Project completed for Big Ideas in Computing and Information under the supervision of Arjun Chandrasekhar.

The project covers what the best neighborhood to live in Pittsburgh is based on three specific metrics: average daily traffic count, bike station locations, and bike lanes, paths, and trails. For this project, my team and I used Jupyter Notebook, Python, pandas, and GeoPandas to produce our data visualizations.

Background

My metric for this project was on bike lanes, paths, and trails. For this metric, I used a dataset from the Western Pennsylvania Regional Data Center to determine that Squirrel Hill North is the best neighborhood to live in for the best bike lanes, paths, and trails.

Limitations

As seen in the image on the left, some limitations and challenges I faced when using revolved around unavailable/missing data. This could result from roads without bike paving or entirely missing data. This is a limitation because the missing data can lead to skewed results. Due to the volume of the data, I still had enough data to provide a representative sample to make a meaningful conclusion that Squirrel Hill North is the best neighborhood for bike lanes, paths, and trails.