Spatial patterns of violent crimes and neighborhood characteristics in Changchun, China

Abstract

Crime is one of the major concerns facing Chinese cities. Using crime data compiled at police precinct level in 2008, this research examines spatial patterns of violent crimes in Changchun, and explores the relationship between the spatial distribution of violent crimes and neighborhood characteristics. Crime rates are applied as a measure of the intensity of violent crimes. Spatial statistics and geographic information systems are used to detect violent crime hot spots, or statistically significant locales of high violent crime rates in Changchun. A multiple linear regression model is calibrated to assess the impacts of contextual neighborhood characteristics on violent crimes. The analytical results demonstrate that the risk or intensity of violent crimes is strongly concentrated in the central city area, and neighborhood socioeconomic, demographic, especially land use characteristics are effective in accounting for the spatial variation in the distribution of violent crimes across the city of Changchun. These findings are largely in line with the routine activities theory, social disorganization theory, and the framework of crime prevention through environmental design, which emphasize the importance of opportunities, local social context, and environmental design in shaping the spatial pattern of and reducing urban crimes.

The concentration of urban crime in space by race: evidence from South Africa

“…The descriptive results of this work largely contradict these sentiments with most crime in Tshwane spatially concentrated in a relatively low number of neighborhoods. These findings reinforce the spatially skewed distributions of crime commonly experienced in countries outside the US including Bangladesh (Dewan, Haider & Amin, 2013); Ghana (Appiahene-Gyamfi, 2002); Israel (Weisburd & Amram, 2014); China (Liu, Song, & Xiu, 2016); and New Zealand (Breetzke, 2013). Moreover, the Pareto principle was largely affirmed in a South African context with most of the 23 categories of crime roughly approximating the 80/20 split.…”

The spatial concentration and stability of crime in a South African township

“…The results of this work largely contradict this with most crime in Khayelitsha spatially concentrated in a relatively low number of small areas despite the township as a whole being among the most violent in the country. These findings reinforce the spatially skewed distributions of crime commonly found in countries outside the US including Bangladesh (Dewan et al, 2013); India (Mazeika and Kumar, 2017); Ghana (Appiahene-Gyamfi, 2002); Israel (Weisburd and Amram, 2014); and China (Liu et al, 2016).…”

Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

“…Similar to the findings of studies conducted in Western countries, the distribution of crimes or hotspots in some Chinese cities displays hourly, daily, weekly and seasonal variation, and some factors that may have impacts on the spatial and temporal pattern of crime have also been verified [7][8][9][10]. The applicability of western criminological theories, such as social disorganization theory [11] and routine activities theory [12], have also been demonstrated by studies conducted in Chinese context [13][14][15]. Nevertheless, few of these studies have focused on long-term changes in crime patterns in China.…”

“…To further analyze and understand the spatial and temporal patterns of crime, regression models are often used to explore the relationships between crime and influential factors based on theories such as social disorganization theory and routine activities theory. The most common factors that may have impacts on crime risks are usually quantified by variables representing the socioeconomic, demographical and land use characteristics across different analytical units [1,2,13,43]. As the data (crime data or other relevant data) are usually available as counts for small areas (e.g., census tracts and neighborhoods), the modeling of crime needs to take into account spatial or spatiotemporal autocorrelation effects, which may result in biased parameter estimates if ignored [44].…”