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Big Data Week: Spatial Data, Visualization, and CartoDB

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.


Joshua Stevens

April 24, 2013

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  1. Open Source Spatial Data Management: CartoDB

  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
  3. Spatial is special. “Everything is related to everything else, but

    near things are more related than distant things.” - Waldo Tobler (1970) Preface:
  4. Geographic visualization should be forward thinking. Step 1: Look ahead.

    What is your goal? How will you accomplish it?
  5. With CartoDB your map could be static... ...or animated.

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

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

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

  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).
  11. So how does this relate to CartoDB? To find out,

    let’s look at what other tools do.
  12. Some tools are great for analysis.

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

  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
  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
  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
  18. Other tools are great for communication.

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

  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!
  24. Let’s make a map! 1 2

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

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

  28. Let’s make a map!

  29. Let’s make a map!

  30. Let’s make a map! Points, lines and polygons = geometric

    primitives essential to GIS + cartography
  31. Let’s make a map! 6 Data type can be String,

    Number (float), Date, or Boolean
  32. Let’s make a map!

  33. Let’s make a map!

  34. Let’s make a map!

  35. Let’s make a map!

  36. Let’s make a map!

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

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

    lines of MapStyle.css
  39. Let’s make a map! More interesting heat map. Notice zoom

    level = 7.
  40. Let’s make a map! Discrete points visible at zoom level

    = 10.
  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
  42. Don’t forget to share your map!

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