Lab 1

In this lab, we began familiarizing ourselves with model builder and automating analysis. We learned inline variable substitution and a collection of skills in the spatial analyst toolbox, including intersect, frequency and join field. 

Lab 2

In this lab, we focused on projections and learning how to correct or troubleshoot errors that arise when projections are incorrect or undefined. We learned about how to predict or interpret projection issues and how to avoid them by looking at metadata and layer source information. 

Exam 2

This exam took the skills we learned in lab 2 and had us apply them independently for a different topic. After defining and correcting data projections, we calculated the nearest national park for each state, as well as the Theisen polygon geometry. 

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Lab 3

In this lab, we became familiar with attribute table calculations and running tools to calculate fields, statistics and geometry through model builder with areal weighted interpolation. Finally, we focused on classification symbology for accurate data interpretation and display. 

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Exam 3

For this exam, we calculated COVID-19 rates amongst Whatcom County school districts given various different metrics, like population or capita. This exam challenged use to retrieve and utilize census data, as well as calculate various statistics using aerial weighted interpolation. 

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Lab 4

In this lab, we dove into raster modeling and analysis. We were given a collection of raster data files to use in calculations, with the end goal being a "wildness indicator" for Washington state and likely wildlife corridors for safe passage between protected areas. 

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Exam 4

This exam built off of the previous lab, but required our own independent raster calculation and analysis. Using distance accumulation, slope and hillshade calculations and reclassifications, we determined travel resistance across Washington state for foot travel, incorporating steepness and barriers to determine likely Indigenous trade routes.

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Lab 5

In this lab, we worked in teams to preprocess raster and vector data, calculate statistics to understand variable significance to focus of study and create a climate envelope suitability analysis for mountain Hemlock in western North America. 

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Exam 5

This exam revisited our original watersheds analysis, but expanded to include three additional counties, precipitation data, population data and dam data. By assessing all of these variables, I was able to create a comprehensive understanding of human influence in and around watersheds of northwest Washington state.

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