A multi-constraint Monte Carlo Simulation approach to downscaling cancer data

Health & Place, Год журнала: 2025, Номер 91, С. 103411 - 103411

Опубликована: Янв. 1, 2025

Язык: Английский

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The spatial differentiation mechanism of intangible cultural heritage and its integration with tourism development based on explainable machine learning and coupled coordination models: a case study of the Jiang-Zhe-Hu in China

Heritage Science, Год журнала: 2024, Номер 12(1)

Опубликована: Ноя. 27, 2024

Abstract As a vital carrier of traditional culture, Intangible Cultural Heritage (ICH) not only preserves historical value but also fosters cultural identity and confidence. This study utilizes explainable machine learning coupled coordination models to analyze the spatial distribution formation mechanisms ICH resources in Jiangsu-Zhejiang-Shanghai (Jiang-Zhe-Hu). The results indicate that (1) Jiang-Zhe-Hu exhibit clustered pattern characterized by “three primary cores two secondary cores.” core areas are Shanghai, Hangzhou, Suzhou, while Yangzhou Nanjing. (2) Population, number religious places, GDP have significant positive impact on Jiang-Zhe-Hu. NDVI road mileage relatively minor effects distribution. (3) In terms resources, Zhejiang Province overall has higher level than Jiangsu Province, with Lishui having highest evaluation most abundant resources. Regarding tourism industry development, Shanghai comprehensive value, followed Nanjing, Wuxi, Changzhou, all which high levels development. (4) According model analysis, demonstrates best coupling degree between industry, achieving good level. integration is better southern (e.g., Suzhou), there still imbalances development northern. an compared Jiangsu, more balanced However, room for improvement deep industry.

Язык: Английский

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SpatioTemporal Random Forest and SpatioTemporal Stacking Tree: A novel spatially explicit ensemble learning approach to modeling non-linearity in spatiotemporal non-stationarity

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 136, С. 104315 - 104315

Опубликована: Дек. 12, 2024

Язык: Английский

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Research on the Nonlinear and Interactive Effects of Multidimensional Influencing Factors on Urban Innovation Cooperation: A Method Based on an Explainable Machine Learning Model

Systems, Год журнала: 2025, Номер 13(3), С. 187 - 187

Опубликована: Март 7, 2025

Within globalization, the significance of urban innovation cooperation has become increasingly evident. However, faces challenges due to various factors—social, economic, and spatial—making it difficult for traditional methods uncover intricate nonlinear relationships among them. Consequently, this research concentrates on cities within Yangtze River Delta region, employing an explainable machine learning model that integrates eXtreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs) investigate interactive effects multidimensional factors impacting cooperation. The findings indicate XGBoost outperforms LR, SVR, RF, GBDT in terms accuracy effectiveness. Key results are summarized as follows: (1) Urban exhibits different phased characteristics. (2) There exist between factors, them, Scientific Technological dimension contributes most (30.59%) significant positive promoting effect later stage after surpassing a certain threshold. In Social Economic (23.61%), number Internet Users (IU) individually. Physical Space (20.46%) generally mutation points during early stages development, with overall predominantly characterized by trends. (3) Through application PDP, is further determined IU synergistic per capita Foreign Direct Investment (FDI), public library collections (LC), city night light data (NPP), while exhibiting negative antagonistic Average Annual Wage Staff (AAS) Enterprises above Designated Size Industry (EDS). (4) For at developmental stages, tailored development proposals should be formulated based single-factor contribution multifactor interaction effects. These insights enhance our understanding elucidate influencing factors.

Язык: Английский

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XAI in geographic analysis of innovation: Evaluating proximity factors in the innovation networks of Chinese technology companies through web-based data

Applied Geography, Год журнала: 2024, Номер 171, С. 103373 - 103373

Опубликована: Авг. 10, 2024

Язык: Английский

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Visual programming-based Geospatial Cyberinfrastructure for open-source GIS education 3.0

Cartography and Geographic Information Science, Год журнала: 2025, Номер unknown, С. 1 - 13

Опубликована: Фев. 24, 2025

Язык: Английский

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