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Visualization of AAG Paper Abstracts André Skupin Dept. of Geography University of New Orleans AAG Pittsburgh, April 5, 2000
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AAG Conference Abstracts
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Web Search Engine Interface
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Research Motivation I Methodology Geography’s role in information visualization –geographic concepts regions scale –cartographic techniques generalization labeling –GIS technology data integration
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Research Motivation II Application Developments in Academic Geography –based on geography’s written output –generalizable for any corpus of documents
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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
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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
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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
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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
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Base Map of AAG Abstracts Complexity –> Generalization ? –> Scale ? Labeling –> Weighted Index ? Visualization –> GIS Software ?
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High-Dimensional Clusters Projected onto Map HierarchicalCoarse SOMK-Means
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Multi-Scale Spatialization w/ Labels
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Map Design for 2D Spatialization Visual Hierarchies Geographic Space Information Space
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Research Directions I Applications visualize trends in geography –author trajectories through time –subject emergence –geography of geography
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Papers by ZIP Code
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Research Directions II Techniques Cluster Solutions –U-matrix (-> contiguous clusters in 2D) –AutoClass (-> with optimized cluster numbers) –quantify performance of cluster solutions Visualization –multi-band thematic visualization
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SOM Plane “GIS”
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SOM Plane “visualization”
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SOM Plane “urban”
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Color Composite “GIS” “urban” “visualization”: Full Extent
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Color Composite “GIS” “urban” “visualization”: Zoom-In
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