May 17 The paper submission deadline has been extended to May 29th.
May 3 The thematic sessions have been announced.
May 1 The SDSS2023 keynote speakers have been announced.
Apr 25 This year's interviewees have been announced.
About the symposium
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.
Paper submission deadline:
May 22, 2023 May 29, 2023
Session submission deadline:
April 14, 2023 April 21, 2023
Paper notification: July 10, 2023
Symposium Dates: September 5-6, 2023
Call for Papers
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
- 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
- Social sensing
- Location-based services
- Humanitarian relief
- Crime analysis
- Urban analytics
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.
This year we are happy to welcome two keynote speakers who are working on the cutting edge of spatial data science.
Judith VerstegenAssistant Professor, Utrecht University
A plea for Pareto frontiers
Finding optimal plans of action becomes more critical as space (e.g. in land use) and time (e.g. in climate change) become more scarce. At the same time, we're aware that in most, if not all, cases, there is no such thing as an optimal plan, as complex problems have multiple stakeholders with multiple conflicting objectives, e.g., preserving biodiversity and increasing food production. That means that trade-offs exist between these objectives. Geoscientists traditionally use simulation models with different scenarios, e.g. a 'nature preservation' scenario versus an 'economic development' scenario to show trade-offs. However, this gives only two points in the entire solution space of action plans and their corresponding spatial arrangements. Spatial optimization methods can contribute to debates by showing the whole Pareto front, i.e. all optimal spatial arrangements given all conflicting objectives. In this talk, I will substantiate this argument, but also point at the geocomputational challenges ahead.
I am an Assistant Professor at the Department of Human Geography and Spatial Planning, Utrecht University, The Netherlands. My research is focused on two sets of methods in Geo-Information Science: geosimulation modelling and spatial optimization. I like working with domain experts to apply these methods to their system of interest, because it allows me to meet interesting people and see the world from different viewpoints.
Ana BasiriProfessor, University of Glasgow
Details to follow.
Along with regular paper presentations, this year's symposium will feature seven thematic sessions organized by a variety of teams from around the globe. The session titles and organizers are listed below. More details will be added as they become available.
Reproducing and Replicating Spatial Data Science
Joseph Holler (Middlebury College), Peter Kedron (ASU), Sarah Bardin (ASU)
Leveraging geographic context at multiple scales: the salience of the neighborhood in statistical learning & causal analysis
Levi Wolf (U Bristol), Taylor Matthew Oshan (U Maryland)
Urban and Wellbeing Analytics
Vanessa Bastos (U Canterbury), Malcolm Campbell (U Canterbury), Lindsey Conrow (U Canterbury)
Street view imagery: Have we answered all the questions with it? What’s left to do?
Koichi Ito (NU Singapore), Winston Yap (NU Singapore)
The use of granular spatial data to examine geospatial mobility in social science research
Noli Brazil (UC Davis), Jennifer Candipan (Brown U)
Spatial Data Science for Disaster Resilience
Yingjie Hu (U Buffalo), Andrew Crooks (U Buffalo)
Spatially Explicit Machine Learning and Artificial Intelligence
Gengchen Mai (U Georgia), Xiaobai Angela Yao (U Georgia), Yao-Yi Chiang (U Minnesota), Yiqun Xie (U Maryland), Rui Zhu (U Bristol)
We will start our symposium with a live/on-stage, interactive interview with Anna Lopez-Carr and Andrew Schroeder from DirectRelief, including their insights into how they make use of Spatial Data Science methods for humanitarian relief, how they use or plan to use GeoAI and knowledge graphs, and key challenges for the future of increasingly data and AI/ML-heavy decision-making in times of crisis. Anna and Andrew will also answer questions from the audience.
Andrew SchroederVP of Research and Analysis
Anna Lopez-CarrMonitoring and Evaluation Specialist Research and Analysis Group
Registration is FREE and will open shortly
UC Santa Barbara
Austrian Institute of Technology
University at Buffalo
University of Bristol
Krzysztof JanowiczGeneral Chair
University of Vienna & Spatial Center,
UC Santa Barbara
- Clio Andris, Georgia Tech
- Vanessa Brum-Bastos, University of Canterbury
- Ling Cai, IBM
- Alessia Calafiore, University of Edinburgh
- Andrew Crooks, University at Buffalo
- Christopher Jones, Cardiff University
- Minh Kieu, University of Aucklange
- Ourania Kounadi, University of Vienna
- Gengchen Mai, University of Georgia
- Vanessa Frias-Martinez, University of Maryland
- Bruno Martins, IST and INESC-ID - Instituto Superior Técnico, University of Lisbon
- Ross Purves, University of Zurich
- Avipsa Roy, University of California, Irvine
- Johannes Scholz, Graz University of Technology, Institute of Geodesy
- Kristin Stock, Massey University
- Yang Xu, The Hong Kong Polytechnic University
- Qunshan Zhao, University of Glasgow
- More to confirm...
SDSS2023 is a distributed/online symposium. Participants are welcome to join one of the symposium hubs distributed around the world. Groups of participants will meet at these hubs to present and discuss with other participants both in person and online.
If you are interested in hosting a hub in your city, please contact firstname.lastname@example.org.
Montreal, CanadaMcGill University
Christchurch, New ZealandUniversity of Canterbury
Calgary, CanadaUniversity of Calgary
Buffalo, USAUniversity at Buffalo
Bristol, UKUniversity of Bristol
Zurich, SwitzerlandETH Zurich
Auckland, New ZealandUniversity of Auckland