|Presenters' slides and posters - International Crime and Intelligence Analysis Conference, 25-26 February, Manchester (UK)|
WHAT WORKS CLASSES
27 September 2016
7-10th November 2016
4th October 2016
24th May 2016
6th December 2016
17th May 2016
7th July 2016
12th July 2016
15th November 2016
5th-16th September 2016
ICIAC 2012 Stream 4: Classes and Workshops
Abstracts and slides
CLASS 4C: Understanding patterns and relationships in spatial data (I)
Brett Rose, Technical Advisor - Federal Technology Center, ESRI
This session will introduce you to spatial pattern analysis and relationship modeling (regression) using tools in the Spatial Statistics tools available in GIS. Attendees will see how these tools may be used to (1) summarize and evaluate geographic distributions; (2) identify statistically significant spatial outliers and spatial clusters (hot spots); (3) describe geographic patterns and trends over time; (4) reveal underlying structures in your data based on multiple variables, spatial and temporal constraints; (5) explore data relationships to help answer “why?” questions, such as “why do we see so higher crime in particular areas?”. With examples from public safety, incident analysis and intelligence, the tools presented in this session will help you find patterns and relationships in your data, facilitating discussion, contributing to research, and informing decision making. The workshop will focus on a broad range of tools in the Spatial Statistics toolbox, including several new tools in ArcGIS 10.1.
CLASS 4D: Measuring repeat and near repeat victimisation (I)
Shane Johnson, Department of Security and Crime Science, University College London
Slides: Yet to be supplied by presenter
It is an understatement to say that the ability to perfectly predict when and where future crimes were to occur would be of considerable value to crime reduction agencies. While we are not there yet, there is much that we already know. For example, there are regularities associated with the timing and location of crime events that can inform methods of crime prediction. In particular, our research has shown that the risk of burglary at a dwelling increases following a first offence, and that incidents of such “repeat victimization” tend to occur swiftly. More generally, it appears that the risk of victimization clusters in space and time, with risk spreading much like patterns observed for contagious diseases. That is, in the case of burglary, neighbours of burgled homes also tend to be at a temporarily elevated risk of victimization following after the offence occurs. Where an offence occurs shortly after, and near to, a previous offence this is referred to as a “near repeat”.
Research demonstrates that near repeats are observed with a higher frequency than would be expected on a chance basis for burglary but also for crimes including theft from motor vehicle, cycle theft, shootings in the US, and even insurgency in Iraq. Where patterns of near repeats are robust, it is possible to make predictions regarding the timing and location of future crimes that are superior to forecasts generated using existing methods of crime hotspot analysis. In this session, I will start with a whistle stop of the research discussed above. However, the focus of the session will be on metrics and methods of measuring repeat and near repeat victimization, including a discussion of freeware that can be used to quantify and establish the reliability of patterns observed. Other types of analyses that may be useful in quantifying and understanding crime patterns will also be discussed. The session will finish with a brief discussion of the direction of our current work.
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