掌握地理加权回归：建立稳健模型的关键因素。

IF 1 4区医学 Q4 HEALTH CARE SCIENCES & SERVICES Geospatial Health Pub Date : 2024-02-29 DOI:10.4081/gh.2024.1271

Behzad Kiani, Benn Sartorius, Colleen L Lau, Robert Bergquist

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引用次数: 0

摘要

地理加权回归（GWR）在空间回归分析中发挥着重要作用，为地理景观中各种变量之间错综复杂的相互作用提供了一个细致入微的视角（Brunsdon 等人，1998 年）。不过，采用 GWR 必须有充分的理由，既可以作为非空间（全球）回归模型的补充，也可以作为其辅助分析（Kiani、Mamiya 等人，2023 年）。此外，适当选择带宽、加权函数或核类型以及变量选择构成了 GWR 分析中最关键的配置（Wheeler，2021 年）。[...].

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Mastering geographically weighted regression: key considerations for building a robust model.

Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...].

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来源期刊

Geospatial Health 医学-公共卫生、环境卫生与职业卫生

CiteScore

2.40

自引率

11.80%

发文量

审稿时长

12 months

期刊介绍： The focus of the journal is on all aspects of the application of geographical information systems, remote sensing, global positioning systems, spatial statistics and other geospatial tools in human and veterinary health. The journal publishes two issues per year.