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My name is Kevin Bone and I recently graduated from the MS program in GIS Technology with the University of Arizona. I am an environmental scientist with six years of experience in natural resources management through civil engineering consulting and local government. What excites me most about these roles is being able to use geospatial data to create connections across disciplines to help solve environmental problems. You can view summaries below that also link to the full project from my graduate program. My Career Story also contains previous GIS experience. Thank you for viewing, and feel free to contact me and connect below.
My name is Kevin Bone and I recently graduated from the MS program in GIS Technology with the University of Arizona. I am an environmental scientist with six years of experience in natural resources management through civil engineering consulting and local government. What excites me most about these roles is being able to use geospatial data to create connections across disciplines to help solve environmental problems. You can view summaries below that also link to the full project from my graduate program. My Career Story also contains previous GIS experience. Thank you for viewing, and feel free to contact me and connect below.
Education
Education
MS in GIS Technology, University of Arizona (May 2025)
Graduate Certificate in Hydrology, Portland State University (December 2018)
BS in Environmental Science, University of Texas at Austin (May 2014)
Skills
Skills
Esri: ArcGIS Pro, ArcMap/ArcCatalog, ModelBuilder, ArcGIS Online, Experience Builder, Dashboards, StoryMaps, JavaScript SDK
Open Source: QGIS, Git
Programming: Python, R, SQL
Data Collection: Trimble Backpack GPS, Field Maps, Survey123, Leica Zeno Mobile One, Leica GG04 Smart Antenna
Drafting: AutoCAD Civil 3D
Design: Adobe Photoshop
Data Visualization: Tableau
Hydrologic/Hydraulic Modeling: HEC-HMS, HEC-RAS
Professional GIS Experience
GIS Intern, National Park Service (October 2024–August 2025)
Developed spatial data management plan for park facilities at Nicodemus National Historic Site in Kansas
Designed geodatabase in ArcGIS Pro to national data standards for feature classes, fields, domains, and metadata
Compiled data from asset management software and historic structure reports to conduct gap analysis
Collected additional GNSS data on park assets using Leica Zeno Mobile One with GG04 smart antenna and field tablet
Produced webmap in ArcGIS Online of assets, buildings, roads, and utilities for park maintenance staff
Revamped map figures in ArcGIS Pro for interpretive wayside exhibits to meet NPS graphic design standards
Project Summaries
Project Summaries
Master's Project
Abstract: Determining areas at risk of flooding is critical to minimizing the damage and losses that floods can cause. Geographic Information Systems can map regions of general flood vulnerability through more rapid methods than complex hydrological models. This project aimed to develop an easily operated tool to locate potentially flood-prone landscape areas by applying the Topographic Wetness Index to measure how terrain influences water runoff and accumulation. The result was an interactive Jupyter Notebook that provided detailed steps on using Python code blocks to perform index calculations. Functions of the tool included automating the downloading process for Digital Elevation Models based on user-provided coordinates and performing raster analysis to determine the input parameters of flow directions, accumulated flow, and slope gradients. A set of optional steps could process the index results according to the needs of the user through low-pass filtering, range scaling, and custom symbolization. The project provided an example of how the tool performs by comparing index values from Digital Elevation Models at differing resolutions to descriptions of localized flooding risks in Lake County, Oregon. The Topographic Wetness Index tool effectively demonstrated a practical approach for land use planning purposes that uses minimal inputs to identify areas susceptible to flooding.
Cartography
Air Pollution & Race in Pierce County, WA
Objective: Determine if spatial relationships exist between air pollution and race at the census tract level using a series of cartographic displays.
Methods:
Reference Map
Graduated Symbols Map
Choropleth Map
Bivariate Choropleth Map
Interactive Visualization
Software: ArcGIS Pro, Tableau
Web GIS
StoryMaps: Ikigai – Purpose and Longevity in Okinawa
Experience Builder:
Dashboards:
Survey123: Flood Damage Survey
ArcGIS Maps SDK for JavaScript: 2020 Presidential Election Results
Vector Analysis
Point Pattern Analysis:
Emerald Ash Borer Presence in Ramsey County, MN
Quadrat Count, Average Nearest Neighbor, Ripley’s K Function
Spatial Regression:
COVID-19 Risk Factors in Santa Clara County, CA
Exploratory Regression, Geographically Weighted Regression
Geospatial Interpolation:
Predicting Nitrate Concentrations in the Ogallala Aquifer in Texas
Inverse Distance Weighting, Spline, Ordinary Kriging
Network Analysis:
Flood Hazard Operations for Johnson Creek in Portland, OR
Optimized Routes, Closest Facilities, Optimal Location of Facilities
Queries and Joins:
Envisioning Effects of the 1980 Mount St. Helens Eruption in the Present
Attribute Queries, Spatial Queries, Spatial Joins
Location Suitability Analysis:
Site Search of Feasible Offshore Wind Farms in Oregon
Boolean Overlay, Weighted Linear Combination
Raster Analysis
Estimating Soil Erosion Using a RUSLE Model in Tillamook Bay, OR
Objective: Develop a raster-based Revised Universal Soil Loss Equation model to estimate annual soil loss.
Methods:
Creating and processing rainfall, soils, slopes, land cover, and preventative measures data
Watershed delineation: sinks/fills, flow direction, flow accumulation, pour points
Raster calculation of annual soil loss for entire watershed
Raster calculation of annual soil loss per subwatershed
Software: ArcGIS Pro, ModelBuilder
Remote Sensing
Applying a New Burn Detection Method to the Eagle Creek Fire in Oregon
Objective: Calculate and compare two different Normalized Burn Ratio techniques using Landsat-8 imagery.
Methods:
Topographic correction of DEM
Calculating burn ratios using NIR, SWIR1, and SWIR2 bands
Unsupervised ISODATA classification
Software: R in RStudio, ArcGIS Pro
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