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

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

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

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

CRIME DATA PREDICTION BASED ON GEOGRAPHICAL LOCATION USING MACHINE LEARNING

This project employs machine learning methods like K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree algorithms to monitor crime data based on location and pinpoint areas with risks. The project implements and tunes the four models to improve the precision of predicting crime levels. These models collaborate to offer a trustworthy evaluation of crime patterns. K Nearest Neighbors (KNN) categorizes locations by examining the proximity of data points considering coordinates and other factors to identify trends linked to increased crime data. Logistic Regression gauges the likelihood of crime incidents by studying the connection, between factors (like location and time ) and the crime activity, assisting in forecasting crimes in various regions. Decision Tree Classifier uses a tree structure to make decisions based on feature values dividing the data into branches representing decision paths. This approach is particularly useful for identifying high-risk areas using crime data. Random Forest Classifier constructs decision trees and combines their results for classification purposes, resulting in enhanced prediction accuracy and robustness by merging outcomes from multiple trees, thus reducing the risks of overfitting and improving generalization to unseen data. The system’s efficiency is assessed using a crime dataset that includes information, about crime occurrences, geographical locations, and time-related data. Metrics, like accuracy, precision, and recall are employed to assess the model’s ability to anticipate crimes and identify hotspots accurately

Predictive Policing

UAE is one of the safest countries to live in, but that does not indicate that the country does not witness crimes, During the COVID-19 pandemic, the country saw an increase in cyber and digital crimes. Apart from cybercrime, there are other types of crimes, such as street crimes and violent crimes. Data analytics aids Dubai Police to predict crimes. Criminal investigation is one of the fields that is very interesting and is taught in colleges and academies. Data analytics opens the door for studying the details of each crime. Data mining tools consist of a variety of techniques that can help solve a problem or indicate a cause or an effect of something. Data analysts use data mining tools through a lot of software that allow the user to analyze data easily and fluently. SAS (statistical analysis system) is one of the reputable software that is used especially for visualizing and analyzing data. In this capstone, we will use SAS since it is a software that is accredited from Dubai Police and we use it already in our workplace. Prediction techniques supports to interpret and facilitate Dubai Police to develop strategies to reduce the crime rate. Hence, it allows UAE to sustain its position as the “safest” country. The capstone idea will actually help us develop what we do at work and stop or reduce crime which is one of the main pillars in Dubai Police. The crime related data will be collected from CID in Dubai police. Link analysis and predictive analysis will be performed in this project to forecast any crime. We will build a predictive model using SAS to predict crime. This proposed project will help to identify the trends of historical crime data. Project timeline has been provided in this writing to have a better outline. The first step is to collect the data from the source which is in our case, the criminal investigation department in Dubai Police. Meeting with the department; they have agreed on giving us datasets of specific crimes that Dubai Police finds critical and needs further analysis from five years. Thus, the data that we will be analyzing will be from the years 2017 to the year 2021. After collecting the data ; the processing took place which is the cleaning part of the data. Since the data is in Arabic and it is old as mentioned earlier that the data of the past five years are collected; there are some missing fields, some inconsistencies and some redundant data. After cleaning the dataset which took 70% of the time working on this project. Now the dataset is ready and can be analyzed in SAS. Importing the dataset through SAS was the first step. Then, we started analyzing the criminals first as we wanted to build a portfolio of the criminals and observe of any patterns found. The highest nationality of the criminals was India. We tried to see if there are higher nationalities in certain years, but in all five years the analysis showed that India was the number one nationality in criminals. Then we wanted to observe the criminals’ education level; the highest education level was unemployed meaning they do not have any degree that supports them. The education level part was very interesting because we found out that even though university degrees did not come first in the highest education level. however there is a sample of the criminals that hold very high level degrees such as PhDs and Masters degrees and this shows us that the stereotype of how uneducated people are bad or are the only people that commit crimes should be disregarded. Next , we analyzed the criminals’ age group and the outcome was that 30 – 45 age groups are the ones that commit crimes the most in Dubai. Finally, we have analyzed the criminals’ gender to see which gender commits most crimes in Dubai and from our analysis; the outcome showed that men are the most that commit crimes in Dubai. After analyzing the criminals’ profiles ; we have moved on to analyzing the crimes in the past five years. The type of crime was the first thing we wanted to analyze to observe what is the most crime committed in Dubai in the last five years. Fraud was the most crime committed in Dubai and this was not a huge shock to us since Dubai is considered a business city and it attracts some people to do their business in it. Dubai has always been interested in building the city financially in the best , legal way possible, however there will always be people that see it as a city to commit fraud in since it has a large population and has many tourists visiting the city. Next, we analyzed the crime replotting per year. 2019 has scored the highest in crime reporting in Dubai; right before the pandemic. We analyzed the police stations that had the most reporting in the past five years in order to observe the locations that are considered crime appealing to criminals. This analysis is very important since every area has a police station assigned to it and the outcome of this analysis was that Bur Dubai police station had the highest number of incidents in the last five years. Lastly, we wanted to analyze what time was the crime committed and the result was that most crimes have been committed in the morning between 9AM and 11AM and that was very shocking and interesting to us because it is know globally that most crimes are committed at night in the dark where no one can see the criminal , but this is due to the type of crime as well , and as we have observed that fraud is the most committed crime, then the morning is the best time to commit this crime since people are awake and willing to do business with other people whether it was online or offline. Finally, the purpose of this whole project is to forecast the crime rates; thus, we built a forecasting model in SAS and it showed us that in the upcoming years, the crime rates in Dubai will decrease dramatically based on the pattern of crimes in the historical data. This is a positive result; however this does not mean that Dubai Police should neglect the surveillance and monitoring of the city due to this forecasting as it is not always accurate

The use of big data and data mining in the investigation of criminal offences

The aim of this study was to determine the features and prospects of using Big Data and Data Mining in criminal proceedings. The research involved the methods of a systematic approach, descriptive analysis, systematic sampling, formal legal approach and forecasting. The object of using Big Data and Data Mining are various crimes, the common features of which are the seriousness and complexity of the investigation. The common tools of Big Data and Data Mining in crime investigation and crime forecasting as interrelated tasks were identified. The creation of databases is the result of the processing of data sources by Data Mining methods, each being distinguished by the specifics of use. The main risks of implementing Big Data and Data Mining are violations of human rights and freedoms. Improving the use of Big Data and Data Mining requires standardization of procedures with strict adherence to the fundamental ethical, organizational and procedural rules. The use of Big Data and Data Mining is a forensic innovation in the investigation of serious crimes and the creation of an evidence base for criminal justice. The prospects for widespread use of these methods involve the standardization of procedures based on ethical, organizational and procedural principles. It is appropriate to outline these procedures in framework practical recommendations, emphasizing the responsibility of officials in case of violation of the specified principles. The area of further research is the improvement of innovative technologies and legal regulation of their application