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

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.