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The Dark Side of Data
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
Powers of Ten Proprietary and Confidential Q: When is a point not a point? A: When you are far enough away
Centroid Overload Proprietary and Confidential
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”
I ♡ Metadata Proprietary and Confidential One Person’s Metadata is Another Person’s Data
There Goes The Metadata! Proprietary and Confidential
Why Can’t We Be Together? Proprietary and Confidential Metadata Data
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)
The Dark Art Proprietary and Confidential ETL: the $10b data gymnastics market nobody has heard of  ERP Business Intelligence Data Warehouse Legacy system
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
Jobs! Proprietary and Confidential talent@urbanmapping.com

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Dark Side Of Data

  • 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