|Programme and registration - International Crime and Intelligence Analysis Conference, 25-26 February, Manchester (UK)|
WHAT WORKS CLASSES
27 September 2016
11th-14th April 2016
7-10th November 2016
19th April 2016
4th October 2016
10th May 2016
6th December 2016
17th May 2016
7th July 2016
12th July 2016
15th November 2016
5th-16th September 2016
ICIAC 2011 Stream 1: classes and workshops
Abstracts and slides
Class 1C: Identifying hotspots: an assessment of common techniques (G)
Spencer Chainey, Department of Security and Crime Science, University College London
A number of methods and techniques exist for identifying hotspots. Different methods produce different results, with some being more suitable than others for understanding where hotspots of crime occur. This workshop will explore the utility of point mapping, thematic mapping of geographic administrative areas, grid thematic mapping, and kernel density estimation for identifying hotspots. For each technique we determine its ability for helping to direct enforcement and crime prevention resources. This is on the basis of considering hotspot mapping as the most basic form of crime prediction - using crimes from the past to predict the future. We also explore the influence of KDE cell size and bandwidth settings. The workshop is interactive, explores the advantages and disadvantages between the techniques and the theory that underpin them.
Class 1D: The principles of problem solving (G)
Sylvia Chenery, Applied Criminology Associates
Proctor, EPIC, Solve, CMM, 5 I’s, SARA and ARA (yep, there was even one called ARA), are just a few amongst the many of models aimed at providing systematic instruction and guidance for decision-making and problem-solving skills. All are aimed at recognising and identifying risks; guiding you towards gathering information, steering you towards identifying suitable options and responses, and enabling you to monitor and assess benefits and harms. Most of us will claim to use at least one of the models, the most common being the SARA Model (Scan, Analyse, Respond, Assess); stemming from the blessed Herman Goldstein’s philosophy of reaching a state of ‘problem oriented’ working….way back in the ‘70s.
But after all these years, how good are we at problem solving…..in a systematic way that is…….really? Most of us will have had some successes, and can have claimed to have taken the problem(s) through its paces, and come up with solutions that probably did have some form of successful impact at the time. Why even some of you will have been a ‘Tilley’ winner. Nick Tilley being another ‘blessed’ soul who, like Herman, continues to strive for perfection, but is equally accepting of the failings of the human race in sticking at anything in a structured and methodical way. So why are ‘problem solving’ techniques still on the agenda in 2011….surely we know it all by now? Guess it’s a little like our lessons at school….we swotted for the exams, we learned to regurgitate the facts, but in life how often do we put them into practice? In this workshop we’ll discuss what’s needed in taking a ‘problem oriented’ approach, and look at what’s often left out, what’s frequently missing and then debate….do we really do it at all?
Class 1E: Advanced time series analysis (A)
Lisa Tompson, Department of Security and Crime Science, University College London
Measuring change in any phenomena over time requires the use of distinct analytical techniques due to the intrinsic temporal ordering of the data. Time-series analysis is used when observations are repeatedly made over a number of time periods (usually 50 or more) to determine characteristics of the data and meaningful statistics. All time-series data have three basic parts; a trend component, a seasonal component and a random component. General trend analysis of these data is therefore complicated, and requires the application of advanced statistical techniques.
This class will introduce time-series analysis to delegates and familiarise them with the analytic process. It will start by outlining some of the fundamental principles of time-ordered data; then, the class will work through a number of real-world crime examples that involve techniques such as seasonal decomposition and ARIMA (autoregressive, integrated, moving average) modelling. A thread running through the whole class will be the types of questions an analyst might want to answer with time-series analysis, and which techniques are the most appropriate for doing so.
Delegates will get the most benefit out of this class if they have some foundation knowledge of statistical testing. An awareness of programming would be helpful, but not essential by any means.
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