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  • The Symposium on Spatiotemporal Data Science: GeoAI for Social Sciences
    900 N Glebe Rd, Arlington, VA 22203
    2024-07-22

    The Symposium on Spatiotemporal Data Science: GeoAI for Social Sciences

    Virginia Tech Research Center – Arlington & George Mason Square

    July 23-24, 2024

    The Symposium on Spatiotemporal Data Science: GeoAI for Social Sciences | China Data Lab (harvard.edu)

     

    Abstract Submission

     

    Online Registration

     

    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 GeoAI for social sciences.

    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

    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

     

    Important Dates:

    • Abstract submission deadline: March 30, 2024
    • Acceptance decision: April 15, 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):

    • Registration for the pre-conference workshop:
      •  US$50 for each workshop; $100 for all four workshops, Before June 30, 2024
      •  US$75 for each workshop; $150 for all four workshops, After (including) June 30, 2024
    • 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

    1. Replicable Data Analysis with Geospatial Analytics for KNIME
    2. Develop GeoAI Tools using ChatGPT
    3. Cloud Computing with Google Earth Engine and GeoAI
    4. 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
    Contact: spatialdatalab@lists.fas.harvard.edu
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