Estimating the probability of local crime clusters: The impact of immediate spatial neighbors

Purpose

To investigate the importance of immediate spatial neighbors when investigating local crime patterns.

Methods

Local indicators of spatial association are used to identify local crime clusters. The classification scheme of these local crime clusters is then modeled in a multinomial logistic regression.

Results

The results show that immediate spatial neighbors are important for understanding local crime patterns. Though (positive) spatial autocorrelation has long been known to be present with crime data, this analysis suggests that negative spatial autocorrelation (if present) has a significantly different implication. Generally speaking, when predicting a local crime cluster type, the immediate spatial neighbors are more important for correct prediction. As such, a low local crime area that is surrounded by high crime areas presents itself as a high crime area in the regression results.

Conclusions

Therefore, efforts to understand the criminal nature of an area must not consider that area in isolation.