Abstract
Numerous studies have suggested a positive correlation between spatial and population densities. However, few have systematically conducted quantitative analysis and deciphered the detailed correlation in block scale. Here, we construct a population–space correlation algorithm to quantify and compare the correlation between mobile phone signalling data and vector spatial data and identify blocks with uneven population density. We analyse the influences of various urban spatial characteristics on population density and the distribution characteristics of the identified city blocks. Changzhou City, China, was selected as the study case. The results indicate that (1) population density distribution is unbalanced only when spatial density exceeds a critical value, reflecting the level and sphere of influence of blocks with varying spatial densities; (2) low population density distribution is concentrated in the zonal space, along the boundary between primary and secondary urban centres; (3) spatial characteristics affecting population density distribution vary with the type of block, and the green landscape’s attractiveness is reduced. Our study provides a novel perspective on quantifying the link between urban form and population distribution. It can help decision-makers and planners in accurately recommending urban intervention in population density distribution by adjusting the spatial morphology and promoting rational use of urban public resources.
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development