GIS & Cartography
This page is a collection of maps produced by myself during my academic and professional career as well as for a personal hobby. In the maps that I produce, I enjoy adding new cartography designs to help attract the audience and help accentuate the stories each map is visually portraying. From advanced spatial analyses to map art, the audiences for my map visualizations range from the general public to scientists and policy makers. My style of cartographic design utilizes clear, orderly placed map features for ease of reading with discernable color palettes to distinguish between the physical aspects of the map. My maps are created through various mediums depending on the audience and include expressive digital maps, interactive maps using R Markdown, and storytelling ArcGIS StoryMaps.
However, geography is not all about maps. Geography is about the underlying processes that create spatial patterns. Given this principle, my maps are more than just what you see. Behind my maps are socio-ecological meanings, thoughtful spatial decision making, and advanced spatial and statistical analyses.
Portland, Oregon
This map art portrays aspects of the Portland Metropolitan Area including the neighborhoods, major highways, parks, and rivers. The hill-shade effect was applied to a digital elevation model of the region to show the topographic features that underlie the Portland area. As the first of a series of personal map art, this map of Portland was created to be an art display in a home, office, or other settings.
Landslide Susceptiblity in the Deepwater Gulf of Mexico
Presented by Duran et al. (2021) at the Carbon Management and Oil and Gas Research Project Review Meeting, this map displays the probability of a seafloor landslide occurrence in a purple-red color ramp. This was predicted using an Artificial Neural Network model trained on landslide data regarding conducive conditions and triggers. Using the Variable Grid Method (Bauer et al., 2019), a gridded uncertainty visualization in black visualizes areas of high prediction certainty in the smallest grid sizes and areas of low prediction certainty in the largest grid sizes.
The interactive map here is in a HTML document created using R Markdown for a class project in Advanced Spatial Statistics where I analyzed the spatial characteristics of fire refugia and locations experiencing second order (i.e., delayed) tree mortality effects. Several spatial analyses were performed including inverse-weighted nearest neighbor distance, Monte-Carlo simulation of global Moran’s I, and spatial regression. The screenshot above displays a map of fire refugia density with low density locations in red identifying areas where there are likely no post-fire seed sources from neighboring surviving trees and thus forest may not grow back. See the full report for more details.
Spatial Statistics of Post-Fire Delayed Tree Mortality to Uncover Ecological Patterns
LiDAR-Derived Forest Structure
The StoryMap presented here was completed for a photogrammetry and LiDAR class. Light Detection and Ranging (LiDAR) is a remote sensing method that uses radar pulses to measure surface elevation and object heights above the ground at a very high spatial resolution. Here, I utilize this method to calculate canopy density and canopy height for two differently managed coniferous forests in the Pacific Northwest to try and discern possible management implications.
References
Bauer, J. R., & Rose, K. (2015). Variable grid method: An intuitive approach for simultaneously quantifying and visualizing spatial data and uncertainty. Transactions in GIS, 19(3), 377-397. https://doi.org/10.1111/tgis.12158.
Duran, R., Mark-Moser, M., Wingo, P., Dyer, A., Zaengle, D., Pantaleone, S., Hoover, B., Bauer, J., & Rose, K. Ocean and Geohazard Analysis. Carbon Management and Oil and Gas Research Project Review Meeting Aug. 26, 2021. https://edx.netl.doe.gov/offshore/wp-content/uploads/2021/12/Ocean-and-Geohazard-Analysis_08262021.pdf
Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z.-L., Quayle, B., & Howard, S. (2007). A Project for Monitoring Trends in Burn Severity. Fire Ecology, 3(1), Article 1. https://doi.org/10.4996/fireecology.0301003.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data. Bulletin of the American Meteorological Society, 91, 363-376. non-government domain. https://doi.org/10.1175/2009BAMS2755.1.
Knapp, K. R., H. J. Diamond, J. P. Kossin, M. C. Kruk, C. J. Schreck, 2018: International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4. [indicate subset used]. NOAA National Centers for Environmental Information. non-government domain. https://doi.org/10.25921/82ty-9e16.
Steel, Z.L., Collins, B.M., Sapsis, D.B., & Stephens, S.L. (2021). Quantifying pyrodiversity and its drivers. Proceedings of the Royal Society B: Biological Sciences 288, 20203202. https://doi.org/10.1098/rspb.2020.3202.