1,721,696 research outputs found
Strava Manchester
No full textOverview:
UBDC researchers have worked extensively with data from Strava, a fitness app that allows users to track a variety of sporting activities. The app is perhaps most popular among cyclists. Data on cycling has traditionally been difficult to gather or has lacked detail but apps like Strava let users log and share their journeys. This enables planners to understand which routes people use, and which origins/destination pairs are busiest. This can help us track how popular cycling is over time, evaluate impacts of infrastructure, and plan new interventions to encourage active travel.
UBDC has the following Strava datasets:
o Strava Scotland data available for 2015 – March 2020
o Strava Glasgow 2013 – March 2020
o Strava Manchester 2015 – 2018
o Strava Tyne and Wear 2015 – 2018
o Strava Sheffield 2017
Strava data:
Strava is a social network, recording data from its users who upload their cycle rides or running activity via smartphone or GPS device. This dataset comprises anonymised data from Strava users in Manchester. The aggregate information provides GIS compatible data that offer an hourly count of users at street level and wait times at intersections based on the Open Street Map roads network. The origin and destination of trips is also available, resolved to census output areas.
Data from Strava, widely used social networking platform tailored for enthusiasts who enjoy activities such as cycling and running, for Scotland and the Northeast of England (Glasgow, Scotland, Manchester, Tyne & Wear, Sheffield). The time period of the data coverage varies for different cities. To ensure user privacy, all the data has been anonymized and also binned.
The GIS compatible data offers:
1.Hourly User Count at Street Level: This gives insights into how many users are active on a specific street on an hourly basis. (Binning: 0 if this number is less than or equal to 3, otherwise rounded up in multiples of 5)
2.Wait Times at Intersections: This information can provide how long Strava users typically wait at various intersections. Such data can be useful for urban planning and traffic management, highlighting potential areas for infrastructure improvement.
3.Origin and Destination of Trips: The data reveals where users start (origin) and finish (destination) their activities.
However, to maintain user privacy, these origins and destinations are grouped by census output areas. This offers a balance between useful data granularity and user confidentiality. This dataset, based on the Open Street Map roads network, offers a rich source of information for a variety of professionals – from urban planners to transport researchers
Access and restrictions:
UBDC data is available for non-commercial, academic research by UK-based academics under an End User Licence. Data can be used for research in the social sciences, including transportation research. Usage of the data for teaching is also permitted.
Access to the Data Strava provides access to the Metroview platform for urban infrastructure planning organizations around the world to understand mobility patterns, identify opportunities for investment and evaluate the impact of infrastructure changes. Access to Metroview is free of charge and is available at Strava’s discretion based on the application requirements. Previous extracts of Strava Metro data for the above geographies and timeframes are available through the UBDC data catalogue. To apply for Strava Metro data in other geographies or for additional timeframes, please see the Strava website https://metro.strava.com/
More information:
Further information can be found at https://metro.strava.com
Strava Scotland
No full textOverview:
UBDC researchers have worked extensively with data from Strava, a fitness app that allows users to track a variety of sporting activities. The app is perhaps most popular among cyclists. Data on cycling has traditionally been difficult to gather or has lacked detail but apps like Strava let users log and share their journeys. This enables planners to understand which routes people use, and which origins/destination pairs are busiest. This can help us track how popular cycling is over time, evaluate impacts of infrastructure, and plan new interventions to encourage active travel.
UBDC has the following Strava datasets:
o Strava Scotland data available for 2015 – March 2020
o Strava Glasgow 2013 – March 2020
o Strava Manchester 2015 – 2018
o Strava Tyne and Wear 2015 – 2018
o Strava Sheffield 2017
Strava data:
Strava is a social network, recording data from its users who upload their cycle rides or running activity via smartphone or GPS device. This dataset comprises anonymised data from Strava users in Manchester. The aggregate information provides GIS compatible data that offer an hourly count of users at street level and wait times at intersections based on the Open Street Map roads network. The origin and destination of trips is also available, resolved to census output areas.
Data from Strava, widely used social networking platform tailored for enthusiasts who enjoy activities such as cycling and running, for Scotland and the Northeast of England (Glasgow, Scotland, Manchester, Tyne & Wear, Sheffield). The time period of the data coverage varies for different cities. To ensure user privacy, all the data has been anonymized and also binned.
The GIS compatible data offers:
1.Hourly User Count at Street Level: This gives insights into how many users are active on a specific street on an hourly basis. (Binning: 0 if this number is less than or equal to 3, otherwise rounded up in multiples of 5)
2.Wait Times at Intersections: This information can provide how long Strava users typically wait at various intersections. Such data can be useful for urban planning and traffic management, highlighting potential areas for infrastructure improvement.
3.Origin and Destination of Trips: The data reveals where users start (origin) and finish (destination) their activities.
However, to maintain user privacy, these origins and destinations are grouped by census output areas. This offers a balance between useful data granularity and user confidentiality. This dataset, based on the Open Street Map roads network, offers a rich source of information for a variety of professionals – from urban planners to transport researchers
Access and restrictions:
UBDC data is available for non-commercial, academic research by UK-based academics under an End User Licence. Data can be used for research in the social sciences, including transportation research. Usage of the data for teaching is also permitted.
Access to the Data Strava provides access to the Metroview platform for urban infrastructure planning organizations around the world to understand mobility patterns, identify opportunities for investment and evaluate the impact of infrastructure changes. Access to Metroview is free of charge and is available at Strava’s discretion based on the application requirements. Previous extracts of Strava Metro data for the above geographies and timeframes are available through the UBDC data catalogue. To apply for Strava Metro data in other geographies or for additional timeframes, please see the Strava website https://metro.strava.com/
More information:
Further information can be found at https://metro.strava.com
Strava Sheffield
No full textOverview:
UBDC researchers have worked extensively with data from Strava, a fitness app that allows users to track a variety of sporting activities. The app is perhaps most popular among cyclists. Data on cycling has traditionally been difficult to gather or has lacked detail but apps like Strava let users log and share their journeys. This enables planners to understand which routes people use, and which origins/destination pairs are busiest. This can help us track how popular cycling is over time, evaluate impacts of infrastructure, and plan new interventions to encourage active travel.
UBDC has the following Strava datasets:
o Strava Scotland data available for 2015 – March 2020
o Strava Glasgow 2013 – March 2020
o Strava Manchester 2015 – 2018
o Strava Tyne and Wear 2015 – 2018
o Strava Sheffield 2017
Strava data:
Strava is a social network, recording data from its users who upload their cycle rides or running activity via smartphone or GPS device. This dataset comprises anonymised data from Strava users in Manchester. The aggregate information provides GIS compatible data that offer an hourly count of users at street level and wait times at intersections based on the Open Street Map roads network. The origin and destination of trips is also available, resolved to census output areas.
Data from Strava, widely used social networking platform tailored for enthusiasts who enjoy activities such as cycling and running, for Scotland and the Northeast of England (Glasgow, Scotland, Manchester, Tyne & Wear, Sheffield). The time period of the data coverage varies for different cities. To ensure user privacy, all the data has been anonymized and also binned.
The GIS compatible data offers:
1.Hourly User Count at Street Level: This gives insights into how many users are active on a specific street on an hourly basis. (Binning: 0 if this number is less than or equal to 3, otherwise rounded up in multiples of 5)
2.Wait Times at Intersections: This information can provide how long Strava users typically wait at various intersections. Such data can be useful for urban planning and traffic management, highlighting potential areas for infrastructure improvement.
3.Origin and Destination of Trips: The data reveals where users start (origin) and finish (destination) their activities.
However, to maintain user privacy, these origins and destinations are grouped by census output areas. This offers a balance between useful data granularity and user confidentiality. This dataset, based on the Open Street Map roads network, offers a rich source of information for a variety of professionals – from urban planners to transport researchers
Access and restrictions:
UBDC data is available for non-commercial, academic research by UK-based academics under an End User Licence. Data can be used for research in the social sciences, including transportation research. Usage of the data for teaching is also permitted.
Access to the Data Strava provides access to the Metroview platform for urban infrastructure planning organizations around the world to understand mobility patterns, identify opportunities for investment and evaluate the impact of infrastructure changes. Access to Metroview is free of charge and is available at Strava’s discretion based on the application requirements. Previous extracts of Strava Metro data for the above geographies and timeframes are available through the UBDC data catalogue. To apply for Strava Metro data in other geographies or for additional timeframes, please see the Strava website https://metro.strava.com/
More information:
Further information can be found at https://metro.strava.com
Strava Glasgow
No full textOverview:
UBDC researchers have worked extensively with data from Strava, a fitness app that allows users to track a variety of sporting activities, perhaps most popular among cyclists. Data on cycling has traditionally been difficult to gather or has lacked detail but apps like Strava let users log and share their journeys. This enables planners to understand which routes people use, and which origins/destination pairs are busiest. This can help track how popular cycling is over time, evaluate impacts of infrastructure, and plan new interventions to encourage active travel.
UBDC has the following Strava datasets:
• Strava Scotland data available for 2015 – March 2020
• Strava Glasgow 2013 – March 2020
• Strava Manchester 2015 – 2018
• Strava Tyne and Wear 2015 – 2018
• Strava Sheffield 2017
Strava:
Strava is a social network, recording data from its users who upload their cycle rides or running activity via smartphone or GPS device. This dataset comprises anonymised data from Strava users. The aggregate information provides GIS compatible data that offer an hourly count of users at street level and wait times at intersections based on the Open Street Map roads network. The origin and destination of trips is also available, resolved to census output areas.
Data from Strava, widely used social networking platform tailored for enthusiasts who enjoy activities such as cycling and running, for Scotland and the Northeast of England (Glasgow, Scotland, Manchester, Tyne & Wear, Sheffield). The time period of the data coverage varies for different cities. To ensure user privacy, all the data has been anonymized and also binned.
The GIS compatible data offers:
1. Hourly user count at street level: this gives insights into how many users are active on a specific street on an hourly basis. (Binning: 0 if this number is less than or equal to 3, otherwise rounded up in multiples of 5).
2. Wait times at intersections: this information can provide how long Strava users typically wait at various intersections. Such data can be useful for urban planning and traffic management, highlighting potential areas for infrastructure improvement.
3. Origin and destination of trips: the data reveals where users start (origin) and finish (destination) their activities.
To maintain users privacy, these origins and destinations are grouped by census output areas. This offers a balance between useful data granularity and user confidentiality. This dataset, based on the Open Street Map roads network, offers a rich source of information for a variety of professionals – from urban planners to transport researchers.
Access and restrictions:
UBDC data is available for non-commercial, academic research by UK-based academics under an End User Licence. Data can be used for research in the social sciences, including transportation research. Usage of the data for teaching is also permitted.
Access to the Data Strava provides access to the Metroview platform for urban infrastructure planning organisations around the world to understand mobility patterns, identify opportunities for investment and evaluate the impact of infrastructure changes. Access to Metroview is free of charge and is available at Strava’s discretion based on the application requirements. Previous extracts of Strava Metro data for the above geographies and timeframes are available through the UBDC data catalogue. To apply for Strava Metro data in other geographies or for additional timeframes, please see the Strava website https://metro.strava.com/
Strava Tyne and Wear
No full textOverview:
UBDC researchers have worked extensively with data from Strava, a fitness app that allows users to track a variety of sporting activities. The app is perhaps most popular among cyclists. Data on cycling has traditionally been difficult to gather or has lacked detail but apps like Strava let users log and share their journeys. This enables planners to understand which routes people use, and which origins/destination pairs are busiest. This can help us track how popular cycling is over time, evaluate impacts of infrastructure, and plan new interventions to encourage active travel.
UBDC has the following Strava datasets:
o Strava Scotland data available for 2015 – March 2020
o Strava Glasgow 2013 – March 2020
o Strava Manchester 2015 – 2018
o Strava Tyne and Wear 2015 – 2018
o Strava Sheffield 2017
Strava data:
Strava is a social network, recording data from its users who upload their cycle rides or running activity via smartphone or GPS device. This dataset comprises anonymised data from Strava users in Manchester. The aggregate information provides GIS compatible data that offer an hourly count of users at street level and wait times at intersections based on the Open Street Map roads network. The origin and destination of trips is also available, resolved to census output areas.
Data from Strava, widely used social networking platform tailored for enthusiasts who enjoy activities such as cycling and running, for Scotland and the Northeast of England (Glasgow, Scotland, Manchester, Tyne & Wear, Sheffield). The time period of the data coverage varies for different cities. To ensure user privacy, all the data has been anonymized and also binned.
The GIS compatible data offers:
1.Hourly User Count at Street Level: This gives insights into how many users are active on a specific street on an hourly basis. (Binning: 0 if this number is less than or equal to 3, otherwise rounded up in multiples of 5)
2.Wait Times at Intersections: This information can provide how long Strava users typically wait at various intersections. Such data can be useful for urban planning and traffic management, highlighting potential areas for infrastructure improvement.
3.Origin and Destination of Trips: The data reveals where users start (origin) and finish (destination) their activities.
However, to maintain user privacy, these origins and destinations are grouped by census output areas. This offers a balance between useful data granularity and user confidentiality. This dataset, based on the Open Street Map roads network, offers a rich source of information for a variety of professionals – from urban planners to transport researchers
Access and restrictions:
UBDC data is available for non-commercial, academic research by UK-based academics under an End User Licence. Data can be used for research in the social sciences, including transportation research. Usage of the data for teaching is also permitted.
Access to the Data Strava provides access to the Metroview platform for urban infrastructure planning organizations around the world to understand mobility patterns, identify opportunities for investment and evaluate the impact of infrastructure changes. Access to Metroview is free of charge and is available at Strava’s discretion based on the application requirements. Previous extracts of Strava Metro data for the above geographies and timeframes are available through the UBDC data catalogue. To apply for Strava Metro data in other geographies or for additional timeframes, please see the Strava website https://metro.strava.com/
More information:
Further information can be found at https://metro.strava.com
Strava, the Aspirational Sport and its Impact on Mental Health
Full text linkNumerous scientific studies have observed how digital social networks significantly affect the users mental health This research sought to investigate whether new sports technology tools which record multiple activity data but also enhance social interaction within digital communities have a detrimental effect on athletes emotional health as they are under the pressure of constant self-improvement due to social scrutiny Strava has become the world s largest sports community and research is largely focused on its user
Interview with Cristiana, Strava "Precarious modernities: assembling state, space and society on the urban margins in Morocco"
No full textWhat does living “precariously” mean in Casablanca? In 2014 it meant being labeled tcharmil (seeming to endanger public order) and swept up by the police, if you were an unemployed young man sporting a banda haircut and gathering with your mates on a street corner. Cristiana Strava witnessed this and other neglected aspects of urban vulnerability while conducting extensive fieldwork in Hay Mohammedi, a renowned working-class neighborhood on the margins of modern Morocco’s economic mecca, Casablanca. In Precarious Modernities: Assembling State, Space and Society on the Urban Margins in Morocco (Bloomsbury, 2021), Strava shares what she learned about how its residents create a sense of place and belonging, despite the manifold insecurities of living in a quarter that is losing both industries and social services. Focusing on the everyday lives and spaces of a mythicized community, and its interaction with heritage activists, international development agendas and technocratic planning regimes, Precarious Modernities documents how the depoliticization of the urban margins aids the consolidation of deeply unequal social, spatial, and economic orders.Middle Eastern Studie
Self-optimization culture & Strava: Self-optimization culture & Strava
No full textI denne opgave undersøger vi, hvordan fitnessplatformen Strava bidrager til en selvoptime-ringskultur gennem socialpsykologiske mekanismer. Med afsæt i tematisk analyse af kvalitative interviews af tre kvindelige Strava brugere, samt teorier som symbolsk interaktionisme, aner-kendelse, social sammenligning samt social identitetsteori analyserer vi, hvordan brugerne an-vender platformen til at fremstille og regulere deres identitet i en præstationsorienteret løbekon-tekst. Derudover undersøger vi, hvordan Strava påvirker deres motivation og selvopfattelse. Gennem Joshua Meyrowitz’ medieanalyse, samt begrebet affordances, undersøger vi derud-over, hvordan Stravas funktioner, herunder kudos, segmenter og leaderboards, skaber et digitalt miljø, hvor præstation, synlighed og social validering bliver centrale elementer. Efterfølgende diskuterer vi, hvordan Stravas funktioner og affordances ligeledes kan bidrage til samt forstær-ke denne selvoptimeringskultur, og hvorvidt informanternes erfaringer stemmer overens med de tendenser, vi har identificeret i interviewene. Projektet konkluderer afslutningsvist, at Strava både forstærker og reproducerer en selvoptimeringskultur, hvor individets værdi i stigende grad måles gennem præstation og deling heraf.This project explores how the fitness platform Strava contributes to a culture of self-optimization through social psychological mechanisms. Based on a thematic analysis of qualitative interviews with three female Strava users, and drawing on theories such as symbolic interactionism, theory of recognition, social comparison, and social identity theory, we analyze how users employ the platform to construct and regulate their identities within a performance-oriented running context. Furthermore, we examine how Strava influences their motivation and self-perception. Through Joshua Meyrowitz’s media analysis and the concept of affordances, we also investigate how Strava’s features, such as kudos, segments, and leaderboards, create a digital environment where performance, visibility, and social validation become central elements. We then discuss how these features and affordances may contribute to and reinforce this culture of self-optimization, and whether the participants’ experiences align with the broader tendencies identified in the interviews. The project ultimately concludes that Strava both reinforces and reproduces a culture of self-optimization, where individual value is increasingly measured through performance and its public display.<br/
Validity and reliability of STRAVA segments: Influence of running distance and velocity
Full text linkThis study aimed to assess the reliability of Strava measurements when manipulating segment distance and running velocity. The tests were carried out on a flat and straight segment. Ten male regular runners were equipped with a Garmin® Forerunner 945 watch and ran over a distance of 1 km of four increasing speeds: 1.39, 2.78, 4.17 and 5 m/s. Different reference positions were accurately determined in order to calculate time at 100 m, 200 m, 500 m, 700 m, and 1000 m. A bike with a wide angle camera was used to standardize the run pace and to record the entire run for reference measurements. Results show a high level of reliability with nearly perfect intra-class correlation (from .997 to 1) when data is analysed accordingly to the distance of the segment or to the running velocity. The validity is also very good with a small average bias (-0.25 s), a standard deviation of differences of 1.84 sec and the limit of agreement range from -3.86 to 3.35 sec. Regardless of the length of the segment, the actual performance of the runner is normally within +/- 2 seconds of the results given by the Strava application. In 95% of cases, the measurement error will be less than four seconds. The relative error is proportionally larger for short segments done at a fast pace. Further studies are needed to explore Strava segments reliability in other specific contexts
Strava detox
Full text linkDenne kvalitative masteroppgaven har som formål å undersøke hva slags betydning Strava og lignende treningsapplikasjoner har for deltagelsen i og opplevelsen av å være i fysisk aktivitet. 8 aktive brukere av Strava deltok i et prosjekt der de frakoblet seg applikasjonen i 3 uker. Deltagerne ble intervjuet før og etter perioden og skrev supplerende loggdata. Dette ga innsikt både i hvordan deltagerne bruker Strava i det daglige og hvordan de erfarte å være frakoblet.
Den tematiske analysen av datamaterialet resulterte i 3 hovedtemaer: (1) «Det digitale treningsfellesskapet», (2) «Selvpresentasjon og identitetsforming», (3) «Sammenligning og tallenes kraft: motivasjon og/eller press?». Selvbestemmelsesteorien av Deci & Ryan og Goffmans dramaturgiske metafor ble anvendt for å tolke dataene. Funnene viser at Strava fungerer som mer enn en treningsdagbok. Det er en sosial arena som kan påvirke både hvordan trening gjennomføres og oppleves. Intervensjonsperioden avdekket ambivalente reaksjoner. Noen savnet motivasjonen og fellesskapet, mens andre opplevde mindre prestasjonspress, økt frihet og bedre regulering av treningsbelastning. Oppgaven konkluderer med at Strava og lignende applikasjoner kan ha betydning for både deltagelse i og opplevelsen av fysisk aktivitet, men at effektene er sammensatte og situasjonsavhengige.
Masteroppgaven gir ny innsikt ved å undersøke frakobling heller enn bruk, og viser hvordan digitale treningsapplikasjoner påvirker motivasjon og identitet på måter som både kan styrke og utfordre individets psykologiske behov.This qualitative master thesis aims to explore the significance of Strava and similar social digital mobile applications, for the participation in and experience of physical activity. 8 frequent Strava users took part in a project where they disconnected from the application for three weeks. The participants were interviewed before and after the intervention-period and wrote supplementary log data to investigate how they use Strava in their daily life and how they experienced being disconnected.
The thematic analysis of the data resulted in three main themes: (1) “The digital fitness community,” (2) “Self-presentation and identity formation,” and (3) “Comparison and the power of numbers: motivation and/or pressure?”. Self-Determination Theory (Deci & Ryan) and Goffman’s dramaturgical metaphor were applied to interpret the data. The findings show that Strava functions as more than a digital training log. It is also a social arena that influences both how training is carried out and how it is experienced. The intervention period revealed ambivalent reactions: some participants missed the motivation and community, while others felt less performance pressure, greater freedom and improved regulation of training load. The thesis concludes that Strava and similar applications have an impact on both participation in and the experience of physical activity, but that these effects are complex and context dependent.
This study offers new insight by examining disconnection rather than usage, highlighting how social digital mobile applications influence motivation and identity in ways that can both support and challenge individuals’ psychological needs
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