Powering geospatial on the web with CartoDB

A presentation given to CIESEN in September 2012

Transcript

1. @andrewxhill
   Powering geospatial data
   on the web with CartoDB
   Wednesday, October 17, 12
   

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2. Wednesday, October 17, 12
   

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3. Guralnick et al., 2007
   Wednesday, October 17, 12
   

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4. Automated analysis
   Guralnick et al., 2007
   Wednesday, October 17, 12
   

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5. Data visualization
   Wednesday, October 17, 12
   

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6. Wednesday, October 17, 12
   

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7. Wednesday, October 17, 12
   

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8. Wednesday, October 17, 12
   

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9. http://
   protectedplanet.net/
   http://protectedplanet.net/
   Wednesday, October 17, 12
   

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10. Wednesday, October 17, 12
    

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11. Wednesday, October 17, 12
    

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12. Wednesday, October 17, 12
    

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13. http://rlat.kew.org/
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14. Wednesday, October 17, 12
    

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15. http://vizzuality.com/projects/imazon
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16. Wednesday, October 17, 12
    

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17. Wednesday, October 17, 12
    

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18. Wednesday, October 17, 12
    

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19. Wednesday, October 17, 12
    

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20. Wednesday, October 17, 12
    

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21. Wednesday, October 17, 12
    

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22. Wednesday, October 17, 12
    

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23. Wednesday, October 17, 12
    

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24. CartoDB Dashboard
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25. CartoDB Dashboard
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26. http://developers.cartodb.com/gallery/maps/
    johnsnow.html data - http://goo.gl/
    2q1Nq
    Data visualization/Data publication
    Wednesday, October 17, 12
    

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27. OpenStreetMap roads in London
    Wednesday, October 17, 12
    

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28. All street trees in San Franscisco
    Wednesday, October 17, 12
    

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29. http://developers.cartodb.com/gallery/maps/
    johnsnow.html data - http://goo.gl/
    2q1Nq
    Population density
    Wednesday, October 17, 12
    

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30. UNEP-WCMC/CBD Carbon Calculator for Protected Areas
    Wednesday, October 17, 12
    

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31. Election results in Spain visualization
    Wednesday, October 17, 12
    

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32. ProtectedPlanet - World Database on Protected Areas
    Wednesday, October 17, 12
    

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33. NGOAIDMAP - Mapping NGO projects in Haiti
    Wednesday, October 17, 12
    

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34. Temporal mapping http://goo.gl/KXIF1
    Wednesday, October 17, 12
    

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35. Wednesday, October 17, 12
    

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36. FILE EXT.
    SHP
    CSV
    KML
    XLS
    OSM
    TIFF
    GEO. TYPES
    ESRI
    JSON
    WKT
    KML
    RAST.
    GPS
    Wednesday, October 17, 12
    

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37. Wednesday, October 17, 12
    

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38. Wednesday, October 17, 12
    

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39. Wednesday, October 17, 12
    

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40. Wednesday, October 17, 12
    

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41. SQL API
    Wednesday, October 17, 12
    

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42. Maps API
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43. Wednesday, October 17, 12
    

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44. Wednesday, October 17, 12
    

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45. The challenges Process and visualize millions or rows.
    Show changes in data over time.
    Visualize up to 5 layers of information.
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46. We host and process the data with CartoDB.
    We generate a JSON file per tile containing
    the deforestation data codified in cells (each
    cell has a value for each time period -
    15days).
    How we did it
    {
    time: 0.00576543211,
    rows: [
    {
    upper_left_x: 16742472,
    upper_left_y: -2589569,
    time_series: [
    0,
    8,
    8,
    8,
    8,
    12,
    12,
    12,
    16,
    ...
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47. {
    time: 0.00576543211,
    rows: [
    {
    upper_left_x: 16742472,
    upper_left_y: -2589569,
    time_series: [
    0,
    8,
    8,
    8,
    8,
    12,
    12,
    12,
    16,
    ...
    Load all the information of the JSON files in
    memory using TypedArrays.
    Render the tiles in the client using the
    codified information -now in memory-. Each
    time the user moves the timeline, the tiles are
    rendered again.
    How we did it
    Wednesday, October 17, 12
    

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48. {
    time: 0.00576543211,
    rows: [
    {
    upper_left_x: 16742472,
    upper_left_y: -2589569,
    time_series: [
    0,
    8,
    8,
    8,
    8,
    12,
    12,
    12,
    16,
    ...
    Load all the information of the JSON files in
    memory using TypedArrays for better
    performance.
    Render the tiles in the client using the
    codified information. Each time the timeline is
    moved, the tiles are rendered again.
    How we did it
    Wednesday, October 17, 12
    

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49. {
    time: 0.00576543211,
    rows: [
    {
    upper_left_x: 16742472,
    upper_left_y: -2589569,
    time_series: [
    0,
    8,
    8,
    8,
    8,
    12,
    12,
    12,
    16,
    ...
    Load all the information of the JSON files in
    memory using TypedArrays for better
    performance.
    Render the tiles in the client using the
    codified information. Each time the timeline is
    moved, the tiles are rendered again.
    How we did it
    Wednesday, October 17, 12
    

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50. Wednesday, October 17, 12
    

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51. Wednesday, October 17, 12
    

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52. Wednesday, October 17, 12
    

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53. Example from Dan Hammer
    CartoDB-R
    library(RCurl)
    library(RJSONIO)
    library(CartoDB)
    library(stats)
    library(cluster)
    account.name cartodb(account.name)
    cartodb.test()
    data columns=c("x", "y"))
    N subset.data as.numeric)
    cid method="single"), h=100)
    png("~/cluster.png")
    plot(subset.data[,2], -subset.data[,1], col=cid)
    dev.off()
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54. GMaps and Leaflet
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55. Wednesday, October 17, 12
    

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56. Wednesday, October 17, 12
    

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57. American Museum of Natural History
    http://www.ecohacknyc.org/
    November 9th
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58. THANK
    YOU
    @ANDREWXHILL
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