The comparative study was conducted using WEKA, an opensource tool. Violent crime trends from the dataset of Communities and Crime Unnormalized and real-time crime statistical data based on three methods, namely linear regression (LR), additive regression (AR), and decision stump (DS), was constructed utilising similar limited sets of characteristics for demonstrating the efficacy of ML approaches in predicting violent crime patterns of criminal hotspots, the test samples were chosen at random. The linear regression (LR) algorithm shows appreciable results among the listed algorithms and tolerates unpredictability in the test data to some extent [16].
The crimes of house burglary, street robbery, and battery were examined retrospectively using an ensemble model to synthesize the findings of logistic regression (LR) and neural network (NN) frameworks using the predictive analytic approach to produce fortnightly and monthly forecasts (based on previous three years of cybercrime datasets) for the year [1]. The authors came to the conclusion that comparing fortnightly forecasts of monthly analysis predictions with splits into day-night datasets significantly enhanced the outcomes. ML was used to examine crime predictions. For the purpose of prediction, crime statistics from the previous 15 years in Vancouver (Canada) were studied. The accumulation of data, data categorization, pattern recognition, prediction, and visualisation are all part of ML-based criminal investigation. The crime dataset was further analyzed using boosted decision tree (BDT) and K-nearest neighbor (KNN) methods. In a separate but similar research, [17] and [18] looked at 560,000 crime statistics from 2003 to 2018 and found that using ML algorithms for crime prediction, the studies predicted crime with an accuracy of 44 per cent to 39 per cent respectively.
The crime dataset from Chicago, the United States. ML and data science (DS) approaches were applied to predict crime details consisting of parameters (scene positioning, type, date, time, and coordinates). Decision trees (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), and Bayesian techniques (BT) are used, with the most accurate model training. With an accuracy of about 0.787, the KNN classification proved to be the most accurate. The authors also utilised several graphics to assist in comprehending the various features of the Chicago crime dataset to better anticipate, identify, and solve crimes, resulting in a reduction in the crime rate.
Data (taken from Chicago crime statistics, demographic and climatic data) accumulation, data preprocessing, predictive model development, dataset training, and testing are included in the proposed system to demonstrate the efficacy of the ML system to forecast violent behaviors, and crime incidences, and precise attributes of criminals. A deep neural network (DNN) [1] forecasts crime attributes and occurrences by combining feature-based multi-model data from the environmental context.ML approaches like regression analysis (RA), kernel density estimation (KDE), and SVM is used in crime prediction systems.
Figure: 4 Dataflow diagram
The suggested DNN has an accuracy of 84.25 %, whereas the SVM and KDE have an accuracy of 67.01 % and 66.33 %, indicating that the suggested DNN was much more accurate than the other prediction models in predicting crime occurrences [4]. The data were analysed and interpreted using approaches such as Bayesian neural networks (BNN), and the Levenberg Marquardt algorithm (LMA) [9], and a scaled algorithm, with the scaled algorithm outperforming the other approaches. Statistical analysis revealed that using the scaled method, the crime rate could be reduced by 78 %, implying an accuracy of 0.78.
RapidMiner was used in a prediction study utilising ML and historical crime trends in data collection, preparation, analysis, and visualization in the four primary visualisation studies [6]. Big data (BD) offers a high throughput and fault tolerance, analyzing huge datasets and providing accurate findings, whilst the ML-based naive Bayes (NB) method can make superior predictions with the existing datasets. Various DM and ML methods utilizable singminal investigations are presented [5]. Our paper makes a contribution by emphasizing the techniques utilized in crime data analytics.
The grid-based crime forecasting framework created a series of spatial-temporal characteristics for a city in Taiwan based on 84 identified geographic locations for anticipating crime in the next slot (month) for every grid. DNN was determined to be the best model among the numerous ML techniques, particularly for a feature and attribute learning [19]. Furthermore, the suggested model architecture exceeded the baseline in terms of crime displacement testing.
Figure: 5 Functionality of proposed approach