Spatial and temporal thinking is important not just because everything happens at some places and at some time, but because knowing where and when things are happening is key to understanding 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 such space- and place-centric 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 in other disciplines ranging from social to biological and 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 the semantics of place types, to classify geo-tagged images, to predict traffic and air quality, to improve resolution of remotely sensed images, to recognize objects in such imagery, to predict and compare trajectories, to name but a few. Geospatial observations may be vague, uncertain, heterogeneous, dependent on other nearby observations, biased, and multimodal; thus, spatial and temporal principles should be included in data science techniques such as deep neural networks. Unsurprisingly, research has shown that by doing so, we can substantially outcompete more general (non-spatial) models when applied to geo-data or applications with a spatial and temporal component.

To keep this discussion alive and help the community to exchange ideas and lessons learned about spatial and temporal aspects of data science, we are hosting the 4th Spatial Data Science Symposium (SDSS 2023) as a distributed virtual meeting. 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. In contrast to classical conferences, the community will decide on those sessions, and the main focus will be on interaction. Hence, we welcome submissions for both papers and sessions (see below). SDSS 2023 will be a distributed symposium in a sense that while the event as such will be online, we will host (and help others to host) individual get-togethers to jointly experience the symposium in person.

Session submission deadline: April 14, 2023

Paper submission deadline: May 22, 2023

Paper notification: July 10, 2023

Symposium Dates: September 5-6, 2023

We welcome short papers (3,000 words) and vision papers (2,000 words) on the following (or similar) topics:

• Geospatial thinking in the arts
• Spatial and temporal knowledge representation and reasoning
• Geospatial artificial intelligence (GeoAI) & spatially explicit machine learning
• Neuro-symbolic representation learning for spatial and temporal data
• Spatial and temporal data mining
• Spatial and spatiotemporal data uncertainty
• Geographic information retrieval
• Geospatial knowledge graphs
• Geospatial semantics
• Spatial statistics / Geostatistics
• Geo-simulation
• Diversity, inclusion, and equity in spatial data science
• Social and environmental ethics in spatial data science
• 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

All submissions must be original and must not be simultaneously submitted to another journal or conference/workshop. All submissions must be in English and formatted according to LNCS templates. Proceedings of the symposium will be publicly available at well-established UC eScholarship and each accepted paper will be assigned an individual DOI. Submissions will be peer-reviewed by the Program Committee. Papers must be submitted via EasyChair.

We invite any person or team who is interested in spatial data science to propose a session for SDSS 2023. Any activity that can fit into a 60-minute time slot is welcome. (Longer sessions may be proposed as a combination of multiple slots.)

Examples of session types include:

• panel discussion
• series of presentations on a topic
• breakout-style discussion
• tutorial
• hackathon/challenge
• education track
• technology track

Submission Guidelines

In a maximal 2-page submission, please indicate:

• Name of the session
• Type of session: Panel | Presentation Series | 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: Kitty Currier (kcurrier@ucsb.edu).