Call for Full Papers

Rethinking the ABCs: Agent-Based Models and Complexity Science in the age of Big Data, CyberGIS, and Sensor networks (full-day), September 27, 2016 at GIScience 2016, Montreal, Canada

Website: http://sites.psu.edu/BigComplexityGISci
E-mail: BigComplexityGISci@gmail.com

Paper Submission Due: May 1st, 2016 May 15th, 2016 (Extended)
Notification of Acceptance: June 1st, 2016 June 30th, 2016 (Extended)

Organizers: Daniel G. Brown (University of Michigan), Eun-Kyeong Kim (Pennsylvania State University), Liliana Perez (Université de Montréal), and Raja Sengupta (McGill University)

Steering Committee: Clio Andris (Pennsylvania State University), David Bennett (University of Iowa), Christopher Bone (University of Oregon), Andrew Crooks (George Mason University), Suzana Dragicevic (Simon Fraser University), Petter Holme (Sungkyunkwan University), Bin Jiang (University of Gävle), Alan M. MacEachren (Pennsylvania State University), Huina Mao (Oak Ridge National Laboratory), Danielle Marceau (University of Calgary), Mir-Abolfazl Mostafavi (Universite Laval), Atsushi Nara (San Diego State University), David O’sullivan (University of California, Berkeley), Shaowen Wang (University of Illinois at Urbana–Champaign), Taha Yessari (Oxford Internet Institute, University of Oxford)

Interest in complex modelling within the GIScience community and related fields has grown rapidly since the early 2000.  There are new opportunities and challenges facing the field, with the availability of Big Data, Cloud Computing platforms, and Sensor Networks, all of which are radically altering the computational platforms and data environments within which the models are executed.  We invite papers for presentation at a workshop on concepts and methodological approaches such agent-based modeling, cellular automata, network theory, and scaling relations, amongst others, in the context of Big Data, CyberGIS and Sensor Networks, linked to GIScience 2016, which focus on and elucidate these linkages.  The topics could include:

1. Multisensor Data Fusion for parameterizing complex models

a. Handling the 5Vs (Volume, Velocity, Variety, Value, and Veracity)
b. Multi-scale interactions in geographic complex phenomena
c. Semantic interoperability

2. Integrating theory with practice:

a. Big data analytics integrated with complexity theories
b. Spatiotemporal analysis in complexity theories
c. Dynamic geo-social network analysis
d. Observer-Expectancy effect of real-time simulations
e. Scaling relations (power laws) in geography
f. Game theoretic approach to geographic problems

3. Output Validation:

a. Can modeled pattern or process outputs be validated with real-time data?
b. How can complex models output be visualized and communicated?
c. How can increased use of massive sensitivity analyses improve process validation?
d. Observer-Expectancy effect of real-time models’ simulations

We will have 5 minute lightning talks by chairs and committee members, followed by 20 minute presentations by selected speakers, and ending with a 10-minute panel discussion.

Submissions are welcome by email: BigComplexityGISci@gmail.com by May 1st, 2016 May 15th, 2016 (Extended).  Please include the words “Big Complexity @ GIScience” in your subject line and attach the paper (formatted in LNCS style, 5000 words including all references and 4 figures maximum) in either word or pdf format to the email.

Submitted papers will be peer-reviewed by the program committee.  Papers will not only be evaluated on scientific quality but also on “visionary approaches”. Accepted papers will be published in the workshop proceedings as a CD-ROM, and possibly submitted for publication as a special issue or edited volume. At least one author of accepted papers will be required to present their research at the workshop.  To participate in this workshop please submit paper by May 1st, 2016 May 15th, 2016 (Extended). Notification of acceptance will be emailed by June 1st, 2016 June 30th, 2016 (Extended).

One or more authors of accepted statements must register for the workshop.