David Anderson

Spatial Data Specialist, GIS Analytics

My Journey

I love maps, weather, data, and all things in-between. While my overall goals in life have changed, I still maintain those same interests in weather and science.

I’m a budding geospatial data engineer, as I’m working with Python and spatial SQL to help workflows that were normally done by button-pushing.

Geospatial & Technical Skillset

Category Skills & Technologies Project Evidence / Application
Programming & Querying Python, Spatial SQL, Bash/Shell, JSON Core language for all pipelines; complex spatial-temporal queries in DuckDB.
Vector Analysis GeoPandas, Shapely, Pygris, OSMnx, Ipumspy, Fiona Managing Census tracts, building footprints, and river networks.
Raster & Point Cloud GDAL/OGR, Rasterio, PDAL, Rasterstats DSM generation from LiDAR; SNODAS snowmelt clipping; zonal statistics for tree canopy.
Data Engineering DuckDB, GeoParquet, PyArrow, ETL Pipelines, Data Auditing Building high-performance pipelines for NWS warning verification and automated vulnerability scoring.
Spatial Analysis Point-in-Polygon, Viewshed Analysis, Zonal Statistics, CRS Management, Proximity Analysis Assessing flood risk, arch visibility, and urban equity.
Visualization & Mapping QGIS (2D/3D), Felt, Leafmap, Folium, Matplotlib, Seaborn Creating interactive web maps and publication-ready choropleth/categorical maps.
Statistics & Modeling Spearman’s Rank Correlation, Normalization, Weighted Scoring, Threshold Selection Identifying income-canopy equity gaps and ranking building flood vulnerability.
Development Tools Git/GitHub, Jupyter Notebooks, Conda, API Integration (IEM, NHGIS, Census) Version control, environment management, and programmatic data acquisition.

Personal Project-Specific Technical Highlights

  • StormVerify-Py: Demonstrated the ability to perform spatio-temporal joins using DuckDB to verify weather warnings against ground-truth reports.
  • Spring Thaw Assessor: Showcased skills in raster-to-vector grid conversion and creating complex vulnerability scoring formulas.
  • Urban Shade Analysis: Highlighted expertise in zonal statistics and using Spearman’s rank correlation for non-normal urban data distributions.
  • Million Dollar View: Proved capability in LiDAR point cloud processing and innovative DSM modification techniques for partial-visibility analysis.