Spatiotemporal Urban Evolution Along the China–Laos Railway in Laos Determined Using Multiple Sources of Remote-Sensed Landscape Indicators and Interpretable Machine Learning DOI Creative Commons
Dongxue Li, Jin Tang, Hu Qiao

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2094 - 2094

Published: Dec. 4, 2024

Constructing high-speed railways (HSRs) is critical for developing countries to stimulate economic growth and urbanization. This study focuses on the Lao section of China–Laos Railway (CLR) employs explicitly spatial remote sensing images investigate urban development surrounding HSR stations. Data-driven machine learning causal inference approaches are integrated quantify spatial–temporal evolution discover its driving factors. The results suggest that CLR has had positive spillover effects space. These have exhibited a distance attenuation pattern, reflecting obvious in 2D rather than 3D Meanwhile, stations adjacent city centers as well functional characteristics, such land use patterns industrialization level, significantly influences development. Specifically, industrial-dominated cities, changes been most significant under influence HSR. Change related industrial residential shown expansion increased utilization efficiency, urbanization primary drivers demand findings offer valuable insights references nations formulate implement management policies initiatives

Language: Английский

Spatiotemporal Urban Evolution Along the China–Laos Railway in Laos Determined Using Multiple Sources of Remote-Sensed Landscape Indicators and Interpretable Machine Learning DOI Creative Commons
Dongxue Li, Jin Tang, Hu Qiao

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2094 - 2094

Published: Dec. 4, 2024

Constructing high-speed railways (HSRs) is critical for developing countries to stimulate economic growth and urbanization. This study focuses on the Lao section of China–Laos Railway (CLR) employs explicitly spatial remote sensing images investigate urban development surrounding HSR stations. Data-driven machine learning causal inference approaches are integrated quantify spatial–temporal evolution discover its driving factors. The results suggest that CLR has had positive spillover effects space. These have exhibited a distance attenuation pattern, reflecting obvious in 2D rather than 3D Meanwhile, stations adjacent city centers as well functional characteristics, such land use patterns industrialization level, significantly influences development. Specifically, industrial-dominated cities, changes been most significant under influence HSR. Change related industrial residential shown expansion increased utilization efficiency, urbanization primary drivers demand findings offer valuable insights references nations formulate implement management policies initiatives

Language: Английский

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