© 2011 ibm corporation informatie analyse laila fettah – associate sales engineer spss 27 january...

© 2011 IBM Corporation

Informatie Analyse

Laila Fettah – Associate Sales Engineer SPSS

27 January 2011


Van informatie op Orde naar Informatie van Waarde – 27 januari 2011

Agenda

Government – Challenges

Data mining

CRISP-DM

Example Application

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Government faces challenges everyday…

Demonstrate Effective Public

Policy

Ongoing Budget Pressures

Lack of Decision-Quality

Information

Transparency & Accountability

Ongoing Improvement,

Less Resources

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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...

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Budgeting & Finance

Budgeting & Finance

Program ExecutionProgram

Execution

Services DeliveryServices Delivery

Workforce/ HR

Workforce/ HR

Executive Leaders

Executive Leaders

Operations/ Readiness


Information TechnologyInformation Technology

Supply ChainSupply Chain

…and silos often persist that impact outcomes...

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Information TechnologyInformation Technology

Budgeting & Finance

Budgeting & Finance

ManagementManagement


Operations/ Readiness Program

ExecutionProgram

Execution

Services DeliveryServices Delivery

Supply ChainSupply Chain

Public Safety Staff

Public Safety Staff

Communities

…analytics can tear down silos

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What is data mining?

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Finding patterns in your data that you can use to do your business better

Business-oriented discovery of patterns producing insight and a predictive capabilitywhich 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



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!”

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Traditional analyses

First by gender or offender?

By count of crime typeBy 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 1Report 1

Report 2Report 2

Report 3Report 3



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 offendersbased 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….

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Descriptive (KPI) Predictive (KPP) Prescriptive (Scenario)

Statistics

Profiling

Clustering

Associations

Classification

Scoring

Prediction

Forecasting

Prediction

Scoring

Forecasting

What If

Underlying analyses

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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.org

Process framework for data mining projects– Process Standardization

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http://www.crisp-dm.org/



CRISP-DM phases

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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

……

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Johnny is arrested for breaking into a carHe 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?

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A citizen reports a burglaryReports 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?

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A Break-in into a shop is reportedThe 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?

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An organized crime unit wants to bust a drugs ringThe detectives are interested in identifying the central players within a narcotics network










Who are the key persons? Who are the leaders?18










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















Crime Data


Surveillance Data

Communication Data

Financial Data

Aspiring Repeat Offender profile…If maleAnd age 14-16And crime =‘car break in’And motive =‘peer pressure’Then repeat risk is HIGH ALERT DA…
















Crime Data


Surveillance Data

Communication Data

Financial Data


Crime profile Team 4Cluster ‘Bogus Official’ - Burglary, - Visit by city official, - Entry ‘Back door’, - Victim “Elderly’
















Crime Data


Surveillance Data

Communication Data

Financial Data



CS profile No Deployment…If Break InAnd NightAnd report>12hrsAnd entry =‘broken window’And object=‘Commercial Property’Then probability evidence is 6%…

















Crime Data


Surveillance Data

Communication Data

Financial Data




Key PlayersFocus on:• Keith Patterson• Colin Wiertz• Markus Haffey















Management Dashboard

Crime Data


Surveillance Data

Communication Data

Financial Data












Capture Predict Act

Crime Data


Surveillance Data

Communication Data

Financial Data

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Capture Predict ActCapture Predict Act

Crime Data


Surveillance Data

Communication Data

Financial Data

Crime Pattern & Hotspot Clustering

Automated Link AnalysisProfiles & Associations


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Crime Data


Surveillance Data

Communication Data

Financial Data



Criminal Career Scoring Model

MO Typology Model

Crime Scene Assessment Model


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Crime Data


Surveillance Data

Communication Data

Financial Data




MO Typology Model


Arresting Officer

Case AssignmentOfficer

CSI Resource Planner

Alert!Aspiring Repeat Offender Risk HIGHAdvise DA and inform parole officer

Alert!Serial Crime ProfileMO fits Team 4

Alert!Very Low Likelihood EvidenceProbability <10% No Deployment


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Crime Data


Surveillance Data

Communication Data

Financial Data




MO Typology Model


Investigative Model Template Repository

Arresting Officer



Investigating Officer


Feedback resultsFeedback loop of new data to improve and adapt predictions





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Crime Data


Surveillance Data

Communication Data

Financial Data




MO Typology Model



Arresting Officer



Analytical Process Automation & OptimizationAutomate prediction & deployment process

Analytical Process Management & ControlMonitor & manage analytics process








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Crime Data


Surveillance Data

Communication Data

Financial Data




MO Typology Model



Arresting Officer



Analytical Process Automation & OptimizationAutomate prediction & deployment process

Analytical Process Management & ControlMonitor & manage analytics process


Management Dashboard







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Start from business understanding… not from data or technique…



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…and use a methodology!


Questions


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© 2011 ibm corporation informatie analyse laila fettah – associate sales engineer spss 27 january...

Documents

ibm corporation informatie

gartner group slide

large data stores

type of crime

count of crime type

useful information

understanding of crime

new crime