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Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

by Hanlin Zhou 1, Lin Liu 1,2,*, Minxuan Lan 1, Bo Yang 3 and Zengli Wang 1,4
Department of Geography and GIS, University of Cincinnati, Cincinnati, OH 45221, USA
School of Geographical Sciences, Center of GeoInformatics for Public Security, Guangzhou University, Guangzhou 510006, China
Department of Sociology, University of Central Florida, Orlando, FL 32816, USA
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 1958;
Received: 25 July 2019 / Revised: 16 August 2019 / Accepted: 18 August 2019 / Published: 21 August 2019
(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)
Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight. View Full-Text
Keywords: crime; edges; nightlight satellite data; NPP-VIIRS crime; edges; nightlight satellite data; NPP-VIIRS
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MDPI and ACS Style

Zhou, H.; Liu, L.; Lan, M.; Yang, B.; Wang, Z. Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA. Remote Sens. 2019, 11, 1958.

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