SpatioTemporal Random Forest and SpatioTemporal Stacking Tree: A novel spatially explicit ensemble learning approach to modeling non-linearity in spatiotemporal non-stationarity DOI Creative Commons
Yun Luo, Shiliang Su

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 136, P. 104315 - 104315

Published: Dec. 12, 2024

Language: Английский

A multi-constraint Monte Carlo Simulation approach to downscaling cancer data DOI
Lingbo Liu,

Lauren Cowan,

Fahui Wang

et al.

Health & Place, Journal Year: 2025, Volume and Issue: 91, P. 103411 - 103411

Published: Jan. 1, 2025

Language: Английский

Citations

1
Visual programming-based Geospatial Cyberinfrastructure for open-source GIS education 3.0 DOI
Lingbo Liu, Weihe Wendy Guan, Fahui Wang

et al.

Cartography and Geographic Information Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: Feb. 24, 2025

Language: Английский

Citations

0
Research on the Nonlinear and Interactive Effects of Multidimensional Influencing Factors on Urban Innovation Cooperation: A Method Based on an Explainable Machine Learning Model DOI Creative Commons

Rui Wang,

Xingping Wang,

Zhonghu Zhang

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(3), P. 187 - 187

Published: March 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.

Language: Английский

Citations

0
XAI in geographic analysis of innovation: Evaluating proximity factors in the innovation networks of Chinese technology companies through web-based data DOI
Chenxi Liu,

Zhenghong Peng,

Lingbo Liu

et al.

Applied Geography, Journal Year: 2024, Volume and Issue: 171, P. 103373 - 103373

Published: Aug. 10, 2024

Language: Английский

Citations

3
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 DOI Creative Commons

Dandan Shao,

Kyungjin Zoh,

Yanfeng Xie

et al.

Heritage Science, Journal Year: 2024, Volume and Issue: 12(1)

Published: Nov. 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.

Language: Английский

Citations

3
SpatioTemporal Random Forest and SpatioTemporal Stacking Tree: A novel spatially explicit ensemble learning approach to modeling non-linearity in spatiotemporal non-stationarity DOI Creative Commons
Yun Luo, Shiliang Su

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 136, P. 104315 - 104315

Published: Dec. 12, 2024

Language: Английский

Citations

3