Small area estimation and disease mapping increasingly rely on areal data where reporting boundaries change over time. We develop a computationally efficient spatio-temporal disaggregation method to recover high-resolution risk surfaces from observed counts under changing boundaries. Our approach extends the spatially aggregated log-Gaussian Cox process and uses the Extended Latent Gaussian Model framework for fast approximate posterior inference. We replace standard lognormal polygon-specific effects with gamma-distributed overdispersion which yields a marginal negative binomial likelihood, and removes one latent variable per polygon-time pair. We illustrate the approach by mapping mortality risk across shifting NUTS-3 boundaries in Belgium and the Netherlands. For the purpose of dissemination we use Codex to leverage the methodology presented in this paper for the analysis of a separate data set concerning the city of Manchester. The methodology is implemented in the open-source R package DAST.
@article{ripstein2026spatiotemporal,title={Spatio-Temporal Disaggregation with Changing Areal Boundaries},author={Ripstein, Noah and Brown, Patrick and Stafford, Jamie},year={2026},journal={arXiv preprint arXiv:2606.25074},}
Patent
Methods and Systems for Handling Incoming Client Calls
Noah Ripstein, Yi Lian, Sara Naeem, and 4 more authors
Feb 2026
U.S. patent application, publication no. 20260059050. Patent pending.
@patent{lian2026incomingcalls,title={Methods and Systems for Handling Incoming Client Calls},author={Ripstein, Noah and Lian, Yi and Naeem, Sara and Babuk, Albina and McConachie, Cheryl and Bachal, Abhishek and White, Isabel},year={2026},month=feb,number={20260059050},note={U.S. patent application, publication no. 20260059050. Patent pending.},}