Human mobility and environmental factors.
Human mobility, exemplified by low‐speed transportation, profoundly influences urban economics, vibrancy, and progression. With the enlarging climate change impact, a detailed exploration of the interplay between low‐speed transportation and environmental elements is crucial yet seldom investigated...
| Publicado en: | Journal of Industrial Ecology Vol. 29; no. 3; pp. 982 - 997 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=186458334&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 186458334 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10881980 FL1 jtl: Journal of Industrial Ecology issn: 10881980 maglogo: Y pubinfo: dt: Jun2025 vid: 29 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186458334 10.1111/jiec.70031 ppf: 982 ppct: 15 formats: tig: atl: Human mobility and environmental factors. aug: au: Li, Chao Keeley, Alexander Ryota Managi, Shunsuke affil: Urban Institute & School of Engineering, Kyushu University, Fukuoka, Japan su: Urban economics Cities & towns Air pressure Wind speed Machine learning sug: subj: Urban economics Cities & towns Air pressure Wind speed Machine learning keyword: GWPR human mobility low‐speed transportation machine learning OD data satellite data GWPR human mobility low‐speed transportation machine learning OD data satellite data ab: Human mobility, exemplified by low‐speed transportation, profoundly influences urban economics, vibrancy, and progression. With the enlarging climate change impact, a detailed exploration of the interplay between low‐speed transportation and environmental elements is crucial yet seldom investigated spatially. Harnessing high‐resolution origin–destination (OD) data with both spatial and temporal granularity, we construct an exceptionally precise predictive model with 88.29% accuracy. This model unravels the relationship leveraging environmental data from satellite data between January 2019 and December 2020. This study pioneers the use of Shapley additive explanation and geographically weighted panel regression to interpret extreme gradient boosting findings and spatial variability in OD data. Our analysis reveals an intriguing dichotomy: urban areas experience a decline in low‐speed transportation with rising temperatures, while a positive correlation surfaces in rural regions. On average, a 1°C increase in temperature correlates to a significant reduction of 466,851 person time in low‐speed transportation in Tokyo, Japan. We also identify that other environmental factors, including air pressure, wind speed, and so on, exert spatially varying impacts on low‐speed transportation. Altogether, this study furnishes robust empirical evidence of the environmental effects on human mobility, presenting valuable insights for academics, policymakers, and society at large. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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