Throughout my geospatial endeavour I've applied both qualitative and quantitative methods to analyzing spatial information and have a firm grasp on database design and development structures for enterprise GIS apps. I also have a talent for creating maps and visual displays that effectively communicate geospatial data, and can clearly explain technical concepts to a diverse audience.

City Mapbook

To aid urban planning, I developed a city mapbook that encompassed maintenance districts for the City of Brandon. I obtained geospatial datasets from both the City of Brandon and college, including boundaries, zoning, transportation, and demographics. I designed multi-scale basemaps highlighting digitized building footprints, land use, and zoning. I created locator maps, , and an index for consistency. I compiled components into a draft mapbook, incorporated feedback, and ensured accuracy; applying cartography, spatial analysis, and geodatabase management skills.

Remote Sensing

These remote sensing projects allowed me to explore digital imagery through image processing, spatial modelling, terrain analysis, and applying GIS and earth observation data to environmental and hazard monitoring applications. Analyzing land cover change, acquiring historic aerial photos and recent drone imagery, performing image classification and change detection. I identified agricultural encroachment on protected land and shifts in sensitive areas. I gained experience with technologies like LiDAR, multispectral satellite sensors, and UAVs for capturing high-resolution spatial data and turning that data into actionable information.

DEM & LiDAR

I utilized geoprocessing tools within ArcGIS Pro, LAStools, and Python for LiDAR data handling, visualization, and analysis. Managing and processing raw point cloud data, terrain modeling and analysis, multi-criteria site suitability assessments, change detection over time, and applying geospatial techniques to environmental planning and monitoring applications allowed me to gain extensive experience in working with LiDAR and DEMs.

Spatial Data Exploration & Analysis

For an environmental analysis capstone, I modeled ozone concentration patterns in California using data from air quality monitoring stations. I interpolated measurements into a continuous surface using kriging to identify regions exceeding health standards. The outputs assisted policymakers in developing targeted emissions controls and public health strategies.

Another project analyzed income distribution in Winnipeg. I obtained census data on median income by dissemination area (DA) and used hotspot analysis to detect clusters of high and low-income DAs. The results highlighted socioeconomic disparities in the city useful for government welfare allocation and urban planning.

A final semester project engaged me in identifying hotspots for break-and-enter crimes in Vancouver. I geocoded police reports to point locations and used the Getis-Ord Gi* statistic to detect spatial clustering of high crime density at multiple distances. The hotspot maps guided law enforcement deployment of resources to high-risk areas and further data analysis of causal factors.

These projects were fun endeavours in spatially interpolating and visualizing environmental data, analyzing demographics, and applying statistical techniques for crime hotspot detection and mapping. I gained experience with various spatial analysis tools, including ordinary kriging, hotspot analysis, and Gi* z-scores, to explore patterns in pollution, income, crime, and other phenomena.

Site Suitability Analysis

One project of mine evaluated potential land for new elementary school locations. I compiled vector data on census blocks, zoning, road networks, natural features, and hazards. I reclassified layers to a common suitability scale, overlayed them in a GIS, and used a group decision method with city officials to weight the importance of each factor. The outcome identified the most viable regions based on existing and projected populations, safe access, appropriate zoning, and affordable real estate.

As a final capstone project, I analyzed the potential distribution of Sprague's Pipit, a threatened grassland bird species. I used a combination of GIS, remote sensing, and ecological modeling techniques to identify suitable habitat based on the pipit's biological requirements. I acquired land cover datasets from satellite imagery to determine the availability of native prairie grasslands, the pipit's preferred habitat. I also obtained LiDAR-derived elevation and slope data to exclude unsuitable areas. I sampled pipit occurrences from wildlife databases and surveys to determine habitat preferences for factors like vegetation height, density, and proximity to water.

The habitat suitability maps identified core areas of high-quality habitat and potential habitat corridors for the pipit. The results guide land managers to focus conservation of existing habitat and restoration of habitat connectivity. My analysis provided a framework to monitor how habitat changes over time in response to phenomena like climate change, wildfire, and human activity.

Rasterization Analysis

For my raster analysis capstone work, I used geospatial techniques to model spatial patterns and simulate environmental processes. One project used multi-criteria evaluation to determine potential slope grades for the RM of North Norfolk. I combined raster layers like topography, vegetation, wind exposure, and road access in a GIS analysis using raster math, reclassification, and weighted overlay tools. The results identified areas with the greatest slope risk to aid in the municipality's future decision-making surrounding the focused area.