Wildfire Risk & Powerlines

Fire Risk and Powerlines Map Fire Risk and Powerlines Map With Roads

3D Map in 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

St Olaf Map

Boulder County Map Dark

Drawing Shapefiles in 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

Washington DC AirBNB Map

Jefferson County Car Crash Map Jefferson County Car Crash Map

Washington DC Airbnb Map

  • 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

Jeffco Car Crashes

Jefferson County Car Crash Map Jefferson County Car Crash Map

Subtle Basemap, Bright Data

  • 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

Data Sources:

Crashes: Jefferson County Crash
Zoning: Jefferson County Zoning
Open Space: Jefferson County Open Space
Water: TIGER/Line Areawater