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Spatial Visualization of AAG Paper Abstracts

Explore the visualization of AAG conference paper abstracts through GIS technology and spatialization techniques. Learn about the capture, indexing, and projection of keyword components to create a comprehensive map of academic geography documents.

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Spatial Visualization of AAG Paper Abstracts

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  1. Visualization of AAG Paper Abstracts André Skupin Dept. of Geography University of New Orleans AAG Pittsburgh, April 5, 2000

  2. AAG Conference Abstracts

  3. Web Search Engine Interface

  4. Research Motivation IMethodology • Geography’s role in information visualization • geographic concepts • regions • scale • cartographic techniques • generalization • labeling • GIS technology • data integration

  5. Research Motivation IIApplication • Developments in Academic Geography • based on geography’s written output • generalizable for any corpus of documents

  6. Data Capture & Pre-Processing • Source Data: • abstracts submitted to AAG 1999 Hawaii • complete abstracts as text file • 2220 abstracts • Pre-Processing: • Separation into three parts: • author information • abstract text • keywords chosen by authors

  7. Keyword Component Indexing • (1) extract keywords chosen by authors • (2) break keywords into components • (3) match components against content of all abstracts • result: • all abstracts indexed • overall richer then only author-chosen keywords • vector-space model with 2220 docs & 741 terms

  8. Spatialization • projection of elements of a high-dimensional information space into a low-dimensional representation (Skupin & Buttenfield 1997) • > project document/keyword matrix into 2D • Technique: Self-Organizing Map (SOM) • input: raw document/keyword matrix • output: two-dimensional grid of neurons with weight for each keyword

  9. Base Map Creation • Implementation: SOM_PAK & C++ • 1. Choose SOM Dimensions • e.g. 85 x 115 neurons • 2. Train Grid of Neurons • each neuron gets weight for each keyword • preservation of high-dim. document topology • 3. Apply SOM to Data Set • documents assigned to single neurons • 4. Assign unique locations to documents

  10. Base Map of AAG Abstracts • Complexity • > Generalization ? • > Scale ? • Labeling • > Weighted Index ? • Visualization • > GIS Software ?

  11. High-Dimensional Clusters Projected onto Map Hierarchical Coarse SOM K-Means

  12. Multi-Scale Spatialization w/ Labels

  13. Map Design for 2D SpatializationVisual Hierarchies Geographic Space Information Space

  14. Research Directions IApplications • visualize trends in geography • author trajectories through time • subject emergence • geography of geography

  15. Papers by ZIP Code

  16. Research Directions IITechniques • Cluster Solutions • U-matrix (-> contiguous clusters in 2D) • AutoClass (-> with optimized cluster numbers) • quantify performance of cluster solutions • Visualization • multi-band thematic visualization

  17. SOM Plane“GIS”

  18. SOM Plane“visualization”

  19. SOM Plane“urban”

  20. Color Composite“GIS” “urban” “visualization”: Full Extent

  21. Color Composite“GIS” “urban” “visualization”: Zoom-In

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