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Dark Side Of Data

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from http://www.meetup.com/webmapsocial/calendar/15036976/

from http://www.meetup.com/webmapsocial/calendar/15036976/

Published in: Technology, Education

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Transcript

  • 1. The Dark Side of Data
  • 2. About Urban Mapping
    Founded in 2006
    San Francisco-based (new office!)
    Profitable
    On-demand 24/7 hosted geo-services via API
    On demand geoservices platform (spatial query, visualizations, base map)
    Data sourcing/standardization/curation:
    Neighborhood boundaries
    Transportation infrastructure (public transit, air travel)
    Enhanced parking
    Airport noise contours
    Crime
    Religious affiliation
    Demographics
    Fiber-lit buildings
    etc…
    Proprietary and Confidential
  • 3. Powers of Ten
    Proprietary and Confidential
    Q: When is a point not a point?
    A: When you are far enough away
  • 4. Centroid Overload
    Proprietary and Confidential
  • 5. Data: Spatial v Attribute
    Proprietary and Confidential
    Staffed Hours
    Station Name
    Stairs
    System Name
    Escalator
    Lines Served
    Hours of
    Operation
    Accessibility
    Rel Elevation
    Directionality
    Elevator
    “Location Doesn’t Matter”
  • 6. I ♡ Metadata
    Proprietary and Confidential
    One Person’s Metadata is Another Person’s Data
  • 7. There Goes The Metadata!
    Proprietary and Confidential
  • 8. Why Can’t We Be Together?
    Proprietary and Confidential
    Metadata
    Data
  • 9. A Rose Is A Rose?
    Proprietary and Confidential
    Exhibit 1
    Exhibit 2
    “Boys age 2-7”
    Data Element ID
    Element Name
    Element Title
    Element Description
    Element Export Name
    (from US consumer expenditure
    data; apparel-related expenditures)
  • 10. The Dark Art
    Proprietary and Confidential
    ETL: the $10b data gymnastics market nobody has heard of
    ERP
    Business Intelligence
    Data Warehouse
    Legacy
    system
  • 11. Enter the Data Curator*
    Proprietary and Confidential
    Part social scientist (empirical methods, demography, statistics)
    Part librarian (taxonomy, editorial, secondary research)
    Part RDBMS wiz
    *”data scientist” presumes fully-described/understood data structure/model, something exceedingly unlikely
  • 12. Jobs!
    Proprietary and Confidential
    talent@urbanmapping.com

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