Introduction to
Analysis of Spatial & Temporal Data
For B* Students
(Code: TBD)
Lecture 1
Nguyen-Xuan Thanh
(nguyen-xuan.thanh@usth.edu.vn)
December - 2023
Introduction
Personal Information
Personal Home Page
http://remosat.usth.edu.vn/~thanhnx/
•Full name:
NGUYEN XUAN THANH
•PhD in:
Environmental Science, Policy and
Management
The Hong Kong University of Science
and Techonology (HKUST)
•Email:
nguyen-xuan.thanh@usth.edu.vn
nxthanhnx@gmail.com
•Mobile:
+84 (0) 375 713 523
2
References
❖ The main contents are modified based on:
❖ R. S. Bivand et al. Applied Spatial Data Analysis with R.
[https://link.springer.com/book/10.1007/978-1-4614-7618-4] 2nd edition. Springer
2013.Book 2, Elsevier 2000
❖ S. Banerjee, B. P. Carlin, and A. E. Gelfand. Hierarchical Modeling and Analysis for
Spatial Data.
[https://www.taylorfrancis.com/books/mono/10.1201/9780203487808/hierarchical-m
odeling-analysis-spatial-data-sudipto-banerjee-bradley-carlin-alan-gelfand-sudipto-ba
nerjee]
❖ Other(s): TBD after the first class
3
4
Objectives
1.
Understand the basic knowledge of spatial and temporal data
2.
To answer questions: How to extract the information, distribution
and trend of objects from spatial-temporal data?
3.
To answer questions: What are the popular models using in spatial
and temporal analysis? What are the applications of these models for
monitoring environment and earth sciences and how to manipulate
the models in
4.
R?
To answer questions: How to evaluate the spatio-temporal
statistical models using referent data?
5
Time Commitment
Component
Attendance
Lecture
20 hours
Tutorial/Exercises
?
Practical/Labwork
16 hours
Total
36 hours
❖ 12 classes in total
❖ 2 classes per week for 6 weeks (19/12/2023 – 26/02/2024)
❖ Personal computers are more welcome
6
Assessment Scheme & Course Policies
Component
Attendance
Percentage
(%)
10 (10)
Exercise
Practical
Reports
Mid-term
Final Exam
10 (20)
0
30 (20)
50
❖ Students can contact to the instructor in case of any concern or
problem via email address and/or mobile phone:
nguyen-xuan.thanh@usth.edu.vn ; +84 (0) 375 713 523
❖ Respect yourself and the others
❖ Honesty
❖ On time (15-min rule)
❖ 4 absences without justification
no final exam, no retake
7
Assessment Scheme & Course Policies
Component
Attendance
Exercise
Practical
Reports
Mid-term
Final Exam
Percentage
(%)
10
20
20
0
0
50
❖ Exercise & Practical:
Problem Sets:
Solved by using R
Rstudio or VS Code is highly recommended
Read & Summary Paper
❖ Final Exam:
TBD
8
Main Contents
Chapter &
Contents
Contents
Lecture
hours
Practice
hours
Chapter 1
Introduction
−
−
−
−
Types of data, geographic data
Structure of geo-referenced vector data and raster data
Collection of temporal and spatial data.
Preparation of data
2
1
Chapter 2
Time series
−
Classification,
components,
decomposition of time series
stationarity,
3
1
Parametric approach
Linear Regression
Non-Parametric approach – Turning Point test, Man-Kendall
Test, Pre Whitened Mann Kendall test, Theil and Sen’s Median
Slope
3
3
Chapter 3
−
Analysis for trend −
detection and
−
slope estimation
concept
of
9
Main Contents
Chapter &
Contents
Contents
Lecture
hours
Practice
hours
Chapter 4
Point processes
frequency
analysis
−
−
−
−
−
−
Poisson processes
Second-order interactions
Spectral analysis
Time-frequency analysis
Non-negative matrix factorization
Spatio-temporal point processes
4
3
Chapter 5
Analysis of
geo-reference
multi-spectral
data
−
−
−
Extracting spatial distribution of objects in multi-spectral data
Un-supervised classification (Isodata, K-means)
Supervised classification (Parallelepiped, Minimum distance, Maximum
likelihood, Spectral angle mapper)
Practice to extract information from panchromatic satellite images
Practice to extract information from multi-hyper spectral satellite images
4
5
Chapter 6
Spatio-temporal
models and its
applications
−
−
−
Kriging for spatio-temporal data
Dynamic Autoregressive Spatio-Temporal Models
Hierarchical models for spatio-temporal data to predict future distribution of
natural phenomena.
Model evaluation: Root Mean Square Error.
Practice to predict the future changes of land cover from multi temporal
satellite images
4
3
−
−
−
−
10
Assessment Scheme & Course Policies
Component
Attendance
Percentage
(%)
10 (10)
Exercise
Practical
Reports
Mid-term
Final Exam
10 (20)
0
30 (20)
50
❖ Students can contact to the instructor in case of any concern or
problem via email address and/or mobile phone:
nguyen-xuan.thanh@usth.edu.vn ; +84 (0) 375 713 523
❖ Respect yourself and the others
❖ Honesty
❖ On time (15-min rule)
❖ 4 absences without justification
no final exam, no retake
11
Warm Up!
1.
https://posit.co/download/rstudio-desktop/
2.
https://www.w3schools.com/r/default.asp
3.
https://www.r-project.org