I turn field data and satellite imagery into decisions people can act on
Recent University of Minnesota grad in Geography (GIS) and Plant Science. I build reproducible Python notebooks and try to make the output legible to whoever actually needs it, not just other GIS people. Most weekends I'm out testing the terrain myself by hiking.
Selected work
Three recent projects spanning remote sensing, renewable siting, and historical map analysis.
Python notebooks
Documented and reproducible. Clone any of these and point them at your own AOI or dataset.
Input_Feature_Class="Street_Centroids_1",
Input_Field="SUM_NumberAvailable",
Output_Feature_Class="HotSpot_SUM",
# fixed distance band, Manhattan distance
Distance_Method="MANHATTAN_DISTANCE")
Scooter Availability vs. How Minneapolis Commutes
Hot Spot analysis of scooter/bike availability, compared against ACS commuting data
# filter to first returns, interpolate a DSM
arcpy.management.MakeLasDatasetLayer(
in_las_dataset=lasd_path,
out_layer="dsm_filter_layer",
return_values=['1']
)
arcpy.conversion.LasDatasetToRaster(...)
LiDAR-to-DSM Toolbox
Custom ArcGIS Python Toolbox: downloads LAZ tiles, decompresses to LAS, and builds a DSM raster
About
I'm a recent graduate of the University of Minnesota, with a B.S. in Geography (GIS) and Plant Science. I was introduced to GIS through a precision agriculture course, and loved it so much that I added it as a second major.
My background spans both plant science and GIS, which gives me a unique toolset for tackling large-scale spatial problems with real scientific grounding behind them: not just the geometry, but why it matters ecologically.
Outside of work I'm usually spending time enjoying what sparked my interest in this field in the first place, the great outdoors. Hiking, kayaking, and plant identification along the way make me feel more in tune with the landscape, and with myself.