The Impact of “Strike Hard” on Repeat and Near-Repeat Residential Burglary in Beijing

“Strike Hard” is an enhanced law-enforcement strategy in China that aims to suppress crime, but measurement of the crime-reducing effect and potential changes in the spatiotemporal concentration of crime associated with “Strike Hard” remain unknown. This paper seeks to examine the impact, if any, of “Strike Hard” on the spatiotemporal clustering of burglary incidents. Two and half years of residential burglary incidents from Chaoyang, Beijing are used to examine repeat and near-repeat burglary incidents before, during, and after the “Strike Hard” intervention and a new technique that enables the comparison of repeat and near repeat patterns across different temporal periods is introduced to achieve this. The results demonstrate the intervention disrupted the repeat pattern during the “Strike Hard” period reducing the observed ratio of single-day repeat burglaries by 155%; however, these same single-day repeat burglary events increased by 41% after the cessation of the intervention. Findings with respect to near repeats are less remarkable with nominal evidence to support that the intervention produced a significant decrease, but coupled with other results, suggest that spatiotemporal displacement may have been an undesired by-product of “Strike Hard”. This study from a non-Western setting provides further evidence of the generalizability of findings related to repeat and near repeat patterns of burglary and further highlights the limited preventative effect that the “Strike Hard” enhanced law enforcement campaign had on burglary.

Near-repeat burglary patterns in Malmö: Stability and change over time

It is well established that previous crime events are valuable indicators for the prediction of future crime. Near-repeat burglaries are incidents that occur in close proximity in space and time to an initial burglary. The current study analyses near-repeat victimization patterns in Malmö, Sweden’s third-largest city. The data, provided by the local police, cover a six-year time frame from 2009 to 2014. The complete dataset, as well as each year’s individual dataset, was analysed using Ratcliffe’s Near Repeat Calculator version 1.3. Results reveal significant near-repeat victimization patterns. For the full dataset, an observed/expected ratio of 2.83 was identified for the first week after an initial incident and an area of 100 metres surrounding the original burglary. Separate analyses of each individual year reveal both similarities and differences between years. Some years manifest near-repeat patterns at longer spatial and temporal distances, indicating a need for further studies on the variability of near repeats. Preventive strategies that include both private and public actors need to be intensified and focused on the first two weeks after a burglary.