Reporting / Dashboards
Citizen Law Enforcement Analysis and Reporting (CLEAR) was an initiative creative by Chicago police in order to keep a track on the crimes (Kieltyka, Kucybala & Crandall, 2016). The initiative of the police has enabled the department to study crime occurrences.
In order to investigate crimes in Chicago dataset2 was used. The dataset of crimes was retrieved from data.world (Data.world, 2018). The dataset contains information of crimes in Chicago for the period of 2014 to 2017. In addition, data for the year 2017 pertains to only the first year.
Through the study of the information it is found that 99999 crimes occurred during the selected period. The crimes were divided into 32 different types of crimes. All crimes were segregated into different location descriptions. From the dataset it is found that there are 114 different location descriptions. Three crimes which had the highest frequency are Theft, Battery and Criminal Damage. Three crimes which have the lowest frequency are human trafficking, other narcotic violation and non-criminal activities. Further, it is found that the information contains crimes for four years from 2014 to 2017. From 11th to 18th January five crimes were committed. Maximum crimes were committed in 2014. The number of crimes in 2014 were 34.41K. The maximum number of crimes were committed in August. The number of crimes in August were 9.44K. In 24.83K crimes Arrest were made. According to CLEAR, Chicago has been divided into 23 districts. In 2017, district 15 reported 2 crimes. Similarly, in 2017, districts 2,6,9 and 11 reported 1 crime each. In 2017 no crime was reported from district 8. The number of domestic crimes were 15.69K. No domestic crimes were reported in 2017. The top five locations of crimes are street, residence, apartment, sidewalk and others. The number of crimes from street were 23.29K. Only one crime was reported from Parking Lot, Cleaners Laundromat, Hallway and Office. On weekends (Sundays and Saturdays) 3.51K and 3.54K crimes were reported from street. The lowest number of crime on weekends (Sundays and Saturdays) were reported from tavern and cemetery. One crime took place on each of Sunday and Saturdays at tavern and cemetery.
The advanced analysis of the dataset shows that of the 24830 domestic crimes committed in 3148 cases arrests were made.
In addition, it is found that most of the crimes on streets resulted in arrest.
The above chart presents the incidence of domestic crimes based on location. It is divulged that the maximum number of domestic crimes took place in Apartments (5.48K). In addition, approximately similar number of domestic crimes took place in residences (5.36K).
Advanced Insights
The above image presents crimes in different districts. The wards of the districts are present as stacked columns. From the analysis it is found that the maximum number of crimes happened in district 11. Moreover, under district 11, the maximum number of crimes took place in ward 28.
The above image presents the analysis of crimes in every district per year. It can be visualised that approximately equal number of crimes took place in each of 2014 to 16 in all the districts.
The present research into the crimes at Chicago has used IBM Watson Analytics. The BI tool uses NLP to provide possible visualizations (Miller, 2016). Post-processing of the visualizations is possible. Post-processing is sometimes necessary to provide a better visual impact to the processed data and also to improve the aesthetics of the image.
A pie chart is used to represent the incidence of domestic violence. Pie chart has been found to suitable when data regarding proportional information has to be depicted. They have been found to be suitable when 6 or fewer variables need to be projected (Phillips, 2015 ). Pie chart can represent both percentage of the proportion or simple the value of the variable. Moreover, with the use of different colours the differentiation of the variables becomes easy. The part of the pie represents the proportion of the variable. Thus one is able to easily discern the variable which has the highest proportion and the one having the lowest proportion (Reys, 2014).
The stacked bar chart is used to represent the incidence of crimes on weekends. Weekends are referred to as Sundays and Saturdays. In order to show both Weekends a stacked bar chart is used. The blue colour represents crimes occurring on Sundays while green colour represents crimes on Saturdays. Thus the stacked bar has been used to show individual number of crimes as part of whole crimes on weekends (Vlamis & Vlamis, 2015). We could have transformed the stacked bar chart into percentage proportions to show the percentage as part of 100% (Munzner, 2014).
Recommendation 1: Location of the Crime
Analysis of the location description of crime shows that maximum number of crimes took place on the streets. The second most important location for It is also found that crimes on streets irrespective of weekends was highest on streets. Studies done by MacDonald, Klick & Grunwald (2016) have shown that there has been a rise in street crimes outside police patrol zones. The researchers had segregated street crimes into assaults, burglaries, snatching of purses, petty robberies and theft from vehicles. According to Reid et al., (2014) conducting a spatial pattern of crimes occurring in the city would show that street is most vulnerable for crimes. Thus it can be suggested that Chicago police should patrol streets more often. This could reduce the incidence of street crimes at Chicago.
Research
Recommendation 2: Vigilance against domestic violence
Analysis of domestic suggests that 15.6% of crimes are domestic in nature. Advanced analysis has suggested that most of the domestic crime do not lead to arrest. Thus the nature of domestic crime is non-criminal in nature. Research done by Root & Brown (2014) points to the fact that the incidence of domestic criminal activities is series in Asian American communities. They have not taken into account social factors which influence the frequency of domestic crimes in western societies. CLEAR initiative needs thus needs to isolate domestic crimes based on cultural distinction. Straus, Gelles & Steinmetz (2017) have studied nature and causes of domestic violence. The researchers have found that domestic violence may occur due to one of or a combination of factors like poor family functioning, economic and financial problems in the family and or week community sanctions.
The analysis of the crimes during the period of 2014-17 shows that a total of 99999 crimes took place. The CLEAR initiative showed that the highest frequency of crimes were theft, battery and criminal damage. Thus it can be envisioned that Chicago police should concentrate most on theft and battery crimes. The analysis showed that the incidence of crime has decreased from 2014 to 16. Thus it suggests that CLEAR initiative of Chicago police is showing results. There has been a decrease in incidence of crimes.
Moreover, it is found that the highest incidence of crimes takes place in the months of July and August. the incidence of crimes increases from February and peaks in August. it then reduces till February. Thus it is found that there is a cyclic pattern in crimes at Chicago.
The analysis of the data elucidates that the incidence of domestic crimes is only 15.6%. Moreover, the advanced analysis has shown that arrests in domestic crimes is low. Further research needs to be carried out to investigate the cultural background of crimes. This is more relevant since research has shown that domestic crimes change with cultural background of the person (Montoya & Rolandsen Agustín, 2013).
We found that on weekends also the incidence of crimes on streets does not reduce. In fact, frequency of crimes on weekends on streets, residences, apartments and sidewalks are similar for both the days. CLEAR program should look into the occurrences of crimes on weekends. Further research needs to be done to extract information on crimes on weekends and weekdays. Further research would provide into cultural, financial background of people committing crimes. This would help CLEAR program to provide rehabilitation services since most of the population of America prefers rehabilitation to a certain extent (Santana et al., 2013).
Recommendations for POLICE CHIEF
This was really an exhilarating experience to use an online business intelligence tool. The natural language processing (NLP) used by IBM Watson Analytics was a new experience (Hoyt et al., 2014). The online tool uses NLP through which suitable questions only needs to be asked. It was a challenge to find the suitable questions (Zhu et al., 2014). However, since the assignment had already provided a set of questions hence the wording of the questions was the only difficult job. Once the questions were asked the BI tool immediately provided a set of suitable visualizations. From the many probable visualisations the one which was most suitable had to be selected. The use of the BI tool was a beautiful experience. We could change the colour of the bars. We could also input the data on the bars. Moreover, we could use stacked bar charts also.
Reference
Data.world. (2018). Data.world. Retrieved from https://data.world/mchadhar/dataset-2-chicago-crime
Hoyt, R. E., Snider, D., Thompson, C., & Mantravadi, S. (2016). IBM Watson analytics: automating visualization, descriptive, and predictive statistics. JMIR public health and surveillance, 2(2).
Kieltyka, J., Kucybala, K., & Crandall, M. (2016). Ecologic factors relating to firearm injuries and gun violence in Chicago. Journal of forensic and legal medicine, 37, 87-90.
MacDonald, J. M., Klick, J., & Grunwald, B. (2016). The effect of private police on crime: evidence from a geographic regression discontinuity design. Journal of the Royal Statistical Society: Series A (Statistics in Society), 179(3), 831-846.
Miller, J. D. (2016). Learning IBM Watson Analytics. Packt Publishing Ltd.
Montoya, C., & Rolandsen Agustín, L. (2013). The othering of domestic violence: The EU and cultural framings of violence against women. Social Politics, 20(4), 534-557.
Munzner, T. (2014). Visualization analysis and design. AK Peters/CRC Press.
Phillips, M. (2015). TIBCO Spotfire–A Comprehensive Primer. Packt Publishing Ltd.
Reid, A. A., Frank, R., Iwanski, N., Dabbaghian, V., & Brantingham, P. (2014). Uncovering the spatial patterning of crimes: A criminal movement model (CriMM). Journal of research in crime and delinquency, 51(2), 230-255.
Reys, R. E., Lindquist, M., Lambdin, D. V., & Smith, N. L. (2014). Helping children learn mathematics. John Wiley & Sons.
Root, M. P., & Brown, L. (2014). An analysis of domestic violence in Asian American communities: A multicultural approach to counseling. In Diversity and complexity in feminist therapy (pp. 143-164). Routledge.
Santana, S. A., Applegate, B. K., Fisher, B. S., Pealer, J. A., & Cullen, F. T. (2013). Public support for correctional rehabilitation in America: Change or consistency?. In Changing attitudes to punishment (pp. 146-165). Willan.
Straus, M. A., Gelles, R. J., & Steinmetz, S. K. (2017). Behind closed doors: Violence in the American family. Routledge.
Vlamis, D., & Vlamis, T. (2015). Data Visualization for Oracle Business Intelligence 11g. McGraw-Hill Education Group.
Zhu, W. D. J., Foyle, B., Gagné, D., Gupta, V., Magdalen, J., Mundi, A. S., … & Triska, M. (2014). IBM Watson content analytics: Discovering actionable insight from your content. IBM Redbooks.
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