Revealing the differences in bicycle theft and motorcycle theft: spatial patterns and contributing factors

There exists abundant literature on vehicle theft, but only a few studies focused on bicycle theft and motorcycle theft. This study aims to reveal and explain differences in spatial distributions of bicycle theft and motorcycle theft in ZG city, China. The key findings are as follows: (1) There are spatial disparities in the hotspots of bicycle theft and motorcycle theft. Bicycle theft hotspots predominantly cluster in the urban core of ZG city, while motorcycle theft hotspots are primarily concentrated in the suburban regions. (2) At the community level, car parks, Internet cafes, and subway stations have a significant positive impact on bicycle theft, while bus stops and shops have a significant positive impact on motorcycle theft. The residential area has significant positive impacts on both bicycle and motorcycle thefts. (3) The proportion of the low-educated has a significant deterrent effect on bicycle theft but a positive impact on motorcycle theft, while the proportion of low-income residents significantly increases motorcycle theft. The proportion of migrant population and residential land area within communities have a significant positive impact on both bicycle theft and motorcycle theft. (4) Surveillance cameras have a significant positive impact on motorcycle theft, but ambient population density has a significant deterring effect on motorcycle thefts. Neither of these two guardianship variables have significant impacts on bicycle thefts. The main theoretical contribution of this study is that it provided a comprehensive assessment on the contrasting spatial distributions between bicycle thefts and motorcycle thefts and on the contrasting contributing factors for the two thefts. These findings provide a scientific basis for effective crime prevention and urban governance. A uniform strategy would not be able to prevent and reduce both bicycle thefts and motorcycle thefts. Effective strategy should target the high concentration areas and intervene the specific contributing factors for each of the two thefts.