News Arrange spatial data Ch 8/9: Spatial Data, Networks • marks for previous 2 weeks published Use Given Geometry –first week was pass/fail for having anything Paper: Genealogical Graphs Geographic –now more fine-grained guidance about expectations with comments Other Derived Paper: ABySS-Explorer • if you didn’t get full credit –in general: don’t just summarize Spatial Fields Ch 8: Arrange Spatial Data Scalar Fields (one value per cell) • today Tamara Munzner Isocontours –pitches first Department of Computer Science Direct Volume Rendering –Q&A, lecture second University of British Columbia Vector and Tensor Fields (many values per cell) CPSC 547, Information Visualization Flow Glyphs (local) Week 6: 17 October 2017 Geometric (sparse seeds) Textures (dense seeds) www.cs.ubc.ca/~tmm/courses/547-17F Features (globally derived) 2 3 4 Idiom: choropleth map Idiom: Bayesian surprise maps Idiom: topographic map Population maps trickiness • use given spatial data • beware! • use models of expectations to highlight surprising values • data –when central task is understanding spatial • absolute vs relative again • confounds (population) and variance (sparsity) –geographic geometry relationships –scalar spatial field • population density vs per capita • data • 1 quant attribute per grid cell • investigate with Ben Jones Tableau • derived data –geographic geometry Public demo –table with 1 quant attribute per region –isoline geometry • http://public.tableau.com/profile/ • encoding • isocontours computed for http://bl.ocks.org/mbostock/4060606 ben.jones#!/vizhome/PopVsFin/PopVsFin specific levels of scalar values Are Maps of Financial Variables just –use given geometry for area mark boundaries Population Maps? –sequential segmented colormap [more later] • yes, unless you look at per capita –(geographic heat map) (relative) numbers [Surprise! Bayesian Weighting for De-Biasing Thematic Maps. Correll and Heer. Proc InfoVis 2016] Land Information New Zealand Data Service [ https://xkcd.com/1138 ] https://medium.com/@uwdata/surprise-maps-showing-the-unexpected-e92b67398865 https://idl.cs.washington.edu/papers/surprise-maps/ 5 6 7 8 Idioms: isosurfaces, direct volume rendering Vector and tensor fields Vector fields Idiom: similarity-clustered streamlines • data • data • empirical study tasks • data –many attribs per cell –finding critical points, identifying their –3D vector field –scalar spatial field types • idiom families • derived data (from field) • 1 quant attribute per grid cell –identifying what type of critical point • task –streamlines: trajectory particle will follow –flow glyphs is at a specific location • purely local • derived data (per streamline) –shape understanding, spatial –predicting where a particle starting at –geometric flow relationships –curvature, torsion, tortuosity a specified point will end up [Interactive Volume Rendering Techniques. Kniss. Master’s thesis, University of Utah Computer Science, 2002.] • derived data from tracing particle (advection) –signature: complex weighted combination • isosurface D trajectories –compute cluster hierarchy across all signatures –derived data: isocontours computed for [Comparing 2D vector field visualization methods: A user study. Laidlaw et al. IEEE Trans. [Comparing 2D vector field visualization methods: A user study. Laidlaw et al. IEEE Trans. • sparse set of seed points Visualization and Computer Graphics (TVCG) 11:1 (2005), 59–70.] Visualization and Computer Graphics (TVCG) 11:1 (2005), 59–70.] –encode: color and opacity by cluster F specific levels of scalar values –texture flow • tasks C • direct volume rendering • derived data, dense seeds –find features, query shape –feature flow –transfer function maps scalar values to B • scalability [Similarity Measures for Enhancing Interactive Streamline Seeding. color, opacity • global computation to detect features McLoughlin,. Jones, Laramee, Malki, Masters, and. Hansen. IEEE Trans. Visualization and Computer Graphics 19:8 (2013), 1342–1353.] E – encoded with one of methods above [Topology tracking for the visualization of time-dependent two-dimensional flows. Tricoche, [Topology tracking for the visualization of time-dependent two-dimensional flows. Tricoche, –millions of samples, hundreds of streamlines [Multidimensional Transfer Functions for Volume Rendering. Kniss, Kindlmann, and Hansen. In The Visualization Handbook, Wischgoll, Scheuermann, and Hagen. Computers & Graphics 26:2 (2002), 249–257.] Wischgoll, Scheuermann, and Hagen. Computers & Graphics 26:2 (2002), 249–257.] edited by Charles Hansen and Christopher Johnson, pp. 189–210. Elsevier, 2005.] 9 10 11 12 Arrange networks and trees Idiom: force-directed placement Idiom: sfdp (multi-level force-directed placement) • visual encoding • data Node–Link Diagrams –link connection marks, node point marks –original: network Connection Marks • considerations –derived: cluster hierarchy atop it NETWORKS TREES –spatial position: no meaning directly encoded • considerations • left free to minimize crossings –better algorithm for same encoding –proximity semantics? Ch 9: Arrange Network Data Adjacency Matrix technique • sometimes meaningful Derived Table • same: fundamental use of space • sometimes arbitrary, artifact of layout algorithm NETWORKS TREES • hierarchy used for algorithm speed/quality but • tension with length [Efficient and high quality force-directed graph drawing. not shown explicitly – long edges more visually salient than short Hu. The Mathematica Journal 10:37–71, 2005.] • (more on algorithm vs encoding in afternoon) • tasks • scalability Enclosure –explore topology; locate paths, clusters Containment Marks • scalability –nodes, edges: 1K-10K NETWORKS TREES –node/edge density E < 4N –hairball problem eventually hits http://mbostock.github.com/d3/ex/force.html 13 14 15 16 http://www.research.att.com/yifanhu/GALLERY/GRAPHS/index1.html
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