Fme to map beauty along a route

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What's the point of mapping beautiful landscapes. And how do you model routing through these landscapes? A case of RouteYou with FME.

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Fme to map beauty along a route

  1. 1. The use of FME in recreational navigation Using FME to map beauty<br />Pascal Brackman, Ph.D.<br />Managing Partner RouteYou<br />
  2. 2. Why would we like to map <br />beauty?<br />And if so,… How?<br />
  3. 3. 1<br />?<br />Why <br />Where?<br />?<br />2<br />How?<br />
  4. 4. 3<br />?<br />Info &<br />surprise <br />4<br />Share &<br />Feedback<br />
  5. 5. The context was/is changing<br />
  6. 6. The context was/is changing<br />
  7. 7. The context is changing<br />
  8. 8. A demo is worth a thousand words<br />Searching for a route<br />Why we need “quality of a route”<br />Do we have enough routes? <br />Planning a route<br />
  9. 9. Just in case<br />
  10. 10. Routing<br />10<br />
  11. 11. Routing… the basics<br />A<br />5<br />2<br />4<br />4<br />B<br />
  12. 12. Routing… the basics<br />A<br />5/beauty<br />2/beauty<br />4/beauty<br />4/beauty<br />B<br />
  13. 13. Routing… the basics<br />A<br />5/5<br />2/1<br />4/4<br />4/2<br />B<br />
  14. 14. How do we map/calculate beauty<br />
  15. 15. Beauty… and uglyness<br />A Grotesque Old Woman orThe Ugly Duchess<br />QuintinMassys in Antwerp<br />
  16. 16. Absolute or relative…<br />90% of people agree on beauty in landscapes…<br />Even by people who don’t have this landscape…<br />Pleistocene landscapes…<br />Denis Dutton <br />a Darwinian theory of beauty<br />
  17. 17. Nice landscapes<br />Hobbit land<br />Mantama<br />http://www.routeyou.com/location/view/47449212/hobbiton-film-set.nl<br />
  18. 18. Nice roads<br />
  19. 19. Nice roads<br />
  20. 20. Nice roads<br />
  21. 21. Nice roads<br />
  22. 22. Nice roads<br />
  23. 23. Nice roads<br />
  24. 24. Nice roads<br />
  25. 25. Nice roads<br />
  26. 26. Nice roads<br />
  27. 27. Nice roads<br />
  28. 28. Ugly landscapes<br />
  29. 29. Ugly roads<br />
  30. 30. Ugly roads<br />
  31. 31. Ugly Roads<br />
  32. 32. Ugly Roads<br />
  33. 33. Ugly roads<br />
  34. 34. The men behind the story<br />Benjamin<br />David<br />Kevin<br />
  35. 35. Worker Module: Get Spatial Data Within Buffer<br /><ul><li>Busy roads
  36. 36. Curvature
  37. 37. Noise (near a highway)
  38. 38. Near forests
  39. 39. BUAs
  40. 40. Parks
  41. 41. Water/rivers/lakes….</li></li></ul><li>2nd source: Professional sources<br />Mapping other sources with your reference data<br />
  42. 42. Snapper & Getwithin Buffer<br />
  43. 43. You always have too little and too much…<br />
  44. 44. 3 layer model<br />Perfect actors…<br />Base Models<br />Actors<br />Omission detectors<br />Commission detectors<br />Detectors<br />Characterisitic detectors<br />Fixers<br />Specific Model fixers<br />Completers<br />
  45. 45. Dead End Cleaner (erosion filter)<br />
  46. 46. Dead End Cleaner (erosion filter)<br />
  47. 47. Dead End Cleaner (2)<br />
  48. 48. Isolated networks<br />
  49. 49. Isolated networks<br />
  50. 50. Specific characteristics<br />E.g. closed network<br />
  51. 51. Dead-End Non-Dead End Disconnectivity<br />
  52. 52. Completers<br />
  53. 53. Isolated Networks<br />
  54. 54.
  55. 55. 3rd source: Community<br />The power of<br />the<br />community<br /><ul><li>It’s very likely that one person is wrong once
  56. 56. Some persons are wrong all the time
  57. 57. It’s very unlikely that all persons are wrong all the time</li></li></ul><li>Community source<br />
  58. 58. Communty data<br />
  59. 59. FME Viewer (debugging<br />
  60. 60. The RouteYou fomulae<br />Benjamin<br />David<br />Kevin<br />N formulae => N scores => resistance for beauty<br />
  61. 61. The RouteYounework<br />
  62. 62. )<br />
  63. 63. Flow of internal workers<br />
  64. 64. The RouteYou network<br />
  65. 65. Python (tile creation) & FME<br />
  66. 66. Tiling and stitching<br />Size and memory management<br />
  67. 67.
  68. 68. The NICEST route<br />Can we measure it? <br />“Cf. search”<br />Can we create is when we don’t have enough? Simulating automa…<br />
  69. 69. Data: RTY Netwerk<br />
  70. 70. All cells are equal, but some are more equal…<br />The Kevin number.<br />
  71. 71. <ul><li>Every cell ‘knows’ which cells are his first order neighbours
  72. 72. The initial cell will combine with every FON to find the best neighbour to merge</li></ul>What is best ?<br />The sum of every segments score multiplied by his length in the outer shell of the merged cell, devided by the total length of the outer shell<br />First order neighbours (FON)<br />Second order neighbours (SON)<br />
  73. 73. GROWN TO BEST NEIGHBOUR<br />The new cell runs through the same flow as the initial cell, if the length criteria isn’t met.<br />
  74. 74. GROWN TO BEST NEW NEIGHBOUR<br />The new cell runs through the same flow as the initial cell, if the length criteria isn’t met.<br />
  75. 75. FINAL SITUATION AFTER GROWING<br />The flow stops when the length criteria is met. <br />
  76. 76. Result<br />
  77. 77. Problems<br />Local minima<br />
  78. 78.
  79. 79. Neighbouring<br />
  80. 80.
  81. 81. Thank You!<br />Questions?<br />For more information:<br />info@routeyou.com<br />Pascal.brackman@routeyou.com<br />RouteYou<br />www.routeyou.com<br />

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