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, which can further inform additional analysis such as those listed above.
Step 1: Find your counter location
Log into Metroview and click on the Map tab at the top.
Click on Edge Counts on the left side panel, and then navigate to the location where your bike counter is installed.
Step 2: Select edges and date range
Select the edges where you’d like to count the number of trips. These edges should correspond to the where your bicycle counter is located. In this example, the counter includes total counts regardless of direction of travel or lane, 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.
On the left side panel, you can select a custom date range.
Step 3: 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 zip file containing a shapefile of the selected edges and CSV. More details about exporting Edge Counts data are available in the Edge Counts Data Export and Download article.
Step 4: Format your spreadsheet and calculate the R-squared value
Next, combine the bicycle count data and the Strava Metro counts, per day, into a single spreadsheet with the Strava Metro trips in one column and the bicycle counter trips in another. Because we're looking at daily data for this example, each day has its own row in the spreadsheet.
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 between the two datasets. If you’re using Excel or Google Sheets, you can calculate the R-squared value by using the RSQ() function. In this example, the R-squared value for the month is 0.93 when comparing daily counts. You can also visualize this by creating a scatterplot chart like the one below.
As you're working with the data in your spreadsheet, this article The power of multiple datasets and the insights hiding in them may provide helpful guidelines for getting the most out of this analysis and being prepared to find and account for outliers.
Step 5: Further analysis and additional reading
There are numerous steps you can take from here using this correlation work, including:
- Calculate what percentage of bicycle trips that were captured by the counter were also a part of the Strava Metro dataset
- 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.