作者: 时间:2019-05-17 点击数:



课程名称:空间数据统计学习(Statistical Learning with Spatial Data)


助教:龚君芳 18971626856

时间:2019年5月20日-5月24日 (23日下午,其他日期上午)

5月20日 上午9:00-12:00

5月21日 上午9:00-12:00

5月22日 上午9:00-12:00

5月23日 下午14:30-17:30

5月24日 上午9:00-12:00


要求:自带笔记本电脑,安装R(https://www.r-project.org/)和ESF Tool(https://thesaar.github.io/


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.  https://www.spatialanalysisonline.com/HTML/index.html

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