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Department of Computational Social Science
Leveraging Crowdsourced data for Agent-based modeling:
Opportunities, Examples & Challenges
Andrew Crooks1 & Sarah Wise2
[email protected], www.gisagents.org, @AndyCrooks
[email protected], http://www.ucl.ac.uk/spacetimelab, @ComplexityWise
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.
A GeoSocial Approach
GeoSocial data mining:The combination of geospatial, social network, and content analysis, to understand the human landscape and gain situational awareness.
• 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
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.
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
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
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>
• 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
• Roads (green primary, red secondary). • Refugee camps emerge (blue).
Source: http://vimeo.com/9182869
Haiti Earthquake 12th January 2010
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
qDelineating 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
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.
Summary & Outlook
Crooks et al., (2015), Crowdsourcing Urban Form and Function, International Journal of Geographical Information Science. DOI: 10.1080/13658816.2014.977905
Questions?
• Contact: [email protected] www.gisagents.org @AndyCrooks
[email protected] http://www.ucl.ac.uk/spacetimelab @ComplexityWise
AcknowledgmentsAnthony Stefanidis, Arie Croitoru, Dieter Pfoser, Jacek Radzikowski & Andrew Jenkins.
www.geosocial.gmu.edu