Spatial Data Science Symposium 2021

Spatial and Temporal Thinking in Data-Driven Methods
December 13-14, 2021 - Virtual / Online

Motivation

Spatial and temporal thinking is important because everything happens at some places and at some time, and understanding where and when things happen help us analyze how and why they happened or will happen. Spatial data science is concerned with the representation, modeling, and simulation of spatial processes, as well as with the publication, retrieval, reuse, integration, and analysis of spatial data. It generalizes and unifies research from fields such as geographic information science/geoinformatics, geo/spatial statistics, remote sensing, environmental studies, and transportation studies, and fosters applications of methods developed in these fields to other disciplines ranging from social to physical sciences.

Data-driven methods, such as machine learning models, have been attracting attention from the Geoscience community for the past several years. For instance, they have been successfully used to quantify semantics of place types, to classify geo-tagged images, to predict traffic and air quality, to improve resolution of remotely sensed images, and among others so on. In contrast to non-spatial information, geospatial information may be vague, uncertain, heterogeneous, and multimodal; thus spatial and temporal thinking should be included in techniques such as deep neural networks. For example, there are many questions to be explored: Whether a larger amount of data can compensate for the lack of spatial and temporal thinking; how large a role spatial and temporal thinking play in such data-driven methods; how to integrate data-driven methods with theory-driven methods, such as agent-based modelling; how to represent spatial and temporal knowledge to facilitate efficient reasoning; and how to take spatial uncertainty into the model.

With these questions in mind, the Center for Spatial Studies at the University of California, Santa Barbara plans to host the 2nd Spatial Data Science Symposium virtually this year with a focus on “Spatial and Temporal Thinking in Data-Driven Methods.” The symposium aims to bring together researchers from both academia and industry to discuss experiences, insights, methodologies, and applications, taking spatial and temporal knowledge into account while addressing their domain-specific problems. The format of this symposium will be a combination of keynotes, scientific sessions, as well as paper presentations. We welcome submissions for both papers and sessions (see below).

Registration

Free, but registration will be required.

Call for Papers

We welcome short papers (6 pages) and vision papers (4 pages) on the following (or similar) topics:

  • Spatial and temporal knowledge representation and reasoning
  • Geospatial semantics
  • Geospatial artificial intelligence (GeoAI) & spatially-explicit machine learning
  • Neuro-symbolic representation learning for spatial and temporal data
  • Geographic information retrieval
  • Geospatial knowledge graphs
  • Spatial statistics / Geostatistics
  • Spatial and temporal data mining
  • Spatial and spatiotemporal data uncertainty
  • Geo-simulation
  • Geospatial applications that use data-driven methods, including but not limited to:
    • Movement analysis
    • Disaster response
    • Environmental studies
    • Geoprivacy
    • Social sensing
    • Location-based services
    • Humanitarian relief
    • Crime analysis
    • Urban analytics

Submission Guidelines

We welcome short papers (6 pages) and vision papers (4 pages). All submissions must be original and must not be simultaneously submitted to another journal or conference/workshop. All submissions must be in English. Proceedings of the symposium will be publicly available at well-established UC eScholarship and each accepted paper will be assigned an individual DOI. All papers must be formatted according to LNCS templates. Submissions will be peer-reviewed by the Program Committee. Papers must be submitted via EasyChair: Easychair Sumbmission System.

Several journals have been contacted about the possibility of organizing a special issue following this event. Selected papers will be invited to submit an extended version to the journal. More details will be announced soon.

Call for Session Proposals

We solicit anyone and any team who is interested in Spatial Data Science to propose a session for SDSS 2021. Any activity that can fit into a 90-minute time slot is welcome (longer sessions may be proposed as a combination of multiple slots).

Examples of session type include:
  • a panel discussion
  • a series of presentations on a topic
  • a breakout-style discussion
  • a tutorial
  • hackathon
  • challenges (bring your own “sharks”)
  • education track
  • technology track

Submission Guidelines

In a maximal 2-page submission, please indicate:

  • Name of the session
  • Type of session: Panel / Presentations / Break-out discussion / Tutorial / Education Track / Technology Track / Hackathon / Challenge / Other (please specify)
  • Short description of the session
  • Names and affiliations of team members that will lead the session (preferably 2-3 people)
  • Speakers (when applicable). It is imperative to specify the confirmed speakers if you are proposing a panel
  • Expected participation (i.e., who would be interested in attending your session)
  • Email the proposal directly to Dr. Rui Zhu: ruizhu@geog.ucsb.edu
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Important Dates

  • Symposium date: December 13–14, 2021
  • Paper submission deadline: October 30, 2021
  • Notification of paper acceptance: November 21, 2021
  • Camera ready version: November 30, 2021
  • Proposal submission deadline: November 15 2021
  • Notification of session acceptance: November 20 2021
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Organizing Committee

General Chair

Program Chairs

  • Rui Zhu, Center for Spatial Studies, University of California Santa Barbara
  • Judith Verstegen, Laboratory of Geo-information Science and Remote Sensing, Wageningen University and Research
  • Ling Cai, Center for Spatial Studies, University of California Santa Barbara
  • Grant McKenzie, Department of Geography, McGill University
  • Ourania Kounadi, Department of Geography, University of Vienna
  • Bruno Martins, Instituto Superior Técnico, University of Lisbon

Local/Virtual Arrangements

  • Karen Doehner, Center for Spatial Studies, University of California Santa Barbara

Program Committee

  • Clio Andris, Georgia Tech University
  • Geoff Boeing, University of Southern California
  • Karl Grossner, University of Pittsburgh
  • Yingjie Hu, University at Buffalo
  • Stefan Keller, Eastern Switzerland University of Applied Sciences
  • Wenwen Li, Arizona State University
  • Gengchen Mai, Stanford University
  • Nick Malleson, University of Leeds
  • Trisalyn Nelson, University of California Santa Barbara
  • Ross Purves, University of Zurich
  • Alina Ristea, University College London
  • Colin Robertson, Wilfrid Laurier University
  • Francisco Rowe, University of Liverpool
  • Johannes Scholz, Graz University of Technology
  • Robert Stewart, Oak Ridge National Laboratory
  • Kristin Stock, Massey University
  • Robert Weibel, University of Zurich
  • Feng Zhang, Zhejiang University
  • Bo Zhao, University of Washington
  • Yunqiang Zhu, Chinese Academy of Sciences
  • Alexander Zipf, Heidelberg University

Contact

For any further information, please contact Rui Zhu or Ling Cai.