Section: New Results

A computer framework for measuring urban land-use mix

The number of people living in cities has been increasing considerably since 1950, from 746 million to 3.9 billion in 2014, and more than 66% of the world’s population are projected to live in urban areas by 2050. As this continuing population growth and urbanization are projected to add 2.5 billion people to the world’s urban population in 30 years, this situation brings new challenges on how to conceive cities that host such amounts of population in a sustainable way. This sustainability question should address several aspects, ranging from economical to social and environmental matters among others. In this work, we focus on the formalization of a measure of mixed use development or land use mix in a city, i.e. how the structure of the city can help to provide a car-free sustainable living. Such type of land use mix has been largely proven to contain beneficial outcomes in terms of sustainability and to positively contribute to societal outcome, health, and public transportation among others. We developped a framework to compute mixed uses development index. A main caracteristic of our approach is to use only crowd-sourcing data (from OpenStreetMap) to extract the geo-localized land uses. Due to the universality of this data source, we are able to process any geographical area in the world, as long as sufficient data are available in OSM. A Kernel Density Estimation is performed for each of the land uses, outputing the spatial distribution of the different land uses. Based on this representation, a measure of land use mix is then calculated using the Entropy Index. The resulting GIS output shows enriched information for urban planners, supporting and aiding the decision-making procedure.

The framework, still in the phase of validation, was applied on the cities of London and Grenoble [9]. Future work includes integrating the LUM output for measuring the urban sprawl phenomenon and performing numerical interpretations of desirable mixed use values. We will also study the potential integration to transportation models, where land use mix correlation with the activities and residential uses can help to improve demand estimation. In addition, further investigation can be done by means of analyzing in detail the different types of activities. Finally, the estimation of LUM can be refined by taking into account, besides their location, the accessibility between different land uses, which is partly conditioned by the transportation infrastructure.