Poicing and Technology ILP

Policing “the Risky”: Technology and Surveillancein Everyday Patrol Work

CARRIE B. SANDERS AND STACEY HANNEM

Wilfrid Laurier University

Alors que de nombreux chercheurs universitaires du domaine de lasurveillance et de la police affirment que l’emergence d’une societe de lasurveillance normalise l’utilisation des technologies de surveillance parles services policiers, nous constatons qu’a cause d’un manque dedonnees empiriques il est difficile de determiner l’impact reel de lagestion des risques, et de la securite et de la surveillance dans le travailde la police. Cette etude s’appuie sur des entrevues approfondies et surl’observation participative de deux services de police canadiens dans lebut d’explorer l’impact que les technologies policieres peuvent avoir surles interactions entre la police et le public. A partir de cette analyse, noussoutenons que le changement organisationnel des activites de police axesur le risque et sur le renseignement ne se manifeste pas sur le terrain.Au contraire, les patrouilleurs utilisent plutot les technologies pourlegitimer l’action policiere envers “les suspects habituels”.

While numerous surveillance and policing scholars argue that the rise ofthe surveillance society has normalized technological surveillance bypolice, the lack of empirical research makes it difficult to discern the trueimpact of risk management, security, and surveillance on police work.The present study uses in-depth interviews and participant observationwith two Canadian police agencies to explore the impact that policetechnologies have on police-public interaction. From this analysis, weargue that the organizational shift toward risk-oriented, intelligence-ledpolicing is not carried out on the ground. Instead, patrol officers oftenutilize technologies to legitimize the policing of the “usual suspects.”

THE DEVELOPMENT OF information technologies (ITs) has substan-tially changed the practice of policing over the past 50 years, away from an

Preparation of this article and the research reported herein was supported by a Social Sciences andHumanities Research Council Insight Development Grant for the lead author.

Carrie Sanders, Department of Criminology, Wilfrid Laurier University, 73 George Street, Brantford,Ontario N3T2Y3, Canada. E-mail: [email protected]

C© 2012 Canadian Sociological Association/ La Societe canadienne de sociologie

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exclusive focus on reactive crime control, toward proactive security,surveillance, and risk management (Murphy 2007). While the develop-ment of policing technologies originated much earlier than September 11,2001, the terrorist attacks of 9/11 provided governments and police agen-cies with a new and compelling justification for intelligence-led policing(ILP; Sheptycki 2000), emphasizing aggressive information gathering andrisk analysis to “target, prioritize and focus interventions” (Cope 2004:199).While definitions of ILP are hard to find, we draw on the Global Intelli-gence Working Group’s definition of ILP as “the collection and analysisof information to produce an intelligence end product designed to informlaw enforcement decision making at both the tactical and strategic lev-els” (Global Intelligence Working Group 2003:3–4, as cited in Ratcliffe2008:81).

Scholars argue that the rise of the “surveillance society” (Lyon 2003b)has normalized technological and human surveillance and increased theuse of personal data by government and private entities for managementpurposes (Marx 1988). However, a lack of empirical, ethnographic researchmakes it difficult to discern the true impact of risk management andsurveillance on police work (Maguire 2000); do claims of risk analysis andthe rise of surveillance practices in policing have empirical validity?

Adopting a social construction of technology perspective, we fill thisgap by qualitatively exploring the impact of IT on patrol work in two Cana-dian police agencies. Drawing on interview data and participant observa-tion, we examine how the organizations construct the functions of policetechnologies, analysis, and risk assessment, and then consider whetherthis rhetoric penetrates individual officers’ everyday interactions with thepublic. Finally, we look at the organizational and situational impedimentsto ILP. From this analysis, we argue that although the rhetoric illustratesa shift toward risk-oriented ILP, examination of the in situ application ofpolice technologies by patrol officers provides a less novel picture. Whilepolice technologies do provide the potential for officers to engage in riskassessment and risk management, in practice the technologies are largelyused to legitimize the policing of those who are already marginalized andsocially profiled—the “usual suspects” (Gill 2000). We conclude by brieflydiscussing the sociopolitical implications of using police technologies totarget marginalized people and places.

POLICING TECHNOLOGY AND THE CONSTRUCTIONOF RISK PROFILES

There are few organizations with greater demand or need for informa-tion management than policing (Abt Associates Inc. 2000; Borglund 2005).Much of the advancement and implementation of IT in policing has re-sulted from the need to “improve effectiveness and efficiency, to satisfy thedemands of external agencies for information and to meet the requirements

Technology and Surveillance in Everyday Patrol Work 391

of new forms of police management and accountability” (Chan 2001:140).IT is perceived as being absent of social bias, leading to accountable, in-formed, and objective decision making (Nesbary 2001). This is particularlyimportant in an occupation such as policing, where allegations of bias maylead to a sense of injustice or a loss of public confidence in police services.

Other than a few notable exceptions (Chan 2001; Manning 2003, 2008;Meehan 1998), scholars have taken these technologies for granted, withlittle consideration of their effect on police/public interactions and orga-nizational change (Sorensen and Pica 2005). Many organizational studieshave difficulty conceptualizing the role of technologies because they havebeen unable to distinguish between the technological and the social with-out reifying and treating them as separate and distinct entities (Clarke2003). The social and technological are co-constituted, with one making upthe other (Clarke and Star 2008); technologies, as social objects, can alterand shape organizational practices and structural arrangements (Barley1986; Roberts and Grabowski 1996).

There is a small body of ethnographic literature that examines thehuman-machine interchange and the effect of IT on police work processes.While these studies illuminate how technologies have contributed to or-ganizational changes—for instance, altering patrol work from reactive toproactive (Meehan 1998)—they more importantly draw attention to theexternal psychological, social, political, and/or cultural factors that are im-plicated in the effect of technologies on social life (Chan 2001:143; Manning2001a; Ratcliffe 2004). For example, while IT may provide the technical ca-pacity for effective crime prevention, closer examination of its applicationwill demonstrate that the scarcity or redirection of resources may stiflethat potential (Dunworth 2000; Ratcliffe 2002). Few studies have exploredhow police use their technology, and its stored information, to respond tocalls for service and construct criminal charges.

Ericson and Haggerty (1997) were the earliest scholars to identifythat policing practices were moving away from preventing and respond-ing to crime and focusing instead on policing risk populations, drawingon information and risk assessments, surveillance, calculations, and anal-ysis. This shift can be understood as reflecting changes in social controland the growth of the risk society (Maguire 2000). In the risk society, ITis perceived as essential for keeping police and the public safe by usingpast “dangerous” or “troublesome” behavior to “predict” future behavior,thereby conceptually transforming abstract notions of danger into seem-ingly concrete “risks” to be managed (Parnaby 2006). The image of publicrisk, therefore, “is interjected into the embodied form of the dangerousother/enemy as a discursive object necessitating immediate regulatory in-tervention” (Hier 2004:551).

Within ILP, data about crime is synthesized, “de-contextualiz[ed] and. . . de-personaliz[ed] . . . in order to develop an overview of the natureof crime problems,” enabling officers to predict and manage them (Cope

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2004:199; see also Ratcliffe 2008). Thus, ILP is the technological effort tomanage information about risks in order to strategically manage the polic-ing mission (Sheptycki 2004). Castel (1991:288) identifies these preventa-tive policies as a “new mode of surveillance”—systematic predetection—“inthe sense that the intended objective is that of anticipating and prevent-ing the emergence of some undesirable event.” Increasingly, technologiesallow the amalgamation of expert opinion and information so that it is eas-ily accessible to front line workers. As a result, fieldworkers such as frontline police officers, no longer act in isolation to assess situations or casesthat they encounter (Castel 1991); rather, electronic access to the collectedassessments of other officers and judicial agents informs and contributesto officers’ constructions of risky populations and identities.

The classifications which officers access through IT are not abstractbinary objects; they are composed of the values, opinions, and rhetoric oftheir designers, organizations, and users (Bowker and Star 2000). Theseclassifications constitute “communication formats” that shape, and in somecases limit, how police think about situations, respond, and justify theiractions (Ericson and Haggerty 1997:33). Examining how officers access,make sense of, interpret, and use the coded and decontextualized infor-mation provides insight into police engagement in risk assessment andsurveillance practices. Therefore, in this analysis we focus on the discur-sive and interactional processes within patrol policing to better understandthe ways that human/machine interactions can reshape the contours of theknowable in police knowledge-work, and their potential to intensify policediscretion.

QUALITATIVE EXPLORATIONS INTO POLICE-PUBLICINTERACTIONS

After receiving university ethics clearance, we collected data throughinterviews and observations with officers from two different Canadianpolice services. The first police department is a 16-officer detachment,located within a small municipality, and part of a larger police organiza-tion. This municipality serves a large rural landscape and a populationof approximately 13,000 people; the 9-1-1 call-center is contracted out toa police department located 257 miles north of the municipality, and thepolice communication center is located in the organization’s headquarters,60 miles west of the municipality. The second police department is locatedin one of Canada’s largest cities with a population of over 1,000,000. Thispolice service employs more than 1,300 officers and has a local 9-1-1 andpolice communication center.

We conducted semistructured interviews with 25 police officers, call-takers and IT trainers. The intensive interviews focused on the partici-pants’ use of IT and their perspectives on the need for and value of thesetechnologies. All interviews were tape-recorded and transcribed verbatim


(Charmaz 2006) and were between one and two hours in length, withmost lasting one and a half hours. We supplemented the interview databy attending the 2011 Association of Law Enforcement Planners (ALEP)Annual meeting, and conducting 50 hours of participant observation ofpolice call-taking and patrol to develop a thick description of how officersuse IT during police-public interactions (Shaffir and Stebbins 1991). Weanalyzed field notes, technology training manuals, and interview tran-scripts, identifying and connecting themes related to risk management,police discretion, and decision making. Using writing as an analytic device(Richardson 2000), we categorized coded sections to identify thematicallycoherent interpretations of the work of police officers. Our constructivistgrounded theory approach is driven by the empirical data, but also in-corporates a reflexive analysis that draws on preexisting theories (socialconstruction of technology) to guide the interpretation (Charmaz 2006).

While the two participating police services have organizational differ-ences, they are technologically similar; each service utilizes GeographicInformation Systems (GIS), Computer-Aided Dispatch (CAD) systems,Record Management Systems (RMS), and Mobile Data Terminals (MDTs).GIS enable police departments to analyze data and chart it spatially (Man-ning 2008); “variations in density by location, types of crimes, or days ofthe week can be mapped, as can offenders’ residences, and patterns ofco-offending” (Manning 2001a:90). CAD systems are described as decision-support programs that “enable precise and exceptionally fast response,through the sharing of ‘real-time information,’ (Intergraph 2006); they pro-vide officers with messaging capabilities, email, and access to “extensivelocation-based information (history, hazards/alerts, contacts, premises de-scription information, etc.)” (Versaterm 2006a). CAD systems are interop-erable with other police databases, enabling call-takers and police officersto run a check on a caller’s name in RMS and Canadian Police InformationCentre (CPIC) databases (Intergraph 2006).

RMS is described as a: “‘state-of-the-art’ police record-keeping system:A fully integrated, versatile investigative and management tool, [that] im-proves the effectiveness and efficiency of police operations” (Niche 2006).RMS includes addresses, caution/hazard data entered by police person-nel, links to incidents, persons, vehicles and property, and access to CPIC(Niche 2006). Information stored on RMS and CAD is accessed and ex-panded through MDTs—the portable laptops used in patrol vehicles:

Extensive person information can be captured, including demographic infor-mation, physical descriptions and cautions, etc. Users can access linked in-formation, including incidents, known associates, next-of-kin and addresses,with a mouse-click. (Niche 2006)

Business and criminal organizations can also be linked to incidents,people, addresses, vehicles, and property, allowing users to trace gangaffiliations and associates (Niche 2006).

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The main screen of an MDT is divided into a series of boxes.1 Onebox displays CAD information (such as status of calls, type, event, andlocation); a second box is connected to the RMS and enables officers tosearch addresses, people, vehicles, and so on; a third box presents a mapof the city/patrol zone.2 A fourth box provides officers with access to aninternal messaging service (allowing emails to be sent between officersand other police personnel).

In what follows, we explore the organizational rhetoric surroundingthe function and utility of ITs, how these technologies are used on theground by patrol officers, and the organizational and situational con-straints imposed on technological functioning and ILP. Finally, we con-sider the sociopolitical implications arising from the integration of ITswithin patrol work.

ORGANIZATIONAL RHETORIC AND SELF-PRESENTATION: THE POTENTIALITIES OF RISKMANAGEMENT

The organizational rhetoric concerning the necessity, value, and use of ITis congruent with theorizing on policing within the risk society, presentinga positive self-image of an efficient and proactive organization. Discourseabout IT emphasizes its value for increasing the legibility of space for effec-tive and efficient resourcing, and for constructing risk profiles to “predict”future crimes and “manage” risky populations.

Increasing the Legibility of Space and Efficient Resourcing

IT designers and top command police personnel note that policing technolo-gies function to increase the legibility of municipal space, allowing policeagencies to allocate officers and resources in a way that is deemed mostefficient. Both police services employ GIS to map criminal events onto thelandscape:

GIS and our spatial information, particularly special address information[cautions / hazard information], is important for capturing police deploymentand creating statistics on the amount of crime in different jurisdictions andzones. (I20, GIS specialist)

As the following excerpt highlights, GIS are perceived by police or-ganizations as a “strategic and tactical tool for law enforcement” (I20,

1. An officer can choose to enlarge or close any of the boxes to customize his/her view.2. Neither police service had GPS in their vehicles at the time of data collection and, therefore, the maps

did not identify the location of officers on patrol.


GIS specialist), assisting with crime prevention and strengthening police-community relations and services:

Every five minutes CAD data is run over to the RMS. RMS data is updatedon the GIS server every 24 hours to enable analysis and visualization ofspatial patterns and connections of crime. Part of our strategic business planis to provide consistent and equitable deployment of police resources whileoptimizing the effectiveness and efficiency of community contact with policeservice. (2011 ALEP Annual meeting)

The use of GIS has rendered the municipal landscape increasingly leg-ible and reconfigured the way that police organizations understand publicspace. GIS information is described as benefitting officers, enabling them“to check on their computer the number of criminal occurrences in theirpatrol zone and to then assess and know how they are going to approachthat area” (I20, GIS specialist).

Organizational Constructions of “Risky” Space, Place, and Identities

Organizational rhetoric concerning the use of IT for risk assessment ele-vates the police agency’s status and solidifies its mandate for crime pre-vention. The following discussion with a police officer on organizationalapproaches to crime prevention illustrates how the use of GIS data worksto characterize the organization in positive terms:

We . . . have an analyst that . . . basically takes all the crime and they willdetermine that based on all of this crime “Break & Enters (B&E’s)” are a bigpriority . . . That sergeant will ask for a study on the past month’s B & Eactivity to see where the problem is, and it will be broken down to two blocksin that area between certain hours . . . so the officers will be dispatched out. . . and they will . . . come up with a plan to target that area to stop crime.(I25, IT security response coordinator)

Upon closer examination it becomes apparent that informationdatabases provide another means by which police organizations catego-rize, classify, and label public space. Above, the officer illustrates howpolicing technologies transform past behaviors into predictors of futureactions, enabling police organizations “to predict the next crimes to oc-cur in those areas” (I20, GIS specialist). IT is discursively constructed asimproving efficiency and effectiveness by enabling officers to identify andpredict dangers. For example, the following quotation from an IT designerillustrates the organizational risk management functions of the “location-driven” CAD system:

Our hazards (criminal and non-criminal caution information) don’t just takeinto account the specific addresses, they take into account radiuses as well.Let’s say that they do have the address off . . . our radius search will pick

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it up and will say, ‘The house next door has a violent gang member who isknown to hate police,’ because perhaps you have the address wrong. . . . Soit is important for the hazard information to be geographic-centric and alsoto take into consideration a little buffer. (I29, IT designer)

For police, space becomes understood within the subjective classifica-tions of emergency calls and undesirable behaviors, and the placement ofpatrol vehicle(s). The past becomes incorporated within and works to guidethe actions of police, who enter spaces armed not only with knowledge oftheir own previous contacts, but with the collected institutional historyof the place in the form of IT classifications. The mining of data aboutthe space becomes a technologically mediated form of surveillance that nolonger relies on physical visibility, but on stored data.

Unlike location-driven CAD, RMS stores coded information that isperson-centered and provides a means to develop profiles on individuals. Asone officer explains, “what police access through the RMS is everything thathas been entered into our databanks . . . it is unbelievable the amount ofinformation that [patrol] can access, useful information from . . . a driver’slicense history, all [the driver’s] contacts, when [he / she] was last stoppedin [the city], who stopped [the driver], and who was with [the driver]” (I25,IT security response coordinator). RMS is defined as the technology where

Intelligence information is kept because people do move to many differentcities around the country and . . . we actually have a product that movespeople around. So what we are interested in on the records side is ‘What haveyou done?’ ‘What have you been involved in?’ So that is something; that iswhere we have a product to share information in, amongst [our] customersand others . . . It is actually open up to any records system that can actuallypublish to it. (I29, IT designer)

When police have access to a master database that incorporates in-formation from various police agencies, they can draw on information pro-vided by other officers to construct their understanding of “the type ofperson” they are dealing with. Versaterm provides their customers with ashared database, the Law Enforcement Information Portal (LEIP).3 LEIP

Enables disciplined information sharing between police agencies, in a region,a province or state or even nationally . . . agencies ‘publish’ indexable andsummary information (on people, places, vehicles, events) to a regional LEIPserver. (Versaterm 2006a)

Any police agency using Versaterm’s RMS automatically publishes toLEIP. Other police agencies not using Versaterm-designed products can

3. Since having completed data collection, LEIP has been renamed the Police Information Portal (PIP)and is now accessible to all Canadian police services regardless of their IT platform (i.e., Niche orVersaterm).


acquire permission to upload information and search the LEIP server. Anestimated 18,000 police officers use or have access to LEIP on a daily ba-sis, and almost one-third of Canada’s police information is shared throughLEIP (Versaterm 2006b:6). LEIP’s information-sharing function is valuedbecause “crime doesn’t respect geographical or municipal boundaries” (I29,IT designer). While all police departments have access to CPIC, whichhouses all Canadian criminal records, police perceive that the “real” in-telligence information is provided by RMS databases. As the following ITdesigner explains:

CPIC is a great system, but it will only tell you what you are charged withcurrently or what you have been charged with and convicted of. The realintelligence information is ‘who were you with when you got charged,’ ‘whatthings did you get stopped for that may not be chargeable offenses,’ which isa lot . . . people don’t start off doing armed robberies. They start off as youngoffenders doing, being troubled in school, you know; they are missing persons,they are group home kids, they are hanging around with known criminals.So it is that kind of intelligence information that is really powerful. (I29, ITdesigner)

These examples draw attention to the organizational rhetoric sur-rounding the use of these technologies for risk assessment and crime pre-vention. As the following IT police trainer notes, “RMS is seen as an inves-tigative tool for storing personal information and cautions about differentaddresses so that officers can know how many times they have been to aparticular address, the demeanor and previous behavior of people at thataddress etc . . . [RMS] is beneficial for solving crime” (I21, police trainer).The ability to mine data from CAD and RMS enables the construction ofrisk profiles on people and places to

a good picture of what [they] are responding to so [they] can do work before[they] get there. So [they] can go in and pull information out such as premisehistories, contact with the police, if this is listed as his/her residence [they]can run that person’s name and get everything [they]

can know about that person . . . [they] can actually go in and mine out. . . before they get there, things that are important to them. IT securityresponse coordinator) Technological data rationalize the deployment ofresourcing and the management of crime in a city.

TECHNOLOGICAL SURVEILLANCE AND EVERYDAYINTERACTIONS

While the organizational construction of police IT leads one to believethat modern-era policing has transformed from reactive policing to ILPand risk management, studying the everyday use of technology paints adifferent picture. IT does provide the option to engage in proactive policing;

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the implementation of MDTs in police cruisers has enabled officers to runchecks and queries on vehicles and people at anytime:

Officer: this is one nice thing about the MDT, or at least whenever Ido a stop (speaking of stopping at a stop light, not necessarilya police traffic stop) I just run the plate and it gives me all theinformation about the person. That’s funny, look at that carahead of us. It has a MADD (Mothers Against Drunk Driving)sticker in the window and yet the driver has had two previousDUI’s (driving under the influence).

Interviewer: I was noticing that you were doing that and realized thatanyone could run my plate while I was driving.

Officer: Yeah, this is where we get a lot of our pull-overs.

(I26, officer ride-along field notes)

Although the searchable functions of IT provide a means for patrolofficers to be more proactive, this is not always the reality of patrol work,as the following officer suggests:

Let’s say I am going to a domestic dispute; have they had any other domesticdisputes before . . . Do they have any weapons registered to that residence?Have we had any previous calls there? Like I can build all that history in thetime that I get there, so I know exactly what I’m dealing with. Now that’san ideal situation and not often does that happen. A lot of times we’re goingin cold. Not knowing exactly—or maybe we have had some past history, sowe know, but a lot of times we are just going in cold. (I05, officer, emphasisadded)

It is challenging to mine information and construct risk profiles whenresponding to a call for service. As one officer explains, the informationprovided by IT is “good to know after the call when you are going to do areport, or like right now we could query a vehicle; we are not going to acall, so it is easy to do. But, going to a call, I don’t need that information”(I10, officer). As another put it:

. . . if I am dealing with somebody, I can query them and can pull up theirmug shot and ensure it is them . . . That is what I like as the purpose of thecomputers ‘ often times there is something specific that I am looking for.’ soI can look at it down here and say ‘okay that is what I am looking for.’ (I20,officer, emphasis added)

Thus, while the proactive risk management possibilities of IT are notalways fully utilized by front line officers, they often mine databases tolegitimize and authenticate decisions being made on the ground.


Marginalizing and Disciplining Space, Place, and Bodies

Organizational rhetoric emphasizes the use of IT for identifying “riskypopulations” and enhancing crime prevention. However, empirical anal-ysis of officers’ use of the decontextualized4 and coded information sug-gests that accessing information may be less about crime prevention thana means of legitimizing police interactions with those who are already“known” to be problematic. For example, a police call-taker described howstored information could be used to determine an “appropriate” course ofaction, even in a situation where no criminal or “risky” activity was takingplace:

When you receive a call the in-house information will be displayed, and itwill show past records of people, for example (types in a person’s name) thiswoman is a prostitute who has been charged, her alias is ‘Chastity,’ she hasbeen arrested four times, she lives in a bad neighbourhood, etcetera. All thatinformation is provided to us. Or, here is another example (types in anothername); he has been charged with assault, known to be violent to police officers,and has a gun behind his door. So, we would give this information to thepolice officers, or if we had transferred the call to ambulance for a medicalemergency we would say ‘hold on there’ and would send a police officer beforethe ambulance. So, if the ambulance arrived first, it would just sit out frontand wait for an officer to arrive. (I08, police communications worker)

Here, it is apparent that the stored and coded information is used toconstruct “risk profiles” that guide the call-taker’s instructions to emer-gency responders. Evidently, there is a communication format effect bywhich key labels and classifications condition a response. Despite no evi-dence of any current criminal activity, the call-taker advises paramedics tonot enter the scene because of the “risk” they may face, dispatching a policeofficer first. Police personnel use the information from RMS and CAD toconstruct the “(un)reliability” and “(un)trustworthiness” of the person theyare interacting with; a history of police contact renders the individual dis-credited and risky (Sanders 2006). The following illustrates how police useRMS information to verify the complainant’s story and his/her “reliability”or “danger”:

So someone calls up and says, ‘Okay, my wife is beating me, blah, blah, blah, ormy husband’s beating me,’ we look it up. Okay, there’s a history there. Theymay not be at the same address; they may be someplace else, but because

4. Decontextualized refers to the way in which IT “offers a synthesis of data about crime that is developedout of context” (Cope 2004:199). For example, a file may have a “hazard classification” noted on it entitled“frequent flyer”—calls 9-1-1 frequently. This code removes the contextual information surroundingthe classification of “frequent flyer”—perhaps it is an elderly woman who lives alone and calls 9-1-1frequently because she mistakes a tree branch hitting the window as someone breaking into the house(field notes).

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they’ve been a complainant in a domestic before, BOOM, that comes up inour ‘Location of Interest.’ (I22, officer)

The stored information provides a historical picture of the individualthat functions to legitimate the actions and decision-making processes ofthe officer. The following field-note excerpts describe exchanges between apatrol officer and “troublesome” members of the city, further illuminatingthe ways that human/machine interaction reshapes the knowable in policework.

Today there is a large city festival planned in the zone we are patrolling andwe are working to ensure the city ‘looks presentable’. As we drive around,we approach a large group of homeless people, and the officer pulls the carover and begins to question one of the female pedestrians he has had previouscontact with. As he questions her, he queries her name into the RMS and CADsystem. Based on his search he notices that she had a court appointment thatmorning and he asks her if she had ‘showed up in court’ that day. She thenyells at him for stopping her for no reason and that she was just returningfrom court ‘Where yous people sent me for trying to get into my own fuckinghouse!! May I return to walking on the street and bothering no one!?’ As sheis yelling, the officer asks her to stay and he continues to query informationon her and what criminal activity she has had in a while. He then asks herwhere she is staying for the night and why she had to go to court. Once hehas read through the information provided on his MDT, he drives on. Theofficer mentions that it is in this area that he gets a lot of contact with peoplebecause there are a lot of homeless people here.

Moments later we pull over a driver, who is driving with a three year expiredlicense-plate sticker. When the officer approaches the car, the driver becomesvisibly irritated by the officer. The officer returns to the car and swipes thedriver’s license through his MDT. While the officer waits for the informationto pop-up on the screen, he begins to fill out a report and a ticket for theexpired license plate. When the MDT has searched RMS, it produces a reportthat is accompanied by the driver’s license picture. This ensures that thedriver is the owner of the vehicle and is also the same person we have pulledover. While MDT continues to search RMS, CAD and its associated CPICdatabase for past criminal charges, the officer returns to the driver. Thistime the driver becomes verbally abusive and says that it is not his vehicleand he is running very late. The officer returns to the car and sees that RMShas pulled up previous police contact information. The MDT shows that thedriver has been pulled over three previous times for his expired sticker. Italso notes that he was verbally abusive and that he was charged previouslyfor assault and later for impersonating a police officer. After reading thisinformation, the officer then pulls out two thick handbooks, one stating allcodes and criminal charges and the other providing the definitions associatedwith the various codes and charges. The officer searches through the differentcharges to see what definitions fit best with the present situation. The officerthen chooses five different offenses, resulting in a charge exceeding $500 andan accompanying court date. (Fieldnotes)


The officer subjectively constructed the charges, responding to thedriver’s present actions and demeanor and his past criminal behavior.From these examples we begin to see how the use of IT has altered publicpatrolling and the determination of criminal charges, grounding them inboth present and past actions. Past information, electronically stored inRMS, can be used to legitimize the “control” of persons profiled as risky. Theofficer stopped the woman on the street, based on his previous interactionswith her, even though she was not engaged in any troublesome behavior; heused his MDT to search for information to legitimize and construct chargeson her but because he was unable to find a missed court appointment orother legal grounds to detain her further, he could not justify further action.

We thus find that, in everyday patrol policing, ITs are not being usedto search and construct “profiles” on previously unidentified “risky popu-lations” or persons, but are more often used to legitimize the policing of“the usual suspects” (Gill 2000)—those who are already known to police.The following quotation illustrates how these technologies can be used toprofile and “track” marginalized persons:

Often times where you get down in the . . . downtown sectors you will look atthe poor people, or bums, what have you, and you will see that they have acouple of warrants here, so they will move down to [a different city] and whenthey move to [that city] they have absolutely no history, but now [because ofRMS] we get their history. Because you know, they do a circuit, they go [onecity], [second city] and head out West for a couple of years, so now we canactually track them, where these people last were, which is nice. (I26, officer)

As the officer notes, police technologies, capable of crossing jurisdic-tional boundaries, have provided technologically enhanced forms of socialsorting, based not on previous encounters with known individuals, but onsocial profiling and group membership (in this case, homelessness). Thishas implications for interactions between police and members of the pub-lic, who are often unaware of the role that technology plays in constructinga “data double” (Lyon 2003a)—the persona that police are responding towhich may be comprised of information on any and all past interactionswith police or emergency responders, even in different cities.

Police services also have multi-agency partnerships, which integratepolice, probation, social services, and ministry of transportation withinone searchable database. Access to the incorporated knowledge of multi-ple agencies, we argue, has expanded and enhanced the scope of policerecords. At a random traffic stop, for example, an officer can run a queryon a person’s license plate that connects the officer to the Ministry ofTransportation database, and provides access to the vehicle owner’s crim-inal record (regardless of who is behind the wheel of the car). Officers usethe information in these databases to establish such intangible traits as“reliability,” “innocence,” and “guilt,” even though the past record may beunrelated to the person’s present behavior.

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When I am in the [cruiser] now . . . we’re linked in with the Ministry ofTransportation, you can . . . be on the MTO website and ching, ching, ching,so when you say to me “I’ve never had an accident or I’ve never done this orthat,” we can punch that up and it’s got a lifelong history there! (I06, officer)

While ILP is viewed as a more efficient use of police resources, ourobservations lead us to believe that an ILP model is not fully utilized by pa-trol officers. Rather than data leading officers to investigate risky persons,known persons are being investigated for evidence of “risk.” The examplesabove highlight the propensity for accumulated data to be accessed andutilized in interactions with persons (or places) that are already labeledas problematic or risky. Organizational practices, such as processes of dif-ferentiation (Manning 2001b)—being attentive to those perceived as morerisky—and rounding up the “usual suspects,” both shape the use of IT andare augmented by it.

ORGANIZATIONAL AND SITUATIONAL CONSTRAINTSFOR ILP

The organizational structure and culture of policing shapes IT use andfunctionality, which in turn shapes the ability to engage in risk manage-ment. Further, the local and situational context of patrol work createsimpediments to technological functioning.

Police As “Notorious Empires”

Police organizations have been described as “information silos” (Shepty-cki 2004)—they are “notorious for being empires and it is at [their] owndetriment . . . there are sometimes difficulties in breaking down the walls”(I26, officer). An officer explains further:

Police agencies are either ‘stand-alone,’ and share information only with eachother and have access to technologically shared information within their ownagency only; or they are part of a larger cooperative . . . what is interestingand frustrating is that now that this technology is available, those membersthat asked for it are not members of the cooperative. (I21, officer)

It seems that some police agencies prefer not to share information,and choose not to employ technologies and databases that would link themto other organizations:

There is not a chance in hell that I will ever see certain agencies sharinginformation with others. The only means of [information sharing] is verbal inmany of these cases because these departments are extremely paranoid, andwith good reason. (I25, IT security response coordinator)


Information hoarding is an organizational problem that restricts theutility of IT designed to breakdown information borders. For ILP to workeffectively, information must be shared within and across police services.Yet even when technologies are employed to enhance information sharing,organizations place limits on what information is shared. For example,when a patrol officer pulls a person over and notices that this personwas recently stopped and charged in a neighboring city, the RMS “doesn’tactually bring up the report . . . it gives me report numbers, contact peopleand occurrences” (I26, officer). The officer must then call the other officeridentified on the report and request permission to see it, limiting his abilityto employ risk analysis in the moment. These organizational challenges totechnological functioning are well recognized.5

Of greater interest, however, are the organizational tracking capa-bilities associated with officer queries and the impediment this trackingcreates to ILP. During field observations, the lead author often requestedto see an officer run a query by offering her own name to search. The of-ficers and call-takers always declined the request noting that they werenot allowed to run a search without the permission of their superior. Offi-cers explained that when querying a vehicle or person the query includesthe badge number of the officer and the query must be justifiable. As oneRMS coordinator notes, “everything is connected . . . you’re so entrustedin respect to sign-ons, and what we teach our members is put down whereyou are, or why you queried [it] so its justifiable” (I04, RMS coordinatorfield notes). The following discussion with a RMS coordinator highlightsthe importance of police justification and accountability in data miningand how concerns around justifiability can create impediments to ILP:

Say we are doing an internal investigation and . . . there’s been an allegationthat one of our police officers is hanging with one of the bad guys, so we putsome silent hits on these bad guys names, just to see if [the officer] is queryingthat . . . are they [the officer] disclosing information, that kind of stuff. Orit could just be simplistically, we want to know what they’re doing on thesystem. (I04, RMS coordinator)

Thus, officers’ abilities to mine data are organizationally constrainedand often structured around claims of justifiability and legitimation—notonly to the courts and public, but most importantly to the organizationwhich has the ‘capability at anytime to find out what you’ve touched andwhen you’ve touched it.’ (I04, RMS police coordinator)

Individual and Situational Impediments to Surveillant Practices

While incompatible technologies and organizational processes can createimpediments to the adoption of ILP, so too can individual skills and the

5. A number of important ethnographies of policing have identified and discussed in detail the technolog-ical impediments to information sharing and ILP (see Chan 2001; Manning 2008).

404 CRS/RCS, 49.4 2012

situational demands of patrol work. The willingness of front line officersto adopt and work with new technology was identified as one barrier tosurveillance practices, because “how do you go about rolling an applicationout when people are not always acceptable to change?” (I20, GIS specialist).A cultural change is required for officers to adopt and use IT as investiga-tive tools.

As the following RMS specialist explains, “some officers grumble aboutusing the technology and taking time to fill out reports in their car . . . Themore information put into the system the better, but it is a chore to turntraining around to do that. The difficulty is to get officers to stop thinkingabout it as paper and move toward RMS as an investigative tool” (I21,RMS police specialist).

While officers are sometimes reluctant to accept new technologies,they may also lack adequate training. During our fieldwork many officersnoted that they were unable to effectively manipulate the system:

It seems like a lot of what we do is trial and error and then gets passed fromperson to person, because it is a complex system and with so many people, itis hard to train. (I26, officer)

One officer suggested that, when it comes to technology, officers areleft “to pretty much learn on their own” (I10, officer), resulting in a vari-ance of knowledge, with some police being more technologically savvy thanothers.6 The following excerpt illustrates the lack of training and techno-logical know-how within patrol:

Returning to the office, we see two older male officers (over the age of 40)typing information into the Records Management System. These two officersask the officer I am shadowing (female under 40) how to import informationdirectly from their records into RMS without having to retype it. She walksover to the computer and shows them how to use the ‘import’ button. Thetwo officers then ask how to import more than two charges at a time withouthaving to start a new document. She explains that she doesn’t add the othercharges and has not been asked about them in the past. This leads the twomale officers to not put their additional charges into the system. (I06, fieldnotes)

Here we see also how the reporting practices of one officer (in omit-ting additional charges) influence those of the officers who are trying tolearn the system. In addition to IT training, patrol officers must be taughtto write reports, which are the building blocks of intelligence databases.Constructing a risk profile requires access to relevant information—yet

6. Lack of training and technological “know-how” are not necessarily mutually exclusive categories. How-ever, the lack of opportunity to learn how to use and manipulate the system is exacerbated whencoupled with difficulty learning. A number of comments were made by officers about the perceivedconnection between age and technological skill and willingness to learn; we recommend this topic forfurther inquiry.


patrol officers did not have a uniform understanding of what consti-tutes relevant information and how to upload/attach that information to areport.

Like we are given training on how to fill out a report properly, but . . . I thinkthere are like 3, 4 things that you must put in and then after that it is prettyloose. Like I said there are a lot of drop down studies . . . I used to workhate crime and if there was a hate crime if the officer was smart the officerwould hit the drop down button and hit special study ‘hate bias’ . . . But, noteveryone checks that off. (I25, IT security response coordinator)

Every police report has to be approved by a sergeant, yet police orga-nizations are “short sergeants to actually check the reports” (I26, officer).During field observations an officer discussed the challenge and frustrationof inadequate reporting for ILP:

We look at one RMS report that a fellow patrol officer had filled out. Thereport had the typical information . . . name, date, time, description of in-cident, but the officer had also noted that they had stopped the vehicle andhad put in a vehicle description and license plate. However, the officer hadnot uploaded the vehicle information into the RMS report—thereby makingit unsearchable. By not connecting the vehicle search to the RMS official re-port, the officer had left this report undetectable to other police. (I21, RMSspecialist field notes)

While organizational and individual contexts may hinder surveillancecapabilities, the local and situational requirements of policing also createimpediments to risk management. Officers disclosed that they did notcollect data prior to attending a call because they lacked time and felt thatthe vast amount of information available was not useful. As one officersuggested:

I wouldn’t do data mining to find crime areas when on the road because ittakes up too much time. Can’t have all that information at one time becauseyou can’t use it all. (I26, field notes)

What Sheptycki (2004) has referred to as “intelligence overload”speaks to concerns about officers’ abilities to identify important informa-tion: “some cops are really good at using the information and some aren’t”(I25, IT security response coordinator). We did not observe that mining in-formation to “paint oneself a picture” (I29, IT designer) prior to attendinga call was a common practice among patrol officers, rendering rhetoricalclaims of risk management questionable.

406 CRS/RCS, 49.4 2012

SOCIOPOLITICAL IMPLICATIONS

While the information provided on CAD, RMS, and MDT holds value forofficer and public safety, this research raises questions about how offi-cers variably incorporate and interpret past information to guide theirwork. More importantly, the findings illustrate how ILP has become amanagement philosophy of evidence-based resource allocation (Ratcliffe2008), which has not been carried to the front line. Although the surveil-lance potentialities of policing IT were not fully evidenced in their in situapplication by patrol officers, the use of this coded and decontextualizedinformation has significant implications. Issues of data integrity and theinterpretation and use of police information are particularly concerning.

Data Integrity

Like all social creations and endeavors, subject to error and misinterpre-tation, we must question the accuracy of the information stored in thesevarious databases. The coding and classification of emergency informa-tion, and its use for calculating “risk,” are inexact sciences (Sheptycki2004), involving selective interpretation by police personnel. For example,the information provided on CAD systems is driven by location addresses,yet “the information does not move when people move, but instead is onlychanged when emergency responders realize that the information is nolonger accurate” (I25, IT security response coordinator). As numerous of-ficers explained, “we don’t have the manpower to verify addresses backinto RMS so there is a level of inaccuracy when you are using the dispatchoccurrence” (ALEP, field notes). If organizational resources and individualactions are influenced by this previously recorded (and possibly inaccurate)information, then unintended social consequences can arise from its use,including the misclassification of individuals living in “risky” neighbor-hoods, and the potentially discriminatory alteration of emergency services(e.g., different emergency classification and response provided to a knownsex worker). If data provided are inaccurate, then its use to legitimize andjustify police decision making is suspect and subject to error.

Interpretation and Use of IT Data

Studying the ways in which IT designers, police organizations, and offi-cers talk about and use their technology points to how IT can legitimizesocial profiling. For example, while police IT may be used to request in-telligence on any member of the public, in practice we see that those whoare already constructed as risky persons and present in public space aremore likely to be subject to such practices of surveillance. This finding mir-rors Hier’s (2004) assertion that while CCTV systems were constructed asa neutral technology of surveillance for the protection of the public, in


practice they effect selective social monitoring, and “the surveillance gazeoverwhelmingly falls upon individuals occupying categories of suspicion—youth, homeless persons, street traders and black men” (p. 543). In thissense, the use of IT may be understood as yet another “organizationalpathology” of police intelligence systems (Sheptycki 2004), acting as a“negative amplificatory information feedback loop”7 to legitimize and reifyexisting understandings of labeled groups and spaces. As exemplified bythe officer’s interaction with the homeless woman recounted above, and an-other officer’s discussion of the utility of tracking “bums,” it appears that“prior encounters” or membership in a “risky” group constitutes sufficientorganizational justification for data mining, even in a noncriminal situa-tion (such as “walking down the street”). Thus, despite official rhetoric, inthe context of patrol, police IT resources are being disproportionately usedto police visible signs of disorder and are arguably not engaged in proactiveresponses to criminal activity.

The databases incorporated within CAD, RMS, and MDTs provide of-ficers with more than information and guidelines to complete their tasks;they provide information that can transform their actions. For example,allocation and deployment of officers and patrol vehicles based on GISdata and the statistical analysis of “risk” can create a power imbalance inthe city. Those areas labeled “dangerous” are likely to be more intenselypoliced, which, in turn, increases the proportion of “crime and disorder”that will be detected and recorded in official crime statistics (Kitsuse andCicourel 1972), reifying the risk profile. Thus, the analysis of location-basedcrime data and integration of IT in policing has the ability to perpetuatethe marginalization of labeled neighborhoods and increase surveillance,transforming these spaces into “criminal zones” and “hot beds of breakand enters.” Labeling neighborhoods as “dangerous” or “risky” has impli-cations in terms of rising insurance premiums, uninsurability, decliningproperty values, and the reluctance of citizens to live and work in “highcrime” areas (Openshaw 1993). Even if the label does not reflect empiricalreality, it may become a self-fulfilling prophecy, “transforming the imageand reality of a place” (Ratcliffe 2002:221). Thus, we argue that widespreaduse of police database information has the potential to stigmatize particu-lar neighborhoods and, implicitly, their inhabitants, who become markedas “risky” persons because they live and work in dangerous spaces.

CONCLUSION

The greatest hope for improving crime control and prevention has beenplaced in IT (Manning 2001b, 2008). While IT may provide the potentialfor ILP, an in situ analysis shows how these technologies are often used

7. We are indebted to an anonymous reviewer for suggesting this term.

408 CRS/RCS, 49.4 2012

by patrol officers as a means to legitimate the policing of populations thatare already socially profiled. Like Sheptycki (2000:312), “we do not con-demn policing surveillance practices wholesale as we reluctantly grantthat when used with great care they may be a necessary evil”; however,we wish to draw critical attention to the potentially negative consequencesof engaging in the technologically mediated surveillance of marginalizedpeople and places. Notably, IT removes the temporal aspect of traditionalpolicing, allowing police to legitimize their actions on the ground on thebasis of stored records of past events that they need not have witnessed.Our analysis also raises questions about the assumed value-neutrality ofpolice technologies, surveillance, and risk management, and the presump-tions about the integrity and objectivity of stored data used to justify policeactions. This in situ analysis of policing IT illuminates how these technolo-gies can (and do) change the knowable in police work and create conditionsof possibility that shape police responses, and their everyday interactionswith citizens, in ways that have not previously been explored.

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