Erratum to:Journal of Earth Science https://doi.org/10.1007/s12583-025-0187-4 The original version of this article unfortunately contained one mistake.The presentation in Page2384 was incorrect.The corrected one is gi...Erratum to:Journal of Earth Science https://doi.org/10.1007/s12583-025-0187-4 The original version of this article unfortunately contained one mistake.The presentation in Page2384 was incorrect.The corrected one is given below.The NTL loss ratio(Figure 4a)was calculated as the variation between pre-earthquake(March 27)and post-earthquake(March 28)radiance values in cloud-free areas.展开更多
Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like pop...Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like population and capital,which may not fully reflect the complexities of economic activities.Nighttime light(NTL)has been validated as an alternative indicator for regional economic development,yet limitations persist in its evaluation.This study integrates OpenStreetMap(OSM)data and NTL data,providing a novel data integration approach for evaluating economic development.The study uses mainland of China as a case,applying ordinary least squares(OLS)and geographically weighted regression(GWR)to evaluate OSM and NTL data across provincial,municipal,and county levels.It compares OSM,NTL,and their combined use,providing key empirical insights for enhancing data fusion models.The study results reveal:(1)NTL data is more accurate for provincial-level economic activity,while OSM data excels at the county level.(2)GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales.(3)Through the integration of both datasets,it is observed that,compared to single-data modeling,the performance is enhanced at the city scale and county scale.The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial,municipal,and county levels.The study offers a straightforward and efficient approach to data integration.The findings offer new research perspectives and scientific support for sustainable regional economic growth,particularly valuable in data-scarce,underdeveloped areas.展开更多
文摘Erratum to:Journal of Earth Science https://doi.org/10.1007/s12583-025-0187-4 The original version of this article unfortunately contained one mistake.The presentation in Page2384 was incorrect.The corrected one is given below.The NTL loss ratio(Figure 4a)was calculated as the variation between pre-earthquake(March 27)and post-earthquake(March 28)radiance values in cloud-free areas.
基金funded by The Third Comprehensive Scientific Investigation in Xinjiang(Grant No.2021xjkk1001)Program of National Social Science Foundation of China(Grant No.22BJL061)+1 种基金Major Project of Xinjiang Social Science Foundation(Grant No.21AZD008)The National Natural Science Foundation of China(Grant No.41461035).
文摘Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like population and capital,which may not fully reflect the complexities of economic activities.Nighttime light(NTL)has been validated as an alternative indicator for regional economic development,yet limitations persist in its evaluation.This study integrates OpenStreetMap(OSM)data and NTL data,providing a novel data integration approach for evaluating economic development.The study uses mainland of China as a case,applying ordinary least squares(OLS)and geographically weighted regression(GWR)to evaluate OSM and NTL data across provincial,municipal,and county levels.It compares OSM,NTL,and their combined use,providing key empirical insights for enhancing data fusion models.The study results reveal:(1)NTL data is more accurate for provincial-level economic activity,while OSM data excels at the county level.(2)GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales.(3)Through the integration of both datasets,it is observed that,compared to single-data modeling,the performance is enhanced at the city scale and county scale.The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial,municipal,and county levels.The study offers a straightforward and efficient approach to data integration.The findings offer new research perspectives and scientific support for sustainable regional economic growth,particularly valuable in data-scarce,underdeveloped areas.
