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Scarc presentation dubois_no_animation

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Scarc presentation dubois_no_animation

  1. 1. Assessing Users’ Cognitive Load When ViewingAnimated Choropleth Maps:A GIS Modeling ApproachMichael DuBois & Sarah Battersby,University of South Carolina
  2. 2. SC ARC 2013 – Columbia, SC 2A (Rather Simple!) AnimationFriendliest Counties, by Decade1980 - 2010Note: a Flash animation was removedfrom this slide for easier posting on theconference website.
  3. 3. SC ARC 2013 – Columbia, SC 3> 1000 tracts
  4. 4. SC ARC 2013 – Columbia, SC 4> 3000 counties
  5. 5. Cognitive Issues in (Choropleth) Maps1. Design Issues– Enumeration unit, extent, scale, classification, etc2. User issues– Familiarity with mapped area– Viewer needs / goalsSC ARC 2013 – Columbia, SC 5
  6. 6. SC ARC 2013 – Columbia, SC 6Animation increases ‘Cognitive Load’1. Challenges to Change Detection– Where do you look?– What are you looking for?2. Need to find balance– Congruence and apprehension– Not overwhelming the user
  7. 7. Map Design Questions1. How to tell if a given animation achieves this balance?2. Needed: a repeatable, objective metric of dynamiccomplexity, and3. A user-friendly way of applying the metric (i.e., a tool ormodel) during the design phaseSC ARC 2013 – Columbia, SC 7
  8. 8. Global Map Complexity ApproachesSC ARC 2013 – Columbia, SC 8Choropleth vs Isopleth(MacEachren 1982)Spatial Autocorrelation τ(Olsen 1975)
  9. 9. Global Map Complexity ApproachesSC ARC 2013 – Columbia, SC 9Choropleth vs Isopleth(MacEachren 1982)Spatial Autocorrelation τ(Olsen 1975)Eye-Tracking (Castner & Eastman1985 | Fabrikant 2010)
  10. 10. Global Map Complexity ApproachesSC ARC 2013 – Columbia, SC 10File Compression(Fairbairn 2006)Choropleth vs Isopleth(MacEachren 1982)Spatial Autocorrelation τ(Olsen 1975)Eye-Tracking (Castner & Eastman1985 | Fabrikant 2010)
  11. 11. A Local Metric11Change Metrics for Dynamic Maps: ‘Magnitude of Change’ (MOC)Goldsberry & Battersby (2009) suggested two change metrics for classedchoropleth maps, ‘Basic Magnitude of Change’ and ‘Magnitude of RankChange’.Goldsberry & Battersby (2009)11Foveal vs Peripheral visionSC ARC 2013 – Columbia, SC
  12. 12. A Local Metric (cont)12Change Metrics for Dynamic Maps: ‘Magnitude of Change’ (MOC)Goldsberry & Battersby (2009) suggested two change metrics for classedchoropleth maps, ‘Basic Magnitude of Change’ and ‘Magnitude of RankChange’.Goldsberry & Battersby (2009)12𝒄𝒐𝒖𝒏𝒕 𝑜𝑟 𝒔𝒖𝒎 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑐𝑙𝑎𝑠𝑠 𝑐𝑕𝑎𝑛𝑔𝑒𝑠 𝑖𝑛 𝑖𝑛𝑠𝑡𝑎𝑛𝑡𝑎𝑛𝑒𝑜𝑢𝑠 𝐹𝐴𝑐𝑜𝑢𝑛𝑡 𝑜𝑓 𝐸𝑈𝑠 𝑖𝑛 𝑖𝑛𝑠𝑡𝑎𝑛𝑡𝑎𝑛𝑒𝑜𝑢𝑠 𝐹𝐴(BMOC)(MORC)SC ARC 2013 – Columbia, SC
  13. 13. SC ARC 2013 – Columbia, SC 13Applying MOC to Dynamic Complexitydynamic complexityacross scenesstatic complexity ofeach scene
  14. 14. Modeling MOC in ArcGIS141. Rasterize 2.Differencegrey no changegreen 1 classred 2 classesSC ARC 2013 – Columbia, SC
  15. 15. Modeling MOC in ArcGIS15Input rasterOutput rasterSpatial Analyst  Neighborhood  Focal StatisticsNeighborhood ShapeNeighborhood SizeStatisticSC ARC 2013 – Columbia, SC
  16. 16. Modeling MOC in ArcGIS16Input rasterOutput rasterSpatial Analyst  Neighborhood  Focal StatisticsNeighborhood ShapeNeighborhood SizeStatisticclass difference rasterradius of Foveal Area, in map unitsMean*circle (weighted?)MOC rasterSC ARC 2013 – Columbia, SC*for MORC. BMOC requiresa binary reclass prior to thisstep
  17. 17. Modeling MOC in ArcGIS17Input rasterOutput rasterSpatial Analyst  Neighborhood  Focal StatisticsNeighborhood ShapeNeighborhood SizeStatisticclass difference rasterradius of Foveal Area, in map unitsMean*circle (weighted?)MOC rasterSC ARC 2013 – Columbia, SC*for MORC. BMOC requiresa binary reclass prior to thisstep
  18. 18. ArcGIS Model Output – SC counties18SC ARC 2013 – Columbia, SCBasic Magnitude of Change Magnitude of Rank Changegrey no changegreen 1 classred 2 classes1.00001.993020 km radius =1256 km2mean SC countyarea = 1741 km2
  19. 19. ArcGIS Model Output – US counties19SC ARC 2013 – Columbia, SCradius of foveal area atoriginal scale: 262 kmgrey no changeblue 1 class change0.1550
  20. 20. Python Version20SC ARC 2013 – Columbia, SCmap units (can’t be degrees)map scaleeye-screen distance (assume 50 cm)Rasterization processWhich fields represent animationframes to be comparedRadius of Search Window (Foveal Area)File Management / Data Prep(cannot be known a priori)I/O ‘scratch’ workspaceanalysis cell sizeEmbedded / derived parameters and settings:dataFrame.mapUnitsdataFrame.scalelyr.symbology.classBreakValuesarcpy.AddField_management( )lyr.ListFields( )Need to be set by the user:
  21. 21. Validating the GIS Model21SC ARC 2013 – Columbia, SC• How well do local complexity metrics perform inrepresenting dynamic change as perceived by mapviewers?random clustered
  22. 22. Ongoing Research22SC ARC 2013 – Columbia, SC• Localizing change: how well does the concept of thefoveal area work when assessing dynamic vs. staticgraphical complexity?• Distance decay: should different weights be assignedto cells based on distance from the precise center ofthe foveal area?• Scale: how does MOC perform at ‘extreme’ scales(mobile devices, projectors)?• Do other metrics yield output that is significantlydifferent from MOC?
  23. 23. Summary23SC ARC 2013 – Columbia, SC• MOC: a local complexity metric for animated maps• Raster-based MOC GIS model• Automatic derivation of model inputs reduces usererror, particularly when sizing the scanning window• Currently validating MOC metrics using human-subject testing
  24. 24. References24SC ARC 2013 – Columbia, SCCastner, H., and Eastman, J. (1984). Eye-Movement parameters and perceived MapComplexity I. The American Cartographer, 11(2), 107-117.Fabrikant, S., Hespanha, S., and Hegarty, M. (2010). Cognitively Inspired and PerceptuallySalient Graphic Displays for Efficient Spatial Inference Making. Annals of the Association ofAmerican Geographers, 100(1), 13-29.Fairbairn, D. (2006). Measuring Map Complexity. The Cartographic Journal, 43(3), 224-238.Goldsberry, K., and Battersby, S. (2009). Issues of Change Detection in Animated ChoroplethMaps. Cartographica, 44(3), 201-215.MacEachren, A. (1982). Map Complexity: Comparison and Measurement. The AmericanCartographer, 9, 31-46.Olson, J. (1975). Autocorrelation and Visual Map Complexity. Annals of the Association ofAmerican Geographers, 65(2), 189-204.

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