Spatial Patterns in the 2011 London Riots

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1 21 Spatial Patterns in the 2011 London Riots Peter Baudains*, Alex Braithwaite** and Shane D. Johnson*** Article Abstract Riots broke out in London on 6th August Many narratives of the riots describe them as having subsequently intensified within and spread across various parts of the city during the following 4 days and nights before finally petering out. This article addresses two important empirical puzzles stemming from the riots. First, we ask whether the riot events did indeed display distinct spatial patterns and, if so, whether these events spread between locations. Second, we discuss how knowledge of the spatial distribution of these events could influence future analytical research on the topic and how, in turn, this work could inform crime reduction strategies for events of this type. Spatial Patterns in the 2011 London Riots Everything happens somewhere and this includes acts of disorder committed during a riot or the actions of the police and their crime reduction partners taken in anticipation of, or in response to, the disorder. In this article, we consider what might be learned from studying the London riots from a spatial perspective and how this might inform policing. Background The riots that disrupted civil order in London across the 5 days from the 6th to the 10th of August 2011 are estimated to have caused in excess of 250 million damage (Met Police, 2012) and resulted in more than 3,000 arrests (Guardian/ LSE, 2012). The riots were largely unprecedented in scale in recent UK history. Moreover, their unpredictable nature, in terms of where apparent hot spots of activity emerged, made their policing especially challenging. These were not riots borne out of pre-planned protests with routes agreed via consultation with the police (as was the case with the student protests in London in late 2010); nor were they riots that targeted traditional, iconic political symbols. Rather, these riots took on the appearance of largely spontaneous and unplanned activities. A considerable police presence was deployed to various parts of the country with a great many forces re-deployed to London from outlying areas nationally. Police officer numbers deployed across London increased considerably during the disorder from 3,000 officers on the first night to 16,000 Peter Baudains, University College London, London, UK. p.baudains@ucl.ac.uk Alex Braithwaite, University College London, London, UK. alex.braithwaite@ucl.ac.uk Shane Johnson, University College London, London, UK. shane.johnson@ucl.ac.uk Advance Access publication: 31 October 2012 Policing, Volume 7, Number 1, pp doi: /police/pas049 ß The Authors Published by Oxford University Press. All rights reserved. For permissions please journals.permissions@oup.com

2 22 Policing Article P. Baudains et al. on the final night (Met Police, 2012). Conflicting reports have emerged since regarding the level of police preparedness to handle events on this scale. The Guardian/LSE Reading the Riots report suggested that many members of the Police had confidence in their general strategy of protecting life, using minimal force, and relying upon CCTV to pursue subsequent prosecutions. They generally accept, however, that they did not mobilize their numbers with sufficient haste (Guardian/LSE, 2012). Thus, while their concentrated deployment to the streets of the capital eventually helped to dampen activities there, many officers at the time felt out of their depth (BBC, 2012). This reflects the fact that policing resources were scarce and very thinly spread. The deployment of these scarce resources during such extreme criminal events is obviously of crucial importance to minimizing the costs and maximizing the effectiveness of policing. This dramatic resource scarcity was reflected in the rather frustrated response of London s Metropolitan Police Service (Met Police, 2012) regarding the identification of the most appropriate tactics for managing disorder of this type and on this scale. Specifically, they were apparently concerned that:...should they send officers forward into a dangerous situation to try to make an arrest, they would then no longer be able to maintain a police cordon which was critical to holding a junction or protecting a location to prevent the spread of disorder or to protect life. Police officers were uncertain as to whether to contain the disorder within defined boundaries or to attempt to proactively arrest rioters during the disorder. The first approach would lead to a concentration of incidents in one area, while the second might cause the rioters and, therefore, their disorder to spread to new locations. In the research literature, some have argued that during episodes of rioting offender behaviour is irrational, with individuals being swept along with the crowd (Le Bon, 1960). In this case, there may be little to learn from studying spatial patterns of rioting, with patterns merely reflecting random fluctuation. However, others (Berk and Aldrich, 1972; Berk, 1974; Mason, 1984; McPhail, 1991; Martin et al., 2009) have argued that even in the extreme circumstances of a riot, offenders make rational choices as to whether to offend and, if so, where they might do so. In this case, there may be much to gain from examining the spatial patterning of such events. Anecdotal evidence suggests that riot events during the 2011 London riots both concentrated at specific locations and displayed distinct patterns of contagion. However, to our knowledge there has been no validation of such assertions using scientific methods, and hence there is currently little insight into the precise patterns. In contrast, research into everyday urban crime (e.g. Weisburd et al., 2009), terrorism and insurgency (e.g. Braithwaite and Li, 2007; Townsley et al., 2008; Johnson and Braithwaite, 2009; Braithwaite and Johnson, 2012; LaFree et al., 2012), and militarized disputes (e.g. Braithwaite, 2005, 2010) has consistently demonstrated that there exist spatial patterns in the location of events, and that the identification of such patterns can usefully inform reduction strategies and tactics. For instance, it has been known for some time that everyday crime is concentrated in space, with a small fraction of areas (Sherman et al., 1989), streets (e.g. Groff et al., 2010; Johnson and Bowers, 2010), and households (e.g. Farrell, 1995) or facilities (Eck et al., 2007) experiencing a disproportionate amount of crime. Moreover, the best available evidence suggests that directing police resources to crime hotspots, be they defined at the area or the household level (for systematic reviews, see Braga, 2005 and Grove et al., 2012, respectively) is an effective way of reducing crime. Accordingly, many police agencies routinely deploy resources to spatial crime hotpots as part of their ongoing policing strategy.

3 Spatial Patterns in the 2011 London Riots Article Policing 23 One question then, is whether such patterns are apparent for events such as the riots that gripped the UK in August In the current article we do two things: (1) present initial analyses that demonstrate that distinct spatial patterns were observed during the riots; and (2) having demonstrated this, discuss some of the more complicated approaches we are currently taking to better understand the spatial decision-making of rioters and how this might inform police deployment and strategy. Data In order to determine whether or not riot event locations clustered spatially or demonstrated any form of contagion, we require data on events aggregated at a consistent spatial unit. For this purpose, we employ the UK census Lower Super Output Area (LSOA) as our unit of observation. Specifically, we employ data for the complete set of 4,765 LSOAs in the Greater London area. Each LSOA typically consists of around 1,500 residents, which ensures that our counts of riot events per unit is approximately normalized by population density. Figure 1: A thematic map of Greater London showing the counts of riot-related offences by LSOA over the duration of the disorder. The riot data were collected by the Metropolitan police service and consist of all offenses that were detected and identified as having been associated with the riots during the period 6 11 August All offenses occurred within Greater London, and each record contained an identifier of the area within which the offence took place. Of the available data (N = 3914), 3,780 records contained entries for the offence location. Only these data were used in the analysis. Figure 1 is a thematic map of London generated using the LSOA area geography. The map depicts the total count of riot-related events that occurred in each area during the course of the riots. This very clearly demonstrates that while some areas experienced more than one riot-related event most experienced none. By virtue of their sparse distribution across units, riot events appear quite clearly to have clustered spatially. Their mapped appearance thus conforms with the anecdotal description of the evolution of the riots. In what follows, we offer a series of robust means by which to evaluate the credibility of this conclusion. Analysis In this section, we outline a series of methods that can be used to assess whether the apparent spatial clustering discussed above was statistically significant and whether or not events (and aggregations of events) diffused spatially. First, we explore the temporal or cyclical distribution of events across each of our spatial units in order to determine whether they occurred non-randomly. Second, we explore whether areas with especially high counts of events tended to occur in close proximity to other areas with high counts in other words, whether events clustered to form hot spots with a clear spatial structure. Third, we offer a simple logistic regression-based technique to explore whether recent activity within an area and within its surrounding areas influences the extent of current activity (i.e. did rioting appear to spread?).

4 24 Policing Article P. Baudains et al. Did the events that comprised the London Riots cluster in space? Figure 2 summarizes the temporal distribution of riot events by hour and by spatial unit per hour. The latter is conveyed by the number of LSOAs in which one or more riots occurred for each hour across the course of the five days of rioting. Figure 2 clearly indicates two things. First, as with other types of urban crime (e.g. Ratcliffe, 2000; Felson and Poulson, 2003), there was a temporal rhythm to the riots, with most occurring later in the day it would seem that even rioters need a break. Second, rather than occurring randomly, the figure suggests that events tended to cluster in some (but certainly not many) of the LSOAs. For example, at 10 pm on the 8th the hour during which most offences occurred it is evident that 336 incidents occurred in 69 LSOAs, indicating that many more than one incident occurred in some LSOAs during this hour. To test whether this pattern is statistically significant, we first compared the observed results to what would be expected if the riot events had occurred on a chance basis. This provides a means of judging whether (despite appearing to the eye to be clustered) the distribution of events actually reflects complete spatial randomness. To do this we use a permutation test. The rationale of such an approach is that if the observed results merely reflect a random process, then they will reflect one permutation of such a (chance) process. Consequently, they should not really differ from other permutations that actually are generated by that chance process. In this case, the simplest chance process would be that the events occurred purely randomly such that each riot-related incident had the same probability of occurring in any one of the LSOAs. It is relatively straightforward to generate such a permutation of the data. For each of the events that occurred in each hour, using a random number generator, we select one of the LSOAs (with every LSOA having the same probability of being selected for each event) to which to allocate that event. Figure 2: Hourly counts of riot-related offences and the number of LSOAs affected.

5 Spatial Patterns in the 2011 London Riots Article Policing 25 Having done so for the complete set of observed events, we can count how many LSOAs experienced events for each simulated hour and compare these simulated counts with those for the actual data. Doing this once is not particularly instructive; however, a full permutation with a dataset in the thousands is virtually impossible. Thus, we use a Monte Carlo (MC) simulation to select a random sample of 499 draws from the population of all possible permutations. Having done so, it is also easy to compute the probability of obtaining a value at least as large as that observed (in the real data) using the approach detailed in North et al. (2002). When we do this, we find that for the simulated data with some minor fluctuations the number of LSOAs affected each hour is generally equal to the number of events recorded. This suggests that the spatial clustering observed exceeded chance expectation. Figure 3 summarizes the data using an empirical cumulative distribution (or ecdf). For this plot, the value on the y-axis indicates the proportions of hourly observations for which the number of LSOAs experiencing (one or more) events was equal to or less than the value shown on the x-axis. Figure 3 indicates that, of those LSOAs that experienced at least one event, far more experienced more than one event per hour than would be expected if the patterns reflected complete spatial randomness. Moreover, of those LSOAs that experienced one or more events, around 20% experienced four or more events (some experienced many more), whereas for the simulated data, none of the LSOAs experienced more than two events. Further analysis (details available upon request) confirms this, with probability of the observed pattern being explained by a chance process being less than A second means by which to assess the extent to which the riots clustered, is to explore whether it was the case that areas with the high counts of incidents tended to be proximate to other areas with Figure 3: Empirical cumulative distribution functions for the number of riot-related offences occurring in each LSOA for both simulated and actual data.

6 26 Policing Article P. Baudains et al. relatively high numbers of incidents. We employ the Moran s I statistic in order to describe the spatial distribution of LSOA event counts (Moran, 1950). As a global test of what is referred to as spatial auto correlation, this is used to see if areas with high (low) counts for a particular variable tend to cluster in space. This statistic has been widely employed to characterize the distribution of levels of criminal (e.g. Anselin et al., 2000) and terrorist (LaFree et al., 2012) activities. The formula for the Moran s I is as follows: I ¼ P i N P j W ij P i P j W ij X i X Xj X 2 X i X P i Where, N is the number of spatial units indexed by areas i (the index area) and j (a proximate area of i), X is the variable of interest, X is the mean of X, and W ij is a matrix of spatial weights indexed by i and j. The null hypothesis for the Moran s I statistic is that patterns reflect complete spatial randomness and so an expected distribution, generated under such conditions, is required to determine if the observed Moran s I value is meaningful. This distribution can again be generated using a permutation approach. To explain, we randomly allocate each observed incident of rioting to one of the LSOAs (each LSOA again has the same probability of being selected for each event). Having done so, we compute a Moran s I statistic for this simulated data. As before, this represents only one permutation of the data and so the process is repeated many times. This generates a distribution of expected Moran s I values that can be compared with the observed value. The Moran s I statistic for the observed data of was small but significantly different to the mean expected value of (P < 0.001). The small value is not unexpected due to the fact that the events were rare and there are so many LSOAs included in the analysis. Thus, these analyses demonstrate that there was clustering of events at the area level although the effect was small. We have shown, thus, that individual acts of rioting clustered within a relatively small number of local areas and, in turn, that relative to complete spatial randomness these locations (that hosted higher numbers of incidents) tended to be located near to other local areas hosting relatively high numbers of incidents. We move next to assess whether, in fact, these patterns may be observed because of the tendency for events to spread between neighbouring areas. Did the events that comprised the London Riots spread between areas? A number of commentators (e.g. Gross, 2011) have suggested that the riots spread as the disorder unfolded. The spreading of disorder could take on a variety of different forms or meanings. In many academic disciplines, the phrase spreading of disorder is most likely to be thought of as reflecting a spatial diffusion process. This has a special meaning and suggests that the likelihood of an event of some kind increases if others have recently occurred nearby the notion of the likelihood of an event occurring at location i being increased by the occurrence of a similar event at nearby location j. This is quite different to simply saying that there has been an increase (or concentration) in the frequency of a particular type of event within the same location which could occur independent of any specific space-time patterning. Here, we explore whether there is any evidence of either spatial diffusion or concentration processes at work. These processes can be measured in a variety of ways. The current article is exploratory, and so we take a simple approach and estimate the extent to which the risk of riots occurring in an area i on a particular day is influenced by whether riot events occurred in area i, or in the surrounding areas j on the previous day. In order to do this, we generated a binary variable for each area, coded with a 1 if one or more incidents occurred in that area on a

7 Spatial Patterns in the 2011 London Riots Article Policing 27 particular day, and zero otherwise. For each area we also computed how many incidents occurred in that area on the preceding day, and how many occurred in the neighbouring areas. To calculate the latter it was necessary to identify the set of areas within close proximity. This can be done in a number of ways, but in the current article we identified all of those areas for which the centroid of the area was within a given distance 1 of the centroid of the target area. To estimate the influence of the activity observed in neighbouring areas on the previous day, we thus generated what is called a spatially lagged variable. This is essentially the average number of events that occurred in the areas j (that surround area i) on the previous day. Rather than simply using the absolute averages, because some areas will be further away from the target area (and hence are likely to have a weaker influence) than others, we compute a weighted average so that those that are closest are considered to have the greatest influence. 2 A range of different geographic boundaries could be used to define the areas for such an analysis. In this case, however, we once again use the UK census LSOA geography. An important reason for this was that using this geography allows us to take account of the influence of other (sociodemographic) variables when conducting the analyses that follow. For the purposes of illustration, we present results for the 9th of August. A number of approaches to modelling could be adopted. Here we use a simple statistical approach. As we are interested in whether or not riot-related events occurred in an area on a particular day we use a logistic regression model that is designed for the analysis of binary outcome data. In this case, the aim is to see if the variables discussed (our covariates) are associated with (or predict) whether or not an area experienced any riot events, and if it did, how important the covariates are in explaining the variation observed across areas. We specify a model in which whether or not a riot is predicted to have occurred in an area is estimated as a function of: the count of riot-related events in that same area on the previous day; the (spatial lag) variable counting the rate of riotrelated events in neighbouring areas on the previous day; and the count of crime in that area in the month of July We also included a collection of sociodemographic and other covariates 4 commonly associated with crime risk. However, so as to not distract the reader from the point of the analyses, the results for these covariates will not be reported. Regardless, it is important to note that the influence of the variables reported is estimated after accounting for the effects of these other covariates. For technical reasons, we take the logarithms of the (independent) variables discussed when estimating the equation parameters. Table 1 shows the results of the analysis. The first column of Table 1 shows the variable of interest. The second column shows the exponentiated (e ) coefficients. These can be slightly difficult to interpret but represent how the odds of an LSOA experiencing one or more incidents on a 1 The value of d is somewhat arbitrary and so we conducted our analyses using different values to ensure that the findings were not sensitive to the value selected. They were not. 2 The results are consistent whether we do this or not. 3 This variable is included because, of course, some areas may simply be more risky than others, regardless of the type of crime committed. Thus, we include a simple estimate of crime risk at the LSOA level. This was generated by counting the total counts of crimes per LSOA for the month of July 2011, as recorded on the website. 4 These were deprivation as measured by the IMD, relative deprivation, ethnic diversity, population churn, population density, the presence of tube stations, retail floor space, the presence of schools, the distance of the LSOA to the centre of London, and the fraction of single parent families.

8 28 Policing Article P. Baudains et al. Table 1: Logistic regression analysis for the likelihood of riot-related events occurring in an LSOA Table 2: Logistic regression analysis for the likelihood of riot-related events occurring in an LSOA e Z-score e Z-score Same LSOA t 1 day 2.65* 7.32 Surrounding LSOAs t 1 day 3.19* 4.75 Crime t 1 month 6.29* 5.66 Null deviance 1,414 Residual deviance 1,133 *P < particular day changes (increases or decreases) with every one-unit increase in the independent variable of interest. The odds simply reflects the probability of one or more incidents occurring in the area divided by the odds of no events occurring in the same area. An e of one would, thus, indicate that the independent variable is not associated with a change in the odds of the event occurring, whereas values above one suggest a positive association (an increase). In this case, it appears that the odds of a riot occurring in an LSOA is (statistically significantly) positively associated with the number of events that occurred in that area on the previous day, with the number of events that occurred on the previous day in the surrounding areas, and with the amount of crime that occurred in the area in the previous month. That is, for the 9th of September (at least), LSOAs that experienced the most incidents on the 8th or that were near to areas that did, were more likely to experience them on the 9th, as well. One issue with such an analysis is that the lagged variables may simply identify areas that are prone to rioting, regardless of the day concerned. In other words, high levels of rioting might be observed because of spatial heterogeneity of areas rather than as a result of spatial dependence between areas. If this were the case, we would expect that lagged variables for days earlier than the previous day to also be significantly associated with where riots occurred on the present day. To examine this potential, we created lagged variables that used data for the day that occurred 2 days before the day of interest (i.e. the 7th). Consistent with the diffusion Same LSOA t 2 days Surrounding LSOAs t 2 days Crime t 1 month 11.13* 7.96 Null deviance 1,414 Residual deviance 1,231 *P < hypothesis, in this case, the lagged variables did not explain any of the variation in the dependent variable (Table 2). Discussion The brief analyses detailed above provides the first empirical evidence to suggest that the events of the London Riots of 2011 clustered and diffused spatially. Demonstrating this formally is important for two reasons. First, showing that the riots were concentrated in space suggests that policing strategies should recognize this and be tailored accordingly. Second, it suggests that other more sophisticated methods from the analytic toolbox may provide further insight into the phenomena. Consequently, a number of questions remain. Many of these questions centre upon the recognition that the patterns which emerged during the riots were the result of the interactions between different actors both rioters and the police. In order to take this multitude of actors into account, we suggest that a series of more advanced methods including some developed within the field of complexity science which deal with such interactions are required. To elaborate, here we explored patterns in the destinations of rioters. This is, however, just one element of the geography of riots. By additionally considering the home locations of rioters (from where they likely travelled from), it is possible to explore patterns in offenders journeys to crime. In further analyses (not reported here) we find that

9 Spatial Patterns in the 2011 London Riots Article Policing 29 like most crime types, the journey to riot conforms to a pattern of distance decay, with most offenders travelling only short distances to engage in the disorder. Moreover, elsewhere, we use a more sophisticated approach to examine those environmental and socio-demographic factors that affect offender spatial decision-making, after controlling for the fact that they are most likely to commit offences close to their home locations (Baudains et al., 2012). Such findings provide insight into what area level factors appear to attract rioters, and which do not, and hence may inform how the police prioritize areas both to prevent this type of disorder, and in the investigative process. If one is willing to make certain assumptions about the processes involved in generating the spatial patterns, then mathematical models may be developed to test them and, if these are able to replicate observed patterns, build explanatory models. Such models may then be used to answer what if questions regarding different scenarios that may arise during riots. For example, Davies et al. (2012) develop a mathematical model of the London riots that combines existing mathematical models of spatial interaction, contagion, and civil violence which generates similar spatial patterns to those observed here. They are then able to investigate policing strategies in particular concerning police deployment strategies in an effort to understand how these might affect outcomes during such extreme events. While such approaches will (of course) never be perfect, they offer a formal approach that can complement or inform expert opinion. In this study, we touched only briefly on the issue of spatial diffusion. Consequently, our findings do not characterize the types of diffusion observed. For example, the disorder may have spread in such a way that reflected an escalation, with affected areas continuing to experience events but with riots also spreading to those nearby. Alternatively, as a result of police action, it is possible that there was a process of relocation, with rioters simply being displaced from one area to another. Unless events are limited to a few areas, such dynamics cannot reliably be inferred from just looking at maps, and hence more formal methods are required. Understanding how patterns unfold was one of the concerns expressed by the police that was highlighted in the introduction, and hence we are developing methods that speak to this issue. Finally, a few caveats are necessary. First, the data used here are for detected offences rather than the complete set of riot-related events, and the reader should be aware of this. Second, at the heart of each of the approaches outlined here and, indeed, any spatial analysis are a series of important decisions regarding the units of analysis used. Different geographical units could be used, including LSOAs, police neighbourhoods, police beats, street segments, and so on. The geography selected should match the unit at which the theory to be tested, or for which police decision making, applies. Thus, future work should explore patterns for different units of analysis, as appropriate. Units of time also require consideration. In our analysis of spatial diffusion, we examined patterns from one day to the next, but during riots patterns may emerge on different time scales. If so, the identification of these may inform policing strategy and help to better tailor tactics to the dynamics of the disorder. Conclusion The aim of the current article was to advance understanding of the spatial distribution of events in the 2011 London Riots. We have offered some straightforward analyses that clearly demonstrate (for the first time) that riot events clustered and diffused spatially. These findings may inform crime control strategies concerning large-scale riots, which is important given the considerable policy attention placed upon the potential for future outbreaks of this type. We have shown that while the riots may have erupted quite unexpectedly, they subsequently formed quite distinct spatial patterns. Such patterns

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