1. A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception
- Author
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Yongqiang Cheng, Haohao Ji, Longmei Han, Linbo Qing, Wang Zhengyong, and Yonghong Peng
- Subjects
Geography (General) ,business.industry ,Computer science ,media_common.quotation_subject ,Geography, Planning and Development ,Big data ,urban space perception ,Urban studies ,data associations ,deep learning ,Data science ,Beijing ,Urban planning ,big data ,Scale (social sciences) ,Perception ,Earth and Planetary Sciences (miscellaneous) ,G1-922 ,urban studies ,Computers in Earth Sciences ,Dimension (data warehouse) ,business ,Built environment ,media_common - 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.
- Published
- 2021
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