Measuring equality in access to urban parks: A big data analysis from Chengdu

Area of Science:

• Environmental Justice
• Urban Planning
• Geospatial Big Data Analysis

Background:

• Spatial equality of urban parks is a critical environmental justice concern, particularly in dense urban areas with limited space.
• Existing studies often overlook subjective preferences and psychological accessibility in park access assessments.
• Fine-scale analysis is needed to understand nuanced disparities in urban park accessibility.

Purpose of the Study:

• To assess spatial and quantitative equality of urban park access in Chengdu using a fine-scale grid resolution (100 × 100 m).
• To investigate the influence of different transportation modes (walking, cycling, driving) on park accessibility.
• To incorporate subjective preferences and psychological accessibility into the evaluation of park access.

Main Methods:

• Employed a supply-demand adjusted two-step floating catchment area (2SFCA) model.
• Utilized multi-source geospatial big data and machine learning approaches.
• Conducted spatial local autocorrelation analysis to identify clustering patterns.

Main Results:

• Park accessibility distribution varied significantly across transportation modes; walking access was influenced by park location, while cycling and driving access were affected by river distribution.
• Park accessibility was nearly equal for driving, relatively equal for cycling, and highly unequal for walking.
• Park accessibility exhibited significant spatial clustering with minimal variation.

Conclusions:

• Urban park accessibility demonstrates significant spatial inequality, particularly for pedestrians, highlighting environmental justice issues.
• Findings provide crucial insights for urban planners to develop targeted strategies for improving park access equity.
• The applied methodology offers a novel perspective for enhancing scientific evaluations of urban resource accessibility.