Haohao Ji
A new data-enabled intelligence framework for evaluating urban space perception
Ji, Haohao; Qing, Linbo; Han, Longmei; Wang, Zhengyong; Cheng, Yongqiang; Peng, Yonghong
Authors
Linbo Qing
Longmei Han
Zhengyong Wang
Yongqiang Cheng
Yonghong Peng
Abstract
The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with a better urban environment. Existing studies for assessing urban perception have primarily relied on low efficiency methods, which also result in low evaluation accuracy. Furthermore, there lacks a sophisticated understanding on how to correlate the urban perception with the built environment and other socio-economic data, which limits their applications in supporting urban planning. In this study, a new data-enabled intelligence framework for evaluating human perceptions of urban space is proposed. Specifically, a novel classification-then-regression strategy based on a deep convolutional neural network and a random-forest algorithm is proposed. The proposed approach has been applied to evaluate the perceptions of Beijing and Chengdu against six perceptual criteria. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators for the built environment and socio-economic data including visual elements, facility attributes and socio-economic indicators. Experimental results show that the proposed framework can effectively evaluate urban perceptions. The associations between urban perceptions and the visual elements, facility attributes and a socio-economic dimension have also been identified, which can provide substantial inputs to guide the urban planning for a better urban space.
Citation
Ji, H., Qing, L., Han, L., Wang, Z., Cheng, Y., & Peng, Y. (2021). A new data-enabled intelligence framework for evaluating urban space perception. ISPRS International Journal of Geo-Information, 10(6), https://doi.org/10.3390/ijgi10060400
Journal Article Type | Article |
---|---|
Acceptance Date | May 26, 2021 |
Publication Date | Jun 1, 2021 |
Deposit Date | Jun 3, 2025 |
Publicly Available Date | Jun 3, 2025 |
Journal | ISPRS International Journal of Geo-Information |
Electronic ISSN | 2220-9964 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 10 |
Issue | 6 |
DOI | https://doi.org/10.3390/ijgi10060400 |
Public URL | https://hull-repository.worktribe.com/output/3796647 |
Make cities and human settlements inclusive, safe, resilient and sustainable
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0
Copyright Statement
© 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
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