There are many reasons planners like to combine Strava Metro data with their bike counter data. The two most common reasons are:
- to find out what share of the biking population Strava Metro represents
- to create expansion factors, so that they can use Strava Metro to analyze across their entire network (not just the places they have counters).
In this guide, we’ll walk you through the steps to complete the correlation analysis.
Example: New York City
Counter location: Manhattan Bridge
Month: April 2019
Step 1: Find your counter location
Log into Metro and click on the Map tab at the top.
Navigate to the location where your bike counter is located, and click on Streets on the left panel.
Step 2: Select edges
Select the edges where you’d like to count the number of trips. In this case, the counter includes total counts regardless of direction of travel, so we’re going to select all of the edges across the middle of the bridge. Now we can see the number of activities that traversed any of these edges.
Step 3: Select the date range
On the left side panel, you can select a custom date range. Let’s start by looking at April 1st. In this example, there were 225 activities across the bridge.
Step 4: Export data
If you only need a small amount of data, you can select each date individually. But in this case, where we’re looking at daily data for a full month, we’re going to export that data so we can quickly combine it with the bike counter data.
To export the data, click on the download icon on the right side panel. This will save the data to your Data page as a CSV.
Step 5: Format your spreadsheet
Next, combine the bicycle count data and the Strava Metro counts, per day, into a single spreadsheet.
Step 6: Calculate the R-squared value
From here, you can calculate the R-squared value (whether changes in one dataset can be predicted by the other dataset). The closer the R-squared value is to 1, the stronger the correlation. If you’re using Excel or Google Sheets, you can calculate the R-squared value by using the RSQ() function. In this case, the R-squared value for April 2019 is 0.93.
Step 7: Calculate percentages
Next, for each day, you can calculate what percentage of bicycle trips that were captured by the counter were also logged on Strava. To do this, divide the number of Strava Metro trips by the number of counter trips, and multiple by 100.
In this case, the median percentage is 6.5%, with a maximum of 8.7% and a minimum of 4.2%
Step 8: Further analysis
There are numerous steps you can take from here using this correlation work, including:
- Develop expansion factors, in order to use the Strava Metro data to estimate the total cycling trips across the entire network
- Find insights hiding in your data, which become apparent when working with multiple datasets
- Conduct volume-adjusted risk assessments
If you’re still determining where to place your bicycle counters, check out this article from Arizona State University researchers about where to locate bike counters.