I recently competed in the Buffalo Civic Innovation Eco Challenge. We finished “in the money”, earning a prize from ESRI for best use of mapping technology. The competition was covered in the local paper! While it is neat to get my name in the Buffalo News, the writeup did not include any detail on our project. I built a web application, Lead Free Buffalo, that has some useful information on lead contamination of drinking water and a couple visualizations of some lead testing performed across the city.

I recently had occasion to use the Google Maps directions API. I needed a series of (lat, lon) points connecting two coordinates, which seemed like a perfect use case for the directions API. It was obvious that I’d get at least a few waypoints scattered throughout the trip. I thought I’d need to interpolate between the waypoints to get the series of points I needed. This was not necessary, as it turned out.

Every day I use more and more spatial data in my projects. I have a few different Python mapping packages to handle spatial visualization and I figured I would summarize my experience with them. Link to git repository The dataset This dataset is drillhole collar coordinates for exploration holes in the Athabasca oil sands in northern Alberta. The coordinates can be found here. The government of Alberta released the coordinates along with well log data I will likely analyze in another post.

I was a student in Georgia Tech’s CS7641 graduate machine learning course in this past Fall. The course is organized around four large projects, each focused on one of the core ML method classes. The semester begins with selecting a dataset suitable for 3 of these projects (Supervised, Unsupervised, and Randomized Optimization). I was very keen to find some data in line with my interests. Unfortunately, none of the classic datasets (UCI repository, etc.

About me I am a recent graduate of Georgia Tech’s OMSCS program. I am currently employed at a software company in Tucson. I graduated from the University of Arizona in 2016 with an engineering degree. I plan to use this blog as a record of my work and my ideas. My current interests include machine learning and data science, natural resource exploration (especially geophysical methods), geostatistics, GIS, and remote sensing.