空間統計

Spatial Statistics

學期

111-2

學分

3 學分

當期課號

536902

永久課號

SCIS30041

開課單位

統計學研究所

授課教師

黃信誠

類別

選修

上課時間表

節	週二
2 09:00–09:50	空間統計 A406 3 節連堂
3 10:10–11:00
4 11:10–12:00

* 根據陽明交大上課時間表所列

概述

This course is intended to provide a basic understanding of statistical methods for analyzing spatial data. The course will cover theory and methods developed for the three major branches of spatial statistics: point-referenced data, areal/lattice data, and point pattern data. Additionally, students will gain hands-on experience in the computational aspects of spatial statistics by using R for analyzing data.

先修科目

Elementary probability, linear models, and R language.

教學方式

參考書 • Bivand, R. S., Pebesma, E., and Gomez-Rubio, V. (2013). Applied spatial data analysis with R, second edition, Springer, NY (download: https://link.springer.com/book/10.1007%2F978-1-4614-7618-4). • Cressie, N. (1993). Statistics for Spatial Data, revised edition. Wiley, New York. • Gaetan, C. and Guyon, X. (2010). Spatial Statistics and Modeling, Springer, New York. • Wikle, C.K., Zammit-Mangion, A., and Cressie, N. (2019). Spatio-Temporal Statistics with R, Chapman & Hall/CRC, Boca Raton, FL (download: https://spacetimewithr.org/). 資料分析競賽 2023 KAUST Competition on Spatial Statistics for Large Datasets (Website: https://cemse.kaust.edu.sa/stsds/news/2023-kaust-competition-spatial-statistics-large-datasets).

評分方式

1. Homework (45%): There will be a series of 5-6 biweekly homework assignments. 2. Spatial data analysis/competition on spatial statistics for large datasets (20%). 3. Project (35%): The final project for this course can take the form of: (1) A comprehensive analysis of a spatial or spatial-temporal data set; (2) An in-depth literature review on a specific topic; (3) A comprehensive introduction with a referee report on a recent publication; (4) An extensive simulation experiment that explores various spatial methodologies. It is mandatory to submit a one-page description of your proposed project, along with a written report and an oral presentation.

週次計畫

週次	主題
第 1 週	Introduction
第 2 週	Point-referenced data
第 3 週	Holiday
第 4 週	Point-referenced data
第 5 週	Point-referenced data
第 6 週	Point-referenced data
第 7 週	Areal/lattice data
第 8 週	Holiday
第 9 週	Areal/lattice data
第 10 週	Point pattern data
第 11 週	Point pattern data
第 12 週	Spatio-temporal data
第 13 週	Spatio-temporal data
第 14 週	Computer experiments
第 15 週	Frequency domain methods
第 16 週	Special topics
第 17 週	Project presentation
第 18 週

教科書

1. Banerjee, S., Carlin, B. P., and Gelfand. A. E. (2014). Hierarchical Modeling and Analysis for Spatial Data, second edition, CRC Press, New York. 2. Handbook of Spatial Statistics (2010), edited by Gelfand, P. J. Diggle, M. Fuentes, P. Guttorp, Chapman & Hall/CRC (download: https://doi.org/10.1201/9781420072884).

Office Hours

地點: Room 424
時間: Tuesday 1:00-3:00 pm (Room 424) or by appointment.
聯絡方式: Email: hchuang@stat.sinica.edu.tw