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The Joy of Hex:
Challenges in creating
and interpreting spatial bins
Sarah Battersby | Daniel Strebe | Michael Finn
The big picture
Lots of data, not a lot of pattern
156,138,722 taxi pick up locations
Simplify - Aggregate
156,138,722 points
vs.
A few hundred bins
Easy to create
Nice, regular pattern
Tricky to make useful
Or…
Let’s dig into the challenges…
What to think about when you want to
think about how people think about spatial bins…
A first decision – bin shape
Option 1
Simple relationship side to area
Quick and easy for aggregation
But…
Strong and distracting horizontal /
vertical...
Subdivides nicely
Option 1
Minimizes edge effects & linear
patterns
More compact shape is ‘pleasing’
But…
More complex relationship of side to area
S...
Also loses the nice subdivision
Option 2
But…
they are ‘edgier’ and people like them
(maybe too much)
Source: http://indiemaps.github.io/hexbin-js/tests/walmart.ht...
Short story on bin shape?
Whatever works for
you
your data
your workflow
your hipness quotient
Second big decision…
What do your readers need to do?
Value for individual location
General patterns
Comparisons across ma...
Value for individual location / general patterns
Are your bins really the same size? Same shape?
Value for individual location / general patterns
Are your bins really the same size? Same shape?
On the plane?
What projec...
Value for individual location / general patterns
Are your bins really the same size? Same shape?
On the WEB MAP plane?
1. ...
Value for individual location / general patterns
Are your bins really the same size? Same shape?
On the WEB MAP plane?
2. ...
Value for individual location / general patterns
Are your bins really the same size? Same shape?
On the WEB MAP plane?
2. ...
Value for individual location / general patterns
Are your bins really the same size? Same shape?
On the WEB MAP plane?
2. ...
Value for individual location / general patterns
Image source:
https://www.mapbox.com/blog/heat
maps-and-grids-with-turf/
...
Value for individual location / general patterns
But can’t I just bin on the sphere and save
myself the headache?
On the s...
A take home message
Be cautious with how your bins are created /
measured
Understand the parameters in the API
Even if the...
Comparison across maps
Multiple hexbin maps?
Be careful with the alignment / origin of your bins
Grid of bins – based on s...
Comparison across maps
Multiple hexbin maps?
Be careful with the alignment / origin of your bins
Grid of bins – based on d...
A take home message
Not all tools for generating spatial bins allow for control of origin /
placement
So, if you want to m...
Which brings up a bigger problem…
Modifiable areal unit problem
Change in size, shape, placement, etc. may give a differen...
(MAUP video)
And an interesting question
What is it that people are going to interpret anyway?
When we encode spatial bins, do people s...
Map shows aggregation on plane:
Bins with same count
Bins with different count
A take home message
We need to understand what people really see in binned visualizations to
figure out how best to visual...
One last point…
Irregular bins to preserve area
But we lose benefit of bin regularity
Computational (point in polygon)
Vis...
But how do I know what that bounding box is??
What in the world was that mathematical scribble?
Calculating the ‘Safe Zone’ to bin in projected space,
and many other go...
Questions?
Sarah Battersby – sbattersby@tableau.com
daan Strebe – dstrebe@tableau.com
Michael Finn – mfinn@usgs.gov
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The Joy of Hex

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NACIS 2016 Presentation
Sarah Battersby, Tableau Software

Complex, large N point datasets present challenges for visualization and synthesis of spatial patterns due to the density of marks and resulting clutter from overlapping mark symbols. One suggested method for dealing with complex point datasets is to partition the space into polygonal bins, and symbolize each bin based on point count inside the bin. Because regular polygonal (e.g., square or hexagonal) bins appear as same size and shape, they are suggested as a method for improving ability to analyze smooth, continuous change in point distributions, while avoiding artifacts from irregular political bin geometry. However, there is a fallacy if regular geographic bins are really considered to represent "same size and shape." In this presentation, we discuss challenges and tradeoffs the cartographer must consider in creating spatial bins, and, more importantly, challenges the map reader faces in interpreting bins in a way that aligns with the cartographer’s intended message.

Publicada em: Design
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The Joy of Hex

  1. 1. The Joy of Hex: Challenges in creating and interpreting spatial bins Sarah Battersby | Daniel Strebe | Michael Finn
  2. 2. The big picture Lots of data, not a lot of pattern 156,138,722 taxi pick up locations
  3. 3. Simplify - Aggregate 156,138,722 points vs. A few hundred bins Easy to create Nice, regular pattern Tricky to make useful
  4. 4. Or…
  5. 5. Let’s dig into the challenges… What to think about when you want to think about how people think about spatial bins…
  6. 6. A first decision – bin shape
  7. 7. Option 1 Simple relationship side to area Quick and easy for aggregation But… Strong and distracting horizontal / vertical lines Potential artefacts with linear cultural features like roads
  8. 8. Subdivides nicely Option 1
  9. 9. Minimizes edge effects & linear patterns More compact shape is ‘pleasing’ But… More complex relationship of side to area Spacing more irregular A little more challenging to aggregate points Option 2
  10. 10. Also loses the nice subdivision Option 2
  11. 11. But… they are ‘edgier’ and people like them (maybe too much) Source: http://indiemaps.github.io/hexbin-js/tests/walmart.html
  12. 12. Short story on bin shape? Whatever works for you your data your workflow your hipness quotient
  13. 13. Second big decision… What do your readers need to do? Value for individual location General patterns Comparisons across maps
  14. 14. Value for individual location / general patterns Are your bins really the same size? Same shape?
  15. 15. Value for individual location / general patterns Are your bins really the same size? Same shape? On the plane? What projection are you using? Equal area projection
  16. 16. Value for individual location / general patterns Are your bins really the same size? Same shape? On the WEB MAP plane? 1. Regular bins in Web Mercator space
  17. 17. Value for individual location / general patterns Are your bins really the same size? Same shape? On the WEB MAP plane? 2. “Regular” bins in “spherical space”
  18. 18. Value for individual location / general patterns Are your bins really the same size? Same shape? On the WEB MAP plane? 2. “Regular” bins in “spherical space”
  19. 19. Value for individual location / general patterns Are your bins really the same size? Same shape? On the WEB MAP plane? 2. “Regular” bins in “spherical space”
  20. 20. Value for individual location / general patterns Image source: https://www.mapbox.com/blog/heat maps-and-grids-with-turf/ Are your bins really the same size? Same shape? On the WEB MAP plane? 2. “Regular” bins in “spherical space”
  21. 21. Value for individual location / general patterns But can’t I just bin on the sphere and save myself the headache? On the sphere? Can’t preserve both areas and angles …and perfect tessellation is a pain Hexagonal tiling with 12 pentagons (the soccer ball problem)
  22. 22. A take home message Be cautious with how your bins are created / measured Understand the parameters in the API Even if they are just “graphics” and the exact bin area doesn’t matter… …it’s important to know how they were made
  23. 23. Comparison across maps Multiple hexbin maps? Be careful with the alignment / origin of your bins Grid of bins – based on specified origin Bins to compare – same spatial location
  24. 24. Comparison across maps Multiple hexbin maps? Be careful with the alignment / origin of your bins Grid of bins – based on data extent Bins to compare – different spatial location Impossible to match aggregation
  25. 25. A take home message Not all tools for generating spatial bins allow for control of origin / placement So, if you want to make valid comparisons of binned data be careful…
  26. 26. Which brings up a bigger problem… Modifiable areal unit problem Change in size, shape, placement, etc. may give a different spatial pattern
  27. 27. (MAUP video)
  28. 28. And an interesting question What is it that people are going to interpret anyway? When we encode spatial bins, do people see density or count? Do they assume that it is just a graphical, planar density? Or is it assumed to be spherical density? Or do they expect it to be both count and correct generic density? Planar = Spherical
  29. 29. Map shows aggregation on plane: Bins with same count
  30. 30. Bins with different count
  31. 31. A take home message We need to understand what people really see in binned visualizations to figure out how best to visualize it My thought on naïve understanding is an assumption of both count and density, so we have a big problem with projections…
  32. 32. One last point… Irregular bins to preserve area But we lose benefit of bin regularity Computational (point in polygon) Visual …or “don’t do this if your geographic area is larger than {insert bounding box}”
  33. 33. But how do I know what that bounding box is??
  34. 34. What in the world was that mathematical scribble? Calculating the ‘Safe Zone’ to bin in projected space, and many other goodies can be found in… “Shapes on a Plane: Evaluating the impact of projection distortion on spatial binning” Download from: http://research.tableau.com
  35. 35. Questions? Sarah Battersby – sbattersby@tableau.com daan Strebe – dstrebe@tableau.com Michael Finn – mfinn@usgs.gov

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