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The Influence of Community Areas, Neighborhood Clusters, and Street Segments on the Spatial Variability of Violent Crime in Chicago

Abstract

Objectives

The influence of three hierarchical units of analysis on the total spatial variability of violent crime incidents in Chicago is assessed. This analysis seeks to replicate a recent study that found street segments, rather than neighborhood units of analysis, accounted for the largest share of the total spatial variability of crime in The Hague, Netherlands (see Steenbeek and Weisburd J Quant Criminol. doi:10.1007/s10940-015-9276-3, 2015).

Methods

We analyze violent crime incidents reported to the police between 2001 and 2014. 359,786 incidents were geocoded to 41,926 street segments nested within 342 neighborhood clusters, in turn nested within 76 community areas in Chicago. Linear mixed models with random slopes of time were estimated to observe the variance uniquely attributed to each unit of analysis.

Results

Similar to Steenbeek and Weisburd, we find 56–65 % of the total variability in violent crime incidents can be attributed to street segments in Chicago. City-wide reductions in violence over the observation period coincide with increases in the spatial variability attributed to street segments and decreases in the variability attributed to both neighborhood units.

Conclusions

Our results suggest that scholars interested in understanding the spatial variation of crime across urban landscapes should be focused on the small places that comprise larger geographic areas. The next wave of “neighborhood-effects” research should explore the role of hierarchical processes in understanding crime variation within larger areas.

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Notes

  1. U.S. Census Quickfacts; http://quickfacts.census.gov/qfd/states/17/1714000.html.

  2. Incident report data was accessed from the data portal in February 2015; https://data.cityofchicago.org/.

  3. See “Appendix 1” for a discussion of the geocoding process.

  4. These 456,060 violent crime incidents represented only 8.0 % of the total crime incidents reported by the Chicago Police Department over our 14-year observation period. 51.6 % of the total violent crime incidents were aggravated assaults, 46.9 % were robberies, and only 1.5 % were homicides. Steenbeek and Weisburd identified 406,683 total crime incidents in The Hague from 2001 to 2009.

  5. Community area boundary files were accessed through the Chicago Data Portal. PHDCN data is publicly available but due to its confidentiality a data access proposal must be submitted to ICPSR. Proposals are submitted at: http://www.icpsr.umich.edu/icpsrweb/PHDCN/. Professor Robert Sampson generously provided the neighborhood cluster boundary files after our research team received approval from ICPSR (see Earls et al. 2007).

  6. A street map was obtained from Chicago’s data portal and then was transformed to a street segment map using ArcGIS version 10.3. As noted in previous research (Weisburd et al. 2014) verifying the validity of spatial units raises some challenges. Specifically, GIS base maps (e.g. street networks) are typically drawn in a manner that reflects city zoning patterns and block level address ranges. This means that many street segments in a street network may not be “true street segments.” In particular, certain street segments (the area between two intersections) may be represented by multiple lines. If left as is, the database would reflect multiple “street segments” where there was only truly a single street segment. To correct such errors, researchers visually reviewed each street unit within ArcGIS (using aerial imagery base maps) to ensure their accuracy, combining separate street segments into single units when necessary. Via this process, the original file of 55,747 street segments was converted to a final dataset comprising 51,650 street segments.

  7. Violent crime incidents occurring on these segments were rare and primarily recorded by the Illinois State Police, which patrols these locations instead of the CPD. As a result, almost all of these incidents did not appear in the Chicago data portal.

  8. 6117 street segments were excluded because they shared boundaries (95.6 % of 6400) and only 283 (4.4 %) were excluded because they crossed boundaries. These street segments were considered in sensitivity analyses to test the robustness of findings. Steenbeek and Weisburd (2015) divided the cross-neighborhood segments into two new segments. This analysis does not utilize this strategy because the number of these segments is negligible and this strategy would devalue the street segments as a stand-alone unit of analysis (i.e. favoring neighborhoods over segments).

  9. 51,650 street segments were initially identified before excluding 9724 thus creating a sample of 81.2 % of the original population. Even without this consideration, identifying the 51,650 street segments as a “population” is to some degree an arbitrary decision since these data could always be conceptualized to belong to a super-population (see Hartley and Sielken, 1975). In other words, the street segments in Chicago could be argued to be a sample of the street segments in Illinois or the violent crime incidents from 2001 to 2014 could be argued to be a sample of 14 years from the 179 years Chicago has been incorporated as a city.

  10. Indeed, the bootstrapping procedure on the Chicago data took approximately 3 days to return estimates from all 500 models. In comparison, a single model yielded estimates in approximately 1 h.

  11. This dependent variable was constructed by adding one to each observation and then taking the natural logarithm of the values: log (raw violent crime count + 1).

  12. Another approach would be to treat the dependent variable as count data and estimate generalized linear mixed models (GLMMs) that use a log link function and the probability mass function for the Poisson distribution. However, estimating a four-level GLMM model using the Chicago dataset presents a formidable computational challenge. Using Stata 14.1, for example, this model would not converge after running for three days. Additionally, as suggested by Steenbeek and Weisburd (2015), GLMMs are limited by a number of considerations for this kind of analysis that make interpretation of results much more complicated. For instance, while a simulation approach has been proposed as a solution (Browne et al. 2005), a disadvantage of GLMM is that the level-1 variance depends on the expected value and is therefore not reported by the Stata 14.1 software used in our analysis (and most other statistical packages). What is more, it was unlikely that our sampling distributions of the parameters were multivariate normal given the relatively small number of units per level in our models. This problem could be addressed by approximating the confidence intervals around our point estimates via parametric boot- strap methods (Efron, 1979). Fortunately, the log transformation of the violent crime measure yielded satisfactory residual diagnostics which allowed for the continued use of straightforward LMMs.

  13. See “Appendix 5” for an illustration of the nested distribution of violent crime incidents at these three levels of analysis in two community areas.

  14. Using the Fisher–Jenks algorithm to divide the distribution into two groups is by no means the most comprehensive strategy to summarize descriptive patterns of crime incidents over time at street segments (see Weisburd et al. 2004; Braga et al. 2010; Curman et al. 2015) but it does provide a preliminary tool to begin to illustrate differences in the spatial distribution of violent crime incidents. Coincidentally these two “low” and “high” groups represent an average of 0–1.9 incidents per year (i.e. 0–27 total incidents; Group 1) and an average 2+ incidents per year (i.e. 28–443 total incidents; Group 2) across the 14 year observation period.

  15. Over the entire study period 75.0 % of street segments experienced at least one violent crime incident.

  16. Lorenz curves for each individual year within the observation period offer similar results; street segments display a slight increase in degree of concentration compared to neighborhood clusters and community areas since neighborhood clusters and community already have practically 100 % of units having an incident (i.e. can’t increase) while street segments only have room to increase over time.

  17. Figure 5. displays an inverted Lorenz curve that is best suited to visualize the spatial distribution of crime incidents while Gini coefficients are calculated on Lorenz curves facing the opposite direction. This does not influence the calculation of the Gini coefficient.

  18. The level-1 residual variance for these LMMs captures the “variance of time that can be attributed to time-varying explanations” (Steenbeek and Weisburd, 2015, pp. 14). This estimate from the final model was observed to be .120.

  19. The point estimate for each variance component was used to calculate the proportions shown in Fig. 7. As each variance represents an estimated parameter in the LMM, there is a 95 % probability that the ±1.96 SD confidence interval around this point estimate captures the true population mean.

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Appendices

Appendix 1: Geocoding Violent Crime Incidents in Chicago

Incident reports in Chicago’s data portal are not attributed to specific addresses but are listed to 100 blocks of addresses. For example, all incidents occurring on the 6600 address block of Halsted Street are recorded as “66XX Halstead Street” in the data portal. While the incident locations are aggregated to the 100 block, the geographic X–Y coordinates for each incident correspond to a specific address, not a shared center point in the 100 block. Map 1 displays the 38 incidents recorded as “66XX Halstead Street” from 2012 to 2014. As illustrated, points fall at unique X–Y coordinates across street segments A and B within the 100 block. It should be noted that the process by which X–Y coordinates are recorded in Chicago’s data portal has been recently modified. Map 2 displays the identical incidents in Map 1 but with new X–Y coordinates that center the incidents on the corresponding street segment within each 100 block. As can be seen, these points are geocoded to several locations at the center of the street segments, rather than the precise street address. Incident reports for Map 1 were accessed in February 2015 while incidents for Map 2 were in May 2016. All incident reports assessed in this study were geocoded using the X–Y coordinates from Map 1 although for the purpose of this analysis both techniques would be appropriate.

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Appendix 2: Violent Crime Rates (count per sq. mile) in Chicago per Community Area (2001, 2008, 2014)

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Appendix 3: Violent Crime Rates (count per sq. mile) in Chicago per Neighborhood Cluster (2001, 2008, 2014)

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Appendix 4: The Spatial Distribution of Violent Crime Incidents at Street Segments in Chicago (2001, 2008, 2014)

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Appendix 5: Variability of the Distribution of Violent Crime Incidents per Unit of Analysis in Two Community Areas in the Southside of Chicago, 2001-2014

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Schnell, C., Braga, A.A. & Piza, E.L. The Influence of Community Areas, Neighborhood Clusters, and Street Segments on the Spatial Variability of Violent Crime in Chicago. J Quant Criminol 33, 469–496 (2017). https://doi.org/10.1007/s10940-016-9313-x

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Keywords

  • Street Segment
  • Crime and place
  • Law of crime concentration
  • Chicago
  • Neighborhoods