机器学习模型和 SHAP 值揭示的地理空间特征与 COVID-19 住院率之间的关系

IF 3.7 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

International Journal of Digital Earth Pub Date : 2024-05-27 DOI:10.1080/17538947.2024.2358851

Lixia Chu, Jeroen Nelen, Alessandro Crivellari, Dainius Masiliūnas, Carola Hein, Christoph Lofi

引用次数: 0

摘要

揭示地理空间特征与 COVID-19 特征之间的关系是一个综合性、复杂性、跨学科和具有挑战性的课题，因为 COVID-19 的传播和影响与地理空间特征之间的关系密切相关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Relationships between geo-spatial features and COVID-19 hospitalisations revealed by machine learning models and SHAP values

Uncovering relationships between geospatial features and COVID-19 features is a comprehensive, confounding, cross-disciplinary and challenging topic, as the spread and effects of COVID-19 are relat...

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来源期刊

International Journal of Digital Earth 地学-遥感

CiteScore

6.50

自引率

3.90%

发文量

审稿时长

3 months

期刊介绍： The International Journal of Digital Earth is a response to this initiative. This peer-reviewed academic journal (SCI-E) focuses on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world. The journal encourages papers that: Progress visions for Digital Earth frameworks, policies, and standards; Explore geographically referenced 3D, 4D, or 5D models to represent the real planet, and geo-data-intensive science and discovery; Develop methods that turn all forms of geo-referenced data, from scientific to social, into useful information that can be analyzed, visualized, and shared; Present innovative, operational applications and pilots of Digital Earth technologies at a local, national, regional, and global level; Expand the role of Digital Earth in the fields of Earth science, including climate change, adaptation and health related issues,natural disasters, new energy sources, agricultural and food security, and urban planning; Foster the use of web-based public-domain platforms, social networks, and location-based services for the sharing of digital data, models, and information about the virtual Earth; and Explore the role of social media and citizen-provided data in generating geo-referenced information in the spatial sciences and technologies.