The centralization and rapid deployment of police agency information technologies: An appraisal of real-time crime centers in the U.S.

Abstract

The newfound ability to deliver information to police in rapid timeframes has resulted in Real-Time Crime Centers (RTCCs) across the United States. Despite their emergence, little is known collectively about them. This study appraised the nature of RTCCs through a national survey of 44 police agencies. Findings revealed that (1) RTCCs have recently begun to diffuse rapidly but are still in an early innovation/adoption phase, (2) there is no single model of their use, (3) most procure a wide variety of technologies and information sources, and (4) most allow for information to be shared with partners in real-time.

Keywords

Real-time crime centers diffusion of innovation information sharing technology crime analysis police innovation police practice

Introduction

Increased efforts to optimize the use of innovative technologies to support police practices have become apparent across law enforcement agencies.¹ A driving force of technological innovation is the idea that agencies are placed at a disadvantage relative to the protection of the public and apprehension of offenders (Byrne & Hummer, 2017). As a result, the trend of increasing reliance on robust intelligence capabilities and the implementation of an expanded range of technologies to meet these demands has gained traction across many local police agencies. Due to these technological innovations, agencies have been able to capitalize on cyber-based information systems with the intention of improving the efficiency and effectiveness of policing (Randol, 2014; Willis and Mastrofski, 2011; Willis et al., 2007).

The ever-increasing need and newfound ability to harness and deliver information to police in rapid timeframes have led to the development of centralized data platforms commonly recognized as real-time crime centers (RTCCs). Real-time crime centers are intended to serve as an operational center capable of delivering information to officers in rapid time en route to either crime in progress or incidents that have recently occurred. The creation of these centers is often contingent on the availability of resources and the extent of technological proficiency maintained by an agency. This is in addition to the nature of crime in a community, stakeholder or citizen investment, and a host of other factors that may also play a role in the degree to which data, information, and intelligence can be used and managed.

As centralized gateways of information and technology, RTCCs offer to deliver a multitude of novel informational resources to line officers, detectives, and other practitioners in a manner that has never been observed in the history of American policing. Despite their emergence and some evaluated success (Hollywood, et al., 2019), the current body of knowledge on the diffusion of these centers remains largely absent. This study, which represents the first endeavor to nationally appraise the nature of real-time crime centers and their equivalent, carried out a voluntary survey of 44 local police agencies housing these units. The study contributes to police practice and the literature on policing innovation in several ways. First, the study examines the diffusion of RTCC platforms within the framework of innovation theory drawing similarities with the diffusion of other policing innovations. Second, it provides a national appraisal of existing RTCCs to better guide their future development as a vehicle for improving responses to crime and disorder within communities. Third, the study identifies consistencies and discrepancies across the different sites and their respective models. Fourth, the findings of the study contribute to the scant body of literature devoted to the understanding of the structure and function of existing RTCCs operating nationwide.

Research context

The term “real-time crime center” was first used in 2005 (NYC Global Partners, 2010), however, the integration of these units into police operations is a relatively new phenomenon that remains largely understudied. The recent diffusion of these centers points to the demand that exists amongst law enforcement agencies to manage the economics of policing and the streamlining of analysis and dissemination of real-time information (Wuschke et al., 2018). Despite lacking a conceptual framework, real-time crime centers are generally described as centralized data hubs that harness information and deliver it to officers in the field in hopes of facilitating outcomes such as improved response times, clearance rates, use of force incidents, and officer safety (Coppola, 2016; Fox, 2014). The actionable information and data derived from the integration of these technologies have led to an increased reliance on these centers by all levels of command, ultimately deeming them an invaluable resource for crime control and investigative purposes. Yet, notwithstanding their continued and increasing deployment, much remains to be known about the extent to which real-time crime centers are implemented and the nuances that exist across agencies.

Innovation

The decision to implement a real-time crime center may best be understood through the lens of policing innovation. Whether an innovation is deemed a significant development in policing practice is highly dependent on what or to whom it is significant (Willis and Mastrofski, 2011). For the purpose of this appraisal, we adopt the idea proposed by Matusiak and King (2021), that innovation is best determined by police practitioners. In a survey conducted by those researchers, it was recognized by police chiefs that real-time policing and the various technologies that exist within such centers indeed constitute an innovation (Matusiak and King, 2021). Nonetheless, organizational innovation can be disaggregated into three separate streams of literature. That is, the (1) determinants of innovation, (2) process of innovation, and (3) diffusion of innovations (King, 2000). Of interest to this particular study is the concept of diffusion, which expounds on the spread of innovation and technology.

Diffusion of innovation

The genesis of research on the diffusion of innovation and its theoretical crux is often traced back to the work of Everett M. Rogers. Rogers (1962) describes the process as involving the adoption and subsequent acceptance of an idea, practice, or object by an individual or specific audience. This process is a sum of four components: (a) the attributes of the innovation, (b) the extent to which communication channels are utilized, (c) the time or rate of diffusion, and (d) the social system in which the innovation is being introduced (Rogers, 2003; Wejnert, 2002; Willis and Mastrofski, 2011). In its most basic form, research on diffusion investigates how these factors interact to impede or facilitate the growth of a specific product or practice among members of a specified adopter unit.

Studies that examine diffusion note a bell-shaped curve associated with the over-time adoption of innovations (see Figure 1). This curve is attributable to those that are considered innovators, mainstream adopters, laggards, and resisters (Grübler, 1996). Specifically, the initial adoption period can be attributed to innovators who experiment with the innovation and demonstrate its utility. Innovators are seen as risk-takers and those that may have the necessary resources to absorb any setbacks that may be associated with or incurred by an unsuccessful implementation. Once a degree of legitimacy is established, the innovation undergoes a mainstream period in which widespread adoption is experienced. This phase eventually flattens out as remaining laggards sign-on. Most often, the success of an innovation is measured by how quickly it enters the mainstream or how long the mainstream process lasts.

Figure 1.

Roger’s Theory of Diffusion

The bell-shaped curve is intended to explain the diffusion of innovation across agencies and organizations. Whether the same sort of distribution can be used to understand the acceptance of RTCCs within individual agencies and by police personnel is uncertain. However, it is feasible that police personnel may adopt technologies at differential rates. An understanding of the diffusion of RTCCs, and other technologies within agencies is an area yet to be fully explored. Though it may be possible to utilize Roger’s bell curve to understand the intra- and inter-organizational diffusion of innovations, it is applied here to gauge the diffusion of RTCCs across police agencies nationally.

Diffusion within policing

Police institutions may be more resistant to organizational change, however, the need for efficient and data-driven decision-making has led to the development and spread of technologies and practices equipped to meet these needs. The diffusion of innovation theory provides a framework that addresses both, the adoption of innovations within policing and the factors that influence the eventual acceptance within the practice. Collectively, policing studies utilizing Rogers’ theory of diffusion seem to suggest that (1) larger agencies tend to be placed at the front end of the process, as smaller agencies may find the implementation less feasible or not an appreciable factor (see Lum et al., 2017; Nix et al., 2020; Weisburd et al., 2003), and (2) innovations emerge with the intent of meeting strategic and administrative demands (see Gayadeen and Phillips 2014; Oliver, 2000; Skogan and Hartnett, 2005; Weisburd and Lum, 2005).

Perhaps the most significant strategic innovation and stepping-stone in the evolution of real-time crime centers was the establishment of the National Fusion Center Network, which commenced at the onset of what has been referred to as the “homeland security era” of policing (Oliver, 2006). As indicated within the Fusion Center Guidelines¹ (2008), a fusion center is defined as an institution that facilitates the flow of information, resources, and expertise between two or more agencies to maximize the detection, prevention, and investigation of criminal or terrorist activity. The term “fusion center” is often used interchangeably with RTCCs although they each exhibit distinct structural and operational characteristics (see Table 1).

Table 1.

Fusion and real-time crime center features.

	Jurisdiction	Role	Function	Crime types	Partnerships	Technology
Fusion center	State or regional	External; horizontal or vertical structure	Intelligence collation, analysis and dissemination; regional risk assessments	Threat and hazards approach; jurisdictional needs	Local; state; federal; private	Access to local state and federal databases
Real-time crime center	Municipal or county	Internal; horizontal or vertical structure	Real-time information sharing capabilities; surveillance; post-incident investigative support	Disorder and disturbance calls; property crime; violent crime; jurisdictional needs	Local; state; federal; and private	Access to local state and federal databases, CCTV; video analytics, automated detection systems, photo recognition

The fusion process is often coordinated and supported at the state or regional level and is considered essential in the sharing of intelligence between both private industries and all sectors of government. These centers are considered external institutions that process information either (1) horizontally, across other existent centers or (2) vertically, to either federal or local law enforcement partners (Global Justice Information Sharing Initiative, 2006). Information is gathered and analyzed in conjunction with a variety of disparate databases that are maintained or controlled by an appropriate representative within the center. The information, if actionable, is then exchanged with the proper entity and per any applicable departmental policies. Operationally, fusion centers take on a preventative approach in which high-level threats and hazards are prioritized. Apart from analytical competencies and access to a vast amount of intelligence data sources, technological capabilities are not emphasized.

By contrast, real-time crime centers are adopted internally by individual police agencies with the primary functions of real-time information sharing, surveillance, and post-incident investigative support. They exemplify a watershed as it concerns the technologies used to gather and deploy actionable intelligence at a more localized level. In addition to intelligence and analytical capabilities, RTCCs are characterized by specific technological features including automatic license plate readers, gun-shot detection systems, and closed-circuit surveillance camera feeds that are used to assist with a vast spectrum of calls and operations such as disorder and disturbance calls, as well as property and violent crimes (Bureau of Justice Assistance, 2019).

Development of real-time crime centers

New York City Police Department (NYPD) was the first to institute a real-time crime center in 2005. Despite the purported benefits of the NYPD RTCC, cities were initially slow to adopt similar concepts within their departments. In 2012, Philadelphia Police Department was only the 10th agency with access to similar depths of information and analytical tools (Gibson, 2019). This suggests that the investment into RTCCs and their incorporation into daily police operations appears to be a more recent phenomenon.

To date, Chicago Police Department’s district-level Strategic Decision Support Centers (SDSCs) serve as the first evaluation of RTCCs in the country. Utilizing a mixed-methods approach, the evaluation report concluded that the SDSCs are a promising tool for supporting crime reduction in districts concerning crime types and time of crime occurrence. The changes in the outcomes of the remaining objectives, including improving officer safety, decreasing response times, and case clearances were not assessed (Hollywood et al., 2019).

Summary

The examination of RTCCs within the context of literature outlining the diffusion of innovation offers many useful insights. First, it reveals that the emergence of RTCCs may follow a common pattern of diffusion, which has been observed in many other previous technologies and practices. Second, diffusions of technologies and practices appear to be based more on expectations of what they may accomplish rather than on demonstrable evidence-based outcomes of their effectiveness. Third, RTCCs represent a logical development within the evolution of strategic police practices, namely CompStat and fusion centers. Still, there remains a void in understanding how and to what extent RTCCs have diffused, what their configurations might be, and what function they play within policing jurisdictions.

The current study

Within this context, the focus of the current study was to centrally examine the diffusion of real-time crime centers nationally and to better understand (1) how they have dispersed as well as (2) the nature of their configurations, and (3) how they operate. Within those broad objectives, the survey study was designed to provide information on eight research questions. For the sake of presentation parsimony, we present each of those specific questions within the structure of the following ‘findings’ section.

Method

Identification of RTCCs

The intention was to identify and administer surveys to every existing RTCC operating within the country at the time of the study. To generate a comprehensive list of existing RTCCs, searches were conducted on several databases. Google and Google News were utilized to identify either press releases or news articles that acknowledged the launch of these centers. Google News is a service developed by Google that offers comprehensive and up-to-date news coverage from aggregated sources. Google Videos and YouTube searches were also conducted to identify agencies that have used these platforms to showcase their centers. Additionally, the International Association of Crime Analysts (IACA)² and LinkedIn platforms were used to obtain any additional insight into the deployment of these centers. A forum was created on the IACA platform in which crime analysts were able to exchange information regarding their real-time crime centers. Further, a search for real-time crime centers on LinkedIn alluded to individuals who worked within these centers and indicated their respective agencies. Within these search results, agencies identified as housing RTCCs were noted, resulting in a final sample of 61 real-time crime centers or their equivalent.

For each of these 61 agencies’ RTCCs contact information was then obtained. This was acquired by first searching for identifiers within publicized reports and then calling each of the police departments directly. This often resulted in either information regarding direct forms of communication with the unit supervisor (e.g., email or phone number) or a transfer to the unit specified. Upon contacting the unit, the objectives of the survey and grant project were communicated, and voluntary participation was verbally requested.

Survey construction

The survey consisted of 25 questions, which were designed to gather information on three substantive areas. These included questions related to basic organizational characteristics, questions that gathered information on the organizational structure of these centers, and questions that determined the overall functionality and operational procedures of the units. The survey was administered through an online platform utilizing Qualtrics software. Qualtrics is considered a powerful online survey tool suited for academic research and allows for the distribution and analysis of responses from a single web-based portal. Questions included both closed (14) and open-ended (11) formats.

Survey dissemination and response rate

The survey was initially distributed in May of 2020. Each agency was sent a formal request letter via electronic mail and prompted via nonresponse follow-up through additional electronic requests and or phone calls. To boost response rates, two rounds of electronic reminder letters were sent via email in 3-week increments, with a final round of targeted electronic requests sent in August of 2020 emphasizing the importance of the project and its contribution to the current and future development of RTCCs. At the conclusion of the data collection period, 44 of the 61 identified RTCCs responded to the Qualtrics survey request. This resulted in a 72 percent response rate. To determine the representativeness of agencies that responded to the survey compared to those that did not, a comparative analysis was conducted (see Appendix 1).

Findings

To what extent have real-time crime centers diffused nationally?

Agencies were asked to disclose the date that their units became operational. This question was open-ended and resulted in straightforward responses that often included both the month and year of launch. Illustrated in Figure 2, the first RTCC was implemented in 2005, and following that, the growth of RTCCs nationally experienced limited progression for nearly a decade. Since 2015, the adoption of RTCCs grew rapidly with a range of three to eight new centers being launched nationally every year. Specifically, 34 of the 44 agencies included within the sample became operational from 2015 or thereafter.

Figure 2.

Cumulative growth of real-time crime centers in the United States, 2005 – 2020

These responses were further utilized to depict the diffusion of real-time crime centers and their current standing within the adoption of the innovation curve presented in Figure 1. Two distinct interpretations are possible. From a broader perspective, when considering the procurement of these centralized units across all police agencies nationally (N = 17,985),³ RTCCs are largely at the front end of the innovator’s stage with only 0.3 percent of all agencies housing these units or their equivalent. Despite the validity of this, the identified agencies (N = 61) within this study which maintain RTCCs consisted of larger-sized organizations (≥100 sworn and civilian personnel). This may suggest that larger police agencies are the most relevant population for the adoption of RTCCs. Accordingly, of the 1468 large-sized agencies (≥50 sworn officers) operating within the United States,⁴ departments with an RTCC represent just 4% of those relevant organizations, which suggests we continue to be in the early adopter’s phase on the innovations curve (see Figure 1). Nevertheless, both interpretations indicate that real-time crime centers continue to be in the beginning stages of development.

Do agency characteristics determine RTCC implementation?

Prior research has noted that the size of police agencies has many organizational implications. Larger agencies tend to have greater access to funding, more diverse job functions, and higher degrees of specialization (Chamard, 2006; Hendrix, et al., 2017; Maguire, 2003; Strom, 2017). As a result of this, larger agencies are likely to have greater information technology capabilities (Nunn, 2001) and a more pressing need to invest in innovative technologies to support organizational functions (Mastrofski et al., 2003; Randol, 2012).

The initial open-ended questions outlined in the survey intended to garner information on the characteristics of participating agencies. These questions were fundamental in providing insight into the regional dispersion of RTCCs and the types of agencies that are most likely to house these centers or their equivalent. To capture agency demographics, survey respondents were asked to provide the name of their agency and the total quantity of sworn and civilian personnel employed. These responses were further aggregated by agency size, agency type, and region⁵ (see Table 2).

Table 2.

Overview of agency characteristics.

N = 44	Frequency	Percent
Agency size^a
Small	7	16%
Medium	10	23%
Large	27	61%
Agency type
Municipal	33	75%
Sheriff or county	10	23%
State or highway	1	2%
Region
Northeast	5	11%
Midwest	4	9%
South	26	59%
West	9	20%

Note. May not equal to 100 percent due to rounding.

^aSmall (51–250 officers); medium (251–500 officers), and large (501 or more officers).

As it concerns agency size, the smallest agency housing a RTCC or equivalent was comprised of 100 personnel, whereas the largest was comprised of 52,000 personnel. The average number of sworn and civilian personnel in the sample of agencies was 2509. These responses were further grouped into three separate categories: small (51–250 officers); medium (251–500 officers), and large (501 or more officers) (see Table 2). Large agencies consisted of the majority, whereas small agencies were underrepresented in the sample.

Specifically, of the 44 respondents, 16 percent represented small agencies, 23 percent represented medium agencies, and 61 percent represented large-sized agencies (see Table 2). Further, based on the agency name, each agency was grouped accordingly into local agency subgroups (i.e., municipal, sheriff and county), or under state or highway patrol. Federal agencies were not included within the sample. The majority of the sample consisted of municipal agencies (75%), followed by sheriff or county (23%), and state or highway (2%). Additionally, most of these agencies were geographically located in the South (59%), followed by the West (20%), Northeast (11%), and Midwest (9%).

What are the most common operating practices found across RTCCs?

Various models may emerge based on the real-time capabilities offered by the individual units. For example, those that respond to calls as they occur in real-time may operate at a vastly different capacity than those who utilize their center for predominantly post-incident investigative work. In recognition that these centers may choose to operate at varying levels, agencies were asked to select all the applicable ways in which their RTCC functions. Eighty-nine percent of respondents stated that they utilize their RTCC to respond to calls as they occur in real-time, 82 percent for active surveillance purposes, 75 percent for post-incident investigative purposes, and 52 percent stated that they utilize their centers for purposes other than those listed. Those respondents that selected “other” were able to expand on their answers. These answers included handling criminal tip lines, counter-terrorism functions, and all things pandemic-related. The following quote is from one of the survey respondents and it provides an example of the dynamic process which they employ:

“The RTCC produces scheduled and unscheduled products based upon customer driven requirements. We provide tactical profiles on every victim (and suspect if identified) of a shooting hit or shooting murder. We provide Impact Zone analysis that identified where shootings are occurring, at what time, and who may be responsible based upon objective review of information streams detailing shootings and gun arrests.”

(Anonymous RTCC administrator)

Further, agencies were asked to select which of the following best described their RTCCs hours of operation: 24 h a day/7 days a week, during set hours (daytime/nighttime), only during peak crime times, only during special or large-scale events, or on an as-needed basis (e.g., per request). The operating hours help dictate many staffing decisions within police agencies. RTCCs operating under a twenty-four-hour-a-day infrastructure may require additional personnel to effectively manage crime events and accommodate peak crime activity times. Agencies that set limited operational hours may have to consider temporary or part-time reassignment of personnel during intermission periods. Based on agency responses, the majority of RTCCs (64%) operate during set hours, followed by 24-hour operations (29%), and lastly on an as-needed or per request basis (7%). None of the respondents described using their RTCCs strictly during peak crime times or only during special or large-scale events.

How does agency size impact RTCC personnel structure?

Without consideration for agency size, real-time crime centers are staffed with a minimum of one individual and a maximum of 82. Of those individuals, real-time crime centers are generally composed of seven sworn officers, two individuals that perform crime analysis duties, four intelligence analysts, and two individuals who hold “other” job functions. Independent contractors were least employed (0.07) within these units. However, to support the general notion that unit structure and operating decisions are based largely on agency resources, Pearson’s correlation coefficient (bivariate analysis) was computed to assess the relationship between the size of an agency and real-time crime center staffing. The findings suggest a positive correlation between the two variables (r = 0.504, n = 43, p = 0.001).

Thus, when accounting for agency size, differences in personnel configurations and command structure become apparent. As evidenced in Table 3, on average, small agencies staff their RTCCs with approximately four individuals, medium agencies with approximately eight, and large agencies with approximately 17. Further, small agencies generally do not staff their unit with sworn personnel, but rather with intelligence analysts and individuals who perform other functions than those listed. By contrast, medium and large agencies are more reliant on sworn personnel and are more likely to include crime analysts and independent contractors within their operating practices. Lastly, independent contractors are least utilized by all agency types.

Table 3.

Average RTCC size by police agency size.

	N	Large	Medium	Small
	N	Mean (SD)	Mean (SD)	Mean (SD)
Total RTCC staff	43	17 (15.72)	8 (3.20)	4 (3.56)
Sworn	42	9 (7.75)	3 (1.94)	0
Crime analysts	42	3 (4.41)	2 (1.95)	0
Intelligence analysts	42	5 (10.12)	2 (1.95)	1 (1.00)
Independent contractor	42	0.07 (0.27)	0.08 (0.28)	0
Other	42	2 (5.04)	1 (1.42)	3 (5.20)

What are common RTCC configurations?

Concerning, it is apparent that the staffing of real-time crime centers generally falls under one of four different models. The range of personnel can include the following: (1) strictly sworn officers or detectives, (2) civilian or sworn crime or intelligence analysts, (3) external contractors, or (4) a hybrid that combines all of the aforementioned models. The argument for a fully sworn unit deals largely with the investigative workload component and the acknowledgment that officers with street-level experience may be better equipped with the experience necessary for communicating relevant information efficiently. The formation of an RTCC that works in unison with crime and intelligence analysts often requires additional training to ensure real-time analytic capabilities (e.g., computer-aided dispatch (CAD) training). Lastly, a hybrid model requires a clear delineation of roles and expectations to limit the duplication of work and minimize territorial issues, such as the ownership of databases and the dissemination of resources.

Once agencies disclosed the total number of personnel staffed within their RTCC and their job functions, these responses were further aggregated into the four models most commonly exhibited within such units. Of the respondents, 64 percent exhibited a hybrid model, 18 percent staffed their units with strictly crime/intelligence analysts, and nine percent operated under a strictly sworn structure. Five percent of agencies did not specify their unit structure, and no agency operated their real-time crime center solely with independent contractors.

Agencies were also asked to designate whether their RTCC operates as a separate unit distinct from other crime analysis units within the department or whether they merge their operations. The decision to integrate RTCC operations may be highly dependent on the scope of work intended, as well as organizational capacity, current capabilities, and any budget restrictions that exist. This question was close-ended and resulted in 77 percent of agencies responding that they operate as a separate unit distinct from other crime analysis units, with 23 percent reporting that they work jointly.

What forms of partnerships are found across RTCCs?

To garner more insight into real-time information-sharing capabilities, agencies were asked to disclose whether their RTCCs operated at a capacity that allowed for information to be shared in real-time with partner agencies. As seen in Figure 3, eighty percent of RTCCs can share information in real-time, whereas twenty percent cannot do so. Subsequently, agencies that do share information in real-time were asked to expand on their answers in an open-ended context. Answers were grouped according to common themes identified. Of those who responded “yes,” three percent stated that they only communicated information with local partners, 71 percent with local, state, and federal partners, and 26 percent with local, state, federal, and private partners.

Figure 3.

Real-time information sharing with partner agencies

Agencies were further asked to identify whether their RTCC could access CCTV camera systems managed by other entities within their jurisdiction (e.g., government, private, personal). Based on the responses (see Table 4), the majority of RTCCs have access to CCTV cameras managed by other entities within their jurisdiction. Of the 80 percent (n = 35) that responded that they did have access to other CCTV cameras, on average they were able to access approximately 104 government CCTV units, 21 publicly maintained cameras, and 131 private camera systems (e.g., access to Ring).

Table 4.

Access to CCTV managed by other entities.

	N (RTCCs)	Mean (CCTVs)	STD	Min	Max
Government	24	103.50	306.22	1	1500
Private	17	20.65	48.24	1	200
Personal	6	130.67	280.43	1	700

What technologies are typically found within RTCCs?

There is a diverse array of information and technologies housed within RTCCs. While 21 different types were reported to be in use across the sample, a third of these were found more commonly than others (see Figure 4). The top seven most common sources of information and databases used within RTCCs include calls for service information (100%), social media (91%), CCTV video footage (91%), license plate readers (88%), intelligence databases (88%), open-source derived information (86%), and criminal history information (84%).

Figure 4.

Sources of information and or databases used within the RTCC

Although often a controversial subject in the realm of policing, respondents were also asked to elaborate on the subject of real-time facial recognition software and whether their deployed CCTV cameras operated with this capacity. Often, most facial recognition work is not conducted in real-time, but instead, it relies on analyst capabilities and third-party searches. Yet, conducting such analysis in real-time may prove to be valuable for the solvability of cases. According to the responses, 95 percent of RTCCs do not utilize real-time facial recognition software, whereas only five percent do. This signals that perhaps RTCCs are hesitant to place sole reliance on video analytics as opposed to analyst review of information.

What measures for effectiveness are employed within RTCCs?

Given the dearth of empirical research on real-time crime center efficacy, agencies were asked to provide insight into all applicable measures of effectiveness employed within their RTCCs. It is suggested that agencies consider various impact assessments of RTCC activities in order to facilitate any necessary changes and provide a standard for best practices. Based on the responses provided, it is apparent that less than half of the sample surveyed employed measures to track common outcomes. That is, only 39 percent of respondents stated that their RTCC documented impact on disrupting crime, 32 percent examined officer safety, 30 percent measured effectiveness through case clearance rates, 11 percent utilized time to clearance as a measure, and only seven percent stated that they examined use of force rates. Thirty-two percent of respondents stated that they utilized “other” forms of measures for determining effectiveness than those listed, and 18 percent did not specify.

Discussion and conclusion

This study reported on findings from a national survey of local police agencies across the United States which had implemented some variety of a real-time crime center. The primary findings indicate that though originally conceived in 2005, RTCCs have only recently begun to diffuse more rapidly, but are still in an early innovation/adoption phase with only 0.3% of all police agencies and just 4% of large agencies nationally having adopted them. The survey findings also indicated that RTCCs vary in their structure and primary functions across agencies yet most centers consistently relied on a wide variety of technologies and information sources. Additionally, most respondents reported that their RTCC platform allowed them to share information with partner agencies in real-time.

The findings of this survey also offer insight into the distinctions that exist across real-time crime centers nationally which could serve as a guide for agencies considering implementing an RTCC within their jurisdiction. While RTCCs share many common characteristics, they also appear to vary in how they operate and are structured across jurisdictions. Given this variation, the nature in which they diffuse and are adopted by new jurisdictions will likely continue to be heterogeneous. This suggests that there may not be a common reference point from which to articulate the dispersion of RTCCs as they continue to evolve. To more effectively facilitate the development and diffusion of RTCC platforms, it may be necessary to establish a national clearinghouse. This could serve as a central resource devoted to cataloging, assessing, and disseminating information on RTCCs. At the very least, much more research is needed to better understand their function within police agencies and their impact on communities.

Considering the backdrop of innovation diffusion processes more generally, the future integration of real-time crime centers might best be considered an evolving process fueled by the advent of new technologies, access to new information sources, and changes in policing tactics as a response to local crime problems. A singular model for implementing, staffing, and launching an RTCC does not appear to exist and may not be useful to impose. Given this, agencies might consider tailoring and structuring operations to meet the needs of their respective jurisdictions within departmental demands. Police agencies may also find it beneficial to recognize the dynamic nature of the activities that funnel through these units and develop procedures on how best to document activities and outcomes.

Nonetheless, the finding in this study that most agencies reported adopting a wide range of technologies within their RTCCs, suggests several things. First, it implies that the implementation of RTCCs may benefit from the development of a clear strategic plan (Strom, 2017). Such a plan might aim to identify the purpose, function, and scope of the RTCC, as well as its relationship to the overall goals of the police agency in serving the community. Second, it suggests RTCCs may be more readily established within police agencies that have some pre-existing internal capacity and experience with transferring data and other cyber-based information systems into actionable information. Third, the diversity of technologies might also underscore the value of establishing standard operating procedures that outline the processes for data collection and dissemination from the various technological systems.

Though not a focal concern of this study, it is acknowledged that the growing commitment to surveillance (as carried out within RTCCs) is not without its challenges (Marlow & Baker, 2018; McQuade, 2020). While the findings of this survey suggest that agencies do not cross their boundaries as it pertains to the use of intrusive technologies (e.g., facial recognition software), the potential for discriminatory impacts must be recognized. For example, whereas technologies on their own cannot be biased in nature, how they are built, where they are positioned, and the purpose behind their implementation may garner controversy (Ferguson, 2017). It may become necessary for real-time crime centers to work proactively with community/citizen protection groups, such as the American Civil Liberties Union, before the installation and placement of surveillance technologies within their jurisdictions. Partnering with such groups may guide the establishment of procedures that concern data and evidence storage, retrieval, and retention.

As it currently stands, the heterogeneous nature of how RTCCs are structured and operate across the country could make it difficult to determine their impact. Beyond their heterogeneity, this challenge may also be the result of several other factors, particularly for individual agencies looking to determine the effectiveness of their work. For one, given the still early and continual development of RTCCs, tracking productivity and documenting efforts may not seem immediately important, particularly when operating practices within given agencies are continually changing. This may be especially true where personnel are unaccustomed to evaluative processes. Second, tracking the life-course of incidents may prove difficult in real-time circumstances as an incident or case number may not be generated until after assistance is provided. Given the fast-paced environment, backtracking to locate missing information may be considered overly distracting and thus, better suited for external researchers rather than integrated as an additional responsibility within the unit. Finally, measuring effectiveness may require an attitudinal shift within agency cultures that emphasizes evaluations of policies, procedures, and technologies to ensure that outcomes meet both departmental and community needs.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by Grant No. 2019-WY-BX-0005, awarded by the Bureau of Justice Assistance. Points of view or opinion in this document are those of the author and do not necessarily represent the official position or policies of the U.S. Department of Justice.

ORCID iD

Kimberly Przeszlowski

Notes

Appendix

Table 1A.

Overview of agency characteristics for respondents and non-respondents.

	Sample respondents (n = 44)	Non-respondents (n = 17)
	Frequency (%)	Frequency (%)
Agency size^a
Small	7 (14%)	1 (6%)
Medium	10 (23%)	1 (6%)
Large	27 (61%)	15 (88%)
Agency type
Municipal	33 (75%)	13 (76%)
Sheriff or county	10 (23%)	4 (24%)
State or highway	1 (2%)	—
Region
Northeast	5 (11%)	5 (29%)
Midwest	4 (9%)	3 (18%)
South	26 (59%)	6 (35%)
West	9 (20%)	3 (18%)

Note. May not equal to 100% due to rounding.

^aSmall (51–250 officers); medium (251–500 officers), and large (501 or more officers).

Table 2A.

Comparative analysis of respondents to non-respondent’s mean agency size^a

	Respondents (n = 44)	Non-respondents (n = 17)	U	p	d
Mean agency size	1418.50	5332.77	250.50	.048	−.330

^aMann–Whitney U test, effect size is given by the rank biserial correlation.

Table 3A.

Comparative analysis of respondents to non-respondents agency region and type^a

N = 61	X ²	df	p
Agency region	4.59	3	.204
Agency type	0.393	2	.821

^aPearson’s chi-square for independence.

Due to the non-normal distribution of the agency size variable, the Mann–Whitney U test was conducted to document the systematic differences between survey respondents (n = 44), and non-respondents (n = 17) (Table 1A). The Mann–Whitney U test revealed that the agency size was significantly lower in the respondent group (M = 1418.50, n = 44), compared to the non-respondent group (M = 5332.77, n = 17), U = 250.50, p = .048, d = −0.330 (See Table 2A). This difference is most likely attributable to the three largest agencies in the country that did not respond to the survey and whom are outliers in the distribution of all police agencies nationally. Despite this difference, the surveyed sample is representative of typical police agencies as well as those agencies that are likely to implement a real-time crime center or its equivalent.

Further, presented in Table 3A Pearson’s chi-square was used to investigate the relationship between the nominal-scale descriptive variables, agency region and agency type. The findings of this non-parametric test suggest that there is no statistically significant difference between respondents and non-respondents as it concerns the police agency geographic region., X² (3, N = 61) = 4.59, p= .204. Further, there is no statistically significant difference between respondents and non-respondents as it pertains to agency type (X² (2, N = 61) = 0.393, p= .821).

References

NYC Global Partners

. (2010, April 27) Best Practice: Real-Time Crime Center: Centralized Data System. New York city: Global Partners. http://www.nyc.gov/html/unccp/gprb/downloads/pdf/NYC_SafetyandSecurity_RTCC.pdf

Bureau of Justice Assistance (2019) Real-time Crime Center Information. Washington, DC: US Department of Justice, Office of Justice Programs. https://bja.ojp.gov/sites/g/files/xyckuh186/files/media/document/RealTimeCrimeCenterInformation.pdf

Bureau of Justice Statistics (2021) Local Police Departments, 2016: Personnel. Washington, DC: US Department of Justice, Office of Justice Programs. Available at: https://bjs.ojp.gov/content/pub/pdf/lpd16p.pdf

Byrne

Hummer

(2017) Technology, innovation, and twenty-first-century policing. In: McGuire

Holt

(eds) The Routledge Handbook of Technology, Crime, and Justice. Abingdon: Taylor and Francis, 375–389.

Chamard

(2006) The history of crime mapping and its use by American police departments. Alaska Justice Forum 23(3): 4–8.

Coppola

(2016, May 26) Real-time crime center serves as force multiplier. TECHBeat. Available at: https://nij.ojp.gov/library/publications/real-time-crime-center-serves-force-multiplier (Accessed on January 10th, 2021).

Ferguson

(2017) The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement. New York: NYU Press.

Fox

(2014, April 14) How real-time crime center technologies are force multipliers. PoliceOne. Available at: https://www.police1.com/police-products/software/data-information-sharing-software/articles/how-real-time-crime-center-technologies-are-force-multipliers-YFZo1KAXSQrHC6q4/ (Accessed on January 14th, 2021).

Gayadeen

Phillips

(2014) The innovation of community policing and the COPS Office: does diffusion of innovation theory hold in a manipulated environment? International Journal of Police Science & Management 16(3): 228–242.

10.

Gibson

(2019 November 20), What is a real-time crime center? Rave Mobile Safety. Available at: https://www.ravemobilesafety.com/blog/what-is-a-real-time-crime-center (Accessed on November 12, 2020).

11.

Global Justice Information Sharing Initiative (2006) Fusion Center Guidelines: Developing and Sharing Information and Intelligence in a New Era. Washington DC: US Department of Justice, Office of Justice Programs. https://www.it.ojp.gov/documents/fusion_center_guidelines_law_enforcement.pdf

12.

Grübler

(1996) Time for a change: on the patterns of diffusion of innovation. Daedalus 125(3): 19–42.

13.

Hendrix

Taniguchi

Strom

, et al. (2017) Strategic policing philosophy and the acquisition of technology: findings from a nationally representative survey of law enforcement. Policing and Society 29(6): 727–743.

14.

Hollywood

McKay

Woods

, et al. (2019) Real-time Crime Centers in Chicago: Evaluation of the Chicago Police Department’s Strategic Decision Support Centers. Santa Monica, CA: RAND Corporation. https://www.rand.org/pubs/research_reports/RR3242.html

15.

King

(2000) Measuring police innovation: issues and measurement. Policing: An International Journal of Police Strategies & Management 23(3): 303–317.

16.

Lum

Koper

Willis

(2017) Understanding the limits of technology’s impact on police effectiveness. Police Quarterly 20(2): 135–163.

17.

Maguire

(2003) Organizational Structure in American Police Agencies: Context, Complexity, and Control. Albany: SUNY Press.

18.

Marlow

Baker

(2018, October 19) How to shift power from the police to the people. American Civil Liberties Union. Available at: https://www.aclu.org/blog/privacy-technology/surveillance-technologies/how-shift-power-police-people (Accessed on December 19, 2020).

19.

Mastrofski

Parks

Wilson

(2003) Influences on the Adoption of Community Policing in the United States. Fairfax, VA: Center for Justice Leadership and Management.

20.

Matusiak

King

(2021) Advancing the study of police innovation: toward an empirical definition and classification of contemporary police innovations. Crime & Delinquency 67(12): 1982–2010.

21.

McQuade

(2020, July 7) The camden model is not a model. It’s an obstacle to real change Jacobin. Available at: https://jacobinmag.com/2020/07/camden-new-jersey-police-reform-surveillance (Accessed on December 19th, 2021).

22.

Nix

Todak

Tregle

(2020) Understanding body-worn camera diffusion in US policing. Police Quarterly 23(3): 396–422.

23.

Nunn

(2001) Police information technology: assessing the effects of computerization on urban police functions. Public Administration Review 61(2): 221–234.

24.

Oliver

(2000) The third generation of community policing: moving through innovation, diffusion, and institutionalization. Police Quarterly 3(4): 367–388.

25.

Oliver

(2006) The fourth era of policing: Homeland security. International Review of Law Computers & Technology 20(1–2): 49–62.

26.

Randol

(2012) The organizational correlates of terrorism response preparedness in local police departments. Criminal Justice Policy Review 23(3): 304–326.

27.

Randol

(2014) Modelling the influence of organisational structure on crime analysis technology innovations in municipal police departments. International Journal of Police Science & Management 16(1): 52–64.

28.

Rogers

(1962) Diffusion of Innovations. 1st edition. New York: Free Press.

29.

Rogers

(2003) Diffusion of Innovations. 5th edition. New York: Free Press.

30.

Skogan

Hartnett

(2005) The diffusion of information technology in policing. Police Practice and Research 6(5): 401–417.

31.

Strom

(2017) Research on the Impact of Technology on Policing Strategy in the 21st Century, Final Report. Washington DC: US Department of Justice.

32.

Weisburd

Lum

(2005) The diffusion of computerized crime mapping in policing: linking research and practice. Police Practice and Research 6(5): 419–434.

33.

Weisburd

Mastrofski

McNally

, et al. (2003) Reforming to preserve: compstat and strategic problem solving in American policing. Criminology & Public Policy 2(3): 421–456.

34.

Wejnert

(2002) Integrating models of diffusion of innovations: a conceptual framework. Annual Review of Sociology 28(1): 297–326.

35.

Willis

Mastrofski

(2011) Innovations in policing: meanings, structures, and processes. Annual Review of Law and Social Science 7(7): 309–334.

36.

Willis

Mastrofski

Weisburd

(2007) Making sense of COMPSTAT: a theory-based analysis of organizational change in three police departments. Law and Society Review 41(1): 147–188.

37.

Wuschke

Andresen

Brantingham

, et al. (2018) What do police do and where do they do it? International Journal of Police Science & Management 20(1): 19–27.