This document discusses using Neo4j and graph databases for intelligence-led policing. It introduces intelligence-led policing as an information-based model that aims to provide strategic knowledge and a targeted approach to crime reduction. The document outlines the POLE data model for representing people, objects, locations, and events in a graph. It then demonstrates Neo4j for implementing a graph database using crime data from Greater Manchester, with nodes for crimes, locations, people, and relationships between them. Finally, it discusses ways the demo could be extended for real-world use, such as adding more detailed relationships and supporting data traceability.

1. Intelligence-led Policing with
Neo4j and the Crime Investigation
Sandbox
Joe Depeau
Sr. Presales Consultant, UK
21st January, 2019
@joedepeau
http://linkedin.com/in/joedepeau

2. • What is Intelligence-led Policing?
• What is the POLE Data Model?
• Why Neo4j?
• Introduction to the Property Graph Model and Cypher
• Demo
• Extending the Demo for Real-world Use
• Q & A
2
Agenda

3. What is Intelligence-led
Policing?
3

4. 4
Intelligence-led Policing: a Definition
”Intelligence-led policing is a business model and managerial
philosophy where data analysis and crime intelligence are pivotal to
an objective decision-making framework that facilitates crime and
problem reduction, disruption, and prevention through both
strategic management and effective strategies that target prolific
and serious offenders.” *
* Source: Ratcliffe, JH (2008) ‘Intelligence-Led Policing’ (Willan Publishing: Cullompton, Devon)
TL;DR: Intelligence-led policing is an information-based operating model that aims to
provide law enforcement with strategic, actionable knowledge and a targeted approach
to combating crime.

5. 5
Benefits of Intelligence-led Policing
• Enhances, rather than replaces, traditional investigative methods
• Standardised approach to gathering, coordinating, and
disseminating intelligence
• Law enforcement organisations can utilise their limited
resources more efficiently
• Enables a strategic, proactive approach to law enforcement
• Investigators can use the same tools as modern criminals
• Improved identification and evaluation of crime trends
• Particularly good at identifying and targeting high-rate, chronic offenders
• Better information and intelligence sharing among different agencies and geographic partners.
• Informs the management of risk in law enforcement

6. 6
But … what about Big Brother?!

7. What is the POLE Data Model?
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8. 8
The POLE Data Model Vehicles
Evidence
Weapons
Documents
Emails
Phones
Victims
Suspects
Witnesses
Investigators
Employers
Family Members
Crimes
Arrests
Meetings
Data Transmissions
Phone Calls
Interventions
Crime Scenes
Home Addresses
Places of Employment
Public Buildings
Landmarks
Travel Destinations
Objects
Persons
Events
Locations

9. • Policing
• Counter Terrorism
• Border Control / Immigration
• Child Protection / Social Services
• Missing Persons
• Offender Rehabilitation
9
POLE Use Cases
Real Time
Proactive
Reactive
Insights

10. Why Neo4j?
10

11. 11

12. Blank Slide
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13. 13
Neo4j — Changing the World
ICIJ used Neo4j to uncover the world’s largest
journalistic leak to date, The Panama Papers,
exposing criminals, corruption and extensive
tax evasion.
The US space agency uses Neo4j for their
“Lessons Learned” database to connect
information to improve search ability
effectiveness in space mission.
eBay uses Neo4j to enable machine
learning through knowledge graphs
powering “conversational commerce”.
Knowledge Graph for AIFraud Detection Knowledge Graph for humans

14. 14

15. Introduction to the Property
Graph Model and Cypher
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16. 16
Car
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Anatomy of a Property Graph Database
Nodes
• Represent the objects in the
graph
• Can be labeled
Relationships
• Relate nodes by type and
direction
Properties
• Name-value pairs that can go on
nodes and relationships.
LOVES
LOVES
LIVES WITH
OW
NS
Person Person

17. 17
A unified view for ultimate agility
• Easily understood
• Easily evolved
• Easy collaboration between
business and IT
#1 Benefit: Project Agility
The Whiteboard Model Is the Physical Model

18. 18
MATCH (:Person { name:“Dan”} ) -[:LOVES]-> (someone)
LOVES
Dan Ann
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE
Introducing Cypher

19. Demo
19

20. 20
Neo4j Sandboxes
• Database environments hosted and
managed by Neo4j
• Completely Free!
• Accessible from your web browser
• Cover a wide variety of use cases
• Include data, step-by-step guides, and
sample queries
• Run for 3 days, extendable to 10

21. • UK street-level crime data is freely available from data.police.uk
• We will be looking at street-level crime data from the Greater Manchester Police for the
month of August 2017
• The crime data provides unique crime IDs, longitude and latitude (at street or ‘block’
level), month, crime type, and last outcome
• The crime data does not include personal identifiers (not even anonymised tokens)
• Longitude/latitude values were translated to UK postcodes using public APIs
• Random data was generated for people, officers, phone calls, crime date, etc.
• The crime and random data where combined and curated to create the demo
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About the Demo Dataset
○ Locations: 14,904 ○ Crimes: 28,762 Relationships: 105,853
○ Officers: 1,000 ○ Persons: 368

22. 22
Let’s have a look!

23. Extending the Demo for
Real-world Use
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24. • Include ‘Personas’ instead of ‘Person’ to account for things like aliases.
• A richer set of relationships between Persons and Crimes (Witness_To, Victim_Of, Suspected_Of,
Convicted_Of, Reported), Locations (Works_At, Visited, etc.), and Objects (Owner_Of, Driver_Of).
• Supporting traceability and auditing of data. For law enforcement it’s very important to understand
the lineage of your data (who entered the information and when, who updated it, has it been
verified, etc.), and to be able to demonstrate you have the right to hold that information (i.e. was it
discovered as part of an investigation, is it publicly available, etc.).
• A robust security configuration, to restrict data access to those who have the right authorisation.
• Adding weighting to our searches and algorithms - for example some crimes might be considered
more dangerous than others (i.e. Violence and Sexual Offences are more serious than Shoplifting),
or some relationships might be considered closer (i.e. ‘Family’ or ‘Lives With’ may be weighted more
than ‘Social Network’).
• Load more data!
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Ways the demo could be extended

25. Q & A
25

26. Thanks for your time!
(the end)
26

27. • Open policing data for England, Wales, and N. Ireland: http://data.police.uk
• Neo4j Graph Algorithms: https://neo4j.com/developer/graph-algorithms/
• Neo4j Sandboxes: https://neo4j.com/sandbox-v2/
• Neo4j Community Site: https://community.neo4j.com/
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Useful Links