Do urban park spatial features influence public emotional responses during jogging? Evidence from social media data

Journal of Outdoor Recreation and Tourism, Journal Year: 2025, Volume and Issue: 50, P. 100864 - 100864

Published: Feb. 26, 2025

Language: Английский

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A Study on the Impact of a Community Green Space Built Environment on Physical Activity in Older People from a Health Perspective: A Case Study of Qingshan District, Wuhan

Sustainability, Journal Year: 2025, Volume and Issue: 17(1), P. 263 - 263

Published: Jan. 2, 2025

(1) Background: In the context of global population aging, how to enhance health older people has become a focus attention in various fields. Although it is widely recognized that effects urban green space built environments on physical activity can substantially improve people, few studies have been conducted understand relationship between spaces, activity, and at community level. This research gap key issue hindering sustainable development among elderly. (2) Methods: study used survey data from 1989 elderly individuals Qingshan District, Wuhan, applied multiple linear regression models explore overall intensity as well with low, moderate, high-intensity levels. (3) Results: The results show education level, income status, companionship, view index, road cleanliness, fitness facilities are positively correlated while gender, age, self-assessed psychological stress, intersection density negatively it. Companionship, recreational low-intensity levels elderly, them. cleanliness moderate-intensity gender For activities, level correlated, correlated. (4) Conclusions: Future could expand sample size incorporating more longitudinal designs, types influencing factors, conduct detailed classifications, carry out broader collection procedures comprehensively analyze environment providing stronger scientific basis for formulation healthy city policies.

Language: Английский

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Unraveling nonlinear and spatial non-stationary effects of urban form on surface urban heat islands using explainable spatial machine learning

Computers Environment and Urban Systems, Journal Year: 2024, Volume and Issue: 114, P. 102200 - 102200

Published: Oct. 4, 2024

Language: Английский

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Examining the nonlinear relationship between neighborhood environment and residents' health

Cities, Journal Year: 2024, Volume and Issue: 152, P. 105213 - 105213

Published: June 21, 2024

Language: Английский

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Interpretable spatial machine learning insights into urban sanitation challenges: A case study of human feces distribution in San Francisco

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 113, P. 105695 - 105695

Published: July 27, 2024

Urban sanitation is critical for public health, with the management of human feces presenting significant challenges in growing urban areas. While prior research has concentrated on health impacts fecal contaminants, spatial distribution and determinants open defecation contexts have received less attention. To address these gaps, this study proposed an interpretable machine learning framework integrating Geographically Weighted Random Forest (GW-RF) SHapley Additive exPlanations (SHAP) analysis to reveal complex heterogeneity factors influencing density cities, taking San Francisco as a case study. Our findings highlight that homelessness, population density, building are drivers distribution. Importantly, higher restroom was linked increased underscoring need planning focus improving accessibility rather than merely increasing their number. Additionally, our suggests green spaces serve mitigating factor, indicating enhancing greenery could be effective strategy addressing challenges. This not only offers insights into Francisco's but also provides practical implications development strategies globally, advocating targeted, evidence-based interventions foster healthier more sustainable cities.

Language: Английский

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Deciphering hot routes in urban parks: the impact of environmental factors on physical activity amount, intensity and diversity

Urban forestry & urban greening, Journal Year: 2025, Volume and Issue: unknown, P. 128684 - 128684

Published: Jan. 1, 2025

Language: Английский

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Investigating 2D/3D factors influencing surface urban heat islands in mountainous cities using explainable machine learning

Urban Climate, Journal Year: 2025, Volume and Issue: 59, P. 102325 - 102325

Published: Jan. 30, 2025

Language: Английский

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Urban Spatial Heat Resilience Indicator Based on Running Activity Z-Score

Urban Science, Journal Year: 2025, Volume and Issue: 9(2), P. 34 - 34

Published: Feb. 5, 2025

The assessment of urban heat resilience has become crucial due to increasing extreme weather events. This study introduces the Running Activity Z-score (RAZ) index based on running activity trajectory data evaluate resilience. Through a case an August 2022 heatwave in Beijing, we examined index’s sensitivity and explored its spatial relationships with key built environment factors, including plot ratio, green coverage, population density, blue space proximity. Our results reveal two findings: (1) RAZ serves as effective real-time, high-precision indicator impacts, evidenced by extremely low values consistently coinciding periods, (2) offers valuable insights for identifying potential areas supporting planning decisions, demonstrated significant correlations factors that align previous studies while uncovering more detailed relationships. Although effectively complements traditional measurement methods, application requires careful consideration external such social dynamics climate variability.

Language: Английский

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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

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(2), P. 80 - 80

Published: Feb. 13, 2025

Outdoor jogging plays a critical role in active mobility and transport-related physical activity (TPA), contributing to both urban health sustainability. While existing studies have primarily focused on participation volumes through survey data, they often overlook the real-time dynamics that shape experiences. This study seeks provide data-driven analysis of volume speed, exploring how environmental factors influence these behaviors. Utilizing dataset over 1000 crowd-sourced trajectories Shenzhen, we spatially linked road-section-level units map distribution average speed. By depicting bivariate behavioral characteristics, identified spatial patterns behavior, elucidating variations A random forest regression model was validated employed capture nonlinear relationships assess differential impacts various The results reveal distinct across city, where is shaped by mixed interplay natural, visual, built environment factors, while speed influenced visual factors. Additionally, highlights effects, particularly identifying threshold beyond which incremental improvements diminishing returns These findings clarify roles influencing offering insights into mobility. Ultimately, this provides data-informed implications for planners seeking create environments support TPA promote lifestyles.

Language: Английский

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Mitigating urban heat island through urban-rural transition zone landscape configuration: Evaluation based on an interpretable ensemble machine learning framework

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: 123, P. 106272 - 106272

Published: March 10, 2025

Language: Английский

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