Street crime prediction model based on the physical characteristics of a streetscape: Analysis of streets in low-rise housing areas in South Korea

Abstract

Previous crime prediction research focusing on regional characteristics is lacking in terms of the examination of physical characteristics of individual crime scenes. This study, therefore, presents a street crime prediction model by analysing streetscape features within an actual field of vision for a low-rise housing area in South Korea, which serves as a gauge for potential offenders to carry out crime. First, we performed logistic regression to analyse the correlation between street crime opportunities and the elements of streets to derive an equation for predicting street crime using selected variables. Next, we created a crime prediction map based on a geographic information system that contains attribute data on these physical characteristics and presented a street crime prediction model based on the derived prediction equation. Finally, to test the prediction model, we compared actual crime data from the selected area with the results obtained from the prediction model. The test results showed that the prediction model classified 11 out of 29 actual crime spots as crime occurrence; among the 312 non-crime spots, 257 were classified as non-crime occurrence. Based on these test results, we confirm that the occurrence of street crime is affected by the physical characteristics within the actual field of vision and discuss the improvement of the prediction model.

Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities

“…As the context of nearby cores is similar, corehoods might overlap. The idea of using overlapping units is not new 16 , 44 , 45 , and it is focused on creating an ego-centric neighborhood for each core (see Supplementary Information (SI) Note 11 for a technical discussion). We describe the characteristics of the place where crime happens through specific features of the core , while we describe the context at which it is embedded through the features at the corehood .…”

“…We geo-reference crimes to cores and, when a crime event happens in a street segment shared between cores, we evenly assign the event to both cores. Due to the limit in accuracy of GPS positioning, we create a buffer of 30 meters for each crime, which is the distance usually employed for stop location detection algorithms 78 and criminology literature at micro-places 21 , 44 , 76 . We have no reason to suspect that the effect of the crime events stops at distances lower than 30 meters (e.g.…”

Grid-Based Crime Prediction Using Geographical Features

“…Geographic features can include crime-related locations, including bars, KTVs, and gang territories, along with crime-inhibiting features such as streetlights, CCTV cameras, police stations, and neighborhood watch. Environmental features include weather and temperature, or physical characteristics such as Natural Surveillance [53]. In addition, displacement features like police actions, controls, and buffer zones can be considered to simulate displacement effects more precisely [54].…”

Developing Machine Learning Based Predictive Models for Smart Policing

“…Crime investigation, for example, illegal practices in the particular dimension and spatial-temporal models [11], [12], [13], [14] have been widely contemplated in recent years. Conventional crime expectation techniques incorporate grid mapping, covering ellipses, and kernel density estimation; delivering expectations dependent on the absence of uniform offense circulation.…”