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Maps & Maps      andScale &   Scale
Lori M Olson@wndxlorihttp://wndx.posterous.com                            Photo Credit: @etrever
Maps are Easy   Right?
Small numbers < 1k
Including Details
More data?limit numbers
Paging
What kinds ofScale problems?
Datatypes
Markers
Polylines
Polygons
Complications
Data exceeds 10k
Discontiguous  segments
Multiplicative factors
1000 polylines * 100    segments/line= 100,000 map items!!!
Size variations
Discontiguous parts
Donuts!
Land lease  with donut
Maybe Not So Easy?
No Code Generation!
Serious Complications
Data exceeds 100k
Hundreds of Thousands
Millions of line segments
Hundreds of Thousands (again)
Event handling
Network latency
Solutions
Data doesn’t change?       TILE
Without and with the grid line tiles
Aggregation
Aggregate wells to fields
Limit data transfer size
On-demand Details
Clustering
Server clusters
Analytic functions -      NTILE
NTILE and group by lat/        long.
Counts!
Client-side
Levels & Cells & Caching
Polyline encoding
Zoom filtering
Cutoffs
Zoom Gotchas
Screen resolution
24” Cinema Display
iPad
WIMBY2Wells In My Back Yard
http://wimby2.herokuapp.comhttps://github.com/wndxlori/wimby2
Thanks!@wndxlori
Maps and Scale
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Maps and Scale

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Maps are easy, right? Right. Except... when they aren't.

What about when you need to mix different data types on your maps?

What about when the simple solutions (paging) to complex problems (too much data) don't cut it with your users?

What about when your scaling problems exceed the bounds of all the available solutions?

When building any kind of GIS on the web, whether with Bing Maps or Google Maps or something else, one of the things you need to realize is that the web is a far different environment from the desktop. Massive datasets have serious performance problems on the web. Although there are some built-in and add-on scaling solutions (clustering, polyline encoding), you can quickly run into issues like "unresponsive script" or just plain old horrible laggy performance on your map, when you attempt to zoom or pan with too many markers or polylines on the map.

In this talk, I'll walk through several such problems we encountered in the development of our Oil & Gas data browsing app, eTriever, and our initial, simplistic solutions, followed by our total re-write to provide a more robust and high-performance web map.

Published in: Technology, Business

Maps and Scale

  1. 1. Maps & Maps andScale & Scale
  2. 2. Lori M Olson@wndxlorihttp://wndx.posterous.com Photo Credit: @etrever
  3. 3. Maps are Easy Right?
  4. 4. Small numbers < 1k
  5. 5. Including Details
  6. 6. More data?limit numbers
  7. 7. Paging
  8. 8. What kinds ofScale problems?
  9. 9. Datatypes
  10. 10. Markers
  11. 11. Polylines
  12. 12. Polygons
  13. 13. Complications
  14. 14. Data exceeds 10k
  15. 15. Discontiguous segments
  16. 16. Multiplicative factors
  17. 17. 1000 polylines * 100 segments/line= 100,000 map items!!!
  18. 18. Size variations
  19. 19. Discontiguous parts
  20. 20. Donuts!
  21. 21. Land lease with donut
  22. 22. Maybe Not So Easy?
  23. 23. No Code Generation!
  24. 24. Serious Complications
  25. 25. Data exceeds 100k
  26. 26. Hundreds of Thousands
  27. 27. Millions of line segments
  28. 28. Hundreds of Thousands (again)
  29. 29. Event handling
  30. 30. Network latency
  31. 31. Solutions
  32. 32. Data doesn’t change? TILE
  33. 33. Without and with the grid line tiles
  34. 34. Aggregation
  35. 35. Aggregate wells to fields
  36. 36. Limit data transfer size
  37. 37. On-demand Details
  38. 38. Clustering
  39. 39. Server clusters
  40. 40. Analytic functions - NTILE
  41. 41. NTILE and group by lat/ long.
  42. 42. Counts!
  43. 43. Client-side
  44. 44. Levels & Cells & Caching
  45. 45. Polyline encoding
  46. 46. Zoom filtering
  47. 47. Cutoffs
  48. 48. Zoom Gotchas
  49. 49. Screen resolution
  50. 50. 24” Cinema Display
  51. 51. iPad
  52. 52. WIMBY2Wells In My Back Yard
  53. 53. http://wimby2.herokuapp.comhttps://github.com/wndxlori/wimby2
  54. 54. Thanks!@wndxlori

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