Quantitative Profiling of
Trafficking Networks;
“Rumsfeldian” Spaghetti
and the Combination of
Science and Art
Jonathan Dalley CEng MIET
Sensor Systems Group
Dstl Sensors & Countermeasures
Room 201-C, Building 005
Dstl Porton Down
Salisbury
Wiltshire SP4 0JQ
United Kingdom
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Telephone : +44 (0) 1980 65 8754
Email :
[email protected]
“There are known knowns; there are things we know we know.
We also know there are known unknowns; that is to say we
know there are some things we do not know.
But there are also unknown unknowns; there are things we do
not know we don't know”
US Secretary of Defence Donald Rumsfeld 12/02/2002
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Scope
• Discuss Networks and Analysis of;
– Open
– Closed
– Partially Open
• Worked example
• Model Maturation
• Conclusions
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Introduction
• Art vs Science, Intelligence vs Analytics
– Intelligence
• Objective/qualitative analysis of information gathered by
observation, interview and intercept.
– Analytics
• Subjective/quantitative analysis based on the application of
statistical models and analysis against measured data.
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Network Science
The National Research Council defines Network Science
as "the study of network representations of physical,
biological, and social phenomena leading to predictive
models of these phenomena.“
• For the purposes of this presentation I have defined
networks as being either;
– Open
– Closed
– Partially Open
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Open Networks (known known)
• Characterised by access available to measure
all aspects of the network
• Measurables define the network
• Examples:
– Passenger flight networks
– Postal network
– Road network
– Production line
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Closed Networks (unknown
unknown)
• Characterised by an inability to observe cause, effect or
function.
• Existence comes out of an recognition that there are
networks acting that we are not aware of.
• Logically impossible to quantify.
• Examples: N/A
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Partially Open Networks (known
unknown)
• Characterised by observed network effect without
quantifiable knowledge of the network interactions.
• Quantified by observation of the effect/output of the
network. Often the only directly measurable aspect.
• Examples:
– Illicit trafficking of Drugs/Humans/Lethal Aid/Immigration
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Terminology
• System
– A set of interacting or interdependent components forming an
integrated whole
• Sub-System
– Sub-component of a system
• Super-System
– A system of systems
• Macro-System
– A system of super-systems
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Worked Example, Exercise Scenario
• Cigarettes have been made illegal in London
• There is continued demand for cigarettes
– Public supplied through ad-hoc illegal road-side salesmen
– Assessed to be run by territorial gangs
• Facilitation/supply assessed to be collaborative but may
include extensive supplier network
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Observation of Cigarette Salesmen
Activity
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Wembley
City
Croydon
Identification of Gang Laydown
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Assumption Generation
• Assume uniform rate of arrest / police counter cigarette
activity
• How many stores have been interdicted?
• Where gang boundaries overlap a 50:50 distribution of
activity is assumed (initially)
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Quantities of Cigarettes by Gang
Randomly Generated Figures (Example)
Total Cigarettes Interdicted (2011), KG
90.00
80.00
70.00
60.00
50.00
40.00
30.00
20.00
10.00
0.00
Coydon
Bromley
Kingston Wembley
Barnett Woodford
Green
City
Total Cigarettes Interdicted (2011), KG
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Identification of Gang Interactions
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External Influence/Supply
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Definition of Points of Entry
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Definition of Gang Interactions
15%
10%
15%
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Quantify Boundary Interactions
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Quantify Points of Entry
(Tactical/Operational Bdry)
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Quantify Gang Feed and Nodes
(Tactical Connectivity)
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So What?
• A lack of activity is indicative of something
• Non-Operational feed network elements could be
indicative of various results:
– Rival gangs
– Separate system
– Different role (e.g. not sales orientated)
– Inactivity in Bromley indicative of:
• Northern supply focus?
• Facilitative focus?
• Highest level of flow within the network
• Largest point of entry into the network
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Quantify Operational & Strategic Connectivity
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Maturing the Model
• The example shown is a fairly simple model
• The in the previous example, the model can be further
matured through the maturation of assumptions:
– Population Density (proxy for criminality density)
– Security force density (may influence arrest/activity rate)
• By population
• By geographic area
– Etc.
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Constraints
• Limited by
– Intelligence Understanding
– Robustness of assumption
– Common data capture
– Knowledge of whether you are correct
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Conclusions
• This approach has been developed from a tactical
perspective.
• The tactical network will be inherently “Wicked”
– Any imposed constraint/change will imply change in the network
• It has not been tested at the operational/strategic level
where it may be more stable to imposed change.
• Benefit may be observed in diverse subject networks.
• This work has been developed in isolation.
– External feedback and common experiences are appreciated.
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Questions
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