{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T12:31:27Z","timestamp":1768739487090,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42001171"],"award-info":[{"award-number":["42001171"]}]},{"name":"National Natural Science Foundation of China","award":["42171218"],"award-info":[{"award-number":["42171218"]}]},{"name":"National Natural Science Foundation of China","award":["42071184"],"award-info":[{"award-number":["42071184"]}]},{"name":"National Natural Science Foundation of China","award":["2022GDJC-M12"],"award-info":[{"award-number":["2022GDJC-M12"]}]},{"name":"National Natural Science Foundation of China","award":["2023A1515011462"],"award-info":[{"award-number":["2023A1515011462"]}]},{"name":"the Graduate Program of Guangzhou University","award":["42001171"],"award-info":[{"award-number":["42001171"]}]},{"name":"the Graduate Program of Guangzhou University","award":["42171218"],"award-info":[{"award-number":["42171218"]}]},{"name":"the Graduate Program of Guangzhou University","award":["42071184"],"award-info":[{"award-number":["42071184"]}]},{"name":"the Graduate Program of Guangzhou University","award":["2022GDJC-M12"],"award-info":[{"award-number":["2022GDJC-M12"]}]},{"name":"the Graduate Program of Guangzhou University","award":["2023A1515011462"],"award-info":[{"award-number":["2023A1515011462"]}]},{"name":"GuangDong Basic and Applied Basic Research Foundation","award":["42001171"],"award-info":[{"award-number":["42001171"]}]},{"name":"GuangDong Basic and Applied Basic Research Foundation","award":["42171218"],"award-info":[{"award-number":["42171218"]}]},{"name":"GuangDong Basic and Applied Basic Research Foundation","award":["42071184"],"award-info":[{"award-number":["42071184"]}]},{"name":"GuangDong Basic and Applied Basic Research Foundation","award":["2022GDJC-M12"],"award-info":[{"award-number":["2022GDJC-M12"]}]},{"name":"GuangDong Basic and Applied Basic Research Foundation","award":["2023A1515011462"],"award-info":[{"award-number":["2023A1515011462"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Both an offender\u2019s home area and their daily activity area can impact the spatial distribution of crime. However, existing studies are generally limited to the influence of the offender\u2019s home area and its immediate surrounding areas, while ignoring other activity spaces. Recent studies have reported that the routine activities of an offender are similar to those of the residents living in the same vicinity. Based on this finding, our study proposed a flow-based method to measure how offenders are distributed in space according to the spatial mobility of the residents. The study area consists of 2643 communities in ZG City in southeast China; resident flows between every two communities were calculated based on mobile phone data. Offenders\u2019 activity locations were inferred from the mobility flows of residents living in the same community. The estimated count of offenders in each community included both the offenders living there and offenders visiting there. Negative binomial regression models were constructed to test the explanatory power of this estimated offender count. Results showed that the flow-based offender count outperformed the home-based offender count. It also outperformed a spatial-lagged count that considers offenders from the immediate neighboring communities. This approach improved the estimation of the spatial distribution of offenders, which is helpful for crime analysis and police practice.<\/jats:p>","DOI":"10.3390\/ijgi13010008","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T04:40:44Z","timestamp":1703565644000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Explaining Theft Using Offenders\u2019 Activity Space Inferred from Residents\u2019 Mobile Phone Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7202-3418","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"first","affiliation":[{"name":"Center of Geo-Informatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"},{"name":"Department of Geography and GIS, University of Cincinnati, Cincinnati, OH 45221, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0162-9640","authenticated-orcid":false,"given":"Chenchen","family":"Li","sequence":"additional","affiliation":[{"name":"Center of Geo-Informatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6318-8958","authenticated-orcid":false,"given":"Luzi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Center of Geo-Informatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Guangwen","family":"Song","sequence":"additional","affiliation":[{"name":"Center of Geo-Informatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"588","DOI":"10.2307\/2094589","article-title":"Social Change and Crime Rate Trends: A Routine Activity Approach","volume":"44","author":"Cohen","year":"1979","journal-title":"Am. 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