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Computer Science
Data Science
Spatial Data Science
1. Foundations of Spatial Data Science
2. Understanding Spatial Data
3. Spatial Data Acquisition and Management
4. Exploratory Spatial Data Analysis
5. Spatial Operations and Geoprocessing
6. Spatial Statistics and Modeling
7. Spatial Machine Learning
8. Advanced Spatial Analysis Topics
9. Big Data and Cloud Computing in Spatial Science
10. Ethics and Privacy in Spatial Data Science
11. Applications and Case Studies
Spatial Statistics and Modeling
Geostatistical Analysis
Theory of Regionalized Variables
Spatial Continuity
Stationarity Assumptions
Isotropy and Anisotropy
Variogram Analysis
Experimental Variogram
Theoretical Variogram Models
Spherical Model
Exponential Model
Gaussian Model
Linear Model
Variogram Parameters
Nugget Effect
Sill
Range
Anisotropic Variograms
Spatial Interpolation Methods
Deterministic Methods
Inverse Distance Weighting
Spline Interpolation
Trend Surface Analysis
Geostatistical Methods
Simple Kriging
Ordinary Kriging
Universal Kriging
Co-Kriging
Indicator Kriging
Interpolation Validation
Cross-Validation
Error Assessment
Uncertainty Quantification
Spatial Regression Analysis
Spatial Dependence in Regression
Spatial Lag Dependence
Spatial Error Dependence
Spatial Durbin Model
Spatial Weight Matrices
Contiguity-based Weights
Queen Contiguity
Rook Contiguity
Distance-based Weights
Fixed Distance
K-Nearest Neighbors
Inverse Distance
Weight Matrix Standardization
Spatial Regression Models
Spatial Lag Model
Spatial Error Model
Spatial Durbin Model
Model Selection Criteria
Geographically Weighted Regression
Local Parameter Estimation
Bandwidth Selection
Model Diagnostics
Interpretation of Results
Spatio-Temporal Regression
Panel Data Models
Space-Time Interaction
Dynamic Spatial Models
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7. Spatial Machine Learning