The Dynamic Heterogeneous Relationship between Urban Population Distribution and Built Environment in Xi’an, China: A Case Study

Abstract

The interaction between the population and built environment is a constant topic in urban spaces and is the main driving force of urban evolution. Understanding urban population distribution and its relationship with the built environment could provide guidance for urban planning, traffic, and disaster management. Following this line of thought, this study conducted an empirical analysis in Xi’an, a rapidly developing western city in China. Well-permeated mobile phone location data were used to represent the spatiotemporal dynamics of the population, and the built environment was characterized from five perspectives—transportation, location, building, greenery, and land use—using multisource geospatial data. Finally, the dynamic heterogeneous influence of built environment factors on population distribution was examined using multiscale geographically weighted regression (MGWR). Overall, the influencing coefficients exhibited a significant dynamic changing process from a temporal perspective and simultaneously demonstrated spatial nonstationarity. Moreover, the specific findings about the influence of each built environment factor facilitate a deeper insight into dynamic population distribution and its determinants.

Exploring the Relationship between Urban Vibrancy and Built Environment Using Multi-Source Data: Case Study in Munich

“…Moreover, the heterogeneous impact of built environment factors on urban vibrancy provides guidance for urban renewal and spatial optimization [22]. The results indicate that the mechanisms and effects of built environment factors in shaping urban vibrancy vary across different areas, emphasizing the importance of context-specific planning and design.…”

“…To avoid overestimating spatial dependency in residuals, it is essential to incorporate spatial effects into the models instead of relying solely on global the MLR model. This study employs geographically weighted regression (GWR), a local model that accounts for spatial heterogeneity [22]. The GWR model assumes that regression coefficients vary based on observation locations and incorporates spatial characteristics of data to examine variations in the dependent factor across space.…”

“…Owing to the accessibility of data, existing studies on the relationship between the built environment and urban vibrancy have primarily focused on Asian countries such as China and Japan, while empirical research in the European context remains relatively limited. The specific concerns include (1) the indicator systems and methods for quantifying and measuring the built environment and urban vibrancy [4,5,12]; (2) the spatial scales and heterogeneity of the built environment that influence urban vibrancy [13,17,22,23]; (3) the relative importance of different built environment dimensions in influencing urban vibrancy [11,17,19,24,25]; (4) and the causal mechanisms and pathways through which built environment factors shape urban vibrancy [9,26,27].…”