Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Big data meets scalable visualizations by JAVIER DE LA TORRE at Big Data Spain 2013

Big data meets scalable visualizations by JAVIER DE LA TORRE at Big Data Spain 2013

The power of visualizing time-series data derived from remote sensing products can not be overestimated. Visualization can give scientists, policy makers, journalists and others immediate insights into how the landscape and environment is changing over time and can lead to quicker understanding and action.


Big Data Spain

December 19, 2013


  1. Big data meets scalable visualizations JAVIER DE LA TORRE

  2. None
  3. 3 picture  on  big  data  awesomeness Big data awesomeness!!!!

  4. 4

  5. 5

  6. 6

  7. Big data without data visualization = #fail

  8. Maps are the most popular type of data visualization Everything

    happens somewhere ! Where are your clients? IP=location ! So everything can be analyzed and visualized on maps
  9. Everybody wants to see data on maps, But making good

    maps is very hard! Ugly map!
  10. Making maps is hard because… Tools are not there yet.

    They are for GIS experts ! Handling 100 points is easy, 1Million is hard ! Data chages! Is not about printing maps online!
  11. 11 Demo  on  meteorites

  12. Wall Street Journal US election maps

  13. Big data analysis and reporting tool - UNEP Carbon calculator

  14. Narrative maps / Story telling - The Hobbit filming Locations

  15. Narrative maps / Story telling - The Rolling Stones tour

  16. German elections real time maps

  17. Visualizing NYC Open Data

  18. Animated geotemporal maps. Everything happens somewhere and at some time.

    Navy of WWI map
  19. Visual analysis - Economic impact of the Mobile World Congress

    2012 in Barcelona
  20. All meteorites fallen on earth

  21. Animated city traffic maps

  22. Mobile ready.

  23. 23

  24. Big data analysis of deforestation How we can track deforestation

    on real time Global Forest Watch
  25. None
  26. None
  27. None
  28. None
  29. None
  30. None
  31. None
  32. None
  33. None
  34. None
  35. None
  36. None
  37. None
  38. None
  39. None
  40. None
  41. None
  42. None
  43. None
  44. None
  45. None
  46. http://en.wikipedia.org/wiki/Bakun_Dam

  47. Most people don’t need Big Data technologies But when you

    can’t…. when it really explodes… You just need to start collecting and analyzing data. Don’t focus on technology, probably your database can already do it ! You are not Facebook, don’t be cheat
  48. Foreign Data wrappers Connect PostgreSQL to almost anything Oracle Hadoop

    MySQL MongoDB CouchDB Redis …. Twitter Email S3
  49. 49 CartoDB Hadoop HBase

  50. Geo-temporal visualizations CartoDB and Torque

  51. None
  52. WITH%hgrid% %%%%%AS%(SELECT%Cdb_rectanglegrid(Cdb_xyz_extent(8,%12,%5),% %%%%%%%%%%%%%%%%Cdb_xyz_resolution(5)%*%4,% %%%%%%%%%%%%%%%%%%%%%%%%%%%Cdb_xyz_resolution(5)%*%4)%AS%cell)% SELECT%x,% %%%%%%%y,% %%%%%%%Array_agg(c)%vals,% %%%%%%%Array_agg(d)%dates% FROM%%%(SELECT%St_xmax(hgrid.cell)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%x,% %%%%%%%%%%%%%%%St_ymax(hgrid.cell)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%y,%

    %%%%%%%%%%%%%%%Count(i.cartodb_id)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%c,% %%%%%%%%%%%%%%%Floor((%Date_part('epoch',%built)%Q%Q10418716800%)%/%32837875)%d% %%%%%%%%FROM%%%hgrid,% %%%%%%%%%%%%%%%us_po_offices%i% %%%%%%%%WHERE%%St_intersects(i.the_geom_webmercator,%hgrid.cell)% %%%%%%%%GROUP%%BY%hgrid.cell,% %%%%%%%%%%%%%%%%%%Floor((%Date_part('epoch',%built)%Q%Q10418716800%)%/%32837875)% %%%%%%%)%f% GROUP%%BY%x,% %%%%%%%%%%y
  53. { %%rows:%[ %%{ %%%%x:%0, %%%%y:%0, %%%%vals:%[2], %%%%dates:%[457] %%}, %%{ %%%%x:%1,

    %%%%y:%0, %%%%vals:%[1,1,4], %%%%dates:%[2,3,4] %%%%} %%] }
  54. 1 10 100 1000 3mb 70mb 300mb 1.5 2 1.2

    300 70 3 Raw Datacube Payload sizes
  55. Think on the value of location on your data, and

    use it! Is very likely you have geospatial data already ! Complete the big data cycle: Don't forget data visualization ! Find the stories inside the data and show them!
  56. None
  57. None