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Spatiotemporal Crime Patterns Across Six U.S. Cities: Analyzing Stability and Change in Clusters and Outliers

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Abstract

Objectives

Examine the degree of crime concentration at micro-places across six large cities, the spatial clustering of high and low crime micro-places within cities, the presence of outliers within those clusters, and extent to which there is stability and change in micro-place classification over time.

Methods

Using crime incident data gathered from six U.S. municipal police departments (Chicago, Los Angeles, New York City, Philadelphia, San Antonio, and Seattle) and aggregated to the street segment, Local Moran’s I is calculated to identify statistically significant high and low crime clusters across each city and outliers within those clusters that differ significantly from their local spatial neighbors.

Results

Within cities, the proportion of segments that are like their neighbors and fall within a statistically significant high or low crime cluster are relatively stable over time. For all cities, the largest proportion of street segments fell into the same classification over time (47.5% to 69.3%); changing segments were less common (4.7% to 20.5%). Changing clusters (i.e., segments that fell into both low and high clusters during the study) were rare. Outliers in each city reveal statistically significant street-to-street variability.

Conclusions

The findings revealed similarities across cities, including considerable stability over time in segment classification. There were also cross-city differences that warrant further investigation, such as varying levels of spatial clustering. Understanding stable and changing clusters and outliers offers an opportunity for future research to explore the mechanisms that shape a city’s spatiotemporal crime patterns to inform strategic resource allocation at smaller spatial scales.

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Notes

  1. If a segment had addresses with ranges greater than 100, Weisburd et al. (2004) created multiple segments defined by “hundred blocks.” For example, they created three segments for a street with house addresses ranging from 1 to 299.

  2. The City of San Antonio was unable to provide the crime incident data for 2017 and 2018 because data sharing is under review with the Texas Office of the Attorney General.

  3. Crime incidents that occurred at intersections were assigned to the nearest street segment. Since this study analyzes total crime incidents and is interested in overall spatial distribution patterns of these incidents, the characteristics of crime incidents that occur at intersections are not different from those at street segments so there is no need to drop or analyze these crime events separately.

  4. Several single-city studies that use street segments as the units of analysis in the same cities report fewer street segments. Many of these studies are predictive analyses that use different methodological approaches and remove portions of the street network such as freeways and tunnels where crime is unlikely to occur. The present study is not predictive but rather examines overall spatial distribution patterns of crime. Removing areas of the street network is not necessary in this type of analysis since locations in the street network where crime is infrequent is accurately revealed in the spatial distribution results. Changing the underlying spatial structure would incorrectly assign some crime incidents and change the neighbors identified for each street segment in the Local Moran’s I analyses, which could lead to incorrect street segment classification.

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Acknowledgements

This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 17STCIN00001-05-00. Disclaimer: The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.

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Authors and Affiliations

  1. Runstad Department of Real Estate, College of Built Environments, University of Washington, 3950 University Way, Gould Hall Box 355727, Seattle, WA, 98195, USA

    Rebecca J. Walter

  2. Department of Criminology and Criminal Justice, University of Texas at San Antonio, 501 W. Cesar E. Chavez Blvd, San Antonio, TX, 78207, USA

    Marie Skubak Tillyer

  3. Runstad Department of Real Estate, College of Built Environments, University of Washington, Seattle, WA, USA

    Arthur Acolin

Authors
  1. Rebecca J. Walter
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  2. Marie Skubak Tillyer
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  3. Arthur Acolin
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Correspondence to Rebecca J. Walter.

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Walter, R.J., Tillyer, M.S. & Acolin, A. Spatiotemporal Crime Patterns Across Six U.S. Cities: Analyzing Stability and Change in Clusters and Outliers. J Quant Criminol 39, 951–974 (2023). https://doi.org/10.1007/s10940-022-09556-7

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