Abstract
Objectives
To examine the spatial concentration and spatial stability of residential burglary at micro places in the context of a substantial city-level burglary drop in Antwerp, Belgium.
Methods
51,337 police recorded home burglary incidents for the period 2005–2016 are geo-referenced to 26,875 street segments. Longitudinal trends in spatial concentrations of burglary are considered using descriptive statistics, generalized Gini coefficients, and local Getis–Ord statistics. Andresen’s (Appl Geogr 29(3):333–345, 2009) non-parametric spatial point pattern test (SPPT) is used to identify spatial stability in burglary point patterns and evaluate the ubiquity of a city-level burglary drop across street segments. A longitudinal extension of the SPPT is implemented.
Results
Residential burglary is substantially concentrated in street segments. Burglary point patterns exhibit a moderate to high degree of spatial stability over time. Local analyses show that 91% of street segments with burglary experienced a net decrease in crime and under 1% of street segments with burglary experienced a net increase. Absolute spatial stability over time is found for just 1.43% of street segments with burglary and minor increases are consistently observed for as few as 11 street segments with burglary.
Conclusions
The citywide home burglary drop manifested itself uniformly across street segments with burglary and the majority of street segments that experienced burglary contributed relatively equally to the crime drop. In other words, we find no strong evidence that the city-level crime drop can be tied to substantial decreases in a few specific places, nor do we find that the reductions in burglary are spatially concentrated.
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Notes
Crime includes incident reports of property crime, disorder, drugs, and prostitution offences, violent personal crime, a range of various non-traffic crime related events such as domestic disputes and missing persons, traffic-related events, and unknown events.
Curman et al. (2015) combine 22 incident categories into a single crime measure: arson, assault, assault in progress, attempted break and enter, attempted theft, break and enter, break and enter in progress, drug arrest, fight, alarm, holdup, homicide, purse snatching, robbery, robbery in progress, shoplifting, stabbing, stolen vehicle, sexual assault, theft from vehicle, theft and theft in progress.
Wheeler et al. (2016) analyze Part 1 and Part 2 UCR crime. Part 1 crimes include homicide, rape, robbery, aggravated assault, burglary, larceny, theft of a motor vehicle, and arson. Part 2 crimes include all criminal law crimes not listed under Part 1 crimes.
Andresen and Malleson (2011) analyze calls for service for assault, burglary, robbery, sexual assault, theft, theft from vehicle, and theft of vehicle.
Andresen et al. (2017) use incident data for commercial break and enter, mischief, theft from vehicle, and theft of vehicle.
The decision to focus on residential burglary data was motivated by two arguments. Since we are interested in studying changes in spatial patterns of crime over time, we required the offence category under study to be a high volume crime with little or no spatial bias in recorded incidents. Residential burglary has a high citizen reporting and police recording rate, ensuring a comprehensive overview of burglary incidents. In part, this is due to most common Belgian fire insurance policies covering material damages caused by (attempted) burglaries provided that the victim files a police report. Furthermore, unlike for other crime types such as robbery, vehicle theft, or personal violence, precise address information is generally available for residential burglary since these incidents take place at a specific residential unit. This ensures high geocoding hit rates and aggregating burglary incidents to the correct street segments thereby minimizing the bias in the spatial patterns under study. The decision to focus on Antwerp was pragmatic more than anything else. The city’s population size and the presence of the largest Belgian local police force ensures having a sizeable number of offences to study. In addition, the ALPD was motivated to collaborate and provided access to the recorded crime data. Finally, international contexts have received little attention in the criminology-of-place literature so far. The external validity of some of the key findings from the criminology-of-place literature may therefore be limited requiring additional studies in different contexts. We acknowledge, however, that our focus on a particular crime type, burglary, in a particular context, Antwerp, limits the focus of our study and, to an extent, the implications of our result.
To take all available burglary data into account and correct for fluctuations in burglary levels over time, a linear trend line was estimated through an ordinary least squares regression model with burglary count as a function of time. A simple comparison of 2005 and 2016 would lead to an even stronger conclusion, namely a burglary decline of − 42%. In addition, we point out that even disregarding 2016, the percentage decline predicted by a linear time trend is − 19%, and disregarding 2013 and 2016 the time trend predicts a − 15% crime reduction. This indeed shows a long-term trend of crime decrease.
An anonymous reviewer suggested we offer potential explanations for the 2012–2013 and 2015–2016 burglary reductions. While identification of the cause(s) of a substantial crime reduction is an interesting question in its own right, we wish to emphasize that this is not the objective of our study. Independently of what may have triggered the burglary reduction, we aim to study to what extent a reduction in burglary volume is a widespread spatial process that manifests itself proportionally across micro places or instead a spatially concentrated process shaped by sharp reductions in just a handful of street segments. In the event of a widespread reduction across many micro places, the factors affecting the Antwerp burglary reduction would be likely rooted in changes in systemic causes that occur across all street segments. A spatially concentrated burglary reduction would be likely due to one or more micro-place processes that are specific to a limited number of street segments. Finally, although we do not wish to suggest causality, we are aware that in 2012 a new local government coalition came into power in Antwerp. The new local coalition quickly implemented a ‘tough-on-crime’ policy. The number of a variety of offences, including burglary, reduced and remained low in comparison to the previous coalition period (Antwerp Local Police Department 2017). While these events may offer context, we encourage readers not to infer causality. Indeed, the largest decline occurred from 2015 to 2016, several years after this event.
Strictly speaking, Antwerp is Belgium’s largest city but the Brussels metropolitan area, which consists of 19 municipalities policed by six different police forces, is the most populous area and is considered Belgium’s largest ‘city’ despite not being an actual city.
The street segments shapefile (Vlaams Wegenregister, downloaded on 3 June 2016) is available for download at the product catalogue of the Flemish Geographical Information Agency (FGIA) [https://download.agiv.be/] and can be accessed online through the FGIA’s online geoportal [http://geopunt.be].
For example, in our data there are six to ten times more street segments than yearly burglary events. Standard measures of concentration, including the standard Gini coefficient, do not account for the fact that the data imposes structural constraints on the maximally possible degree of crime concentration.
Relative event counts ensure that datasets with different event volumes can be compared. Since total burglary volume declined during the study period, this is an appealing and appropriate analytical approach to study burglary spatial patterns independently from yearly fluctuations in total burglary volume.
Of course, stability in spatial patterns does not exclude the possibility that a small number of high crime localities contribute substantially to the observed crime decline. Spatial patterns are considered globally stable if no more than 20% of spatial units exhibit significant spatial change. As such, considerable spatial change could still underlie global stability of spatial patterns.
Instead of describing one of the three patterns chosen a priori by Andresen et al. (2017), our approach allows to describe any longitudinal pattern by combining the local similarity sum scores and the associated volatility scores (see below).
Since there are 12 time periods in our study period, there are 11 consecutive pairwise comparisons that are relevant to gauge the stability of burglary patterns longitudinally (i.e., 2005–2006, 2006–2007, …, 2015–2016).
The following example is instructive to understand how we define volatility. Consider three street segments in a 12-year study period each with a sum score of − 7 but with different associated volatility scores. First, assume a street segment with the following local similarity index values: (− 1, − 1, − 1, − 1, − 1, − 1, − 1, 0, 0, 0, 0). This street segment experienced seven consecutive burglary decreases and no significant change thereafter, receiving a volatility score of 0 because no changes other than a consecutive decrease occurred. A second street segment is characterized by (− 1, − 1, − 1, − 1, − 1, − 1, − 1, 0, 1, 0, − 1). Next to the seven decreases, this segment also experienced one increase and one decrease. The sum score is − 7 but the associated volatility score is 1 because one increase is offset by one decrease. Finally, consider a street segment with an even higher degree of volatility: (− 1, − 1, − 1, − 1, − 1, − 1, − 1, 1, − 1, 1, − 1). With a sum score of − 7, this street segment has a volatility score of 2 because next to seven decreases, two increase/decrease changes also occurred. Coincidentally, a volatility score of 2 is also the maximum possible volatility score associated with a sum score of − 7 and twelve time periods.
However, note that 50% of all home burglaries in 2005 (= 4628/2 = 2314) can only occur in about (2314/26,875 × 100% =) 8.6% of all street segments under maximum spatial dispersion, i.e., when every event occurs on a different street segment. In 2016, this percentage of possible street segments is further reduced to 5.0%. These structural constraints to the maximally possible degree of burglary concentration help put the reported concentrations in perspective, and are also accounted for in the generalized Gini calculations.
Reviewing an earlier version of this paper, an anonymous reviewer suggested to verify whether the two steepest reductions (2012–2013, 2015–2016) are distributed proportionally across Antwerp street segments similarly to the overall reduction (2005–2016). The multivariate robust global similarity indices (top right, Table 3) for the 2012–2013 (.71) and the 2015–2016 (.73) comparison suggest moderate to high spatial stability of residential burglary concentration. In line with the general pattern, just over seven out of ten street segments (that had at least one burglary at any time during the study period) experience the same percentage of home burglaries from 2012 to 2013 and from 2015 to 2016. This indicates that both burglary changes indeed scale proportionally across street segments.
The observed negative correlation between number of burglaries in 2005 and size of proportional reduction 2005–2016 indicates that, generally, the highest burglary count street segments exhibited the greatest changes. The Spearman correlation between 2005 burglary count and size of proportional reduction 2005–2016 is − .85 (N = 3581; p < .001) for street segments with any local change in burglary volume and − .85 (N = 2319; p < .001) for streets with significant local change in burglary volume.
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Acknowledgements
Christophe Vandeviver and Wouter Steenbeek are both to be regarded as co-first authors of this article. Christophe Vandeviver is a Postdoctoral Fellow of the Research Foundation—Flanders (FWO). Part of this work was carried out while Christophe Vandeviver was International Fellow at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR).
Funding
Vandeviver’s contribution to this work was supported by the Research Foundation—Flanders (FWO) Postdoctoral Fellowship funding scheme and the Research Foundation—Flanders (FWO) Long Stay Abroad funding scheme [FWO15/PDO/242 to C.V., V4.303.16N to C.V.]. Steenbeek’s contribution to this work was supported by the Netherlands Organisation for Scientific Research (NWO) Innovational Research Incentives Scheme [VENI 451-12-011 to W.S.].
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Vandeviver, C., Steenbeek, W. The (In)Stability of Residential Burglary Patterns on Street Segments: The Case of Antwerp, Belgium 2005–2016. J Quant Criminol 35, 111–133 (2019). https://doi.org/10.1007/s10940-017-9371-8
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DOI: https://doi.org/10.1007/s10940-017-9371-8