Simulation of urban development in the City of Rome: Framework, methodology, and problem solving

Simone Di Zio; Armando Montanari; Barbara Staniscia

doi:10.5198/jtlu.v3i2.154

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Published

2010-09-23

Issue

Vol. 3 No. 2 (2010)

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Articles

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How to Cite

Di Zio, S., Montanari, A., & Staniscia, B. (2010). Simulation of urban development in the City of Rome: Framework, methodology, and problem solving. Journal of Transport and Land Use, 3(2). https://doi.org/10.5198/jtlu.v3i2.154

Simulation of urban development in the City of Rome: Framework, methodology, and problem solving

Simone Di Zio

G. d'Annunzio University

Armando Montanari

Rome Sapienza University

Barbara Staniscia

Rome sapienza University

DOI: https://doi.org/10.5198/jtlu.v3i2.154

Keywords: Land Use, Development, Density

Abstract

In Italy’s case, the implementation of the UrbanSIM model involved the territory of Rome, including the municipalities of Rome and Fiumicino. The main goal was to build scenarios regarding the future of economic deconcentration. Rome is the largest municipality in Europe, with an inhabited surface area only slightly smaller than that of Greater London and almost double that of the inner Paris suburbs (the Petite Couronne). The spatial distribution of buildings within the municipality is distinctive. Unbuilt areas comprise 73 percent of the territory. These voids are often farmland (paradoxically, Rome is the largest rural municipality in Italy) or areas with high environmental, historic or cultural value. Fiumicino, previously part of the municipality of Rome, became an independent municipality in 1991. Its autonomy, made all the more significant because Fiumicino hosts the international airport, marked the start of an extensive process of economic deconcentration along the route connecting Rome to the airport. In Italy’s case, the implementation of the UrbanSIM model posed several challenges, notably the availability, homogeneity and completeness of data. This paper uses four specific cases (land use, travel times, accessibility, and residential land values) to propose a general methodology to solve problems related to missing or non-homogeneous data. For the land use, we simply combine two different land use data sources, while for accessibility and travel time data, we propose the use of geostatistical methods in order to estimate missing and unavailable data, calculating also the accuracy of the predictions. For the residential land values, which are discrete data, we suggest the use of deterministic interpolation techniques. While it has not yet been possible to implement the calibration stage, some simulation outputs are presented.

Author Biography

Armando Montanari, Rome Sapienza University

Professor, Department of European and Intercultural Studies