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About

Coming from a background of math and computer science, I develop algorithms and statistical methods for spatial data science. My work aims to better represent and mathematically model spatial problems and to chip away at our understanding of core geographical concepts, such as process and scale. Through algorithm design, I also seek to advance the principled use of (spatial) data science in social science. For more information about my research, click here (slightly outdated). In my spare time, I love watching movies, playing piano, and biking. Previously, I was a recipient of the NSF Graduate Research Fellowship and a Ph.D student at Arizona State University in the Spatial Analysis Research Center (SPARC).

News and Updates

28 Sep 2023: My paper, "Controlling for spatial confounding and spatial interference in causal inference", has been published in Annals of GIS! Check it out for a review of spatial causal modeling ideas and to explore relative performance of these models on different spatial datasets! (With Peter Kedron.)

28 Apr 2023: I will be presenting at the Texas A&M Institute of Data Science's Built Environment Talk Series! The talk is on May 11 from 10-10:30am central time. You can find out more about the talk from the flyer.

17 Apr 2023: Our contribution to the Geographic Information Science Body of Knowledge online encyclopedia has been published! Find out more about spatial autoregressive models at this link.

7 Apr 2023: Version 0.0.1 of my first Python package is now live! spycause, a package for spatial causal inference in Python, can be installed from PyPI using pip install spycause. Learn more at spycause.readthedocs.io or visit the repository to see the source code!

26 Mar 2023: I was selected as the 2nd place winner of the 2023 Spatial Analysis and Modeling John Odland Student Paper Competition! Stay tuned for the full paper!

23 Mar 2023: How does access to green space impact gentrification? Our project, selected as one of the winners from MDI’s Green Space Data Challenge can begin to answer this question! (With Timara Crichlow and Shaylynn Trego.)

21 Oct 2022: I was featured on the NumFOCUS Google Summer of Code wrap-up blog post!

11 Oct 2022: I received my Master's in Passing! I'll be graduating with my M.A. in Geography this fall from ASU. One more step checked off on the way to a Ph.D!

28 Feb 2022: I presented at UCSB's Spatial Lightning Talks 2022 about Private Wojtek, a bear who fought in World War 2. The video can be found here.

19 Nov 2021: I was featured during ASU Geo Week 2021!

Publications

  1. T. D. Hoffman, P. Kedron. (2023). "Controlling for spatial confounding and spatial interference in causal inference: Modeling insights from a computational experiment." Annals of GIS, open access, 1--11. PDF. Link.
  2. P. Kedron, T. D. Hoffman, S. Bardin. (2023). "Chapter 18: Reproducibility and Replicability in GeoAI." Handbook of Geospatial Artificial Intelligence. S. Gao, Y. Hu, W. Li (Ed.).
  3. T. D. Hoffman, P. Kedron. (2023). "Spatial Autoregressive Models." The Geographic Information Science & Technology Body of Knowledge (2nd Quarter 2023 Edition). John P. Wilson (Ed.). Link.
  4. T. D. Hoffman, P. Kedron. (2022). "Operationalizing Spatial Causal Inference." UCSB Spatial Data Science Symposium 2022 Short Paper Proceedings. PDF. Link. Recorded presentation.
  5. P. Kedron, S. Bardin, T. D. Hoffman, M. Sachdeva, M. Quick, J. Holler. (2022). "A Replication of DiMaggio et al. (2020) in Phoenix, AZ." Annals of Epidemiology, 74, 8-14. PDF. Link.
  6. W. F. Fagan, C. Saborio, T. D. Hoffman, E. Gurarie, R. S. Cantrell, C. Cosner. (2022). "What's in a resource gradient? Comparing alternative cues for foraging in dynamic environments via movement, perception, and memory." Theoretical Ecology, open access, 1-16. PDF. Link.
  7. T. D. Hoffman, T. Oshan. (2021). "A Supervised Heuristic for a Balanced Approach to Regionalization." GIS Research UK Conference. PDF. Link.
  8. T. Hoffman*, A. Swain*, K. Leyba, W. F. Fagan. (2021). "Trade-offs in sensory characteristics shape the evolution of perception." Frontiers in Ecology and Evolution, 9. PDF. Link.
  9. A. Lawson, T. Hoffman, Y. Chung, K. Keegan, S. Day. (2021). "A density-based approach to feature detection in persistence diagrams for firn data." Foundations of Data Science. PDF. Link.
  10. W. F. Fagan, T. Hoffman, D. Dahiya, E. Gurarie, R. S. Cantrell, C. Cosner. (2019). "Improved foraging by switching between diffusion and advection: benefits from movement that depends on spatial context." Theoretical Ecology, 13 (2), 127-136. PDF. Link.

*equal contributions

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