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Article

A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception

1
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
2
Chengdu Institute of Urban Planning and Design, Chengdu 610081, China
3
Department of Computer Science and Technology, University of Hull, Hull HU6 7RX, UK
4
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Maria Antonia Brovelli and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(6), 400; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060400
Received: 18 February 2021 / Revised: 11 April 2021 / Accepted: 26 May 2021 / Published: 9 June 2021
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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. View Full-Text
Keywords: urban space perception; deep learning; big data; data associations; urban studies urban space perception; deep learning; big data; data associations; urban studies
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MDPI and ACS Style

Ji, H.; Qing, L.; Han, L.; Wang, Z.; Cheng, Y.; Peng, Y. A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception. ISPRS Int. J. Geo-Inf. 2021, 10, 400. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060400

AMA Style

Ji H, Qing L, Han L, Wang Z, Cheng Y, Peng Y. A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception. ISPRS International Journal of Geo-Information. 2021; 10(6):400. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060400

Chicago/Turabian Style

Ji, Haohao, Linbo Qing, Longmei Han, Zhengyong Wang, Yongqiang Cheng, and Yonghong Peng. 2021. "A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception" ISPRS International Journal of Geo-Information 10, no. 6: 400. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060400

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