Analyzing Chicago Crime Data Using Watson Analytics Software

Project Description

This project is aimed at analyzing crime data from the Chicago Police Department concerning the reported crimes from the year 2012. This will be accomplished by use of the Watson Analytics software. The analysis will help to give an informed decisions and lay strategies on improving the security of the Chicago city.

This project entails use of Watson analytics software to analyze data obtained from the Chicago police department. The dataset to be analyzed is about the reported crimes in Chicago from the year 2012. The project aims analyzing the Chicago crime data, present the findings in a clear manner and then provide an automated predictive analysis by analyzing the data in order to get an overview of the crimes in Chicago from the year 2012 so as to come up with informed preventive and corrective measures of curbing crime cases in Chicago so as to increase the safe city index of the City.

The dataset given entails different aspects of crime cases in Chicago city reported to the Chicago Police Department beginning from the year 2012. The data describes different aspects of the crimes such as the day and time a crime was committed, the area where the crime was committed, the type of crime committed, the number of cases cleared in comparison with the number of reporting made among others. This details will greatly inform the Chicago Police Departments in laying strategies in an endeavor of reducing crime rate of Chicago.

The use of the Watson Analytics Software in the data analysis is a very good choice it is able to analyze big chunks of data in a user friendly manner as it uses simple language understandable by the user (Watson, 2014) It also presents it in an easily understandable manner by use of inforgraphics (Hoyt, 2014), that is in form of graphs and charts and hence ease the data interpretation and decision making process. The size and the nature of data is also not a problem once this software is used because it is able to analyze unformatted data very quickly by use of an isolation button (Epstein, 2012). The software data storage system is also cloud based making it able to store large amounts of data without a limitation of space (Kambatla, 2014).

• Due to the high number of crimes in Chicago, its safe city index is quite low (Mori, 2015).
• The crime clearance ratio in Chicago is quite low. These means that the number of arrests and prosecutions done for crime perpetrators is very minimal compared to the crimes reported (Roberts, 2008).
• Theft crime has the least crime clearance ratio while narcotics related crimes has the highest crime clearance ratio. This means that the number of theft crime perpetrator arrested and prosecuted is low as compared to that of narcotic crime perpetrators.
• The residential areas and the streets in Chicago has high prevalence of crimes. This means that these areas are the crime hot spots in Chicago.
• Reduction of crime numbers reported in Chicago from the year 2012 0onwards shows that much has been done in cubing the crimes by the Chicago Police Department (Ratcliffe, 2011).

The Business Intelligent (BI) solutions/dashboards, and in this case the Watson Analytics Software, has been chosen because of the following reasons.

• It is a powerful decision making support system tool (Clemen, 2013)- The Watson Analytics Software is very crucial in decision making process as because it helps improve human visual ability of the analyzed data hence help predictive analytics process required especially in the Chicago Police department.
• Processes unstructured data (Chen, 2012)- The Watson analytics Software has an ability to analyze unstructured data. After feeding the data into the software, the isolation button is used to analyze one type of data at a time and therefore makes it easy to analyze large data chunks hence filling human limitations of being unable to analyze large data chunks.
• The Watson Analytics software improves the whole process of data analysis by giving high quality data analyzed by presenting it in inforgraphics form, that is in graphs and charts making it very easy for the analysts to draw conclusions of the analyzed data as shown below
• Handle enormous quantities of data- The Watson Analytics Software storage is Cloud based and therefore has no limitation on data storage. This makes it possible for it to store and analyze large chunks of data for a very long time. For instance, for the Chicago Police Department, it has been used to handle the information of 361,741 districts
• It is customizable (Hart, 2011)- Depending on the data available and required analysis, the software is not fixed but rather can be customized by the user to fit their requirements. For instance, the Chicago Police Department has analyzed the crime cases reported in terms of type, days and area of occurrence.

Data Analysis Method

Based on the result from the analysis of the data, I would like to make the following recommendations that will help improve the Operations of the Chicago Police Department.

• Increase the number of personnel on patrol in the streets and the resident areas because these are the crime hotspots as shown by the figure below
• Creation of special security force to handle the theft crime cases in Chicago would help in increasing the theft crime clearance rate which is currently very low as shown by the figure below
• There is high prevalence of crimes in Chicago city. This leads to a low safe city index of the city. In order to increase the safe city index, the Chicago police department should work closely with other bodies such as Drug Enforcement Administration to reduce some crimes such as narcotics crimes.
• Security should be made a bit tight on Sundays by probably increasing the number of police on patrols as this is the day when most of the crimes are committed as shown below
• The above diagram also shows that there is not a single day when crimes are not committed. Therefore, regular patrols on each day should be done in the city of Chicago including at the cemetery and the forest reserve areas as there are still high levels of crimes in this areas as shown below

Lighting up these areas could also be a method of reducing crime cases.

I am currently working for Federation University Australia as a data analyst. I undertook the project of analyzing the big data on crimes reported and recorded by your security department. The information given to me was comprehensive giving different aspects of crimes such as the crime hot spots, the crime clearance rates, the crime prevalence, crime type, day of crime commission among others. After the data analysis, I have made different observations that will be very crucial in helping you come up with informed comprehensive strategies in reducing the crimes in Chicago so as to improve on the its safe city index. Some of the insights and their recommendations are listed below.

• The crime clearance rate of the city is very low. Much more need to be done to ensure that the cases reported are followed up and perpetrators arrested, prosecuted and cases cleared. This will help a great deal in reducing crimes in Chicago hence improve its safe city index
• Theft is the principle damage crime in Chicago. However, there is usually very little theft crime cases cleared. This calls for improvement in theft crime perpetrators hunting operations. This can be done by forming a special security unit to do this job.
• Narcotics crimes are very high in Chicago, working closely with drug fighting bodies such as drug enforcement administration will help reduce these crimes.
• The streets and the residential areas are highly affected by crimes. Increase of patrol in this areas will help cub this.

It is my sincere hope that the information I provided plays an important role in helping you improve the security of Chicago. May it also please you to consider the recommendations I have given to you.

Using the Watson Analytic Software has imparted us with knowledge about its usage that we initially didn’t have. We have also learnt a lot and improved on big data analysis skills that will be very crucial for us in the course of our future leaning and application. The project was however challenging as much effort and time had to be applied in order to learn how to use the new software and also in coming up with conclusive conclusion of the data after analysis.

Conclusion

Data analysis using the Watson Analytic Software has given a very comprehensive data analysis and presented it in an eye catching inforgraphics making the whole process of predictive analysis for the Chicago police department. From the analyzed data about the crimes in Chicago, different observations, insights and recommendations has been given in order to improve the operations of the Chicago Police department.

References

Watson, H.J., 2014Tutorial: Big data analytics: Concepts, technologies, and applications. CAIS, 34 , 5^th ed. Georgia:  p.65.

Epstein, E.A., Schor, M.I., Iyer, B.S., Lally, A., Brown, E.W. and Cwiklik, J., 2012. Making watson fast.  IBM Journal of Research and Development, 56(3.4), 56^th ed. Toronto: pp.15-1.

Kambatla, K., Kollias, G., Kumar, V. and Grama, A., 2014. Trends in big data analytics.  Journal of Parallel and Distributed Computing, 74(7), 74^th ed. Minnesota: pp.2561-2573.

Hoyt, R.E., Snider, D., Thompson, C. and Mantravadi, S., 2016.  IBM Watson analytics: automating visualization, descriptive, and predictive statistics. JMIR public health and surveillance, 2(2) 157^th ed. New York. Pp 58

Mori, K. and Yamashita, T., 2015. Methodological framework of sustainability assessment in City Sustainability Index (CSI): A concept of constraint and maximisation indicators. Habitat International, 45, 45^th ed. Amsterdam:  pp.10-14.

Roberts, A., 2008. The influences of incident and contextual characteristics on crime clearance of nonlethal violence A multilevel event history analysis. Journal of Criminal Justice, 36(1) 36^th ed. London, pp.61-71.

Ratcliffe, J.H., Taniguchi, T., Groff, E.R. and Wood, J.D., 2011. The Philadelphia foot patrol experiment: A randomized controlled trial of police patrol effectiveness in violent crime hotspots. Criminology, 49(3), 1^st ed. Philadelphia :pp.795-831.

Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: from big data to big impact. MIS quarterly, 36^th ed. Arizona: pp.1165-1188.

Hart, W.E., Watson, J.P. and Woodruff, D.L., 2011. Pyomo: modeling and solving mathematical programs in Python. Mathematical Programming Computation, 3(3), 3^rd ed. Duke: p.219.