“...criminal acts require convergence in space and time of likely o�enders, suitable targets and the absence of capable guardians...” (Cohen and Felson, 1979)

Discrete Choice Methods

Graph Theoretic Methods

Example Practical Outcomes

Forthcoming Publications

Example Theoretical Outcomes

Analysing and predicting crime using discrete choice and graph theoretic methods

Mr. Michael Frith (uctzmjf@ucl.ac.uk), Prof. Shane Johnson and Dr. Hannah Fry

Traditional methods for analysing this convergence either ana-lyse the target or guardianship but ignore the o�ender (regres-sion methods). Or they analyse the o�ender but ignore the target or guardianship (journey-to-crime methods). Discrete-choice methods can analyse these simultaneously.

These methods work by modelling o�ence locations as a choice where the o�ender chose that location above all others. These models then relate that choice to the attributes of the person (the o�ender) or to the attributes of the alternatives available.

Frith, M., Johnson, S.D., and Fry, H. An analysis of residential burglars and guardian-ship using graph theory and mixed logit. Submitted for peer review (Criminology).

Frith, M., and Johnson, S.D. A comparative analysis of the o�ence location choices of di�erent types of serious acquisitive crime o�enders in York (UK). In preparation.

Frith, M., Johnson, S.D., and Fry, H. A meta-analytic review of graph theoretic and space syntactic methods for estimating pedestrian and vehicular movement �ows. In preparation.

Traditional analyses also simplify the mechanics of this conver-gence and ignore how the street network determines navigation and movement. For example, how o�enders �nd and reach tar-gets and where potential victims and guardians may accumulate.

Here, graph theory can be used to analyse the con�gurational ef-fects of the built-environment. For example, existing metrics (e.g. betweenness) can be used to estimate aggregated movement �ows and novel idiosyncratic metrics (in forthcoming paper) may estimate an o�ender’s movements and their ‘awareness space’.

These methods and results will then be applied and evaluated for their practical uses in:

Geographic Pro�lingGiven a series of connected o�ences, can we use o�ence location preference data, and for example the new idiosyncratic graph theory metrics, to improve current methods for identifying the most probable area than an o�ender resides or otherwise frequents?

Crime ForecastingGiven o�ence location preference data (in-cluding on repeats and near-repeats), and the locations of o�enders (or an assumption of their geographic distribution), can we im-prove on current methods for predicting of-fence locations and hotspots?

O�ender Pro�lingGiven o�ence location data for a speci�c of-fender, we can estimate their speci�c prefer-ences. This can then be used, for example, for pre-emptive patrols or strategic housing (by probation) of ‘ex-o�enders’ away from their preferred and tempting targets.

Targets - When selecting targets, do o�enders follow a hierarchical ‘hunting process’? If so, at what spatial units? (See A)- For non-stationary targets (e.g. robberies), can we use the victims’ estimated movements and their journey-to-crime to model and analyse their convergence with the o�ender?

Guardianship - Can we accurately model movement �ows using graph theory? And if so, can we do this for di�erent times of the day using travel journal trip distributions? (See B) - Can we model the quality (and quantity) of passer-by guardianship using local-weighted graph theory metrics?

O�enders - Can we accurately model an o�ender’s likely movements and their awareness space? - By allowing preference heterogeneity amongst o�enders, how do their preferences vary? And can we use this to quantitatively build typologies of o�enders? (See C)

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Individual targets

Area-level a�uence

Passer-by volume

CCTV sites and lines of sight

Proximity to o�ender’s home

Awareness space

Real world

Morning Afternoon Evening

‘Target Area’ ‘Target Street’ ‘Actual Target’

A

B

C Distance

A�uence

Dislikes Likes

Preference HeterogeneityHeterogeneity modelled

into discrete classes

Type A:Prefers closer targetsIndi�erent to a�uence“Marauder”

OverallType AType B

Dislikes Likes

Type B:Indi�erent to distancePrefers a�uent targets“Commuter”

Interpret classes/typologies

To City Centre

Area AArea B

Before Current Future(Actual)

PredictedTraditional

MethodUsingThis

Research

OverallType AType B

Hostel A (Not Preferred)

Hostel B(Preferred)

For pre-emptive patrols

Most probablehome location

School

Shops

Bars

School

Shops

Bars

School

Shops

Bars