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Weighted Flow graphs for statistics Edwin de Jonge NTTS February 2009

Mar 27, 2015



  • Slide 1

Weighted Flow graphs for statistics Edwin de Jonge NTTS February 2009 Slide 2 Statistics and flows Many official statistics are flow data Demography Migration International trade But also balance systems: System of National Accounts (SNA) Energy balance Slide 3 Statistics and visualisation Visualisation exploits visual system to: Reveal and highlight patterns in data (trends, correlation, distribution) Most common visualisations line and bar charts scatter and bubble plots Cartographic choropleth Slide 4 Flow visualization Many official statistics are flow data But not presented as flows! Flow diagram is weighted directed graph G = (V,E,w) Not many visualisation research for weighted directed graphs Slide 5 Flow visualisation (2) Options Standard node and edge visualisation Not real option: does not encode the weights (= data) Sankey diagrams Very good for energy statistics etc.! Cartographic flows Arrows on a cartographic map Slide 6 Cartographic flows Flow maps: Many are hand made Flow routing is hard Number of flows is limited to 50 Most are unidirectional Computer generated cartographic flow layout is still scarce Slide 7 Slide 8 Experiment: large flow map Most statistical datasets are large! Experiment to visualise Thousands of flows, that are bidirectional, every flow may have a counter flow It should: give overview of all flows show main flows reveal flow patterns Slide 9 Experiment: Internal migration Migration between 459 municipalities in the Netherlands Migration is matrix M(i,j) i, j = 1..N m ij = migration from i to j Large number of flows and bidirectional Slide 10 Experiment: Internal migration Data summary: 60,000 movements (of the 210,000) Mean = 10, Max = 2880, Median = 2 = Skewed! Technology: Google Earth, KML file Generate arrows as polygons in KML Slide 11 Nave implementation Too many arrows Visual clutter: no overview no main flows no flow patterns Slide 12 Naive implementation 2 Slide 13 Visual encoding Use visual encoding to reduce clutter Arrow Width: logarithmic scale Encodes size of flows Transparency: logarithmic scale Reduces visual clutter Height: linear scale Focus on main flows Slide 14 User interaction / Results Use user interaction to filter data user can select regions (no flows) Results Clear overview of overall flows Main flows are visible Non local flows are also visible But no other patterns! Slide 15 Slide 16 Discussion Result is ok, but should be further improved Better user interaction GE user interaction very limited Select and filter for flows Reveal patterns in flow data Use cluster techniques to group flows User cluster techniques to group regions