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, and multimodal; thus, spatial and temporal principles should be included in 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 3rd Spatial Data Science Symposium (SDSS 2022) 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 2022 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.

Invited Speakers

Vanessa Frias-Martinez
University of Maryland, College Park

Shih-Lung Shaw
University of Tennessee, Knoxville

Daniel Sui
Virginia Tech

Details to follow

More speakers to be announced...

Important Dates

Symposium date: September 22-23, 2022

Paper submission deadline: July 18, 2022 July 26, 2022

Notification of paper acceptance: August 18, 2022 August 25, 2022

Camera ready version: August 25, 2022 September 8, 2022

Proposal submission deadline: July 26, 2022

Notification of session acceptance: August 2, 2022


Converging on Spatial Data Science


Marcela Suarez
Penn State University

Lauren Bennett

Canserina Kurnia

This panel will explore some of the challenges and opportunities as spatial analysis and data science converge. Topics will include teaching spatial data science beyond the traditional classroom, the impact of imposter syndrome, and what the future might look like. MORE

Geoethics Spectrum


Dara Seidl
Colorado Mountain College

Ourania Kounadi
University of Vienna

This session seeks to address the variable ethical viewpoints on handling of personal geodata in different domains and topics such as police investigation, health insurance, health research, UAVs, transportation, privacy in public, trust & trustworthiness, and social justice. We will encourage an open discussion (with all participants) around scientifically driven yet subjective or controversial issues and highlightthe opportunities and risks related to personal (geo) data and their protection. The session will provide an opportunity to compare how spatial data scientists’ viewpoints on geoprivacy issues mirror or differ from other documented public opinions and practices on these issues. MORE

Leveraging KnowWhereGraph for Geospatial Data Acquisition


Thomas Thelen

Zhining Gu
Arizona State University

Meilin Shi
UC Santa Barbara

In this tutorial, we’re going to present a workflow for using KnowWhereGraph to discover and download geospatial data. The key areas of focus are 1. Finding data using the Knowledge Explorer 2. Downloading the data by using the schema to write SPARQL queries 3. Visualizing the data with geopandas. MORE

Understanding the structure of cities through the lens of data


Martin Fleischmann
University of Liverpool

James D. Gaboardi
Oak Ridge National Laboratory

This tutorial introduces a purely data-driven method under the umbrella of urban morphometrics. It presents the current state of urban morphology in the Python world, focusing predominantly on vector data capturing building footprints and street networks. The main part of the tutorial introduces momepy, a toolkit from the PySAL family allowing a complex multi-scale analysis of urban form from the perspective of measuring the spatial configuration of simple vector features. MORE

Forget Data Analytics for Mobility – we need it for Accessibility!


Monika Sester
Leibniz Universität Hannover,

Alexandra Millonig
Austrian Institute of Technology

Martin Tomko
University of Melbourne

Stephan Winter
University of Melbourne

Urban transport is a major contributor to human‐induced climate change and the only sector not showing any sign of successful carbon reduction. Technological solutions such as more efficient vehicles or alternative propulsion systems are not able to achieve the required massive reduction in the ever‐shrinking time frame for mobility transition or are merely shifting the problem to other sectors, especially the energy sector facing the same problem. Thus, there is a sharp necessity to change mobility behavior, as reductions that cannot be realized technologically must be compensated by sufficiency in transport – as much as really needed, as little as possible. This means that the focus should be changed from mobility (improving and optimizing getting around with transport services) to accessibility (improving and optimizing the possibility to reach essential destinations with as little motorized transport required as possible), e.g., by creating local mixed‐use centers providing most if not all functionalities of everyday live. This would reduce the need for motorized transport to non‐routine trips, which should consequently also be shared and made as efficient as possible. MORE

Call for Papers

We welcome short papers (6 pages) and vision papers (4 pages) 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
  • 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 Submission System.

Call for Session Proposals

We invite any person or team who is interested in Spatial Data Science to propose a session for SDSS 2022. 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 type 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 Dr. Shirly Stephen: shirly.stephen@geog.ucsb.edu

Organizing Committee

General Chair

Krzysztof Janowicz

University of Vienna and University of California, Santa Barbara

Program Chairs

Ana Basiri

University of Glasgow, UK

Kitty Currier

University of California, Santa Barbara, USA

Grant McKenzie

McGill University, Canada

Johannes Scholz

Graz University of Technology, Austria

Shirly Stephen

University of California, Santa Barbara, USA

Rui Zhu

University of Bristol, UK

Program Committee

Clio Andris, Georgia Tech University
Geoff Boeing, University of Southern California
Vanessa Brum-Bastos, University of Canterbury, New Zealand
Ling Cai, University of California, Santa Barbara
Jed Long, Western University, Canada
Bruno Martins, University of Lisbon
Ross Purves, University of Zurich
Kristin Stock, Massey University
Emmanouil Tranos, University of Bristol
Yang Xu, The Hong Kong Polytechnic University
Quanshan Zhao, University of Glasgow
More to confirm.


Registration is free. Register via the AirMeet link below.