Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Mingchen Feng,Jiangbin Zheng,Xiuxiu Li,Yue Xi,Qiaoyuan Liu,Amir Hussain,Jinchang Ren

doi:10.1109/access.2019.2930410

Abstract

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.

Highlights

In recent years, Big Data Analytics (BDA) has become an emerging approach for analyzing data and extracting information and their relations in a wide range of application areas [1]
The results showed that Prophet model and LSTM model performed better than traditional neural network models as demonstrated in table. 1 that neural network seems has lower Root Mean Square Error (RMSE) but the correlation between predicted values and the real ones is low
By exploring the Prophet model, a neural network model, and the deep learning algorithm LSTM, we found that both the Prophet model and the LSTM algorithm perform better than conventional neural network models

Summary

Introduction

Big Data Analytics (BDA) has become an emerging approach for analyzing data and extracting information and their relations in a wide range of application areas [1]. Due to continuous urbanization and growing populations, cities play important central roles in our society. Such developments have been accompanied by an increase in violent crimes and accidents. To tackle such problems, sociologists, analysts, and safety institutions have devoted much effort towards mining potential patterns and factors [34]. In relation to public policy there are many challenges in dealing with large amounts of available data.

Objectives

Results

Conclusion