Skip to main content

Advertisement

SpringerLink
Log in

How and Why is Crime More Concentrated in Some Neighborhoods than Others?: A New Dimension to Community Crime

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

Abstract

Objectives

Much recent work has focused on how crime concentrates on particular streets within communities. This is the first study to examine how such concentrations vary across the neighborhoods of a city. The analysis evaluates the extent to which neighborhoods have characteristic levels of crime concentration and then tests two hypotheses for explaining these variations: the compositional hypothesis, which posits that neighborhoods whose streets vary in land usage or demographics have corresponding disparities in levels of crime; and the social control hypothesis, which posits that neighborhoods with higher levels of collective efficacy limit crime to fewer streets.

Methods

We used 911 dispatches from Boston, MA, to map violent crimes across the streets of the city. For each census tract we calculated the concentration of crime across the streets therein using the generalized Gini coefficient and cross-time stability in the locations of hotspots.

Results

Neighborhoods did have characteristic levels of concentration that were best explained by the compositional hypothesis in the form of demographic and land use diversity. In addition, ethnic heterogeneity predicted higher concentrations of crime over and above what would be expected given the characteristics of the individual streets, suggesting it exacerbated disparities in crime.

Conclusions

The extent to which crime concentrates represents an underexamined aspect of how crime manifests in each community. It is driven in part by the diversity of places in the neighborhood, but also can be influenced by neighborhood-level processes. Future work should continue to probe the sources and consequences of these variations.

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

Similar content being viewed by others

Notes

  1. Formally calculated as the area between the line of equality and the Lorenz curve divided by the total area under the line of equality. This is also equal to twice the area between the line of equality and the Lorenz curve as the line of equality delineates a right triangle on a set of axes with scale 0–1, thus having a total area of .5.

  2. All dispatches are immediately geocoded to the City’s street and address management (SAM) system at the time of receipt to specify unique latitude and longitude. The Boston Area Research Initiative then uses these latitude–longitude pairs to spatially join to the containing or nearest parcel (i.e., address), using parcel polygons provided by the City and included in BARI’s Geographical Infrastructure for the City of Boston (O'Brien et al. 2018). 56,654 violent events were contained within the footprint of an address (84% of events). These were then linked directly to the containing street segment from the Geographical Infrastructure. An additional 7,486 were spatially joined to the nearest street segment (11% of events). All records were within 30 m of the nearest street, indicating strong fidelity in this process. The 5% of events not geocoded all were outside the city or lacked latitude and longitude in the original record.

  3. Given that some street segments form the border between two tracts, rather than lying clearly inside one or another, the Geographical Infrastructure links each street to the single tract containing its centroid. This process assigns them randomly to one of the census tracts between which they form the border, limiting any systematic bias in the subsequent analyses. One consequence of this method is that 11% of streets have parcels that fall in a census tract other than the one to which they were attributed (i.e., on the other side of the census tract border from the street’s centroid; 13% of all parcels). Counts of violent crimes for each street included events occurring at all addresses on the street, regardless of whether they fell in the same tract as the street’s centroid. This was deemed as the most appropriate way to deal with disagreements between an addresses’ street and tract, as the fundamental unit of analysis was the street segment. Further, it altered the presumed census tract of only 1,097 of violent events (2% of those geocoded).

  4. Readers will note that these numbers differ somewhat from O’Brien & Winship (2017), which uses the same basic database and analytic structure. The 2018 update of the Boston Area Research Initiative’s Geographical Infrastructure of Boston geocoded properties and parcels in a more precise manner that altered the street attribution of a modest number of parcels.

  5. The confirmatory factor analyses were based on counts of events of case types for census block groups, maximizing the extent to which case types included in a single category of events (e.g., public violence) were co-incident at this level of geography. We did not re-run the confirmatory factor analysis at streets because the low frequency of events relative to streets (< 1 per street for almost all case types) would make such an analysis hard to interpret as stochasticity can obscure the correlations between types of events.

  6. This was incorporated as an interaction with land use type of the street, as it has a different interpretation vis-à-vis routine activities and crime for residential streets compared to other types of zonings (e.g., commercial). Indeed, streets dominated by single-family residential parcels with higher value per square foot had fewer crimes than other streets in the neighborhood, whereas the opposite was true for commercial streets.

  7. This information is not readily accessible from the US Census as demographics are tabulated for census blocks and street blocks form the borders between census blocks. In order to impute information from census blocks to streets blocks, we first determined how many parcels on a street were contained in each bordering census block (per the GI). This was then the basis for a weighted average of the form \({p}_{i}=\sum_{b}\frac{{p}_{ib}*{parcels}_{b}}{\sum_{b}{parcels}_{b}}\), where pi is the estimated proportion of residents of ethnicity i for the street block, pib is the same proportion for block b, and parcelsb is the number of parcels on the street in block b. This was done for proportion Asian, Black, Latino, and White.

References

  • Andresen MA, Malleson N (2011) Testing the stability of crime patterns: Implications for theory and policy. J Res Crime Delinq 48(1):58–82

    Article  Google Scholar 

  • Banton M (1983) Racial and ethnic competition. Cambridge University Press, Cambridge

    Google Scholar 

  • Bernasco W, Steenbeek W (2017) More places than crimes: implications for evaluating the law of crime concentration at place. J Quant Criminol 33:451–467

    Article  Google Scholar 

  • Blalock HM (1967) Toward a theory of minority-group relations. Capricorn Books, New York

    Google Scholar 

  • Braga AA, Hureau DM, Papachristos AV (2011) The relevance of micro places to citywide robbery trents: A longitudinal analysis of robbery incidents at street corners and block faces in Boston. J Res Crime Delinq 48(1):7–32

    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 

  • Brantingham PL, Brantingham PJ (1993) Environment, routine, and situation: toward a pattern theory of crime. In: Clarke RV, Felson M (eds) Routine Activity and Rational Choice. Transaction Publications, New Brunswick, NJ

    Google Scholar 

  • Browning CR, Calder CA, Soller B, Jackson AL, Dirham J (2017) Ecological networks and neighborhood social organization. Am J Sociol 122(6):1939–1988

    Article  Google Scholar 

  • Bursik RJ, Grasmick H (1993) Neighborhoods and Crime: The Dimensions of Effective Community Control. Lexington Books, New York

    Google Scholar 

  • Center, Injury Control Research, and Boston Area Research Initiative2019) Boston neighborhood survey boston area research initiative harvard dataverse

  • Cohen LE, Felson M (1979) Social change and crime rate trends: A routine activity approach. Am Sociol Rev 44(4):588–608

    Article  Google Scholar 

  • Colabianchi N, Dowda M, Pfeiffer KA, Porter DE, Almeida MJC, Pate RR (2007) Towards an understanding of salient neighborhood boundaries: Adolescent reports of an easy walking distance and conveniient driving distance. Int J Behav Nutr Phys Activity 4(1):66

    Article  Google Scholar 

  • Coulton CJ, Korbin J, Chan T, Marilyn Su (2001) Mapping residents’ perceptions of neighborhood boundaries: A methodological note. Am J Community Psychol 29(2):371–383

    Article  Google Scholar 

  • Curiel RP, Delmar SC, Steven RIchard Bishop. (2018) Measuring the distribution of crime and its concentration. J Quant Criminol 34:775–803

    Article  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 

  • Durkheim E (1895/1964) The Rules of Sociological Method. Translated by S. A. Solovay and J. H. Mueller. New York: Free Press.

  • Eck JE, Clarke RV, Guerette RT (2007) Risky facilities: Crime concentration in homogeneous sets of establishments and facilities. Crime Prev Stud 21:225–264

    Google Scholar 

  • Farrell G, Pease K (2001) Repeat Victimization. Criminal Justice Press, Monsey, NY

    Google Scholar 

  • Glas I, Engbersen G, Snel E (2019) Going spatial: applying egohoods to fear of crime research. Br J Criminol 59(6):1411–1431

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Guest AM, Lee BA (1984) How urbanites define their neighborhoods. Popul Environ 7(1):32–56

    Article  Google Scholar 

  • Handcock MS (2016) reldist: relative distribution methods (Version 1.6-6). CRAN. Retrieved from http://www.stat.ucla.edu/~handcock/RelDist

  • Hipp JR, Kim Y-A (2017) Measuring crime concentration across cities of varying sizes: Complications based on the spatial and temporal scale employed. J Quant Criminol 33(3):595–632

    Article  Google Scholar 

  • Johnson SD (2008) Repeat victimisation: A tale of two theories. J ExpCriminol 23:201–219

    Google Scholar 

  • Johnson SD, Bernasco W, Bowers KJ, Elffers H, Ratcliffe J, Rengert G, Townsley M (2007) Space-time patterns of risk: A cross national assessment of residential burglary victimization. J Quant Criminol 23:201–219

    Article  Google Scholar 

  • Kling JR, Jens L, Katz LF (2005) Neighborhood effects on crime for female and male youth: evidence from a randomized housing voucher experiment. Q J Econ 120(1):87–130

    Google Scholar 

  • Klinger DA, Bridges GS (1997) Measurement error in calls-for-service as an indicator of crime. Criminology 35(4):705–726

    Article  Google Scholar 

  • Lee YJ, Eck JE, SooHyun O, Martinez NN (2017) How concentrated is crime at places? A systematic review from 1970 to 2015. Crime Science 6. https://doi.org/10.1186/s40163-017-0069-x

  • Legewie J, Schaeffer M (2016) Contested boundaries: Explaining where ethnoracial diversity provokes neighborhood conflict. Am J Sociol 122(1):125–161

    Article  Google Scholar 

  • Loeffler C, Flaxman S (2018) Is gun violence contagious? A spatiotemporal test. J Quant Criminol 34(4):999–1017

    Article  Google Scholar 

  • Mohler G, Jeffrey Brantingham P, Carter J, Short MB (2019) Reducting bias in estimates for the law of crime concentration. J Quant Criminol 35:747–765

    Article  Google Scholar 

  • O’Brien D, Ciomek A (2017) Massachusetts Census Indicators, edited by B. A. R, Initiative

    Google Scholar 

  • O’Brien D, Sampson RJ (2015a) Public and Private Spheres of Neighborhood Disorder: Assessing Pathways to Violence Using Large-Scale Digital Records. J Res Crime Delinq 52:486–510

    Article  Google Scholar 

  • O’Brien DT (2019) The action is everywhere, but greater at more localized spatial scales: Comparing concentrations of crime across addresses, streets, and neighborhoods. J Res Crime Delinq 56(3):339–377

    Article  Google Scholar 

  • O’Brien DT, Phillips N, De Benedictis-Kessner J, Shields M, Sheini S (2018) 2018 Geographical Infrastructure for the City of Boston, edited by B. A. R, Initiative

    Google Scholar 

  • O’Brien DT, Sampson RJ (2015b) Public and private spheres of neighborhood disorder: Assessing pathways to violence using large-scale digital records. J. Res. Crime Delinq. 52(4):486–510

    Article  Google Scholar 

  • O’Brien DT, Sampson RJ, Winship C (2015) Ecometrics in the age of big data: Measuring and assessing “broken windows” using administrative records. SociolMethodol 45:101–147

    Google Scholar 

  • O’Brien DT, Winship C (2017) The gains of greater granularity: The presence and persistence of problem properties in urban neighborhoods. J Quant Criminol 33:649–674

    Article  Google Scholar 

  • Olaghere A, Lum C (2018) Classifying “micro” routine activities of street-level drug transactions. J Res Crime Delinq 55(4):466–492

    Article  Google Scholar 

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

    Google Scholar 

  • Pratt TC, Cullen FT (2005) Assessing macro-level predictors and thories of crime: a meta-analysis. Crime and Justice 32:373–450

    Article  Google Scholar 

  • Sampson RJ (2012) Great American City: Chicago and the Enduring Neighborhood Effect. University of Chicago Press, Chicago

    Book  Google Scholar 

  • Sampson RJ, Raudenbush S, Earls F (1997) Neighborhoods and violent crime: A multilevel study of collective efficacy. Science 277:918–924

    Article  Google Scholar 

  • Schnell C, Braga AA, Piza EL (2017) 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. https://doi.org/10.1007/s10940-016-9313-x

    Article  Google Scholar 

  • Shaw C, McKay H (1942/1969). Juvenile Delinquency and Urban Areas. University of Chicago Press, Chicago

  • Sherman LW, Gartin PR, Buerger ME (1989) Hot spots of predatory crime: Routine activities and the ciminology of place. Criminology 27(1):27–55

    Article  Google Scholar 

  • St. Jean, Peter K.B. (2007) Pockets of Crime: Broken Windows, Colelctive Efficacy, and the Criminal Point of View. University of Chicago Press, Chicago, IL

    Book  Google Scholar 

  • Steenbeek W, Weisburd D (2016) Where the action is in crime? An examination of variability of crime across different spatial units in The Hague, 2001–2009. J Quant Criminol 32(3):449–469

    Article  Google Scholar 

  • Trickett A, Osborn DR, Seymour J, Pease K (1992) What is different about high crime areas? Br J Criminol 32(1):81–89

    Article  Google Scholar 

  • Weisburd D (2015) The law of crime concentration and the criminology of place. Criminology 53(2):133–157

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Weisburd D, Groff ER, Yang S-M (2012) The Criminology of Place: Street Segments and Our Understanding of the Crime Problem. Oxford University Press, New York

    Book  Google Scholar 

  • Weisburd D, Groff E, Yang S-M (2013) Understanding and controlling hot spots of crime: the importance of formal and informal social controls. PrevSci 15(1):31–43

    Google Scholar 

  • Weisburd D, Shay M, Amram S, Zamir R (2017) The relationship between social disorganization and crime at the micro geographic level: Findings from Te Aviv-Yafo using Israeli census data. AdvCriminol Theory 22:97–120

    Google Scholar 

  • Weisburd D, White C, Wooditch A (2020) Does collective efficacy matter at the micro geogrpahic level?: findings from a study of street segments. Br J Criminol 60(4):873–891

    Article  Google Scholar 

  • Wilson M, Daly M (1997) Life expectancy, economic inequality, homicide, and reproductive timing in Chicago neighbourhoods. BMJ 314:1271–1274

    Article  Google Scholar 

Download references

Funding

Funding was provided by Division of Social and Economic Sciences (Grant Numbers SES-1637124, SES-1921281).

Author information

Authors and Affiliations

  1. School of Public Policy and Urban Affairs, Northeastern University, 310 Renaissance Park, 1135 Tremont St, Boston, MA, 02120, USA

    Daniel T. O’Brien

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

    Daniel T. O’Brien & Riley Tucker

  3. Boston Area Research Initiative, Northeastern University, Cambridge, USA

    Daniel T. O’Brien, Alexandra Ciomek & Riley Tucker

  4. Boston Area Research Initiative, Harvard University, Cambridge, USA

    Daniel T. O’Brien, Alexandra Ciomek & Riley Tucker

  5. Department of Sociology, Harvard University, Cambridge, USA

    Alexandra Ciomek

Authors
  1. Daniel T. O’Brien
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexandra Ciomek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Riley Tucker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel T. O’Brien.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table

Table 4 Complete parameter estimates from multilevel models predicting counts of reports of violent crime on a street relative to other streets in the same neighborhood
Full size table

4.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

O’Brien, D.T., Ciomek, A. & Tucker, R. How and Why is Crime More Concentrated in Some Neighborhoods than Others?: A New Dimension to Community Crime. J Quant Criminol 38, 295–321 (2022). https://doi.org/10.1007/s10940-021-09495-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-021-09495-9

Keywords

Search

Navigation