Spatial Modeling in Neighborhood and Crime Studies: An Empirical Examination

We theoretically discuss why addressing spatial dependence is important and empirically demonstrate its methodological advantages in the context of neighborhood and crime studies. We found that as the uncertainty in measuring neighbors increases, the bias in the coefficient estimates increases. However, importantly, we also observed that even with a high rate of uncertainty in the spatial matrix, the bias is smaller than in the non-spatial models. Likewise, as the uncertainty in defining neighbors increases, models tend to underestimate the standard errors. However, even with higher uncertainty in the spatial weight matrix, underestimation seems smaller than that of the non-spatial model.