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Open AccessArticle

Using Open Source Data to Identify Transit Deserts in Four Major Chinese Cities

by Junfeng Jiao 1 and Mingming Cai 2,*
1
Urban Information Lab, the University of Texas at Austin, Austin, TX 78705, USA
2
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(2), 100; https://doi.org/10.3390/ijgi9020100
Received: 7 January 2020 / Revised: 25 January 2020 / Accepted: 27 January 2020 / Published: 6 February 2020
The concept of transit deserts stems from the concept of food deserts. There is substantial research on transit deserts in developed countries. However, there is no known research that has studied this subject in Chinese cities. Using open-source data, this paper identified transit desert areas in four major Chinese cities (Beijing, Shanghai, Wuhan, Chengdu). The results show that: (1) In these four cities, the transit desert areas are mainly concentrated in city centers and hardly occur in any suburban areas, which is very different from the cases in the US. (2) Shanghai has the largest transit-dependent population living in transit deserts, followed by Beijing, Chengdu, and Wuhan. Chengdu has the smallest transit desert areas, followed by Shanghai, Wuhan, and Beijing. (3) An oversized transit-dependent population and incomplete transit systems in these cities might contribute to the transit deserts’ occurrences. (4) Different distribution of population density, traveling preference, and transportation investment policy in Chinese and American cities might contribute to the different findings. By examining transit desert problems in major Chinese cities, this study brought people’s attention to the gap between transit demand and supply in China.
Keywords: transit desert; transit demand; transit supply; China transit desert; transit demand; transit supply; China
MDPI and ACS Style

Jiao, J.; Cai, M. Using Open Source Data to Identify Transit Deserts in Four Major Chinese Cities. ISPRS Int. J. Geo-Inf. 2020, 9, 100.

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