Calculating spatial urban sprawl indices using open data

Luciano Gervasoni 1 Martí Bosch 2 Serge Fenet 3 Peter Sturm 1
1 STEEP - Sustainability transition, environment, economy and local policy
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Urban sprawl has been related to numerous negative environmental and socioeconomic impacts. Meanwhile, urban areas have been growing at alarming rates, urging for assessing sprawl towards sustainable development. However, sprawl is an elusive term and different approaches to measure it have lead to heterogeneous results. Moreover, most studies rely on pri-vate/commercial data-sets and their software is rarely made public, impeding research reproducibility and comparability. Furthermore, many works give as result a unique value for a region of analysis, dismissing local spatial diversity that is vital for urban planners and policy makers. We present in this paper an open source framework for assessing urban sprawl using open data. Locations of residential and activity units are used to measure mixed use development and built-up dispersion, whereas the street network is used to measure accessibility between different land uses. Sprawl patterns are identified, and the resulting spatial information allows focusing on particular neighborhoods for a fine-grained analysis, as well as visualizing each sprawl dimension separately.
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15th International Conference on Computers in Urban Planning and Urban Management, Jul 2017, Adelaide, Australia. 2017, 〈http://www.unisa.edu.au/cupum〉
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Luciano Gervasoni, Martí Bosch, Serge Fenet, Peter Sturm. Calculating spatial urban sprawl indices using open data. 15th International Conference on Computers in Urban Planning and Urban Management, Jul 2017, Adelaide, Australia. 2017, 〈http://www.unisa.edu.au/cupum〉. 〈hal-01535469〉

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