Description
Technology has created a shift to an extremely data rich environment with significant increases in the resolution and storage of spatio-temporal attribute data. This challenges traditional analysis with exponential demands on computational resources. Data mining and knowledge construction address this issue by reducing the volume and complexity of data. However, these methods do not indicate the significance of their results and thus require the intervention of an expert. This means that solutions to the proliferation of data require both computer methods and human intelligence. The method of interest for this thesis is the selforganizing map (SOM), which creates a model of the topological structure within a dataset that can be used for further analysis. These models have some spatial qualities, allowing them to be combined with geographic information systems (GIS) to provide practical interactive solutions for the analysis and exploration of large high-dimensional datasets. This approach takes advantage of existing human and computer resources. A review of the literature and existing software reveals that while the combination of SOM and GIS is not unprecedented the approach taken here is unique. This software is a plug-in for a market-leading commercial GIS product, ArcGIS, and facilitates leveraging established GIS visualization and analysis techniques. By relying on a commercial off-theshelf GIS, development was focused on integration and takes advantage of existing user skills. The software, called SOM Analyst, contains tools for data preprocessing, SOM computation, and SOM visualization. These tools were tested using sample data and evaluated by experts. The tools are documented in a help file, in the code, and through a tutorial and are available on CD-ROM. The CD-ROM, an appendix to the thesis, is available for viewing at the Media Center of Love Library. SOM Analyst makes it easier to use the SOM method within GIS and demonstrates how SOMs can be useful for GIS-based analysis. Conversely, it also shows how GIS enhances the spatial nature of SOM models, such that GIS becomes applicable to even nongeographic data. The practical demonstration of this mutually beneficial relationship of GIS and SOM is among the main methodological contributions of this thesis. SOM Analyst supports several common data formats and has all the basic functions needed for data preparation, SOM computation, and SOM visualization. For simplicity, only the traditional SOM method is supported, but SOM Analyst is a significant contribution to which many enhancements could be added. It serves as the basis of simple and advanced SOM visualization and analysis including the ability to quickly and easily produce attribute trajectories. These capabilities are meant to enhance knowledge construction based on the analysis of large high-dimensional datasets.
