Biking Down the Economic Growth Trail with Data!

How Greenville, South Carolina’s Swamp Rabbit Trail Might Be Stimulating Economic Growth

This is my first data science project where I have compiled data Greenville, SC’s Swamp Rabbit Trail Survey data and Industry Growth by Employees in the workforce to evaluate any potential for correlation of having a bike/walk trail can stimulate economic growth for a Metro area.

Greenville South Carolina is located in near the upper northwest of the state. It is the largest county in the state and is growing both in population and economically. Greenville’s growth and influence has been a stimulating force to its surrounding city and county neighbors as well.

One of the factors in Greenville’s growth is the Swamp Rabbit Trail. The Swamp Rabbit Trail is a 22-mile multi-use (walking & bicycling) connected greenway network that traverses along the Reedy River. It is an old railroad corridor that connects Greenville city parks to Travelers Rest.

The trail opened up in 2009 and is one of the most popular recreational attractions in the Greenville Metro area. It has been so inspirational that the surrounding counties of Pickens, Anderson, and Spartanburg have also implemented either bike path or trails. Both the Doodle Trail in Pickens County connecting the cities Easley to Pickens and the Swamp Rabbit Trail was inspired by the Rails to Trails initiative where communities are repurposing no-longer-used train trail routes and converting them to bike/walk trails. Spartanburg has established an in-town bike path system called Bike Town that has a bicycle sharing program call B-Share.

Greenville’s Swamp Rabbit Trail had a survey conducted that ran from 2011 to 2014 to analyze the community Impact. Here is an infographic from that study:

Using data from the Greenville Rec’s Google Drive I created this data frame:

Note: Years 2015 and 2016 were not a part of the survey. The values reflected for those years are predicted values since the data for those years is not available.

Using the data from that data frame, I used Plotly(because of its interactive tools) and to code one line graph to show the growth of path usage and the other to show economic impact.

The Economic data is used in with is data is based on this survey report summary as follows:

Nineteen managers/owners of retail businesses directly abutting and/or within close proximity to a GHS SRT access point were interviewed in Year 3, however, this number increased to 27 in Year 4, including six retail bicycle shops.

Selection criteria for the business interviews were as follows in Year 4:

Questions for the business interviews in Year 4, similar to Years 1–3 were taken from Stewart and Barr 4 examining promotion methods used by hospitality-related firms in close proximity to rail/trails. The business interviews were primarily designed to determine: If businesses located near the GHS SRT access points have observed any change in business after the trail was built?

Overall, the GHS SRT has a significant impact on business since inception. The managers/owners interviewed in Year 4 were asked a host of questions with the focus on the advantages and disadvantages of having a business in close proximity to the GHS SRT.

I find it interesting that although trail usage did not change at all between the years 2013 and 2014, the revenue of the retail businesses continued to grow. I believe that this is a strong indication of people moving to the area finding financial stability and growth.

For this data, I sourced it from the US Census website for the years of 2013–2016. Here is the data frame that I used:

You can also read this post with the dataset I used on my GitHub Pages site:

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