Evaluating cities' vitality and identifying ghost cities in China with emerging geographical data

Jin, Xiaobin; Long, Ying; Sun, Wei; Lu, Yu‐Ying; Yang, Xuhong; Tang, Jingxian

doi:10.1016/j.cities.2017.01.002

Cited by 245 publications

(119 citation statements)

References 16 publications

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“…Jacobs () contended that vitality in urban space refers to diversity constituted by people's activities and spaces. To calculate urban vitality, Jin et al () delineated the concept into three components, namely, urban morphology, urban function, and urban society. In this study, these three components are integrated into the analysis.…”

Section: Introductionmentioning

confidence: 99%

Space, function, and vitality in historic areas: The tourismification process and spatial order of Shichahai in Beijing

Wang

et al. 2018

Journal of Tourism Research

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In the development of historic areas, functions change following socioeconomic transformation. Nowadays, tourism is popular in historic areas. We examined this to understand the internal motivation, spatial logic, and functional transformation of a historic area to sustainably maintain its vitality. For Shichahai in Beijing, the selected study area, space syntax and kernel density analyses were conducted to understand the development vitality and determine the inner order of space and function in tourism development. The results clarified the inertia retained by historic genes to provide vitality in tourism.

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Section: Introductionmentioning

confidence: 99%

Space, function, and vitality in historic areas: The tourismification process and spatial order of Shichahai in Beijing

Wang

et al. 2018

Journal of Tourism Research

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“…Jahromi, Zignani, Gaito, and Rossi (2016) conducted a theoretical analysis of POI applications and believed that POI could better reflect human activities and social behaviors in urban spaces. Jin et al (2017) obtained POI data from web crawler technology, as well as combined road intersection data and human activity data, of location-based services (LBS) to evaluate the vitality of cities in China. Bakillah, Liang, Mobasheri, Arsanjani, and Zipf (2014) used POI data extracted from open street maps (OSM) to estimate HAD from a single building.…”

mentioning

confidence: 99%

Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps‐based analysis of Wuhan, China

Zuo

Xiao²,

Luo

et al. 2019

Growth and Change

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With the development and popularity of mobile Internet technology, data sources of human activity in urban centers are rapidly updated and play an important role in supporting urban planning and management. Therefore, it is critical to integrate different data sources and detect spatially implicit information in the spatial pattern of relationships between urban human activity and related geographical factors. A new analytical framework is first proposed to integrate multisource location‐based big data and use these data to analyze dynamic real‐time human activity density (HAD). Taking Wuhan, the largest city in central China as an example, using the Baidu’s thermal data, this paper analyzes spatiotemporal characteristics of HAD distributions at different points on weekends and weekdays, and further combines the relevant cities’ points of interest data to analyze the correlations between different spatial elements and HAD distributions. The results show that: (a) Using a new indicator and data processing method can simply achieve effective utilization of Baidu’s thermal data; (b) Combined with standardized grids, spatial density estimation can match the two different data sources in this study; (c) The greater the HAD, the greater is the elasticity of change, and in the active population area, the densities of human activity on weekends and weekdays at different times have significant differences; and (d) Different geographically weighted regression models effectively distinguish the influence of different urban elements on weekdays and weekends. In particular, the impact patterns of the workplace, education, and cityscape reflect the unique spatial patterns of research cases. These findings, as well as visual analytics, help in the understanding of the potential value of Baidu heatmaps in urban study and provide support for more scientific and accurate urban planning and space management for the better consideration of real‐time changes in human activity.

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“…There are some studies on ghost cities [7,8]. The problem was first reported and named by the western media.…”

Section: Introductionmentioning

confidence: 99%

Ghost City Extraction and Rate Estimation in China Based on NPP-VIIRS Night-Time Light Data

Yang

Zhu

et al. 2018

IJGI

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Abstract:The ghost city phenomenon is a serious problem resulting from the rapid urbanization process in China. Estimation of the ghost city rate (GCR) can provide information about vacant dwellings. This paper developed a methodology to quantitatively evaluate GCR values at the national scale using multi-resource remote sensing data. The Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer (NPP-VIIRS) night-time light data and moderate resolution imaging spectroradiometer (MODIS) land cover data were used in the evaluation of the GCR values in China. The average ghost city rate (AGCR) was 35.1% in China in 2013. Shanghai had the smallest AGCR of 21.7%, while Jilin has the largest AGCR of 47.27%. There is a significant negative correlation between both the provincial AGCR and the per capita disposable income of urban households (R = −0.659, p < 0.01) and the average selling prices of commercial buildings (R = −0.637, p < 0.01). In total, 31 ghost cities are mainly concentrated in the economically underdeveloped inland provinces. Ghost city areas are mainly located on the edge of urban built-up areas, and the spatial pattern of ghost city areas changed in different regions. This approach combines statistical data with the distribution of vacant urban areas, which is an effective method to capture ghost city information.

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Evaluating cities' vitality and identifying ghost cities in China with emerging geographical data

Cited by 245 publications

References 16 publications

Space, function, and vitality in historic areas: The tourismification process and spatial order of Shichahai in Beijing

Space, function, and vitality in historic areas: The tourismification process and spatial order of Shichahai in Beijing

Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps‐based analysis of Wuhan, China

Ghost City Extraction and Rate Estimation in China Based on NPP-VIIRS Night-Time Light Data

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