Data-Driven Policing

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This Blog is written by Kumar Shubham from KIIT School of Law, Odisha. Edited by Anshika Porwal.



By data-driven, we mean the acquisition, analysis, and use of a good variety of digitized data sources to tell deciding, improve processes, and increase actionable intelligence for all personnel within a police service, whether or not they are operating at the front-line or in positions of strategic leadership. By public value, we mean the total value that a police force contributes to society across a variety of measurable dimensions, including outcomes in relation to crime, the efficient use of public funds, and the quality of the police relationship with the general public. More specifically, during this report, we’ve explicitly Draw non, and further adapted, the concept of a ‘policing bottom line’ first developed by Professor Mark H.Moore at Harvard University. This means that the police, and sometimes businesses and citizens themselves can deliver public value in one or more of nine distinct ways in reference to policing and crime, including through:

• Reducing crime.

• Improving crime detection.

• Reducing public fear.

• Reducing public vulnerability.

• The utilization of police authority and force in a very fair and just way.

• Action to boost trust and confidence within the police and therefore the wider criminal Justice system.

• The delivery of a top-quality service experience to citizens.

• The efficiency and enjoyment of public funds.

Our target these dimensions of public value has shaped our approach to both the gathering and presentation of evidence during this report. In deploying it, the working assumption throughout has been that it captures something important about the goals and methods of a consent-based model of policing of the kind we have already got, and value, within the UK.

In the material that follows, and in exploring the possible benefits and disadvantages of a data-driven approach, we are therefore less fascinated by what data can neutralize the abstract, and more inquisitive about what it offers across each of those nine indicators of public value.


I have chosen data-driven approaches and public value as they both interlinked to each other, and the most important reasons were First, to develop a stronger evidence base regarding data-driven approaches to policing. The use of data-driven technologies is thought by many to carry out the promise of a brand new era, bringing advances in many areas including police workforce productivity and wellbeing all the way through to raised crime investigation,detection and prevention. The appliance of the latest technology might also sometimes cause a shift of law enforcement officials from the road to back-office functions which, though potentially very effective and efficient within the effort to fight crime, may undermine perceptions of public safety. We hope this report can help police leaders, policy-makers, and the public to work out the balance of opportunities and risks involved in adopting data-driven approaches and help us as a society to navigate our way through the challenges.

Second, the pace of technological change is accelerating and therefore the question of how the police should adapt to alter, not whether or not they should adopt, is already pressing. during this context, it’s worrying that a lot of forces are experimenting and changing in a very vacuum with limited advice and guidance on what’s and is not good practice. This report helps to fill that gap by mapping out what police agencies do here and around the world and considers not just how the police must adapt but how the regulation of data-driven policing, and the way the general public and political debate, must adapt too.

Third, massive budget cuts are already impacting policing within the UK and are demanding forces up and down the country explore for new ways to work to urge either more out of the identical resource or more for fewer. This financial driver of change is playing out alongside the technology driver, and while technology may hold some of the keys to the efficiency gains we seek, the two drivers can and do also collide at the purpose at which business cases need to be made for brand new Investments in technology and data-led approaches, as against investments in other areas of policing.
While this could be superficially attractive, it ignores the way within which the various elements of public value relate to every other. Top-quality relationships between the police and also the public often translate into improved intelligence that may both help prevent and detect crime. Unless the complete notion of the policing bottom line we’ve outlined here is protected, there is a danger that experimentation with data-driven approaches could become limited to a really narrow range of police activity. Within the long-run, this is able to be to the detriment of UK policing.


Policing within the US and around the world is rapidly changing.  One in all the most solutions that helps enforcement adapt to the current change is adopting a sound data-driven policing strategy.
Data-driven policing is that the use of information to tell deciding and increase actionable intelligence for all personnel within the bureau. When establishing an Information driven policing strategy, it’s important to stay focused. For this reason, you must consider it in terms of 5 core goals: Increase efficiency, increase effectiveness, direct resources to what matters most, increase community connections, and uncover deep, actionable internal insights for your department of local government. Leveraging data within an enforcement agency may be very successful and may be adopted by any department. Let’s explore how.

1. Increase Efficiency

Data driven policing offers the flexibility to require advantage of the vast amounts of internal data retained by a local department and supply insights regarding how bureau can get the foremost bang for his or her buck. Any patrol officer can explain the vast amount of documentation that’s completed for several police activities. Most if not all of these records are recorded during a computer-aided dispatch system, similarly as a records management system. As a result, there’s more data than one can imagine within those systems. Harnessing that data and putting it to figure with data-driven strategies enables departments to produce relevant guidance that produces police agencies more efficient.

Increased efficiency comes from both preventing crime through better targeting of the correct problems and communicating the knowledge needed to spot them with greater ease. Predictive hot spot policing is in a position to utilize a major portion of police incident and or entail service data and supply very accurate maps of where crime is possible to occur. These sophisticated maps can show a political candidate exactly where to travel and by that specialize in identified hot Spots, the officer can get the foremost out of his or her presence. a correct data-driven strategy would be to also make sure that the maps are easily understood and produced in order that all officers have access to the current value information.

2. Increase Effectiveness

Efficiency must be not to mention effectiveness. A local department can be extremely efficient but hasn’t any impact on the issues within the community. Data-driven policing will allow the workplace to utilize actionable intelligence (gained via numerous data sources) and deploy to the correct places at the correct times to own the best impact on crime and other community issues. The goal is to use data to tell deciding. With smarter decisions being made on a way to tackle controversy, issues are going to be mitigated sooner and overall quality of life will improve within the community.

All departments collect large volumes of information, like need service address data, incident level data, and accident level data. Some modern departments have GPS altogether cruisers or attached to any or all law officer radios. The info from these devices is simply sitting in databases, but if leveraged properly it provides deep revelations about how a small tweak in an officer’s activity could yield significant results. As an example, predictive hot spot maps enable a patrol officer to specialize in the proper areas at the proper time to either prevent crime or catch a criminal within the act. This level of effectiveness ends up in crime reductions that might otherwise not be gained. Many police departments already use a number of their data; however, a correct data-driven policing strategy will leave detailed insights to be gained instantly without the loss of valuable hours to crunching numbers.

3. Identify Real Problems

Perhaps the foremost important aspect of information-driven policing is unlocking the flexibility to style a good strategy for pointing enforcement leaders to the foremost significant problems in a very community. Oftentimes, issues that are perceived as “major problems” are the results of uninformed opinions. Data can provide insights that refute or confirm those perceptions and help uncover what the important problems are. Through using the correct data, police agencies are ready to quickly identify the foremost important problems then implement data-driven policing initiatives to mitigate those issues

Manchester Heroin Graphs, In fact, it’s possible that advanced uses of knowledge-driven policing could have spotted the rising Opioid/heroin crisis before it became the foremost issue that’s now plaguing communities across the state. The right monitoring of certain data points, like demand service, crime types, and open-source data sets could have highlighted the rising heroin problems. a talented enforcement leader with a well-developed data-driven policing strategy could have used such insights to induce ahead of the epidemic and contain this full-fledged crisis.

4. Connect with the Community

Imagine having the flexibility to realize instant insight into community sentiment about the local department, problems in a very neighborhood, or perceptions of certain events. Data driven policing can provide this type of rich information to enforcement. Previously, community surveys are accustomed to gauge this sort of sentiment; however, within the era of massive data, there are such a big amount of more resources that will be harnessed and wont to gain a deeper understanding of the populations’ different departments serve. A police officer can study developing problems or negative sentiment and so provide direction to subordinates about a way to best intervene.

“I’ve had people from neighbourhoods where their cars have all been broken into, they’ve just been riddled with car breaks, set out and thank me at 2 o’clock within the morning because they see the cruiser driving around and they’re saying it’s an absolute deterrent.”
For instance, certain neighbourhoods may have previously undetectable problems, like disorderly pedestrians during the late night hours. Normal policing routines might cause this type of issue to miscarry the cracks because information isn’t passed along and it’s not considered a significant problem like robbery or burglary. the matter is that if a department of local government doesn’t address this sort of quality of life issue, then community members will want they’re not cared about and trust within the police will diminish. Data driven policing techniques can uncover this exact reasonably problem, with officers to retort to the problems, solve the matter, and increase the standard of life for the residents of the neighbourhood. When this can be accomplished, citizens remain confident in their local department and trust is maintained.

5. Gain Internal Insights

Responding more efficiently and effectively to community problems is vital, but even as important is that the ability to spot and understand the interior workings of a local department. Using select key performance indicators, a supervisor or police executive can examine insights about department morale and agency strengths and weaknesses to induce a far better handle on administrative functions. Proper visualization and communication of this type of information to finish users allows for a fast recognition of internal issues. A Chief should be tuned in to any morale issues or budgetary issues before they become major detriments to the department. Data driven policing can give solutions that keep the Chief and other stakeholders several steps prior these matters. The goal here is to stay executives prepared in the slightest degree times in order that appropriate decisions may be made in an exceedingly timely manner


Communities with high crime rates and traffic crashes suffer a lowered quality of life. Enforcement agencies within these communities must work collaboratively with local stakeholders to scale back social harm. Problematically, today’s increased demands for enforcement services are including rising operating costs and limited resources, leading many enforcement agencies to allocate tax dollars with greater efficiency and intelligence. to seek out a resolution, enforcement executives must integrate technological advances and data-driven strategies into their decision-making processes. This issue of Geography and Public Safety discusses initial results of a replacement operational initiative that uses modern statistical analysis and geographic software to assist enforcement executives deploy resources effectively and efficiently. The initiative, called Data-Driven Approaches to Crime and Traffic Safety (DDACTS), was developed through a partnership between the National Highway Traffic Safety Administration within the U.S. Department of Transportation and also the Bureau of Justice Assistance and National Institute of Justice within the U.S. Department of Justice. It emphasizes using geographic mapping to locate crime and traffic crash hot spots and target these areas with highly visible traffic enforcement. DDACTS was created as a Nationwide initiative to assist local communities improve public safety by both reducing their crime rates and decreasing their traffic crashes. The initiative began within the summer of 2008 with seven demonstration sites,1 each of which has used timely and accurate data to make localized policing strategies and tactics. DDACTS offers guidelines for communities that want to implement this kind of information driven enforcement operational model. Using the DDACTS model can help enforcement agencies cultivate long-term change in their communities. Currently, many communities face serious public safety dilemmas. Incorporate services have increased, and since of a weakened economy, policymakers often need strong justification to extend funding for public safety efforts. Enforcement executives must find some way to justify their budgetary needs and spend the resources they need wisely. Law enforcement executives can easily justify their needs if they collect local crime and traffic data and use them to focus on community hot spots. Using spatial statistical techniques to spot clusters of crime and crashes can provide conclusive evidence about where both are occurring within the same places. Creating strategies that focus on these hot spots helps guide workflow and increase efficiency. By consistently monitoring and evaluating their agencies’ progress, they will determine which methods work and the way strategies should be revised within the future. Additionally, visible officer presence in high crime or traffic-heavy areas lets citizens maintain a way of safety and well-being. Officers can improve community relations by partnering with community stakeholders and sharing information. During this way, officers keep citizens directly informed about safety and crime prevention, and enhance media relations. Enforcement agencies must face today’s challenges with dynamic strategies that employ technological resources to resolve problems efficiently. Community-focused, place-based enforcement practices have emerged as a good thanks to reduce social harm and improve public safety. Intelligent policing improves citizens’ lives.

The imminent changes to the criminal justice system make the employment of information driven policing imperative. Insights gained from the correct quite data that are then leveraged appropriately will help enforcement approach problems within the simplest and efficient ways possible. Even as the private sector has adopted the employment of analytics as a core component of the business process, policing has to implement similar strategies to be effective and maintain legitimacy within the communities it serves.
If you’d wish to quickly get a Information driven policing initiative in situ for your community, Iron side can help. Our Iron Shield solution provides a versatile predictive policing framework with a predictive hot spots module that’s ready right out of the box. We glance forward to helping you protect your community and reduce crime.



[2]  “Conclusion: Questions for the Future.” The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement, by Andrew Guthrie Ferguson, NYU Press, New York, 2017, pp. 187–202. JSTOR, Accessed 25 July 2020.

[3] Ferguson, A. (2017). Conclusion: Questions for the Future. In The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement (pp. 187-202). New York: NYU Press. Retrieved July 25, 2020, from

[4] Ferguson, Andrew Guthrie. “Conclusion: Questions for the Future.” In The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement, 187-202. New York: NYU Press, 2017. Accessed July 25, 2020.

[5] “Introduction: Big Data Policing.” The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement, by Andrew Guthrie Ferguson, NYU Press, New York, 2017, pp. 1–[6] . JSTOR, Accessed 25 July 2020.

[6] Ferguson, A. (2017). Introduction: Big Data Policing. In The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement (pp. 1-6). New York: NYU Press. Retrieved July 25, 2020, from

[7] Ferguson, Andrew Guthrie. “Introduction: Big Data Policing.” In The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement, 1-6. New York: NYU Press, 2017. Accessed July 25, 2020.