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The article has been written by Kartik Bohra, from Symbiosis Law School, Hyderabad. In this article, the author discusses the role of data-driven policing in law enforcement and processes and practices of predicting policing.


Data-driven policing is generally the usage of analytical information and technologies, especially the quantitative technologies to acknowledge and identify the relevant crimes or targets where urgent police intervention is required. This information majorly helps in the prevention of crime or solve crimes using various technologies which include statistical prediction and procedure capabilities. Data-driven policing aims at using quantitative technologies with a combination of reliable information and evidence of a crime which helps in early detection and prevention of crime. The basic presumption of data-driven policing is that crime is not necessarily happening randomly in different locations but has a predefined pattern which is identified using this technology. Thus, instead of using traditional technologies to investigate the crimes, the agency now started using data-driven policing methods to identify the patterns of crimes by using the power of analytics. 

Data-driven policing does not aim to remove the traditional technologies in the investigation of crime but to fasten the investigation by using data analytics and various other methods to help forecast and prevent crime. By using new data analysis techniques on different data tools, law enforcement authorities can detect and predict the happening of an incident and deploy police officials accordingly. Nowadays, technology is considered to be a significant medium for law enforcement tools and strategies in developing or developed nations. These technologies have provided better and fast access to citizens where the police assistance is required. The communication technology and other technological advancements in the field of security have made significant improvement in law enforcement and made various data technologies available to the authorities that were unheard of by their predecessors. These algorithmic technologies help in reducing the biases that sometimes present in human decision making. A developing country like India is also taking major efforts to make it a data-rich jurisdiction and advancement in the field of data-driven policing. 

Presently some of the prominent methods that help in forecasting and determining crime using data-driven technologies are:

  • Predicting the place and time of the crime.
  • Predicting future prospective criminals with the data sets.
  • Predicting individuals in society who are more prone to victims.
  • Creating and initiating the investigation through past records.

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What is Data-driven policing?

Data-driven policing can be understood as the current use of various data sources for the purpose of effective and efficient decision making, improved procedures and raised actionable intelligence for all the officers within a police service. Data-driven policing is a technology which uses a number of community channels to merge the crime and traffic data and take suitable actions to reduce the crime rates and offences. This technology uses data to determine the previous records and ‘hot spots’ where criminal activity has taken place. Thus, police can deploy its resources and enforcement officers to targeted areas. It is also called Data-Driven Approaches to Crime and Traffic Safety (DDACTS). It was first started in India in 2008 by a partnership among the Bureau of Justice Assistance, National Institute of Justice and National Highway Traffic Safety to reduce road accidents and other offences on Highways.

These data-driven policing techniques are used to make the relevant information available to frontline officers for counter purposes and also to formulate preventive strategies.   

Data-driven policing and public value

Data-driven policing is a method or technique that uses and procures a variety of digitised data sources to formulate decisions. By public value, the author means that the value which law enforcement agencies contribute to the public at large. It creates a positive impact on citizens with the police authorities and perceived legitimacy of the police. 

The project aims to contribute good public relations by bringing data-driven policing and public value together. Advancement in technology and the innovation of new systems around the world are transforming the traditional methods of policing and creating public utility. Police agencies are using reliable information systems with GIS technology to reduce criminal activities and to ensure a free and safe environment for the public. Thus, data-driven policing systems are also enhancing and creating public utility around the world. 

Processes and practices of Data-driven policing

Data-driven policing does not only make predictions about the future happening of any crime in the society rather it is a comprehensive and complex process and procedure of which data-driven policing is a part. Some of the prominent methods in data-driven policing involve an analysis of statistical data while other methods include the organization of data standard techniques to detect and investigate a crime. The process of data-driven policing involves the following steps:

  • Collection of Data

All data-driven policing techniques are generally based on data. There are various tools available to collect data that is necessary for data-driven policing. In many countries across the world, a common integrated police technique or method is used to collect data from a source of information and investigate the offence through the channel of communication. India also uses relevant sources to collect the information as revealed by the Ministry of Home Affairs (MHA), Government of India through CCTNS (Crime & Criminal Tracking and Network System) that collects the data from various authentic sources such as an existing report, registers, case files, pictures, charge sheet, FIR, arrest memo, conviction memo and digital data from the police record rooms.

New York Police Department (NYPD) uses the Domain Awareness System to collect data which integrates the numerous data sources within the department. This technique makes the information available to the authorities for combating and preventing purposes. Thus, the collection of data is the primary function in the process of data-driven policing methods.

  • Data Analysis

Various techniques such as data mining and regression are used to evaluate and analyze the data obtained in the first step. These techniques generally provide insights into crime by using various tools and formulate common patterns that are unique to a given region. Generally, these procedures are divided into three phases such as identification, acquisition and implementation for the purpose of analysis. Identification is the first step of analysis where agencies create awareness of the technology. The acquisition is related to the pursuit of buying in a contract with a specific technology. Lastly, implementation is the process where technology is being integrated into police practice.

Major data analysis methods are as follows:

  • Data Mining

Data mining is the most significant method of data analysis as it may recognize the data patterns and use that data for future predictions and presumptions. One of the most prominent technologies used in data mining is that various types of data mining inputs applied in policing are different types of algorithms that are generally used to segment the data mine in different directions and ways. In this method, specialized software is used to discrete the available data which results in clear visualization of criminal activities including the main hotspot.

  • Hotspot analysis

In this method of hotspot analysis, data is used to predict and presume areas which have a high tendency of crime being committed based on the historical set of data. The basic assumption in this method is that crime tends to be lumpy. In general terms, hotspot analysis is used to map out the previous criminal records in order to prevent potential future crimes.

  • Regression models

A regression method aims to observe the interference between independent and certain variables to analyse future predictions. Thus, this model is useful to determine the various variables in data instead of only criminal history. Violent crimes in any area could be predicted by using leading indicator data.

  • Police operations

This is the third step used in data-driven policing technology. The analysis will be of no use to bring down potential future crimes if it does not affect the present policing. Locations identified, and information received through data sources may require additional efforts to detect and investigate the crime. Police intervention is required to reduce the crime rates as some criminals may be arrested using these methods of data-driven policing and some criminals may opt to stop committing crimes. Thus, there is a need for police operations in this technology so that an action can be taken from the authorities on that location. In this way, a new cycle will begin with the identification of data, analysis and intervention or operations. 

The method of data collection, analysis and intervention is used to determine the appropriate response to the circumstance. Even after collecting the required information and good analysis, it will not help in reducing the crime rate in society if there are no effective and efficient police operations. Therefore, swift police action and intervention is required under data-driven policing to reduce criminal activity.

  • Criminal Response

This is the last step in data-driven policing. After effective police intervention, the outcome of the action largely depends upon the risks involved in the operation and on the resources deployed by the authorities. It is always possible that some criminals get arrested and some may change their identified locations before police intervention. Thus, this can change the modus operandi and may alter the data patterns as the occurrence of crime may change with the changed criminal activity. The data collection, analysis and policy intervention will become obsolete if changed in the criminal response. Thus, it is necessary to have updated data collection, analysis and police operation processes.

Benefits of Data-driven policing

The most significant benefit of data-driven policing is to prevent crime but there are also the broader benefits to a nation. The major advantage of predicting policing is that it significantly transforms the mechanism from reacting to crime to predict the potential future crime and deploying law enforcement agencies to overcome or mitigate crime. The existing traditional methods are being replaced by these new predicting techniques to reduce the crime rate in society by using surveillance systems and hotspot policing. 

This technology has given various tools to law enforcement agencies who developed a better understanding of criminal behaviour and enable better forecasting tools to analyze the hotspot areas and to determine where and when crimes may occur. It also provides better analytical methods which are not available in the traditional approach, to anticipate the potential future crime and enables timely actionable intelligence. Another significant advantage of data-driven policing is that it assists in analyzing various demographic trends and economic conditions that largely affect the crime rates in hotspot areas.

The intelligible agencies use different methods in predicting policing such as to check previous criminal records to provide reliable information to police authorities about the intervention which reduces the number of crimes in society. Crime anywhere around the world creates a huge impact on society at national as well as individual levels. It is important for law enforcement agencies to anticipate potential future crime and deploy resources to reduce crime rates in society. In the US, police officers use data-driven methods and GIS (Geographic Information System) systems to identify hotspot areas for automobile crime involving vandalism and increasing police resources in those areas for swift and timely action in response to criminal acts.

Due to lack of resources and overburdening of work, the Indian police authorities are facing various challenges in deploying resources at the right place and at the right time. Thus, technology and methods that provide a better allocation of law enforcement agencies which results in the reduction of crime rates are desirable in India. Therefore, predictive policing or data-driven policing provides an opportunity for police agencies as it enables better administration of rules and regulations by focusing on crime-prone areas. 

Data-driven policing in India

Data-driven policing is a new concept in a developing country like India. Nowadays, Indian police are using various data-driven techniques that involve the storage of data and analysis of the volume of data to curb criminal activities in India. This technique aimed at assuming and predicting the patterns to know the hub of criminal activities. Various law enforcement authorities have started using data-driven technology to detect and investigate crime in a faster way. This technology also provides authorities with an option to identify and analyze the real-time data to know about the different patterns of crime within their respective jurisdictions.


There are various challenges faced by a democratic country, where it is the duty of the state to uphold the Fundamental Rights of an individual. Thus, while using these technologies, the government must take care of the rights of individuals in the society and predictive approaches which are followed by the authorities must be avoided. One of the most prominent challenges of this technology is that there are chances of erosion of privacy and other human rights as well as fundamental rights in the society. The main challenges to data-driven policing are:

  • Privacy

Use of data and information to identify the criminal hub or hotspot may not be a threat to privacy but the use of potential data to know individual criminals may lead to a threat to the privacy of an individual. Analysis of personal information of an individual is against the fundamental rights guaranteed under the Indian Constitution. Thus, a lot of people fear about sharing personal information to the authorities and sometimes try to hide their behaviour.

  • Misuse

Data-driven policing sometimes fails to safeguard the usage of personal information of an individual. The preventive action of this technology sometimes causes a threat to an individual’s liberty and infringes fundamental rights. Thus, any arrest without authentic information may lead to abuse of power by the authorities and the government.

  • Data storage

Data or information in the form of data can be captured from different sources according to the relevancy and authenticity. The storage of data is itself a big challenge in front of the law enforcement agencies. There are various data sources available to track the information of an individual such as the National Crime Records Bureau (NCRB), UIDAI, etc. and causes a problem in the storage of data.

  • Cybercrime

Cybercrime is another major challenge to be addressed. The information in the form of data harnessed by authorities to prevent crime by using data-driven policing is also misused by criminals as it is valuable for them to commit more sophisticated cyber-crimes. 

  • Security and Amplifications

Data-driven policing systems are more prone to amplifying the data and inequities in knowledge. Further, the security of the data is also one of the major concerns in data-driven policing as it will be used for the purpose of analysis. Thus, it is necessary to protect and secure the data and need for good infrastructure to ensure confidentiality and security of data.

Data-driven policing: the future of law enforcement

Police departments around the world are taking an active part in law enforcement by using predictive analysis techniques to know people who are likely to become victims or perpetrators of crime. This technology is used for the early detection and investigation of crime using various methods of data-driven policing. Various police departments across the world are using predictive analysis to determine and prevent crime and to control their day-to-day operations. Data-driven policing helps police authorities to determine the hotspot areas and to forecast crime in particular places. 

Using data technology to observe the potential “hotspot”, a hub for criminal activity, police authorities are deploying their resources for the purpose of law enforcement in these targeted areas. These officers use geo-mapping techniques to ascertain the particular places where the highest number of crimes are committed. These agencies use surveillance systems to target specific areas and deploy their officers and traffic enforcement in these places. Data-driven policing has transformed into powerful methods that provide mechanisms through which agencies can pinpoint their resources, prevent any criminal activity and put a restriction on the predators of crime. This technology has given analytical data to intelligence agencies and improved their working system of being at the right place at the right time.

Data-driven policing also helps in early detection and investigation of the potential crime through the use of analytical data which protects and prevents misuse and infringement on civil as well as human rights. Sometimes law enforcement uses a predictive system to detect and investigate the crime to avoid mishappening and harm to the public at large. Data-driven policing uses advanced probabilistic algorithms and community policing to deploy authorities on the hotspot areas. However, this system of predicting crime might be effective but police authorities cannot always rely on these technologies or methods. Thus, police officers have to analyze the things as far as their instincts and must use their experience to control the things in the right place.


  1. Data-driven policing technology must use strategic planning for the deployment of resources at the specific areas so that prompt action can be taken without any delay.
  2. Privacy and transparency challenges must be solved by establishing a privacy and ethics committee to address issues relating to privacy and transparency. This is necessary as data-driven policing methods generally use data sources for the purpose of identification.
  3. The Central and State governments should deploy additional resources at hotspot areas, identified by using data-driven policing to reduce crime rates.
  4. Educational training and awareness should be developed among police authorities in pursuance to data accuracy in predictive policing methods.
  5. The maintenance of public confidentiality is necessary for the purpose of societal and public value. Thus, it is required for the law enforcement agencies to maintain high-degree public confidentiality while working under data-driven policing.


The study concludes that data-driven policing techniques help in the reasonable deployment of available resources to reduce the crime rates in society. The deployment of resources largely depends upon the nature and behaviour of criminals and to determine the hotspot areas and to forecast crime in particular places. These models are used to reduce the crime rate effectively and efficiently. The identification and collection of data and the prediction of individuals and geospatial locations helps early detection and investigation of the crime by developing the policing intervention. This also helps in preparing strategies and tactics to prevent future potential crimes in the society.

The methods and tools of data-driven policing provide an opportunity for authorities to ensure a proper law enforcement system so that crime can be reduced effectively with prompt action.   It is necessary for the present scenario to detect and investigate future potential crimes to safeguard and protect the rights of victims. Nowadays, criminals are using a wide range of technologies and methods to commit a crime. Thus, law enforcement agencies have to adopt predicting policing measures to curb and control crimes rates in society. It is rightly said that “prevention is better than cure.” Police authorities need to ensure an effective and efficient response to the data collected under data-driven policing and analyze the hotspot areas to control and prevent future potential crimes. 

Although there are various benefits associated with the use of data-driven policing for law enforcement in society, there are also several drawbacks relating to cybersecurity and privacy policy as discussed above. The drawbacks related to transparency and  Thus, law enforcement agencies need to ensure that the method of data-driven policing is used diligently so that it cannot infringe other persons liberty and security. Further, police authorities must be given adequate training to adapt to the methods and tools of data-driven policing. It is also necessary to create awareness amongst the general public about the uses of this technology.

Based on our study, we arrive at the conclusion that the adoption of the technology of predicting policing in the field of crime detection and investigation is needed in the country like India where crimes are increasing day by day at an alarming rate. This technology uses data methods for identification and detection of crime and helps in the deployment of resources at specific areas after analyzing the previous criminal records. Therefore, data-driven policing is a boon in the country as it prevents future potential crimes and reduces the crime rate in society.


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