Description
ABSTRACT
Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining we can extract previously unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc we are focusing mainly on crime factors of each day.
TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWELDGEMENT
ABSTRACT
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE PROJECT
- PROBLEM STATEMENT
- AIM/OBJECTIVE OF THE PROJECT
- PURPOSE OF THE PROJECT
- SCOPE OF THE PROJECT
- SIGNIFICANCE OF THE PROJECT
- PROJECT ORGANISATION
CHAPTER TWO
LITERATURE REVIEW
- REVIEW OF THE STUDY
- KNOWLEDGE DISCOVERY IN DATABASES
- CRIME FACTS REVIEW
- CRIME RECORDING PROCESS
- CRIME PATTERN THEORY
- REVIEW OF CURRENT CRIME AND OFFENDING PREDICTION TECHNIQUES
- OVERVIEW OF CRIME ANALYSIS
CHAPTER THREE
- MATERIAL AND METHODS
- CRIME DATA
- THE PROPOSED FRAMEWORK
- DATA PREPROCESSING
- DATA WAREHOUSE CREATION PHASE
- THE PROPOSED ETL STRATEGY
CHAPTER FOUR
TEST AND RESULT ANALYSIS
- EVALUATION PHASE AND EXPERIMENTALRESULTS
CHAPTER FIVE
- CONCLUSION AND FUTURE WORK
- REFERENCES
CHAPTER ONE
- INTRODUCTION
Day by day the crime rate is increasing considerably. Crime cannot be predicted since it is neither systematic nor random. Also the modern technologies and hi-tech methods help criminals in achieving their misdeeds. According to Crime Records Bureau crimes like burglary, arson etc have been decreased while crimes like murder, sex abuse, gang rape etc have been increased. Even though we cannot predict who all may be the victims of crime but can predict the place that has probability for its occurrence.
The predicted results cannot be assured of 100% accuracy but the results shows that our application helps in reducing crime rate to a certain extent by providing security in crime sensitive areas. So for building such a powerful crime analytics tool we have to collect crime records and evaluate it [1].
It is only within the last few decades that the technology made spatial data mining a practical solution for wide audiences of Law enforcement officials which is affordable and available. Since the availability of criminal data or records is limited we are collecting crime data from various sources like web sites, news sites, blogs, social media, RSS feeds etc. This huge data is used as a record for creating a crime record database. So the main challenge in front of us is developing a better, efficient crime pattern detection tool to identify crime patterns effectively. The main challenges we are facing are:
- Increase in crime information that has to be stored and analyzed.
- Analysis of data is difficult since data is incomplete and
- Limitation in getting crime data records from Law Enforcement
- Accuracy of the program depends on accuracy of the training
Finding the patterns and trends in crime is a challenging factor. To identify a pattern, crime analysts takes a lot of time, scanning through data to find whether a particular crime fits into a known pattern. If it does not fit into an existing pattern then the data must be classified as a new pattern. After detecting a pattern, it can be used to predict, anticipate and prevent crime.
Before this clustering algorithms have been used for crime analysis. For instance, one site it is revealed that suspect has black hair and from next site/witness it is revealed that suspect is youth and from third one reveals that the offender has tattoo on his left arm etc. By describing the offender details it gives a complete picture from different crime incidents. Today most of it is manually done with the help of multiple reports that the detectives usually get from the computer data analysts and their own crime logs.
The reason for choosing this method is that we have only data about the known crimes we will get the crime pattern for a particular place. Therefore, classification technique that will rely on the existing and known solved crimes, will not give good predictive quality for future crimes. Also nature of crimes change over time, so in order to be able to detect newer and unknown patterns in future, clustering techniques work better.
There are steps in doing Crime Analysis:
- Data Collection
- Classification
- Pattern Identification
- Prediction
- Visualization
Fig. 1. Steps in Crime analysis
1.1 BACKGROUND OF THE STUDY
Crime are public social problem affecting the economy of communities and life [1], also it define the places should be avoided by people [2]. There is a strong body of evidence to support the opinion that crime is predictable because criminals tend to operate in their areas. Therefore, criminals tend to repeat the same type of crimes that they have committed successfully in the past in the same time and area [3]. In the past, solving crimes have been the prerogative of the law enforcement officers. Nowadays, the increasing use of the computers to track crimes, data analysis with computers has helped the law enforcement specialists to speed up the process of solving crimes [4].
Enhancing information awareness is a critical objective for the government, thus government sponsors the National Project for Law Enforcement to the speed of litigation procedures. This project includes several sectors (police, justice and forensic medicine) which will lead to the existence of a huge database.
Due to the increasing data, there is a need of technologies to analyze these data, so this study will use data mining, as it is an effective technique which allows searching for useful information and valuable in huge volumes of data [5], also the complexity of relationships between crime data have made study of crime an appropriate field for applying data mining techniques, in addition the knowledge that is gained from data mining techniques is useful to support Law Enforcement [6].
A lot of technologies were integrated in this study to apply data mining techniques for crime prediction such as data warehouse which helps to support decision making [7]. One of the most important tasks of the data warehouse is gathering heterogeneous data from several sources and integrates them into a single dataset to monitor historical trends and patterns [8].
The proposed data warehouse (FMA-DW) has been built using (MS SQL Server 2008) to crime prediction from forensic medicine database, thus FMA-DW included the measured data about crime prediction (crime type, areas, date, crime’s persons, gender, age). The important step to create a data warehouse is to extract, transform, and load data to database (ETL), ETL system refer to extract data from the several sources, then these sources can be used together to generate a suitable data, and finally ETL delivers this data in a suitable format to DW, so that the end users can make decisions [9]. Generally in this work a framework has been built to analyze country forensic medicine data for crime prediction by applying data mining techniques (DMT).
The remaining sections in this paper are organized as follows: First, section (2) showed related work. In section (3) research methodology and the proposed framework are presented in details. In Section (4) evaluation phase, the experimental results and the inference phase are discussed. Finally, in section (5) conclusion and suggested future work are showed.
1.2 PROBLEM STATEMENT
Day by day the crime rate is increasing considerably. Crime cannot be predicted since it is neither systematic nor random. Also the modern technologies and hi-tech methods help criminals in achieving their misdeeds. According to Crime Records Bureau crimes like burglary, arson etc have been decreased while crimes like murder, sex abuse, gang rape etc have been increased. Even though we cannot predict who all may be the victims of crime but can predict the place that has probability for its occurrence. This study brings solution to this problem. it involves using computer and information technology in reducing crime rate to a certain extent by providing security in crime sensitive areas.
1.3 AIM OF THE PROJECT
This work aims to present a proposed framework for crime prediction analysis using data mining techniques.
1.4 PURPOSE OF THE STUDY
The main purpose of this work is to help the government of any country to make strategically decisions to reduce crimes.
1.5 SCOPE OF THE STUDY
Police analysts are required to unravel the complexities in data to assist operational personnel in arresting offenders and directing crime prevention strategies. However, the volume of crime that is being committed and the awareness of modern criminals make this a daunting task. The ability to analyse this amount of data with its inherent complexities without using computational support puts a strain on human resources. This paper examines the current techniques that are used to predict crime and criminality.
1.6 SIGNIFICANCE OF THE STUDY
1. Improves Crime Prevention
The primary selling point of predictive policing centers on the prevention of crime before it happens based on data-driven and technology-centric approaches. It specifically involves data analytics, as well as other technologies such as artificial intelligence or machine learning and Big Data applications, to provide law enforcers with relevant insights and targets for intervention, as well as process optimization and automation.
2. Informed Decision-Making
Note that data analytics has the primary purpose of discovering useful information to support decisions. When applied to law enforcement, particularly through predictive policing, data analytics promote informed decision-making across numerous facets such as the analysis of crime patterns and prevention of crime, predicting risks and determining identities of offenders, and identifying the vulnerabilities of a community or its members.
3. Advances the Justice System
Another benefit that comes from predictive policing centers on how it advances the criminal justice system. Note that the practice promotes informed decision-making. What this means is that it not only prevents or reduces crimes but also improves both the quantity and quality of arrests, as well as support how law enforcers determine the criminal liability of suspected offenders.
1.7 PROJECT ORGANISATION
The work is organized as follows: chapter one discuses the introductory part of the work, chapter two presents the literature review of the study, chapter three describes the methods applied, chapter four discusses the results of the work, chapter five summarizes the research outcomes and the recommendations
CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 OVERVIEW OF THE STUDY
The role of computers has been increased in all walks of life from the finance sector to supermarkets. In recent years police forces have been enhancing their traditional method of crime reporting with new technological advancements to increase their output by efficiently recording crimes to aid their investigation (Adderley and Musgrove 1999). Data is not just a record of crimes, it also contains valuable information that could be used to link crime scenes based on the modus operandi (MO) of the offender(s), suggest which offenders may be responsible for the crime and also identify those offenders who work in teams (offender networks) etc. In today’s world, computers are playing a major role in the investigation of all types of crime from those that are considered as volume crime (burglary, vehicle crime etc.) to major crime such as fraud, drug trafficking, murder etc.
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