Phillips, Peter (2011) Mining cross-patterning in large areal aggregated spatial datasets. PhD thesis, James Cook University.

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Abstract

Intelligent crime analysis allows for a greater understanding of the dynamics of unlawful activities, providing possible answers to where, when and why certain crimes are likely to happen. With the growth of geo-referenced data and the sophistication and complexity of spatial databases, data mining and knowledge discovery techniques have become essential tools for the successful analysis of large spatial datasets. Crime analysis requires a combination of heterogeneous data, such as socio-economic and socio-demographic factors, geospatial features and crime datasets so that interesting patterns can be discovered.

The Queensland Police Service (QPS) and Australian Bureau of Statistics (ABS) record crime and census information in areal aggregated datasets. The primary reason for this is to protect the privacy of individuals. To enable intelligent crime analysis with these areal aggregated spatial datasets, the cross-patterning relationship between different spatial layers across locations needs to be modelled and quantified.

This thesis focuses on developing a framework for the discovery and visualisation of crosspatterning in areal aggregated spatial datasets. We show that cross-patterning can be modelled in three ways. Cross-association patterns that model the relationship between multiple datasets for each region while ignoring the effect of neighbouring regions, cross-varying patterns that take into consideration the effect of all local neighbouring regions and cross-distribution patterns that consider one local neighbouring region and the global distribution of the dataset. With areal aggregated datasets, local neighbours are defined as those that share a boundary.

The most suited type of cross-patterning is dependent on the application and dataset. We present a generic framework that can discover all types of cross-patterning from areal aggregated spatial datasets. The user can either select a specific type of pattern they wish to discover, or can use the framework to discover all cross-patterning and then use the visualisation environment to highlight patterns of interest.

We develop algorithms to discover each type of cross-patterning. To model cross-association we propose an Association Rules Mining (ARM) based approach. To model cross-varying we extend Lee's bivariate spatial correlation approach [51], and to model cross-distribution we propose two new techniques based on the spatial distribution of the dataset. The framework also includes a visualisation environment that easily allows the user to interpret discovered patterns and highlight patterns that show overlapping cross-patterning (for example both crossassociation and cross-distribution).

The algorithms developed for discovering cross-patterning relationships are designed so they can be applied to areal aggregated spatial datasets from a wide variety of disciplines. We test the framework with synthetic datasets to verify their correctness and then present a case study using real crime data from Brisbane, Australia. The computational performance of the algorithms is analysed and is acceptable for large datasets.

Item ID:	31323
Item Type:	Thesis (PhD)
Keywords:	data mining; decision support systems; pattern recognition; spatial correlation; spatial cross-patterning
Related URLs:	http://researchonline.jcu.edu.au/19845/ http://researchonline.jcu.edu.au/5771/ http://researchonline.jcu.edu.au/9911/ http://researchonline.jcu.edu.au/7921/ http://researchonline.jcu.edu.au/8240/ http://researchonline.jcu.edu.au/7919/ http://researchonline.jcu.edu.au/3109/ http://researchonline.jcu.edu.au/3173/
Additional Information:	Publications arising from this thesis are available from the Related URLs field. The publications are: Phillips, Peter, and Lee, Ickjai (2011) Crime analysis through spatial areal aggregated density patterns. Geoinformatica, 15 (1). pp. 49-74. Lee, Ickjai, and Phillips, Peter (2008) Urban crime analysis through areal categorized multivariate association mining. Applied Artificial Intelligence, 22 (5). pp. 483-499. Lee, Ickjai, Pershouse, Reece, Phillips, Peter, Lee, Kyungmi, and Torpelund-Bruin, Christopher (2010) What-if emergency response through higher order Voronoi diagrams. Annals of Information Systems, 9. pp. 77-95. Phillips, Peter, and Lee, Ickjai (2009) Mining top-k and bottom-k correlative crime patterns through graph representations. In: Proceedings of the IEEE International Conference on Intelligence and Security Informatics 2009, pp. 25-30. From: IEEE International Conference on Intelligence and Security Informatics 2009, 8-11 June 2009, Dallas, Texas, USA. Phillips, Peter, and Lee, Ickjai (2009) Criminal cross correlation mining and visualization. In: Proceedings of the Pacific-Asia Workshop on Intelligence and Security Informatics 2009, pp. 2-13. From: Pacific-Asia Workshop on Intelligence and Security Informatics 2009, 27 April 2009, Bangkok, Thailand. Phillips, Peter, and Lee, Ickjai (2009) Multivarite areal aggregated crime analysis through cross correlation. In: Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application (IITA) (2), pp. 295-298. From: 2008 Second International Symposium on Intelligent Information Technology Application (IITA) , 21-22 December 2008, Shanghai, China. Phillips, Peter, and Lee, Ickjai (2007) Areal aggregated crime reasoning through density tracing. In: Proceedings ICDM Workshops 2007 - Seventh IEEE International Conference on Data Mining - Workshops, pp. 649-654. From: Seventh IEEE International Conference on Data Minining, 28-31 October 2007, Omaha, Nebraska, USA. Lee, Ickjai, Pershouse, Reece, Phillips, Peter, and Christensen, Chris (2007) What-if emergency management system: a generalized Vironoi diagram approach. In: Lecture Notes in Computer Science (Proceedings of the Pacific Asia Workshop PAISI 2007) (4430), pp. 58-69. From: Intelligence & Security Informatics - Pacific Asia Workshop PAISI 2007, 11-12 April 2007, Chengdu, China.
Date Deposited:	07 May 2014 06:36
FoR Codes:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 34% 08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080605 Decision Support and Group Support Systems @ 33% 08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080606 Global Information Systems @ 33%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 100%
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