Welcome to Strava Metro! We’re excited to support your work in improving active transportation for everyone. Here’s a quick guide to using the platform.
You can try out a demo version now at metro.strava.com/demo.
Strava Metro provides bicycling and pedestrian data. Choose which mode you’d like to explore using the selector at the top of the page.
Cycling data includes activities recorded on conventional bicycles only, and does not include e-bikes or scooters. If you are interested in exploring e-bike data, please contact us.
Pedestrian data is a blend of running, walking and hiking data. Unlike our bicycling data, you may notice a skew towards recreational use in pedestrian data due to the large numbers of Strava members who record runs. Pedestrian data is a new offering and we’re continually reviewing the available functionality to improve the discoverability of different trip types.
On the dashboard, high-level insights and statistics are surfaced from your area of interest on a rolling basis.
Your area of interest is shown at the top of the screen. If your organization has multiple areas, you can switch between them using the dropdown menu.
Just below you can see highlights from the last data cycle within your area of interest (weekly or monthly), with comparisons to the previous period so you can see how numbers are trending.
In this chart you can see how traffic patterns are changing over time in your area.
You can see volumes of trips, filtering by commutes or leisure rides. either monthly or weekly, comparing across multiple years.
On the right you can see the volume of people who actually made those trips, monthly or weekly, across multiple years, and whether those people are locals or visitors to your area
At the bottom, you will find demographic information about people in your area, with the ability to compare across multiple years.
In the Tourism chart, “tourists” and “locals” are defined according to the boundaries of your area of interest – usually a city, county, state or equivalent. A person’s home location is set by the place they have been most active in the last six months. We are working on offering more granular options related to this data.
The map view contains four different layers for you to explore active transportation data geospatially. You can navigate between them using the left-hand menu.
This layer shows Strava activities overlaid on a map of your area, with brightness as a factor of density of GPS points. Use the heatmap to understand which parts of your network are being used most and least often.
The Corridors feature enables you to identify the highest volume corridors that people are using. You can see information about each popular corridor, and charts that visualize usage by time of day, day of week, and month of year. Pan and zoom the map to explore further, and click on a corridor line to see more information.
It’s easy to differentiate between short, commute trips and longer, leisure trips. Simply move the trip length slider to a shorter distance to isolate commute trips. After clicking on a corridor, you’ll be able to see on the right sidebar the pattern of those trips. Below we see a typical Monday-Friday commute trip pattern on a popular corridor in San Francisco.
By default, the Corridors feature shows trips of 0-8km (or 0-5 miles depending on your region).
The Routes feature enables you to explore activity between two specific points of interest to understand route choices people are making.
Move the origin and destination anchors to your desired points. Here you can see the most direct route between those two places in blue, and compare it to the most popular route, in red. A divergence between these two lines suggests there may be obstacles to completing this trip directly – physical barriers such as hills, or that people are choosing longer alternative routes in order to use better infrastructure.
For more detailed insights, the Streets feature enables you to analyze at the street block level. First you’ll see a breakdown of all the trips and people in your area. Choose a date range to narrow down your search.
Now zoom in to load the street edges for an area.
Click on an individual edge to see information about usage patterns and trip purpose. Click over to People to learn more about the people using this facility. You can see demographic and activity information.
To expand this analysis, click on multiple edges. They can be next to each other, if you’re exploring a continuous section of road or path, or separated, such as if you want to understand multiple entry points to a place of interest from different directions.
The trips panel now shows two numbers – the total number of people who crossed all of the 4 edges you selected during your selected timeframe, and the number of people who crossed any of the four.
You can also perform this same analysis on the People tab. As before, total is the number of people who crossed all 4 edges.
To explore change on an edge or collection of edges over time, use the date picker on the left sidebar.
To narrow down your search further, click Custom date range.
To download the data from the Streets view, click the download icon at the top of the right sidebar. Your download will be queued and you can access it on the Data page.
Any data that you have saved will be displayed on this page. CSV files can be joined to the shapefiles we have provided to you. You can sort files by creation date to more easily find what you’re looking for. Click the Date Created column header to reverse the order.
Any datasets you have previously purchased will also appear here, in the Resources section.
The exported data contains the following columns:
edge_id – the identifier for the street block, which can be joined to a shape file
activity_type – the type of activity, e.g. (ride, run, walk, hike)
date – one day per row
For each row, the following pairs of columns are provided.
People by gender:
Bike Counter Correlation
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.
Strava Metro is based on activities uploaded by Strava members. Strava is free to use application, and members can track their activities using a wide range of devices, such as a smartphone, smart watch, GPS watch, bike computer, FitBit or similar.
Strava members can opt out of inclusion in the Metro dataset at any time.
If you have any questions, please click on the help icon at the top of any page in the application.
This product is in active development, so we welcome your feedback and suggestions.
Please also take a minute to check out our FAQ.