Statistics Professor Higham authors publications on spatial statistics and spatial statistics software.

Spatial data commonly arises in ecological and epidemiological applications and consists of observations that are indexed by spatial coordinates (like Latitude and Longitude). Matt Higham has published the following work on spatial data and applications in the statistical software R, one of the most popular environments for statistical computing:

1. A paper on spatio-temporal modeling has been published in the Journal of Agricultural, Biological, and Environmental Statistics (link to the paper abstract: https://link.springer.com/article/10.1007/s13253-023-00565-y).

2. A software paper introducing sptotal, an R package for practioners to use for their own data analysis, has been published in the Journal of Open Source Software. The sptotal package has been downloaded over 10,000 times since it’s public release in the summer of 2020 (link to the paper: https://joss.theoj.org/papers/10.21105/joss.05363.pdf).

3. Higham also co-taught a workshop on spmodel, another R package for spatial analysis, at the 2023 Spatial Statistics conference in Boulder, Colorado. The spmodel package has been downloaded over 3,000 times since it’s public release in the fall of 2022 (link to the workshop materials: https://usepa.github.io/spmodel.spatialstat2023/).

Dr. Matt Higham is a statistician and data scientist whose research interests are in spatial statistics applied to ecological settings and in development of packages for the statistical software R. He holds a doctorate in Statistics from Oregon State University and he has worked extensively with the Alaska Department of Fish and Game. He is also a graduate of Miami University (OH) where he earned degrees in Botany, Statistics and Zoology. At St. Lawrence, Professor Higham regularly teaches the Foundations of Data Science course as well as a Data Visualization course that he designed.