the challenges of crowdsourcing multidimensional data
TRANSCRIPT
The Challenges of Crowdsourcing
Multidimensional Data
Sean Gorman PhD
Timbr.io
@seangorman
Data Collection Data Processing
& Analysis
Data Products
A Traditional Geospatial Workflow
Data’s full life cycle was monolithic
Creating inherently authoritative data
A fundamental shift in who is creating data
Authoritative Data
Volunteered Geographic Information
Ambient Geographic Information
CROWDSOURCING
Geography Network, OSM, GeoCommons, AGOL, ESRI Open Data
OSM, WikiMapia, Waze (?)
Twitter, FourSquare, Instagram, Fitbit, Strava, Google Traffic
103,726
2,000,000
550,000,000
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
1,000,000,000
GeoCommons OpenStreetMap Twitter
Users
Users
Waze Connected Citizens Program
Ambient Geographic Information
•The graph of connections that messages are propagated across
•The content of messages transmitted across the network
•The time stamp provided for the transmission of content
•The location of where content was sent or the locations discussed in content
Geo Temporal
Network Textual
Data Dimensions
How do we deal with data diversity?
A Multitude of Algorithms
for Multidimensional Data
Can we crowdsource algorithms
and match them to data structures?
Our current
work
