WS06 — SPACESCANS: Tools for Ontology-Driven Spatiotemporal Linkage and Analysis of the Spatial and Contextual Exposome
A half-day pre-conference workshop at ISEE 2026 · Munich, Germany · 30 August – 2 September 2026
All attendee materials are in one shared folder — open the Google Drive folder ↗. No sign-in required.
It contains:
1.
The workshop tutorial (PDF)
2.
Two demo datasets of 10,000 simulated participants — one minimal file for the linkage step, one with outcome and covariates for the analysis step
3.
The R scripts for the ExWAS and prediction pipelines, in
the RScripts subfolder
1.
Background on the spatial and contextual exposome and presentation of an ontology to organize natural, built, and social environmental domains, highlight metadata standards, and support harmonization across datasets.
2.
Overview and live demonstration of the SPACESCANS spatiotemporal linkage workflow, including defining geographic units and buffers, aligning spatial grids and time windows, and linking multiple environmental datasets to cohort data. Participants will be guided through hands-on exercises using a demo dataset.
3.
Demonstration of downstream analysis pipelines using linked SPACESCANS outputs, including exposome-wide association studies (ExWAS) and predictive modeling. We will discuss approaches for handling high-dimensional, correlated exposures, feature engineering, and model validation.
4.
Question and answer session and discussion of future directions for spatial/contextual exposome tools and standards.
The hands-on exercises run in a browser — nothing to install. Open the SPACESCANS web app repository ↗ in GitHub Codespaces and the linkage app, RStudio, and the workshop scripts all start together.
The same R scripts ship in that repository under
ISSE26/forAttendee/, alongside the demo cohort.
If you attended the workshop, please take the SPACESCANS Web App survey ↗. It takes about 10–15 minutes and shapes what we build next.
The survey is anonymous: no name, email address, or other identifying information is collected. It opens with a study information sheet, and taking part is entirely voluntary. Approved by the Indiana University IRB, protocol #33011.
Hui Hu — Brigham and Women's Hospital, Channing Division of Network Medicine, Boston, Massachusetts, United States of America
Shuteng Niu — Mayo Clinic, Department of Artificial Intelligence and Informatics, Jacksonville, Florida, United States of America
Xing He — Indiana University, Department of Biostatistics & Health Data Science, Indianapolis, Indiana, United States of America