Systematic Literature Review of Crime Prediction and Data Mining (original) (raw)
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International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Crimes are treacherous social problems which are faced worldwide. It is the most serious and predominant issue of our society. It affects various key features bound to the society and an individual's life like the quality of life, reputation, economic growth and societal safety. With the advancement of technology there is an enormous growth in the crime rate. There are laws that have been enforced to take preventive measures but there is a need for advanced approaches for protecting the society and the individuals in the society. Hence analyzing of crime helps in detecting the patterns and trends in crime. The real time crime predictions will be a helping hand to reduce crime rate. Various visualization approaches and algorithms are used in this study to anticipate the distribution of crime in a given area. This paper's pre-processed dataset was obtained from Kaggle. After that, the system is trained using the ARIMA model. The next step is Visualization of data after which the result is predicted after analyzing the data with many machine learning calculations and modules.
A Survey Paper on Crime Prediction Technique Using Data Mining
2014
Crime prediction is an attempt to identify and reducing the future crime. Crime prediction uses past data and after analyzing data, predict the future crime with location and time. In present days serial criminal cases rapidly occur so it is an challenging task to predict future crime accurately with better performance. Data mining technique are very useful to solving Crime detection problem. So the aim of this paper to study various computational techniques used to predict future crime. This paper provides comparative analysis of Data mining Techniques for detection and prediction of future crime. KeywordsCrime Data, Crime Prediction, Data Mining Technique, Predictive Accuracy.
Crime Analysis and Prediction Using Data Mining
Crime analysis and prediction is a systematic approach for identifying and analyzing patterns and trends in crime. Crime analysis is an area of vital importance to the police department. Study of crime data can help the police department to analyze crime patterns, interrelated clues and important hidden relations between the crimes, that is why data mining can be of great aid to analyze, visualize and predict crime using crime data sets. [1] Classification and correlation of data sets makes it easy to understand similarities & differences amongst the data objects. The focus is on criminality of places rather than the tracing of individual criminals. The main users of the system will be the police force who from time to time shall be able to predict the possibility of crime occurrence in the nearest future as well a particular time the crime will likely occur. In this paper we basically look at kmeans clustering algorithm for data mining and use it to generate hotspots of criminal activity and also use it to predict the chance of crime occurring in the nearest future. This analysis may help the law enforcement of the country to take a more accurate decision for example allocation of more resources like police officers in the crime prone areas [2].
Crime prediction with machine learning
Crime prediction with machine learning
This study aggregates, summarizes, and evaluates the spatial-temporal crime prediction and detection techniques through conducting a Systematic Literature Review (SLR). A systematic Literature review identifies, selects and critically appraises research in order to provide a solution to a well formulated problem. (A, 2016)) . [The systematic review follows a clearly defined protocol or a plan where criteria is clearly stated before the review is conducted]. It was possible to find a comprehensive study on crime hotspot detection and prediction while conducting the SLR. However, there was an attempt to critically analyze the existing literature reviews while also studying the challenges facing the current crime prediction and detection systems. A thorough study was done in determining the type of operations and undertakings carried out by major security bodies in Kenya which included; The Kenya police Service (KPS), Directorate of Criminal Investigation (DCI), National Intelligence service (NIS) and National Crime Research Centre (NCRC). A keen evaluation on the techniques used by the NCRC was performed to obtain vital information on crime and how the body manipulate data to gain useful insights on crime. It was found out clearly that NCRC perform crime research and study across the country after every two years. The claim is that, one year is used for collection of data from across the country based on public opinion on crime while the other year is used for analysis of crime data. With this type of technique the current data being used may not be up to date or even sufficient and accurate enough for accurate prediction of crime. Furthermore, the whole process of crime data analysis is not a real time process and thus it is rendered infective. A study on other different sources pertaining the studies on crime hotspot detection and prediction was also performed. These included the use of machine learning and data analysis; more specifically on the use of Classification & Clustering Algorithms like the KK-means, Decision trees, Time series Analysis and the Bayes theorem in predicting crime incidences. Hereby, it is presented in great details how the fore mentioned techniques can be used in predicting crime proactively.
Crime Forecasting Using Data Mining Techniques
2011 IEEE 11th International Conference on Data Mining Workshops, 2011
Crime is classically "unpredictable". It is not necessarily random, but neither does it take place consistently in space or time.
Crime Analysis and Prediction Using Data Mining – CAP a Survey
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.
CRIME ANALYSIS AND PREDICTION USING MACHINE LEARNING
Crime is one of our society's most serious and pervasive problems, and preventing it is a critical duty. This necessitates keeping note of all offences and creating a database for future reference. The present issue is keeping a reliable crime record and analysing this data to aid in the prediction and resolution of future crimes. The objective of this paper is to analyze dataset, which consist of numerous crimes and predicting the type of crime, which may happen in future depending upon various conditions. In this project, we will be using the technique of machine learning and data science for crime prediction of Indian crime data set. Crime analysis and prediction is a methodical way to spotting crime. This algorithm can anticipate and depict crime-prone areas. Using the notion of machine learning, we may extract previously unknown, meaningful information from unstructured data. The extraction of new information is anticipated using current datasets. Crime is a perilous and widespread societal issue that affects people all around the world. Crime has an impact on people's quality of life, economic prosperity, and the nation's reputation. To safeguard their communities from crime, modern technology and novel techniques to enhancing crime analytics are required. We present a system that can analyse, identify, and forecast various crime probabilities in a given location. This study describes many sorts of criminal analysis and crime prediction using machine learning approaches.
Crime Pattern Analysis, Visualization And Prediction Using Data Mining
International Journal of Advance Research and Innovative Ideas in Education, 2015
Crime against women these days has become problem of every nation around the globe many countries are trying to curb this problem. Preventive are taken to reduce the increasing number of cases of crime against women. A huge amount of data set is generated every year on the basis of reporting of crime. This data can prove very useful in analysing and predicting crime and help us prevent the crime to some extent. Crime analysis is an area of vital importance in police department. Study of crime data can help us analyse crime pattern, inter-related clues& important hidden relations between the crimes. That is why data mining can be great aid to analyse, visualize and predict crime using crime data set. Classification and correlation of data set makes it easy to understand similarities & dissimilarities amongst the data objects. We group data objects using clustering technique. Dataset is classified on the basis of some predefined condition. Here grouping is done according to various ty...
Survey on Crime Interpretation and Forecasting Using Machine Learning
IRJET, 2022
Understanding the crime pattern and taking safety measures to solve the crime problems has become the important factor in today's world. The main aim here is to locate the crime location and find the pattern based on the time and place in Bangalore city. There is a lot of rise in the software systems which helps the officers to solve the crime issues. We will be looking at the various machine learning algorithms which helps to detect the crime pattern and helps in solving the crime in very less time. There are different algorithms in machine learning like k-means clustering algorithm which can be used in the process of identification of crime. The Data mining approach is used to predict the features of crime dataset that affects the high crime rate. There are two types of algorithms i.e., supervised and unsupervised, supervised algorithms that is used to evaluate the training dataset for its accuracy and unsupervised algorithms helps in solving the unlabelled data into classes or clusters. There are other few different learning methods like random forest in data mining based on unknown and the previous year collected datasets which can be used for predicting the crime pattern based on the time and place.
Crime Pattern Detection, Analysis & Prediction using Machine Learning
Criminal analysis is a methodical approach for identifying and analyzing patterns and trends in crime. With the increasing origin 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, system can analyze previously unknown, useful information from an unstructured data. Predictive policing means, using analytical and predictive techniques, to identify criminal and it has been found to be pretty much effective in doing the same. Because of the increased crime rate over the years, system will have to handle a huge amount of crime data stored in warehouses which would be very difficult to be analyzed manually, and also now a day's, criminals are becoming technologically advance, so there is need to use advance technologies in order to keep police ahead of them. In this paper, the main focus is on the review of algorithms and techniques used for identify the criminals.