GIS and Spatial Interpolation Course

Lesson 1 • Coordinate Systems and Projections

Explains geographic vs. projected coordinate systems and their effect on spatial analysis. Students learn to identify and assign correct projections to datasets.

Lesson 2 • Importing and Inspecting Spatial Data

Covers common spatial file formats and tools for loading and validating data. Students gain hands-on practice opening and auditing real datasets.

Lesson 3 • Spatial Relationships and Queries

Introduces spatial predicates and attribute-based queries for filtering data. Prepares students to subset datasets before running interpolation workflows.

Lesson 4 • What GIS Is and Does

Defines GIS, its core components, and real-world applications across industries. Establishes the conceptual framework used throughout the course.

Lesson 1 • Visualizing Spatial Patterns

Uses choropleth maps, scatter plots, and variogram clouds to visualise data structure. Visual diagnostics guide method selection in later chapters.

Lesson 2 • Detecting and Handling Outliers

Identifies spatial and attribute outliers using visual and statistical techniques. Outlier treatment decisions directly affect interpolated surface accuracy.

Lesson 3 • Spatial Distribution and Clustering

Examines point pattern analysis and clustering metrics to reveal spatial structure. Informs decisions about which interpolation methods suit the data distribution.

Lesson 4 • Descriptive Statistics for Spatial Data

Reviews mean, variance, skewness, and kurtosis applied to spatial attribute values. Provides the statistical baseline needed to evaluate interpolation assumptions.

Lesson 1 • Error, Uncertainty, and Validation Concepts

Introduces RMSE, bias, and cross-validation as tools for assessing interpolation quality. Connects validation metrics to method comparison in later chapters.

Lesson 2 • Sampling Design and Data Requirements

Covers sample size, spatial coverage, and measurement error as inputs to interpolation. Poor sampling design is identified as the leading cause of surface model failure.

Lesson 3 • Classification of Interpolation Methods

Organises methods into deterministic and geostatistical families with subcategories. Gives students a decision map for selecting methods in applied chapters.

Lesson 4 • Scale, Resolution, and Edge Effects

Examines how output cell size and study area boundaries influence interpolated surfaces. Students learn to set resolution and manage boundary artefacts appropriately.

Lesson 1 • Trend Surface Analysis

Fits polynomial trend surfaces to capture broad spatial gradients in data. Residual analysis connects trend surfaces to geostatistical methods introduced later.

Lesson 2 • Inverse Distance Weighting

Explains IDW weighting logic, power parameter effects, and search neighbourhood settings. Students tune IDW parameters and evaluate surface smoothness vs. accuracy trade-offs.

Lesson 3 • Spline Interpolation Techniques

Covers regularised and tension splines, explaining smoothness constraints and overfitting risks. Students apply splines to terrain and environmental datasets.

Lesson 4 • Comparing Deterministic Method Outputs

Uses cross-validation and visual comparison to rank deterministic methods on a shared dataset. Builds critical evaluation skills applied throughout the course.

Lesson 1 • Semivariogram Computation

Calculates experimental semivariograms from point data using lag distance and direction. Students interpret sill, range, and nugget from computed variogram clouds.

Lesson 2 • Theoretical Variogram Models

Introduces spherical, exponential, Gaussian, and power variogram model families. Students fit models to experimental variograms and assess goodness of fit.

Lesson 3 • Anisotropy Detection and Modelling

Identifies geometric and zonal anisotropy in directional variograms and corrects for it. Anisotropy modelling improves kriging accuracy in directionally structured datasets.

Lesson 4 • Introduction to Geostatistics

Traces geostatistics from mining origins to modern spatial analysis applications. Frames the regionalised variable theory underlying all kriging methods.

Lesson 1 • Simple and Universal Kriging

Contrasts simple kriging's known mean assumption with universal kriging's trend removal. Students choose between variants based on stationarity diagnostics.

Lesson 2 • Co-Kriging with Secondary Variables

Incorporates densely sampled secondary variables to improve primary variable predictions. Students build cross-variograms and run co-kriging workflows.

Lesson 3 • Ordinary Kriging Fundamentals

Derives ordinary kriging equations and explains the best linear unbiased estimator property. Students run ordinary kriging and interpret prediction and variance output maps.

Lesson 4 • Indicator and Probability Kriging

Transforms continuous data into binary indicators to estimate exceedance probabilities. Applied to contamination thresholds and categorical spatial phenomena.

Lesson 1 • Machine Learning for Spatial Prediction

Applies random forests and gradient boosting to spatial prediction with spatial cross-validation. Addresses spatial autocorrelation in train-test splits to avoid overfitting.

Lesson 2 • Temporal Interpolation and Space-Time Methods

Extends spatial interpolation into the time dimension using space-time variograms. Students model dynamic phenomena such as air quality and soil moisture over time.

Lesson 3 • Data Fusion and Multi-Source Interpolation

Merges ground observations with remote sensing and model output grids for improved coverage. Students apply area-to-point and downscaling techniques.

Lesson 4 • Regression Kriging Workflow

Combines linear regression with kriging of residuals to exploit environmental covariates. Students build end-to-end regression kriging pipelines on real datasets.

Lesson 1 • Capstone Project Delivery

Students complete an end-to-end interpolation project from data acquisition to final map products. Deliverables include a technical report, validated surfaces, and an oral presentation.

Lesson 2 • Natural Resource and Terrain Analysis

Uses interpolation to generate DEMs, mineral grade models, and forest biomass surfaces. Students apply terrain derivatives to support resource management decisions.

Lesson 3 • Urban and Infrastructure Spatial Analysis

Interpolates noise levels, traffic density, and utility demand across urban environments. Students integrate interpolated surfaces with infrastructure planning workflows.

Lesson 4 • Environmental Monitoring Applications

Applies interpolation to air quality, soil contamination, and precipitation mapping scenarios. Students select methods, validate outputs, and interpret results for environmental decisions.