Offenders’ routine activity locations—where they live, work, and carry out other non-criminal activities—play a significant role in shaping their crime locations [1
]. Understanding the nature and spatial distribution of individual offenders’ activity locations thus has important implications for crime and policing policy and practice: for example, identifying places that individuals are at higher risk of offending, or identifying, in police investigations, suspects who would be more likely to have committed a given crime in a given location [4
]. Yet little research to date has studied the full range of offenders’ routine activity locations, or addressed practically important questions such as the extent to which offenders’ activity locations are shared or can be differentiated, as we elaborate below.
Many police jurisdictions maintain databases which store the details of calls for service, criminal investigations, intelligence reports, arrests, stops/searches and other routine police activities that involve interacting with members of the public [5
]. The details of such records can include information about the locations of offenses, incidents, police interactions, the home addresses of the parties involved and even where they work or attend school. Provided the data contains location and timing information with sufficient specificity, and can be collated if stored across multiple databases, it could be a rich source of information about offenders’ activity spaces (This paper focuses on locations where offenders have carried out routine activities, or travelled between them, collectively making up their ‘activity space’ [1
]. The related concept of ‘awareness space’ additionally includes places known through sources other than direct experience [1
], which are not identifiable from the present data.), particularly for those who have frequently come into contact with police as offenders, or as victims, witnesses, or members of the community. However, such data have not yet been used to study offenders’ activity locations other than their residential addresses and prior crime locations [3
The aims of this paper are therefore two-fold: first, from a methodological perspective, to consider the range of routine activity locations that can be identified using the kind of police data described above; second, to answer several exploratory research questions relating to the nature and distribution of offenders’ activity locations as revealed by these data. We examine the distributions of activity locations of offenders identified for a burglary, robbery or sexual offense over timeframes of varying lengths prior to the most recent offense and geographic units of analysis of varying sizes. We focus on crimes that typically involve some form of search for a suitable victim or target (rather than the targeting of a specific victim already known to the offender) which is influenced by the offenders’ prior activity locations. The subset of crime types included here reflect a mixture of environments and land uses (residential, business/retail), motivations (material/non-material) and planning (strongly versus weakly premeditated) and thus are likely to be representative of a wider set of crime types. We also explore the extent to which the available activity locations of these offenders differentiate between them. The question of differentiation is particularly important for geographic profiling methods that seek to predict, given the locations of unsolved crimes, who may have committed them or where the offender might be found [10
]. If activity spaces were shared by many offenders, they would be of little use for prioritizing among suspects.
We begin with a review of the small extant literature that has examined offenders’ routine activity spaces and expand on the concept of differentiation. We then provide details of the data used in this research and the methods used in the analyses before presenting and discussing the results. We conclude by highlighting the benefits and limitations of the data for both research and practice.
1.1. Offenders’ Activity Spaces
Understanding the nature of offenders’ activity spaces beyond their homes is an important research endeavor, given well established links between their activity spaces and offense locations. As Routine Activities Theory asserts, crime requires a convergence of motivated offenders and potential targets in time and space [12
]. Furthermore, as Crime Pattern Theory explains, this convergence happens when and where the routine activities of offenders (forming their activity space) overlap with crime opportunities [1
]. Offenders’ non-criminal and criminal activities alike equip them with knowledge of possible crime locations that is brought to bear on future decisions about where to commit crime [1
]. Studies of aggregate crime patterns confirm that crime concentrates in or near routine activity locations likely to be common to many offenders, such as central business districts, shopping precincts and transit hubs [13
]. At an individual level, many studies demonstrate that people are more likely to commit crime closer to their home than further away [15
]. Furthermore, interview studies of small samples of offenders have highlighted that offenders tend to commit crimes at or near a range of other routine activity locations such as work, friends and family members’ homes, recreation sites, or prior crimes [16
]. Studies using Discrete Spatial Choice Modelling (DSCM) have even quantified the increase in odds of a location being chosen based on its proximity to offenders’ previous addresses, their family members’ current or previous addresses, and the locations of their previous crimes, when controlling for proximity to their home [3
]. Two further DSCM studies, both on young offenders, incorporated additional types of activity nodes to confirm that the odds of crime increase with proximity to any activity node [27
However, few studies have described, quantitatively, offenders’ routine activity spaces in terms of the number of locations they frequent, or the geographic extent of these locations, over a given time period. Those that have vary in location, cohort, and method, but provide a rough baseline with which to compare the present study. For example, location tracking data from fourteen 19- to 44-year-old parolees on GPS monitoring in Florida revealed that offenders visited 4 non-home nodes (specific sites) on average (range 2 to 6) during the week prior to re-offending [29
]. Their activity spaces covered an average of 27 miles squared (median 12, range 0.2 to 70).
In the Netherlands, 78 young offenders aged 18–26 who participated in an online survey reported visiting 6 nodes (neighborhoods) on average (range 1 to 15) in the month preceding their offenses [28
]. These included home, school/work, friend/family residences and leisure activity nodes, with most offenders reporting at least one of each. The maximum distance between any individual offender’s nodes was 200 km (of a possible maximum distance between any two neighborhoods in the Netherlands of 300 km). In another Dutch interview-based study [27
], 70 13- to 16-year-olds who offended in the subsequent 4 years reported an average of 7 activity nodes (200 m × 200 m cells in a map grid covering the Hague) in a 4-day period preceding the interview (median 5, range 1 to 15). Almost 75% of their time was spent at just two nodes (home and typically school), producing a relatively short (average 3 km) “radius of gyration”, a measure of the size of an individual’s activity space weighted by time spent in each node.
Considering not the number or geographic range of nodes but their overlap between individuals, a UK study found that there was a high correlation between the aggregated self-reported awareness spaces of 17 prolific property offenders and 13 non-offenders from the same county [20
]. In other words, the nodes common to many offenders were also common to the general population in the study area. We expand on the issue of commonality versus differentiation in activity space in the next section.
1.2. Homology and Differentiation in Activity Spaces
In geographic profiling for criminal investigations, differentiation between suspects’ activity spaces would enable prioritization among suspects when comparing suspects’ known activity locations with the predicted base(s) of the offender [11
]. Differentiation would mean that suspects fit the “geographic profile” to varying degrees. Conversely, if offenders’ activity spaces are relatively homogenous, it would be difficult to prioritize among the many potential suspects whose activity spaces are all equally consistent with them having committed an offense(s) in a given location.
This issue has been raised in the behavioral profiling literature. The ability to infer an offender’s characteristics from attributes of the crime relies on core assumptions of homology and differentiation [31
]. Homology means offenders sharing certain crime attributes will also share certain personal characteristics; differentiation means differences in crime attributes indicate differences in offender attributes. The more distinctive (i.e., differentiating) the attribute(s)—of crime and criminal—the more reliably suspect pools can be narrowed [31
With respect to the spatial dimension of offending, homology means that offenders sharing the locations of their crimes will also share other parts of their activity spaces. For example, if two burglars commit an offense in the same street, homology means they are more likely to live in the same neighborhood, attend the same school, or visit the same shopping center, as compared to two burglars offending in different streets. The DSCM studies discussed above are suggestive of homology between offenders’ crime locations and activity spaces. Since activity node locations predict crime locations [27
], offenders with similar activity node locations would, logically, offend in similar locations, having gained awareness of the same crime opportunities. However, the reverse does not logically follow, though geographic profiling relies on inferring potential activity node locations from offense locations. Illustrative of this point, Costanzo et al. [34
] found that offenders with nearby home addresses tended to travel in a similar direction to offend, but that offenders with nearby offenses had not necessarily come from similar directions to offend.
Exploring the link between co-offenders’ activity spaces and crime locations, Lammers [35
] found that offenses committed at the same place and time (i.e., by co-offenders) were more likely to be committed in a neighborhood where at least two of the co-offenders had lived or committed a previous crime, than elsewhere. However, the co-offending pairs shared less than 50% of their residential or prior offense neighborhoods. Tayebi et al. [36
] found that offenders who were more closely connected in a co-offending network lived closer together and shared more home and past offense neighborhoods (however, Malm et al. [37
] found no correlation between network proximity and home proximity in a cannabis production network). Thus, past or present co-offending may entail similarity of some, but not all, elements of co-offenders’ activity spaces.
There is also evidence that offenders might be differentiated by their crime and routine activity locations. Nearby crimes are more likely to have been committed by the same offender than different offenders [38
]. That all people exhibit highly distinctive spatio-temporal routine activity patterns [43
], suggests that among offenders, activity spaces may also be distinctive.
However, neither the extent to which individual offenders’ activity spaces are shared (other than with co-offenders), nor the extent to which homology and differentiation are present in the relationship between offenders’ activity spaces and their crime locations, has been directly examined by any studies to date.
1.3. Present Study
We therefore aimed to explore the range of routine activity locations that can be identified in police data and to answer the following research questions relating to the nature, distribution and spatial similarity (or differentiation) between offenders’ activity spaces as revealed by these data. (1) What is the distribution of the number activity nodes per offender? (2) What is the distribution of activity space size per offender? (3) What proportion of offenders’ activity nodes are shared with other offenders? (4) Do offenders who offended in closer proximity to each other have similar activity spaces preceding the offense, relative to offenders who offended in different locations? (The present research considers purely spatial similarity, rather than the overlap of offenders’ activity space in both time and space. Offenders who have frequented the same location years apart could identify the same criminal opportunities to the extent that those opportunities reflect stable features of the environment. Future research might consider whether offenses close together in both time and space are associated with activity space overlapping in both time and space, which was not possible with the present data.)
We used routinely collected police data to provide insight into offenders’ activity spaces in terms of the number of activity nodes, their geographic range, and the extent to which they are shared between or differentiate offenders. We believe this to be the largest and most comprehensive study of offender activity space to date, considering the frequency and types of activity nodes included.
On the surface the one-year activity node distributions appear comparable to the results of Rossmo et al.’s [29
] study of US parolees and Menting [28
] and Bernasco’s [27
] studies of young Dutch offenders, considering the different timeframes involved. Our medians were 13 to 15 for burglary/robbery and 5 for sex offenses, for nodes that were current during the year prior to the reference offense, compared with 4-7 nodes in the above studies over much shorter timeframes. However, our distributions were wider and appeared more skewed: a minority of offenders had no prior activity nodes on record; many offenders had few nodes; a few had very many. This result is likely a combination of both missingness and reality: we have more complete data for some individuals than for others, and some individuals genuinely have more activity nodes than others, as found in criminal cohorts in the 3 studies above and in the general population [51
]. Future research could help establish the extent of (in)completeness by comparing police data at an individual level with alternative data sources such as surveys or GPS data as used in the studies above.
That the activity spaces frequently spanned multiple urban areas is consistent with the few NZ studies of the distances between offenders’ home addresses and their offenses, and of the mobility of New Zealanders in general. In NZ, the home-crime distances of sex offenders tend to be longer on average than overseas, with higher proportions of “commuter” offenders whose homes are outside the radius of their offenses [61
]. Davidson’s [65
] study of Christchurch burglars’ home-crime distances found more geographically constrained patterns but may not represent contemporary trends, given changes in societal travel patterns and mobility. In 21st Century New Zealand, people move frequently [66
], family and friends can be widely dispersed across different towns and cities, and domestic travel by car or plane is common, despite the long distances involved [67
]. The inclusion of prisons as activity nodes, sometimes recorded in the address data, would also have contributed to these distances because offenders may be transferred to prisons a long way from their community; they may also not return to the same community on release [73
] (Prison addresses were not always readily identifiable in the data, precluding a comprehensive investigation into the extent to which they accounted for wide activity space ranges.). It is no surprise, therefore, that NZ offenders’ activity spaces spanned much longer distances than those of the offenders studied by Rossmo et al. [29
], Menting [28
] and Bernasco [27
], given these studies were more geographically and temporally constrained, and involved cohorts with likely more limited mobility (offenders on GPS monitoring and young people). However, the extent to which our results are unique to New Zealand or indicate that offender populations have wider activity ranges than captured by studies with smaller study areas warrants further investigation. We encourage future studies of offender activity space in other countries to widen their spatial scope.
The analyses of the number and geographic range of activity nodes reveal potential and problems for the use of this data in research and practice. In terms of volume, the data are promising: there were a variety of activity nodes beyond home addresses available for the burglary, robbery and—to a lesser extent—sex offenders included in this study. There was also considerable information gain when extending the timeframe to less recent activity nodes (burglary median 29 nodes, robbery 34, sex offenses 11), though it remains for future research to explore whether this additional information yields any signal: we do not yet know whether these “older” activity nodes bear on the locations of future offenses. In contrast, the distributions were very similar regardless of the unit of analysis, which may partly reflect the small size of SA1s in the urban areas in which activity nodes were concentrated. Research and analysis may therefore benefit from the use of aggregate spatial units of SA1 size without much loss of information, at least in urban areas.
The geographic span of the offenders’ activity spaces indicates a potential limitation of this data. Given that offenders’ activity nodes were often widely dispersed, a high proportion of these nodes are likely to have little bearing on their choice of crime locations at a micro-geographic or neighborhood level. There may even be no activity nodes known to police in proximity to offenders’ latest offense locations. In crime location choice research, if the data do not include—with sufficient frequency—the more proximal activity nodes likely to have influenced a given crime location choice, models may fail to identify relationships between activity nodes and crime locations. In crime investigations, there is potential to miss possible suspects by narrowly focusing on those with local nodes in police data, highlighting the importance of supplementing police information with other sources of information on suspects’ activity nodes, be it through data sharing agreements with other agencies or on an individual basis for named suspects in a given investigation.
Any given activity node (SA1) was likely to be shared with other offenders who had committed the same reference offense, though these nodes were not necessarily “active” at the same time. This result is consistent with Hart and colleagues’ [60
] finding that 80% of the paths between young Australians’ activity nodes were shared. The most frequently shared nodes reflected places expected to have high numbers of offenders residing or visiting (prisons, police stations, court houses) or high numbers of people in general (consistent with the findings of Menting et al. [20
]). The latter would generate more activity node records in police data through higher levels of crime opportunity and higher odds of encountering police during proactive patrols.
Comparing any two offenders, however, showed much less overlap between their particular activity spaces, signaling considerable individual differences in offenders’ routine activity patterns as captured in this data. The homology and differentiation results suggest that those routine activity spaces were—marginally—more likely to converge the closer together offenders’ latest offenses were. Our results thus provide evidence of a small degree of spatial homology and differentiation, and some insights into its causes.
For example, reference offense co-offenders had greater activity space similarity than offenders who offended in the same location but independently. One potential explanation for this finding is that it reflects co-offenders who have also committed other crimes together in the past, and who thus share prior crime nodes. Other potential explanations reflect possible familial or social connections between co-offenders. On occasion, co-offenders may be family members [74
], who may therefore share home addresses, family home address, or school nodes. However, more frequently, co-offenders are connected socially [76
]. Human mobility studies have shown that people more closely connected in a social network have more similar activity spaces than those who are not socially connected [78
]. Co-offenders’ social ties could be a product of living in close proximity or attending the same school, and both a cause and effect of sharing “hangout” nodes [77
]. In NZ, as elsewhere, co-offending can occur as a part of membership of gangs, from the fluid, loosely connected structures of youth gangs, to more hierarchical organized crime groups [84
]. Many youth gangs align themselves to particular neighborhoods reflective of the shared activity space of their members, and more formalized gangs are arranged into local “chapters” [84
]. Consistent with Lammers’ study [35
], however, co-offenders did not share most of their activity nodes.
However, since co-offenders only made up a small proportion of offender pairs used for the homology correlations (and excluding them made no difference to the results), other mechanisms must be at play. For example, those who share activity space are likely to become aware of the same criminal opportunities; those with different activity spaces are exposed to different opportunities. Social networks also play a role: previous
co-offenders (who share prior crime nodes) share information with each other about crime opportunities that influences where they offend in the future not only together but separately [89
Robbery offenders displayed less homology than burglars. This could be expected since robbery tends to concentrate in commercial areas with high numbers of potential targets [13
], which would attract potential offenders with disparate residential nodes [2
]. That homology was still present may reflect the range of nodes in the data: offenders committing robberies in the same commercial area may also have committed prior offenses or have had prior interactions with police in the vicinity.
Those committing sex offenses appear to be a more spatially heterogenous group, also displaying less spatial overlap or proximity in their activity spaces. This might be an effect of there being fewer nodes in the dataset for these offenders, which would mean less chance of finding overlapping or proximal nodes. It might also be an effect of the heterogenous nature of these offenses. The sex offenses included a wide range of behaviors, from indecent exposure to rape, and included offenses against children and adults. They could have occurred at the offender’s home, the victim’s home, or elsewhere [93
]. Sex offenders’ strategies for identifying, approaching, and attacking victims vary widely from extended grooming to opportunistic “blitz” attacks [98
]. Offenders might identify their victims through social networks (online or offline) or search for victims in target rich environments such as near schools or night-time economy districts [94
]. All these factors would lead to variation in the relationship between offenders’ activity spaces and their crime locations. Furthermore, social connection and sharing of information between sex offenders, such as about the locations of criminal opportunities, appears to be less common than with property crime [97
], which would reduce network driven homology effects. Future research might seek to isolate spatial homology effects for different subtypes of sex offenders.
Several limitations of the data are also worth considering in interpreting our results. First, the data only identifies co-offenders where there was sufficient evidence to proceed against each offender. In some cases, multiple offenders may have been involved in a crime but not recorded as offenders due to a lack of evidence. Furthermore, offender pairs were only identifiable as co-offenders if the offense was both offenders’ reference (i.e., latest) offense. Offender pairs would not be identified as co-offenders if one had committed a subsequent offense. To the extent that any co-offenders were thus treated as having offended independently, the differences between co-offenders and independent offenders will have been under-estimated.
Second, the data were not systematically recorded; they were only on file where collected for operational purposes. As noted, some offenders had more complete records, representing more of their routine activity space. Our conclusions are therefore limited to offenders’ activity spaces to the extent identifiable by routinely collected police data.
An additional caveat is that not all activity space is equal with respect to crime location choice. Activity nodes that are more recent, more frequently visited, and have been in activity space for a longer time have stronger associations with crime locations [27
], as do activity nodes that provide the most relevant knowledge of criminal opportunity: prior crimes of the same nature [26
]. We could therefore expect greater spatial similarity in those parts of offenders’ activity spaces that have had the greatest influence in producing similarity of their crime locations. Indeed, our results suggest that offenders who offend in spatially similar locations are spatially similar in parts
of their activity space. Further research would be needed to determine, with the present dataset, which activity nodes are most predictive of crime locations, and whether homology effects are larger when activity space is isolated to, or weighted by, these most influential activity nodes.