Presentation is loading. Please wait.

Presentation is loading. Please wait.

© 2011 IBM Corporation Informatie Analyse Laila Fettah – Associate Sales Engineer SPSS 27 January 2011.

Similar presentations


Presentation on theme: "© 2011 IBM Corporation Informatie Analyse Laila Fettah – Associate Sales Engineer SPSS 27 January 2011."— Presentation transcript:

1 © 2011 IBM Corporation Informatie Analyse Laila Fettah – Associate Sales Engineer SPSS 27 January 2011

2 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Agenda  Government – Challenges  Data mining  CRISP-DM  Example Application 2

3 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Government faces challenges everyday… Demonstrate Effective Public Policy Ongoing Budget Pressures Lack of Decision-Quality Information Transparency & Accountability Ongoing Improvement, Less Resources 3

4 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 How satisfied do citizens feel? Have job creation programs helped curb benefits applications? Have new crime fighting tactics been effective? What fraud patterns are emerging? How have collection strategies impacted budgets? What is likely to happen in the long- term? …and must answer critical questions everyday... 4

5 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Budgeting & Finance Program Execution Services Delivery Workforce/ HR Executive Leaders Operations/ Readiness Information Technology Supply Chain …and silos often persist that impact outcomes... 5

6 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Information Technology Budgeting & Finance Management Operations/ Readiness Program Execution Services Delivery Supply Chain Public Safety Staff Communities …analytics can tear down silos 6

7 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 What is data mining? 7  Finding patterns in your data that you can use to do your business better  Business-oriented discovery of patterns producing insight and a predictive capability which can be deployed widely  Process of autonomously retrieving useful information or knowledge (“actionable assets”) from large data stores or set “Predictive analysis helps connect data to effective action by drawing reliable conclusionsabout current conditions and future events.” Gareth Herschel, Research Director, Gartner Group

8 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 What’s in a name?  Data Mining is not a great metaphor –Would mean people who dig for gold are “rock miners”!  Other early candidates: –Knowledge Discovery in Databases (KDD) –“Torturing the data until it confesses” •“…and if you torture it long enough, it’ll confess to anything!” 8

9 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Traditional analyses First by gender or offender? By count of crime type By time of offence What do I do NOW??? What is the profile of the repeat offenders in my district? Give me the number of males and females within the repeat offenders Give me the times that crimes where committed Give me a count of the types of crimes Report 1 Report 2 Report 3

10 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Make individual profiles A descriptive question I know from my understanding of crime that gender, time, place, type of crime, age can be important Youth gangs from cities A and B that are mostly active on Thursday night in the center. Addicts that are mostly active around the central station as pick pockets ……….. There are several profiles for repeat offenders. The most important are…. Data Mining What is the profile of the repeat offenders in my district? Let me think…. Data Mining Technology Create profiles of repeat offenders based on gender, time, location, type of crime… Ok, so I need to talk with the railway and with local authorities in city A and B…. 10

11 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Descriptive (KPI)Predictive (KPP)Prescriptive (Scenario) Statistics Profiling Clustering Associations Classification Scoring Prediction Forecasting Prediction Scoring Forecasting What If Underlying analyses 11

12 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 CRISP-DM  CRoss Industry Standard Process for Data Mining –Funding from European commission –Non-proprietary –Application/Industry neutral –Tool neutral –Focus on business issues as well as technical analysis –www.crisp-dm.orgwww.crisp-dm.org  Process framework for data mining projects –Process Standardization 12

13 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 CRISP-DM phases 13

14 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Example Application Areas:  Public Safety –Reduce crime –Improve border protection –Proactive disease surveillance –Intrusion and insider threat detection  Customs & Excise, Tax, Social security –Predict & prevent fraud –Improve collections –Focus investigators & inspectors  Defense –Increase battle readiness of assets –Improve employee acquisition, retention & growth  Citizen satisfaction –Implement continuous citizen feedback loop –Improve operational processes  …… 14

15 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? 15

16 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? A citizen reports a burglary Reports that her house was burglarized while she was talking to a representative from the city council Does this crime resemble others? Is it serial? Do we have a team working on similar crimes that we can assign it to? 16

17 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? A citizen reports a burglary Reports that her house was burglarized while she was talking to a representative from the city council Does this crime resemble others? Is it serial? Do we have a team working on similar crimes that we can assign it to? A Break-in into a shop is reported The perpetrators entered by breaking a window probably between 3am and 5am. Crime was discovered at 6 pm next day Does it make sense to send out a CSI team? Is it likely that they’ll find useful evidence? 17

18 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 An organized crime unit wants to bust a drugs ring The detectives are interested in identifying the central players within a narcotics network Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? A citizen reports a burglary Reports that her house was burglarized while she was talking to a representative from the city council Does this crime resemble others? Is it serial? Do we have a team working on similar crimes that we can assign it to? A Break-in into a shop is reported The perpetrators entered by breaking a window probably between 3am and 5am. Crime was discovered at 6 pm next day Does it make sense to send out a CSI team? Is it likely that they’ll find useful evidence? Who are the key persons?Who are the leaders? 18

19 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? A citizen reports a burglary Reports that her house was burglarized while she was talking to a representative from the city council Does this crime resemble others? Is it serial? Do we have a team working on similar crimes that we can assign it to? A Break-in into a shop is reported The perpetrators entered by breaking a window probably between 3am and 5am. Crime was discovered at 6 pm next day PD uses predictive analytics to profile crimes & criminals to improve solved crime rates and optimize resource usage Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Predictive Modeling for Crime Pattern Detection An organized crime unit wants to bust a drugs ring The detectives are interested in identifying the central players within a narcotics network Does it make sense to send out a CSI team? Is it likely that they’ll find useful evidence? Who are the key persons?Who are the leaders? 19

20 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? A citizen reports a burglary Reports that her house was burglarized while she was talking to a representative from the city council Does this crime resemble others? Is it serial? Do we have a team working on similar crimes that we can assign it to? A Break-in into a shop is reported The perpetrators entered by breaking a window probably between 3am and 5am. Crime was discovered at 6 pm next day PD uses predictive analytics to profile crimes & criminals to improve solved crime rates and optimize resource usage Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Aspiring Repeat Offender profile … If male And age And crime =‘car break in’ Andmotive =‘peer pressure’ Thenrepeat risk is HIGH  ALERT DA … Predictive Modeling for Crime Pattern Detection An organized crime unit wants to bust a drugs ring The detectives are interested in identifying the central players within a narcotics network Does it make sense to send out a CSI team? Is it likely that they’ll find useful evidence? Who are the key persons?Who are the leaders? 20

21 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? A citizen reports a burglary Reports that her house was burglarized while she was talking to a representative from the city council Does this crime resemble others? Is it serial? Do we have a team working on similar crimes that we can assign it to? A Break-in into a shop is reported The perpetrators entered by breaking a window probably between 3am and 5am. Crime was discovered at 6 pm next day PD uses predictive analytics to profile crimes & criminals to improve solved crime rates and optimize resource usage Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Aspiring Repeat Offender profile … If male And age And crime =‘car break in’ Andmotive =‘peer pressure’ Thenrepeat risk is HIGH  ALERT DA … Crime profile  Team 4 Cluster ‘Bogus Official’ - Burglary, - Visit by city official, - Entry ‘Back door’, - Victim “Elderly’ Predictive Modeling for Crime Pattern Detection An organized crime unit wants to bust a drugs ring The detectives are interested in identifying the central players within a narcotics network Does it make sense to send out a CSI team? Is it likely that they’ll find useful evidence? Who are the key persons?Who are the leaders? 21

22 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? A citizen reports a burglary Reports that her house was burglarized while she was talking to a representative from the city council Does this crime resemble others? Is it serial? Do we have a team working on similar crimes that we can assign it to? A Break-in into a shop is reported The perpetrators entered by breaking a window probably between 3am and 5am. Crime was discovered at 6 pm next day PD uses predictive analytics to profile crimes & criminals to improve solved crime rates and optimize resource usage Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Aspiring Repeat Offender profile … If male And age And crime =‘car break in’ Andmotive =‘peer pressure’ Thenrepeat risk is HIGH  ALERT DA … Crime profile  Team 4 Cluster ‘Bogus Official’ - Burglary, - Visit by city official, - Entry ‘Back door’, - Victim “Elderly’ CS profile  No Deployment … If Break In And Night Andreport>12hrs And entry =‘broken window’ Andobject=‘Commercial Property’ Thenprobability evidence is 6% … Predictive Modeling for Crime Pattern Detection An organized crime unit wants to bust a drugs ring The detectives are interested in identifying the central players within a narcotics network Does it make sense to send out a CSI team? Is it likely that they’ll find useful evidence? Who are the key persons?Who are the leaders? 22

23 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 An organized crime unit wants to bust a drugs ring The detectives are interested in identifying the central players within a narcotics network Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? A citizen reports a burglary Reports that her house was burglarized while she was talking to a representative from the city council Does this crime resemble others? Is it serial? Do we have a team working on similar crimes that we can assign it to? A Break-in into a shop is reported The perpetrators entered by breaking a window probably between 3am and 5am. Crime was discovered at 6 pm next day PD uses predictive analytics to profile crimes & criminals to improve solved crime rates and optimize resource usage Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Aspiring Repeat Offender profile … If male And age And crime =‘car break in’ Andmotive =‘peer pressure’ Thenrepeat risk is HIGH  ALERT DA … Crime profile  Team 4 Cluster ‘Bogus Official’ - Burglary, - Visit by city official, - Entry ‘Back door’, - Victim “Elderly’ CS profile  No Deployment … If Break In And Night Andreport>12hrs And entry =‘broken window’ Andobject=‘Commercial Property’ Thenprobability evidence is 6% … Key Players Focus on: • Keith Patterson • Colin Wiertz • Markus Haffey Predictive Modeling for Crime Pattern Detection Does it make sense to send out a CSI team? Is it likely that they’ll find useful evidence? Who are the key persons?Who are the leaders? 23

24 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 Johnny is arrested for breaking into a car He is 15 years old and confesses that he wanted to belong to a group of friends Will he become a repeat offender? If YES: advise DA and later parole officer? A citizen reports a burglary Reports that her house was burglarized while she was talking to a representative from the city council Does this crime resemble others? Is it serial? Do we have a team working on similar crimes that we can assign it to? A Break-in into a shop is reported The perpetrators entered by breaking a window probably between 3am and 5am. Crime was discovered at 6 pm next day PD uses predictive analytics to profile crimes & criminals to improve solved crime rates and optimize resource usage Management Dashboard Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Predictive Modeling for Crime Pattern Detection Aspiring Repeat Offender profile … If male And age And crime =‘car break in’ Andmotive =‘peer pressure’ Thenrepeat risk is HIGH  ALERT DA … Crime profile  Team 4 Cluster ‘Bogus Official’ - Burglary, - Visit by city official, - Entry ‘Back door’, - Victim “Elderly’ CS profile  No Deployment … If Break In And Night Andreport>12hrs And entry =‘broken window’ Andobject=‘Commercial Property’ Thenprobability evidence is 6% … Key Players Focus on: • Keith Patterson • Colin Wiertz • Markus Haffey An organized crime unit wants to bust a drugs ring The detectives are interested in identifying the central players within a narcotics network Does it make sense to send out a CSI team? Is it likely that they’ll find useful evidence? Who are the key persons?Who are the leaders? 24

25 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 CapturePredictAct Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data 25

26 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 CapturePredictAct Capture PredictAct Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Crime Pattern & Hotspot Clustering Automated Link Analysis Profiles & Associations Predictive Modeling for Crime Pattern Detection 26

27 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 CapturePredictAct Capture PredictAct Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Crime Pattern & Hotspot Clustering Automated Link Analysis Profiles & Associations Criminal Career Scoring Model MO Typology Model Crime Scene Assessment Model Predictive Modeling for Crime Pattern Detection 27

28 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 CapturePredictAct Capture PredictAct Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Crime Pattern & Hotspot Clustering Automated Link Analysis Profiles & Associations Criminal Career Scoring Model MO Typology Model Crime Scene Assessment Model Arresting Officer Case Assignment Officer CSI Resource Planner Alert! Aspiring Repeat Offender Risk HIGH Advise DA and inform parole officer Alert! Aspiring Repeat Offender Risk HIGH Advise DA and inform parole officer Alert! Serial Crime Profile MO fits  Team 4 Alert! Serial Crime Profile MO fits  Team 4 Alert! Very Low Likelihood Evidence Probability <10%  No Deployment Alert! Very Low Likelihood Evidence Probability <10%  No Deployment Predictive Modeling for Crime Pattern Detection 28

29 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 CapturePredictAct Capture PredictAct Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Crime Pattern & Hotspot Clustering Automated Link Analysis Profiles & Associations Criminal Career Scoring Model MO Typology Model Crime Scene Assessment Model Investigative Model Template Repository Arresting Officer Case Assignment Officer CSI Resource Planner Investigating Officer Predictive Modeling for Crime Pattern Detection Feedback results Feedback loop of new data to improve and adapt predictions Key Players Focus on: •Keith Patterson •Colin Wiertz •Markus Haffey Key Players Focus on: •Keith Patterson •Colin Wiertz •Markus Haffey Alert! Aspiring Repeat Offender Risk HIGH Advise DA and inform parole officer Alert! Aspiring Repeat Offender Risk HIGH Advise DA and inform parole officer Alert! Serial Crime Profile MO fits  Team 4 Alert! Serial Crime Profile MO fits  Team 4 Alert! Very Low Likelihood Evidence Probability <10%  No Deployment Alert! Very Low Likelihood Evidence Probability <10%  No Deployment 29

30 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 CapturePredictAct Capture PredictAct Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Crime Pattern & Hotspot Clustering Automated Link Analysis Profiles & Associations Criminal Career Scoring Model MO Typology Model Crime Scene Assessment Model Investigative Model Template Repository Arresting Officer Case Assignment Officer CSI Resource Planner Analytical Process Automation & Optimization Automate prediction & deployment process Analytical Process Management & Control Monitor & manage analytics process Predictive Modeling for Crime Pattern Detection Feedback results Feedback loop of new data to improve and adapt predictions Investigating Officer Key Players Focus on: •Keith Patterson •Colin Wiertz •Markus Haffey Key Players Focus on: •Keith Patterson •Colin Wiertz •Markus Haffey Alert! Aspiring Repeat Offender Risk HIGH Advise DA and inform parole officer Alert! Aspiring Repeat Offender Risk HIGH Advise DA and inform parole officer Alert! Serial Crime Profile MO fits  Team 4 Alert! Serial Crime Profile MO fits  Team 4 Alert! Very Low Likelihood Evidence Probability <10%  No Deployment Alert! Very Low Likelihood Evidence Probability <10%  No Deployment 30

31 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari 2011 CapturePredictAct Capture PredictAct Crime Data Crime record notes and call logs Surveillance Data Communication Data Financial Data Crime Pattern & Hotspot Clustering Automated Link Analysis Profiles & Associations Criminal Career Scoring Model MO Typology Model Crime Scene Assessment Model Investigative Model Template Repository Arresting Officer Case Assignment Officer CSI Resource Planner Analytical Process Automation & Optimization Automate prediction & deployment process Analytical Process Management & Control Monitor & manage analytics process Predictive Modeling for Crime Pattern Detection Management Dashboard Feedback results Feedback loop of new data to improve and adapt predictions Investigating Officer Key Players Focus on: •Keith Patterson •Colin Wiertz •Markus Haffey Key Players Focus on: •Keith Patterson •Colin Wiertz •Markus Haffey Alert! Aspiring Repeat Offender Risk HIGH Advise DA and inform parole officer Alert! Aspiring Repeat Offender Risk HIGH Advise DA and inform parole officer Alert! Serial Crime Profile MO fits  Team 4 Alert! Serial Crime Profile MO fits  Team 4 Alert! Very Low Likelihood Evidence Probability <10%  No Deployment Alert! Very Low Likelihood Evidence Probability <10%  No Deployment 31

32 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari Start from business understanding… not from data or technique…

33 © 2011 IBM Corporation Van informatie op Orde naar Informatie van Waarde – 27 januari …and use a methodology!

34 © 2011 IBM Corporation Questions Van informatie op Orde naar Informatie van Waarde – 27 januari


Download ppt "© 2011 IBM Corporation Informatie Analyse Laila Fettah – Associate Sales Engineer SPSS 27 January 2011."

Similar presentations


Ads by Google