AAG 2019 Symposium on Frontiers in Geospatial Data Science

Geospatial data science represents an emerging interdisciplinary and transdisciplinary field intersecting among three broad knowledge domains: geospatial sciences and technologies, mathematical and statistical sciences, and cyberinfrastructure and computational sciences. The core of this intersection encompasses fertile synergies and interactions between big data and cyberGIS with geospatial principles guiding discovery and innovation.

Figure 1: The scope of geospatial data science

CyberGIS is defined as geographic information science and systems (GIS) based on advanced computing and cyberinfrastructure. Though geospatial big data have played important roles in many domains with significant societal impacts, geospatial data science remains to be established for advancing data-intensive geographic research and education in the era of big data and cyberGIS.  At AAG 2019 annual meeting, the Symposium on Frontiers in Geospatial Data Science will be held to provide an exciting and timely forum for sharing recent progress and future trends on geospatial data science and related fields.  A suite of paper and panel sessions will address cutting-edge advances of geospatial data science with a particular focus placed on the following themes: foundations, principles, and theories of geospatial data science; data-driven geography; artificial intelligence and data-intensive approaches to geographic problem solving; geographic knowledge discovery enabled by cyberGIS; education advances and challenges; and spatial cyberinfrastructure.

Sponsorship

Cyberinfrastructure Specialty Group (CISG), Geographic Information Science and Systems (GISS) Specialty Group, Remote Sensing Specialty Group, and Spatial Analysis and Modeling (SAM) Specialty Group

Panel Sessions

• CyberGIS and Geospatial Data Science Curriculum
• Geospatial Data Science: Frontiers and Opportunities
• Uncertainty and Bias in Geospatial Data Science

Paper Sessions

• Big Data Computing for Geospatial Applications
  • Big Data and the Old Scale Issues, Liem Tran
  • MapReduce Based Spatial Hotspots Detection Using Polygon Propagation, Jian Chen
  • A Cyberinfrastructure Approach for Big Data-Driven Microtopographic Analysis, Wenwu Tang
  • Utilizing a Spark-Based Cloud Computing Framework to Address Big Remote-Sensing Data Processing: Multi-scale Large Landsat 8 Dataset and MODIS as an Example, Hai Lan
  • Application of real-time GIS cloud-based context-aware information services for local government emergency preparedness in the era of big data for resilient and smart city management, Mbongowo Mbuh
• Geospatial Artificial Intelligence: Machine Learning and Deep Learning
  • Employing Deep Learning for Real-Time Sewage Level Prediction within Smart Cities, Jin Xing
  • Machine learning algorithms for identifying crop types in smallholder farms, Preeti Rao
  • Automatic crater detection based on random forest, existing crater map, and spatial structural information from DEM, Cheng-Zhi Qin
• Data-Intensive Geography
  • Optimization techniques for Improving Geospatial Semantic Web Query, Chuanrong Zhang
  • When geospatial big data meets high-performance computing in 3D GIS, Tianyang Chen
• Education Advances and Challenges in Geospatial Data Science
  • Integrating GIS Research into the Geospatial Undergraduate Student Experience, Susan Kamei
  • Understanding the Ecosystem of Geospatial Research and Service in Universities, Weihe Wendy Guan
  • Geospatial Software Institute Community Survey Results, Rebecca Vandewalle
  • Hour of Cyberinfrastructure: Using Data-Driven Education to Improve CyberGIScience Education, Coleman Shepard
• Foundations, Principles, and Theories of Geospatial Data Science
  • A Map Algebra Approach to Analyzing Spatiotemporal Data, Xingong Li
  • Using the smartphone-based activity survey to understand individual mobility and accessibility in the urban environment across time: an object-oriented framework, Ying Song
  • Spatio-Temporal Data Mining and Analyses in a Multi-Scale Framework, Yi Qiang
• Spatial Prediction
  • Spatial Prediction and Laws of Geography, A-Xing Zhu
  • Spatio-temporal modeling of PM2:5 concentrations with missing data problem: a case study in Beijing, China, Qiang Pu
  • Dynamically Weighted Spatiotemporal Interpolation for Modeling Distribution of Social Media Population, Dandong Yin
• Synergizing Geospatial Data Science with Domain Applications I: Climate Science
  • Climate change or natural cycle? Not sure, but it gets 2 hot and 2 cold 4 sure, Bin Zhang
• Synergizing Geospatial Data Science with Domain Applications II: Public Health
  • Patient’s time and travel costs in the treatment of atrial fibrillation: Savings achievable with the shift from warfarin to new oral anticoagulants, Aapeli Leminen
  • Vulnerability to heat exposure at the county level in Florida, Jihoon Jung
  • The Association between Heat and Mortality in Virginia Climate Divisions, Gina Li
• Synergizing Geospatial Data Science with Domain Applications III: Remote Sensing
  • Toward a Scalable Agriculture Classification Model: Comparison of Remote Sensing Crop Classification Methods in the Red River Valley, Minnesota, Jesse Bakker
  • Improving the Crop Type Classification Feasibility for Large Geographic Areas, Yaping Cai
  • Comparison of statistical and deep learning methods for spatiotemporal fusion of satellite images, Rohit Mukherjee
• Synergizing Geospatial Data Science with Domain Applications IV: Disaster Management
  • A CyberGIS-Enabled Multi-Criteria Spatial Decision Support System: A Case Study on Flood Emergency Management, Zhe Zhang
  • An Effective Pothole Identification and Measurement System Using Mobile Devices, Xiao Li
  • A framework for CyberGIS-enabled multi-scale space-time index for analyzing urban social vulnerability dynamics, Shakil Kashem
  • Correlation and Discovery of Disaster Big Data, Juanie Wang

Co-Chairs

• Guofeng Cao, Texas Tech University
• Jing Gao, University of Delaware
• Harvey Miller, the Ohio State University
• Shaowen Wang, University of Illinois at Urbana-Champaign
• Dawn Wright, Esri and Oregon State University
• Dandong Yin, University of Illinois at Urbana-Champaign

Program Committee

• Luc Anselin, University of Chicago
• Marc Armstrong, the University of Iowa
• Peter Atkinson, Lancaster University
• Budhendra L. Bhaduri, Oak Ridge National Laboratory
• Ling Bian, University at Buffalo
• Christopher Brunsdon, Maynooth University
• Alexis Comber, University of Leeds
• Giles Foody, University of Nottingham
• Stewart Fotheringham, Arizona State University
• Song Gao, University of Wisconsin – Madison
• Michael Goodchild, University of California – Santa Barbara
• Yingjie Hu, University at Buffalo
• Myeong-Hun Jeong, Chosun University
• Karen Kemp, University of Southern California
• Carsten Kessler, Aalborg University Copenhagen
• Mei-Po Kwan, University of Illinois at Urbana-Champaign
• Wenwen Li, Arizona State University
• Zhenlong Li, University of South Carolina
• Yu Liu, Peking University
• Binbin Lu, Wuhan University
• Steven Manson, University of Minnesota
• Grant McKenzie, McGill University
• Ali Mansourian, Lund University
• Shawn Newsam, University of California – Merced
• Anand Padmanabhan, University of Illinois at Urbana-Champaign
• Ed Parsons, Google
• Serge Rey, University of California – Riverside
• Shih-Lung Shaw, University of Tennessee – Knoxville
• Wen-Zhong Shi, Hong Kong Polytechnic University
• Eric Shook, University of Minnesota – Twin Cities
• Kathleen Stewart, University of Maryland – College Park
• Daniel Sui, University of Arkansas
• Wenwu Tang, University of North Carolina at Charlotte
• Tao Pei, Chinese Academy of Sciences
• Ming-Hsiang (Ming) Tsou, San Diego State University
• E. Lynn Usery, U.S. Geological Survey
• Fahui Wang, Louisiana State University
• Monica Wachowicz, University of New Brunswick
• John Wilson, University of Southern California
• Ningchuan Xiao, the Ohio State University
• Chaowei (Phil) Yang, George Mason University
• Xinyue Ye, New Jersey Institute of Technology
• May Yuan, University of Texas at Dallas
• Chuanrong Zhang, University of Connecticut
• Zhe Zhang, Texas A&M University

References

• Armstrong, M.P., Wang, S., and Zhang, Z. (2018) “The Internet of Things and Fast Data Streams: Prospects for Geospatial Data Science in Emerging Information Ecosystems.” Cartography and Geographic Information Science, https://doi.org/10.1080/15230406.2018.1503973
• Bowlick, F.J. and Wright, D.J. (2018) “Digital Data-centric Geography: Implications for Geography’s Frontier”. The Professional Geographer, DOI: 10.1080/00330124.2018.1443478
• Miller H.J. (2017) “Geographic Information Observatories and Opportunistic GIScience”. Progress in Human Geography, 41, 489-500
• Miller, H.J. (2018) “Mesogeography: Social physics, GIScience and the Quest for Geographic Knowledge”. Progress in Human Geography, 42, 600-609
• Miller H.J. and Goodchild, M.F. (2016) “Data-driven Geography”. GeoJournal, 80, 449-461
• Wang, S. (2016) “CyberGIS and Spatial Data Science”. GeoJournal. 81(6), 965-968
• Wang, S. and Goodchild, M.F. (2018) “CyberGIS for Geospatial Innovation and Discovery”. Springer, Dordrecht, Netherlands, DOI: 10.1007/978-94-024-1531-5
• Wright, D. J. and Wang, S. (2011) “The Emergence of Spatial Cyberinfrastructure”. PNAS, 108(14): 5488-5491