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Liu Y, Chen M, Wang M, Huang J, Thomas F, Rahimi K, Mamouei M. An interpretable machine learning framework for measuring urban perceptions from panoramic street view images. iScience 2023; 26:106132. [PMID: 36843850 PMCID: PMC9950426 DOI: 10.1016/j.isci.2023.106132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/24/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
The proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their end-to-end structure and "black-box" nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability. By utilizing the MIT Place Pulse data, the developed framework can systematically extract six dimensions of urban perceptions from the given panoramas, including perceptions of wealth, boredom, depression, beauty, safety, and liveliness. The practical utility of this framework is demonstrated through its deployment in Inner London, where it was used to visualize urban perceptions at the Output Area (OA) level and to verify against real-world crime rate.
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Affiliation(s)
- Yunzhe Liu
- Informal Cities, Oxford Martin School, University of Oxford, Oxford OX1 3BD, UK,MRC Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK,Corresponding author
| | - Meixu Chen
- Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZT, UK,Corresponding author
| | - Meihui Wang
- SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E 6BT, UK
| | - Jing Huang
- Informal Cities, Oxford Martin School, University of Oxford, Oxford OX1 3BD, UK,Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
| | - Fisher Thomas
- Informal Cities, Oxford Martin School, University of Oxford, Oxford OX1 3BD, UK
| | - Kazem Rahimi
- Informal Cities, Oxford Martin School, University of Oxford, Oxford OX1 3BD, UK
| | - Mohammad Mamouei
- Informal Cities, Oxford Martin School, University of Oxford, Oxford OX1 3BD, UK
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