How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China
Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional dis...
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MDPI AG
2024-12-01
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| author | Chunzhu Wei Xufeng Liu Wei Chen Lupan Zhang Ruixia Chao Wei Wei |
| author_facet | Chunzhu Wei Xufeng Liu Wei Chen Lupan Zhang Ruixia Chao Wei Wei |
| author_sort | Chunzhu Wei |
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| description | Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various levels. This study thus employed five advanced multiscale geographically and temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, and STWR—to analyze the spatio-temporal relationships between ten key conventional socio-economic indicators and per capita GDP across different administrative levels in China from 2000 to 2019. The findings highlight a consistent increase in regional disparities, with secondary industry emerging as a dominant driver of long-term economic inequality among the indicators analyzed. While a clear inland-to-coastal gradient underscores the persistence of regional disparity determinants, areas with greater economic disparities exhibit pronounced spatio-temporal heterogeneity. Among the models, STWR outperforms others in capturing and interpreting local variations in spatio-temporal disparities, demonstrating its utility in understanding complex regional dynamics. This study provides novel insights into the spatio-temporal determinants of regional economic disparities, offering a robust analytical framework for policymakers to address region-specific variables driving inequality over time and space. These insights contribute to the development of targeted and dynamic policies for promoting balanced and sustainable regional growth. |
| format | Article |
| id | doaj-art-94a4e6d36ce54ebcb9ac9852bb9d9ff4 |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-12-01 |
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| series | Land |
| spelling | doaj-art-94a4e6d36ce54ebcb9ac9852bb9d9ff42025-01-24T13:37:44ZengMDPI AGLand2073-445X2024-12-011415910.3390/land14010059How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in ChinaChunzhu Wei0Xufeng Liu1Wei Chen2Lupan Zhang3Ruixia Chao4Wei Wei5School of Geography and Planning, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geography and Planning, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geography and Planning, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geography and Planning, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geography and Planning, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Economic, Guangxi University for Nationalities, Nanning 530006, ChinaRapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various levels. This study thus employed five advanced multiscale geographically and temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, and STWR—to analyze the spatio-temporal relationships between ten key conventional socio-economic indicators and per capita GDP across different administrative levels in China from 2000 to 2019. The findings highlight a consistent increase in regional disparities, with secondary industry emerging as a dominant driver of long-term economic inequality among the indicators analyzed. While a clear inland-to-coastal gradient underscores the persistence of regional disparity determinants, areas with greater economic disparities exhibit pronounced spatio-temporal heterogeneity. Among the models, STWR outperforms others in capturing and interpreting local variations in spatio-temporal disparities, demonstrating its utility in understanding complex regional dynamics. This study provides novel insights into the spatio-temporal determinants of regional economic disparities, offering a robust analytical framework for policymakers to address region-specific variables driving inequality over time and space. These insights contribute to the development of targeted and dynamic policies for promoting balanced and sustainable regional growth.https://www.mdpi.com/2073-445X/14/1/59social disparityspatio-temporal non-stationarityspatial and temporal kernelsspatio-temporal patternsgeographically and temporally weighted regression models |
| spellingShingle | Chunzhu Wei Xufeng Liu Wei Chen Lupan Zhang Ruixia Chao Wei Wei How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China Land social disparity spatio-temporal non-stationarity spatial and temporal kernels spatio-temporal patterns geographically and temporally weighted regression models |
| title | How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China |
| title_full | How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China |
| title_fullStr | How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China |
| title_full_unstemmed | How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China |
| title_short | How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China |
| title_sort | how do temporal and geographical kernels differ in reflecting regional disparities insights from a case study in china |
| topic | social disparity spatio-temporal non-stationarity spatial and temporal kernels spatio-temporal patterns geographically and temporally weighted regression models |
| url | https://www.mdpi.com/2073-445X/14/1/59 |
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