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Revealing Spatial Patterns and Environmental Influences on Jogging Volume and Speed: Insights from Crowd-Sourced GPS Trajectory Data and Random Forest
16 references·1 citing·44 second hopsampled
Revealing Spatial Patterns and Environmental Influences on Jogging Volume and Speed: Insights from Crowd-Sourced GPS Trajectory Data and Random Forest (2025)Revealing Spatial Patterns and Environmen…Measuring the age-friendliness of streets' walking environment using multi-source big data: A case study in Shanghai, China (2024)Measuring the age-friendlines…Urban dynamics through the lens of human mobility (2023)Urban dynamics through the le…Exploring nonlinear effects of built environment on jogging behavior using random forest (2023)Exploring nonlinear effects o…Examining the influence of neighborhood and street-level built environment on fitness jogging in Chengdu, China: A massive GPS trajectory data analysis (2023)Examining the influence of ne…Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data (2023)Environmental factors for out…Bias and precision of crowdsourced recreational activity data from Strava (2023)Bias and precision of crowdso…Assessing impacts of objective features and subjective perceptions of street environment on running amount: A case study of Boston (2023)Assessing impacts of objectiv…Sustainable design of running friendly streets: Environmental exposures predict runnability by Volunteered Geographic Information and multilevel model approaches (2022)Sustainable design of running…How Are Macro-Scale and Micro-Scale Built Environments Associated with Running Activity? The Application of Strava Data and Deep Learning in Inner London (2022)How Are Macro-Scale and Micro…Cluster analysis of microscopic spatio-temporal patterns of tourists’ movement behaviors in mountainous scenic areas using open GPS-trajectory data (2022)Cluster analysis of microscop…To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults (2021)To walk or not to walk? Exami…Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach (2021)Non-linear associations betwe…Examining non-linear built environment effects on elderly’s walking: A random forest approach (2020)Examining non-linear built en…Where we ride faster? Examining cycling speed using smartphone GPS data (2019)Where we ride faster? Examini…Are buffers around home representative of physical activity spaces among adults? (2017)Are buffers around home repre…Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study (2016)Physical activity in relation…Effects of road greenery and sky openness on non-commuting cycling route choice: insights from online map navigation (2025)Effects of road greenery and …AN ECOLOGICAL APPROACH TO CREATING ACTIVE LIVING COMMUNITIES (2005)The development of a walkability index: application to the Neighborhood Quality of Life Study (2009)Priority actions for the non-communicable disease crisis (2011)Systematic literature review of built environment effects on physical activity and active transport – an update and new findings on health equity (2017)The Built Environment as a Determinant of Physical Activity: A Systematic Review of Longitudinal Studies and Natural Experiments (2018)Urban planning and quality of life: A review of pathways linking the built environment to subjective well-being (2021)How derived is the demand for travel? Some conceptual and measurement considerations (2001)Travel and the Built Environment: A Synthesis (2001)Model-Dependent Variance Inflation Factor Cutoff Values (2002)Time-varying and non-linear associations between metro ridership and the built environment (2022)Travel demand and the 3Ds: Density, diversity, and design (1997)Travel and the Built Environment (2010)COVID-19 effects on property markets: The pandemic decreases the implicit price of metro accessibility (2022)Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis (2022)Using machine-learning models to understand nonlinear relationships between land use and travel (2023)What about people in Regional Science? (1970)Gis methods in time‐geographic research: geocomputation and geovisualization of human activity patterns (2004)Forecasting Chinese tourist volume with search engine data (2014)A Survey on Spatio-Temporal Big Data Analytics Ecosystem: Resource Management, Processing Platform, and Applications (2023)Capturing urban recreational hotspots from GPS data: A new framework in the lens of spatial heterogeneity (2023)Tracking tourist mobility in the big data era: insights from data, theory, and future directions (2024)In search of causality: a systematic review of the relationship between the built environment and physical activity among adults (2011)Destination and Route Attributes Associated with Adults’ Walking (2012)Connectivity and physical activity: using footpath networks to measure the walkability of built environments (2015)The effects of street environment features on road running: An analysis using crowdsourced fitness tracker data and machine learning (2023)How does visual environment affect outdoor jogging behavior? Insights from large-scale city images and GPS trajectories (2024)Applying mobile phone data to travel behaviour research: A literature review (2017)Big data and understanding change in the context of planning transport systems (2018)Strava Metro data for bicycle monitoring: a literature review (2020)Estimating daily bicycle counts with Strava data in rural and urban locations (2023)Gender differences in urban recreational running: A data-driven approach (2025)Uncovering spatial patterns of environmental influence on the paces of active leisure travel (2025)Local Indicators of Spatial Association—LISA (1995)Unraveling nonlinear and interaction effects of multilevel built environment features on outdoor jogging with explainable machine learning (2024)Coverage and bias of street view imagery in mapping the urban environment (2025)A review on the effects of physical built environment attributes on enhancing walking and cycling activity levels within residential neighborhoods (2015)Applying a random forest method approach to model travel mode choice behavior (2018)Crafting a jogging-friendly city: Harnessing big data to evaluate the runnability of urban streets (2024)Investigating urban mobility through multi-source public transportation data: A multiplex network perspective (2024)Nonlinear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree (2021)Examining the nonlinear relationship between neighborhood environment and residents' health (2024)Utilizing Multi-Source Geospatial Big Data to Examine How Environmental Factors Attract Outdoor Jogging Activities (2024)The 15-minute city for all? – Measuring individual and temporal variations in walking accessibility (2022)Exploring the spatiotemporal patterns and correlates of urban vitality: Temporal and spatial heterogeneity (2023)
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References

  • 2016Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study1,388 cited
  • 2021To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults462 cited
  • 2020Examining non-linear built environment effects on elderly’s walking: A random forest approach352 cited
  • 2021Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach250 cited
  • 2023Assessing impacts of objective features and subjective perceptions of street environment on running amount: A case study of Boston186 cited
  • 2023Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data176 cited
  • 2022Cluster analysis of microscopic spatio-temporal patterns of tourists’ movement behaviors in mountainous scenic areas using open GPS-trajectory data100 cited
  • 2023Bias and precision of crowdsourced recreational activity data from Strava97 cited
  • 2023Exploring nonlinear effects of built environment on jogging behavior using random forest96 cited
  • 2022Sustainable design of running friendly streets: Environmental exposures predict runnability by Volunteered Geographic Information and multilevel model approaches83 cited
  • 2022How Are Macro-Scale and Micro-Scale Built Environments Associated with Running Activity? The Application of Strava Data and Deep Learning in Inner London78 cited
  • 2023Examining the influence of neighborhood and street-level built environment on fitness jogging in Chengdu, China: A massive GPS trajectory data analysis

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71 cited
  • 2023Urban dynamics through the lens of human mobility67 cited
  • 2024Measuring the age-friendliness of streets' walking environment using multi-source big data: A case study in Shanghai, China62 cited
  • 2017Are buffers around home representative of physical activity spaces among adults?58 cited
  • 2019Where we ride faster? Examining cycling speed using smartphone GPS data54 cited
  • Cited by

    • 2025Effects of road greenery and sky openness on non-commuting cycling route choice: insights from online map navigation5 cited