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Where the Action is in Crime? An Examination of Variability of Crime Across Different Spatial Units in The Hague, 2001–2009

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Abstract

Objectives

To identify how much of the variability of crime in a city can be attributed to micro (street segment), meso (neighborhood), and macro (district) levels of geography. We define the extent to which different levels of geography are important in understanding the crime problem within cities and how those relationships change over time.

Methods

Data are police recorded crime events for the period 2001–2009. More than 400,000 crime events are geocoded to about 15,000 street segments, nested within 114 neighborhoods, in turn nested within 44 districts. Lorenz curves and Gini coefficients are used to describe the crime concentration at the three spatial levels. Linear mixed models with random slopes of time are used to estimate the variance attributed to each level.

Results

About 58–69 % of the variability of crime can be attributed to street segments, with most of the remaining variability at the district level. Our findings suggest that micro geographic units are key to understanding the crime problem and that the neighborhood does not add significantly beyond what is learned at the micro and macro levels. While the total number of crime events declines over time, the importance of street segments increases over time.

Conclusions

Our findings suggest that micro geographic units are key to understanding the variability of crime within cities—despite the fact that they have received little criminological focus so far. Moreover, our results raise a strong challenge to recent focus on such meso geographic units as census block groups.

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Notes

  1. Andresen and Malleson (2011; p. 62) do not report how a street segment is attributed to census tracts: “Our criteria for a successful geocoded event is for the event to be geocoded to the correct 100-block (street segment). All data are geocoded to the street network and then aggregated to the census tracts and dissemination areas using a spatial join function.” However, problems could arise if its two block faces fall within two separate census tracts, i.e. when a street is itself the border of a census tract.

  2. One caveat concerns where street segments cross neighborhood borders. We discuss the construction of the multilevel dataset in more detail in section “Units of Analysis”.

  3. As in prior studies, property crimes including thefts make up a large proportion of the crimes studied. About 65 percent of the crimes examined here are non-violent thefts. Weisburd et al. (2004) found that about 50 percent of the crimes examined in their longitudinal sample in Seattle were non-violent property crimes.

  4. Visual inspection shows that the pattern of these approximated crime events is very similar to the pattern of non-approximated locations, leading to the tentative conclusion that there is no spatial bias in the location of approximated crime event locations. Some disparities also occur, especially near the coast.

  5. The street segment shapefile (Nationaal Wegenbestand of the year 2010) was obtained from the public domain under license http://creativecommons.org/publicdomain/zero/1.0/ from the Dutch National Georegister, which facilitates access to publicly available datasets. The maintenance of the street segment shapefile falls under the Ministry of Infrastructure and the Environment. The neighborhood and districts shapefiles are obtained from Statistic Netherlands.

  6. This happens for 1152 of the 14,375 street segments.

  7. Of course several studies have modeled crime using HLM, e.g. street segments nested in output areas, nested in medium super output areas (Davies and Johnson 2015). However, the focus of these studies is often on obtaining parameter estimates of covariates and not on the variance decomposition of crime per spatial level and its change over time.

  8. An alternative approach of the log-transformation is to use generalized linear mixed models (GLMM), which directly model the non-normal error distribution of the response variable. For our (count) data, one possible GLMM uses a log link function and the probability mass function for the Poisson distribution. A disadvantage of GLMM is that inference is much more complicated than for linear models. The likelihood can only be approximated, e.g. using numerical integration, in contrast to LMM. While the accuracy increases as the number of integration points increases, the number of evaluations also increases exponentially with the number of random effects; the size of the dataset also impacts computation time a lot. For this reason, the statistical package used in this paper (lme4 in R) only allows a single integration point (i.e. Laplace approximation) when estimating more than one random effect. Moreover, it is assumed that the sampling distributions of the parameters are multivariate normal, and this is unlikely with fewer units per level, although confidence intervals for random effects can be approximated using (e.g.) parametric bootstrap methods (Efron 1979). Finally, a disadvantage of GLMM is that, in contrast to LMM, the level-1 variance depends on the expected value and is therefore not reported by most statistical software—although a simulation method has been proposed as a solution (Browne et al. 2005). In short, residual diagnostics plots indicated that a linear mixed model can adequately model a logged version of crime, and we therefore present these results instead of GLMM outcomes.

  9. Johnson (2010) points out that the Gini coefficient is dependent upon the shape of the estimated line of equality, the shape of which varies for different units of analysis. More specifically, Johnson (2010) encounters this problem because he studies home burglary and the number of homes is unequally concentrated per street segment. Here, we only use the Gini coefficient as a tentative measure of crime concentration across street segments, and use HLM to address the same question more directly.

  10. There is also a residual variance component of time, capturing the variance of crime that can be attributed to time-varying explanations. The proportions reported are the proportion of variance in crime of each spatial unit as compared to the total variance explained by the three spatial units.

  11. The real estate agent had discovered zones in the city of Chicago when he made up an inventory of price changes of houses and real estate. He contacted Burgess regarding his findings, which led to the now famous geographic model of crime and social problems in the urban context.

  12. In reality only half circles because Chicago is situated at the border of Lake Michigan.

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Author information

Authors and Affiliations

  1. Netherlands Institute for the Study of Crime and Law Enforcement, PO Box 71304, 1008 BH, Amsterdam, The Netherlands

    Wouter Steenbeek

  2. College of Humanities and Social Sciences (Criminology, Law, and Society), George Mason University, Fairfax, VA, USA

    David Weisburd

  3. Faculty of Law, Institute of Criminology, The Hebrew University, Jerusalem, Israel

    David Weisburd

Authors
  1. Wouter Steenbeek
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  2. David Weisburd
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Corresponding author

Correspondence to Wouter Steenbeek.

Appendix

Appendix

See Fig. 8.

Fig. 8
figure 8

Mean estimate of variance estimates as a function of number of replications (all crimes)

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Steenbeek, W., Weisburd, D. 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, 449–469 (2016). https://doi.org/10.1007/s10940-015-9276-3

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