This book contains a selection of papers presented during a special workshop on Complexity Science organized as part of the 9th International Conference on GIScience 2016. Expert researchers in the areas of Agent-Based Modeling, Complexity Theory, Network Theory, Big Data, and emerging methods of Analysis and Visualization for new types of data explore novel complexity science approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a Big Data Era.

Provides timely discussions on how cutting-edge technologies of data collection, management, processing, and analysis change trends in complexity science approaches in geospatial sciences Introduces the-state-of-the-art frameworks/methodologies to leverage advanced computational infrastructure to improve modeling, simulation, and data analysis Introduces novel analysis/visualization methods for new types of geospatial big data Shows how such advanced technologies and frameworks can be adapted to various application domains across natural and human phenomena

Autorentext

Liliana Perez is an Assistant Professor at the Department of Geography and director of the Laboratory of Environmental Geosimulation (LEDGE), University of Montreal, Canada. Liliana is interested in advancing GIScience methods applied to ecology, by developing modelling approaches to simulate ecological complexities in order to understand their behavior and dynamics as well as to use them as a starting point to begin planning and preparing management strategies in face of climate change. She has developed and implemented a series of simulation tools focusing on forestry, landscape ecology, biodiversity and climate change.

Eun-Kyeong Kim is a Ph.D. candidate in the GeoVISTA Center in the Department of Geography at the Pennsylvania State University. Eun-Kyeong's research attempts to advance spatiotemporal data analysis methodologies by integrating methods from statistical physics and complexity science. She also has an interest in geospatial big data visualization with advanced technologies. She has served NSF-sponsored Big Data Education project as a graduate researcher, and is a co-author of big data analytics online textbook.

Raja Sengupta is Associate Professor, Geography and School of Environment at McGill University. Dr. Sengupta is interested in research on both Artificial Life and Software Agents, and applying GIScience to environmental management issues and water resources management. He was an editorial board member for the journal Transactions in GIS (2011-2016) and is currently an editorial board member for Water International.

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Inhalt

1. Developing High Fidelity, Data Driven, Verified Agent Based Models of Coupled Socio-Ecological Systems of Alaska Fisheries.- 2. Leveraging Coupled Agent-Based Models to Explore the Resilience of Tightly-Coupled Land Use Systems.- 3. Deconstructing geospatial agent-based model: Sensitivity analysis of forest insect infestation model.- 4.An Agent-Based Model to Identify Migration Pathways of Refugees: the Case of Syria.- 5. Rule extraction for Agent Mobility from Animal Big Data: Trends and Possibilities.- 6. Wealthy Hubs and Poor Chains: Constellations and Routing in the U.S. Urban Migration System.- 7. Uncovering geographic and structural characteristics of the interpersonal communication. network on Twitter: A complex networks perspective.