The primary result of this analysis is that the results for estimating local crime clusters for both low-crime communities surrounded by high-crime communities ("low-high") and high-crime communities surrounded by low-crime communities ("high-low") were more similar to high-crime communities surrounded by high-crime communities ("high-high") than low-crime communities surrounded by low-crime communities ("low-low"). The statistical results for estimating the probabilities of low crime areas surrounded by low crime areas ("low-low") were generally the most consistent with the theoretical expectations of social disorganization theory and routine-activity theory. Thus, "low-low" spatial arrangements for communities decreased the likelihood of social disorganization and opportunities for crime to occur. A high-crime community surrounded by high-crime communities ("high-high") were found to be the least consistent with the expectations of social disorganization theory and routine-activity theory in predicting local crime clusters. Further research is needed in order to understand why this is the case. This study also discusses why the characteristics of immediate spatial neighbors impact the nature of local crime areas. Local indicators of spatial association were used to identify local crime clusters. The classification scheme of these local crime clusters was then modeled in a multinomial logistic regression. Variables were measured with crime data and census data. 7 tables, 6 figures, and 61 references