Reflections from my Regression course and explaining the Hat matrix and its significance in determining outliers.

Last post I left off on getting the neighborhood regions and grocery markers on the map. This month was largely spent on getting the data set up for food desert calculations. I spent a bit of time on the theoretical aspect to create the algorithm. However, in practice doing a step like projections to XY coordinates took a long time. This blog post, I’m breaking down the methodology/theory of determining a food desert region and then I will be discussing the progress in my application.

Link to Github Link to Heroku Website I first started off by finding another dataset with grocery stores in Chicago because the first one was not all of the grocery stores. Then, I used Grocery Stores - 2013, however, this has convinience stores and liquor stores counting as Grocery stores. After searching for more, I decided to settle on this dataset for now since I needed to start coding. My first goal was loading the dataset as markers.

I joined the Chipy mentorship program in hopes of expanding my Python abilities and learning better methods of writing Python code. I really wanted to meet like-minded people and be in the company of people working towards similar goals. The first challenge was figuring out my project and the scope of it. I have been closely studying the food desert crisis in the Southside and the abundance of fast food and dearth of fresh produce.

I have always been interested in learning about cutting edge technologies and enjoy applying logic to solve problems. During the summer and fall of 2017, I learned about high performance computing and modified CQSim, the HPC queue simulator. Project Learning and Exploring Cobalt – the HPC Job Management Suite Weekly Updates Update One (7-17-17) I spent this week getting familiar with Cobalt’s administrative and user commands. I have almost finished installing the simulation environment on a CentOS 6.