Automated spatial information retrieval and visualisation of spatial data

Walker, Arron R. (2007) Automated spatial information retrieval and visualisation of spatial data. PhD thesis, Queensland University of Technology.

Abstract

An increasing amount of freely available Geographic Information System (GIS) data on the Internet has stimulated recent research into Spatial Information Retrieval (SIR). Typically, SIR looks at the problem of retrieving spatial data on a dataset by dataset basis. However in practice, GIS datasets are generally not analysed in isolation. More often than not multiple datasets are required to create a map for a particular analysis task. To do this using the current SIR techniques, each dataset is retrieved one by one using traditional retrieval methods and manually added to the map. To automate map creation the traditional SIR paradigm of matching a query to a single dataset type must be extended to include discovering relationships between different dataset types. This thesis presents a Bayesian inference retrieval framework that will incorporate expert knowledge in order to retrieve all relevant datasets and automatically create a map given an initial user query. The framework consists of a Bayesian network that utilises causal relationships between GIS datasets. A series of Bayesian learning algorithms are presented that automatically discover these causal linkages from historic expert knowledge about GIS datasets. This new retrieval model improves support for complex and vague queries through the discovered dataset relationships. In addition, the framework will learn which datasets are best suited for particular query input through feedback supplied by the user. This thesis evaluates the new Bayesian Framework for SIR. This was achieved by utilising a test set of queries and responses and measuring the performance of the respective new algorithms against conventional algorithms. This contribution will increase the performance and efficiency of knowledge extraction from GIS by allowing users to focus on interpreting data, instead of focusing on finding which data is relevant to their analysis. In addition, they will allow GIS to reach non-technical people.

Impact and interest:

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ID Code: 17258
Item Type: QUT Thesis (PhD)
Supervisor: Moody, Miles P.
Keywords: Visualisation of Spatial Data, automated Spatial Information Retrieval
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Institution: Queensland University of Technology
Deposited On: 02 Feb 2009 05:07
Last Modified: 09 Feb 2011 13:53

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