2019年暑期名家课程(一):Statistical Learning with Spatial Data 空间数据统计学习-澳门新葡亰平台9411-www.941.net-新葡萄京官网

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2019年暑期名家课程(一):Statistical Learning with Spatial Data 空间数据统计学习


主讲老师: 李斌 美国中密歇根大学地理系教授,曾任中密歇根大学地理系和地理信息科学中心主任,现任中密歇根大学科学技术学院中国事务院长助理、武汉大学讲座教授和国际地学计算联合中心副主任、中密歇根大学-江西师大中美大湖流域联合研究中心执行主任、华南师大特聘教授。

 

助教:龚君芳 18971626856

 

时间:2019520-524日 (23日下午,其他日期上午)

520日 上午900-1200

521日 上午900-1200

522日 上午900-1200

523日 下午1430-1730

524日 上午900-1200

 

地点:信息楼201

 

要求:自带笔记本电脑,安装Rhttp://www.r-project.org/)和ESF Toolhttp://thesaar.github.io/

 

课程简介:

This course introduces the methods and techniques for performing regression modeling with geographically reference data. The first part of the course reviews linear regression and Generalized Linear Regression, through which the importance of geographic effects is illustrated. The second part introduces implementations of spatial models for both linear models and GLM, which focuses on the Spatial Autoregressive Models and Eigenvector Spatial Filtering. The course concludes with real world applications that demonstrate concepts and procedures of conducting spatial regression modeling. The class will be taught with a combination of lecture and real time demonstrations. Software R and ESF Tool will be used for labs and demonstration. Students are expected to have backgrounds in linear algebra and introductory statistics.

 

Below is the list of the tentative topics.

Day 1 Introduction to statistical learning; exploratory data analysis; linear regression

Day 2 Generalized Linear Regression

Day 3 Spatial autoregressive models

Day 4 Eigenvector spatial filtering

Day 5 Applications

 

The following books are used as references.

James, et al., An Introduction to Statistical Learning, Springer. http://www-bcf.usc.edu/~gareth/ISL/

Smith and Goodchild, Geospatial Analysis. http://www.spatialanalysisonline.com/HTML/index.html

Chun and Griffith, 2013. Spatial Statistics & Geostatistics, Sage.


说明:

1、研究生以在研究生选课系统中选课为准,可获得学分;

2、本科生直接发邮件至助教老师邮箱(25971743@qq.com)报名,报名信息包括:姓名+班级+学号+联系电话+QQ号,报名截止时间:51917:00。完成课程且通过课程考核者获得创新创业学分。