{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T14:23:28Z","timestamp":1769264608881,"version":"3.49.0"},"reference-count":68,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T00:00:00Z","timestamp":1691539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese National Nature Science Foundation","award":["41461035"],"award-info":[{"award-number":["41461035"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study introduces a novel approach to urban public safety analysis inspired by a streetscape analysis commonly applied in urban criminology, leveraging the concept of micro-geographical units to account for urban spatial heterogeneity. Recognizing the intrinsic uniformity within these smaller, distinct environments of a city, the methodology represents a shift from large-scale regional studies to a more localized and precise exploration of urban vulnerability. The research objectives focus on three key aspects: first, establishing a framework for identifying and dividing cities into micro-geographical units; second, determining the type and nature of data that effectively illustrate the potential vulnerability of these units; and third, developing a robust and reliable evaluation index system for urban vulnerability. We apply this innovative method to Urumqi\u2019s Tianshan District in Xinjiang, China, resulting in the formation of 30 distinct micro-geographical units. Using WorldView-2 remote sensing imagery and the object-oriented classification method, we extract and evaluate features related to vehicles, roads, buildings, and vegetation for each unit. This information feeds into the construction of a comprehensive index, used to assess public security vulnerability at a granular level. The findings from our study reveal a wide spectrum of vulnerability levels across the 30 units. Notably, units X1 (Er Dao Bridge) and X7 (Gold Coin Mountain International Plaza) showed high vulnerability due to factors such as a lack of green spaces, poor urban planning, dense building development, and traffic issues. Our research validates the utility and effectiveness of the micro-geographical unit concept in assessing urban vulnerability, thereby introducing a new paradigm in urban safety studies. This micro-geographical approach, combined with a multi-source data strategy involving high-resolution remote sensing and field survey data, offers a robust and comprehensive tool for urban vulnerability assessment. Moreover, the urban vulnerability evaluation index developed through this study provides a promising model for future urban safety research across different cities.<\/jats:p>","DOI":"10.3390\/rs15163944","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T10:21:50Z","timestamp":1691576510000},"page":"3944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Urban Vulnerability Analysis Based on Micro-Geographic Unit with Multi-Source Data\u2014Case Study in Urumqi, Xinjiang, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Jianghua","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Geographical and Remote Sensing Science, Xinjiang University, Urumqi 830046, China"},{"name":"Key Lab of Smart City and Environmental Modelling, Xinjiang University, Urumqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4470-7011","authenticated-orcid":false,"given":"Danlin","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ 07043, USA"}]},{"given":"Chuqiao","family":"Han","sequence":"additional","affiliation":[{"name":"College of Geographical and Remote Sensing Science, Xinjiang University, Urumqi 830046, China"}]},{"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geographical and Remote Sensing Science, Xinjiang University, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fang, C., and Yu, D. 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