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Zechun Cao

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. Talk Organization. Introduction Methodology Preliminary Results Future Work. Urban Computing.

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Zechun Cao

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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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