Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-the-art data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process.

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Big data analytics and mining for effective visualization and trends forecasting of crime data

Crime analysis and prediction is a systematic approach for analyzing and identifying different patterns, relations and trends in crime. In this paper we conduct exploratory data analysis to analyze criminal data in San Francisco, Chicago and Philadelphia. We first explored time series of the data, and forecast crime trends in the following years. Then predicted crime category given time and location, to overcome the problem of imbalance, we merged multiple classes into larger classes and did feature selection to improve accuracy. We have applied several state-of-the-art data mining techniques that are specifically used for crime prediction. The experimental results show that the Tree classification models performed better on our classification task over k-NN and Naive Bayesian approaches. Holt-Winters with multiplicative seasonality gives best results when predicting crime trends. The promising outcomes will be beneficial for police department and law enforcement to speed up the process of solving crimes and provide insights that enable them track criminal activities, predict the likelihood of incidents, effectively deploy resources and make faster decisions.

Big Data Analytics and Mining for Crime Data Analysis, Visualization and Prediction

Context: Crime is a prevalent social problem, then, government and society, in general, have had enormous problems caused by this phenomenon. However, with the increasing ability to collect and sto...

Applied Intelligent Data Analysis to Government Data Related to Criminal Incident: A Systematic Review

Crime Predictive Model Using Big Data Analytics

The analysts belonging to the police forces are obliged for exposing the complexities found in data, to help the operational staff in nabbing the criminals and guiding strategies of crime prevention. But, this task is made extremely complicated due to the innumerous crimes, which take place and the knowledge levels of recent day offenders. Crime is one of the omnipresent and worrying aspects concerning society, and preventing it is an important task. Examination of crime is a systematic means of detection as well as an examination of crime patterns and trends. The data work involving includes two important aspects, analysis of crime and prediction of perpetrator identity. Analysis of crime has a significant role to play in these two steps. Analysis of the crime data can be of massive help in the prediction and resolution of crimes from a futuristic perspective. To avert this issue in the police field, the crime rate must be predicted with the help of AI (machine learning) approaches and deep learning techniques. The objective of this review is to examine the AI approaches and deep learning methods for prediction of crime rate that yield superior accuracy, and this review article also explores the suitability of data approaches in the attempts made toward crime prediction with specific predominance to the dataset. This review evaluates the advantages and drawbacks faced by crime data analysis. The article provides extensive guidance to the evaluation of model parameters to performance in terms of prediction of crime rate by carrying out comparisons ranging from deep learning to machine learning algorithms.

A Thorough Analysis of Machine Learning and Deep Learning Methods for Crime Data Analysis

Data mixed up with discrete and continuous features makes negative affect to the classification for existing models which discretizes the continuous features or even without any treatment is not able to deal with it. The noise which is not subject to Gaussian distribution also severely affected the result of classification. In this paper, a deep neural network mixed with random forests, Stacked Denoising Autoencoder and Multilayer Perception is proposed to improve the classification effect of mixed features input. Furthermore, to improve pre-training effect of the Stacked Denoising Autoencoder, Perlin noise is added to the Gaussian Noise in noise layer. A certain promotion of this method has been proved through the experiments with the crime data between 2003 and 2015 in San Francisco, and comparisons by 10-fold cross-validation with other common methods of classification shows that this method has advantages on data mixed up with discrete and continuous features.

Hybrid Neural Network mixed with Random Forests and Perlin noise

The invention discloses an optimization method for mapping virtual machines to physical machines. The method comprises the following steps of S1, modeling virtual machine demands; S2, determining a threshold; S3, establishing a priority queue; S4, carrying out batch dequeuing; S5, designing an optimization kernel; and S6, carrying out algorithm parallelization. According to the optimization methodfor mapping the virtual machines to the physical machines provided by the invention, the validity and low delay property of the algorithm are improved. According to the method, allocation positions of the virtual machines can be timely and effectively determined according to the specific virtual machine demands; and when the virtual demands are dynamically changed, the targeted dynamic adjustmentis carried out for the mapping from virtual machine resources to physical machine resources. In an application process, a utilization rate of cloud resources can be improved, and the operation cost of a cloud service provider is reduced.

Fengqin Zhang

Papers

Hybrid Neural Network mixed with Random Forests and Perlin noise

Optimization method for mapping virtual machines to physical machines