On the Use of Data Mining Techniques for Crime Profiling

Crime is today a salient fact, an integral part of the risks we face in everyday life. The concern about national andinternational security has increased significantly since the incident of September 11th, 2001 attacks. However, informationoverload thwarts the effective and efficient analysis of criminal activities. Application of data mining in the context of lawenforcement and intelligence analysis holds the promise of solving such problems. The benefit of data mining for policeseems tremendous, yet only a few limited applications are documented. Data mining can be used to model crime detectionproblems. Any research that can help in solving crimes faster will pay for itself. This paper gives reviews current trends inprofiling crime using data mining techniques. We proposed the use of clustering algorithm as a data mining approach to helpdetect the crimes patterns and speed up the process of solving crime.Key words: Crime, profiling, data mining, criminals, attacks and detectio

An Intelligent Analysis of Crime through Newspaper Articles - Clustering and Classification

Crime analysis is one of the most important activities of the majority of the intelligent and law enforcement organizations all over the world. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. A major challenge faced by most of the law enforcement and intelligence organizations is efficiently and accurately analyzing the growing volumes of crime related data. The vast geographical diversity and the complexity of crime patterns have made the analyzing and recording of crime data more difficult. This paper presents an intelligent crime analysis system which is designed to overcome the above mentioned problems. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. The proposed system is a web-based system which performs crime analysis through news articles. In this paper we use a clustering/ classification based model to automatically group the retrieved documents into a list of meaningful categories. The data mining techniques are used to analyze the web data

Time series forecasting on crime data in Amsterdam for a software company

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn recent years, there have been many discussions of data mining technology implementation in the fight against terrorism and crime. Sentient as a software company has been supporting the police for years by applying data mining techniques in the DataDetective application (Sentient, 2017). Experimenting with various types of predictive model solutions, selecting the most efficient and promising solution are the objectives of this internship. Initially, extended literatures were reviewed in the field of data mining, crime analysis and crime data mining. Sentient provided 7 years of crime data which was aggregated on daily basis to create a univariate dataset. Also, an incidence type daily aggregation was done to create a multivariate dataset. The prediction length for each solution was 7 days. The experiments were divided into two major categories: Statistical models and neural network models. Neural networks outperformed statistical models for the crime data. This paper provides the overview of statistical models and neural network models used. A comparative study of all the models on similar dataset gives a clear picture of their performance on available data and generalization capability. Evidently, the experiments showed that Gated Recurrent units (GRU) produced better prediction in comparison to other models. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in DataDetective

Crime Rate Prediction using KNN

Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. Thus, it becomes necessary to study various reasons, factors and relationship between different crimes that are occurring and ?nding the most appropriate methods to control and avoid more crimes. The main objective of this project is to classify clustered crimes based on occurrence frequency during different years. Data mining is used broadly in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. In this work, various clustering approaches of data mining are used to analyze the crime data. The K-Nearest Neighbour (KNN) classi?cation is used for crime prediction. The proposed system can predict regions which have high probability for crime rate and can forecast crime prone areas. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etcit will focuse mainly on crime factors of each day

Indirect association rule mining for crime data analysis

Crime data analysis is difficult to undertake. There are continuous efforts to analyze crime and determine ways to combat crime but that task is a complex one. Additionally, the nature of a domestic violence crime is hard to detect and even more difficult to predict. Recently police have taken steps to better classify domestic violence cases. The problem is that there is nominal research into this category of crime, possibly due to its sensitive nature or lack of data available for analysis, and therefore there is little known about these crimes and how they relate to others. The objectives of this thesis are 1) develop an indirect association rule mining algorithm from a large, publicly available data set with a focus on crimes of the domestic violence nature 2) extend the indirect association rule mining algorithm for generating indirect association rules and determine its impact --Leaf iv

Crime in Chicago: Red Light Violations and its Association with Violent Crime

This paper examines red light camera traffic violations and violent crime, specifically assault and battery, in the city of Chicago, IL. Using cluster analysis, a data mining technique that identifies groups within a dataset, and crime data from the Chicago Data Portal, we searched for crime correlations among different police districts in Chicago. The results found here could give Chicago crime-fighters and government officials insight into the occurrences of overall crime as they work to prevent it

An interactive human centered data science approach towards crime pattern analysis

The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making. In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme. The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph. This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime. We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis

Estimating the spatial distribution of crime events around a football stadium from georeferenced tweets

Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime

Appling Data Mining Technique for Crime Prevention: The Case of Hossaena Town Police Office

The Law enforcement agencies like that of police today are faced with large volume of data that must be processed and transformed into useful information and hence data mining can greatly improve crime analysis and aid in reducing and preventing crime. The purpose of this study is to construct predictive models that could help in the effort of crime pattern analysis with the aim of supporting the crime prevention activities at the Hossaena town police office. For this study, a six-step hybrid knowledge discovery process model is followed, due to the nature of the problem and attributes in the dataset. The classification technique such as J48 decision tree and Naive Bayes used to build the models. Performance of the models is compared using accuracy, True Positive Rate, False Positives Rate, and the area under the Relative Optical character curve. J48 decision tree registers better performance with 96.34% accuracy. Lastly for extracting the knowledge the researcher develop the prototype for the user for support the decision which crime is assigned under the serious, medium or low for this purpose the researcher generate the prototypes. Keywords: Classification, Crime, Data Mining, Hybrid, WEKA DOI: 10.7176/CEIS/11-1-03 Publication date: January 31st 2020