Abstract
The analysis of the processes of urban growth and sprawl should be conducted taking into account the temporal evolution of a plurality of parameters: economic, demographic and socio-cultural. These factors are so related that the complex territorial system sometimes seems indecipherable. Thus, as the quantitative modeling suggests, the real phenomenon is simplified, identifying a limited number of exogenous variables and researching the effect that these variables generate on the simplersystem, object of the study. The goal is to find the functional relationships that govern the events.
In the present study, the temporal evolution of the built in a metropolitan area is investigated, in order to determine the effect that the age of construction and geographical location have on the market value of the property for residential use. Then, correlations are recorded with both the labor or capital income that the population perceives on the territory, and with the spatial distribution of the unemployment rate.
The construction of the reference dataset is conducted on the basis of official information provided in Italy by the National Institute of Statistics (ISTAT) and the Property Market Observatory (OMI) of Territorial Agency.
Finally, the implementation of Geographic Information Systems (GIS), at the level of Census and OMI units, allows to draw thematic maps, from which comparisonthe results of the study are derived (Both the authors conceived and designed the research and the paper. Massimiliano Bencardino collected data on the evolution of the built-up area and Antonio Nesticò collected data on real estate values, both wrote parts in every section.).
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- 1.
The only exception is given by the ancient agglomerates of Bacoli and Monte di Procida on one hand, of Portici and San Giorgio a Cremano on the other, both with a unique characters and in the coastal area.
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Bencardino, M., Nesticò, A. (2017). Urban Sprawl, Labor Incomes and Real Estate Values. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10405. Springer, Cham. https://doi.org/10.1007/978-3-319-62395-5_2
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