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

Abstract

Urban livability has become a major policy and practice priority in many parts of the world, but its attainment remains challenging in many cities of developing and emerging economies. The lack of data with appropriate quality, coverage, and spatial/temporal resolution often complicates the assessment of livability in such cities, and the identification of priority areas for improvement. Here we develop an innovative framework to mobilize and synthesize open-source data to analyze spatially urban livability patterns in Shanghai. The framework brings together diverse open-source data such as housing prices, 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 environment in Shanghai, as well as provide useful information to urban planners and decision-makers. Furthermore, the developed method has the potential for application in other cities, subject to data availability.