Skip to main content
SpringerLink
Log in

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

  • Original Paper
  • Published:
Journal of Quantitative Criminology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

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.

References

  • Agnew R (1992) Foundation for a general strain theory of crime and delinquency. Criminology 30:47–87

    Article  Google Scholar 

  • Agnew R (1999) A general strain theory of community differences in crime rates. J Res Crime Delinq 36:123–155

    Article  Google Scholar 

  • Andresen MA, Malleson N (2011) Testing the stability of crime patterns: implications for theory and policy. J Res Crime Delinq 48:58–82

    Article  Google Scholar 

  • Bellair PE (2000) Informal surveillance and street crime: a complex relationship. Criminology 38:137–170

    Article  Google Scholar 

  • Bernasco W, Block R (2011) Robberies in Chicago: a block-level analysis of the influence of crime generators, crime attractors, and offender anchor points. J Res Crime Delinq 48:33–57

    Article  Google Scholar 

  • Black D (1970) The production of crime rates. Am Soc Rev 35:733–748

    Article  Google Scholar 

  • Block LR, Block CB (1995) Space, place, and crime: Hot spot areas and hot places of liquor-related crime. In: Eck J, Weisburd D (eds) Crime and place: crime prevention studies, vol 4. Criminal Justice Press, Monsey, pp 145–183

    Google Scholar 

  • Boessen A, Hipp J (2015) Close-ups and the scale of ecology: land uses and the geography of social context and crime. Criminology 53:399–426

    Article  Google Scholar 

  • Bowers K (2014) Risky facilities: crime radiators or crime absorbers? A comparison of internal and external levels of theft. J Quant Criminol 30:389–414

    Article  Google Scholar 

  • Braga AA, Clarke RV (2014) Explaining high-risk concentrations of crime in the city: social disorganization, crime opportunities, and important next steps. J Res Crime Delinq 51:480–498

    Article  Google Scholar 

  • Braga AA, Papachristos AV, Hureau DM (2010) The concentration and stability of gun violence at micro places in Boston, 1980–2008. J Quant Criminol 26:33–53

    Article  Google Scholar 

  • Braga AA, Hureau DM, Papachristos AV (2011) The relevance of micro places to citywide robbery trends: a longitudinal analysis of robbery incidents at street corners and block faces in Boston. J Res Crime Delinq 48:7–32

    Article  Google Scholar 

  • Braga AA, Papachristos AV, Hureau DM (2014) The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Justice Q 31:633–663

    Article  Google Scholar 

  • Brantingham PJ, Brantingham PL (1981) Environmental criminology. Sage Publications, Thousand Oaks

    Google Scholar 

  • Brantingham PJ, Brantingham PL (1993) Environment, routine and situation: toward a pattern theory of crime. In: Clarke R, Felson M (eds) Routine activity and rational choice. Transaction, New Brunswick, pp 259–294

    Google Scholar 

  • Browne WJ, Subramanian SV, Jones K, Goldstein H (2005) Variance partitioning in multilevel logistic models that exhibit overdispersion. J R Stat Soc 168:599–613

    Article  Google Scholar 

  • Burgess EW (1925) The growth of the city: an introduction to a research project. In: Park RE, Burgess EW (eds) The city: suggestions for the investigation of human behavior in the urban environment. The University of Chicago Press, Chicago, pp 47–62

    Google Scholar 

  • Bursik RJ, Grasmick HG (1993) Neighborhoods and crime: the dimensions of effective community control. Lexington Books, Lanham

    Google Scholar 

  • Bursik RJ, Webb J (1982) Community change and patterns of delinquency. Am J Soc 88:24–42

    Article  Google Scholar 

  • Chicago Fact Book Consortium (1990) Local community fact book: Chicago metropolitan area. Academy Chicago Publishers, Chicago

    Google Scholar 

  • Clarke RV (1995) Situational crime prevention. In: Tonry M, Farrington D (eds) Building a safer society: strategic approaches to crime prevention. The University of Chicago Press, Chicago

    Google Scholar 

  • Cloward RA, Ohlin LE (1960) Delinquency and opportunity: a study of delinquent gangs. The Free Press, Glencoe

    Google Scholar 

  • Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Soc Rev 44:588–605

    Article  Google Scholar 

  • Cornish DB, Clarke RV (1986) The reasoning criminal. Springer, New York

    Book  Google Scholar 

  • Curman ASN, Andresen MA, Brantingham PJ (2015) Crime and place: a longitudinal examination of street segment patterns in Vancouver, BC. J Quant Criminol 31:127–147

    Article  Google Scholar 

  • Deryol R, Wilcox P, Logan M, Wooldredge J (2016) Crime places in context: an illustration of the multi-level nature of hot spot development. J Quant Criminol. doi:10.1007/s10940-015-9278-1

    Google Scholar 

  • Earls F, Buka SL (1997) Project on human development in Chicago neighborhoods. National Institute of Justice, Washington, DC

    Google Scholar 

  • Earls FJ, Brooks-Gunn J, Raudenbush SW, Sampson RJ (2007) Project on human development in chicago neighborhoods: community survey, 1994–1995. ICPSR02766-v3. Inter-university consortium for political and social research [distributor], Ann Arbor. 2007-10-29. 10.3886/ICPSR02766.v3

  • Eck JE, Eck EB (2012) Crime place and pollution: expanding crime reduction options through a regulatory approach. Criminol Public Policy 11:281–316

    Article  Google Scholar 

  • Eck JE, Weisburd D (1995) Crime places in crime theory. In: Eck J, Weisburd D (eds) Crime and place: crime prevention studies, vol 4. Criminal Justice Press, Monsey, pp 1–34

    Google Scholar 

  • Eck JE, Gersh JS, Taylor C (2000) Finding crime hot spots through repeat address mapping. In: Goldsmith V, McGuire PG, Mollenkopf JH, Ross TA (eds) Analyzing crime patterns: frontiers of practice. Sage Publications, Thousand Oaks, pp 49–64

    Chapter  Google Scholar 

  • Efron B (1979) Bootstrap methods: another look at the jackknife. Anna Stat 7:1–26

    Article  Google Scholar 

  • Fisher WD (1958) On grouping for maximum homogeneity. J Am Stat Assoc 53:789–798

    Article  Google Scholar 

  • Gastwirth JL (1972) The estimation of the Lorenz curve and Gini index. Rev Econ Stat 54:306–316

    Article  Google Scholar 

  • Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, New York

    Google Scholar 

  • Grannis R (1998) The importance of trivial streets: residential streets and residential segregation. Am J Soc 103:1530–1564

    Article  Google Scholar 

  • Groff E, Weisburd D, Morris NA (2009) Where the action is at places: examining spatio-temporal patterns of juvenile crime at places using trajectory analysis and GIS. In: Weisburd D, Bernasco W, Bruinsma G (eds) Putting crime in its place: units of analysis in geographic criminology. Springer, NewYork, pp 61–86

    Chapter  Google Scholar 

  • Groff ER, Weisburd D, Yang SM (2010) Is it important to examine crime trends at a local “micro” level?: a longitudinal analysis of street to street variability in crime trajectories. J Quant Criminol 26:7–32

    Article  Google Scholar 

  • Hartley HO, Sielken RL (1975) A “super-population viewpoint” for finite population sampling. Biometrics 31:411–422

    Article  Google Scholar 

  • Hipp J, Boessen A (2015) Neighborhoods, networks, and crime. In: Cullen F, Wilcox P, Sampson R, Dooley B (eds) Challenging criminological theory: the legacy of Ruth Kornhauser. Transaction, New Brunswick, pp 275–300

    Google Scholar 

  • Hunter A (1974) Symbolic communities: the persistence and change of Chicago’s local communities. The University of Chicago Press, Chicago

    Google Scholar 

  • Hunter A (1985) Private, parochial, and public social orders: the problem of crime and incivility in urban communities. In: Suttles G, Zald M (eds) The challenge of social control: citizenship and institution building in modern society. Ablex, Norwood, pp 230–242

    Google Scholar 

  • Johnson SD (2010) A brief history of the analysis of crime concentration. Eur J Appl Math 21:349–370

    Article  Google Scholar 

  • Johnson S, Bowers K, Birks D, Pease K (2009) Predictive mapping of crime by ProMap: accuracy, units of analysis, and the environmental backcloth. In: Weisburd D, Bernasco W, Bruinsma G (eds) Putting crime in its place: units of analysis in geographic criminology. Springer, New York, pp 179–198

    Google Scholar 

  • Kirk DS, Papachristos AV (2011) Cultural mechanisms and the persistence of neighborhood violence. Am J Soc 116:1190–1233

    Article  Google Scholar 

  • Kuhn TS (1962) The structure of scientific revolutions. The University of Chicago Press, Chicago

    Google Scholar 

  • Laub JH (2004) The life course of criminology in the United States: the American Society of Criminology 2003 Presidential Address. Criminology 42:1–26

    Article  Google Scholar 

  • Lorenz MO (1905) Methods of measuring the concentration of wealth. Publ Am Stat Assoc 9:209–219

    Google Scholar 

  • Mears D, Bhati D (2006) No community is an island: the effects of resource deprivation on urban violence in spatially and socially proximate communities. Criminology 44:509–548

    Article  Google Scholar 

  • Merton RK (1938) Social structure and anomie. Am Soc Rev 3:672–682

    Article  Google Scholar 

  • Miethe TD, Meier RF (1994) Crime and its social context: toward an integrated theory of offenders, victims, and situations. State University of New York Press, Albany

    Google Scholar 

  • Morenoff JD, Sampson RJ, Raudenbush SW (2001) Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology 39:517–559

    Article  Google Scholar 

  • Oberwittler D, Wikstrom P (2009) Why small is better: Advancing the study of the role of behavioral contexts in crime causation. In: Weisburd D, Bernasco W, Bruinsma G (eds) Putting crime in its place: units of analysis in geographic criminology. Springer, New York, pp 35–59

    Chapter  Google Scholar 

  • Park R (1915) Suggestions for the investigations of human behavior in the urban environment. Am J Soc 20:577–612

    Article  Google Scholar 

  • Peterson RD, Krivo LJ (2010) divergent social worlds: neighborhood crime and the racial-spatial divide. Russell Sage Foundation, New York

    Google Scholar 

  • Pierce GL, Spaar S, Briggs LR (1988) The character of police work: strategic and tactical implications. Northeastern University Center for Applied Social Research, Boston

    Google Scholar 

  • Rabe-Hesketh S, Skrondal A (2012) Multilevel and longitudinal modeling using stata volume I: continuous responses, 3rd edn. Stata Press, College Station

    Google Scholar 

  • Raudenbush SW, Bryk AS (2002) Hierarhical linear models: applications and data analysis methods, 2nd edn. Sage Publications, Thousand Oaks

    Google Scholar 

  • Rengert G, Chakravorty S, Bole T, Henderson K (2000) A geographic analysis of illegal drug markets. Crim Prev Stud 11:219–240

    Google Scholar 

  • Rice KJ, Smith WRC (2002) Socioecological models of automotive theft: integrating routine activity and social disorganization approaches. J Res Crime Delinq 39:304–336

    Article  Google Scholar 

  • Robinson W (1950) Ecological correlations and the behavior of individuals. Am Soc Rev 15:351–357

    Article  Google Scholar 

  • Sampson RJ (2011) The community. In: Wilson JQ, Petersilia J (eds) Crime and public policy. Oxford University Press, New York, pp 210–236

    Google Scholar 

  • Sampson RJ (2012) Great American city: chicago and the enduring neighborhood effect. The University of Chicago Press, Chicago

    Book  Google Scholar 

  • Sampson RJ (2013) The place of context: a theory and strategy for criminology’s hard problems. Criminology 51:1–31

    Article  Google Scholar 

  • Sampson RJ, Groves WB (1989) Community structure and crime: testing social-disorganization theory. Am J Soc 94:774–802

    Article  Google Scholar 

  • Sampson R, Raudenbush S (1999) Systematic social observation of public spaces: a new look at disorder in urban neighborhoods. Am J Soc 105:603–651

    Article  Google Scholar 

  • Sampson RJ, Wilson WJ (1995) Toward a theory of race, crime, and urban inequality. In: Hagan J, Peterson RD (eds) Crime and inequality. Stanford University Press, Stanford, pp 37–56

    Google Scholar 

  • Sampson R, Raudenbush S, Earls F (1997) Neighborhoods and violent crime: a multi-level study of collective efficacy. Science 277:918–924

    Article  Google Scholar 

  • Sampson RJ, Morenoff JD, Earls F (1999) Beyond social capital: spatial dynamics of collective efficacy for children. Am Soc Rev 64:633–666

    Article  Google Scholar 

  • Sampson RJ, Morenoff JD, Gannon-Rowley T (2002) Assessing “neighborhood effects” social processes and new directions in research. Annu Rev Soc 28:443–478

    Article  Google Scholar 

  • Schneider VW, Wiersema B (1990) Limits and use of uniform crime reports. In: MacKenzie PJ, Roberg RR (eds) Measuring crime. State University of New York Press, Albany, pp 21–48

    Google Scholar 

  • Shaw C, McKay H (1942) Juvenile delinquency and urban areas. The University of Chicago Press, Chicago

    Google Scholar 

  • Shaw CR, Zorbaugh FM, McKay HD, Cottrell LS (1929) Delinquency Areas: a study of the geographic distribution of school truants, juvenile delinquents, and adult offenders in Chicago. The University of Chicago Press, Chicago

    Google Scholar 

  • Sherman LW, Gartin PR, Buerger ME (1989) Hot spots of predatory crime: routine activities and the criminology of place. Criminology 27:27–56

    Article  Google Scholar 

  • Simcha-Fagan O, Schwartz JE (1986) Neighborhood and delinquency: an assessment of contextual effects. Criminology 24:667–699

    Article  Google Scholar 

  • Slocum TA, McMaster RB, Kessler FC, Howard HH (2005) Thematic cartography and geographic visualization. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Spelman W (1995) Criminal careers of public places. In: Eck J, Weisburd D (eds) Crime and place: crime prevention studies, vol 4. Criminal Justice Press, Monsey, pp 115–144

    Google Scholar 

  • St. Jean PKB (2007) Pockets of crime: broken windows, collective efficacy, and the criminal point of view. The University of Chicago Press, Chicago

    Book  Google Scholar 

  • Steenbeek W, Weisburd D (2015) Where the action is in crime? An examination of variability of crime across spatial units in The Hague, 2001–2009. J Quant Criminol. doi:10.1007/s10940-015-9276-3

    Google Scholar 

  • Suttles GD (1972) The social construction of communities. The University of Chicago Press, Chicago

    Google Scholar 

  • Taylor RB (1997) Social order and disorder of street blocks and neighborhoods: ecology, microecology and the systemic model of social disorganization. J Res Crime Delinq 34:113–155

    Article  Google Scholar 

  • Taylor RB (2015) Community criminology: fundamentals of spatial and temporal scaling, ecological indicators, and selectivity bias. New York University Press, New York

    Book  Google Scholar 

  • Weisburd D (2015) The law of crime concentration and the criminology of place. Criminology 53:133–157

    Article  Google Scholar 

  • Weisburd D, Amram S (2014) The law of concentration of crime at place: the case of Tel Aviv-Jaffa. Police Pract Res Int J 15:101–114

    Article  Google Scholar 

  • Weisburd D, Bushway S, Lum C, Yang S (2004) Trajectories of crime at places: a longitudinal study of street segments in the city of Seattle. Criminology 42:283–322

    Article  Google Scholar 

  • Weisburd D, Bernasco W, Bruinsma G (2009) Putting crime in its place: units of analysis in geographic criminology. Springer, New York

    Book  Google Scholar 

  • Weisburd D, Groff ER, Yang S (2012) The criminology of place: street segments and our understanding of the crime problem. Oxford University Press, New York

    Book  Google Scholar 

  • Weisburd D, Telep C, Lawton B (2014) Could innovations in policing have contributed to the New York City crime drop even in a period of declining police strength?: The case of stop, question and frisk as a hot spots policing strategy. Justice Q 31:129–153

    Article  Google Scholar 

  • Wheeler AP, Worden RE, McLean SJ (2015) Replicating group-based trajectory models of crime at micro-places in Albany, NY. J Quant Criminol. doi:10.1007/s10940-015-9268-3

    Google Scholar 

  • Wilcox P, Land KC (2015) Social disorganization and criminal opportunity. In: Cullen F, Wilcox P, Sampson R, Dooley B (eds) Challenging criminological theory: the legacy of Ruth Kornhauser. Transaction, New Brunswick, pp 237–258

    Google Scholar 

  • Wirth L, Bernert EH (1949) Local community fact book of Chicago. The University of Chicago Press, Chicago

    Google Scholar 

  • Wolfgang ME, Ferracuti F (1967) Subculture of violence: towards an integrated theory in criminology. Sage Publications, Thousand Oaks

    Google Scholar 

  • Zorbaugh HW (1929) The gold coast and the slum: a sociological study of chicago’s near north side. University of Chicago Press, Chicago

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Criminal Justice, Rutgers University, 123 Washington Street, Newark, NJ, 07102, USA

    Cory Schnell

  2. School of Criminology and Criminal Justice, Northeastern University, Boston, MA, USA

    Anthony A. Braga

  3. John Jay College of Criminal Justice, City University of New York, New York, NY, USA

    Eric L. Piza

Authors
  1. Cory Schnell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anthony A. Braga
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eric L. Piza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cory Schnell.

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.

figure a

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

figure 9

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

figure 10

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

figure 11

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

figure 12

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-016-9313-x

Keywords

Search

Navigation