slide 1 1 “the geek and the gumshoe” or “can mathematics and computers really solve crimes?”...

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“The Geek and the Gumshoe”

or “Can Mathematics and

Computers Really Solve Crimes?”

Michael “theprez98” SchearerFrank “Thorn” Thornton

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Introduction

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Who are we, and why are we here?

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The Geek: Michael Schearer

Fascinated by the application of mathematics to real-world situations

Recently separated from nearly 9 years in the U.S. Navy (flying aircraft)

Currently working for a U.S. government contractor in Maryland (flying a desk)

Contributing author to Penetration Tester's Open Source Toolkit (Volume 2) Netcat Power Tools (April 2008), and maybe more!

Football coach and proud father of three

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When did Frank start as a cop?

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Looking forward…

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The Gumshoe: Frank Thornton

Law Enforcement Officer, 1980 – 2002. Served in a variety of ranks and positions from Patrol Officer to Chief of Police. Also worked in VT Forensic Lab on Latent Fingerprints and crime scene investigations. Rated as a Class I (Homicide) Death Investigator by Vermont’s Office of the Chief Medical Examiner

Hacking computers since ~1973 Helped create ANSI Standard “ANSI/NIST-CSL

1-1993 Data Format for the Interchange of Fingerprint Information”

Author and co-author of a half-dozen books on computer security. (Cheerfully blatant plug!)

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Agenda

Introduction– Explanation– Videos– Perceptions

Math, Computers & Crime– Math in everyday life– Math and crime-fighting

Conclusions Questions & Answers

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Police Investigations Are ALL About Collecting Data

WhoWhatWhenWhereHow

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Investigations differ from other data collection in several areas

Everyone lies to the police. Fact has to be separated from:

-Lies.-Fiction.-Opinion.-Other false positives. (May be thousands)

Eye witnesses have a high credibility with prosecutors and juries, less so with cops.

Everyone lies to the police. Failure can be dangerous to the public. Did we mention that everyone lies to the police?

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Information = Data

This is sometimes recognized at some level. Joseph Wambaugh, ex-LAPD Detective, award winning mystery novelist and screenwriter wrote this in The Black Marble:

“Clarence looked around at the roar of activity, at the grinding paper mill. Paper everywhere. Take away my gun and car, but please don't take my pencils.”

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Information = Data

“You walked in with information and a pretty face.

You can’t leave with both.”

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Doesn’t it really work like on CSI?

The CSI Effect Perception and Reality

– DNA Testing– AFIS Searches– School-Associated Violence– Cops are always doing exciting things like

getting in fights or shooting bad guys

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So, knowing all that, what other tools are available to help

investigations?

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So let’s explore how math and computer technology can help

with investigations…

“Time for science!”

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Math is everywhere.

Elections– Voting, exit polls, voter identification/analysis

Sports– Statistics, sabermetrics, betting/sports book

Lottery– Probability (or perhaps improbability!)

Math in advertising– frequency atlas, Google advertisements, British

two pound coin

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Billboard say what?

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What is wrong with this picture?

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Can Mathematics and Computers Really Solve

Crimes?

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Crash Reconstruction

Collision evidence– positions of rest, skid marks, roadway markings,

damage to vehicles, damage to property Other evidence

– Witness recollections, traffic control devices, weather conditions, lighting issues

Available specifications– Newton’s laws of motion

Collision reconstruction techniques– Damaged-based– trajectory-based

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Image Deblurring

“Enhance…enhance…enhance…” Blurring is typically caused by movement

during the capture process by the camera or by the subject, or an out of focus lens

Deblurring involves finding a mathematical description of how the image was blurred

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Image Deblurring

Before… After

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Image Deblurring

A camera captured this image

Image deblurring produced this image

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Deblurring Fingerprints

A very touchy subject! By deblurring a fingerprint, are non-existent details being added to a latent print?

Typically, any enhancement (fingerprint or otherwise) must be verifiable and able to be duplicated by another expert

The risk in “crossing the line” is highly dependent upon use of tools

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Fingerprint Matching

Different vendors use different algorithms 10 different Fingerprint Individuality models Minutiae matching vs. Pattern matching Speed and throughput vs. accuracy Error rates

– Type I (FP, FRR) vs. Type II (FN, FAR)– Crossover or Equal Error Rate– Security vs. Forensic Science

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Receiver Operating Characteristics

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Fingerprint Classification

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Escape Math Variables

– Time, Method of travel, Achievable speeds, Traffic density, Traffic choke points

Dijkstra’s algorithm– Link-state routing protocols (OSPF), MapQuest,

Google Maps Random walks

– Calculate distance escaped POWs could travel in WW2

Trawler problem Drive-time calculations (MapPoint) Social network analysis (to be discussed

later)

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Dijkstra’s Algorithm

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Random walks

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Trawler problem

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Narrowing the Suspect Pool

Profiling– Psychological/criminal– Geographic (to be discussed later)

Venn diagrams

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Social Networks Social network analysis

– Google’s PageRank algorithm is an example of network analysis

– Organized crime, gangs, terrorist cells, individuals, other organizations

– Social relationships in terms of nodes and ties– Determine the social capital of individual actors

Things to consider– Who are someone’s closest friends/associates?– Where might that person flee to?– Structural cohesion: could you eliminate a

specific individual from a group which could cause that group to collapse?

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Social network of a project team

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Social network of 9/11 terrorists

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Crime Mapping

Choropleths Pin Mapping Hot Spot Analysis Geographic Profiling

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France, 1829

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London, September

1854

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Choropleths and Pin Mapping

NYPD has used traditional pin mapping since at least 1900

University of Chicago researchers mapped crime in Chicago neighborhoods (1920-30s)

These methods of mapping helped to identify relationships between crime and neighborhoods, social disorganization, poverty, and physical deterioration

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Automated Crime Mapping

Automated mapping began in the late 1960s– Did not really “take off” until the 1990s

Hot Spot Analysis– Finding geographic concentrations of types of

crimes; finding causes for those hot spots; aggressive policing in those areas

Geographic profiling– If psychological profiling tells you “who”,

geographic profiling tells you “where”

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Hot Spot Analysis

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Hot Spot Analysis

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Geographic Profiling

If Psychological/criminal profiling tells you “who”, geographic profiling tell you “where”

Suitable for serial crimes: murder, rape, robbery, arson, predatory crimes

Gives police a starting point from which to narrow down lists of suspects

Does not replace traditional investigative techniques, but supplements them to help manage the large volume of information

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Geographic Profiling

CrimeStat, Dragnet, Predator, Rigel Theory is based upon “journey to crime” and

“principle of least effort” “Journey to crime” varies among type of

crime, age, race, etc. Includes a buffer zone around the offender’s

home or base of operations

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Saanich Serial Arsonist

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Jeopardy Surface

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Probability of Offender Residence

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GeoProfile

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Some Other Examples

Spherical Trigonometry: Determining position on Earth based on two like photographs (with a few caveats…)

Prisoner’s dilemma: Is it better to cooperate or defect?

Steganography and covert channels: Finding hidden information

Predictive Analysis: Predicting the location of a serial event

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The Future is Now: RTTC

26-member staff, on 24/7/365 15 workstations, divided among teams of

officers Each team has a particular assignment, such

as homicides or shootings Satellite imaging Precinct-by-precinct maps Ties together information to solve crimes 2-story tall projection screens

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NYPD Real Time Crime Center

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The Future is Now: RTTC

Cognos data warehouse, using IBM OmniFind 8.2 on SUSE Linux blade servers

Link Analysis Capacity can call up all known addresses for a suspect and known associates

When a crime occurs, any number of searches of public records are then run:– Over 5 million NYS criminal records, parole and

probation files– Over 20 million New York City criminal complaints,

911/311 calls and summonses spanning five years– Over 31 million national crime records– Over 33 billion public records.

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Conclusions

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References

Mark Bridger, Northeastern University Valdis Krebs @ orgnet.com David Weisburd and Tom McEwen, “Crime

Mapping and Crime Prevention” Dr. Kim Rossmo, Texas State University Rob Gebeloff, NJ Star-Ledger Dr. Raymond Chan, CUHK Mitsubishi Electronic Research Laboratories Zeno Geradts, Netherlands Forensic Institute Henry C. Lee and R.E. Gaensslen, Advances in

Fingerprint Technology, 2nd Ed.

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Questions & Answers