White Paper Big Data and UK Policing Imran Zaman @ DAYWATCHER.COM 12 th August 2013 1 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
Table of Contents: - Police setup in England and Wales - Technology and UK police - Big data and big data analytics - Big data and predictive policing - Predictive policing in UK - Predictive policing around the world - Predictive policing technology - Predictive policing concerns - The future - Conclusion - Appendix 2 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
1. Introduction "The primary object of an efficient police is the prevention of crime: the next that of detection and punishment of offenders if crime is committed. To these ends all the efforts of police must be directed. The protection of life and property, the preservation of public tranquillity, and the absence of crime, will alone prove whether those efforts have been successful and whether the objects for which the police were appointed have been attained." - Richard Mayne, 1829. While crime prevention is the bedrock of the police service mission, majority of police work involves tackling a problem once it has already taken place. Even with the use of technology, policing by nature is mostly reactive rather than proactive. Big Data analytics is changing the policing scene from crime response to crime prevention in a big way. 2. Police Setup in England and Wales Territorial police forces and Special police forces are two main police agencies in England and Wales. The other police agencies include non-police law enforcement agencies and miscellaneous police forces. Territorial police forces: There are 43 territorial police forces in England and Wales. Each force is setup to cover a police region that comes under an independent Police Authority. These forces are responsible for the majority of policing in England and Wales. Special police forces: These are national police forces with non-regional jurisdiction. British Transport Police is one such force that polices the Great Britain railway network. The Civil nuclear Constabulary is responsible for guarding civilian nuclear installations and non-military nuclear material in transit. Not to be confused with the military police, the Ministry of Defence Police focuses on the defence community by providing armed security at key defence sites and protecting Britain s nuclear deterrent and critical national infrastructure. Non-police law enforcement agencies: Authorised to enforce laws, the officers from these agencies are not police constables. The Serious Organised Crime Agency (SOCA), dubbed Britain s FBI, is a non-police law enforcement agency. This unit tackles serious organised crime such as class A drug dealings, people smuggling, human trafficking, major gun crime, fraud, computer crime and money laundering. 3 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
Miscellaneous police forces: Officers of these forces are responsible for policing certain specific local areas or activities, such as ports and parks. 3. Technology and UK police The UK police went live with the Police National Computer (PNC) in 1974. It started off as a database for Stolen Vehicles. Since then, it has expanded considerably and contains information about criminal convictions, people, buildings and vehicles. According to data released by the National Policing Improvement Agency, as of October 2009 the PNC contains over 9.2 million personal records, 52 million driver records and 55 million vehicle records. The potential to use this massive volume of data has not been missed by the UK police. PNC is more as an investigative tool now rather than a record keeping tool. In addition to the PNC, police in the UK rely heavily on technology for a variety of law enforcement functions. Several police forces use electronic notebooks while at the scene of a crime. This allows them to access information from sources such as the automated fingerprint identification system, vehicle records, driving records, warrants, protection orders, interagency information sharing and criminal history records. The backbone of law enforcement is patrolling. With limited resources, it is important to dispatch patrol resources to the right place at the right time. Other than using computers for crime investigation and information sharing, the police also use technology for crime analysis and crime mapping. Crime analysis and mapping allow the police to identify hotspots where crime is most likely to occur and dispatch patrol accordingly. When not responding to calls for service, the officers use the crime mapping system to identify hotspots for patrolling. This process often called CompStat (Comparative Statistics) along with data and intelligence-driven patrol approaches has resulted in remarkable decline in crime in certain areas. These crime maps are also accessible by the public on the web sites of all 43 police forces. However, all this technology is still by nature reactive. The analysis relies on recent and historical trends to indicate hotspots. It does not come up with any emerging or future patterns from studying the current data. This is where big data analytics comes into the picture. 4 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
4. Big Data and Big Data Analytics Every day, systems around the world record terabytes, petabytes or even exabytes of information from sources such as social media sites, surveillance cameras, stock markets, GPS signals and mobile phones. Closer to home, the UK police forces have built up massive crime databases from street camera records, car recognition software, finger prints, shoe foot prints, DNA and other crime related data. Big Data refers to the collection of all of these loosely structured and unstructured data. Big Data analytics is the process of analysing this mass of data to find patterns, trends, correlations, valuable relationships, sequences, affinities and any other useful information. The applications of big data analytics in law enforcement are numerous. The results allow the police to filter out useful information from huge volumes of data. The agencies can use this information to visualise links between disparate information to solve a crime. Another ambitious application of Big Data is predictive policing. 5. Big Data and Predictive Policing Predictive policing refers to the use of advanced technological tools and big data analytics to find patterns from data and pre-empt crime using proactive measures. Successful predictive policing entails less reactive policing and more proactive policing. Phenomenon s such as the spread of infectious diseases, estimates of earthquake aftershocks and weather, can be forecast because they follow a specific pattern. Similarly, criminal activity also forms and follows a certain pattern in time and space. Dr George Mohler, developer of the PredPol predictive policing algorithm, says, Criminals want to replicate their successes, they go back to similar locations, and they repeat their crimes. It is almost identical to how aftershocks roll out after earthquakes, following predictable fault lines and timetables. Predictive policing is not new. What is new is the massive infusion of data into the picture. There are several predictive policing softwares in today s market that use this data to make good crime predictions. The predictive algorithms work on the premise that human behaviour is predictable and can be mathematically modelled and used to forecast what people are most likely to do over time and space. 5 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
The algorithms use a combination of historical crime data and constantly calibrated current crime data to predict in real-time the possibility of a particular type of criminal activity taking place in a given location at a given time. To further fine-tune forecasts, the systems use other information found in big data such as, details of road networks and human psychology. For example, Muggers do not like getting wet, says Ron Fellows, IBM s expert. To date, the most successful application of predictive policing has been in the reduction of property crime rates. It is very effective at preventing burglary and other property crimes such as thefts of vehicles or their contents. This is because crimes such as property crimes lend themselves to modelling and prediction. For example, a burglary in a certain area indicates a much higher burglary risk for neighbouring properties with the threat shrinking rapidly if there are no further offences. 6. Predictive policing softwares There are several predictive policing softwares solutions in the market today. Following is a high level overview of a few of them. KeyLines: KeyLines is a flexible network visualisation technology from Cambridge Intelligence. It renders any kind of data with nodes and links and responds to user interactions like clicking, touching and moving nodes. It is a great tool to visualise and make sense of an otherwise loosely structured big data. PredPol: Real time crime prediction software, it assigns probabilities of future crime events to regions of space and time. It presents an estimated crime risk in a usable framework that allows law enforcement decision makers to deploy resources more efficiently and accurately. GeoEye Analytics Signature Analyst: This predictive policing software provides hot-spot maps using empirical and statistical approach that focuses on social, cultural and physical variables. Risk Terrain Modeling: Risk terrain modeling creates a risk terrain map that displays risk factor intensity for each spatial unit by measuring multiple risk factors across spatial units. The map can be used to identify locations where conditions are conducive for crimes to occur. 6 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
IBM SPSS Statistics: The predictive policing software from IBM uses the Champion-Challenger approach to compare competing models every time new data is streamed. The software uses a tree-based model that allows a drill down to investigate the root causes of new hotspots. The policing system watches big local events, weather and payday proximity to predict the frequency and location of law breaking. The predictive policing softwares are not a one-size fit-all solution. While one product may be better at predicting a certain type of crime, a different solution may be better for other situations. Each product comes with its own advantages and disadvantages. The customised product varies by department. There are a lot of variables to consider before picking the right software for a particular police force. It is important to spend time on literature searches and expert interviews to determine the best fit. The main focus must be on whether or not the final product is successful in preventing crime. Also, complicated models do not necessarily mean better predictions. 7. Predictive policing in UK The UK police have implemented Big Data predictive policing software for visualisation and crime prevention in several of its forces. The most notable is the implementation of predictive policing by the Kent police force. In December 2012, the Kent police began a trial of the PredPol crime prevention software. The algorithm used in this software is the same as the one used to predict earthquakes. The software analyses crime data from records and human behaviour and predicts hotspots within 150 square meter boxes. According to experts in policing, the software is surprisingly accurate in highlighting likely crime spots. The system predictions come in twice a day. The officers decide where to patrol based on these updates. While the Kent police already put in a lot of effort into crime prevention, the software has taken crime prevention to a whole new level. Police analysts note that while their predictions scored five percent in the accuracy scale, the software scored 8.5 percent when predicting a crime. In the four-month trial period, 8.5 percent of all crimes took place within the hotspots predicted by the software and plenty more occurred in adjacent neighbourhoods. The force is able to use these modest gains to reduce crime substantially by combining the results with intelligent policing. 7 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
In another example, UK s National Fraud Prevention Service has deployed KeyLines to explore visually, the fraudulent activity network seen within fraud cases. The software allows the Fraud Prevention Service to visualise the complex inter-linking networks and relationships often found in fraud cases. UK Ministry of Justice uses the IBM SPSS Statistics and IBM SPSS Modeler Premium software to analyse a huge volume of crime and offender data to figure out proactive measures to prevent recidivism. Using the software, the ministry s violent crime recidivism prediction went up from 68 to 74 percent, and general offenses recidivism prediction went up from 76 percent to 80 percent. In addition to crime prediction, the software analysis has helped the ministry develop targeted treatment programs for the prisoners throughout their sentence to bring down the probability of the prisoners committing crimes again upon their release. In the field of research, researchers in London are using the model of spread of infectious disease to develop a model to predict the spread of riot once it begins. This model makes it possible for the police force to prepare and proactively respond in case of a riot. 8. Predictive policing around the world Big data predictive policing software is finding widespread applications within police forces world-wide. Blue CRUSH is an impressive case of the results of the implementation of IBM SPSS predictive analytics software by the Memphis police department in the United States. The predictive software has brought about a 30 percent reduction in serious crime and a 15 percent reduction in violent crime. The implementation of predictive policing by the Santa Cruz, LA police department in July 2011, within a year, resulted in a 27 percent drop in the number of reported burglaries and a 19 percent reduction in crime rates. Due to its success, the department bumped its program from an experimental phase to full operation in July 2012. To reduce New Year s Eve random gunfire incidents in Richmond, VA, the police used predictive policing to anticipate the time, location and nature of the gunfire incidents. In 2003, Richmond police strategically placed officers at those locations for crime prevention and quick response. As a result, there was a 47 percent drop in random gunfire and a 246 percent increase in the number of weapons seized. Efficient use of police resources also saved the department over 9,000 in personnel costs. 8 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
9. Predictive policing concerns The advantages and potential of predictive policing are numerous but, there are several problems, concerns and challenges to overcome before predictive policing becomes the norm. A major concern is privacy. The valid question arises: who is watching who and where to draw the line? There are also problems with civil liberty infringement that comes with the nature of the collected data. There is also a real possibility of data misuse and overuse, which amplifies bias. The following is an example of bias amplification. Robbery and theft crimes are more readily reported than drug dealing and other violence. Rich neighbourhoods readily turn to the police, whereas crimes in poor neighbourhoods, tend to go unreported. The bias is clear in these cases. The predictive reports, without any human input, amplify robbery and theft crimes in the first case and favour rich neighbourhoods in the second. Predictive crime perpetration is another serious concern. Most of the data used by predictive softwares are public domain. Hence it is not too far a stretch to say that criminals have access to the same data that the police do and that they leverage it to predict police location. In this case, positioning police in predicted hotspots may discourage opportunistic crime, but may encourage planned criminal activity to move to less likely areas. Since predictive policing pre-empts crime, the resulting arrest increases the risk of authorities being overly tough on low-risk offenders while also running the risk of under-supervising serious offenders. Also, a valid fear is that of the judges and juries coming to trust the accuracy of crime prediction tools blindly and thereby jeopardising justice, all due to the perceived transparency of predictive systems. 9 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
10. The future Despite a reduction in the number of officers in force today, statistics indicate that the number of crimes have reduced significantly year-to-year in the UK. Over the past year, crime in England and Wales has dropped by nine percent, a 4,516 decrease from the previous year. This is despite the economic downturn and austerity measures. Where a decade ago there were around six million recorded offences annually, there are only 3.7 million recorded offences today. There is a drop in nearly all types of crime: 15 percent in car theft, seven percent fall in burglary, six percent fall in violent crime and 13 percent fall in vandalism. Not surprisingly, this coincides with the police forces embracing newer crime analysis techniques including predictive policing as standard practice. Predictive policing allows for the most efficient deployment of limited personnel resources. In the end, the success of predictive policing comes down to reliable big data analytics. Predictive policing is a holistic approach. It relies not just on data within the police department, but also data outside its walls such as medical data, behavioural data, school data and land-use data. The main challenge is making sense of all the available data and then figuring out a method to bring it all together. The inclusion of human behavioural data into crime analysis is what sets apart predictive policing from older crime analysis techniques, which only involves studying crime data. The current softwares are close to the accuracy limits with the available data. Newer data in this area will drive the future of predictive policing. The future involves incorporating more and more behavioural data into predictive softwares to predict criminal behaviour more accurately. Although it is a privacy rights nightmare, environmental criminology has a huge potential for growth. This refers to examining a person s journey to a life of crime, their routine activities and habits. Data scientists can then use these criminal signatures and other factors to make better predictions by matching temporal patterns and geographic patterns together. Experts believe that data from social networks and virtual environments is another gold mine when it comes to predictive policing. Data from these environments is virtually unexploited today. In the future, for example, instead of responding to an event, departments will use information from virtual existences and social media data streams to assign a patrol team within half a mile of the suspected disturbance, ready to move in when needed. How will experts deal with predictive perpetration in the future? Will criminals 10 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
alter their behaviour by studying data from social networks and virtual environments? Experts say that although this possibility has to be accounted for, it is very difficult for anybody to engage in truly random behaviour. McCue, author of Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, says that location preferences of humans tend to be a subtle and unconscious decisions in most cases despite humans awareness of a vast majority of their behaviours. McCue further explains that it is very difficult to bypass these unconscious decision making processes and that this is the reason crime analysis and predictive policing works. 11. Conclusion Despite significant advances in artificial intelligence, human intuition and interpretation are indispensable. The future holds more complex algorithms to evaluate mountains of new data, but trusting software with decision making will not happen anytime soon. Softwares use rules to make decisions and offenders do not always follow rules. This necessitates the need for human adaptive intervention. There is an old historic saying, The police are the public and the public are the police. Predictive policing strategy cannot replace an officer's knowledge that is accumulated on the street, or replace relationships built with members of the community. Rather, it is another tool to perform the basic policing tenet of crime prevention and not just making an arrest after it is too late for the victim. Even though in its infancy, predictive policing using Big Data analytics has the potential to revolutionise law enforcement and let the police agencies in the future regularly predict and prevent crime rather than simply respond to it. 11 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.
12. Appendix About the DAYWATCHER.COM Business & Technology Blog The daywatcher.com Blog offers free and unique articles covering the latest in business and technology developments. To find out more about the Blog, get your hands on the latest articles, white papers and much more visit www.daywatcher.com. Thanks, Imran Zaman. Editor DAYWATCHER.COM - The Business & Technology Blog Email: imran.zaman@daywatcher.com Web: http://www.daywatcher.com Twitter: http://twitter.com/daywatcher LinkedIn: http://linkedin.com/in/izaman 12 Copyright Imran Zaman, DAYWATCHER.COM 2009-13. All rights reserved.