The 2nd SDSS: The 2nd Spatial Data Science Symposium - Spatial and Temporal Thinking in Data-Driven Method Virtual December 13-14, 2021 |
Conference website | http://sdss2021.spatial-data-science.net/ |
Submission link | https://easychair.org/conferences/?conf=sdss2021 |
Submission deadline | October 30, 2021 |
Notification of acceptance | November 21, 2021 |
Camera ready version | November 30, 2021 |
Space and time matter not only for the obvious reason that everything happens somewhere at some time, but because knowing where and when things happen is critical to understanding why and how 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 intensive attention from 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 so on. In contrast to non-spatial information, geospatial information is often vague, uncertain, heterogeneous, and multimodal; thus certain spatial and temporal thinking must be adapted to generic advanced techniques such as deep neural networks. For example, there are still 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 ingrate 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 and paper presentations.
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-esablished UC eScholorship and each accepted paper will be assigned with an individual DOI. All papers must be formatted according to LNCS templates. Submissions will be peer-reviewed by the Program Committee. Paper submission should be done via EasyChair: [Link TBA].
- Short papers (6-page) describe your most recent work where spatial and temporal thinking plays roles and discuss their roles.
- Vision papers (4-page) describe your vision of the role spatial and temporal thinking plays in data-driven appraoches in GISicence and geography in general.
We are contacting journals to potentially organize 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.
List of Topics
- Spatial and temporal knowledge representation and reasoning
- Spatial cognition & 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
Organizing Committees
General Chair:
Krzysztof Janowicz, Center for Spatial Studies, University of California Santa Barbara
Program Chairs:
- Rui Zhu, Center for Spatial Studies, University of California Santa Barbara
- Judith Verstegen, Center for Spatial Studies, University of California Santa Barbara
- Ling Cai, Center for Spatial Studies, University of California Santa Barbara
- Grant McKenzie, Department of Geography, McGill University
- Bruno Martins, Instituto Superior Técnico, University of Lisbon
Local/Virtual Arrangements
Karen Doehner, Center for Spatial Studies, University of California Santa Barbara
Program Committee:
TBD
Invited Speakers
- TBD
Publication
Proceedings of the symposium will be publicly available at eScholorship and each accepted paper will be assigned with an individual DOI. All papers must be formatted according to LNCS templates. Submissions will be peer-reviewed by the Program Committee.
Venue
The conference will be held in virually this year and welcome scientists from all the world.
Contact
All questions about submissions should be emailed to Rui Zhu (ruizhu@ucsb.edu) or Ling Cai (lingcai@ucsb.edu).
Sponsors
TBD