The Challenges of Crowdsourcing Multidimensional Data

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

THANKS Sean Gorman

[email protected]

202-321-3914

@seangorman