Eick, Christoph F.2017-02-052017-02-05December 22014-12December 2http://hdl.handle.net/10657/1613In recent years the widespread usage of scanning device, such as GPS-enabled devices, PDAs, and video cameras, has resulted in an abundance of spatial data. Therefore, there is an increasing interest in mining hidden patterns in spatial data. Discovery of co-location patterns has been a research area in association analysis for several years. In this thesis, we designed and implemented a user-friendly, interactive Co-location Analysis Tool which can be used to extract co-location patterns from spatial datasets. By using this tool, we are able to extract co-location patterns at different levels of granularity; these results can help with business decision-making, ecology research, and urban planning. The tool provides two approaches to analyze collocation patterns: Ripley's K-function approach, and a novel approach called K-Nearest-Neighbor distance approach. Both approaches compute spatial statistics for different neighborhood sizes and compare these characteristics with spatial characteristics obtained by placing objects randomly to determine the presence of collocation and anti-collocation. The second approach uses summaries of K-nearest neighbor distances of objects in the dataset to diagnose the presence of collocation patterns. In addition, the tool provides visualization techniques to present the data analysis experimental results. Finally, we validated the tool and compared the two collocation analysis approaches for a building dataset.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Co-locationRipley's k-functionK-nearest neighbor distance2017-02-05Thesisborn digital