Many planners and agencies combine Strava Metro data with physical counter data to better understand how activity observed at specific locations relates to broader network patterns. Two common reasons include:
- 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).
This article outlines one example approach for comparing Strava Metro Edge Counts data with counter data. While the example below uses a bicycle counter, the same general workflow can be applied to other types of counters where appropriate. Results may vary based on location, facility type, timeframe, and how data is collected and aggregated.
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 that best correspond to the location and coverage of your counter. This may include selecting multiple edges if the counter captures activity across directions or lanes. In this example, the counter records total bicycle volume regardless of direction, so all relevant edges across the bridge are selected.
Use the date picker in the left-hand panel to choose a custom date range that matches the period covered by your counter data.
Keep in mind that correlation results may vary depending on how the selected edges align with the physical counter location.
Step 3: Export Edge Counts data
If you’re reviewing a short period, you can view counts directly in Metroview. For longer timeframes or daily comparisons, exporting the data allows you to combine Strava Metro counts with your counter data more efficiently.
To export, click the download icon in the right-hand panel. The export will be available on your Data page as a ZIP file containing a shapefile of the selected edges and a CSV. Additional details are available in the Edge Counts data export article.
Step 4: Combine datasets and compare trends
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.
If you’re working with counter data directly in Metroview, you can also reference guidance on adding and viewing your own counter data within the platform.