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Itech 7407 report

The document analyzes crime data from Chicago between 2012-2016 using an analytics tool to create a dashboard for business intelligence teams and CEOs. It collects data on crime types, locations, dates and outcomes. Analysis finds theft and battery as the most common crimes. It also shows crime rates by month and area to identify patterns. The dashboard will visualize this analysis to help authorities address high crime areas and periods to improve public safety.

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Crime Data Analysis using SAP Expert Analytics Tool
Executive Summery Crime is one of the primary issues for which the nation cannot stay in rest and peace. Crime is the general scenario of most of the nations. The fact that make the nation sensitive is the high amount of crime. The police and the intelligence bureau are there in every nation to protect the nation from such crime. But in most of the cases it can be seen the failure of the police and agencies for the lack of information and due to non-organised action against those. This make the nation with the execution of different types of crimes as the criminals can see the misprediction and the mis witness of the criminal booking records and hence they can move forward easily (Félix Mata, 2016). But some of the nation perform better and hence they are able to diminish or at least decrease the count for the crime. The feature of the criminals is they can see and understand the steps taken by the police and government and soon they approach to them, the criminals can flee away. There is some other measure available for the intelligence bureau through which they can check the activity without directly watching them and that is the prediction of the crimes. This can be possible if the required data for the crime and the criminals will be available and almost all the country generally stores the records. If the predictive analysis will be done on the data, the generated outcome can show the probable outcome of the crime in different places and for the years also (H. Zhang, 2015). With watching this, the Intelligence bureau can take steps and probably prior to the happening of the crime as they are executing their operation based on prediction. This one if helpful to the government agencies as they have exerted less labour and so they can focus upon the operation and actions to be taken against the criminals (D. Reis, 2006). The predictive analysis should be multidimensional and hence comprises of the analytical view of different crimes with respect to the location and the year and on the basis of the number of crime happenings. Now a day, there are many options are open to make the predictive analysis (Jensen, 2013). The predictive analysis may consist of the coding or may not be as there are several Business Intelligence tools are available, through which the analysis upon the data can be done with less labour and with high accuracy. In this paper, the crime analysis will be performed on the basis of the collected data of crime and the dashboard will be shown which will display the analytical outcome of the crime at that nation and will eb helpful to the Business Intelligence team and the CEO of the organizations who are about to initiate the action against crime (E. Galbrun, 2016).
Table of Contents Introduction................................................................................................................................ 4 Data Collection........................................................................................................................... 4 Tool Selection............................................................................................................................ 10 Dashboard and Analysis ........................................................................................................... 10 Recommendation to CEO ......................................................................................................... 13 Cover letter to CEO.................................................................................................................. 19 Conclusion................................................................................................................................ 20 References................................................................................................................................. 20
Introduction In this paper, the analysis of crime is performed. As the crime is the growing problem of the nation in almost all over the world, this needs to be analysed with an objective to rectification of the nation from such crimes and the criminal activities as well. With such goal of the work, the crime data is collected and hence the data will be analysed to view the statistics of crime so that the outcome can be recommended to the Business Intelligence team and the CEO of the organization who want to check the analysis for taking proper steps against such crimes (C.-H. Yu, 2014). Hence the analysis along with the visualizations are done on the basis of the data and with all those analytical outcomes, the Dashboard is done to show the view of the crime result. The dashboard is done so that all of the analysis and the visualizations can be available in same place and the CEO need not to swipe for multiple visualization as it is the problematic if the CEO want to compare the result and check among the analysis (T. Wang, 2013). Dashboard provides the compact view of the analysis of the data through which the outcome can be predicted and hence the CEO can make the decision for the relevant actions to be taken for the elimination of the crime. So, after doing the analysis using the analytical tool, the dashboard will be sent to the Business Intelligence team that is lead by the CEO with the respective data so that the objective of the both side can be fulfilled and the CEO will be get helped of the analytical view of the data by watching the visualization (D. Reis, 2006). This the dashboard can be helpful to the Business Intelligence team and the CEO as well for taking the proper action against the crime and hence they can proceed for the relevant steps. The next section will be dealing with the data collection for the crime and the description of the data is given to understand the insight of the data (Félix Mata, 2016). Data Collection For the analysis of crime, the crime data for Chicago is chosen. As Chicago is one of the renowned cities in United States of America and the crime factor is high in that city. So, the data for the crime is Chicago has the relevancy for the analysis. Hence the data is collected for the period of 2012 to 2016 and the analysis of the data will reflect the outcome of required operation (Leitner, 2013). The data has its several parameters like:  CrimeID  Case Number  Date  Month  Day  Block  IUCR  Primary Type  Description  Location  Arrest  Domestic  Beat  District
 Ward  Community Area  FBI Code  X Coordinate & Y Coordinate  Latitude & Longitude  Location  Year  Updated On Those attributes have their own meaning and those are helpful for the analysis (M. H. Bhuyan, 2014). The briefs of the important parameters are described below: CrimeID: This shows the type of crime that are recorded and hence will be applicable to be counted for the occurrence with respect to the location, year and districts. Primary Type: This shows the type of crimes have been taken place and hence can be identified by the Crime ID. Arrest: This attribute shows the status of arrest with respect to the crime occurred. Beat: This attribute shows about the activity of the authority and is helpful to create the analysis wit respect to the Crime ID and other related parameters. District, Ward: This attribute basically shows the location of crime and hence is useful for the analysis of crime in district and ward wise to see the insight of the happenings of the crime. Year: As per the name of the attribute, it shows the records of the crimes and the other parameters to be analysed with respect to it to count the frequency. So, with those attributes, the data analysis will be done. Primarily, the crime happenings are computed with respect to the month to see the crime occurrence per month and hence it will show the name of the month for high crime (N. Padhy, 2012). This will be helpful to predict for the sensitive month and the BI team can understand about their steps. The table of data is shown below: Table-1: Analysis for Crime Month basis Crime Month wise Month Crimes Occurred January 325413452791 February 268713531675 March 273318228906 April 277083620573 May 274006906795 June 265302088752 July 272856574332 August 271524112689 September 273446839041 October 272071139249
November 269341993580 December 268421768980 After the observation of the crime scenario by month, the crime data is fetched with respect to the type of crime. This is essential to see the happening of crime. As there are different types of crime but all of those crimes are not happening with same amount and to find the highest occurance of crime, the analysis is required for future prediction such that the relevant steps can be taken to diminish such types of crime (Dhannoon, 2017). Hence, the table is shown below: Table-2: Analysis of Crime by Type Type of Crime Crime Type Crimes Occurred THEFT 751002561233 BATTERY 587599855032 NARCOTICS 368052097473 CRIMINAL DAMAGE 334806395720 ASSAULT 199907953494 OTHER OFFENSE 196617243992 BURGLARY 193033921844 DECEPTIVE PRACTICE 154255739292 MOTOR VEHICLE THEFT 138394954104 ROBBERY 124364707167 CRIMINAL TRESPASS 86794104837 WEAPONS VIOLATION 36557891705 PUBLIC PEACE VIOLATION 32510334600 OFFENSE INVOLVING CHILDREN 25533916382 PROSTITUTION 19460580047 INTERFERENCE WITH PUBLIC OFFICER 14340208312 CRIM SEXUAL ASSAULT 14289242733 SEX OFFENSE 11315282643 GAMBLING 6512039696 LIQUOR LAW VIOLATION 5047772654 ARSON 4350569172 KIDNAPPING 2693574221 STALKING 1750155860 INTIMIDATION 1520992113 OBSCENITY 381276790 PUBLIC INDECENCY 153887077 OTHER NARCOTIC VIOLATION 91701634 NON-CRIMINAL 83840055 NON - CRIMINAL 38660439 HOMICIDE 19766859
HUMAN TRAFFICKING 10110085 NON-CRIMINAL (SUBJECT SPECIFIED) 8920098 Though such types of crimes are happening in Chicago, the required data is to determine the status of arrest is shown below. It can be seen that, for most of the cases, the criminals are not arrested and thus they can find the way to execute their next crime (Jensen, 2013). With the predictive analysis is done in the next section will help the CEO and the BI Team to get the opportunity to arrest the criminals as the crimes can be detected by the predictive analysis. Table-3: Analysis for the status of arrest Status of Arrest Arrest Crime Records FALSE 2376176861896 TRUE 935323395467 The below table shows the analysis of Crime by district as it is the required parameter to find the happening of crime in different districts (C.-H. Yu, 2014). The districts are shows with the district codes. Table-4: Analysis of Crime by District District wise Crime District ID Crimes 1 129746794377 2 137187411076 3 172538653389 4 198094903257 5 152010345314 6 196186678718 7 194725973755 8 221548937215 9 161167918486 10 151036845081 11 234445870155 12 151907474919 14 116157126031 15 150947332125 16 111812921498 17 93248847602 18 138866926969 19 150441835804 20 55115340528 22 107667539589
24 93979369768 25 192561517767 31 94529791 FBI code is the allotted for the identification of the crime type and hence that is required to be analysed along with the previous issues. So, the data for the analysis is shown below: Table-5: Crime by FBI Code Crime by FBI Code FBI Code Crimes 2 15953981951 3 124364707167 5 193033921844 6 751002561233 7 138394954104 9 4286255046 10 18109525064 11 134505631912 12 641832884 13 998749432 14 334806395720 15 36557891705 16 19507210155 17 13551969417 18 350207111505 19 6529894786 20 13516260090 22 5047772654 24 42153332499 26 319300116124 01A 133124 01B 19633735 04A 49313973119 04B 76269045885 08A 152096587061 08B 511330809147 As the previous data shows the analysis for the crime types which are required to determine and predict the crime to be happened in future, another parameter which is required to show the frequency of crime that have been taken place by year and that data is shown below:
Table-6: Analysis by Year Analysis by Year Year Crimes 2012 1152478880717 2013 1113373039049 2014 1027357673759 2015 2148095646 2016 16142568192 It can be seen that the crime is decreasing by year though the crime that is seen in the last year is higher enough and the reason is already discussed with required data in the earlier section (H. Zhang, 2015). The ward wise crime is show in the next table. Table-7: Ward wise Crime Analysis Ward wise Crime Ward No. Crime 1 56603256520 2 124152541339 3 87373929855 4 53458823569 5 81862462223 6 109864474319 7 91127186174 8 93866006934 9 85283094757 10 61194844946 11 44793655576 12 40522760193 13 41447583471 14 45759380400 15 88945038771 16 93595395802 17 112749983448 18 55456806310 19 28542241927 20 104929683302 21 102000577471 22 40089600369 23 39426177393 24 141697771043 25 46685347284 26 51088283540
27 115277584672 28 168093136031 29 81003178351 30 46107758287 31 47081855884 32 52823075761 33 32200274653 34 92847818979 35 40102089283 36 31795615447 37 90534215720 38 34719610839 39 29571770454 40 34120270465 41 37769024708 42 140230575084 43 41468862334 44 52006848841 45 34321340171 46 45618718123 47 32252910552 48 30851637187 49 44249579690 50 33899159405 So, using these data, the analysis and the dashboard will be done and based on the analysis and the dashboard, the report will be forwarded to the CEO. Tool Selection SAP analytics tool is used for the analysis of the Chicago Crime data. The SAP tool is the service and tool from IBM and is capable of analysis of the big data as well any kind of data and the most precious feature of the tool is that it is capable of analysing the data using prediction and the drag and drop facility enrich the visualization technique and save the time for the analysis. Hence, the tool is useful for the analysis of the data and to prepared the dashboard. Dashboard and Analysis The dashboard is prepared using the SAP analytics and it is show below:
Fig-1: Dashboard for Crime data This dashboard will be said to be presented to the CEO by writing the cover letter. The CEO will take the decision for the future action ad hence the analysis will be described to him. So, in this section, the analysis will be described to signify the insight of the dashboard and to predict the future happening of the crime. First the no of beat by month is analysed to check the status of the number of beats to the criminals by the Chicago police. It can be seen that in January the value is highest so the month can be denoted as the sensistive one (Leitner, 2013) . The analysis s shown below: Fig-2: Analysis of beat by month Next the analysis is done for the beat value by the community area. This is required analysis to show the number of crimes happened at the general and community area to show the security aspects of Chicago (E. Galbrun, 2016). The analysis, hence, is shown below:
Fig-3: Analysis of beat by Community area In the analysis chain the third one is the analysis of the beat by the district denote the number of crimes that is happend in the districts (M. H. Bhuyan, 2014). This will help for the prediction for the crime as the police can now determine for the sensitive districts and hence e more tight there. Fig-4: Analysis of Beat by District So, in the previous three analysis shows the value of the beat that is the measures taken by the authority but apart from that there are many cases are pending for which no actions are taken yet (D. Reis, 2006). The next analysis will deal with the crime generated by district which will predict the future crime to be happened there. Fig-5: Crime by District
Another sensitive issue that is analysed here is the crime the analysis with respect to ward and hence it will help to determine the sensitive crime in Chicago. Eventually, it can be seen that the crime type Theft was executed for highest times with respect to the wards (Leitner, 2013). So, this needs the precaution for the happening of the crime in next days in those wards. The analysis is shown below: Fig-6: Analysis of Primary Type Crime in Wards To check for the analysis in Fig-6, the subordinate analysis is done for the Primary type by the Crime ID and here the same result is obtained that the Theft was occurred for highest times (M. H. Bhuyan, 2014). The analysis is shown below: Fig-7: Analysis of Primary Type Crime by Crime ID So, based on these analysis and visualizations, the recommendations will be placed to the CEO for their further actions. So, the letter will be written to the CEO informing about the scenario of the present crime and its future probable happenings. Recommendation to CEO With the analysis, it can be said that the crimes are still in Chicago in huge amount and that should be recovered for making the society healthy and free to move and out of fear from the crimes. From the analysis, it can be seen that, District 11 is highly sensitive of the crime and so it needs the urgent attention (H. Zhang, 2015). The crime Theft and the Battery taken t and second positions respectively and so these will be resolved from the further occurance of crime there. Additionally, the community area 24 is highly affected by the crime and so need the
urgent action so that the place will be free from any kind of crime (Jensen, 2013). So, the letter to the CEO specifically contains the recommendations and the QR code through which he can observer the crime analysis regularly and from any place the data can be downloaded. Using the power of analytics, the following suggestion can be recommended: 1. How many numbers of reported crimes? 2. How many different number of reported crime types? (primary types) 3. Provide a list of top 21 location descriptions with respect to crimes. 4. Provide a list of least 10 locations descriptions with respect to crimes .
5. What is the top three most primary type? 6. What is the least three most primary type? 7. How many years of years of reported crime is in the data file 8. How many number of reported crimes were logged every year in December?
9. Which month generated the most reported crime in Chicago? 10. Which year generated the most reported crime in Chicago? 11. How many number of reported crimes whether an arrest was made?
12. How many number of district in the dataset? 13. What are the top 3 districts in terms of reported crime? 14. What are the least 3 districts in terms of reported crime? 15. What are the primary type that reported most crimes from district 8 in 2014?
16. How many number of domestic reported crimes made in Chicago? 17. How many domestics number of reported crimes were made in 2012 to 2014? 18. Which day is the busiest day of the week in terms of committed crimes? 19. Which location description has the most number of crime reported on weekends?
20. Which location description has the least number of crime reported on weekends? Cover letter to CEO To The CEO, FBI. Sub: Report on the Analysis of Crime in City of Chicago Respected Sir, As per the analysis is done with the specified data, the report shows the crime happening in Chicago and this still very higher than assumed. Chicago is still bearing high record for Theft followed by the Battery and others. While analysis, it is found that District 11 is much sensitive to the crime for various types and if no steps will be taken then in future days, the probability of crimes can be higher end the city and the districts will not be believable enough to stay or for freely move. Apart from that, as the community area is the sensitive places where the society exists in its prominent form, there also the amount of crime is higher and more specifically saying in the community area 24 itself. From the analysis, the following recommendations can be suggested to control the crime: 1. The mentality and causes for the motive behind the crime needs to be understood 2. The rate of crime can be controlled by spreading public awareness 3. Grooming the people for the true fact of crime and the punishment and thus make them educated not to be a criminal 4. Implementing Discipline and Rule of Law in police force 5. Increasing the interaction among the public 6. Analysis of the police report frequently Therefore, it is the humble request to consider the fact so that the future crime may be diminished. The real time Quick Response code is also attached through which the real
time crime scenario will be observer just by scanning it. The scan will be result in the Dashboard which is made on behalf of the predicting purpose and hence it is provided to you for the understanding of the true scenario of Chicago Crime. Thanking you, Faithfully yours, ------------------- Conclusion In thus paper, the required analysis done on the Chicago Crime data and the analytical dashboard is made using the SAP analytics tool though which the data insight for the crime and the prediction both can be done. On the basis of the analysis, the cover letter is written to the CEO with the analytical report and additionally, with the QR code so that he will check the crime scenario every time he scans it. References C.-H.Yu, W. D. P. C. a. M. M., 2014. Crime forecastingusingspatio-temporal patternwithensemble learning. Advancesin KnowledgeDiscovery and Data Mining:18th Pacific-Asia Conference,PAKDD 2014, Tainan,Taiwan,May 13–16, p. 174–185. D. Reis,A.M. A. L. V. C.a. V. F.,2006. Towardsoptimal police patrol routeswithgeneticalgorithms. IEEE InternationalConferenceon Intelligenceand Security Informatics,ISI2006, San Diego,CA,USA, May 23-24, Volume 3975, p. 485–491.
Dhannoon,R.F. N. a. B. N., 2017. Classificationforintrusiondetectionwithdifferentfeature selectionmethods:asurvey(2014–2016). InternationalJournalof Advanced Research in Computer Science and SoftwareEngineering, 7(5). E. Galbrun,K. P.a. E. T.,2016. Urban navigationbeyondshortestroute:the case of safe paths. Information Systems, Volume 57,p.160–171. Félix Mata,M. T.-R.G. G. R. Q. R. Z.-F.M. M.-I.a. E. L., 2016. A Mobile InformationSystemBasedon Crowd-SensedandOfficialCrime DataforFindingSafe Routes: A Case Studyof MexicoCity. Mobile Information Systems. H. Zhang,Y. X.a. X. W., 2015. Optimal shortestpathsetprobleminundirectedgraphs,. Journalof CombinatorialOptimization, 29(3),p.511–530. Jensen,V.C.a. C. S.,2013. Routingservice quality—local driverbehaviorversusroutingservices. Proceedingsof theIEEE 14th InternationalConferenceon MobileData Management(MDM'13), Volume 1,p. 97–106. Leitner,M.,2013. Crime ModelingandMappingUsingGeospatial Technologies. Springer,Dordrecht, The Netherlands, Volume8. M. H. Bhuyan,D. K.B. a. J. K. K.,2014. Networkanomalydetection:methods,systemsandtools. IEEE CommunicationsSurveysand Tutorials, 14(1),p.303–336. N.Padhy,P. M. a. R. P., 2012. The surveyof data miningapplicationsandfeature scope. InternationalJournalof ComputerScience,Engineering and Information Technology, 2(3),p.43–58. T. Wang, C. R. D. W. a. R. S.,2013. Learningto detectpatternsof crime. MachineLearning and KnowledgeDiscovery in Databases, Volume8190, p. 515–530.
Itech 7407 report
Itech 7407 report

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Itech 7407 report

  • 1.
    Crime Data Analysis using SAPExpert Analytics Tool
  • 2.
    Executive Summery Crime isone of the primary issues for which the nation cannot stay in rest and peace. Crime is the general scenario of most of the nations. The fact that make the nation sensitive is the high amount of crime. The police and the intelligence bureau are there in every nation to protect the nation from such crime. But in most of the cases it can be seen the failure of the police and agencies for the lack of information and due to non-organised action against those. This make the nation with the execution of different types of crimes as the criminals can see the misprediction and the mis witness of the criminal booking records and hence they can move forward easily (Félix Mata, 2016). But some of the nation perform better and hence they are able to diminish or at least decrease the count for the crime. The feature of the criminals is they can see and understand the steps taken by the police and government and soon they approach to them, the criminals can flee away. There is some other measure available for the intelligence bureau through which they can check the activity without directly watching them and that is the prediction of the crimes. This can be possible if the required data for the crime and the criminals will be available and almost all the country generally stores the records. If the predictive analysis will be done on the data, the generated outcome can show the probable outcome of the crime in different places and for the years also (H. Zhang, 2015). With watching this, the Intelligence bureau can take steps and probably prior to the happening of the crime as they are executing their operation based on prediction. This one if helpful to the government agencies as they have exerted less labour and so they can focus upon the operation and actions to be taken against the criminals (D. Reis, 2006). The predictive analysis should be multidimensional and hence comprises of the analytical view of different crimes with respect to the location and the year and on the basis of the number of crime happenings. Now a day, there are many options are open to make the predictive analysis (Jensen, 2013). The predictive analysis may consist of the coding or may not be as there are several Business Intelligence tools are available, through which the analysis upon the data can be done with less labour and with high accuracy. In this paper, the crime analysis will be performed on the basis of the collected data of crime and the dashboard will be shown which will display the analytical outcome of the crime at that nation and will eb helpful to the Business Intelligence team and the CEO of the organizations who are about to initiate the action against crime (E. Galbrun, 2016).
  • 3.
    Table of Contents Introduction................................................................................................................................4 Data Collection........................................................................................................................... 4 Tool Selection............................................................................................................................ 10 Dashboard and Analysis ........................................................................................................... 10 Recommendation to CEO ......................................................................................................... 13 Cover letter to CEO.................................................................................................................. 19 Conclusion................................................................................................................................ 20 References................................................................................................................................. 20
  • 4.
    Introduction In this paper,the analysis of crime is performed. As the crime is the growing problem of the nation in almost all over the world, this needs to be analysed with an objective to rectification of the nation from such crimes and the criminal activities as well. With such goal of the work, the crime data is collected and hence the data will be analysed to view the statistics of crime so that the outcome can be recommended to the Business Intelligence team and the CEO of the organization who want to check the analysis for taking proper steps against such crimes (C.-H. Yu, 2014). Hence the analysis along with the visualizations are done on the basis of the data and with all those analytical outcomes, the Dashboard is done to show the view of the crime result. The dashboard is done so that all of the analysis and the visualizations can be available in same place and the CEO need not to swipe for multiple visualization as it is the problematic if the CEO want to compare the result and check among the analysis (T. Wang, 2013). Dashboard provides the compact view of the analysis of the data through which the outcome can be predicted and hence the CEO can make the decision for the relevant actions to be taken for the elimination of the crime. So, after doing the analysis using the analytical tool, the dashboard will be sent to the Business Intelligence team that is lead by the CEO with the respective data so that the objective of the both side can be fulfilled and the CEO will be get helped of the analytical view of the data by watching the visualization (D. Reis, 2006). This the dashboard can be helpful to the Business Intelligence team and the CEO as well for taking the proper action against the crime and hence they can proceed for the relevant steps. The next section will be dealing with the data collection for the crime and the description of the data is given to understand the insight of the data (Félix Mata, 2016). Data Collection For the analysis of crime, the crime data for Chicago is chosen. As Chicago is one of the renowned cities in United States of America and the crime factor is high in that city. So, the data for the crime is Chicago has the relevancy for the analysis. Hence the data is collected for the period of 2012 to 2016 and the analysis of the data will reflect the outcome of required operation (Leitner, 2013). The data has its several parameters like:  CrimeID  Case Number  Date  Month  Day  Block  IUCR  Primary Type  Description  Location  Arrest  Domestic  Beat  District
  • 5.
     Ward  CommunityArea  FBI Code  X Coordinate & Y Coordinate  Latitude & Longitude  Location  Year  Updated On Those attributes have their own meaning and those are helpful for the analysis (M. H. Bhuyan, 2014). The briefs of the important parameters are described below: CrimeID: This shows the type of crime that are recorded and hence will be applicable to be counted for the occurrence with respect to the location, year and districts. Primary Type: This shows the type of crimes have been taken place and hence can be identified by the Crime ID. Arrest: This attribute shows the status of arrest with respect to the crime occurred. Beat: This attribute shows about the activity of the authority and is helpful to create the analysis wit respect to the Crime ID and other related parameters. District, Ward: This attribute basically shows the location of crime and hence is useful for the analysis of crime in district and ward wise to see the insight of the happenings of the crime. Year: As per the name of the attribute, it shows the records of the crimes and the other parameters to be analysed with respect to it to count the frequency. So, with those attributes, the data analysis will be done. Primarily, the crime happenings are computed with respect to the month to see the crime occurrence per month and hence it will show the name of the month for high crime (N. Padhy, 2012). This will be helpful to predict for the sensitive month and the BI team can understand about their steps. The table of data is shown below: Table-1: Analysis for Crime Month basis Crime Month wise Month Crimes Occurred January 325413452791 February 268713531675 March 273318228906 April 277083620573 May 274006906795 June 265302088752 July 272856574332 August 271524112689 September 273446839041 October 272071139249
  • 6.
    November 269341993580 December 268421768980 Afterthe observation of the crime scenario by month, the crime data is fetched with respect to the type of crime. This is essential to see the happening of crime. As there are different types of crime but all of those crimes are not happening with same amount and to find the highest occurance of crime, the analysis is required for future prediction such that the relevant steps can be taken to diminish such types of crime (Dhannoon, 2017). Hence, the table is shown below: Table-2: Analysis of Crime by Type Type of Crime Crime Type Crimes Occurred THEFT 751002561233 BATTERY 587599855032 NARCOTICS 368052097473 CRIMINAL DAMAGE 334806395720 ASSAULT 199907953494 OTHER OFFENSE 196617243992 BURGLARY 193033921844 DECEPTIVE PRACTICE 154255739292 MOTOR VEHICLE THEFT 138394954104 ROBBERY 124364707167 CRIMINAL TRESPASS 86794104837 WEAPONS VIOLATION 36557891705 PUBLIC PEACE VIOLATION 32510334600 OFFENSE INVOLVING CHILDREN 25533916382 PROSTITUTION 19460580047 INTERFERENCE WITH PUBLIC OFFICER 14340208312 CRIM SEXUAL ASSAULT 14289242733 SEX OFFENSE 11315282643 GAMBLING 6512039696 LIQUOR LAW VIOLATION 5047772654 ARSON 4350569172 KIDNAPPING 2693574221 STALKING 1750155860 INTIMIDATION 1520992113 OBSCENITY 381276790 PUBLIC INDECENCY 153887077 OTHER NARCOTIC VIOLATION 91701634 NON-CRIMINAL 83840055 NON - CRIMINAL 38660439 HOMICIDE 19766859
  • 7.
    HUMAN TRAFFICKING 10110085 NON-CRIMINAL(SUBJECT SPECIFIED) 8920098 Though such types of crimes are happening in Chicago, the required data is to determine the status of arrest is shown below. It can be seen that, for most of the cases, the criminals are not arrested and thus they can find the way to execute their next crime (Jensen, 2013). With the predictive analysis is done in the next section will help the CEO and the BI Team to get the opportunity to arrest the criminals as the crimes can be detected by the predictive analysis. Table-3: Analysis for the status of arrest Status of Arrest Arrest Crime Records FALSE 2376176861896 TRUE 935323395467 The below table shows the analysis of Crime by district as it is the required parameter to find the happening of crime in different districts (C.-H. Yu, 2014). The districts are shows with the district codes. Table-4: Analysis of Crime by District District wise Crime District ID Crimes 1 129746794377 2 137187411076 3 172538653389 4 198094903257 5 152010345314 6 196186678718 7 194725973755 8 221548937215 9 161167918486 10 151036845081 11 234445870155 12 151907474919 14 116157126031 15 150947332125 16 111812921498 17 93248847602 18 138866926969 19 150441835804 20 55115340528 22 107667539589
  • 8.
    24 93979369768 25 192561517767 3194529791 FBI code is the allotted for the identification of the crime type and hence that is required to be analysed along with the previous issues. So, the data for the analysis is shown below: Table-5: Crime by FBI Code Crime by FBI Code FBI Code Crimes 2 15953981951 3 124364707167 5 193033921844 6 751002561233 7 138394954104 9 4286255046 10 18109525064 11 134505631912 12 641832884 13 998749432 14 334806395720 15 36557891705 16 19507210155 17 13551969417 18 350207111505 19 6529894786 20 13516260090 22 5047772654 24 42153332499 26 319300116124 01A 133124 01B 19633735 04A 49313973119 04B 76269045885 08A 152096587061 08B 511330809147 As the previous data shows the analysis for the crime types which are required to determine and predict the crime to be happened in future, another parameter which is required to show the frequency of crime that have been taken place by year and that data is shown below:
  • 9.
    Table-6: Analysis byYear Analysis by Year Year Crimes 2012 1152478880717 2013 1113373039049 2014 1027357673759 2015 2148095646 2016 16142568192 It can be seen that the crime is decreasing by year though the crime that is seen in the last year is higher enough and the reason is already discussed with required data in the earlier section (H. Zhang, 2015). The ward wise crime is show in the next table. Table-7: Ward wise Crime Analysis Ward wise Crime Ward No. Crime 1 56603256520 2 124152541339 3 87373929855 4 53458823569 5 81862462223 6 109864474319 7 91127186174 8 93866006934 9 85283094757 10 61194844946 11 44793655576 12 40522760193 13 41447583471 14 45759380400 15 88945038771 16 93595395802 17 112749983448 18 55456806310 19 28542241927 20 104929683302 21 102000577471 22 40089600369 23 39426177393 24 141697771043 25 46685347284 26 51088283540
  • 10.
    27 115277584672 28 168093136031 2981003178351 30 46107758287 31 47081855884 32 52823075761 33 32200274653 34 92847818979 35 40102089283 36 31795615447 37 90534215720 38 34719610839 39 29571770454 40 34120270465 41 37769024708 42 140230575084 43 41468862334 44 52006848841 45 34321340171 46 45618718123 47 32252910552 48 30851637187 49 44249579690 50 33899159405 So, using these data, the analysis and the dashboard will be done and based on the analysis and the dashboard, the report will be forwarded to the CEO. Tool Selection SAP analytics tool is used for the analysis of the Chicago Crime data. The SAP tool is the service and tool from IBM and is capable of analysis of the big data as well any kind of data and the most precious feature of the tool is that it is capable of analysing the data using prediction and the drag and drop facility enrich the visualization technique and save the time for the analysis. Hence, the tool is useful for the analysis of the data and to prepared the dashboard. Dashboard and Analysis The dashboard is prepared using the SAP analytics and it is show below:
  • 11.
    Fig-1: Dashboard forCrime data This dashboard will be said to be presented to the CEO by writing the cover letter. The CEO will take the decision for the future action ad hence the analysis will be described to him. So, in this section, the analysis will be described to signify the insight of the dashboard and to predict the future happening of the crime. First the no of beat by month is analysed to check the status of the number of beats to the criminals by the Chicago police. It can be seen that in January the value is highest so the month can be denoted as the sensistive one (Leitner, 2013) . The analysis s shown below: Fig-2: Analysis of beat by month Next the analysis is done for the beat value by the community area. This is required analysis to show the number of crimes happened at the general and community area to show the security aspects of Chicago (E. Galbrun, 2016). The analysis, hence, is shown below:
  • 12.
    Fig-3: Analysis ofbeat by Community area In the analysis chain the third one is the analysis of the beat by the district denote the number of crimes that is happend in the districts (M. H. Bhuyan, 2014). This will help for the prediction for the crime as the police can now determine for the sensitive districts and hence e more tight there. Fig-4: Analysis of Beat by District So, in the previous three analysis shows the value of the beat that is the measures taken by the authority but apart from that there are many cases are pending for which no actions are taken yet (D. Reis, 2006). The next analysis will deal with the crime generated by district which will predict the future crime to be happened there. Fig-5: Crime by District
  • 13.
    Another sensitive issuethat is analysed here is the crime the analysis with respect to ward and hence it will help to determine the sensitive crime in Chicago. Eventually, it can be seen that the crime type Theft was executed for highest times with respect to the wards (Leitner, 2013). So, this needs the precaution for the happening of the crime in next days in those wards. The analysis is shown below: Fig-6: Analysis of Primary Type Crime in Wards To check for the analysis in Fig-6, the subordinate analysis is done for the Primary type by the Crime ID and here the same result is obtained that the Theft was occurred for highest times (M. H. Bhuyan, 2014). The analysis is shown below: Fig-7: Analysis of Primary Type Crime by Crime ID So, based on these analysis and visualizations, the recommendations will be placed to the CEO for their further actions. So, the letter will be written to the CEO informing about the scenario of the present crime and its future probable happenings. Recommendation to CEO With the analysis, it can be said that the crimes are still in Chicago in huge amount and that should be recovered for making the society healthy and free to move and out of fear from the crimes. From the analysis, it can be seen that, District 11 is highly sensitive of the crime and so it needs the urgent attention (H. Zhang, 2015). The crime Theft and the Battery taken t and second positions respectively and so these will be resolved from the further occurance of crime there. Additionally, the community area 24 is highly affected by the crime and so need the
  • 14.
    urgent action sothat the place will be free from any kind of crime (Jensen, 2013). So, the letter to the CEO specifically contains the recommendations and the QR code through which he can observer the crime analysis regularly and from any place the data can be downloaded. Using the power of analytics, the following suggestion can be recommended: 1. How many numbers of reported crimes? 2. How many different number of reported crime types? (primary types) 3. Provide a list of top 21 location descriptions with respect to crimes. 4. Provide a list of least 10 locations descriptions with respect to crimes .
  • 15.
    5. What isthe top three most primary type? 6. What is the least three most primary type? 7. How many years of years of reported crime is in the data file 8. How many number of reported crimes were logged every year in December?
  • 16.
    9. Which monthgenerated the most reported crime in Chicago? 10. Which year generated the most reported crime in Chicago? 11. How many number of reported crimes whether an arrest was made?
  • 17.
    12. How manynumber of district in the dataset? 13. What are the top 3 districts in terms of reported crime? 14. What are the least 3 districts in terms of reported crime? 15. What are the primary type that reported most crimes from district 8 in 2014?
  • 18.
    16. How manynumber of domestic reported crimes made in Chicago? 17. How many domestics number of reported crimes were made in 2012 to 2014? 18. Which day is the busiest day of the week in terms of committed crimes? 19. Which location description has the most number of crime reported on weekends?
  • 19.
    20. Which locationdescription has the least number of crime reported on weekends? Cover letter to CEO To The CEO, FBI. Sub: Report on the Analysis of Crime in City of Chicago Respected Sir, As per the analysis is done with the specified data, the report shows the crime happening in Chicago and this still very higher than assumed. Chicago is still bearing high record for Theft followed by the Battery and others. While analysis, it is found that District 11 is much sensitive to the crime for various types and if no steps will be taken then in future days, the probability of crimes can be higher end the city and the districts will not be believable enough to stay or for freely move. Apart from that, as the community area is the sensitive places where the society exists in its prominent form, there also the amount of crime is higher and more specifically saying in the community area 24 itself. From the analysis, the following recommendations can be suggested to control the crime: 1. The mentality and causes for the motive behind the crime needs to be understood 2. The rate of crime can be controlled by spreading public awareness 3. Grooming the people for the true fact of crime and the punishment and thus make them educated not to be a criminal 4. Implementing Discipline and Rule of Law in police force 5. Increasing the interaction among the public 6. Analysis of the police report frequently Therefore, it is the humble request to consider the fact so that the future crime may be diminished. The real time Quick Response code is also attached through which the real
  • 20.
    time crime scenariowill be observer just by scanning it. The scan will be result in the Dashboard which is made on behalf of the predicting purpose and hence it is provided to you for the understanding of the true scenario of Chicago Crime. Thanking you, Faithfully yours, ------------------- Conclusion In thus paper, the required analysis done on the Chicago Crime data and the analytical dashboard is made using the SAP analytics tool though which the data insight for the crime and the prediction both can be done. On the basis of the analysis, the cover letter is written to the CEO with the analytical report and additionally, with the QR code so that he will check the crime scenario every time he scans it. References C.-H.Yu, W. D. P. C. a. M. M., 2014. Crime forecastingusingspatio-temporal patternwithensemble learning. Advancesin KnowledgeDiscovery and Data Mining:18th Pacific-Asia Conference,PAKDD 2014, Tainan,Taiwan,May 13–16, p. 174–185. D. Reis,A.M. A. L. V. C.a. V. F.,2006. Towardsoptimal police patrol routeswithgeneticalgorithms. IEEE InternationalConferenceon Intelligenceand Security Informatics,ISI2006, San Diego,CA,USA, May 23-24, Volume 3975, p. 485–491.
  • 21.
    Dhannoon,R.F. N. a.B. N., 2017. Classificationforintrusiondetectionwithdifferentfeature selectionmethods:asurvey(2014–2016). InternationalJournalof Advanced Research in Computer Science and SoftwareEngineering, 7(5). E. Galbrun,K. P.a. E. T.,2016. Urban navigationbeyondshortestroute:the case of safe paths. Information Systems, Volume 57,p.160–171. Félix Mata,M. T.-R.G. G. R. Q. R. Z.-F.M. M.-I.a. E. L., 2016. A Mobile InformationSystemBasedon Crowd-SensedandOfficialCrime DataforFindingSafe Routes: A Case Studyof MexicoCity. Mobile Information Systems. H. Zhang,Y. X.a. X. W., 2015. Optimal shortestpathsetprobleminundirectedgraphs,. Journalof CombinatorialOptimization, 29(3),p.511–530. Jensen,V.C.a. C. S.,2013. Routingservice quality—local driverbehaviorversusroutingservices. Proceedingsof theIEEE 14th InternationalConferenceon MobileData Management(MDM'13), Volume 1,p. 97–106. Leitner,M.,2013. Crime ModelingandMappingUsingGeospatial Technologies. Springer,Dordrecht, The Netherlands, Volume8. M. H. Bhuyan,D. K.B. a. J. K. K.,2014. Networkanomalydetection:methods,systemsandtools. IEEE CommunicationsSurveysand Tutorials, 14(1),p.303–336. N.Padhy,P. M. a. R. P., 2012. The surveyof data miningapplicationsandfeature scope. InternationalJournalof ComputerScience,Engineering and Information Technology, 2(3),p.43–58. T. Wang, C. R. D. W. a. R. S.,2013. Learningto detectpatternsof crime. MachineLearning and KnowledgeDiscovery in Databases, Volume8190, p. 515–530.