Getting Started
Welcome!

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 .

Modes

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.

Dashboard

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.

Map

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.

Heatmap

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.

Corridors

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).

Routes

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.

Streets

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.

Data

Any data that you have saved will be displayed on this page. Before the data is ready to download, the Link column will display “processing…” When the data is ready to download, the Link column will display an orange Download button.

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.

Data Fields

The exported data contains the following columns:

edge_UID  – the identifier for the street block activity_type  – the type of activity, e.g. (ride) date  – one day per row

“Forward”  indicates trips traveling in the direction the street was digitized into OSM (from the first point of the line to the last point of the line).

“Reverse”  indicates trips traveling in the opposite direction the street was digitized into OSM (does not indicate wrong-way travel).

“Trip”  counts indicate the number of bicycle or pedestrian trips on that edge during the given timeframe.

“People”  counts indicate the number of unique people who completed a bicycle or pedestrian trip on that edge during the given timeframe. For instance, 10 people may have completed 30 trips on an edge during the timeframe.

For each row, the following pairs of columns are provided.

Trip counts: forward_trip_count, reverse_trip_count
forward_commute_trip_count, reverse_commute_trip_count
forward_leisure_trip_count, reverse_leisure_trip_count
forward_morning_trip_count, reverse_morning_trip_count
forward_evening_trip_count, reverse_evening_trip_count
forward_hour_{0-24}_trip_count, reverse_hour_{0-24}_trip_count (trip counts per hour)

People counts: forward_people_count, reverse_people_count

People counts by Gender: forward_male_people_count, reverse_male_people_count
forward_female_people_count, reverse_female_people_count
forward_unspecified_people_count, reverse_unspecified_people_count

People counts by Age: forward_13_19_people_count, reverse_13_19_people_count
forward_20_34_people_count, reverse_20_34_people_count
forward_35_54_people_count, reverse_35_54_people_count
forward_55_64_people_count, reverse_55_64_people_count
forward_65_plus_people_count, reverse_65_plus_people_count

Average Speed: forward_average_speed, reverse_average_speed (meters per second)

GIS Data Export

AOI Export

To download data for your entire region, navigate to the Map tab and select Streets on the left side panel. After clicking on Streets, a panel will appear on the right side of the screen, indicating a few statistics for your entire area of interest.

Under the date is an orange Download button. If you haven’t selected any street segments (edges), this will download data for the date range and the entire area of interest shown in the right side panel.

To export data for a different date range, use the time period filter on the left side panel before clicking the download button.

After clicking on the Download button, a screen will pop up in the middle of the page where you can edit the filename and description of the data export.

Then, click on the Save to Data button. This will start the process of creating the data export – a zip file which includes the shapefile for the area of interest and a CSV file which includes the data. You can access this at any time by clicking on the Data tab at the top of the page. (Depending on the size of your area of interest, it may take some time for the shapefile export to finish processing.)

Selected Edges Export

To download data for a few specific street segments (edges), navigate to the Map tab and select Streets on the left side panel. A “Zoom in to load edges” button will appear near the left panel; click this to zoom in and load edges in the extent shown.

Select edges by clicking on the segments you want to export. A panel will appear on the right side of the screen, with an orange Download button below the date range. Now when you click on the Download button, only the selected edges will export (instead of the full area of interest). Edit the filename and description in the popup window and click Save to Data. You can download the data at any time from the Data tab.

Working with the GIS Data

When you export GIS data from the Metroview dashboard, you’ll have access to two files – a CSV of data and an  Open Street Map  shapefile basemap.

To provide the Streets data, we map the Strava activity data onto OSM street and trail segments. We periodically pull new versions of OSM throughout the year, and then run additional processing on it to make it a routable basemap. We run a process that breaks all streets/trails/etc at intersections (decision points) to create Edges. When this occurs, we then need to apply a new ID to each Edge, because one OSM ID may (and likely does) now apply to multiple Edges. These Edge IDs are unique to the Strava data, and are unique to the specific basemap used at the time the data is created.

We have included two ID fields in the shapefile:  osmId , which indicates the OSM geometry the segment is a part of, and  edgeUID , which is used to join the data in the CSV to the shapefile.  NOTE: the data cannot be joined to the osmId field. This is for reference only.

Depending on the date range you have selected for the export,  you may need to do additional filtering of the CSV file before joining it to the shapefile.  This is because the CSV includes daily data, so there’s a row in the CSV for every day that an edge had bicycle trips that traversed it. For instance, if you exported March 2020 data for Denver County, the CSV will include each edgeUID for every day in March that it had activities, meaning that most edges will appear multiple times in the CSV (and up to 31 times in March). Make sure you filter the data first before joining to the shapefile.

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:

  1. to find out what share of the biking population Strava Metro represents

  2. 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

Timeframe:  Daily

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:

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.

About Metro

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.

Please see our FAQ for further information .

Questions?

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 .