A lot of hydrological information can be derived from a DEM, so I decided to automate a typical exploratory analysis of the White Mountains, NH using R and GRASS GIS.

Predicting where invasive species are likely to spread is an important step in applying preventative measures. I utilized R and GBIF to download data and run optimized MaxEnt models for multiple species of interest.

A Habitat Suitability Model (HSM) is a tool used to predict the suitability of a given habitat for a particular species, based on specific environmental conditions and the species' requirements. These models integrate various factors, including climate, vegetation type, availability of water, and presence of predators or competitors to estimate how well a particular area supports the target species.

Assessing landscape distributions of points is an important part of any analysis, so I scripted a method to mathematically determine clustering and dispersion in point datasets!

Spatial regression modeling is an important part of any spatial ecology project, and is often hindered by assumption violating autocorrelation. I looked to solve this in a Thrush dataset by accounting for spatial autocorrelation through various methods, including trend surface modeling and eigenvector mapping!

Spatial autocorrelation is a centerpiece in any analysis as the degree to which a variable is correlated with itself can change what analytics you can do. I scripted a method to create variograms and correlograms to visualize this autocorrelation, and utilized our variogram to krige!

By using the Analytical Hierarchy Process (AHP) in tandem with landscape metrics, we can determine which site is best suited for restoration! I created an interactive site selection shiny app to see which sites are best as our priorities change!

We can assess the health and fragmentation of ecosystems by calculating their landscape metrics with land cover classification data!

Invasive species spread quickly throughout introduced regions, so visualizing a species' spread over time is an important way to gauge their threat. I looked to utilize EDDMaps in order to visualize citizen science reports of invasive occurences.

Forest surveys can be difficult to impossible in large areas, so I scripted a method to identify treetops and canopies from drone flights in R!

This is the coursework I developed for the graduate level course at the University of Massachusetts, Amherst. The course focuses on coding skills, and how to apply them to conduct non-trivial spatial analytics in R. The first half of the semester focuses on R fundamentals and how to apply it to spatial projects, while in the second half of the semester we take the time to dive into specific spatial analytics and how students can use them to conduct their own final projects!

Visualize your terrain
in 3D! I looked to
render rasters
with R using
raytracing
to create
images
   &
GIFs!