{"title":"Using Remote Sensing Data in Urban Crime Analysis: A Systematic Review of English-Language Literature from 2003 to 2023","authors":"Vania Ceccato, Ioannis Ioannidis","doi":"10.1177/10575677241237960","DOIUrl":null,"url":null,"abstract":"Drawing from environmental criminology principles, this article explores the existing literature to assess the utility of remote sensing data in detecting and analysing features in the urban environment that are associated with crime occurrence. A systematic review of the literature in the English language from 2003 until the first half of 2023 from two major databases, Scopus and Science Direct, is carried out. As many as 910 publications were selected, from which 36 publications satisfied the selection criteria. Findings show that neighborhood's design has a quantifiable imprint that is possible to be observed with very high spatial-resolution imagery. Given its high spatial and temporal resolution, remote sensing data can to different degrees support the identification of criminogenic features in urban environments (streets and roads, property boundaries, housing density, characteristics and density of vegetation as well as luminosity levels), but when it is used for the detection of potentially illegal activities, infringement of people's privacy and methods lacking validation still present serious concerns. The article concludes with a discussion of the opportunities and challenges of using remote sensing data in crime analysis.","PeriodicalId":51797,"journal":{"name":"International Criminal Justice Review","volume":"23 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Criminal Justice Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/10575677241237960","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CRIMINOLOGY & PENOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Drawing from environmental criminology principles, this article explores the existing literature to assess the utility of remote sensing data in detecting and analysing features in the urban environment that are associated with crime occurrence. A systematic review of the literature in the English language from 2003 until the first half of 2023 from two major databases, Scopus and Science Direct, is carried out. As many as 910 publications were selected, from which 36 publications satisfied the selection criteria. Findings show that neighborhood's design has a quantifiable imprint that is possible to be observed with very high spatial-resolution imagery. Given its high spatial and temporal resolution, remote sensing data can to different degrees support the identification of criminogenic features in urban environments (streets and roads, property boundaries, housing density, characteristics and density of vegetation as well as luminosity levels), but when it is used for the detection of potentially illegal activities, infringement of people's privacy and methods lacking validation still present serious concerns. The article concludes with a discussion of the opportunities and challenges of using remote sensing data in crime analysis.
期刊介绍:
International Criminal Justice Review is a scholarly journal dedicated to presenting system wide trends and problems on crime and justice throughout the world. Articles may focus on a single country or compare issues affecting two or more countries. Both qualitative and quantitative pieces are encouraged, providing they adhere to standards of quality scholarship. Manuscripts may emphasize either contemporary or historical topics. As a peer-reviewed journal, we encourage the submission of articles, research notes, and commentaries that focus on crime and broadly defined justice-related topics in an international and/or comparative context.