By completing this eight-topic course, students will develop the skills required to conduct end-to-end spatial analysis using advanced models and software, and will be equipped to apply these methods to practical problems and high-level research publications.
Download the Lecture Outline and Reading List (PDF) for foundational references and topic-by-topic readings.
- Each day, write a 200-word essay on that day's topic.
- Submit a combined Word document with essays for at least four days out of the eight.
- Submission: PDF file (named with student's name), emailed to yongze.song@outlook.com
- IEEE IGARSS Summer School Training Materials: https://ausgis.github.io/igarss25ss/
Topic 1. Introduction to Spatial Modelling and Prediction
Why spatial methods are needed for prediction; GeoAI concepts, history, and emerging trends; sustainable infrastructure case study; OPGD model and the GD R package for determinant analysis.
Topic 2. Spatial Modelling for Heterogeneity and Determinant Analysis
Concepts of local and spatial stratified heterogeneity; the GOZH model for optimal zone-based analysis; LISP for local driver analysis.
Topic 3. Spatial Modelling for Prediction
General process of spatial prediction; Kriging fundamentals and geostatistics; Second-Dimension Spatial Association (SDA); Geographically Optimal Similarity (GOS) model.
Topic 4. Spatial Patterns for Prediction
Spatial pattern identification and quantification (clusters, gradients, hotspots); Geocomplexity theory and measurement; Second-Dimension Outliers (SDO) for prediction.
Topic 5. Research Design, Spatial & Remote Sensing Data Collection, and Pre-processing
Experiment design for spatial prediction; data collection strategies; spatial stratified random sampling; pre-processing workflow (missing values, multicollinearity, outliers, normalization).
Topic 6. Spatial Validation Methods
Why spatial validation is critical; k-fold cross-validation; spatial block cross-validation; comparison of random CV vs. block CV vs. NNDM approaches.
Topic 7. Spatial Decision-Making and Applications
Model-based spatial decision-making; entropy-weighting for multi-criteria aggregation; constructing composite urbanization indices; spatial mapping and visualization.
Topic 8. Geospatial Intelligence (GeoAI) and Spatial Big Data for Urban Sustainability
GeoAI definition, scope, and history; emerging trends (heterogeneity-aware GeoAI, geo-foundation models, spatial representation learning); machine learning with GAM using the caret package.
Answers to students' questions are continuously updated here.
- Topic 4
- analysis code.R, line 27: change sf_data to sf.data
- Topic 5
- Australia extent:
ext_aus <- ext(112, 154, -44, -10) - China extent:
ext_china <- ext(73, 135, 18, 54) - Canada:
ext_canada <- ext(-141, -52, 41, 83) - Europe:
ext_europe <- ext(-25, 45, 34, 72) - Africa:
ext_africa <- ext(-20, 55, -35, 38) - South America:
ext_south_america <- ext(-82, -34, -56, 13) - US:
ext_us <- ext(-125, -66.5, 24, 49.5)
- Australia extent: