leveraging crowdsourced data for agent-based modeling: opportunities, examples and challenges
TRANSCRIPT
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.
Thematic Spaces (neighborhood example)
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
Event Responses in Twitterdom
Event Responses in Twitterdom
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
Model Inputs: All Geo-referenced
Spread of Information
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
Note: word size normalized relative to the occurrence of “fire”
Frequently Adopted Toponym Terms
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
Three-dimensional perspective
Source: Wise 2014
Deriving Mood
Building Agent Populations
• ~~
Source: Wise 2014
Source: Wise 2014
Social media for validating agent-based models
Source: Wise 2014
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