How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China DOI Creative Commons
Chunzhu Wei, Xufeng Liu, Wei Chen

и другие.

Land, Год журнала: 2024, Номер 14(1), С. 59 - 59

Опубликована: Дек. 31, 2024

Rapid economic growth in China has brought about a significant challenge: the widening gap regional development. Addressing this disparity is crucial for ensuring sustainable However, existing studies have largely overlooked intrinsic spatial and temporal dynamics of disparities on various levels. This study thus employed five advanced multiscale geographically temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, STWR—to analyze spatio-temporal relationships between ten key conventional socio-economic indicators per capita GDP across different administrative levels from 2000 to 2019. The findings highlight consistent increase disparities, with secondary industry emerging as dominant driver long-term inequality among analyzed. While clear inland-to-coastal gradient underscores persistence determinants, areas greater exhibit pronounced heterogeneity. Among models, STWR outperforms others capturing interpreting local variations demonstrating its utility understanding complex dynamics. provides novel insights into determinants offering robust analytical framework policymakers address region-specific variables driving over time space. These contribute development targeted dynamic policies promoting balanced growth.

Язык: Английский

How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China DOI Creative Commons
Chunzhu Wei, Xufeng Liu, Wei Chen

и другие.

Land, Год журнала: 2024, Номер 14(1), С. 59 - 59

Опубликована: Дек. 31, 2024

Rapid economic growth in China has brought about a significant challenge: the widening gap regional development. Addressing this disparity is crucial for ensuring sustainable However, existing studies have largely overlooked intrinsic spatial and temporal dynamics of disparities on various levels. This study thus employed five advanced multiscale geographically temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, STWR—to analyze spatio-temporal relationships between ten key conventional socio-economic indicators per capita GDP across different administrative levels from 2000 to 2019. The findings highlight consistent increase disparities, with secondary industry emerging as dominant driver long-term inequality among analyzed. While clear inland-to-coastal gradient underscores persistence determinants, areas greater exhibit pronounced heterogeneity. Among models, STWR outperforms others capturing interpreting local variations demonstrating its utility understanding complex dynamics. provides novel insights into determinants offering robust analytical framework policymakers address region-specific variables driving over time space. These contribute development targeted dynamic policies promoting balanced growth.

Язык: Английский

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