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

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.

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Fig. 1
Fig. 2

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.

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Funding

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

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Correspondence to Daniel T. O’Brien.

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Appendix

Appendix

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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

4.

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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

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Keywords

  • Hotspots
  • Urban criminology
  • Communities and crime
  • Quantitative methods