Assessing urban livability in Shanghai through an open source data-driven approach

Urban livability has become a major policy and practice priority in many parts of the world. However, its attainment remains challenging in many cities of developing and emerging economies. The lack of data with appropriate quality, coverage, and spatial and temporal resolution often complicates both the assessment of livability in such cities and the identification of priority areas for improvement. Here we develop a framework to mobilize and synthesize open-source data to analyze spatially urban livability patterns in Shanghai. The framework brings together diverse types of open-source data including housing characteristics, population distribution, transportation networks, and points of interest to identify city areas with low livability, and thus priority areas for improvement. Such findings can provide a comprehensive overview of the residential living conditions in Shanghai, as well as useful information to urban planners and decision-makers. Furthermore, subject to data availability, the proposed method has the potential for application in other cities.

Yin Long, Yi Wu, Liqiao Huang, Jelena Aleksejeva, Deljana Lossifova, Nannan Dong, Alexandros Gasparatos