Nonlinear and threshold effects of the built environment, road vehicles and air pollution on urban vitality

Landscape and Urban Planning, Journal Year: 2024, Volume and Issue: 253, P. 105204 - 105204

Published: Sept. 19, 2024

Language: Английский

---

Spatiotemporal variations of private e-bike trips with explainable data-driven technologies

Cities, Journal Year: 2025, Volume and Issue: 158, P. 105712 - 105712

Published: Jan. 6, 2025

Language: Английский

---

Crafting a jogging-friendly city: Harnessing big data to evaluate the runnability of urban streets

Journal of Transport Geography, Journal Year: 2024, Volume and Issue: 121, P. 104015 - 104015

Published: Oct. 1, 2024

Language: Английский

---

Utilizing Multi-Source Geospatial Big Data to Examine How Environmental Factors Attract Outdoor Jogging Activities

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 3056 - 3056

Published: Aug. 20, 2024

In the post-pandemic era, outdoor jogging has become an increasingly popular form of exercise due to growing emphasis on health. It is essential comprehensively analyze factors influencing spatial distribution activities and propose planning strategies with practical guidance. Using multi-source geospatial big data multiple models, this study constructs a comprehensive analytical framework examine association between environmental variables frequency in Guangzhou. Firstly, trajectory were collected from fitness app, potential selected based perspectives built environment, street perception, natural environment. For example, using street-view imagery, objective elements such as greenery subjective safety perception extracted human-centric perspective. Secondly, included three models: backward stepwise regression, optimal parameters-based geographical detector, geographically weighted regression (GWR) model. These models served, screen significant variables, identify synergistic effects among quantify heterogeneity effects, respectively. Finally, area was clustered results GWR model urban clear positions significance. The indicated following: (1) Factors related environment significantly influence distribution. (2) Public sports facilities, level greenery, identified key activities, representing aspects service (3) Specifically, each factor displayed variation. instance, facilities positively correlated city center. (4) Lastly, divided into four clusters, different local associative characteristics activities. zonal recommendations have implications for planners policymakers aiming create jogging-friendly environments.

Language: Английский

---

A cross-scale indicator framework for the study of annual stability of land surface temperature in different land uses

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 105936 - 105936

Published: Oct. 1, 2024

Language: Английский

---

Disentangling the non-linear relationships and interaction effects of urban digital transformation on carbon emission intensity

Urban Climate, Journal Year: 2025, Volume and Issue: 59, P. 102283 - 102283

Published: Jan. 5, 2025

Language: Английский

---

What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: Jan. 23, 2025

Introduction This study, based on Volunteered Geographic Information (VGI) and multi-source data, aims to construct an interpretable macro-scale analytical framework explore the factors influencing urban physical activities. Using 290 prefecture-level cities in China as samples, it investigates impact of socioeconomic, geographical, built environment both overall activity levels specific types mobile Methods Machine learning methods were employed analyze data systematically. Socioeconomic, indicators used explanatory variables examine their influence willingness intensity across different activities (e.g., running, walking, cycling). Interaction effects non-linear patterns also assessed. Results The study identified three key findings: (1) A significant difference exists between intensity. Socioeconomic primarily drive willingness, whereas geographical have a stronger (2) vary significantly by type. Low-threshold walking) tend amplify promotional inhibitory factors. (3) Some display typical effects, consistent with findings from micro-scale studies. Discussion provide comprehensive theoretical support for understanding optimizing among residents. Based these results, proposes guideline-based macro-level intervention strategies aimed at improving through effective public resource allocation. These can assist policymakers developing more scientific targeted approaches promote activity.

Language: Английский

---

Nonlinear and spatial non-stationary effects of land finance on urban expansion at the county level in China: Insights from explainable spatial machine learning

Cities, Journal Year: 2025, Volume and Issue: 160, P. 105850 - 105850

Published: Feb. 28, 2025

Language: Английский

---

Environmental determinants of dynamic jogging patterns: Insights from trajectory big data analysis and interpretable machine learning

Applied Geography, Journal Year: 2025, Volume and Issue: 178, P. 103596 - 103596

Published: March 14, 2025

Language: Английский

---

Exploring the factors influencing visits to urban parks: A case study of Beijing's central urban area

Applied Geography, Journal Year: 2025, Volume and Issue: 178, P. 103613 - 103613

Published: April 3, 2025

Language: Английский

---