So far, I've focused on vector-based geospatial analysis, implementing a Point-In-Polygon (PIP) query and working with practical representations of spatial data. I'm interested in expanding into different aspects of geospatial software engineering, particularly how spatial data, algorithms, and applications come together in practical use cases.
A C++ performance demonstration with a Python 3 geoprocessing phase. It extracts coordinate data from a California Open Data GeoJSON file and applies a Mercator projection to measure the runtime difference between calculating distances via Great Circle Arcs vs. Euclidean lines.
A C++ raster-model application demonstrating a spatial filtering use case for environmental public health data. It applies a 3x3 smoothing mean filter to a raster grid of Air Quality Index (AQI) values to reduce sensor noise and computes its gradient magnitude to highlight areas of rapid AQI change.
A C++ vector-model application demonstrating a practical environmental use case. It performs a spatial query through a point-in-polygon classification of tree locations within protected zones to ensure compliance with conservation regulations.