economics: decoding accessibility using property value and housing price in Cardiff, Wales. In: Proceedings of the Eighth International Space Syntax Symposium, Santiago, Chile (2012)
Caniggia, G., Maffei, G.L.: Architectural composition and building typology: interpreting basic building. Alinea Editrice, Florence, Italy (2001)
Lynch, K.: The Image of the City. MIT press, Cambridge (1960)
Batty, M.: Cities as Complex Systems: Scaling, Interaction, Networks, Dynamics and Urban Morphologies. Research report Centre for Advanced Spatial Analysis (CASA), no. 131 (2009)
Fusco, G., Perez, J.: Bayesian Network clustering and self-organizing maps under the test of Indian districts. A comparison. Cybergeo Eur. J. Geogr. (2020). https://doi.org/10.4000/cybergeo.31909. http://journals.openedition.org/cybergeo/31909
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser. B 39(1), 1–38 (1977)
Rissanen, J.: Information and Complexity in Statistical Modeling. Springer, New York (2007)
Levasseur, S.: La loi SRU et les quotas de logements sociaux 15 ans après, quel bilan? Research report OFCE (Sciences Po) no. 54 (2015)
Matan, A., Newman, P.: People Cities: The life and Legacy of Jan Gehl. Island Press, Washington (2016)
Andersson, C.: Public space and the new urban agenda. J. Public Space 1(1), 5–10 (2016)
Manganelli, B., Pontrandolfi, P., Azzato, A., Murgante, B.: Using geographically weighted regression for housing market segmentation. Int. J. Bus. Intell. Data Min. 9(2), 161–177 (2014)
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.
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Venerandi, A., Fusco, G. (2020). Describing the Residential Valorisation of Urban Space at the Street Level. The French Riviera as Example. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_37
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