- Housing Discrimination Today
- Volume 17, Number 3
- Managing Editor: Mark D. Shroder
- Associate Editor: Michelle P. Matuga
SpAM: Predicting Local Crime Clusters Using (Multinomial) Logistic Regression
Martin A. Andresen
Simon Fraser University
SpAM (Spatial Analysis and Methods) presents short articles on the use of spatial statistical techniques for housing or urban development research. Through this department of Cityscape, the Office of Policy Development and Research introduces readers to the use of emerging spatial data analysis methods or techniques for measuring geographic relationships in research data. Researchers increasingly use these new techniques to enhance their understanding of urban patterns but often do not have access to short demonstration articles for applied guidance. If you have an idea for an article of no more than 3,000 words presenting an applied spatial data analysis method or technique, please send a one-paragraph abstract to email@example.com for review.
Understanding hot spots of crime has been a concern of spatial criminology for nearly 200 years. A number of methods are used to identify and calculate hot spots, such as dot maps, surface interpolation (kernel density estimation), and statistically identified cluster analysis. Relating to the latter set of methods, local Moran’s I is one of the more commonly used methods for identifying local crime clusters. One important aspect for this method for subsequent analysis is that it uses areas, such as census boundary units, to identify local clusters of crime. Consequently, census data may be used to predict and better understand these local crime clusters. In this article, I use multinomial logistic regression and census variables to predict the local crime clusters identified by local Moran’s I. This analysis shows a number of nuances regarding local crime clusters, and the spatial patterns of crime, more generally, can be identified using this two-stage approach. Such an approach provides a better understanding of spatial crime patterns than the more common regression methods.
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