Investigating the relationship between built environment and urban vitality using big data

Area of Science:

• Urban studies
• Environmental science
• Spatial analysis

Background:

• Urban vitality is crucial for social and economic development, influencing urban quality and resident well-being.
• Existing research often neglects big data integration and spatial heterogeneity in urban environments.
• Understanding these factors is vital for sustainable urban planning and development.

Purpose of the Study:

• To measure urban vitality using multi-source big data in Yinchuan.
• To analyze the influence of 2D/3D built environment variables on urban vitality.
• To investigate the spatially heterogeneous relationships between the built environment and urban vitality using Geographically Weighted Regression (GWR).

Main Methods:

• Utilized multi-source big data for urban vitality measurement.
• Incorporated 2D/3D variables to characterize the built environment.
• Applied a Geographically Weighted Regression (GWR) model to explore spatial heterogeneity.

Main Results:

• Built environment factors significantly impact urban vitality, especially in central districts.
• Positive spatial autocorrelation and clustering patterns were observed in the influence of built environment on vitality.
• Significant spatial heterogeneity was found across different regions of Yinchuan.

Conclusions:

• The built environment is a key determinant of urban vitality, with spatially varying effects.
• Targeted, multidimensional strategies are proposed for enhancing urban vitality.
• Findings offer theoretical support and practical guidance for Yinchuan and other cities facing similar urban challenges.