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Discriminating Patterns for Empirical Discovery in Geospatial Data

Presenter: Wei Ding Wei.Ding@umb.edu Computer Science Department University of Massachusetts Boston. Discriminating Patterns for Empirical Discovery in Geospatial Data. The NSF Workshop on GeoSpatial and GeoTemporal informatics ∙ January 8-9, 2009. Solved or Almost Solved.

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Discriminating Patterns for Empirical Discovery in Geospatial Data

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  1. Presenter: Wei Ding Wei.Ding@umb.edu Computer Science Department University of Massachusetts Boston Discriminating Patterns for Empirical Discovery in Geospatial Data The NSF Workshop on GeoSpatial and GeoTemporal informatics ∙ January 8-9, 2009

  2. Solved or Almost Solved • Spatial Association Rules [Koperski & Han 1995] • Spatial Co-Location Patterns [Huang & Shekhar] • Regional Association Patterns [Ding & Eick] • Geospatial Discriminating Patterns • Spatial Association Rules • Spatial Co-Location Patterns • Regional Association Patterns [huang2004] Wei Ding . UMass Boston . Discriminating Patterns

  3. Failed & Missing • Modeling causal relationship among geo-variables • Complex chain of mutual interactions • Nonlinearity and spatial variability • Efficient pattern summarization • Laziness: large amount of patterns • “super-patterns” • Robust & practical knowledge discovery tools to build empirical models for domain experts Wei Ding . UMass Boston . Discriminating Patterns

  4. What is next? Provide the scientific community with an efficient tool for auto-analyzing the root causes behind observed patterns in geospatial data. Wei Ding . UMass Boston . Discriminating Patterns

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