Big Data Week: Spatial Data, Visualization, and CartoDB

This talk expands upon my previous demo of spatial tools and visualization with CartoDB. Topics covered include design considerations, scientific visualization vs communication, relevance of big data, and a step-by-step walkthrough on basic map creation in CartoDB.

Presented during Big Data Week (http://bigdataweek.com/) at Penn State.

Transcript

1. Open Source Spatial
   Data Management:
   CartoDB
   

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2. Table of contents:
   1.............Considerations
   2.............Big Data
   3.............Art, Science, and The Scary
   Places in the Middle
   4.............Why CartoDB?
   5............Making a Map with
   CartoDB
   6............Q & A
   

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3. Spatial is special.
   “Everything is related to everything else,
   but near things are more related than
   distant things.”
   - Waldo Tobler (1970)
   Preface:
   

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4. Geographic visualization should be
   forward thinking.
   Step 1: Look ahead. What is your
   goal? How will you accomplish it?
   

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5. With CartoDB your map could be
   static...
   ...or animated.
   

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6. Your map could stand alone...
   ...or be part of a larger story.
   

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7. Big Data are
   more than
   just ‘big.’
   “Big data is more than simply a
   matter of size; it is an opportunity to
   find insights in new and emerging
   types of data and content...”
   - IBM
   Step 2: Consider your data.
   

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8. Volume Variety Velocity Vinculation
   of big data
   V’s
   The four
   

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9. Geographic data are frequently big (one or more V’s).
   

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10. Step 3: Decide if you’re looking for answers or communicating results.
    (or both)
    - Andy Woodruff, “Apart from being dead, Art and Science are strong in web
    cartography.”http://andywoodruff.com/blog/apart-from-being-dead-art-and-science-
    are-strong-in-web-cartography/
    “Web cartography is not about maps;
    it’s about hacks for moving data
    around.”
    Graphic from MacEachren, A.M. (1994) “Visualization in modern
    cartography: Setting the agenda.” Redrawn by Roth (2011).
    

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11. So how does this
    relate to
    CartoDB?
    To find out, let’s look at
    what other tools do.
    

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12. Some tools are great for
    analysis.
    

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13. http://at-cam.blogspot.com
    ArcGIS
    http://blog.ushahidi.com
    ✓ De facto standard GIS
    ✓ 10.1 on campus labs
    ✓ 1-year licenses for students
    ✓ Raster and vector analysis
    ✓ Hundreds of tools
    ✓ Model builder (see left)
    ✓ Python
    

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14. ArcGIS Online
    http://www.arcgis.com
    

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15. Quantum GIS
    (QGIS for short)
    ✓ Windows, OS X, Linux
    ✓ Open Source
    ✓ GDAL
    ✓ PostgreSQL/PostGIS, OSM
    ✓ Raster and vector analysis
    ✓ Hundreds of tools
    ✓ Python
    ✓ Increasingly mainstream
    

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16. PostGIS
    Spatially-enabled PostgreSQL
    ✓ Windows, OS X, Linux
    ✓ Open Source (GPL)
    ✓ Very powerful. Very fast.
    ✓ Often used in conjunction with
    other tools
    No direct GUI
    

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17. R
    With Spatial Packages
    ✓ A stats favorite
    ✓ Excels at point patterns, trends,
    and interpolation
    ✓ So-so visualization
    ✓ Recommend packages: ggplot2,
    maps, splancs, spatstat, mapproj
    R Studio w/ ggplot2 + maps
    http://www.statisfaction.wordpress.com
    

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18. Other tools are great for
    communication.
    

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19. TileMill
    ✓ A MapBox project
    ✓ CartoCSS for styling
    ✓ Smart selectors + compositing
    ✓ Shapefiles, PostGIS, SQLite,
    GeoJSON, CSV...
    ✓ Export to web, mobile, tiles, PDF,
    SVG, PNG...
    ✓ Publish to MapBox (free and $)
    ✓ Open Source
    

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20. MapBox
    ✓ Cloud-based map design
    ✓ Add public layers, base maps
    ✓ Load data from TileMill
    ✓ Embed or share
    ✓ Free account = 3,000 views per
    month
    ✓ MapBox.js library
    

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21. D3
    ✓ JavaScript for SVG styled with
    CSS
    ✓ Huge (and growing!) set of
    examples
    ✓ Impressive geographic capability
    ✓ Steep learning curve
    ✓ Maps are only one facet of D3
    Data-Driven Documents
    

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22. Where does CartoDB fit in all of this?
    Communication
    Analysis Cloud-ready
    

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23. Where does CartoDB fit in all of this?
    DB-driven (PostgreSQL, PostGIS)
    Scriptable tools and extensions
    Support for SHP, CSV, GeoJSON
    Tie into R for analysis
    Cloud-based online editor
    Pair with JavaScript libraries (D3,
    MapBox.js, etc)
    Base map from TileMill, MapBox
    SVG rendering
    Animation
    Interaction
    Communication
    Analysis Cloud-ready
    You guessed it!
    

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24. Let’s make a map!
    1
    2
    

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25. Let’s make a map!
    3
    Any plans for academics?
    “Yes we have. Contact us for getting
    more information. We are quite
    friends of academics so, you will
    get a lot of benefits.”
    - CartoDB
    

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26. Let’s make a map!
    4
    Need data? No sweat: https://gist.github.com/jscarto/4541842
    

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27. Let’s make a map!
    Need data? No sweat: https://gist.github.com/jscarto/4541842
    5
    

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28. Let’s make a map!
    

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29. Let’s make a map!
    

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30. Let’s make a map!
    Points, lines and polygons = geometric primitives essential to GIS + cartography
    

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31. Let’s make a map!
    6
    Data type can be String, Number (float), Date, or Boolean
    

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32. Let’s make a map!
    

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33. Let’s make a map!
    

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34. Let’s make a map!
    

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35. Let’s make a map!
    

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36. Let’s make a map!
    

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37. Let’s make a map!
    Copy (ctrl- or ⌘-c) all 67 lines of MapStyle.css
    

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38. Let’s make a map!
    Paste (ctrl- or ⌘-v) all 67 lines of MapStyle.css
    

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39. Let’s make a map!
    More interesting heat map. Notice zoom level = 7.
    

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40. Let’s make a map!
    Discrete points visible at zoom level = 10.
    

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41. Let’s make a map!
    1
    2
    3
    CartoCSS Breakdown
    1 2 3
    + + +... = multilayer symbol for heat map Smart selectors enable variable-driven design
    

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42. Don’t forget to share your map!
    

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43. Thanks!
    Questions?
    Email:
    josh.stevens@psu.edu
    Twitter:
    @jscarto
    Slides online at:
    speakerdeck.com/jscarto
    

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