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.

1. Big data meets scalable visualizations
JAVIER DE LA TORRE

2. Big data meets scalable visualizations
Javier de la Torre - @jatorre
2

3. Big data awesomeness!!!!
picture
on
big
data
awesomeness
3

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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. Ugly
map!
Everybody wants
to see data on
maps,
But making good
maps is very hard!

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. Demo
on
meteorites
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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 map

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

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.

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24. Big data analysis of deforestation
How we can track deforestation on real time
Global Forest Watch

46. http://en.wikipedia.org/wiki/Bakun_Dam

47. Most people don’t need Big Data
technologies
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
But when you can’t…. when it really explodes…

48. Connect PostgreSQL to almost anything
Foreign Data wrappers
Oracle
Hadoop
MySQL
MongoDB
CouchDB
Redis
….
Twitter
Email
S3

49. CartoDB
Hadoop
HBase
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50. CartoDB and Torque
Geo-temporal visualizations

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. Raw
Datacube
1000
300
100
70
10
3
1
2
3mb
1.2
Payload sizes
70mb
1.5
300mb

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. Thanks!!
Javier de la Torre - @jatorre
56