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How to Predict a Serial Arsonist Eva Ljungkvist Björn Granvik

How to Predict a Serial Arsonist

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Serial arsonist are like most people. Habits drive us even when putting a house fire. As such they display patterns in their modus operandi which are visible. In this session we cover what we learnt so far and how we believe that working across disciplines stand a greater chance of predicting the arsonist's actions.

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Page 1: How to Predict a Serial Arsonist

How to Predict a Serial ArsonistEva Ljungkvist Björn Granvik

Page 2: How to Predict a Serial Arsonist

Dan Granvik !former County Sheriff’s Department Special Unit focusing on Organized Crime & Repeat Offenders

Eva Ljungkvist !Fire protection engineer Arson investigator i.e. Fire-nerd

Björn Granvik !Civil Engineer Programmer i.e. IT-nerd

Page 3: How to Predict a Serial Arsonist

• Like most peoplehabits, patterns, …

• Modus operandi

• Evidence?

The Serial Arsonist

Page 4: How to Predict a Serial Arsonist

Copper

Page 5: How to Predict a Serial Arsonist

High risk?

Page 6: How to Predict a Serial Arsonist
Page 7: How to Predict a Serial Arsonist

© Guillaume Baviere, Flickr

Page 8: How to Predict a Serial Arsonist
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Method Triangulation

Page 10: How to Predict a Serial Arsonist

Powered by Fika

Page 11: How to Predict a Serial Arsonist

Timings

Device Primed Device Ignited Fire Discovered

Case 33 23:19

Case 34 07:12

Page 12: How to Predict a Serial Arsonist

In the Head of Dan

© TheKarenD, Flickr

Page 13: How to Predict a Serial Arsonist

In the head of an Arsonist?

• Data is too complex

• …and hidden

• Power in relationships

© Jenny Spadafora, Flickr

Page 14: How to Predict a Serial Arsonist

A Graph• Node

”noun”

• Relationship”structure”

• Properties

Aname: ”Björn” headsize: 60

technology: ”Neo4j”

strength: 10

B

LIKES

C USES

name: ”Eva”

USES

Dproject: ”Helmuth”

USED_IN

HAS_MEMBER

Page 15: How to Predict a Serial Arsonist

Pattern Matching

A

B C

Page 16: How to Predict a Serial Arsonist

Pattern Matching

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An example

Hospital

Arson Device

name: ”Heimlich Hospital” adress: ”21 Prune Street" city: ”Snicket Town"

lives_at House A

name: ”Hus A” adress: ”22 Prune Street" city: ”Snicket Town"

description: ”Toarulle”

Fire

description: ”Big poff” with

happened_at

Timings

name: ”Count Olaf”

next_to

started

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Example Queries◦ Do we have a pattern, a serial arsonist?!◦ What new targets? High vs. low risk?!◦ Where and when should we apply preventive

measures?

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How to Predict?

◦ Crossover ◦ Support complex

world descriptions ◦ Usability

© Moyan Brenn, Flickr

Page 20: How to Predict a Serial Arsonist

Results so far◦ Weather conditions!◦ Time frame!◦ Trends of arson !◦ Location & arson device!◦ High risk objects & the need of fire protection!◦ Worst case scenario & the need of resources!◦ Physical security & immediate surroundings

Serial

Page 21: How to Predict a Serial Arsonist

Eva Ljungkvist!

!

[email protected]

http://evasbrandblogg.se/

Björn Granvik!

!

[email protected]

@bjorngranvik

http://bit.ly/bjorngranvikblog

Thanks!