Data analytics involve gathering, sorting, examining and explaining data to produce insights and guide decisions. The decisions can range from providing direction to patrol officers, to setting investigative priorities and administrative insights.
Data analytics can assist in processing and comprehending large amounts of data from different sources, such as crime reports, social media, CCTV cameras, sensors, calls for service (CFS) and more. Data analytics can also help detect patterns, trends, anomalies and correlations in the data that can offer useful information for solving problems, enhancing performance and optimizing outcomes.
Types of data analytics
Data analytics can be classified into four types, depending on the level of complexity and the purpose of the analysis.
These are:
1. Descriptive analytics: This type of analytics answers the question of what happened by summarizing and visualizing historical data. For example, descriptive analytics can show the number of crimes, arrests, or calls for service in a given period or area.
2. Diagnostic analytics: This type of analytics answers the question of why something happened, by exploring and comparing data to find the root causes or factors behind an event or phenomenon. For example, diagnostic analytics can reveal the reasons for a spike or drop in crime rates, such as weather, holidays, or social unrest.
3. Predictive analytics: This type of analytics answers the question of what will happen, by using statistical models and machine learning to forecast future outcomes or scenarios based on historical and current data. For example, predictive analytics can estimate the probability of a crime occurring in a certain location or time, or the risk of a suspect being involved in a violent incident.
4. Prescriptive analytics: This type of analytics answers the question of what should be done, by using optimization and simulation techniques to recommend the best actions or strategies to achieve a desired goal or objective. For example, prescriptive analytics can suggest the optimal allocation of resources, such as personnel, equipment, or vehicles, to prevent or respond to crime.
Role of data analytics in RTCCs
Real Time Crime Centers (RTCCs) use data analytics and technology to support operational and tactical decision-making by collecting and integrating data from multiple sources, such as crime databases, 911 calls, GPS, license plate readers, facial recognition and gunshot detection, and analyze them in real-time to provide actionable intelligence and situational awareness to officers in the field.
RTCCs also use data analytics to monitor and evaluate the performance and effectiveness of their operations and to identify and address emerging issues and trends.
Data analytics plays a vital role in enhancing the capabilities and efficiency of RTCCs, by enabling them to:
- Boost awareness of the situation: By gathering and displaying data from different sources and platforms.
- Improve decision-making: By giving them analytics that can predict and suggest the best way to proceed. Also by tracking and showing key measures of success, such as crime prevention, speed of response, or case resolution.
- Raise operational effectiveness: By distributing them based on the demands and importance of the situation. Also by streamlining and connecting data gathering and analysis, and by minimizing the need for manual and repetitive tasks.
The data dashboard
The data dashboard is a key tool for RTCCs to use data analytics. It is a graphical interface that shows the most important and relevant data and metrics clearly and simply. A data dashboard can help RTCCs to access data analytics anytime and anywhere and to share data insights with other stakeholders effectively. Some data can be public facing providing insights for the community and building trust. A data dashboard can also help RTCCs to tailor their data analytics, by letting them choose, filter, and explore the data based on their preferences and objectives.
A well-designed data dashboard can improve the efficiency and output of RTCCs by offering these advantages:
- Increased visibility: A holistic and granular view of their data by displaying them in various formats, such as charts, graphs, maps, or tables. Also monitor and track the changes and trends in their data, by using indicators, such as colors, icons, or alerts.
- Enhanced analysis: A holistic and granular view of their data by displaying them in various formats, such as charts, graphs, maps, or tables. Also monitor and track the changes and trends in their data, by using indicators, such as colors, icons, or alerts.
- Improved action: Translate their data insights into action, by providing them with actionable and relevant recommendations and suggestions. Also measure and evaluate the results and outcomes of their action, by providing them with feedback and reports.
Data analytics now and in the future
These are some ways that data analytics can help RTCCs with various parts of policing activities. These are not all the possible ways, but they show how data analytics can be useful and versatile in RTCCs.
1. Crime prevention and deterrence
Real-Time Crime Centers (RTCCs) primarily focus on using data to prevent and deter crime. This is achieved by identifying and targeting high-risk areas, offenders and behaviors, and deploying resources and interventions accordingly. RTCCs utilize data to conduct various forms of crime analysis, including hotspot mapping, crime trend analysis, crime pattern analysis and predictive analysis. These analyses help identify where, when, how and why crime occurs, and forecast future crime risks.
In addition, RTCCs conduct intelligence analysis, such as link analysis, network analysis and threat assessment. These analyses identify who is involved in crime, their connections, motivations and capabilities. By leveraging these data-driven analyses, RTCCs provide actionable information and recommendations to officers and commanders. This includes guidance on where to patrol, whom to target, and how to intervene, aiming to prevent and deter crime before it happens.
This approach not only aids in crime prevention but also addresses community concerns regarding practices such as stop and frisk and unnecessary pre-text stops. By focusing on data-driven, targeted interventions, RTCCs can contribute to safer communities while respecting the rights and concerns of their residents.
2. Quality of life and recidivism impact
RTCCs are currently being deployed with the primary functions of using data to prevent and deter crime, detect and respond to crime, investigate and aid in the prosecution of crime, and strategize for personnel deployment. However, these functions can only be fully realized with a human-centered approach through community policing avenues. This entails an additional step in the RTCC process not currently being addressed.
The next step is disposition efforts through the human-centered lens that includes the community policing approach. By using various AI methods, the RTCCs of the future will still use CAD, RMS, ALPR, CCTV and other databases to identify the locations, times, vehicles and suspects, but will also collaborate with community stakeholder programs and community stakeholder databases to provide direction with respect to call disposition and recommendations for predictive outcomes.
Instead of just predicting where the subject can be apprehended, the next step in prediction will be what is the direction for the disposition. Should the officer make an arrest, issue a citation, refer to a restorative justice program, connect the subject to a clinician, or establish some sort of poly approach incorporating more than one disposition that results in crime reduction and lifestyle enhancement for the offender? This further contributes to the overall goals of the community and builds greater trust in the system.
Many of the public and legislators are trying to address the lack of having the best interest of people in mind by legislating various actions incumbent upon the police for dispositions. Police lack the capacity to properly administer dispositions and programs legislated. Therefore there is a continued frustration and divide. Adding the disposition recommendation to an RTCC will heighten the success for the offender rehabilitation providing the best direction for prosecutors and other stakeholders, as well as effectively reducing recidivism rates. This establishes greater trust.
Future directions of the RTCC
While data analytics can provide significant benefits to RTCCs and law enforcement agencies, there are challenges to be addressed.
Data quality and integration
Data quality and integration are essential for ensuring the validity and reliability of data analytics in RTCCs. However, data quality and integration can be compromised by data errors, inconsistencies, incompleteness, duplication and heterogeneity. Therefore, RTCCs need to adopt and implement data quality and integration standards and protocols, and to employ data cleaning, validation, and transformation techniques and tools.
Moreover, RTCCs need to deal with the challenges of data interoperability and compatibility, especially when integrating data from different sources and systems, both internal and external to law enforcement agencies. RTCCs need to develop and use common data models, formats and vocabularies, and to leverage data exchange and integration platforms and technologies.
Data privacy and security
Data privacy and security are critical for ensuring the protection and confidentiality of data and information in RTCCs. However, data privacy and security can be threatened by data breaches, unauthorized access, misuse and leakage. Therefore, RTCCs need to adopt and implement data privacy and security policies and regulations, and to employ data encryption, authentication, and authorization techniques and tools. Moreover, RTCCs need to deal with the challenges of data governance and accountability, especially when sharing and using data from different sources and systems, both internal and external to law enforcement agencies. RTCCs need to establish and follow data governance and accountability frameworks and mechanisms, and to ensure data transparency and traceability.
Data ethics and bias
Data ethics and bias are important for ensuring the fairness and impartiality of data analytics in RTCCs. However, data ethics and bias can be affected by various factors, such as data collection, processing, analysis, and use. Therefore, RTCCs need to adopt and implement data ethics and bias principles and guidelines, and to employ data auditing, monitoring, and evaluation techniques and tools.
Moreover, RTCCs need to deal with the challenges of data awareness and literacy, especially when communicating and interacting with data stakeholders, such as law enforcement personnel, public officials, and citizens. RTCCs need to educate and train data stakeholders on the benefits and limitations of data analytics, and to involve and engage them in data analytics processes and outcomes.
