This is a project I did analyzing the demographic distribution of populations near pollution sites in King County, WA.

This report looks at known pollution sites as well as Superfund sites and analyzes the demographics of a 4 kilometer range around them.

First, a 4 kilometer buffer is created around each point. Then, using several geoprocessing tools outlined in the report, statistics are calculated for the Census block groups within each buffer.

The goal of this project was to find the optimal corridor route for wildlife between protected areas like National Parks while obtaining skills in raster analysis.

By combining a multitude of different geographic variables all calculated in respect to their effects on wildlife habitability, I was able to create a wildness score surface of Washington state that I used alongside a distance accumulation raster to determine optimal paths between protected areas.

The variables included land cover, ruggedness, human population density, and proximity to highways. I scored these variables before combining them to create a cost surface raster. The negative of this cost surface raster is our wildness surface.

For this project, students were allowed to select their own topic of interest. I chose mountain goats and urban growth, as I wanted to examine the spatial relationship between two disparate data sources.

The analysis included generating a cost distance accumulation raster and examining average distance values within zones. Data included Habitat Core polygons from USGS, as well as Urban Growth Area polygons from WA Dept. of Ecology.

We also were tasked with creating a StoryMap to present our findings. It was a lot of fun, and you can check it out here: 

https://arcg.is/15PWza0

This report served multiple purposes, from proper coordinate use to global protected area analysis.

To begin, a showcase of the differences between coordinate systems in regards to area, shape, distance, and direction. The report includes a table to demonstrate how values change across coordinate systems, then several maps visualizing the differences.

After that, the report becomes an analysis of protected area across the globe using two different coordinate systems. First with the popular Web Mercator and then with Equal Earth.

Finally, the report concludes with a map using Thiessen polygons to determine the closest National Park to any location in the contiguous United States.

This small project for a practical exam focused on fixing broken data layers before combining them to create a walkability index.

The broken data consisted of local crosswalks, streets, waterbodies, and neighborhoods. Each of them was either unprojected or was using the incorrect projection. After correcting the data, I was able to do the analysis.

Higher walkability was determined to be neighborhoods that have a higher density of crosswalks.

Another exam project, this one only consists of the one layout and the model. The report uses raster analysis to determine the most likely paths for historical Indigenous trade routes.

Starting with just elevation data and historical village locations, I began my analysis. I started by determining the rate of elevation change over the study area. Then, I created a distance accumulation raster between the villages. By combining the costs of distance and elevation change, I was able to determine the lowest cost paths between each village.

This final exam uses a combination of raster and vector analysis methods. To determine watershed vulnerability, I looked at a few different relevant variables: human pollution and activity, watershed productivity, and the presence of dams.

To understand each watersheds level of human pollution and activity, this model finds the density of intersections between roads and streams within each watershed. Watersheds with more road/stream crossings will have a higher chance of human pollution.

I combined this with a population density raster I created for a different project, an annual average precipitation raster, and a point layer of dams in the study area. These variables together help us derive which watersheds are the most vulnerable and in need of increased protection and management.

This project was the first one for GIS 3. The model used in this project was a precursor to the one used in the final exam. This report focuses more explicitly on the intersections between roads and streams.

Road and stream intersections are found, counted, and assigned to each watershed they reside in. Also, total stream length for each watershed was calculated. This allows us to have two maps: one of road/stream crossing density to watershed area, and one of road/stream crossing density to total stream length within a watershed.

The report also has a layout regarding streamflow of the Nooksack river. This map uses streamflow data from 4 different streamgage measuring locations on the river and its tirbutaries. This analysis helps us understand where the Nooksack gets most of its water at different times of the year.

This group project involved the four of us using satellite imagery to determine the land cover status across the Salish Sea bioregion.

We began by each selecting a tile from the Landsat dataset. Then, we explored the different visualizations available through the various bands provided by the imagery. We analyzed natural color, false color composite, NDVI, and NDBI. Each of these were then mosaicked into complete images of the bioregion.

Next, we used supervised classification to examine differences in land cover. Using a selection of bands, each team member created 30-50 training sites for each land cover class. These final rasters were then mosaicked to give a complete classified map of the Salish Sea.

The goal of this research was to determine the current and future possible extent of the Alaska Yellow-Cedar.

This report began as a challenge to discover the variables necessary ourselves. To do this, my team members and I read through literature about Alaska Yellow-Cedar relating to its typical climate, growing conditions, and historical importance. Our assignment was to focus on climate variables alone, so variables such as soil type were ignored.

Using statistical analysis, we determined the climate variables that help determine where Alaska Yellow-Cedar grows. Then, using climate projection data, we were able to map the future extent.