Date:
Location:
The Symposium on Spatiotemporal Data Science
Virginia Tech Research Center – Arlington & George Mason Square
July 23-24, 2024
Website: https://stds2024.stcenter.net/
Watch the video for last years' symposium on Youtube
Co-sponsored by the Vice President’s Office of Virginia Tech, the Vice President’s Office of George Mason University, the NSF-sponsored Spatiotemporal Innovation Center (STC), the Spatial Data Lab project (SDL), the Future Data Lab, and the Journal of Urban Informatics, this symposium aims to promote replicable and expandable spatiotemporal data science with new methodology and innovative technology. The symposium will discuss open data sources, advanced methodology, and cutting-edge technology with a focus on spatiotemporal data science.
The symposium will feature plenary speakers, parallel sessions, and posters. The program will include onsite and online sessions. The organization committee plans to select a group of high-quality papers presented at the symposium and recommend them for publishing in a special issue at the Journal of Urban Informatics. Papers submitted for consideration of the special issue still need to go through a regular review process. This symposium will offer an excellent opportunity for participants to network, collaborate, and learn about recent developments in spatiotemporal data science.
There will be a one-day workshop (July 22, 2024) on spatiotemporal innovation before the symposium.
Co-Hosts: Virginia Tech and George Mason University
Steering Committee
Co-Chairs:
- Daniel Sui, Virginia Tech
- Andre Marshall, George Mason University
Co-Chairs:
- Siqin Wang, University of South California
- Peter Kedron, UCSB
- Xiao Huang, Emory University
Co-Chairs:
- Mengxi Zhang, Virginia Tech
- Fengxiu Zhang, George Mason University
Topics (not limited to):
· New Technology for Reproducible and Replicable Spatiotemporal Data Analysis
· Open Data Access, Integration, and Online Data Sharing and Visualization
· Advances in Geospatial Technology
· GeoAI for Socioeconomic Data Analysis
· GeoAI for Environmental Data Analysis
· GeoAI for Remote Sensing Data Analysis
· GeoAI for Business Data Analysis
· GeoAI for Social Media and Big Data Analysis
· GeoAI for Healthcare Data Analysis
· Curriculum Development for Spatiotemporal Science Labs
· The Ethnics, Privacy, and Other Social Issues of GeoAI
Keynote Speakers:
- John Wilson, University of South California
- Michael Goodchild, University of California Santa Barbara
- Krzysztof Janowicz, University of Vienna & UCSB
Important Dates:
- Abstract submission deadline: May 3, 2024
- Acceptance decision: May 10, 2024
Submission:
Please submit your abstract (less than 500 words) from https://harvard.az1.qualtrics.com/jfe/form/SV_2bZJ06ISlc9KNzU before March 30, 2024. Detailed agenda and lodging information will be sent to those accepted authors later. Participants are responsible for their own travel and lodging expenses. Please contact the organization committee at spatialdatalab@lists.fas.harvard.edu if there are any questions.
Registration Fee (same for onsite and online attendees):
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Registration for the pre-conference workshop:
- US$100 for all four workshops, Before June 30, 2024
- US$150 for all four workshops, After (including) June 30, 2024
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Registration for the symposium:
- US$250, Before June 30, 2024
- US$350, After (including) June 30, 2024
The online registration is now available at https://www.eventbrite.com/e/812024947477
Location:
The onsite event will be placed on Virginia Tech Research Center (900 N Glebe Rd, Arlington, VA 22203, map) and George Mason Square (3351 Fairfax Dr, Arlington, VA 22201, map). The online sessions will be conducted via Zoom.
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One-day Workshop on Spatiotemporal Innovation and GeoAI Applications
Time: July 22, 2024 (Monday), one day before the Symposium on Spatiotemporal Data Science (July 23-24, 2024)
Venue: Virginia Tech Research Center – Arlington, 900 N Glebe Rd, Arlington, VA 22203
Organized by
- The Spatiotemporal Innovation Institute Program
Description of the Workshop
This workshop aims to provide a comprehensive overview of the potential and challenges of some cutting-edge technologies in advancing geospatial science and applications, fostering a deeper understanding and encouraging further exploration and innovation in the field. The workshop will introduce some new tools and applications of GeoAI built on the workflow technology as well as their applications in social science and public health. Participants will learn the GeoAI-based methodology for geospatial analysis, GeoAI tools and packages for spatial data analysis, as well as their applications.
Topics: 6 hours
- Replicable Data Analysis with Geospatial Analytics for KNIME
- Develop GeoAI Tools using ChatGPT
- Cloud Computing with Google Earth Engine and GeoAI
- Geospatial Methods and Tools for the Spatial Assessment of Healthcare Accessibility
Workshop Registration: https://www.eventbrite.com/e/812024947477
Audience: Anyone who is interested in learning the essentials of replicable workflow technology, GeoAI methods and applications in cross-disciplinary domains.
Abstracts and Instructors:
I. Replicable Data Analysis with Geospatial Analytics for KNIME
This workshop will introduce the recent development of workflow technology for replicable spatial data analysis. The topics include: (1) Introduction to KNIME, a free tool for workflow data analysis; (2) Introduction to Geospatial Analytics Extension for KNIME; (3) GeoAI data analysis with KNIME; (4) Case studies of GeoAI and KNIME applications for environmental and socioeconomic studies with big data.
Instructor:
- Lingbo Liu, Center of Geographic Analysis, Harvard University
II. Develop GeoAI Tools using ChatGPT and Python packages
The first half of this session will introduce GeoLocator - a GeoAI tool re-developed from ChatGPT to detect the location based on the image that was input into the ChatGPT. It will more broadly introduce how to reformulate ChatGPT for geographic studies and research. The second half of this session will introduce a few advanced geospatial and GeoAI methods for spatial prediction, including geographical detectors model, geographically optimal similarity model, the second-dimension spatial association model, and popular R packages which have been downloaded over 120,000 times globally. Case studies of using these methods and tools will be introduced in the session.
Instructors:
- Siqin Wang, Spatial Sciences Institute, University of Southern California
- Yongze Song, School of Design and the Built Environment, Curtin University
III. Cloud Computing with Google Earth Engine and GeoAI
This workshop explores the integration and applications of cloud computing technologies, specifically Google Earth Engine, with Geographic Artificial Intelligence (GeoAI) to address complex spatial problems. The presentation aims to showcase how cloud computing offers scalable and efficient computing resources for processing vast amounts of geographic data, enabling researchers, scientists, and developers to perform advanced spatial analysis and machine learning tasks without the need for extensive hardware infrastructure. Case studies or examples are provided to illustrate the practical applications of combining Google Earth Engine and GeoAI.
Instructors:
- Xiao Huang, Department of Environmental Sciences, Emory University
- Qiusheng Wu, Department of Geography & Sustainability, University of Tennessee
IV. Geospatial Methods and Tools for the Spatial Assessment of Healthcare Accessibility
The workshop will introduce some recent development of methodology and technology for the spatial study of health accessibility. The topics include: (1) Overview of the issues on spatial accessibility, (2) Two-Step Floating Catchment Area (2SFCA) Method, (3) Generalized 2SFCA (G2SFCA), (4) Inverted 2SFCA (i2SFCA) method for estimating potential crowdedness in facilities, (5) Two-Step Virtual Catchment Area (2SVCA) method for measuring accessibility via internet or virtual accessibility, and (6) the ArcGIS toolkit and KNIME workflows for automated implementation of a case study of various accessibility measures for primary care physicians in Baton Rouge metropolitan region.
Instructors:
- Fahui Wang, Graduate School, Louisiana State University
- Changzhen Wang, Department of Geography, University of Alabama
- Mengxi Zhang, Carilion School of Medicine, Virginia Tech
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Call for Submissions: "Replicability and Reproducibility of Geospatial Artificial Intelligence (GeoAI) Models and Their Application in Social Science"
The Journal of Urban Informatics (https://link.springer.com/journal/44212) invites submissions for a special issue entitled "Replicability and Reproducibility of GeoAI Models and Their Application in Social Science." The deadline for submissions is December 30, 2024.
Guest Editors:
Dr. Lingbo Liu, Center for Geographic Analysis, Harvard University
Dr. Xiao Huang, Department of Environmental Sciences, Emory University,
Dr. Siqin Wang, Spatial Sciences Institute, University of Southern California,
Aim & Scope:
This special issue aims to delve into the burgeoning field of GeoAI, focusing on its applications in social sciences. We are particularly interested in contributions that examine the replicability and reproducibility of GeoAI models, shedding light on their effectiveness and dependability in social science research. Our goal is to foster interdisciplinary dialogue, bridging theoretical and empirical approaches in the realm of GeoAI. We welcome submissions that demonstrate how GeoAI models can be used to tackle complex issues in social science research.
The potential submissions should critically analyze the application of GeoAI in various social science disciplines, including sociology, economics, political science, human geography, and more. Papers that utilize innovative methodologies, offer novel theoretical insights, or present case studies demonstrating the application of GeoAI in real-world social science research with replicable data and code are particularly encouraged.
In addition to regular research papers, we welcome a variety of scholarly contributions related to GeoAI in social science. These include talk summaries, discussion collections, and opinions, submitted in the formats of review papers, perspective papers, or commentary articles.
Possible Topics:
- GeoAI Model applications in social science research
- Theoretical discussions on GeoAI's role in understanding social phenomena
- Methodological advancements in GeoAI for social science
- Reviews of trends and future directions in GeoAI and social science
- Cross-disciplinary approaches involving GeoAI and fields like sociology, economics, or political science
- Ethical considerations in using GeoAI in social science
- GeoAI used in the education of social science
- GeoAI integrating with ChatGPT, Generative AI and other large language models (LLM) in social science
Special Requirements for GeoAI Model Papers
For submissions focusing on GeoAI models, authors are required to provide:
Data: Original or sample data from the study;
Workflow: Executable workflows based on code of Python, R, or KNIME workflows that can be replicable in future studies.
These materials are crucial for ensuring the replicability and reproducibility of the research.
Submission Process:
Manuscripts should be submitted through the journal’s online system (www.editorialmanager.com/uinf/default.aspx), ensuring adherence to the high standards of Urban Informatics. Please select this special issue in the "Additional Information" section during submission.
Deadline for Submissions: December 30, 2024
We are looking forward to your valuable contributions and joint efforts to foster a rich academic discourse at the intersection of GeoAI and social science.