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机译:用于建模时空非固定关系的地理上和时间神经网络加权回归
Zhejiang Univ Sch Earth Sci Hangzhou Peoples R China|Zhejiang Prov Key Lab Geog Informat Sci Hangzhou Peoples R China;
Zhejiang Univ Sch Earth Sci Hangzhou Peoples R China;
Zhejiang Univ Sch Earth Sci Hangzhou Peoples R China|Zhejiang Prov Key Lab Geog Informat Sci Hangzhou Peoples R China;
Chinese Univ Hong Kong Dept Geog & Resource Management Shatin Hong Kong Peoples R China|Chinese Univ Hong Kong Inst Space & Earth Informat Sci Shatin Hong Kong Peoples R China;
Zhejiang Univ Sch Earth Sci Hangzhou Peoples R China|Zhejiang Prov Key Lab Geog Informat Sci Hangzhou Peoples R China;
Zhejiang Univ Sch Earth Sci Hangzhou Peoples R China|Zhejiang Prov Key Lab Geog Informat Sci Hangzhou Peoples R China;
Spatiotemporal non-stationary relationship; spatiotemporal non-stationarity; geographically and temporally weighted regression; spatiotemporal proximity neural network; geographically and temporally neural network weighted regression;
机译:用地理和时间加权回归模型模拟人口分布的时空关系
机译:使用地理上和时间加权回归模拟Covid-19传输与人口流动性之间的时空关联
机译:气候因素对儿童手,脚和口腔疾病的时空效应:用混合地理和颞加权回归模型进行案例研究
机译:探索时尚变化的回归关系:地理加权面板回归分析
机译:建模每日温度范围与城市土地覆盖之间的关系:地理加权回归方法
机译:使用地理上和时间加权回归模拟Covid-19传输与人口流动性之间的时空关联
机译:基于地理和临时加权回归的时空变形建模方法