Urban Intensity in Theory and Practice: Empirical Determining Mechanism of Floor Area Ratio and Its Deviation from the Classic Location Theories in Beijing

Qing Lu, Jing Ning, Hong You and Liyan Xu ()
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Qing Lu: College of Urban and Environmental Sciences, Peking University, 5 Summer Palace Road, Beijing 100871, China
Jing Ning: College of Architecture and Landscape Architecture, Peking University, 5 Summer Palace Road, Beijing 100871, China
Hong You: Department of Urban Regeneration and Planning, Beijing Municipal Institute of City Planning, 60 Nanlishi Road, Beijing 100045, China
Liyan Xu: College of Architecture and Landscape Architecture, Peking University, 5 Summer Palace Road, Beijing 100871, China

Land, 2023, vol. 12, issue 2, 1-16

Abstract: Background: Classic locational principles predict a picture of urban intensity decaying from the city center to the periphery under ideal assumptions. However, various exogenous factors can influence the real-world urban intensity and often deviate from the theoretical pattern. The specific mechanisms are worthy of exploration and are of potential theoretical and practical significance. Methods: In this paper, we consider two city districts with typical urban locations, namely, Changping and Chaoyang in Beijing, and construct mechanistic models of the status quo urban intensity (floor area ratio, FAR ) utilizing multisource spatiotemporal big data. We further compare these models with the “theoretically ideal” FAR patterns as would be predicted by applied locational theories. Results: We find that the status quo FAR distribution generally conforms to the theoretical predictions but still exhibits regional deviations that can be explained by historical inertia and influence from particular policies. Conclusion: We conclude this paper with discussions on the findings’ methodological and practical implications for urban planning institutions, especially in a transition economy context.

Keywords: urban intensity; floor area ratio ( FAR ); locational theory; spatiotemporal big data; Beijing (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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