A framework for evaluating urban land use mix from crowd-sourcing data

Luciano Gervasoni 1 Martí Bosch 2 Serge Fenet 3, 1 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 : Population in urban areas has been increasing at an alarming rate in the last decades. This evidence, together with the rising availability of massive data from cities, has motivated research on sustainable urban development. In this paper we present a GIS-based land use mix analysis framework to help urban planners to compute indices for mixed uses development, which may be helpful towards developing sustainable cities. Residential and activities land uses are extracted using OpenStreetMap crowd-sourcing data. Kernel density estimation is performed for these land uses, and then used to compute the mixed uses indices. The framework is applied to several cities, analyzing the land use mix output.
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Luciano Gervasoni, Martí Bosch, Serge Fenet, Peter Sturm. A framework for evaluating urban land use mix from crowd-sourcing data. 2nd International Workshop on Big Data for Sustainable Development, Dec 2016, Washington DC, United States. pp.2147-2156, ⟨10.1109/BigData.2016.7840844⟩. ⟨hal-01396792⟩

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