Decentralization and Polycentricity: Spatial Changes of Employment in Beijing Metropolitan Area, China

Lv, Yongqiang; Zheng, Xinqi; Zhou, Lin; Zhang, Lulu

doi:10.3390/su9101880

Cited by 19 publications

(12 citation statements)

References 35 publications

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“…Our results show that the standard deviation of the curvature in the landform is statistically nonsignificant, differing from previous studies (Liu and Wang 2016). The study area can influence the results of the identified subcenters (Lv et al 2017). PLACMDs, which are much smaller than Chinese cities at the prefectural level and above, are used as the study area in this study.…”

Section: Resultsmentioning

confidence: 99%

“…The compact development of city centers has economic and social advantages (He et al 2016; Kong et al 2016), but also produces major urban diseases, such as lower quality of life, environmental pollution, high housing prices, traffic jams, etc. (Kong et al 2016; Lv et al 2017; Wang and Kwan 2017; Zheng and Kahn 2017). Government officials and urban planners in the West are exerting efforts to establish a polycentric urban spatial structure because they believe that polycentricity could enhance their cities’ competitiveness and environmental sustainability (Parr 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Notably, different methods of center identification use different data types (Li, Long, and Dang 2018); however, but the polycentric development of Chinese cities by different data are highly correlated with each other. While some studies have used nighttime lights (Chen et al 2017) or building volumes (Taubenböck et al 2017) to analyze the urban spatial structure, most studies have been based on population or employment data (McMillen 2001; Garcia‐López and Muñiz 2010; Kim, Chang, and Kwon 2014; Lv et al 2017; Li et al 2018). Employment centers are more important than population centers for understanding the urban spatial structure (Giuliano and Small 1991), but few studies have examined the employment structure in Chinese cities because of either a lack of data or doubts about the quality of the data (Tao and Jin 2009; Huang, Liu, and Zhao 2015).…”

Section: Introductionmentioning

confidence: 99%

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Polycentric Urban Development and its Determinants in China: A Geospatial Big Data Perspective

Lan²,

Kan

et al. 2020

Geographical Analysis

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The urban structure of large Chinese cities has been well researched, but a systematic analysis of polycentric urban development and the determinants of subcenter formation across municipal districts in cities at the prefectural level and above (PLACMD) is lacking. Using geospatial big data and spatial analysis methods, we measure the urban spatial structure of all 294 PLACMDs to determine the polycentric urban structure in China and conduct an exploratory regression analysis of 59 PLACMDs (due to data restrictions) to explore the formation of polycentric cities. Our results suggest that using location‐based data allows for a timelier and more accurate center identification of detailed urban structural features than using other data. Each PLACMD in China has at least one center, and polycentricity is currently the most common urban spatial structure. PLACMDs with higher populations are more polycentric. Compared with the results obtained from large American urban areas, our regression results imply that population alone accounts for most of the variation in the polycentric index and that commuting costs provide a weak explanation of the existence of Chinese PLACMDs. Both economic development and agglomeration economics are associated with the polycentric index. In contrast, the topographical features are statistically nonsignificant in the regression model.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Polycentric Urban Development and its Determinants in China: A Geospatial Big Data Perspective

Lan²,

Kan

et al. 2020

Geographical Analysis

Self Cite

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“…2019, 8, 283 2 of 20 gradually declined [12]. In efforts to address this problem, it has been shown that decentralization and the development of polycentric cities can effectively alleviate 'big city disease' [13]. Moreover, the production efficiency and annual average wage in polycentric areas are generally higher than those in monocentric areas [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Detecting Urban Polycentric Structure from POI Data

Deng

Liu

Yang³

et al. 2019

IJGI

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It is meaningful to analyze urban spatial structure by identifying urban subcenters, and many methods of doing so have been proposed in the published literature. Although these methods are widely applied, they exhibit obvious shortcomings that limit their further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we propose the density contour tree (DCT) method for detecting urban polycentric structures and their spatial distributions. Conceptually, this method is based on an analogy between urban spatial structure and terrain. The point-of-interest (POI) density is visualized as a continuous mathematical surface representing the urban terrain. Peaks represent the regions of the most frequent human activity, valleys represent regions with small population densities in the city, and slopes represent spatial changes in urban land-use intensity. Using this method, we have detected the urban “polycentric” structure of Beijing and determined the corresponding spatial relationships. In addition, several important properties of the urban centers have been identified. For example, Beijing has a typical urban polycentric structure with an urban center area accounting for 5.9% of the total urban area, and most of the urban centers in Beijing serve comprehensive functions. In general, the method and the results can serve as references for the later research on analyzing urban structure.

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“…Many studies, mainly empirical studies examining population distribution and land prices [2][3][4][5][6][7][8][9], have investigated the polycentric spatial structure in China's mega cities, and a few studies have done this from the perspective of employment distribution [10][11][12]. These studies have demonstrated that many mega cities in China have been experiencing polycentric development resulting from the emergence of subcenters and the increasing intensity of development in the suburbs.…”

Section: Introductionmentioning

confidence: 99%

Polycentric Development and the Role of Urban Polycentric Planning in China’s Mega Cities: An Examination of Beijing’s Metropolitan Area

Liu

2018

Sustainability

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An emerging polycentric spatial structure in China's mega cities has been confirmed in the literature, but few studies have addressed the linkage between the change in spatial structure and urban development policies. Using Beijing as a case study, this paper examines the role of urban polycentric planning in the polycentric evolution process from a morphological perspective. We observed that urban polycentric planning significantly promoted the emergence of a polycentric urban form in Beijing's metropolitan area from 2001 to 2010. Firstly, most of the planned subcenters were identified as real employment subcenters by a relative threshold method in 2001 and 2010 and comprised the majority of the employment subcenters. Secondly, the planned subcenters significantly increased their employment share and influence on employment and population distribution from 2001 to 2010, and their performances referring to these aspects were much better than the unplanned subcenters. Thus, our findings provide evidence that changes in urban spatial structure can be strongly affected by urban development policies, although it is determined by market forces in theory.

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Decentralization and Polycentricity: Spatial Changes of Employment in Beijing Metropolitan Area, China

Cited by 19 publications

References 35 publications

Polycentric Urban Development and its Determinants in China: A Geospatial Big Data Perspective

Polycentric Urban Development and its Determinants in China: A Geospatial Big Data Perspective

Detecting Urban Polycentric Structure from POI Data

Polycentric Development and the Role of Urban Polycentric Planning in China’s Mega Cities: An Examination of Beijing’s Metropolitan Area

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