Crimes are created by the interactions of potential offenders with potential targets in settings that make doing the crime easy, safe and profitable (see, e.g., Clarke, 1992; Brant ingham and Brantingham, 1993a and 1993b; Felson, 1994). Fear of crime is created by situations and settings that make people feel vulnerable to victimization (see, e.g., Fisher and Nasar, 1992a and 1992b; Nasar and Fisher, 1992 and 1993; Brantingham et al., 1995). The urban settings that create crime and fear are human constructions, the by-product of the environments we build to support the requirements of everyday life: homes and residential neighbourhoods; shops and offices; factories and warehouses; government buildings; parks and recreational sites; sports stadia and theatres; t ransport systems, bus stops, roadways and parking garages. The ways in which we assemble these large building blocks of routine activity into the urban backcloth can have enormous impact on our fear levels and on the quantities, types and timing of the crimes we suffer.
Although criminologists have argued this point in various ways for at least a hundred years (e.g., Ferri, 1896; Burgess, 1916; Shaw and McKay, 1942; Jeffery, 1971; Brantingham and Brantingham, 1993a and 1993b) it is only recently that large mul t i -purpose municipal data bases, in conjunction with police information systems, have begun to make it possible to actually explore how the juxtaposi t ion of land uses and transport networks shapes the backcloth on which crime occurs. This paper a t tempts to set out some of the next steps in unders tanding the construction of the backcloth and its effects on crime. The model that will eventually emerge should provide us with a planning tool that will
1 Schoolo f Criminology, Simon Fraser University, Burnaby, British Columbia,
Canada V5A 1S6.
allow us to estimate the criminogenic and fear-generating potentials of different planning and development decisions in context in the way that traffic engineers can presently predict the potential of different land uses in generating car journeys. It will be based on a large-scale empirical analysis of crime data patterns of the sort that allowed Block et al. (1985) to estimate the victimization risks attached to hundreds of different occupations.
In such an undertaking it will also be important to remember that the sites, situations, or general socio-economic, demographic and media conditions that create fear may not necessarily relate to actual risks of victimization or patterns of crime. For example, it is well known that the elderly express high levels of fear of crime, but run low risks of actual victimization; teenagers and young adults generally express low levels of fear of crime, but run the highest risks of criminal victimization (Fattah, 1991). Note that places marked by darkness and isolation are generally feared as likely crime sites, but (with a few exceptions) tend to be relatively low-frequency crime locations (Brantingham et al., 1995). Introduction of higher levels of street lighting into high-fear locations appears in general to have little beneficial impact on crime levels (Atkins et al., 1991; Ramsay and Newton, 1991). Vandalism, litter and graffiti are known to make people feel uneasy, to raise their fears of crime in an area, but do not often constitute the territorial markers of actual crime hot spots (Ley and Cybriwsky, 1974; Skogan, 1988). The public view of 'crime' often turns out to be tied to the presence of noise, traffic, beggars, alcoholics and contact between groups of 'different' people as much as to criminal code events.
Crime may often be high in situations and sites where people feel safe and express little fear. This is predicted by Angel's (1968) target density model and by what is known about the environmental psychology of crime (Brantingham and Brantingham, 1993b). So, robberies are known to concentrate along busy shopping streets (Wilcox, 1973) where people generally express little fear. University crimes in general concentrate in high-activity areas such as the library or student union or dormitory laundromats where students say they feel safe (Brantingham et al., 1977; Brantingham et al., 1995). Car thefts and thefts from cars concentrate in and around parking lots where people feel their cars are safe (Poyner, 1992; Eck and Spellman, 1994; Fleming et al., 1994) or in exposed locations such as the street close to home where people feel their cars are safe (Clarke and Mayhew, 1994).
Both crime and fear may constitute problems at particular locations in space-time, of course. Such dual hot spots of crime and fear often occur along the edges of ' en ter ta inment ' Granville Street in Vancouver, for instance. They occur in the danger zones half a block away from major transit stops (Brantingham et al., 1991). They occur on the edges or borders between neighbourhoods of distinctly different character and social status (Brantingham and Brantingham, 1975 and 1993b; Brant ingham et al., 1977). They occur on major pathways and at major nodes where large numbers of potential offenders are brought together, through routine activities, with large numbers of potential victims and targets.
This array of possibilities means that it is important to unders tand the construction of the environmental backcloth and how its elements contribute to the choice of targets and target areas by offenders; and the development of fear on the part of individuals. Different land uses in different juxtapositions, arrayed in different ways on the t ransport network will have different potentials.
There are four broad types of urban sites that need to be considered: crime generators; crime attractors; cr ime-neutral sites; and fear generators.
Crime generators are particular areas to which large numbers of people are attracted for reasons unrelated to any particular level of criminal motivation they might have or to any particular crime they might end up committing. Typical examples might include shopping precincts; enter ta inment
districts; office concentrat ions; or sports stadiums.
In metro Vancouver these might include the downtown core; the Granville Island shopping and theatre district; the s tadium complexes on False Creek; the Metrotown complex in suburban Burnaby. Major travel nodes, where many different travel paths and transit modes converge or intersect, can form crime generators. Bus interchanges, transit system stops, massive 'park and ride' parking lots can all become crime generators because of the volumes of people that pass through them.
Crim e generators produce crime by creating particular t imes and places that provide appropriate concentra t ions of people and other targets (Angel, 1968) in settings that are conducive to particular types of criminal acts. Mixed into the people gathered at generator locations are some potential offenders with sufficient general levels of criminal motivation that al though they did not come to the area with the explicit intent of doing a crime, they notice and exploit criminal opportunit ies as presented (either immediate ly or on a subsequent occasion). Both local insiders and outsiders may be t empted into commit t ing crimes at crime generator locations.
Crime attractors are particular places, areas, neighbourhoods, districts which create well-known criminal oppor tuni t ies to which strongly motivated, intending criminal offenders are at tracted because of the known opportunit ies for particular types of crime. Examples might include bar districts; prosti tution areas; drug markets; large shopping malls, particularly those near major public transit exchanges; large, insecure parking lots in business or commercia l areas. The intending offender goes to rough bars looking for fights or other kinds of 'action'. The intending offender goes to red-light districts looking to solicit an act of prostitution; or, in the case of serial offenders, looking for a victim (Alston, 1994; Rossmo, 1994). The intending offender is drawn to a drug market area to deal in drugs. The intending offender is drawn to malls or stores with poor security a r rangements looking for opportuni t ies to shoplift. The intending offender is drawn to large, insecure parking lots looking for cars or car parts to steal.
Crimes in such locations are often commi t ted by outsiders to the area. Strongly motivated offenders will travel relatively long distances in search of a target. (When insiders commi t crimes in such areas, they may have previously moved to those areas because of their crime attracting qualities; or, as in many cities, because poor areas are located near commercial areas thus creating m a n y accessible targets near home.)
The attraction is created by an ecological label (Brantingham and Brantingham, 1991 and 1993b), often supp lemen ted by the intending offender's personal past history, establishing that location as a known place to go for that kind of crime. As studies by Rengert (1994) and by Langworthy and LeBeau (1992a and 1992b) have shown, such crime attracting areas can also generate other types of crime that are auxiliary or serendipitous by-products of the intending offender having been at tracted to the area by the prospect of commit t ing the pr imary crime.
There are also cr ime-neutral areas in mos t cities. Crime-neutral areas nei ther
attract intending offenders because they expect to do a
part icular crime in the area, nor do they produce crimes by creating criminal opportuni t ies that are too tempt ing to resist. Instead, they experience occasional crimes by local insiders. Simple distance decay and pa thway models can describe the geography of crime in such locations. The offence mix is different from the offence mix at either crime at t ractor or crime generator locat ions (Brantingham and Brantingham, 1994). It is impor tan t to note that areas are unlikely to be pure attractors or pure generators or purely neutral. Most areas will be mixed, in the sense that they may be crime at tractors for some types of crime, crime generators for o ther types of crime, and neutral with respect to still other types of crime.
Fear of crime is complex. There are many types of fear, but they seem overall to be related to five broad categories:
direct fear of another person; m fear of being alone; fear at night, in the dark;
- -
- -
- -
fear in unknown areas; fear of encounters with 'scary' people.
Fear of crime is a general fear of being attacked, of suffering some physical harm, of suffering an int rus ion that destroys privacy and dignity. It is not generally tied to a concern for proper ty loss. Fear is enhanced by:
personal physical vulnerability: people who because of age or lack of strength feel much more at risk of harm if attacked, feel much more fearful; lack of control over the si tuation: people who feel at risk in a s i tuat ion but feel they cannot do anything about it are much more fearful. This is why subway trains are so scary: a passenger cannot be sure who might get on at the next station; and if someone scary gets on, there is no help and no escape until the next stat ion. Fear is greater with higher perceived vulnerability, more isolat ion from 'known' people, less control of what is happen ing or might happen. Fear is higher for a potent ial ly vulnerable person when alone in publ ic space with no sure knowledge of what is around, when necessary pathways cross those of others seen as 'potent ia l at tackers ' or when there are signs that there are 'problems' - Wilson and Kelling's (1982) broken windows, Skogan's (1988) indicators of incivility such as lit ter and graffiti - in the area.
People commit offences close to the central places (nodes) in their lives: their homes; the places where they work; school; their favourite recrea tion sites; their normal shopping centres. People are also victimized close to the central places in their lives: their homes; the places where they work; school; their favourite recreation sites; their normal shopping centres (Brantingham and Brantingham, 1991). Both individual and aggregate crime patterns cluster around offender and victim nodes and along the principal pathways between them. Property offenders - robbers and burglars - commit nearly all of their offences in the aware ness spaces defined by the nodes and paths of their routine activities (Maguire, 1982; Rengert and Wasilchick, 1985; Gabor et al., 1987; Wright and Decker, 1994). The same appears to be true of serial rapists (Canter and Larkin, 1993; Alston, 1994) and serial killers (Rossmo, 1994). People tend to share many of their life nodes. Thousands of people shop at the same malls, work in the same office complexes, change buses at the same interchanges, go to the same sports stadia, go to the same cinemas, etcetera. The mixture of uses at such nodes, and the exact ways they are clustered together in the built environment can go a long way to determining whether particular nodes are crime attractors, crime generators, fear generators or crime-neutral spots. Moreover, some uses may have additive or even multiplicative effects if they are clustered together.
Nodal concentrations of crime appear both in research using objective, Euclidean measures (Capone and Nichols, 1976; Sherman et al., 1989) and in research using cognitive images or non-Euclidean measures (Carter and Hill, 1979; Brantingham and Brantingham, 1981). This is so because the character of the built environment, the clustering of land uses and the temporal routines of daily life cluster nodes, channel movement and force a convergence of uncountable individual path potentials into a limited number of actual paths between nodes (Chapin, 1974; Lowe and Moryadas, 1975; Whyte, 1988). The character of actual paths can be measured in a variety of ways.
The criminogenic characteristics of activity nodes are sometimes increased by the types of activities carried out at them or by the particular high-risk users (e.g., teenagers, or motorcycle gangs, or alcoholics or drug users or singles intent on meeting new people) who frequent them. For instance, people may go to a bar simply to drink, but
1 1
if it is a bar where many people become drunk it is likely to experience a lot of assaults. People who go to such bars with no prior intent may nevertheless be swept up into fights.
Paths are critically important in shaping routine activities, everyday life and special events as well. Paths determine where people go and what they learn about the city. People spend long hours in routine paths, travelling to and from work, school, shopping, entertainment. Paths determine where people search for criminal targets and where people are victimized. Because paths are so important, street networks, traffic and transit patterns strongly influence the distribution of crimes (Bevis and Nutter, 1977; Beavon et al., 1994). Offenders who live close to one another tend to travel in the same direction toward the sites where they commit offences. Nodal crime sites such as a city centre bar district, a shopping mall, or a secondary school tend to attract offenders from many different directions (Costanzo et al., 1986). This pattern is very similar to the more general pattern of movement in relation to more mundane activities such as shopping. Criminal events cluster near major traffic arteries and near major intersections between arteries (Wilcox, 1973; Duffala, 1976; Bevis and Nutter, 1977; Alston, 1994; Beavon et al., 1994). Crime hot spots often centre on subway exits, bus stops, and freeway exits (Fink, 1969; Maguire, 1982; Brantingham et al., 1991), but are often restricted to times at which specific levels of traffic flow are generated. Neighbourhood traffic permeability appears to have a substantial effect on neighbourhood crime rates (Bevis and Nutter, 1977; White, 1990; Beavon et al., 1994). The theoretical model that predicts crime and offender patterns also predicts that vict imization patterns will be tied to the victim's routine paths and activity nodes. Although not researched to the same degree as the offender's journey to crime, the available literature seems to provide empirical support for this theoretical prediction. Research into the crime mobility triangles defined by the victim's residence, the offender's residence and the crime site shows that victim movement patterns are often as important in determining where and when a crime occurs as offender movement patterns (Rand, 1986; Burgess, 1925a and 1925h). This makes particular sense when it is remembered that studies in the victim precipitation tradition (Fattah, 1991), in the lifestyle tradition (Hindelang et al., 1978) and the self-report tradition (Gabor, 1994) all indicate that potential v ic t im/potent ia l offender status is a fuzzy set (McNeill and Freiberger, 1993), not a dichotomy. The movement pat terns of both potential offenders and potent ial victims must be considered in understanding crime aggregate pat terns, because it is often not certain which is which until criminal events unfold.
Criminal events should occur where offender and victim activity spaces intersect. The aggregate pat terns of high-probabil i ty criminal event zones in some particular place such as a city, a neighbourhood or, as Felson (1994, p. 134) notes, smaller places such as a factory, an office complex, a shopping mall or a housing estate, will be defined by the topological product of the activity spaces of the set of potential offenders and set of potential victims.
The environment is full of physical and perceptual edges, places where there is enough distinctiveness from one bit to another that the change is noticeable. At an extreme, the land border ing on a river is an edge; the houses behind a commercial strip deve lopment and the businesses on the strip form a perceptual edge. Parks have edges. Residential areas have edges. Commercial areas have edges. Land use zoning and t ransport planning frequently work in t andem with the result that major roads follow perceptual edges between different types of areas. Major roads themselves can consti tute an edge.
Edges can be considered in terms of physical barriers; or in terms of the strong cognitive images created by paths with diverse land uses on either side of a road (Lynch, 1960); or in terms of the limits of perceptual comfort felt by outsiders entering unknown areas (Sacks, 1972; Reppetto, 1974; Brantingham and Brantingham, 1975; Carter and Hill, 1979; Rengert and Wasilchick, 1985; Cromwell et al., 1991; Wright and Decker, 1994). They can also be considered as areas of potential territorial conflict between different groups or land uses (Shaw and McKay, 1942; Suttles, 1968).
The areas around edges often experience high crime rates (Shaw and McKay, 1942; Suttles,.1968; Brant ingham and Brantingham, 1975 and 1978; Brantingham et al., 1977; Herber t and Hyde, 1985; Walsh, 1986). Edges may create areas where strangers are more easily accepted because they are frequently and legit imately present, while the interiors of areas may consti tute territories where strangers are uncomfor table and subject to challenge. Edges may also contain mixes of land uses and physical features - crime generators and attractors - that concentrate
13
criminal opportunities. This seems particularly likely on edges formed by major roads, which tend to concentrate large numbers of businesses and high-density residential blocks (Beavon et al., 1994).
Of particular impor tance are the spatial and temporal edge effects relating to crowds and to high-activity areas. Many of the crimes that occur at high-activity locations such as sport ing arenas or commercial centres, or that occur at high-activity times such as store closing or bar closing, in fact occur at the edges of the high-activity location or high-activity time. Crimes cluster on the street near the subway station or bus stop, at the edge of the normal waiting area (Shellow et al., 1974; Levine and Wachs, 1985; Brantingham et al., 1991). Crimes often cluster in the alley behind a strip of shops (Wilcox, 1973). Robberies in Oakland, California have been shown to cluster on the fringe of parking tots and in the temporal edge half an hour after closing time in the commercial areas when most people have already depar ted (Wilcox, 1973). Angel (1968) has also conducted an interesting analysis of crime clustered on activity and temporal edges in Oakland.
While edges somet imes identify an open-access space, they may also identify territorial limits or boundaries that separate areas of high and low crime rates. Ley and Cybriwsky (1974) and Taylor (1988) have shown how graffiti serve as territorial markers for groups of urban teenagers, defining the limits of their normal activity spaces. Suttles (1968) showed how complex territorial cues at ne ighbourhood edges can somet imes form buffer zones between ne ighbourhoods that reduce social conflict and crime for those areas.
Sometimes the edges between different types of ne ighbourhoods can form psychological and perceptual barriers that deflect external offenders (Brantingham and Brantingham, 1975; Wright and Decker, 1994). While offenders invariably identify rich neighbourhoods as good locations for hunting targets; they consistently commit crimes in neighbourhoods they personally know well or that are very similar in physical, social and economic characteristics to their home neighbour hoods (Reppetto, 1974; Rengert and Wasilchick, 1985; Cromwell et al., 1991; Wright and Decker, 1994). Edges may also form psychological barriers that keep ne ighbourhood insiders locked within the neighbour hood as welt as keeping outsiders out of the area. When this happens, most local crime will be commit ted by insiders. Offenders will be much harder for neighbourhood watchers to identify. As ne ighbourhood insiders, they will not stand out against the local environmental backcloth. This leads to a considerat ion of crimes commit ted by local area insiders and outsiders.
1 4
Local land use policies that physically cluster or disperse uses that are attractive to particular types of people can be analyzed to help predict where common forms of crime are most likely to occur and to help explain why crime rates are high in one part of a city and low in another. Housing patterns, shaped by market forces, public policy, and personal choices, cluster people of similar social background together.
The juxtaposit ion between land uses can affect the crime rates of entire neighbourhoods (Rengert, 1988). Some juxtaposit ions can expose potential targets in one area to large numbers of potential offenders in an adjacent area and create high inter-area crime rates. Some juxta positions between different types of land uses can form criminogenic zones in which offenders can operate with relative freedom from scrutiny (Brantingham and Brantingham, 1975; Rhodes and Conly, 1981). Such zones of anonymity often occur along or near arterial and collector roads, reinforcing the criminogenic character of major paths and further concentrat ing
crime on them.
One way of approaching the problem of building a sufficient under standing of the crime risks associated with different urban forms and structures is to begin with a mapping of cr ime occurrence patterns, then looking to see what sorts of crime generators and crime attractors might be present. So many generators and at tractors are clustered in city centres by design that they pose a much more difficult task to address. To illustrate the approach here, we have elected to look at the municipali ty of Burnaby, one of the largest and mos t densely popula ted suburbs in the greater Vancouver region. 2
Figure 1 (p. 16) depicts criminal code offences known to police in Burnaby in 1991. Three major cr ime peaks are labelled. (The pat terns are essentially identical when rates are plotted.) Each represents a collection of crime generators and crime attractors.
Peak 3 shows the effect of a crime generator, a major bus interchange. This bus interchange, which connects three major municipalit ies, is
2 We are indebted to Jonathan Alston who gathered the site data reported in this illustration.
located in what is principally a residential neighbourhood with few additional crime generators or crime attractors nearby. The neighbour hood itself is relatively low-income and high-density. More than a third of the criminal code offences reported to the police in the neighbour hood (37%) occur within 500 metres of the bus interchange. There is no secondary school, no teenage attractor such as a video game arcade or recreation centre within reasonable walking distance. At the far edge of the neighbourhood, about a kilometre and a half away along the major highway that traverses the area, there is a notorious bar reputed to attract criminals.
P e a k 2 combines the effect of a major bus interchange with other crime attractors and crime generators, in this case a major shopping mall. The immediate neighbourhood also features a major recreation centre, a public library, a variety of fast-food restaurants, and a number of youth-oriented businesses within easy walking distance of the bus interchange and mall entrance. Much of the neighbourhood is high-rise, high-density residential development. The mall itself includes a multi screen budget cinema, a video arcade, and a food fair. More than one fourth (27%) of all crimes reported to the police in the neighbourhood occur within 250 metres of the bus interchange/mall entrance that forms the centre of this high-crime node.
P e a k
I shows the effect of combining a large list of crime attractors and crime generators. It combines the largest shopping mall in British Columbia with a major bus interchange and a metro station. The mall includes two multiplex movie theatres, several food courts, video arcades, restaurants and franchise hamburger shops, and a casino. Close by are some bars with reputations as criminal attractors. The combination of large-volume destinations, intersections of major transit routes, juvenile-attracting destinations, and crime attractors support the highest crime rate in the municipality.
Figure 2 (p. 16) shows the distribution of total complaints to the Royal Canadian Mounted Police in Burnaby's policing District 2 during 1994 by policing atom. District 2 forms the northeast quadrant of the municipa lity and includes peak 2 from figure 1 as well as Simon Fraser University. Note that, consistent with findings from a recent campus victimization survey (Brantingham et al., 1995), the university does n o t form one of the significant crime hot spots in District 2 despite the fact that it has a large number of students who travel to campus by bus each day. The university's relative isolation on top of a small mountain, surrounded on all sides by wilderness park, largely removes it from the activity spaces of
Figure : 1
0 0
Figure : 2
' N
most campus outsiders, including most potent ia l outs ider offenders. Mall 2 was identified as Peak 2 in figure 1. It is the dominan t crime locus in District 2. In 1994, as in 1991, the locat ion combined a series of generators and attractors - a bus interchange, low-end shops and budget c inemas in the mall, fast-food outlets adjacent , as well as an adjacent recreation centre - that suppor ted high levels of crime.
Kensington is a different kind of crime locus. It is a smaller, local shopping centre anchored by a large supermarke t and a government l iquor store with many smaller shops and fast--food restaurants in the complex. It is adjacent to a park and to the largest high school (senior secondary school, for adolescents aged 14-18) in the municipality. It is located on one of the major commute r highways running through the municipality. Although there is a local bus stop, there is no major bus interchange at this location. There is no video arcade, and few busi nesses that specially cater to adolescents . There is no bar nearby. The effect here - a combinat ion of a local shopping centre and a high school and the movements between them - generates a local crime peak within District 2. This peak shows as a smaller peak adjacent to peak 2 in figure 1.
A ridge of slightly elevated crime levels can be seen running from Kensington to Mall 2, following the pr incipal pathway between them. This pathway skirts the base of the small mounta in on which university is located. As a major pathway, the route of least resis tance between these crime generator dest inat ions itself becomes a crime generator. Note that both figure 1 and figure 2 also show some areas, in terspersed with the crime peaks and ridges, that exhibit very low crime rates. These are the cr ime-neutra l areas where there are no crime at tractors and no crime generators. Crimes occur in such areas, but in low frequency and low concentrat ion.
Another approach to unders tanding cr ime at t ractors and crime generators and how they fit together is to analyze the dis t r ibut ion of crimes across different types of land uses. We illustrate this approach with some old data from Cambridge, England.
During the early 1970s we had an oppor tun i ty to obtain 1971 crime data for the City of Cambridge from the Cambridgeshi re police. These data recorded crimes known to the police according to a s tandardized land use classification scheme. We were able to develop a data file describing the land use at each address in the city in 1971 by merging information from several sources: business use records maintained by the Depart ment of Environmental Health to enable sanitary inspections under various statutes; commercially published city directories; the 1971 Cambridge telephone directory; and some county records. By separately collecting address level data on burglaries known to the police, we were able to make of estimate of the known burglary rates for a large number of land use types. Table 1 shows the results rank-ordered by 1971 rates per 100 land uses in that category. The two most frequently burgled land uses were sports (and other) clubs and youth clubs. These uses had clear crime generator characteristics: they pulled in huge numbers of people in the ordinary course of doing business so that they fit into the activity and awareness spaces of large numbers of people. At the same time, their clientele tended to fit the demographic profiles - young, male, lower income - of potential offenders. A micro-analysis of which clubs were most frequently burgled and which were not against the backdrop of the transport patterns of the time would be very interesting. Note that the two next most burgled uses, restaurants and laundries, are also high traffic uses that can be expected to be to be found in many people's activity and awareness spaces. They also feature alcohol, and are likely to have cash from evening operations stored on the premises at night. At the other extreme are ironmongers, doctors' offices, college hostels, pubs and tailors' shops. Some, such as the ironmongers' shops, are unlikely to contain much that would be attractive to burglars. Others, such as tailors and college hostels have a very specialized clientele and are likely to fit into only a few people's routine activity spaces, even in a college town. The low burglary rates experienced by pubs is somewhat surprising to North Americans since their cognates, bars and taverns, seem to be criminogenic everywhere in North America (Roncek and Pravatiner, 1989; Roncek and Maier; 1991; Verma, forthcoming 1995). Few North American bars, however, have resident owners in the way that pubs in Cambridge had 25 years ago. Things might be different in Cambridge now. We also suspect that doctors ' offices would run a higher risk in North America since they might be thought to house a variety of drugs in their dispensaries. Note that there are a number of land uses that are not treated in this table. Most are omitted because they experienced no known burglaries during the year. No one, for instance broke into any of the town's many museums and libraries in order to steal paintings or rare books. Some uses are omitted because while they had high burglary rates there were very few of them: hospitals are a case in point. Some uses were not
Table : 1
addressed in the 1971 police data set - primary and secondary schools most notably.
The point of this exercise is to suggest that it is now possible to conduct such analyses in many cities in North America and Europe. A bank of such studies, for many different types of crime, could begin to give us the base for estimating the criminal victimization risks associated with different types of land uses.
We thinks that a research p rog ramme expanding on the considerations set out in this paper could lead to the development of an empirical tool for estimating the criminogenic impact of planning decisions. Such a tool would allow police and town planners to est imate the likely increase in calls for police services (and consequent need for increased police and other criminal justice system personnel and resources) inherent in all kinds of planning decisions: changes in businesses operat ing out of a specific address; individual site redevelopments; large new develop ments such as green field housing estates and shopping centres; traffic reroutings and changes to transit services; relocation of institutions such as hospitals or schools; and so forth. To accomplish this we suggest several parallel lines of research which we plan to expand or begin in Vancouver, and which we hope other scholars will undertake in other cities.
First, we plan to expand the scope of our c r ime-mapping exercises to cover much larger parts of the Vancouver metropol i tan area, and to expand coverage over time. High-crime nodes identified through the mapping exercises will be subjected to micro-analyses to determine land use mixtures, path placements, and clientele. Activity pat terns at each site will be studied. This should provide us with a bet ter understanding of crime generators and crime attractors.
Second, we plan to merge police and planning data bases to begin constructing crime risk tables by land use in Vancouver. This will, of course, involve some extensive data purification and clarification exercises along the way.
Third, we plan to explore the situational characterist ics of high and low-risk establishments within part icular use categories. Location on
t ransport
networks, position on ne ighbourhood edges and location with
respect to large nodes such as schools and shopping malls will receive part icular
attention.
Fourth, we plan to conduct formal juxtaposi t ion analyses that look at part icular uses in conjunction with other, different types of uses. Is a bar, for instance, more likely to experience problems if it is si tuated adjacent to other bars, or if it is instead sur rounded by theatres and restaurants, or if it is located in the middle of a residential neighbourhood? In Vancouver it is possible to explore the current but it is also possible Cin at least some communit ies) to view
pat tern, things over t ime by utilizing various business licensing data bases. This has the effect of creating many different natural experiments from which it may prove possible to draw very strong conclusions. Fifth, we hope to conduct potent ia t ion analyses that look at the spatial and temporal crime fields created by cr ime generator and crime attractor nodes. This is an expansion of work that was begun by Marcus Felson in his piece on predicting crime at any point on the city map (1986). Research by our students has already demonst ra ted that major roads in Vancouver have criminogenic fields that reach out approxi mately half a kilometre on either side (Weigman and Hu, 1992). We have shown that one large Burnaby mall has an apparent criminogenic field as well (Brantingham and Brantingham, 1994). We suspect that it may be possible, eventually, to develop cr iminogenic field est imates for manY different crime generators and crime attractors. The goal of all this will, eventually, be to merge the findings f rom these studies into a data base that can provide police and town planners with a tool for estimating the criminogenic consequences of their normal planning decisions.
Burnaby CBC), Simon Fraser Univer sity, School of Criminology, 1994 I~unpublished master's thesis)
Berkeley {Cal.), Center for Planning and Development Research, 1968 Working paper no. 75
London, Home Office Police Depart ment, 1991 Crime Prevention Unit Series, paper no. 28
The influence of street networks on the patterning of property offenses.
Brantingham, P.L., P.J. Branting ham
Residential burglary and urban form Urban Studies, vol. 12, 1975, pp. 273-284
Brantingham, P.J., P.L. Branting ham
A theoretical model of crime site selection. In: M.D. Krohn, R.L. Akers (eds.), Crime, Law and Sanctions Beverly Hills (Cal.), Sage, 1978, pp. 105-118
Mobility, notoriety, and crime: a study in the crime patterns of urban nodal points
Journal of Environmental Systems,
vol. 11, 1981, pp. 89-99
Brantingham, P.J., P.L. Branting ham
Prospect Heights (111.), Waveland Press, 1991
Environment, routine and situation: toward a pattern theory of crime
Advances in Criminological Theory,
vol. 5, 1993a, pp. 259-294
Brantingham, P.L., PJ. Branting ham
Nodes, paths and edges: considerations on environmental criminology
Journal of Environmental Psychology,
vol. 13, 1993b, pp. 3-28
Brantingham, P.L., P.J. Branting ham
La concentration spatiale relative de la criminalit~
Crimino/ogie, vol. 27, 1994, pp. 81-97
Brantingham, P.J., P.L. Branting ham, D. Butcher
Monsey (NY), Criminal Justice Press, 1986, pp. 139-159
Perceptions of crime in a dreadful enclosure Ohio Journal of Science, vol. 77, 1977, pp. 256-261
Crime and fear at a Canadian univer sity. In: B.S. Fisher, J.J. Sloan (eds.),
Campus Crime: Legal Social, and Poficy Perspectives
Springfield CIII.), Charles C. Thomas, 1995, pp. 123-155
How public transit feeds private crime: notes on the Vancouver 'Skytrain' experience Security Journal, vol. 2, 1991, pp. 91-95
Journal of the American Institute of Criminal Law and Criminology, vol. 6,
1916, pp. 724-728
Chicago, University of Chicago Press, 1925a, pp. 47-62
Can neighborhood work have a scientific basis? In: R.E. Park, E.W. Burgess, R.D. Mackenzie Ceds.),
Chicago, University of Chicago Press, 1925b, pp. 142-155
The environmental range of serial rapists
Journal of Environmental Psychology,
vol. 13, 1993, pp. 63-69
American Behavioral Scientist,
vol. 20, 1976, pp. 199-201
New York, Pergamon, 1979
Human Activity Patterns in the City: Things People Do in Time and Space
New York, Wiley, 1974
Situational Crime Prevention: Successful Case Studies
(ed.), Crime Prevention Studies, vol. 3
Monsey (NY), Criminal Justice Press, 1994, pp. 91-107
The procedural analysis of offending and its relevance for situational prevention. In: R.V. Clarke (ed.),
Crime Prevention Studies, voL 3
Monsey (NY), Criminal Justice Press, 1994, pp. 151-196
New York, Springer-Verlag, 1966
Costanzo, C.M., W.C. Halperin, N. Gale
Criminal mobility and the directional component in journeys to crime. In: R.M. Figlio, S. Hakim, G.F. Rengert
Monsey (NY), Criminal Justice .Press, 1986, pp. 73-95
Cromwell, P.F., J.N. Olson, D.W. Avary
Breaking and Entering: An Ethno graphic Analysis of Burglary
Newbury Park (Cal.), Sage, 1991 Duffala, D.C. Convenience stores, armed robbery, and physical environmental features
American Behavioral Scientist,
vol. 20, 1976, pp. 227-246
Thefts from vehicles in ship yard parking lots. In: R.V. Clarke (ed.),
New York, Harrow and Heston, 1992, pp. 164-173
Scarborough (Ont.), Prentice-Hall, 1991
Monsey (NY), Criminal Justice Press, 1986, pp. 127-136
Crime and Everyday Life: Insight and Implications for Society
Thousand Oaks (Cal.~), Pine Forge Press, 1994
New York, Appleton, 1896
Einsbruchstatorte vornehmlich an einfallstrassen? Kriminalistik, vol. 23, 1969, pp. 358-360
Fear of crime in relation to three exterior site features
Environment and Behavior, vol. 24,
1992a, pp. 35-65
Students' fear of crime and its relation to physical features of the campus
Journal of Security Administration,
vol. 15, 1992b, pp. 67-79
Fleming, Z., P. Brantingham, P. Brantingham
Exploring auto theft in British Columbia. In: R.V. Clark Ced.~}, Crime
Prevention Studies, voL 3
Monsey CNY'J, Criminal Justice Press, 1994, pp. 47-90
Gabor, T.
Toronto, University of Toronto Press, 1994
Springfield CIII.), Charles C. Thomas,
1987
Environmental criminology: testing some area hypotheses
Transactions, Institute of British Geographers, vol. 10, 1985,
pp. 259-274
Hindelang, MJ., M.R. Gottfredson, J. Garofalo
Cambridge CMass.), Ballinger Publishing Company, 1978
Crime Prevention Through Environ mental Design
Beverly Hills (~Cal0, Sage, 1971
Journal of Criminal Justice, vol. 20,
1992a, pp. 135-145
Journal of Criminal Justice, vol. 20,
1992b, pp. 442-451
Factors Affecting the Incidence of Bus Crime in Los Angeles
Washington CDC), Office of Techni cal Assistance, Urban Mass Trans
port Administration, of Transport, 1985 Report no. CA-06-0195
US Department
Annals of the Association of Ameri can Geographers, vol. 64, 1974,
pp. 491-5O5
The Geography of Movement
Cambridge (Mass.), MIT Press, 1960
New York, Simon and Schuster, 1993
London, Heinemann, 1982
Design for vulnerability: cues and reactions to fear of crime
Sociology and Social Research,
vol. 76, 1992, pp. 48-58
Hot spots of fear and crime: a multi-method investigation
Journal of Environmental Psychology,
vol. 13, ]993, pp. ]87-206
Situational crime prevention in two parking facilities. In: R.V. Clarke
New York, Harrow and Heston, 1992, pp. 174-184
Monsey ONYX, Criminal Justice Press, 1986, pp. 117-126
L'impact du commerce de drogues
sur le tissu urbain Criminologie, vol. 27, 1994, pp. 69-80
Springfield (111.), Charles C. Thomas, ] 985
Cambridge (Mass.), Ballinger, 1974
The effect of high schools on cr.ime in their neighborhoods
Social Science Quarterly, vol. 64,
1983, pp. 598-613
Additional evidence that taverns enhance nearby crime
Sociology and Social Research,
vol. 73, 1989, pp. ]85-188
Bars, blocks and crime revisited: linking the theory of routine activi ties to the empiricism of hot spots Criminology, vol. 29, 1991, pp. 725-753
Targeting victims: serial killers and the urban environment. In: T. O'Reilly-Fleming, S. Egger (eds.),
Toronto, University of Toronto Press, 1994
New York, Free Press, 1972, pp. 280-293
Delinquency and Urban Areas
Chicago, University of Chicago Press, 1942
Crime in rapid transit systems: an analysis and a recommended security and surveillance system
Transportation Research Record,
vol. 487, 1974, pp. 1-12
Hot spots of predatory crime: routine activities and the criminology of place Criminology, vol. 27, 1989, pp. 27-55
Disorder, crime and community decline. In: T. Hope, M. Shaw (eds.),
London, HMSO, 1988, pp. 48-61
Chicago, University of Chicago Press, 1968
Cambridge, Cambridge University Press, 1988
Burnaby (8C), forthcoming (unpublished Ph.D. dissertation)
1995
New York, Springer-Verlag, 1986, pp. 38-56
Vancouver Using the Network Capabilities of the Arc Info Geo graphic Information System
Burnaby, British Columbia Institute of Technology, 1992
Neighborhood permeability and burglary rates Justice Quarterly, vol. 7, 1990, pp. 57-67
New York, Doubleday, 1988
The Geography of Robbery; the Prevention and Control of Robbery, voL 3
Davis, Center of Administration of Justice, University of California, 1973
Broken windows: the police and neighborhood safety Atlantic Monthly, March ]982, pp. 29-38
Burglars on the Job: Streetlife and Residential Break-ins
Boston, Northeastern University Press, 1994