Hi, I'm Connor --- a data-driven product manager with a background in computer engineering & data science and a passion for geospatial problems. I've built products in deep tech, health-tech and education-tech.
GEOFF (GEOspatial Fact Finder) aims to turn natural language prompts like "how many bike lanes are near school zones?", turn them into Spatial SQL, and display the results on a web map. Project progressing ~ Live Demo | 🔗 GitHub Repo
Using trained LULC U-net classifier to autoamtically predict land use land cover of the entire City of Toronto aerial at 2 px per meter. Technologies: gdal, leaflet, Vite, Tensorflow/Keras, Python
Trained a Convolutional Neural Network (CNN) with U-net architecture on aerial imagery to classify land use in downtown Toronto. Technologies: CNN, U-net, Tensorflow/Keras, Map Digitization
Mapped urban heat islands in Toronto using remote imagery and overlaid demographic data to highlight vulnerable communities. Technologies: QGIS, GDAL, Raster Analysis, Remote Sensing, Landsat
🗺️ Full Story on StoryMaps! | 🔗 GitHub Repo
Analyzed Toronto's bike share data (2016-2024) using spatial SQL and geospatial visualization to assess impact of changes in infrastructure. Technologies: PostGIS, QGIS, Python, PyQGIS
🗺️ Full Story on StoryMaps! | 🔗 GitHub Repo
I'm always open to discussions about geospatial data, urban analytics, and climate tech.
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