This project performs an analysis of images from Google Street View, with the aim of evaluating the quality of streetscapes in and around the Atlanta BeltLine. Through this methodology, we set out to contextualize the following question: which access points (and areas around access points) to the BeltLine exhibit a poor degree of walkability and pedestrian oriented design. In order to perform this analysis, we identify a series of points around each of the major access points to the BeltLine. This process was done manually in ArcGIS. Each of these points represents a Google Street View image of the road corridor. Then, using the PSPNET Computer Vision model, we identify and calculate the various metrics that determine the quality of the streetscape. Across each series of images (aggregated across each BeltLine access point), we aggregate the calculated metrics and determine a final streetscape "score" for each sector. Once the analysis is complete, we will plot on a map each of the access points alongside their calculated scores. The goal of this study is to provide a spatial analysis of which parts of the BeltLine need enhanced pedestrian infrastructure, in order to ameliorate heightened injury risk at the points of access.

Analysis of walkability of street segments using PSPNET computer vision model.

Calculating the travel times from local stations to midtown stations.

Intro to Urban Analytics Mini Assignment 4

Tidied Yelp data for campgrounds and coffee shops in White County, GA, with a bit of EDA

Yelp data for campgrounds and coffee shops in White County, GA