The document discusses using CartoDB and Torque to create geo-temporal visualizations of global deforestation over time using big data. It describes how datacubes can be used to analyze and summarize multidimensional data binned by variables. The document provides an example of aggregating postal office location and construction date data into a grid to visualize counts and dates over space and time in a compact JSON format that can be transferred to a browser. Geo-temporal visualizations and data cubes allow understanding how spatial data changes over millions of records.

1. Global&Deforesta-on&through&Time&
Big$Data$Meets$Scalable$Visualiza2ons$|$Javier$de$la$Torre$;$CartoDB$

3. MAKE BEAUTIFUL MAPS - BUILD GEOSPATIAL APPLICATIONS - LET YOU GAIN INSIGHT FROM YOUR DATA

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

31. Geo-temporal visualizations
CartoDB and Torque

34. Datacubes
•BI/Finance analysis
•multidimensional
•summary data
•binned variables

36. 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

37. {
%%rows:%[
%%{
%%%%x:%0,
%%%%y:%0,
%%%%vals:%[2],
%%%%dates:%[457]
%%},
%%{
%%%%x:%1,
%%%%y:%0,
%%%%vals:%[1,1,4],
%%%%dates:%[2,3,4]
%%%%}
%%]
}

38. 1
10
100
1000
3mb 70mb 300mb
1.5
2
1.2
300
70
3
Raw Datacube
Payload sizes

41. Geo-temporal visualizations
We have defined a format to transfer data cube “data” to the
browser.
One or many dimensions: use of coded images or JSON
Geo-temporal visualizations and data cubes are a great
way of understanding how spatial data changes
We can handle millions of vector data or processed rasters

42. Global&Deforesta-on&through&Time&
Big$Data$Meets$Scalable$Visualiza2ons$|$Javier$de$la$Torre$;$CartoDB$