Agent-based models and complexity science in the age of geospatial big data : selected papers from a workshop on agent-based models and complexity science (GIScience 2016)

• 作者： International Symposium on Geographic Information Science (9th : 2016 : Montreal, Quebec)
• 其他作者：
  • Perez, Liliana, editor.
  • Kim, Eun-Kyeong, editor.
  • Sengupta, Raja, editor.
  • SpringerLink (Online service)
• 其他題名：
  • GIScience 2016
  • Advances in geographic information science.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Advances in geographic information science,
• 主題： Geographic information systems--Congresses. , Geography. , Geographical Information Systems/Cartography. , Simulation and Modeling. , Big Data/Analytics. , Big Data. , Algorithm Analysis and Problem Complexity.
• ISBN： 9783319659930 (electronic bk.) 、 9783319659923 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: 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.
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• 摘要註: This book contains a selection of papers presented during a special workshop on Complexity Science that took 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.
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