The data contains running activity data from March 2022 to December 2023. The data has been downloaded using the Strava API.

The data can be used for exploratory data analysis, building data visualizations,dashboards, performance analysis, machine learning models(eg. for predicting pace, predicting kudos etc.)

Context

Strava is a popular platform for tracking and collecting user data. I have used Strava for years, mostly for sharing my running with friends. I was curious about do my own analysis with data and finding some interesting insights. Hopefully, Strava provides a great API that makes it easy.

Content

I extracted interesting attributes for me from my activities objects, like a average speed, distance, time, heart rate, elevation gain and etc.

Inspiration

I want to find something interesting in my data and use it for my running goals in the future. I am thinking about building a predictive model for finishing time on marathons or average speed.

Traffic analytics, rankings, and competitive metrics for strava.com as of October 2025

The UTMB (Ultra-Trail du Mont-Blanc) is the most prestigious trail race in the world. To complete this race the athletes must run 171 kilometers and 10,000 meters of positive elevation gain around the Mont-Blanc through Italy, Switzerland and France in a maximum time of 46h30. I started trail running in 2016 and registed all my training data in Strava since then. It was just when I finished my first 100km race that I started planning my training to one day run the UTMB. I finished the UTMB race in 2022 with a total time of 40h43. So, after sucessfully complete my dream race, I decided to get some analysis of my training data.

This dataset contains user reviews of the Strava application collected from the Google Play Store. The data consists of textual reviews submitted by users, along with associated metadata such as review date, user rating (on a scale of 1 to 5), and reviewer name or alias. In some cases, the dataset may also include the number of likes each review received and the version of the app being reviewed. These reviews offer valuable insights into user experiences, satisfaction, complaints, feature requests, and perceptions of the app’s performance and usability.

Context

This is a download from Pedro Junqueira activities on Strava. Strava.com which is a sports activity tracker

A analysis of this data was performed in the following Kernel

https://www.kaggle.com/pedrojunqueira/predicting-strava-kudos

The Cycling Computer With Strava Integration market has witnessed significant expansion in recent years, reflecting the growing trend toward health and fitness awareness coupled with advancements in cycling technology. These devices serve as vital tools for cycling enthusiasts, offering detailed performance metrics

Dataset consists of the data produced by nine cyclists. Data were directly exported from their Strava or Garmin Connect accounts. Data format of sport s activities could be written in GPX or TCX form, which are basically the XML formats adapted to specific purposes. From each dataset, many following information can be obtained: GPS location, elevation, duration, distance, average and maximal heart rate, while some workouts include also data obtained from power meters.

Discover the booming running apps market! This in-depth analysis reveals a $648.6 million market in 2025, projected to grow at 14.2% CAGR until 2033. Learn about key drivers, trends, and top players like Nike+ and Strava. Get insights for investors and businesses in the fitness tech space.

Smartphones with integrated global navigation satellite system (GNSS) functionality are increasingly used in various apps beyond communication, including positioning, navigation, and tracking. This study explores the potential of smartphone GNSS data to improve ski slope safety through motion data analysis. Apps such as iSKI, Skitude, Slopes, and Strava measure speeds, distances, and altitude differences, generating valuable data on skiers’ movements. These data help ski resorts in planning and accident prevention by identifying high-risk areas based on movement patterns. We compared the accuracy of position and speed data from four apps across four smartphone models (two Android and two iOS) against a differential GNSS (dGNSS) reference system. Data were collected at two ski resorts during the winter of 2022/23, with smartphones recording at 1 Hz and dGNSS at 50 Hz. Analysis focused on downhill runs, excluding initial recording phases and vertical position data. Accuracy was assessed by calculating the Euclidean distance between the time-synchronized smartphone data and dGNSS reference data. High-end smartphones provided more accurate position data, with an average error of approximately 4 m, compared to 6 m for low-end models. Speed data were reliable across all devices, with an average error

The Sports Training AI market is booming, projected to reach $1.24 Billion by 2033 with a 9.2% CAGR. Discover key drivers, trends, and leading companies leveraging AI for personalized athlete training and performance optimization. Learn more about this rapidly expanding sector.

Explore the booming Turbo Trainer Apps market, projected to reach USD 1.5 billion by 2025 with a 22% CAGR. Discover key drivers, trends, and leading companies shaping virtual cycling.

Discover the booming market for runner workout apps! This in-depth analysis reveals key trends, growth drivers, and leading companies in this dynamic sector, projecting a $6.1 billion market by 2033. Explore market size, CAGR, regional breakdowns, and competitive landscapes.

Introduction

This travel time matrix records travel times and travel distances for routes between all centroids (N = 13132) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by walking, cycling, public transportation, and private car. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below.

The grid follows the geometric properties and enumeration of the versatile Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.

Data formats

The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably.

Helsinki_Travel_Time_Matrix_2023.csv.zst: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the Zstandard algorithm, that many data science libraries, for instance, pandas, support transparently, directly, and automatically.

Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip: an OGC GeoPackage standard file containing all data columns and the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.

Helsinki_Travel_Matrix_2023_travel_times.csv.zip: a set of 13132 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv where 5787545 is replaced by the to_id by which the rows in the file are grouped. Use the from_id column to join with the geometries from one of the files below.

Geometry, only:

Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip: an OGC GeoPackage standard file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.

Helsinki_Travel_Time_Matrix_2023_grid.shp.zip: an ESRI Shapefile archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files.

Table structure

from_id: ID number of the origin grid cell to_id: ID number of the destination grid cell walk_avg: Travel time in minutes from origin to destination by walking at an average speed walk_slo: Travel time in minutes from origin to destination by walking slowly bike_avg: Travel time in minutes from origin to destination by cycling at an average speedbike_fst: Travel time in minutes from origin to destination by cycling fastbike_slo: Travel time in minutes from origin to destination by cycling slowlypt_r_avg: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed pt_r_slo: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed pt_m_avg: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed pt_m_slo: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed pt_n_avg: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed pt_n_slo: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed car_r: Travel time in minutes from origin to destination by private car in rush hour traffic car_m: Travel time in minutes from origin to destination by private car in midday traffic car_n: Travel time in minutes from origin to destination by private car in nighttime traffic walk_d: Distance from origin to destination, in meters, on foot

Data for 2013, 2015, and 2018

At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations' results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format.

For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to DOI:10.5281/zenodo.3247563.

Methodology

Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. 'Rush hour' refers to an 1-hour window between 8 and 9 am, 'midday' to 12 noon to 1 pm, and 'nighttime' to 2-3 am.

All routes have been calculated using r5py, a Python library making use of the R5 engine by Conveyal, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications:

Walking

Walking speeds, and in turn walking times, are based on the findings of Willberg et al., 2023, in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use the average measured walking speed in summer conditions for walk_avg (as well as the respective pt_*_walk_avg), and the slowest quintile of all measured walker across all conditions for walk_slo (and the respective pt_*_walk_slo).

Cycling

Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from Strava data, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast, slow, and average cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the Helsinki City Bike bike-share system, and the mean between the two.

Further, in line with the values observed by Jäppinen (2012), we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination.

Public Transport

We used public transport schedules in General Transit Feed Specification (GTFS) format published by the Helsinki Regional Transport Authority, and adjusted the walking speeds (for connections between vehicles, as well as for access and egress to and from public transport stops) using the same methods as described above for walking.

Private motorcar

To represent road speeds actually driven in the Helsinki metropolitan region, we used floating car data of a representative sample of the roads in the region to derive the differences between the speed limit and the driven speed on different road classes, and by speed limit, see Perola (2023) for a detailed description of the methodology. Because these per-segment speeds factor in potential waiting times at road crossings, we eliminated turn penalties from R5.

Our modifications were carried out in two ways: some changes can be controlled by preparing input data sets in a certain way, or by setting model parameters outside of R5 or r5py. Other modifications required more profound changes to the source code of the R5 engine.

You can find a fully patched fork of the R5 engine in the Digital Geography Lab's GitHub repositories at github.com/DigitalGeographyLab/r5. The code that handles input data mangling and model parameter estimations is kept together with the logic to read input parameters and to collate output data, in the repository at github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices.

Cohen’s d values for the speed error across applications.

Cohen’s d values for the horizontal plane position error across apps.

Summary of skiing activity included in the study, showing distance, altitude drop, average and maximum speed, and duration of the activity for the two recreational skiers.

Mean time offsets from the true GPS time for each app.

The horizontal plane position error with the four smartphone apps.

Speed error across the three data collection days at the two locations.