Point pattern analysis of nighttime residential burglary in relation to remotely sensed light intensity, socioeconomic context and road access

Authors: Zhiyong Hu*, University of West Florida, Yanyan Liu, University of West Florida, Jilin Hu, University of West Florida
Topics: Spatial Analysis & Modeling, Quantitative Methods, Remote Sensing
Keywords: crime analysis, burglary point pattern analysis, VIIRS, remote sensing, nighttime light
Session Type: Poster
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded

Existing studies of nighttime street light effect on crime has conflicting findings. This research presents a point pattern analysis of nighttime residential burglary in Los Angles in relation to remotely sensed VIIRS light, socioeconomic context and road access. Data used in this study include year 2010 to 2018 nighttime residential burglary locations, 2015 Visible Infrared Imaging Radiometer Suite (VIIRS) image data, and census 2015 data. Using R, a software environment for statistical computing and graphics, a series of point pattern analysis functions were run to examine to spatial pattern correlation, including exploratory spatial data analysis, intensity investigation, tests of complete spatial randomness, maximum likelihood for Poisson processes, distance methods, and a Gibbs model. The research found that nighttime light does not reduce burglary but burglary is more concentrated in areas with lower socio-economic-status and easier access to main roads.

Abstract Information

This abstract is already part of a session. View the session here.

To access contact information login