Geographic Crime Analysis

Newton Crime Sci (2015) 4:30 DOI 10.1186/s40163-015-0040-7

R E S E A R C H

Crime and the NTE: multi-classification crime (MCC) hot spots in time and space Andrew Newton*

Abstract This paper examines crime hot spots near licensed premises in the night-time economy (NTE) to investigate whether hot spots of four different classification of crime and disorder co-occur in time and place, namely violence, disorder, drugs and criminal damage. It introduces the concept of multi-classification crime (MCC) hot spots; the presence of hot spots of more than one crime classification at the same place. Furthermore, it explores the temporal patterns of identified MCC hot spots, to determine if they exhibit distinct spatio-temporal patterns. Getis Ord (GI*) hot spot analysis was used to identify locations of statistically significant hot spots of each of the four crime and disorder clas- sifications. Strong spatial correlations were found between licensed premises and each of the four crime and disorder classifications analysed. MCC hot spots were also identified near licensed premises. Temporal profiling of the MCC hot spots revealed all four crime types were simultaneously present in time and place, near licensed premises, on Friday through Sunday in the early hours of the morning around premise closing times. At other times, criminal damage and drugs hot spots were found to occur earlier in the evening, and disorder and violence at later time periods. Criminal damage and drug hot spots flared for shorter time periods, 2–3 h, whereas disorder and violence hot spots were present for several hours. There was a small spatial lag between Friday and Saturday, with offences occurring approxi- mately 1 h later on Saturdays. The implications of these findings for hot spot policing are discussed.

Keywords: Policing, Licensed premises, Alcohol, Multi-classification crime (MCC) hot spots, Spatio-temporal analysis

© 2015 Newton. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Background There is a longstanding recognition that the locations of alcohol consumption and crime co-occur (Gorman, Speer, Gruenewald, & Labouvie, 2001; Home Office, 2003; Scott and Dedel, 2006; Newton and Hirschfield, 2009a). This often fuels the wider debate over the ‘causal’ versus ‘non-causal’ relationship between alcohol and crime (Dingwall, 2013; Horvath and Le Boutillier, 2014). A growing concern is the prevalence of clusters of crime, termed hot spots, in urban areas with concentrations of licensed premises, synonymous with the Night-Time Economy (NTE). For the purposes of this paper licensed premises are considered those selling alcohol for on and or off premise consumption; examples include pubs, bars, nightclubs, hotels, off licenses, supermarkets, con- venience stores, restaurants, cafes, takeaways, cinemas and social clubs. Sherman (1995, p 36) defines crime hot

spots as ‘small places in which the occurrence of crime is so frequent that it is highly predictable, at least over a 1-year period and this paper examines hot spots over 12–36  months. In addition to the known geographical clustering of crime near licensed premises, NTE hot spot areas also exhibit clear temporal patterns, especially on Friday and Saturday evenings and early mornings, which correspond with premise closing times (Block and Block, 1995; Newton and Hirschfield 2009b; Popova, Giesbre- cht, Bekmuradov, & Patra, 2009; Uittenbogaard and Cec- cato, 2012; Conrow, Aldstadt, & Mendoza, 2015). Thus there are clear spatial and temporal patterns to NTE crime hot spots.

There is a sound theoretical basis for the presence of hot spots in the vicinity of licensed premises. Routine activity theory (Cohen and Felson, 1979) and crime pattern the- ory (Brantingham and Brantingham, 1993) contend that persons, both potential offenders and victims, exhibit sys- tematic movement patterns governed by their day to day undertakings, termed routine activities. Certain places

Open Access

*Correspondence: a.d.newton@hud.ac.uk The Applied Criminology Centre, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK

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are frequented regularly, for example home, place of work or leisure, termed activity nodes. The routes travelled between nodes are known as paths. This movement devel- ops a person’s awareness space, and crime is shown to be more likely on the edges of these activity nodes (Bow- ers, 2014). Places at which several offenders and victims converge form multiple awareness spaces, which increase the likelihood of crime. Eck, Clarke, and Guerette (2007) identify a number of ‘risky facilities’ where concentrations of crime are evident. Indeed, a small minority of facili- ties contribute the majority of offences at all risky facili- ties, termed the ‘iron law of troublesome places’ (Wilcox and Eck, 2011: 476). Examples include shopping centres, busy road junctions, hospitals, schools, train and bus sta- tions, and entertainment districts. Places with clusters of licensed premises represent recreational activity nodes, where there is a convergence of people in time and space. This coming together may create unplanned but favoura- ble crime opportunities, termed crime generators; or draw in offenders to bars and localities with known opportuni- ties for offending, termed crime attractors (Brantingham & Brantingham, 1995). Within NTE areas both of these eventualities are plausible.

A number of explanations exist for the occurrence of crime in NTE areas (for good overviews see Finney, 2004; Graham & Homel, 2008). These include: cultural factors, relating to societies use and acceptance of alcohol; per- son factors based on an individual’s responses and beliefs about alcohol consumption; the psychopharmacologi- cal properties of alcohol and their influence on an indi- vidual’s behaviour; and contextual factors, the physical and social circumstances of where and when alcohol is consumed. Recently a focus for NTE research has been on premise density and premise opening hours. Explana- tions for crime have focussed on: NTE places deemed to have ‘too many’ licensed premises, those saturated with a high density of premises (Livingston, 2008; Pridemore & Grubesic, 2013); and, premises open ‘too long’, with con- cerns over the length of time premises can remain open for, based around extensions granted in trading hours (Chikritzhs & Stockwell, 2002; Holmes et al., 2014). What is clear is the relationship between crime and alcohol is multi-faceted. A useful explanation is offered by Elvins and Hadfield (2003) who suggest a combination of fac- tors are likely account for crime in NTE areas, including: places with high densities of licensed premises in urban areas; the convergence of large number of persons at these places; crowding of persons within drinking ven- ues in close proximity in confined spaces, often leading to ‘vertical drinking’; the consumption of alcohol, often in large quantities; poor management of NTE places; and, the cumulative build up of ‘environmental stresses’ over the course of an evening.

Efforts to tackle problems of crime in the NTE have predominantly but not exclusively focussed on: better place management (Madensen & Eck, 2008); alcohol edu- cation and awareness schemes; regulation of licensing, legislation and enforcement (Hadfield and Newton 2010); increasing the costs of unit prices of alcohol (Booth et al., 2008); regulating the number of, and opening times of premises (Chikritzhs & Stockwell, 2002); and high vis- ibility police patrols. Whilst the merits of each approach have and will continue to be debated in the literature (see Graham & Homel, 2008; Humphreys & Eisner, 2014; Hol- mes et  al., 2014), the focus of this paper is on the use of police patrols in NTE areas.

A recent movement in policing has been a resurgence of hot spot policing, ‘targeted on foot patrols’, fuelled by the willingness of a number of police forces to implement randomised control trials (RCTs) of hot spot policing effectiveness (Ratcliffe, Taniguchi, Groff, & Wood, 2011; Braga, Papachristos, & Hureau, 2012; Groff et  al., 2015). Successes are evident for hot spot policing targeting bur- glary, repeat calls for service, nuisance bars, drugs, and violent crime, in particular when focussed on hot spots defined tightly in both place and time. A caveat identi- fied in the literature is that the effectiveness of the polic- ing tactic used often is dependent on the type of hot spot policed.

The process of hot spot policing involves identifying hot spot areas, and then subsequently targeting patrols at these places in a systematic fashion. It is contended here that this reflects more general current trends in policing,1 of using evidence gleaned from crime analysis or crime intelligence to inform police response. Many including the author advocate a problem solving/evidence based approach to policing and crime reduction. Two of the most well know examples of this are Problem Orientated Policing (Goldstein, 1990) and Intelligence Led Policing (Ratcliffe, 2008). At the simplest level of explanation, the analyst or police officer is encouraged to: firstly identify a crime problem through some form of suitable analysis of crime or other data; then, to further examine the identi- fied problem to understand the mechanisms driving it and the context of its setting; the next step is to identify and implement possible solutions; and the final stage is to monitor and or evaluate the effectiveness of the measure implemented.

This paper focusses on the first stage of the pro- cess, known as ‘scanning’ in the SARA model (Ashby & Chainey, 2012) or ‘Intelligence’ in the 5Is approach

1 In the UK the College of Policing has recently launched the What Works Crime Reduction Centre, http://whatworks.college.police.uk/Pages/default. aspx; the US has a long standing Centre for Problem Orientated Policing (POP) http://www.popcenter.org/about/?p=whatiscpop; and the Society of Evidence Based Policing launched in 2012 http://www.sebp.police.uk/.

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(Ekblom, 2011). The process of identifying crime hot spots for subsequent deployment of hot spot policing tends to be atemporal. This is a reflection of both soft- ware availability and analytical skills (Newton and Fel- son, 2015). Furthermore, sample sizes are larger when crime is not dissected by time of day, which increases the robustness of hot spot analysis. Moreover, once a crime hot spot has been identified, subsequent analysis by time of day enables identification of when to implement hot spot policing at detected hot spots. Perhaps an important component of high crime places overlooked here is that analysts are encouraged to be crime specific, and thus tend to examine single crime classifications, for example violent crime. This is not unexpected, the spatial patterns of burglary will not closely resemble those of street rob- bery, nor should they be expected to.

However, areas with concentrations of licensed prem- ises are known to be highly criminogenic and not just for violence. Associations have been demonstrated between licensed premises and a number of crime types, most notably violence and aggression, but also criminal dam- age, disorder, and drug use (Scott & Dedel, 2006; Graham & Homel, 2008; Newton and Hirschfield, 2009b). Indeed Yang (2010) demonstrated longitudinally that correlations in time and place exist between violence and disorder. Furthermore, offenders have been shown to be versatile in the types of crime they commit (Roach & Pease, 2014), and indeed police may overestimate the specialised nature of offending. Thus, if offenders are known to commit sev- eral types of crime, and several types of crimes have been shown to be related to NTE places, should analysis of crime at these places be focussed on single crime classifications?

This discussion has demonstrated that: particular NTE places experience more than one crime type; offenders are known to be versatile in the types of crime they com- mit, and that one of the limitations of spatio-temporal analysis is that segmenting data in both time and place can substantially reduce sample size. Combing several ‘related’ crime types into a single analysis is a possible solution here. Therefore, this research aims to investi- gate whether multi-classification crime (MCC) hot spots exist near licensed premises, and if so, do they exhibit distinctive spatio-temporal patterns. More specifically, it examines four crime types known to be associated with licensed premises, namely violence against the person, criminal damage, drugs, and disorder incidents (anti- social behaviour), to ascertain how these crimes manifest in NTE hot spots both in time and place. The following research questions were formulated for this study.

Research questions:

• Is there spatial correspondence between the locations of hot spots for different crime and disorder classi-

fications near licensed premises (violence, criminal damage, disorder and drugs)?

• Do MCC hot spots correspond temporally, that is to say, when a place is a hot spot for violence, is it also a hot spot for criminal damage?

• Do MCC hot spots fluctuate over time, for example does a place experience criminal damage, and then later in the day or a different day of the week experi- ence violence against the person?

Methods Data This study used crime and disorder data for an anonymised case study area in England. Its residential population is approximately 1.5  million persons and includes a mixture of large towns and several rural vil- lages, covering a geographical area of approximately 600  km2. Offence data were obtained for the 3  years period 1st January 2007 to 31st December 2009 for crimes categorised as violence against the person (VAP), criminal damage (CD), and drugs; based on the UK Home Office 2010 counting rules for recorded crime. Incident data for calls for service for disorder (non-crimed) were also obtained for the 12  month period 1st January to 31st December 2007. An addi- tional dataset used was a licensed premise database for the case study area, and 6047 premises were iden- tified as ‘open’ during the considered time period (2007–2009).

Data processing The crime and disorder data were cleaned to include only those containing a known time of offence, and those with geo-spatial references outside of the case study area were also excluded. This resulted in a sam- ple of: 64,440 VAP offences; 83,159 CD offences; 18,270 drugs offences, and 346,022 disorder incidents. A Geo- graphical Information Science (GIS) software program was used to calculate the distance from each offence or incident to the nearest licensed premise, and the results of this are shown in Table  1. This demonstrates that for all crime and disorder types the mean distance to a licensed premise was approximately 130–170  m. Median distances ranged from 80 to 125 m. Considering these distances and other studies using buffer analysis to examine crime near licensed premises (Newton and Hirschfield, 2009b; Ratcliffe, 2012), a 250  m thresh- old was selected as an appropriate distance to repre- sent crime and disorder ‘near’ licensed premises in this study. As shown in Table  2, for all crime and dis- order types analysed, 50–65 % of all crime and disorder offences (varying by crime or disorder classification) occurred within 250 m of a licensed premise.

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The temporal nature of offences It was previously identified that NTE hot spots exhibit distinct spatial and temporal patterns, with crime peaks evident on Friday and Saturday evening, or the early hours of Saturday and Sunday morning, around premise closing times. In order to examine this further the time of all crime and disorder in NTE hot spots (within 250  m) were re-coded with a value representing both the time of day and day of week (termed week-hour, ‘WH’ for this study). There are a total of 168 h in a week, and thus each crime and disorder incident was assigned a WH2 value from 6 to 173.

Figure  1 shows the weekly temporal distribution of each crime and disorder type and reveals distinctive pat- terns in the WH of VAP, CD, drugs and disorder. For all crime and disorder types there are clear peaks during the evening and early hours of the morning on all days. How- ever, there are some differences in the patterns observed; the highest peaks for disorder are on Friday evening fol- lowed by Saturday evening, with lower peaks from Sun- day though to Thursday; VAP peaks on Saturday evening, followed by Sunday, Saturday, and Monday, with lower peaks Tuesday to Thursday; drug offences peak on Satur- day evenings, followed by Friday and Sunday, with more

2 A value of 6 represents the time period 6.00 a.m. to 6.59 a.m. on a Sunday morning; 23 represents 11.00 p.m. to 11.59 p.m. on a Sunday evening; 24 represents midnight to 0.59 a.m. on a Monday morning; 47 represents 11.00 p.m. to 11.59 p.m. on a Monday evening; 48 is midnight to 0.59 a.m. on a Tuesday; and so forth. A look up reference for this is provided in Additional file 1: Appendix S1.

irregular peaks during the rest of the week; for CD the highest peaks are Sunday evening, followed by Saturday and Friday; peaks during the rest of the week are again lower, but the reduction is less than that of other crime types. Disorder, CD and drugs also exhibit two separate peaks during Saturday evenings which are not evident for VAP. CD tends to have two distinct peaks in the evening most days of the week, unlike disorder and VAP which have single evening peaks all days except Saturday. Over- all, there are clear and distinct temporal patterns evident for each crime type.

It is possible that using 3  years of data may skew the results as the temporal patterns of each crime may have changed over time. In order to test this the WH val- ues for each time period were compared by year, thus WH values for 2007 were compared with those of 2008 (2007–2008), and WH values for 2008 compared with those of 2009 (2008–2009). Mann–Whitney tests were used to compare the means (non-parametric independ- ent samples). The results were as follows: for VAP 2007– 2008, z = − 0.253, p = 0.8; for VAP 2008–2009 z = − 0.7, p  =  0.48; for CD 2007–2008 z  =  −  0.35, p  =  0.25; for CD 2008–2009 z = −0.18, p = 0.6, for drugs 2007–2008 z = −1.5, p = 0.12, and for drugs 2008–2009 z = −0.46, p = 0.09. This suggests that there were no significant dif- ferences in WH crime times for VAP, CD or drugs over any of the comparative time periods, and therefore that the WH temporal patterns of each of the three crime types remained stable over the 3  years period. As only 12  months of data were available for disorder, tests for this were not conducted. However, it is assumed that these are also likely to have remained stable, based on the stability of the recorded crime results.

Identifying hot‑spots A range of methods can be used to identify crime hot spots including thematic mapping, kernel density estima- tions, nearest neighbourhood hierarchical clustering, and the Getis Ord GI* statistic (Eck, Chainey, Cameron, & Wilson, 2005; Chainey & Ratcliffe, 2005; Levine, 2015). For this analysis the Getis-Ord GI* method (Getis & Ord, 1992; Ratcliffe, 2010; Chainey, 2014) was used to identify significant hot spot areas of crime around licensed prem- ises. The advantage of this method over other hot spot mapping techniques is that it identifies small grid areas that are statistically significant, and returns a z3 score that measures the strength or intensity of the clustering and its significance. This method also produces tightly defined hot spot areas appropriate for hot spot policing.

3 The higher the z score the greater the clustering, and a z score equal to or above 1.960 is significant at the 95 % confidence level, and equal to or above 2.576 significant at the 99 % level.

Table 1 Average distances of  offences to  licensed prem- ises (metres)

Offence/incident N Distance to nearest licensed premise (m)

Mean Median SD

Disorder 346,022 167.5 119.5 197.7

Violence against person 64,640 132.4 84.2 173.4

Criminal damage 83,159 163.4 124.6 178.6

Drugs 18,270 149.1 85.4 225.6

Table 2 Percentage of offences and incidents near licensed premises (within 250 m)

Offence/incident N < 250 m Percentage Total N

Disorder 188,756 54.6 346,022

Violence against person 41,538 64.3 64,640

Criminal damage 44,570 53.6 83,159

Drugs 11,870 65.0 18,270

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Using the GIS software a 250 m grid matrix was gener- ated across the study area resulting in 104,958 grids. A GIS was used to count the number of crimes in each grid repeated for VAP, CD drug offences, and disorder inci- dents. This analysis used all crimes within the case study area. An alternative approach would be to only select crimes within 250  m of premises, but this may skew the hot spot generation. For each of the four classifications of crime and disorder, GI* hot spots were calculated4 using ArcGIS spatial statistics toolbox. Figure 2 shows the case study area, the 250  m grids, and the location of licensed premises. The results of the hot spot analysis are shown in Fig. 3a–d, which maps the location of hot spots. Note in these maps only grids which are clustered with 99  % confidence or greater (z ≥ 2.576) are displayed, with hot spots superimposed by the locations of licensed premises

4 The parameters for this were to use a fixed distance band, with a threshold (spatial lag) of 355 m (based on 250 m grids).

in the case study area. The images are rotated for anonymity.

There are distinct spatial hot spots evident in Fig.  3, which correlate with urban areas containing high densi- ties of licensed premises. Upon first glance similar hot spot patterns are apparent for VAP, CD, disorder and drugs. However a more detailed visual inspection reveals subtle differences. The extent of the hot spots around urban centres is greater for VAP and disorder, and more tightly concentrated for drugs and CD. Towards the bot- tom of the case study area there are hot spots of VAP, CD and disorder, but not for drug offences. Towards the right of the map there is an area with large concentrations of VAP, drugs, disorder, and CD, but close inspection reveals the extent of this is much more spread for VAP than the other three crime types. On these maps only grid cells that are significant hot spots at 99 % confidence interval are displayed. There were 2970 such cells, and these cells are now examined further.

Fig. 1 Weekly-hourly2 crime frequencies (Sunday to Saturday) four each of four crime types (a–d). CD criminal damage, VAP violence against person

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Results The first research question was to examine the degree to which hot spots of different crime classifications co- exist spatially, in other words occur at the same place. Analysis of all grids in the study area using Spearman’s Rank revealed strong statistically significant correlations for each crime and disorder type (Table 3) with the loca- tion of licensed premises; the strongest relationship was between premises and disorder, followed by CD, VAP, and drugs. All crime and disorder types were correlated with premises at R > 0.7, p < 0.01 which indicates a high degree of correlation between the location of licensed premises, and crime and disorder events in the case study area.

Further analysis was undertaken using only grids sig- nificant at the 99  % level (2970) which contained a sig- nificant hot spot for at least one of the four crime and disorder classifications examined. 2435 grids contained a licensed premise, and unsurprisingly all of these grids were identified as a statistically significant hot spot for at least one crime type. Further analysis revealed 2485 grids of the 2970 were hot spots for VAP (83  %), 2385 for CD (80  %), 2160 for disorder (72.7  %), and 1307 for drugs (44 %). Each grid could contain a hot spot for one, two, three, or all four crime types, and a Conjunctive Case Analysis (CCA, Miethe, Hart, & Regoeczi, 2008) was used to examine the 256 (44) possible combinations

here.5 The results of this are presented in Table  4. This found 1214 grids, 40 % of the significant crime hot spot grids, were statistically significant hot spots for all four crime classifications. A further 663 grids (22  %) were significant hot spots for at least three types of crime. This shows strong evidence of an overlap in the location of hot spots for VAP, disorder, CD and drugs near licensed premises and suggests strong evidence in the case study area that MCC hot spots are present near licensed premises.

Profiling the ‘hottest’ hot spots The research has thus far demonstrated that MCC hot spots are present spatially, thus hot spots of VAP are also hot spots of CD for example. The purpose of research questions two and three are to further examine the MCC hot spots temporally, to ascertain whether the different crime types found in the MCC hot spots occur at the same time, at different times of day, or different days of the week. Therefore the top twenty hot spot grids were identified for further profiling. To determine these top twenty cells, the ‘hottest hot spots’, cells that were statisti- cally significant hot spots for all four types of crime and disorder (VAP, CD, drugs and disorder) were identified. There were 1214 of these cells. Cells with the highest combined z scores6 were selected to represent the twenty ‘hottest’ hot spots. A profile of each of these cells is pro- vided in Table 5. At these twenty 250 m grid cells over the 3 years period (12 months for disorder) there were a high number of crime and disorder incidents ranging from: 78 to 802 for VAP; 252 to 1736 for disorder; 37 to 182 for CD; and 8 to 265 for drugs. The number of license prem- ises in each grid ranged from a minimum of 3 to a maxi- mum of 96. In order to examine the temporal profiles of these cells, the WH values of each crime type for each cell was calculated, and the results of this are presented in Fig. 4. The frequencies of offences by time of day were divided into five equal quintiles, and these are colour coded as per the table key. Those in red represent the 20  % of times with the highest levels of crime for each classification, VAP, CD, disorder and drugs.

Figure  4 shows the temporal profiles of the 20 hot- test MCC hot spots. There were seven WH time periods (each WH is 1  h of the week) that had high levels (col- oured red in Figure) of crime and disorder for all four crime and disorder categories at the same time and same place: Thursday 2.00 a.m. to 2.59 a.m.; Friday 1.00

5 An alternative here may be the use of Multiple Classification Analysis (MCA), also known as factorial ANOVA. However, as this is used for linear data, and spatial crime data often follows a negative binomial distribution, this was not considered appropriate here. 6 Calculated as combined z score of each of four crime classifications from GI* analysis.

Fig. 2 Case study area with 250 m grids and licensed premises

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a.m. to 2.59 a.m.; and Saturday midnight to 02.59 a.m. There were some further distinctive temporal patterns identified in the MCC hot spots. Disorder is prevalent

Wednesday through Sunday evenings; on Sunday peaks were at 7.00 p.m., 9.00 p.m., and from midnight to 2.59 a.m.; on Wednesday from 1.00 a.m. to 2.59 a.m.; on Thursday from midnight to 3.59 a.m.; on Friday from 6.00 p.m. until 2.59 a.m.; and then on Saturday from 7.00 p.m. until 3.59 a.m. Thus there is an extended period of disor- der on Friday and Saturday, which last for several hours. There are also some disorder peaks on Tuesday afternoon not found for other crime types. VAP followed similar patterns to that of disorder. However, the length of the peaks was shorter, occurring slightly later on Sunday until 3.59 a.m., and generally VAP starts later in the evening

Fig. 3 GI* hot spot maps of crime and licensed premises by each of four crime types (a–d) (>99 % significant hot spots shown). CD criminal dam- age, VAP violence against person

Table 3 Correlations between licensed premises and crime hot spots (250 m grid based analyses)

Spearman’s Rho correlation with licensed premises

VAP CD Drugs Disorder

N 10,948 10,948 10,948 10,948

P 0.805 0.913 0.712 0.937

Sig 0.001 0.001 0.001 0.001

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than disorder. The corresponding periods of disorder and violence also seem to occur 1  h later on a Saturday than they do on a Friday. Drugs followed a more unusual pat- tern; offences occurred on Thursday to Sunday evenings correlating with VAP and disorder, and there were some unique peaks early Friday morning at 9.00 a.m. and 11.00 a.m. Drug offence peaks tended to be for 1  h only with the exception of Thursday through Sunday. CD tended

to occur at much earlier time periods during the day, for example: on Sunday between 6.00 p.m. and 8.00 p.m., and then 10.00 p.m. to midnight; at 5.00 p.m. on a Monday and Thursday; and 5.00 p.m. and 7.00 p.m. on a Saturday.

Discussion of findings The top 20 ‘hottest’ hot spots identified (based on 250 m grid cells) accounted for less than half a percent of all the

Table 4 Hot spot grids (99 % significance) and crime and disorder types

Crime type VAP CD Disorder Drugs Total

Number of grids 2485 2385 2160 1307 2970

Percentage of grids (%) 83.7 80.3 72.7 44.0 100.0

CCA analysis of grids by hot spot types

VAP CD Disorder Drugs Number of cells

Presence (1) or absence of a hot spot (0) of a hot spot

1 1 1 1 1214

1 1 1 0 609

1 0 1 1 25

0 1 1 1 16

1 1 0 1 5

1 0 1 1 5

0 1 1 1 3

Table 5 Top 20 grid profiles (the hottest hot spots)

Z score based on Getis Ord (GI*) hot spot significance (>2.576 = 99 % significant)

Grid_ID Premises (N) VAP (N) VAP (z score)

Disorder (N) Disorder (z score)

CD (N) CD (z score)

Drugs (N) Drugs (z score)

Total z All Crime (N)

54124 63 530 106.86 784 79.88 143 53.27 115 88.87 4920.74 1602

54125 17 146 110.88 800 85.18 58 71.53 42 98.01 7206.67 1056

54126 5 92 53.64 338 54.96 85 60.76 28 51.21 3220.20 553

54417 19 187 92.54 532 64.97 37 36.61 39 78.63 3035.92 809

54418 44 756 126.20 1736 94.52 172 58.94 187 109.04 6647.91 2871

54419 35 468 120.32 876 90.04 182 71.75 129 103.21 7615.46 1685

54420 3 126 55.78 384 53.56 126 57.49 54 52.79 3143.88 704

54712 9 224 103.58 498 83.26 101 55.67 53 92.81 5353.51 887

54713 49 78 95.60 266 74.95 67 66.00 22 85.40 5807.23 439

54714 8 87 50.04 252 41.41 56 52.54 26 49.79 2707.54 427

55006 75 124 56.56 472 50.91 90 43.30 27 62.40 2809.36 718

55007 96 83 54.28 348 50.54 72 51.74 30 53.89 2893.05 538

55301 48 205 58.52 266 56.72 103 60.62 49 49.79 3133.43 635

55595 7 96 54.69 338 50.27 79 57.71 8 46.01 2760.01 527

62448 16 202 78.72 542 57.60 93 36.24 56 73.11 2786.24 910

62449 8 181 88.94 642 68.99 63 43.52 83 87.14 3950.09 981

62450 4 100 73.80 436 62.44 49 41.63 28 81.31 3520.88 622

62742 11 185 80.64 458 61.20 66 38.65 35 81.78 3302.21 756

62743 22 802 90.40 1234 76.17 182 49.05 265 94.39 4796.07 2539

62744 23 166 77.13 1018 64.33 78 41.48 42 82.57 3566.55 1319

Totals 562 4838 12,220 1902 1318 20,578

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grids that contained a crime or disorder incident (6165 cells), yet contained over 5  % of all crime and disor- der incidents analysed across the entire case study area. Moreover, a 7 h time window (Thursday 2.00 a.m. to 2.59 a.m., Friday 1.00 a.m. to 2.59 a.m., and Saturday midnight to 02.59 a.m.), which represented 4  % of the 168 WH intervals over a week), accounted for nearly 15  % of all crimes at these top 20 hot spots alone. Therefore crime is highly concentrated at these times in these places. This 7  h time frame is important as at these times MCC hot spots co-existed both in time and space, for all four crime classifications examined. The most plausible explanations for this are the high volumes of persons likely to be pre- sent at these times and places create multiple opportuni- ties for crime, supported by crime pattern theory, routine activity theory, and the non-specialised nature of many offenders. Indeed conterminously at the same places and locations there may be suitable targets and lack of capa- ble guardians in these micro places for drugs, criminal damage, disorder and violence. At these time periods hot spot policing may require a range of tactics, due to the diverse nature of multiple crime types prevalent.

At other times of the day MCC hot spots were also evident but not for all crime types. On Friday and Satur- day afternoons disorder was evident from 6.00 p.m. until the early hours of the morning, whereas violent offences tended to occur after midnight. This may be reflective of a number of factors, perhaps disorder is a signal crime of later violence (similar to the Innes, 2004). Alternatively later in the evening, the number of persons in the NTE

settings may increase, but to fewer locations; cumula- tively more alcohol is consumed, and the result that dis- order may escalate into more serious violence. Criminal damage offences occur earlier in the evening than vio- lence. An interesting finding is the apparent spatial lag between Friday and Saturday; both have similar patterns but offences are approximately 1  h earlier on Fridays. This may reflect cultural difference and routines; those who partake in the NTE on Friday’s may do so straight from work, whereas those who go out on Saturdays may have constrained activities on Saturday afternoons, or go out with different friend groups or their partners, thus drinking in the NTE may start slightly later on Saturdays.

There are a number of limitations to this study. Police recorded crime and disorder data is known to be sub- ject to both underreporting and errors in the accuracy of geo-coding (Chainey and Ratcliffe, 2005; Newton and Hirschfield, 2009a). It would be useful to supplement this data with hospital accident and emergency data (A & E) or ambulance data. According to Shepherd, Ali, Hughes, and Levers (1993) six in seven of those attending A & E for violent injuries are not in recorded crime statistics. However, health data does not always contain location specific information on when and where crime occurs, and this data is not always available to the police. It is suggested a more robust future analysis incorporating A & E data is likely to confirm the presence of MCC hot spots near licensed premised.

There are limitations in the arbitrary 250  m buffer distance, and the use of the GI* statistic. Analysis using

Time of Day 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 Sunday Disorder 22 30 18 22 26 26 46 48 64 48 50 50 80 92 74 96 74 72 112 110 128 54 60 20

VAP 4 1 8 2 4 6 9 6 15 34 24 22 31 29 43 37 62 97 71 113 90 74 35 10 CD 4 5 2 1 4 1 6 5 6 8 6 19 22 25 15 17 22 15 27 14 14 18 21 5

Drugs 0 2 0 0 2 0 2 5 2 4 4 7 9 9 11 15 8 41 16 17 10 9 1 0

Monday Disorder 4 10 12 30 34 44 76 58 50 72 50 78 76 68 64 66 62 74 60 88 80 48 16 4 VAP 6 3 6 6 5 9 12 11 21 19 27 21 23 19 19 27 36 69 56 58 57 61 21 3 CD 1 4 3 7 1 1 9 6 6 10 12 30 18 18 16 16 17 27 14 23 12 16 8 6

Drugs 0 0 0 0 2 5 2 4 7 10 11 8 7 6 4 9 6 13 19 20 6 8 2 0

Tuesday Disorder 6 6 26 24 32 64 60 94 102 64 104 80 72 84 74 64 52 64 56 68 76 58 20 6 VAP 1 3 2 3 10 12 10 17 21 29 20 18 15 28 22 25 41 37 37 48 50 40 20 1 CD 0 0 3 5 3 5 6 7 9 6 12 27 20 11 9 18 15 13 16 14 12 10 6 5

Drugs 2 0 1 0 4 0 6 1 9 9 6 2 7 3 11 7 16 9 28 12 0 1 6 0

Wednesday Disorder 4 12 20 30 58 52 74 84 90 86 66 54 72 62 82 88 68 74 74 94 100 60 22 8 VAP 2 2 1 7 6 5 9 17 24 33 21 15 21 29 27 29 24 50 35 44 51 39 16 5 CD 1 3 0 4 4 7 6 5 6 12 9 18 23 9 11 17 15 20 14 18 16 7 8 0

Drugs 0 0 0 8 4 5 9 5 5 8 4 2 3 8 13 7 8 3 4 8 5 3 4 0

Thursday Disorder 6 2 14 32 44 50 52 80 72 68 82 66 82 66 62 66 58 80 132 154 138 166 80 22 VAP 4 0 4 7 13 10 14 7 13 22 25 16 18 10 21 23 26 37 73 82 90 83 37 7 CD 1 2 4 2 3 0 3 4 3 3 13 27 8 19 10 10 8 13 18 19 22 24 12 3

Drugs 0 0 1 7 3 6 5 9 7 2 3 7 0 4 8 4 2 11 52 22 18 5 4 3

Friday Disorder 6 6 22 28 40 60 42 54 74 88 90 68 92 122 128 140 140 172 226 228 190 150 84 30 VAP 1 5 1 4 5 15 17 10 18 20 17 19 15 10 20 29 37 48 92 112 101 90 45 6 CD 1 4 4 5 5 1 10 5 11 5 14 22 19 11 17 15 17 23 21 31 23 18 10 6

Drugs 0 0 2 14 6 12 5 8 3 2 2 4 4 7 2 4 10 22 24 42 16 20 10 2

Saturday Disorder 22 14 26 22 48 50 70 66 92 82 96 118 82 106 108 104 146 202 286 338 312 244 142 54 VAP 2 1 4 5 5 8 11 7 20 21 16 28 17 25 32 44 37 65 123 178 173 121 58 17 CD 2 1 2 1 0 4 6 8 6 5 8 30 16 26 10 19 22 15 33 35 28 28 26 14

Drugs 0 0 0 3 0 5 0 4 4 4 5 5 6 12 11 11 13 36 45 76 51 27 11 6

Fig. 4 The ‘Hottest’ hot spot profiles by time of day and crime type (MCC hot spots): values indicate crime counts

Page 10 of 12Newton Crime Sci (2015) 4:30

alternative buffers (100  m, 400  m) found no discernible differences in patterns of crime observed. A possible lim- itation of the GI* is it identifies too many hot spot areas significant at 99 %. Future analysis could compare the use of a corrected Bonferonni approach rather than Gausian for determining Z-score (Chainey, 2014). This technique also identifies cells that have low crime counts, as it is based on neighbourhoods surrounding cells rather than just inside a cell in its calculation; alternative hot spot techniques should be used explored and compare MCC hot spots.

Conclusions This paper has presented strong evidence for the pres- ence of MCC hot spots near clusters of premises, known to be particularly criminogenic places. This is not surprising, given the literature on crime oppor- tunity, crime pattern theory, routine activities, risky facilities, and crime attractors and generators. How- ever, what this research does begin to question is the conventional wisdom of hot spot analysis and hot spot policing being wholly crime specific, using single crime classifications at highly criminogenic places. Hot spots of VAP, CD, drugs and disorder were identified at the same locations in the study area, near to licensed prem- ises. Moreover, the results show that at particular time periods (seven hourly periods of a 168  h  week) all four crime and disorder types occurred conterminously in both time and space. At other times only one or two hot spots were present, and at some times of the day hot spots were not found. This has clear implications for hot spot policing in terms of tactics used and when best to target resources. Further exploration and explanation of these patterns is warranted to assist in effective hot spot policing deployment and tactics at MCC hot spot locations.

A range of methods could be incorporated to refine future analysis. In particular more statistical time based analysis should test: whether MCCs are clustered in time and space; if the space–time clustering occurs continu- ously or within defined time periods; or if there is a space time interaction (Levine, 2015). Suggested tests here are to use the Knox and Mantel tests to examine the interac- tions between licensed premises and the MCC hot spots identified. Furthermore circular statistics could be incor- porated, for example the use of Rayleigh’s test to exam- ine significant clustering by time of day, or the Watsons U test to examine for differences in two temporal data- sets (Wuschke, Clare, & Garis, 2013) by month, season or year.

As observed by Townsley (2008) characteristics of crime hot spots can alter over time, with periods of emer- gence, persistence, and decline. Therefore any future

analysis that is developed should also consider how MCC hot spots may emerge and dissipate over time near licensed premises, and whether they are stable hot spots or occur more sporadically. Moreover, there are seasonal variations in crime patterns and discretionary routines influenced by daylight hours and temperature (Tompson & Bowers, 2015) and this may influence MCC hot spots near licensed premises.

At present there are a number of studies using predic- tive crime mapping or crime forecasting (Chainey, 2014). Perhaps predicting MCC hot spots should form part of this research. Indeed, Shekhar, Mohan, Oliver, and Zhou (2012) attempt to do similar, by testing for the emergence of crime trends with multiple crime types. MCC hot spots have been identified near licensed premises, but perhaps alternatives exist, for example: burglary hot spot analysis could also consider patterns of theft of, and theft from vehicle; the locations of street robbery could be compared with pickpocketing and theft from person; at drug locations a number of crimes associated with illicit trade could be examined. In other places known to be criminogenic, it may be important to identify alternative configurations of MCC hot spots.

VAP, CD, drugs and disorder have all been shown to relate to licensed premises, but more detailed informa- tion on types of premises, density and opening hours should also be taken into account before prioritising hot spot policing. Indeed a final question that remains is the implications of this research for hot spot polic- ing and resource targeting. It is possible to continue to police hot spots based on single crime types effectively. It is not known if focussing on the places and times of MCC hot spots is likely to be more effective in reduc- ing crime, as theoretically more offenders are likely to be present at MCC than single crime hot spots, thus police may be more likely to deter or apprehend offend- ers at MCC hot spots. However, tactically it may be more difficult to police MCC areas, targeting multiple types of crime may require several concurrent tactics that may conflict. MCC hot spots have been shown to contain different crime types over time, criminal dam- age and disorder earlier in the day and violence at later times. It is not known if early intervention here would reduce crime at later times of the day, or if police would need to remain at these MCC hot spots for longer time periods. It is suggested an RCT of MCC hots spot patrols near licensed premises may shed some light on this question.

Additional file

Additional file 1. Appendix 1: Look up table for ‘WH’ weekly-hour values in Fig. 1.

Page 11 of 12Newton Crime Sci (2015) 4:30

Abbreviations CD: criminal damage; GIS: geographical information science; MCC: multi-clas- sification crime; NTE: night-time economy; VAP: violence against the person; WH: week hour.

Competing interests The author declares no competing interests.

Received: 3 July 2015 Accepted: 30 September 2015

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