https://orcid.org/0000-0002-5004-9975
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Abstract
The assessment of closed-circuit television (CCTV) as an infrastructure designed to improve the policing environment has gained widespread attention. The effect of CCTV is influenced not only by the time of installation, however, but also by locations, making it challenging to assess accurately from a single dimension. Existing studies have analyzed the differences in assessment results due to the period of installation, but little has been said about the sensitivity of assessment results to spatial changes. Moreover, existing assessment results rarely consider the varying degrees of influence that CCTV might have in different locations. To address these issues, we use the weighted displacement quotient (WDQ) algorithm to assess the effect of 603 CCTVs installed by the Green Light Project in Detroit from 2016 to 2019. This assessment examines the sensitivity of the WDQ algorithm to spatial changes, particularly when the radius of the target area is altered. We also optimize the matching algorithm and consider the spatial heterogeneity of CCTV’s effects in the assessment process. The results show that over 50 percent of the CCTVs installed have a diffusion of benefits on crime reduction (WDQ > 0), and the assessment results obtained using the WDQ algorithm are highly sensitive to spatial changes. These findings provide valuable insights for subsequent assessments of CCTV, and for the optimization of CCTV installation and layout to enhance their effect.
闭路电视(CCTV)是改善治安环境的基础设施, 对CCTV的评估得到了广泛关注。然而, CCTV的效果不仅受安装时间还受地理位置的影响, 难以从单一维度进行准确评估。现有的研究分析了不同安装周期导致的评估结果的差异, 但很少研究评估结果对空间变化的敏感性。现有评估结果很少考虑CCTV在不同地点的不同影响。我们采用加权位移商(WDQ)算法, 评估了2016年至2019年美国底特律Green Light项目603台CCTV的效果。研究了WDQ算法对空间变化、尤其是对目标区域半径变化的敏感性。优化了匹配算法, 在评估过程中考虑了CCTV效果的空间异质性。结果表明, 超过50%的CCTV对减少犯罪具有扩散效益(WDQ > 0), 基于WDQ算法的评估结果对空间变化具有高度敏感性。本研究为CCTV的后续评估、CCTV的安装和布局优化, 从而改善CCTV的效果, 提供了有益的见解。
La evaluación de la televisión de circuito cerrado (CCTV), como infraestructura diseñada para mejorar el entorno policivo, ha ganado atención generalizada. Sin embargo, el efecto de la CCTV está influido no solo por el tiempo de la instalación, sino también por las ubicaciones, lo cual imprime un reto al proceso de evaluar con precisión desde una dimensión sencilla. Los estudios existentes han analizado las diferencias en los resultados de la evaluación debidas al período de la instalación, aunque poco se ha dicho acerca de la sensibilidad de los resultados de la evaluación en los cambios espaciales. Además, los resultados de evaluación existentes raramente consideran los diferentes grados de influencia que la CCTV podría tener en diferentes ubicaciones. Para abocar estas cuestiones, usamos el algoritmo del cociente ponderado de desplazamiento (WDQ), para evaluar el efecto de 603 CCTV instaladas por el Proyecto de Green Light en Detroit, de 2016 a 2019. Esta evaluación examina la sensibilidad del algoritmo WDQ a los cambios espaciales, en particular cuando se altera el radio del área seleccionada. También optimizamos el algoritmo de coincidencia y consideramos la heterogeneidad espacial de los efectos de la CCTV en el proceso de evaluación. Los resultados muestran que más del 50 por ciento de las CCTV instaladas tienen una difusión de los beneficios por la reducción del crimen (WDQ > 0), y los resultados de la evaluación obtenidos usando el algoritmo WDQ son altamente sensibles a los cambios espaciales. Estos descubrimientos proveen valiosas perspectivas para subsiguientes evaluaciones de la CCTV y para optimizar la instalación de la CCTV y su diseño, para fortalecer su efecto.
Acknowledgments
We extend our sincere gratitude to the editor and the anonymous reviewers for their valuable comments. Additionally, we would like to express our heartfelt appreciation to all the participants who generously dedicated their time to this study.
Disclosure Statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Ruidun Chen
RUIDUN CHEN is a PhD Student in the School of Remote Sensing and Information Engineering at Wuhan University, Wuhan 430079, China. E-mail: [email protected]. He focuses on research on the geographic elements affecting crime and the evaluation of CCTV cameras.
Cong Fu
CONG FU is a Postdoctoral Fellow in the School of Remote Sensing and Information Engineering at Wuhan University, Wuhan 430079, China. E-mail: [email protected]. His research focuses on crime analysis, spatial optimization, and public health.
Shanhe Jiang
SHANHE JIANG is a Professor in the Department of Criminology and Criminal Justice at Wayne State University, Detroit, MI 48201. E-mail: [email protected]. His research interests include institutional and community corrections, comparative criminology and criminal justice, and criminology of place.
Minxuan Lan
MINXUAN LAN is an Assistant Professor in the Department of Geography and Planning at The University of Toledo, Toledo, OH 43606. E-mail: [email protected]. His research interests include geography of crime, public health, and big data.
Yanqing Xu
YANQING XU is a Professor in the School of Remote Sensing and Information Engineering at Wuhan University, Wuhan 430079, China. E-mail: [email protected]. Her research interests include GIScience, crime geography, and health geography.