Ryan Zimmerman Portfolio

QGIS

Blender

Exported map from QGIS in B&W or color raster layers
Imported B&W raster layers to Blender to drive mesh deformation, making a 3D model of the data
Imported other B&W layers to control Cycles material properties
Imported color raster layers to create emissive and colored materials
Rendered fully textured 3D model of the map using Blender Cycles rendering engine

Data Sources:
Powerlines:	Homeland Infrastructure Foundation-Level Data (HIFLD) Electric Power Transmission Lines
Fire Risk:	Wildfire Hazard Potential (WHP) for the conterminous United States (270-m GRID), version 2018 classified (2nd Edition)
Road System:	US Census Tiger/Line

QGIS

Illustrator

Imported satellite imagery from Mapbox and Google Earth into Adobe Illustrator
Drew outlines around all paths and roads
Used Illustrator's Pathfinder tools to ensure each feature was a single, enclosed path
Converted paths to shapefile polygons
Georeferenced polygons to WGS84 projection
Extracted topo contours from 1m LIDAR data
Transformed and overlaid other georeferenced data

Data Sources:
Satellite Imagery:	Mapbox (Gaia Subscription), Google Earth
LIDAR:	Minnesota Geospatial Commons
Vegetation:	GAP/LANDFIRE National Terrestrial Ecosystems 2011
Natural Lands Pond:	St. Olaf GIS
Steam Tunnel System:	St. Olaf Framework Plan 2016

QGIS

R

Python

Maps intended to visualize inputs for a price-prediction regression
Upper map circles represent AirBNBs, colored by price and sized by the number of people each AirBNb accomodates
Hard light blending mode makes it easier to see all the datapoints, while allowing them to overlap and without losing saturation and intensity like if they were blended with opacity
Lower map colors all buildings by the LD50 sound level at that point in the city—louder areas are red
Original plan was to test if sound level was significant; as a proxy test for missing variables, but it was not significant.
Surprisingly, a geospatially lagged model did not significantly outperform a standard regression

Data Sources:
Building Footprints:	Open Data DC Building Footprints
Swimming Pools:	Open Data DC Swimming Pools
Railroads:	Open Data DC Railroads
Trees:	Open Data DC Urban Forestry Trees
Curbs:	Open Data DC Curbs
Roads:	Open Data DC Roads
Sidewalks:	Open Data DC Sidewalks
Alleys & Parking:	Open Data DC Alleys and Parking
Water:	Open Data DC Waterbodies
Topography	Open Data DC 10 foot contours Open Data DC 2 foot contours
Sound Level	Geospatial sound modeling. 2013-2015, NPS IRMA

QGIS

Gathered zoning areas, water areas, and open space areas to create subtly textured basemap
Overlaid all recorded Jeffco crashes as tiny points
Rendered transparent 5mm radius linear heatmap of crashes
Rendered transparent 20mm radius linear heatmap of crashes on top
Combined, the two heatmaps approximate a non-linear heatmap falloff which makes the data more comprehensible at a glance

Mapping