Predictive Analytics for Law Enforcement: In Theory and In Practice

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Predictive Analytics for Law Enforcement: In Theory and In Practice

Predictive Analytics for

Law Enforcement: In

Theory and In Practice

35 th Annual IACP Law Enforcement Information

Management Training Conference and Exposition

June 15, 2011

1

Company Confidential - For Internal Use Only

Copyright © 2010, SAS Institute Inc. All rights reserved.


Agenda

In Theory, In Practice

• Vincent Talucci, Principal Advisor –Law Enforcement, SAS

Las Vegas: Practical Challenges

• Patrick Baldwin, Manager – Homeland Security Bureau / Analytical

Section, Las Vegas Metropolitan Police Department

Las Vegas: Practical Solutions

• Jodi Blomberg, Principal Technical Architect, SAS

Questions and Answers

2

Company Confidential - For Internal Use Only

Copyright © 2010, SAS Institute Inc. All rights reserved.


Company Confidential - For Internal Use Only

Copyright © 2010, SAS Institute Inc. All rights reserved.

3


Predictive Analytics: What It Is Not

It is not a crime fighting panacea

It will not predict where the next crime will occur

It will not identify the next offender

It will not solely maximize your agency’s resources

It is not a strategy

4

Company Confidential - For Internal Use Only

Copyright © 2010, SAS Institute Inc. All rights reserved.


Predictive Analytics: What It Is

It is a tool:

• To help prevent crime and disorder

• To determine probability of occurrence geospatially

• To unearth previously undetected patterns and relations

• To maximize your agency’s finite resources

• To support your agency’s policing strategy

• That comes in many forms and builds upon your agency’s

existing data sources

5

Company Confidential - For Internal Use Only

Copyright © 2010, SAS Institute Inc. All rights reserved.


Company Confidential - For Internal Use Only

Copyright © 2010, SAS Institute Inc. All rights reserved.

6


Predictive Analytics: How?

Prevention of crime, crashes and disorder through predictive

analytics based on existing data sources and other identified

variables

Strategic allocation of finite resources via probability of crime or

crash occurrence

Data‐driven problem identification to ensure comprehensive policy

and operational responses

Generating timely, actionable insight via:

• Accessing near real‐time data

Integrating and validating disparate, relevant data sources

• Analyzing unstructured data to add context to structured data

• Providing illustrative snapshots to decision‐makers on unearthed

safety issues

• Forecasting probability of occurrence based on coalesced data

and other variable drivers (e.g. weather, time of day, time of year)

7

Company Confidential - For Internal Use Only

Copyright © 2010, SAS Institute Inc. All rights reserved.


LAS VEGAS METROPOLITAN

POLICE DEPARTMENT

DOUGLAS C. GILLESPIE, SHERIFF

Predictive Analytics

IACP LEIM, June 15, 2011


SNCTC Overview





14 Partner Agencies

24/7 Watch Desk and analytic component

Crime Analysis Group

• 18 Crime Analysts

• 6 Watch Desk Officers

Counter-Terrorism Analysis Group

• 7 Intelligence Analysts

• 3 Investigative Specialists


SNCTC Overview


Operational Component

• Counter-Terrorism Detectives

• ARMOR Unit

• Terrorism Liaison Coordinators


Predictive Analytics



What does this mean to an analytic

section?

Data

• Structured and Unstructured

• Future Data?

• Historical Data


Predictive Analytics



Types of Models

• Assessment or Verification

• Standardization

• Effectiveness/Resource Allocation

Las Vegas Lessons

• New Year’s Eve 2009

• Creation of Critical Infrastructure

Queries

• Las Vegas Boulevard


Predictive Analytics

• Crime

• Criminal Precursors to Terrorism

• Special Events

• Operation Safe Strip


Predictive Analytics: Using Text

Text Mining: discovering and extracting

meaningful patterns and relationships from text

• Apply Natural Language Processing to the docs

– Parse out words, terms, proper nouns, etc

– Determine synonyms, phrases, entities, etc.

• Use it for analysis and investigation


Text Mining Example: Las Vegas

Call notes narrative is:

“BTWN TROPICANA & MGM ON BRIDGE

WALKWAY,,SUBJS ARE 416'G Original Location :

TROPICANA HOTEL”

Classify this as occurring “on the street” instead

of at the Tropicana


Street Violence at Casino Nightclubs


Box Truck: Suspicious vs. Not

Suspicious


Chemical Substances

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