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1. Introduction
As police organizations automate their operations and implement more
modern computer systems, taking advantage of advances in information tech-
nology such as open architecture database systems, enterprise wide computer
applications and ever increasing microprocessor and network speeds, more and
more information will become available to police officers at the click of a mouse
button. Moreover, all of this information will be linked together from various
sources and organized in ways which were previously unheard of. Police inves-
tigators will likely find this wealth of information a boon to their work, but
crime analysts and police administrators may well find themselves faced with
information overload.
At the same time that police departments are making increasing use of
computer technology they are also undergoing a change in law enforcement phi-
losophy. Evidence of this change can be seen in the fact that many police de-
partments are implementing Community Oriented Policing (C.O.P.) in an effort
to emphasize proactive rather than reactive law enforcement. While the concept
of Community Oriented Policing is certainly not new (for a review of early
C.O.P. initiatives see Trojanowicz, 1986) the way in which information is util-
ized in Community Oriented Policing has changed over the years. In many cities
desktop personal computers have replaced the daily log for foot patrol officers
and in some cities the time honored tradition of a notebook and pencil has given
way to hand held, pen based mobile computers.
An abundance of tools and methodologies have been developed that sup-
port traditional reactive law enforcement. Practical examples include investiga-
tive tools such as linkage analysis, geographic offender profiling and modus op-
erandi systems. Geographic information systems have also played a large role,
both from a practical and a research perspective. Research examples include
measuring the geographic displacement of drug offenders (Green, 1993), moni-
toring the effects of law enforcement strategies on nuisance bar activity (Cohen
et al., 1993) and point pattern analysis of crime locations (Canter, 1993). Other
examples of more general purpose crime mapping systems for law enforcement
include the Drug Market Analysis Program (DMAP) effort undertaken in Jersey
City, Hartford, San Diego, Pittsburgh and Kansas City (McEwen and Taxman,
1994; Maltz, 1993) and PA-LEGIS (Pennsylvania Law Enforcement Geographic
Information System), an integrated GIS and police records management system
developed for smaller police departments (Bookser, 1991).
There is no doubt that tools for reactive policing will always play an im-
portant role in law enforcement. However, proactive law enforcement will re-
quire an entirely new set of tools, the development of which has only just begun.
Proactive problem solving by detectives, community oriented police officers and
police officials not only requires access to up-to-date information on criminal
activity, but perhaps more importantly the ability to anticipate emerging crime
trends. This in turn requires the ability to mine the vast amounts of data pro-
1. Introduction As police organizations automate their operations and implement more modern computer systems, taking advantage of advances in information tech- nology such as open architecture database systems, enterprise wide computer applications and ever increasing microprocessor and network speeds, more and more information will become available to police officers at the click of a mouse button. Moreover, all of this information will be linked together from various sources and organized in ways which were previously unheard of. Police inves- tigators will likely find this wealth of information a boon to their work, but crime analysts and police administrators may well find themselves faced with information overload. At the same time that police departments are making increasing use of computer technology they are also undergoing a change in law enforcement phi- losophy. Evidence of this change can be seen in the fact that many police de- partments are implementing Community Oriented Policing (C.O.P.) in an effort to emphasize proactive rather than reactive law enforcement. While the concept of Community Oriented Policing is certainly not new (for a review of early C.O.P. initiatives see Trojanowicz, 1986) the way in which information is util- ized in Community Oriented Policing has changed over the years. In many cities desktop personal computers have replaced the daily log for foot patrol officers and in some cities the time honored tradition of a notebook and pencil has given way to hand held, pen based mobile computers. An abundance of tools and methodologies have been developed that sup- port traditional reactive law enforcement. Practical examples include investiga- tive tools such as linkage analysis, geographic offender profiling and modus op- erandi systems. Geographic information systems have also played a large role, both from a practical and a research perspective. Research examples include measuring the geographic displacement of drug offenders (Green, 1993), moni- toring the effects of law enforcement strategies on nuisance bar activity (Cohen et al., 1993) and point pattern analysis of crime locations (Canter, 1993). Other examples of more general purpose crime mapping systems for law enforcement include the Drug Market Analysis Program (DMAP) effort undertaken in Jersey City, Hartford, San Diego, Pittsburgh and Kansas City (McEwen and Taxman, 1994; Maltz, 1993) and PA-LEGIS (Pennsylvania Law Enforcement Geographic Information System), an integrated GIS and police records management system developed for smaller police departments (Bookser, 1991). There is no doubt that tools for reactive policing will always play an im- portant role in law enforcement. However, proactive law enforcement will re- quire an entirely new set of tools, the development of which has only just begun. Proactive problem solving by detectives, community oriented police officers and police officials not only requires access to up-to-date information on criminal activity, but perhaps more importantly the ability to anticipate emerging crime trends. This in turn requires the ability to mine the vast amounts of data pro- 30
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