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Methodology for Identifying Uniform Regions in Spatial Data and Its Urban Analysis Applications

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This paper presents a novel methodology for identifying uniform regions within spatial data, particularly in urban environments. The approach aims to maximize uniformity based on expert-defined criteria and employs a framework that incorporates both spatial and non-spatial attributes to analyze urban building compositions effectively. Preliminary results indicate successful partitioning of spaces into uniform regions, revealing insights into building types and spatial heterogeneity. Future work will focus on improving clustering techniques and conducting sensitivity analyses to enhance the quality of results.

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Methodology for Identifying Uniform Regions in Spatial Data and Its Urban Analysis Applications

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  1. A Methodology for Finding Uniform Regions in Spatial Data and its Application to Analyzing the Composition of Cities Zechun Cao UH DMML Advisor: Dr. Eick

  2. Talk Organization Introduction Methodology Preliminary Results Future Work UH-DMML

  3. Urban Computing • Serves for the modern cities with rapid progress of urbanization and civilization. • Aims to understand the nature and science behind the phenomenon. UH-DMML

  4. Research Tasks • Analyzes the composition of the buildings in a city. • Develops framework that captures the spatial heterogeneity by non-spatial attributes. UH-DMML

  5. Research Goals • Partitions a given space into uniform regions based on a domain expert’s notion of uniformity by maximizing a plug-in measure of uniformity. • Provides analysis functions to create summaries for the identified uniform regions. UH-DMML

  6. CLEVER UH-DMML

  7. Interestingness • Purity • Low Variance UH-DMML

  8. Interestingness • Building Type Signature (90%, 5%, 1%, 3%, 0%, 1% ) UH-DMML

  9. Dataset UH-DMML

  10. Preliminary Results Building Type Purity Experiment Cluster#0: Commercial Area UH-DMML

  11. Preliminary Results Building Size Distribution Experiment • 30 out of 36 clusters has the area standard deviation lower than the dataset standard deviation. UH-DMML

  12. Preliminary Results Query Spatial Dataset by Signature UH-DMML

  13. Future Work • Sensitivity analysis of initialization. • Merging the clusters across multiple runs to gain better clustering quality. • Parallel spatial data mining algorithms. UH-DMML

  14. Thank you!

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