ICIAC 2011 Stream 3: Classes and workshops

Abstracts and slides

Class 3C: Understanding Organised Crime  through Organised Crime Group Mapping (OCGM) (I)

Mike Baker and John Unsworth, ACPO National Coordinators Office for Organised Crime

OCGs dominate much of the criminality that has a considerable impact on the UK. Organised crime manifests itself through harms in local communities. It is at this local level that criminal markets exist and it is where violence and the exercise of control take place.

The face of organised crime has changed, the threat is now broader and more complex than ever. The local impact represents the end of a continuum that often starts on another continent that stretches across the world. Globalisation and new technologies have led to new types of offending and created constant challenges to LEAs and Governments of being able to keep pace with, let alone get ahead of criminal capability.

In order to improve our knowledge and understanding, the OCGM process was designed and introduced across UK LE. It follows a problem solving methodology of identify, assess, manage and review. OCGM allows us to better understand the threats, risks and harm, and allows for recognition of opportunities to minimise and mitigate it. Through assessing an OCGs criminal activities, intent and capabilities we are able to identify and prioritise OCGs and their criminality for intervention.

Exploiting the knowledge and understanding of OC that OCGM provides allows LEAs and partner agencies to inform strategic and tactical decision making.

SORRY. SLIDES NOT AVAILABLE

Class 3D: Advanced hotspot analysis - spatial significance mapping using the Gi* statistic (A)

Spencer Chainey, Department of Security and Crime Science, University College London

In this class we explore the use of the Gi* statistic as a practical method for incorporating spatial statistical significance testing into hotspot analysis. The workshop begins by briefly reviewing the utility of common hotspot mapping techniques: spatial ellipses, choropleth mapping of administrative/jurisdictional geographic units (e.g. Census Tracts, Police Patrol Neighbourhoods), choropleth mapping using grid cells, and kernel density estimation (KDE). Research shows that KDE is the best of these common techniques, however it is not without its flaws – the main two relating to its tendency to ‘oversmooth’ and draw the map readers attention to the larger ‘blobs’ at the expense of smaller, highly concentrated areas of crime; and the lack of rules for systematically determining choropleth legend threshold values, leaving it to the ‘whims and fancies’ of the map designer to produce a KDE map with many hotspots, or very few (using exactly the same data).

The focus of the workshop is to explain in full the principles behind the Gi* statistic and its use as a technique for spatial significance mapping. This includes a step by step guide on how to use the Gi* statistic and advice for determining appropriate parameter settings. We show evidence from research that the Gi* statistic goes beyond KDE because it can identify (in map form) those areas where the clustering of crime points is significant. That is, it can determine areas that can be statistically defined as hot from those that are not, plus represent this thematically by the level of statistical significance i.e. 90%, 95%, 99% or 99% confidence levels.

We illustrate the use of the Gi* statistic by using the free Excel-based software, Rook's Case (with outputs being imported into any GIS software), and the functionality in standard ArcGIS. Delegates who attend the class will be able to leave feeling confident that they can apply these techniques, and refer to video clips of captured on-screen operations that we will post online that will remind them of the process.

Presenter's slides: ICIAC11_3D_SChainey

Class 3E: Improving Community Safety Partnership intelligence-led business processes (G)

John Chapman, Drug Action and Community Safety Team, Dorset County Council

This session will focus on the value of an evidence-led approach to Community Safety Partnerships, and how maximising the use of data and analysis are central to an effective partnership business framework.

Intelligence-led business processes are a key hallmark of Delivering Safer Communities guidance, generating an expectation that Community Safety Partnerships are compliant with the business principles of the National Intelligence Model (NIM).

The basic underlying principle of the NIM – the use of crime analysis to facilitate an intelligence-led approach to problem solving – is key. With a wide range of community safety issues to address, partnerships need to prioritise their efforts to reduce crime and disorder in local communities effectively. Routine analysis of a broad range of evidence from partner agencies is therefore vital in helping to provide a more holistic understanding of the dynamics of crime, and in supporting the identification of priority areas and prevailing risks.

Certain analytical products are central to informing the partnership knowledge base. Strategic Assessments are a core component of the intelligence-led decision making process, and this session will outline their intended role, scope and benefit in identifying the key issues and threats in local areas, and helping determine the strategic priorities that require attention. It will also explore the actual process for producing a Strategic Assessment, including critiques of current approaches and elements for success in producing an effective document. The session will also discuss the role of other analytical products such as tactical assessments and problem profiles, how they should link with strategic assessments, and their value to the overall partnership business framework and planning cycle. The importance of robust performance management regimes to partnerships will also be outlined.

The emphasis of this session will be on audience interaction, with attendees encouraged to share their experiences and successes in regard to the key themes discussed. 

Presenter's slides: ICIAC11_3E_JChapman

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