Do Gated Communities Aggravated Urban Green Space Disparity: A Case Study in Panyu District, Guangzhou
Topics:
Keywords: Urban Green Space, Gated Community, Spatial Disparity, Spatial Justice, Landscape Index, Gentrification
Abstract Type: Paper Abstract
Authors:
Yizhou Wang Cornell University
Abstract
This paper aims to examine the potential impact of gated communities on the disparity of urban green spaces in the distinct neighborhoods of Panyu District, Guangzhou. Since the 1990s, Panyu has undergone rapid population growth and suburbanization, fostering a diverse population with locals, migrant workers, and commuters residing in varied settings. Responding to the rising demand for “safe exclusive communities”, developers integrate green design into gated communities, appealing to the eco-conscious middle and upper class. Therefore, a potential urban green space disparity should be acknowledged. The peri-urban areas of Panyu are characterized by three distinct types of neighborhoods: i) redeveloped villages, ii) non-gated commodity housing, and iii) commodity housing. The research seeks to understand how gated communities both contribute to and intensify disparities in access to green spaces within urban areas. The classification of neighborhoods in Panyu District is conducted utilizing GIS and property transaction data. Subsequently, the calculation of green space ratios and landscape indexes of green spaces in different kinds of communities will be performed using analyzing land cover data. The landscape indexes include area-weighted mean shape index (SHAPE_AM), mean Euclidean nearest neighbor distance (ENN_MN), patch density (PD), and metrics of percentage of landscape (PLAND). Finally, the paper discovered the relationship between gated communities and landscape index and discovered that there is a difference in spatial patterns of urban green space between gated and non-gated communities.
Do Gated Communities Aggravated Urban Green Space Disparity: A Case Study in Panyu District, Guangzhou
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Paper Abstract
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Submitted By:
Yizhou Wang Cornell University
yw2552@cornell.edu
This abstract is part of a session: GeoAI and Deep Learning Symposium - Responsible GeoAI II: Justice and Accuracy