The 3rd Spatial Data Science Symposium
News
[September 29] Session recordings are posted on the Center for Spatial Studies' YouTube channel.
[September 14] There are only 5 spaces left for conference registration. For urgent registration requests, please contact Kitty Currier at kcurrier@ucsb.edu
[September 10] Titles and abstracts have been added for the Invited Talks.
[September 1] The preliminary Program is now available.
[August 19] While the symposium well be virtual, we are encouraging participants to get together at physical locations around the world to interact and participate in the symposium in small groups. To this end, a number of 'hosting parties' are being organized. Please contact one of the organizers listed below to join their local event. If you would like to host one in your own city, please let us know.
- Vienna, Austria - Krzysztof Janowicz (krzysztof.janowicz@univie.ac.at)
We will meet at the University of Vienna, Conference Room (C0520) of NIG for the Sep. 22 morning sessions, and in the Green Lounge of the Main Building for the Sep. 22 afternoon sessions and all Sep. 23 sessions. Feel free to join us for all or just some of the sessions. See flyer for detailed times, addresses and directions - Montreal, Canada - Grant McKenzie (grant.mckenzie@mcgill.ca)
- Glasgow, United Kingdom - Ana Basiri (Ana.Basiri@glasgow.ac.uk)
- Lisbon, Portugal - Bruno Martins (bruno.g.martins@tecnico.ulisboa.pt)
- Santa Barbara, USA - TBD
[August 15] Registration is now open. Click the button in the upper right corner.
About
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
Data-driven decision making for accessible and equitable cities
The pervasiveness of cell phones, mobile applications and social media generates vast amounts of digital traces that can reveal a wide range of human behavior. From mobility patterns to social networks, these signals expose insights about human behaviors and social interactions. In this talk, I will discuss approaches that can help local governments and non-profit organizations better understand the spatial dynamics of cities and communities - offering additional insights beyond more traditional sources of information - and assisting in the design of more accessible, equitable and humane cities.
I will first give a high-level overview of the research that I lead in the Urban Computing Lab; followed by an in-depth description of two projects. The first project will present a novel approach to create cycling safety maps at large scale. These maps can be used by cycling activists and departments of transportation to understand safety perceptions as well as to identify accessibility to safe routes by different communities. In the second project, I will describe a new method to enhance the fairness of mobility-based crime prediction models by addressing crime under-reporting bias that can negatively affect minority communities. This work highlights that controlling for bias can improve both the fairness and the accuracy of mobility-based crime prediction models.
A Space-Place Framework for Human Dynamics Research in A Hybrid Physical-Virtual World: Are we ready for a quantum leap in the emerging metaverse?
We now live in a hybrid physical-virtual world enabled by modern technologies where we work, shop, and socialize with others in both physical and virtual spaces. With the emerging metaverse based on immersive virtual reality, human dynamics, which refers to all forms of human activities and interactions, are likely to undergo further transformations in the coming years. The new human dynamics will be a seamless integration of physical and virtual activities. Built on our earlier work in human dynamics research, we argue that the current discussions in spatial data science for human dynamics research are conceptually constrained by their physical and virtual silos. The new hybrid physical-virtual approach we are envisioning aims to transcend the simplistic dichotomy by integrating both space and place perspectives. This presentation also draws on basic concepts in quantum theory and earlier discussions on their potential applications in geography and GIScience to espouse a quantum turn in exploring the human dynamics in a hybrid physical-virtual world. It explores how concepts, methods, and understandings from the quantum physics and computing can be translated into addressing human dynamics research under a Space-Place (Splatial) GIScience framework.
