Exploring land use functional variance using mobile phone derived human activity data in Shanghai

Abstract

Land use functions can categorize places where people perform different socioeconomic activities. This classification plays an important role in urban management, policy making, and resource allocation. However, due to the rapid changes of built environment and living demands, human activities might vary significantly, in space and time, even within the same land use function as conventionally defined, impeding the formulation of targeted and user-oriented planning policies. This study took the first step to explore land use subcategorization using mobile phone-derived human activities. The study area is the 5,298 census tracts in Shanghai. Sixteen million mobile phone users’ data were collected from Shanghai Mobile Co., Ltd., in 2014. We proposed a multi-dimensional indicator framework to capture collective features of activities and identified land use subcategories using the K-Means clustering method. Analysis of variance (ANOVA) was applied to detect the proportion of activity variances captured by the classification results. Subcategory labelling method was applied to reveal the relationship between land use subcategories and built environment factors. The results show that (1) Conventional land-use functional zones (LFZs) cannot fully capture the activity variances, especially in behavioral regularity and temporal variation; (2) According to the variance analysis, at least four to five subcategories should be identified upon current LFZs to capture the main activity variances; and (3) In the case of Shanghai, land use subcategories presented palpable spatial regularity, which revealed a citywide structure deserves for further study. We concluded that data-derived activity features can provide an innovative perspective complementary to existing land use classification standards and facilitate policymakers with their decision-making processes on urban resource allocation.