Assessing community-level livability using combined remote sensing and internet-based big geospatial data

With rapid urbanization, retrieving livability information of human settlements in time is essential for urban planning and governance. However, livability assessments are often limited by data availability and data update cycle, and this problem is more serious when making an assessment at finer spatial scales (e.g., community level). Here we aim to develop a reliable and dynamic model for community-level livability assessment taking Linyi city in Shandong Province, China as a case study. First, we constructed a hierarchical index system for livability assessment, and derived data for each index and community from remotely sensed data or Internet-based geospatial data. Next, we calculated the livability scores for all communities and assessed their uncertainties using Monte Carlo simulations. The results showed that the mean livability score of all communities was 59. The old urban and newly developed districts of our study area had the best livability, and got a livability score of 62 and 58 respectively, while industrial districts had the poorest conditions with an average livability score of 48. Results by dimension showed that the old urban district had better conditions of living amenity and travel convenience, but poorer conditions of environmental health and comfort. The newly developed districts were the opposite. We conclude that our model is effective and extendible for rapidly assessing community-level livability, which provides detailed and useful information of human settlements for sustainable urban planning and governance.