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A new data-enabled intelligence framework for evaluating urban space perception

Ji, Haohao; Qing, Linbo; Han, Longmei; Wang, Zhengyong; Cheng, Yongqiang; Peng, Yonghong

Authors

Haohao Ji

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
This output contributes to the following UN Sustainable Development Goals:

SDG 11 - Sustainable Cities and Communities

Make cities and human settlements inclusive, safe, resilient and sustainable

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