{"title":"Studying the impact of streetlights on street crime rate using geo-statistics","authors":"Srikanth Vadlamani, M. Hashemi","doi":"10.1109/IRI49571.2020.00040","DOIUrl":null,"url":null,"abstract":"Lack of adequate streetlights likely affect public safety, particularly in neighborhoods with higher crime rates. Several researchers have studied the influence of streetlights on crime. However, those studies compare the crime rate during the day and not night or explore crime patterns in socially disorganized communities. This study focuses on detecting the pattern of nighttime street crime near a broken or due-for-repair streetlights. Historical crime data and data on city streetlight service requests studied in this project. Analytical approaches for this projects include the least squares linear regression model applied to determine the relationship between streetlight and crime data and Ripley’s K function is used to detect crime clusters near broken streetlights. The Moran’s I index is used to measuring the spatial correlation between broken streetlights and crime rates. Optimized hotspot analysis is used to predict crime locations. This study found that broken streetlights cause increasing trends of crime near them The Moran’s I index’s large positive value underscored the statistically-significant clustering of street crimes around broken streetlights","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI49571.2020.00040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Lack of adequate streetlights likely affect public safety, particularly in neighborhoods with higher crime rates. Several researchers have studied the influence of streetlights on crime. However, those studies compare the crime rate during the day and not night or explore crime patterns in socially disorganized communities. This study focuses on detecting the pattern of nighttime street crime near a broken or due-for-repair streetlights. Historical crime data and data on city streetlight service requests studied in this project. Analytical approaches for this projects include the least squares linear regression model applied to determine the relationship between streetlight and crime data and Ripley’s K function is used to detect crime clusters near broken streetlights. The Moran’s I index is used to measuring the spatial correlation between broken streetlights and crime rates. Optimized hotspot analysis is used to predict crime locations. This study found that broken streetlights cause increasing trends of crime near them The Moran’s I index’s large positive value underscored the statistically-significant clustering of street crimes around broken streetlights
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利用地理统计学研究路灯对街道犯罪率的影响
缺乏充足的路灯可能会影响公共安全,特别是在犯罪率较高的社区。几位研究人员研究了路灯对犯罪的影响。然而,这些研究比较的是白天的犯罪率,而不是夜晚的犯罪率,或者探索社会混乱社区的犯罪模式。这项研究的重点是在损坏或需要维修的路灯附近检测夜间街头犯罪的模式。本课题研究了历史犯罪数据和城市路灯服务请求数据。该项目的分析方法包括最小二乘线性回归模型,用于确定路灯与犯罪数据之间的关系,以及使用Ripley的K函数来检测损坏路灯附近的犯罪集群。莫兰指数用于衡量路灯损坏与犯罪率之间的空间相关性。利用优化的热点分析预测犯罪地点。研究发现,路灯破损导致路灯附近的犯罪呈上升趋势。Moran 's I指数的大正值强调了路灯破损附近街道犯罪的统计学显著聚集
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