Multiscale identification of urban functional polycentricity for planning implications : An integrated approach using geo-big transport data and complex network modeling
(2020) In Habitat International 97.- Abstract
Polycentrism has gradually become a newly emergent dimension of global urbanization. Many countries worldwide have tailored plans suited to functional polycentricity, in light of the prevalent “ghost cities” or “empty towns” as lessons from the morphologically polycentric development practices. However, the subject of defining and measuring functional polycentricity is still in an initial development phase, both in theory and in methodology. This paper first establishes a general theoretical framework for understanding functional polycentricity from the lens of interactive human mobility among spatial units. Then, a new approach is proposed to identify and measure urban functional polycentricity from a multiscale perspective and further... (More)
Polycentrism has gradually become a newly emergent dimension of global urbanization. Many countries worldwide have tailored plans suited to functional polycentricity, in light of the prevalent “ghost cities” or “empty towns” as lessons from the morphologically polycentric development practices. However, the subject of defining and measuring functional polycentricity is still in an initial development phase, both in theory and in methodology. This paper first establishes a general theoretical framework for understanding functional polycentricity from the lens of interactive human mobility among spatial units. Then, a new approach is proposed to identify and measure urban functional polycentricity from a multiscale perspective and further applied to the case of Shanghai, China. More specifically, the pick-up and drop-off points from taxi GPS data are used to examine the linkages among different urban units across various scales (e.g., census tract, 3000-m grid, 5000-m grid, and community). Complex network modeling, together with the sensitivity analysis, is further employed to identify the centers according to the spatial importance of each unit. The results show that (1) the approach proposed can effectively identify functional centers within urban setting; (2) an obvious polycentric structure exists in Shanghai and is sensitive to scale effects; (3) the estimates are more accurate and precise with the shrink of analysis unit size from community level to census tract level; and (4) under the same spatial scale, the grid-based analysis produces a more elaborated polycentric pattern compared with the traditional administration-based analysis. Finally, scale-dependent differences between morphological and functional polycentricity are distinguished for providing implications for urban planning. Our study is believed to renew the knowledge of polycentricity conceptualization.
(Less)
- author
- Wei, Lai ; Luo, Yun ; Wang, Miao LU ; Cai, Yuyang ; Su, Shiliang ; Li, Bozhao and Ji, Hangyu
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Big data, Polycentricity, Scale effect, Space-time activities, Taxi ridership, Urban spatial structure
- in
- Habitat International
- volume
- 97
- article number
- 102134
- publisher
- Elsevier
- external identifiers
-
- scopus:85079777095
- ISSN
- 0197-3975
- DOI
- 10.1016/j.habitatint.2020.102134
- language
- English
- LU publication?
- yes
- id
- 5b5554b8-1409-480a-a39b-9467b8be3e4b
- date added to LUP
- 2020-12-30 13:20:34
- date last changed
- 2022-04-26 22:58:02
@article{5b5554b8-1409-480a-a39b-9467b8be3e4b,
abstract = {{<p>Polycentrism has gradually become a newly emergent dimension of global urbanization. Many countries worldwide have tailored plans suited to functional polycentricity, in light of the prevalent “ghost cities” or “empty towns” as lessons from the morphologically polycentric development practices. However, the subject of defining and measuring functional polycentricity is still in an initial development phase, both in theory and in methodology. This paper first establishes a general theoretical framework for understanding functional polycentricity from the lens of interactive human mobility among spatial units. Then, a new approach is proposed to identify and measure urban functional polycentricity from a multiscale perspective and further applied to the case of Shanghai, China. More specifically, the pick-up and drop-off points from taxi GPS data are used to examine the linkages among different urban units across various scales (e.g., census tract, 3000-m grid, 5000-m grid, and community). Complex network modeling, together with the sensitivity analysis, is further employed to identify the centers according to the spatial importance of each unit. The results show that (1) the approach proposed can effectively identify functional centers within urban setting; (2) an obvious polycentric structure exists in Shanghai and is sensitive to scale effects; (3) the estimates are more accurate and precise with the shrink of analysis unit size from community level to census tract level; and (4) under the same spatial scale, the grid-based analysis produces a more elaborated polycentric pattern compared with the traditional administration-based analysis. Finally, scale-dependent differences between morphological and functional polycentricity are distinguished for providing implications for urban planning. Our study is believed to renew the knowledge of polycentricity conceptualization.</p>}},
author = {{Wei, Lai and Luo, Yun and Wang, Miao and Cai, Yuyang and Su, Shiliang and Li, Bozhao and Ji, Hangyu}},
issn = {{0197-3975}},
keywords = {{Big data; Polycentricity; Scale effect; Space-time activities; Taxi ridership; Urban spatial structure}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Habitat International}},
title = {{Multiscale identification of urban functional polycentricity for planning implications : An integrated approach using geo-big transport data and complex network modeling}},
url = {{http://dx.doi.org/10.1016/j.habitatint.2020.102134}},
doi = {{10.1016/j.habitatint.2020.102134}},
volume = {{97}},
year = {{2020}},
}