Crime data mining: an overview and case studies

International Journal of Engineering Research and, 2016

As crime rates keep spiralling each day, new challenges are faced by law enforcement agencies. They have to keep their forces on the lookout for any signs of criminal activity. This may only cause more burden on their resources. The law enforcement agencies should therefore be able to predict such increases or decreases or trends in crime, such as the approximate number of murders, rapes, thefts, or any such crimes that may occur in a particular area in a particular month, year, or any timespan, or, the overall number of crimes occurring in a country in a particular year in the future, or any other prediction or projection of future crime statistics. First, our system proposes to extract data from crime record repositories, on which we intend to perform data mining. Data classification and regression algorithms then help in forecasting and predicting this is proposed to be done by first training a set and then applying the learned rules on the test set in order to determine the predicted output. Using this, law enforcement agencies can better understand how the crime pattern across a certain region, or interval of time is, and using this data, such agencies can take proactive action to stem the rise of particular crimes in particular regions, or during particular times. This would save them a lot of time, money and effort. Our system proposes to mine this data and thus run appropriate algorithms on such data. This predicted output could also be presented to the user in the form of clusters using a data visualization algorithm like K-means clustering algorithm. The final endproduct could thus be a system where some future predictions would made by training crime data sets, and the output could be visualized in order to be simple to comprehend for the user.

Police department have right to use a expeditiously volume of data, pooled with the vigorous kind and density of criminal activities, these needs led to the use of data mining techniques in crime record bureaus and police stations. An experience police official working as analyst can examine crime trends accurately, when amount of data available is reasonably small, but as the cases and difficulty of crime rises quantity of facts and figures also increases respectively. This has resulted in increase analysis time, further humanoid mistakes are certain to creep in, by increasing efficacy and minimizing errors data mining techniques can enable crime investigator to detect crime and predict its occurrence in advance. In this paper a detailed survey of existing tools and techniques used for crime analysis and crime prediction is provided.

2012

There had been an enormous increase in the crime in the recent past. Crime prevention is a primary concern of police as they perform their central role of protecting the lives and property of citizens. However, information and technology overload hinders the effective analysis of criminal and terrorist activities. Crime deterrence has become an upheaval task. The cops in their role to catch criminals are required to remain convincingly ahead in the eternal race between law breakers and law enforcers. Police often have at their disposal vast amounts of least utilized crime data (such as crime incident reports) which if analyzed could reveal some hidden information such as crime committing trends useful in crime prevention. Use of Information Systems techniques such as data mining and Geographic Information Systems for analyzing these data is promising in boosting the police efforts. This paper reviews the applicability of various data mining methods and Geographic Information Systems...

Data mining is vast field for mining knowledge in various fields of life. Crime mining is one of the applications focused here. Credit card and web based crime are increasingly as more technologies are rising high. To deal and overcome fraud clustering and classification techniques are implemented. Framework and process models are designed that provide user results, graphs and trees that help user to find criminals without any complex computation.

International Journal of Advanced Computer Science and Applications

The advancement in Information Technology permits high volume of data to be generated in databases of institutions, organizations, government, including Law Enforcement Agencies (LEAs). Technologies have also been developed to store and manipulate these data to enhance decision making. Crime remains a severe threat to humanity. Criminals currently, exploit highly sophisticated technologies to perform criminal activities. To effectively combat crime, LEAs must be adequately equipped with technological tools such as data mining technology to enable useful discoveries from databases. To achieve this, a Real-time Integrated Crime Information System (RICIS) was developed and mobile phones were used by informants (general public) to capture information about crimes being committed within Southern-East, Nigeria. Each crime information captured is being sent to the LEA responsible for the crime type and the information is stored in the agency database for data analysis. Thus, this study uses data mining algorithms to analyze crime trends and patterns in Southern-Eastern part of Nigeria between 2012 and 2013. The algorithms adopted were Classification and Rule Induction. The data set of 973 were collected from Eleme Police station, PortHarcourt (2012) and Nsukka Police station (2013). The analysis enables identifications of some trends of crimes and criminal activities from various LEAs databases, enhancing crime control and public safety.

2018

In large datasets, data mining is one of the most powerful ways of knowledge extraction or we can say it is one of the best approaches to detect underlying relationships among data with the help of machine learning and artificial intelligence techniques. Crime Detection is one of the hot topics in data mining where different patterns of criminology are identified. It includes variety of steps, starting from identification of crime characterization till detection of crime pattern. For this purpose, various crime detection techniques have been discussed in literature. In this paper, we have selected widely adapted data mining techniques that are specifically used for crime detection. The analytical study is presented with an extraction in form of strengths and weakness of each technique. Each technique is specific to its use, for example to identify the social ties and roles of criminal in any network, Social Network Analysis techniques is best suited because of its degree, density an...

INTERNATIONAL JOURNAL OF ADVANCE RESEARCH, IDEAS AND INNOVATIONS IN TECHNOLOGY

Data mining can be used to model crime detection problems. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. About 10% of the criminals commit about 50% of the crimes. Here we look at the use of a clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving the crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We applied these techniques to real crime data from a sheriff's office and validated our results. We also use a semi-supervised learning technique here for knowledge discovery from the criminal records and to help increase the predictive accuracy. We also developed a weighting scheme for attributes here to deal with limitations of various out of the box clustering tools and techniques. This easy to implement data mining framework works with the geospatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counterterrorism for homeland security.

—We propose an approach for the arrangement and execution of bad behavior area and criminal recognizing confirmation for Indian urban groups using data mining frameworks. Our approach is parceled into six modules, to be particular—information extraction (DE), information preprocessing (DP), grouping, Google outline, characterization and WEKA® execution. To begin with module, DE expels the unstructured wrongdoing dataset from various wrongdoing Web sources, in the midst of the season of 2000– 2018. Second module, DP cleans, facilitates and diminishes the removed wrongdoing data into sorted out 5,038 wrongdoing events. We address these events using 35 predefined wrongdoing attributes. Secure measures are taken for the wrongdoing database accessibility. Rest four modules are useful for bad behavior acknowledgment, criminal recognizing evidence and desire, and bad behavior affirmation, independently. Wrongdoing acknowledgment is explored using k-suggests gathering, which iteratively makes two wrongdoing bundles that rely upon equivalent wrongdoing properties. Google portray observation to k-infers. Criminal conspicuous verification and estimate is dismembered using KNN portrayal. Bad behavior check of our results is done using WEKA®. WEKA® checks an exactness of 93.62 and 93.99 % in the course of action of two bad behavior clusters using picked bad behavior attributes. Our approach contributes in the change of the overall population by helping the looking at workplaces in bad behavior area and guilty parties' recognizing confirmation, and in this way decreasing the bad behavior rates. Wrongdoings are a social unsettling influence and cost the overall population to an awesome degree from various perspectives. Any examination that can help in separating and comprehending wrongdoing speedier pays for itself. Crime data mining has the capacity of extricating helpful data and concealed examples from the substantial wrongdoing informational indexes. The crime data mining challenges are getting to be fortifying open doors for the coming years. Since the writing of crime information mining has expanded energetically as of late, it winds up obligatory to build up a diagram of the cutting edge. This orderly survey centers around crime data mining procedures and innovations utilized as a part of past investigations. The current work is grouped into various classifications and is introduced utilizing perceptions. This paper additionally demonstrates a few difficulties identified with crime data research.

2005

It is well recognized that advanced filtering and mining in information streams and intelligence bases are of key importance in investigative analysis for countering terrorism and organized crime. As opposed to traditional data mining aiming at extracting knowledge form data, mining for investigative analysis, called Investigative Data Mining (IDM), aims at discovering hidden instances of patterns of interest, such as patterns indicating an organized crime activity. An important problem targeted by IDM is identification of terror/crime networks, based on available intelligence and other information. We present an approach to an IDM solution of this problem, using semantic link analysis and visualization of findings. The approach is demonstrated in an application by a prototype system. The system finds associations between terrorist and terrorist and is capable of determining links between terrorism plots occurred in the past, their affiliation with terrorist camps, travel record, and funds transfer, etc. The findings are represented by a network in the form of an attributed relational graph. Paths from a node to any other node in the network indicate the relationships between individuals and organizations. The system also provides assistance to law enforcement agencies, indicating when the capture of a specific terrorist will likely destabilize the terrorist network.

Data mining is a process of extracting knowledge from huge amount of data stored in databases, data warehouses and data repositories. Crime is an interesting application where data mining plays an important role in terms of prediction and analysis. Clustering is the process of combining data objects into groups. The data objects within the group are very similar and very dissimilar as well when compared to objects of other groups. This paper presents detailed study on clustering techniques and its role on crime applications. This study also helps crime branch for better prediction and classification of crimes.