• #30daymapchallange Day Green

    August 27, 2020

    For the seventh day of the #30daymapchallange, I made a map using Open Street Map Data to map my old neighborhood. I feel lucky to have grown up near mountains in a city.

  • #30daymapchallange Day 1 Points

    August 27, 2020
  • Wildfires in the West

    August 27, 2020
  • First R Package

    August 26, 2020

    I’ve been trying to participate in #tidytuesday. While making plots I found myself consistantly repeating the same theme() attributes for each plot. To solve this repetition, I decided to produce a package with my own theme.

  • TidyTuesday: Energy Usage in Europe

    August 16, 2020

    I haven’t had a ton of time lately. On a recent road trip, I tried out a Tidy Tuesday submission on European evergy usage. Because I didn’t have enough time, I wanted to make something simple and work on making it really easy to read. I think I did that. I would have liked to do a bit more with the theme. Maybe next time.

  • Will There be a Raftable Release out of McPhee Reservoir

    February 09, 2020

    I live next to the Dolores River. It’s an often overlooked gem of the southwest. It runs from just outside Rico, Colorado at its headwaters to the Colorado River near Moab, Utah. Rafting it is an experience.

  • Colorado: Hex Plots, API packages, and R

    November 30, 2019
  • Spatially Balanced Sample Designs in R with `spsurvey::grts()`

    November 19, 2019

    We’ve been using spatially balanced stratified study designs more frequently at work these days. They are a good way to make probabilistic inference over large areas. A popular method of creating these designs is using the R function spsurvey::grts(). The following is a basic (very basic) explainer of how to get up and running with grts() function and what it is. But a bit about GRTS and spatially balanced study design before we get coding.

  • Statistics in R: Resources for Understanding Statistics in R

    September 19, 2019

    This is a collections of resources that have helped me learn and understand statistics in R.

  • Kernel Density Estimation in R

    September 11, 2019

    For a recent project I needed to run a kernel density estimation in R, turning GPS points into a raster of point densities. Below is how I accomplished that.

  • Cumulative Distribution Function

    April 11, 2019

    Cumulative distribution functions allow you to answer the questions, what percent of my sample is less than or greater than a value. For example I work with sage-brush cover frequently. With a cumulative distribution function I can answer the question, what proportion of my plots with sagebrush have greater than 90% cover.

  • Subset Raster Extent w/ R

    March 27, 2019

    A little snippet that helps subset raster extents.

  • Remote Sensing Tools

    March 11, 2019

    A collection of tools and documentation on remote sensing.

  • Extract Raster Values

    March 11, 2019

    Below is a method to use the raster package extract() function to get a subet of rasterBrick values. To be specific, I need to extract all raster values that are within a polygon boundary. In the past I have used crop(), mask() and then the getValues() functions from the raster package to subset data values within a polygon. But that method returns a data frame with a ton of NA values (anything outside of the crop area in the raster is an NA). This is fine most of the time but the current project that I am working on requires almost all of the memory on my computer. I’m working with extremely large rasters (2Gb). Removing the NA values after the crop(), mask(), and getValues() process crashes my computer. So I need a more effecient process.

  • Random Forest Resources and Notes

    February 26, 2019

    Resources to understand and run random forests in r.

  • Creating a polygon from scratch in R

    February 14, 2019

    A quick little snippet for making a polygon with coordinates out of thin air in r.

  • Raster Distance Calculations

    December 18, 2018
  • Classifying High Resolution Aerial Imagery - Part 2

    December 01, 2018

    I have been attempting to use random forests to classify high resolution aerial imagery. Part one of this post series was my first attempt. The aerial imagery dataset that I am working on is made up of many ortho tiles that I need to classify into vegetation categories. The first attempt was to classify vegetation on one tile. This note documents classifying vegetation across tiles.

  • Colorado Avalanches By The Numbers in R

    November 20, 2018

    A look at avalanches in Colorado. Please not I’m not an avalanche expert, so please take these interpretations with a healthy dose of skepticism.

  • Data Science Resources

    November 20, 2018

    A collection of resources on data science and machine learning primarily in R.

  • Classifying High Resolution Aerial Imagery - Part 1

    November 20, 2018

    The following note documents a proof of concept for classifying vegetation with 4 band 0.1m aerial imagery. We used sagebrush, bare ground, grass, and PJ for classes. approximately 300 training polygons were used as a training data.

  • Making a Chloropleth Map in R

    November 20, 2018
  • Working with DIMA Tools and Making a Plant List from Species Richness Table

    August 31, 2018

    This is a series of notes that works with the DIMA database. The DIMA was produced by the Jornada Research Center for the Assessment Inventory and Monitoring framework.

  • A Method for counting in a sequence, reset by a binary event in R

    August 24, 2018

    A method for creating a variable that sequential counts until an binary event occurs in another vairiable.

  • How to download and work with LSAT data - a better approach

    August 13, 2018

    My last post was about working with the r getlandsat package to work with landsat data from NASA and the USGS. This post will be a brief refinement on that process.

  • Cultural Model R Scripts

    August 01, 2018

    The following are scripts that I used to make a cultural prediction model. It uses topographic, hydrologic and biological GIS information to predict areas where arc sites likely occur on the landscape.

  • Landsat First Try

    July 31, 2018

    {r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE)