Tyler Hoffman


(he/him) · Spatial data scientist, statistician, computational thinker.

Email me: tdhoffman@asu.edu




Google Scholar


GSoC 2022 Progress Journal

Kedron Lab @ ASU




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 For more information about my research, click here (slightly outdated).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. I am a recipient of the NSF Graduate Research Fellowship and I’m currently a Ph.D student at Arizona State University in the Spatial Analysis Research Center (SPARC).

Fun stuff

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.

I was featured during ASU Geo Week 2021!


  1. T. D. Hoffman, P. Kedron. (2022). "Operationalizing Spatial Causal Inference." UCSB Spatial Data Science Symposium 2022 Short Paper Proceedings. PDF. Link. Recorded presentation.
  2. 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.
  3. 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.
  4. T. D. Hoffman, T. Oshan. (2021). "A Supervised Heuristic for a Balanced Approach to Regionalization." GIS Research UK Conference. PDF. Link.
  5. 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.
  6. 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.
  7. 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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