Talk slides from the AAG 2015

1. Department of Computational Social Science
Leveraging Crowdsourced data for
Agent-based modeling:
Opportunities, Examples &
Challenges
Andrew Crooks1 & Sarah Wise2
1acrooks2@gmu.edu, www.gisagents.org, @AndyCrooks
2s.wise@ucl.ac.uk, http://www.ucl.ac.uk/spacetimelab,
@ComplexityWise

2. Harvesting Ambient Geographic
Information
• Web 2.0 and Social Media:
• Volunteered Geographical Information (VGI) and
Ambient Geographical information (AGI).
• Provides a new lens to study the human landscape as a
living, evolving social organism:
• Advanced situational awareness.
• Unique opportunities for actionable knowledge discovery
and modeling:
• Can it be leveraged to help understand human behavior
and actions?
Stefanidis, Crooks, & Radzikowski. (2013), Harvesting Ambient Geospatial Information from Social Media Feeds, GeoJournal 78, (2): 319-338.

3. A GeoSocial Approach
GeoSocial data mining:
The combination of geospatial, social
network, and content analysis, to
understand the human landscape
and gain situational awareness.

4. • Twitter: 645 million accounts
(288 active users).
• flickr: 8 billion photos (1.4
million photos uploaded
every day).
• Facebook: 1.4 billion users,
and 350 million photos
uploaded daily.
• QQ has 829 million active
users.
Source: http://en.wikipedia.org/wiki/List_of_countries_by_population
Ambient Information in Numbers

5. Traffic Speeds
Crooks et al., (2015), Crowdsourcing Urban Form and Function, International Journal of Geographical Information Science. DOI: 10.1080/13658816.2014.977905
Changing traffic situation as
detected by floating car data –
Berlin, Germany (only major
roads shown).
(a) 16 December 2013 – 1 am.
(b) 8 am.
(c) 5:30 pm.

6. Thematic Spaces (neighborhood example)

7. Opportunities: Supplement Traditional Data
Crooks et al., (2015), Crowdsourcing Urban Form and Function, International Journal of Geographical Information Science. DOI: 10.1080/13658816.2014.977905

8. Event Responses in Twitterdom

9. Event Responses in Twitterdom

10. Adjusted times between event occurrence and tweets Tweets delineating the impact area
Crooks, A.T., Croitoru, A., Stefanidis, A. and Radzikowski, J. (2013), #Earthquake: Twitter as a Distributed Sensor System, Transactions in GIS, 17(1): 124-147
Event Responses in Twitterdom
#Earthquake: Twitter as a Distributed Sensor
System

11. Agent-Based Modeling
• How can we use the crowd here?
– New sources of spatial data.
– Near “real time” information.
– New ways to explore how people
perceive & use the space.
– Insights into human behavior?
– Rob Axtell: “… there is a large
research program to be done
over the next 20 years, or even
100 years, for building good
high-fidelity models of human
behavior and interactions”
Crooks & Heppenstall (2012), Introduction to Agent-based Modelling, in Heppenstall, Crooks, See & Batty (eds.), Agent-based Models of Geographical Systems..
Mobile agents
Immobile agents
Artificial World
If <cond> then
<action1> else
<action2>

12. • Instant reports from media and Web 2.0 technology
(e.g. Twitter, Ushahidi etc..)
• Data released over the internet:
Haiti Earthquake 12th January 2010
- Mostly from the “bottom-up” via
crowdsourcing and VGI
- E.g. Google Map Maker, OpenStreetMap
etc...
– Ground damage, tent cities etc...
• Can ABM and GIS be integrated
to assist post-disaster relief
operations rather than just
evacuations?
Crooks & Wise (2013), GIS and Agent-Based models for Humanitarian Assistance, Computers, Environment and Urban Systems, 41: 100-111.
ABM and GIS for Disaster Relief

13. • Roads (green primary, red secondary).
• Refugee camps emerge (blue).
Source: http://vimeo.com/9182869
Haiti Earthquake 12th January 2010

14. Model Inputs: All Geo-referenced

15. Spread of Information

16. Colorado Wildfires
• June and July of 2012
• Wildfires in northern and central Colorado
prompted the evacuation of over 30,000 citizens
• Research question: Can social multimedia be used
to delineate the extent of the wildfire and fused with
an agent-based model?
• Case Study: Waldo Canyon

17. Note: word size normalized relative to the
occurrence of “fire”
Frequently Adopted Toponym Terms

18. q
Delineating Events: Flickr Images
Panteras, Wise, Lu, Croitoru, Crooks, & Stefanidis, (2014), Triangulating
Social Multimedia Content for Event Localization using Flickr and Twitter,
Transactions in GIS. DOI: 10.1111/tgis.12122

19. Three-dimensional perspective

20. Source: Wise 2014
Deriving Mood

21. Building Agent Populations
• ~~
Source: Wise 2014

22. Source: Wise 2014

23. Social media for validating agent-based models
Source: Wise 2014

24. Summary & Challenges
• Crowdsourced data:
• Provides a new lens for understanding of how people perceive,
use and are affected by space over time.
• Provides links across scales: from micro to macro phenomena.
• Challenges:
• Collection and storage of data.
• Short time scales vs. long term problems.
• Validation (cross source), participation bias etc…..
• Emerging research opportunities for Geosimulation:
• Lots of work to be done.

25. Summary & Outlook
Crooks et al., (2015), Crowdsourcing Urban Form and Function, International Journal of Geographical Information Science. DOI: 10.1080/13658816.2014.977905

26. Questions?
• Contact:
acrooks2@gmu.edu
www.gisagents.org
@AndyCrooks
s.wise@ucl.ac.uk
http://www.ucl.ac.uk/spacetimelab
@ComplexityWise

27. Acknowledgments
Anthony Stefanidis, Arie Croitoru,
Dieter Pfoser, Jacek Radzikowski &
Andrew Jenkins.
www.geosocial.gmu.edu