How to Validate a Model
Residuals, holdout design, sensitivity analysis, and uncertainty reporting for any geographic model.
Methods and Tools
Cross-cutting resources for GIS, modelling workflow, quantitative methods, and practical setup.
Cross-Cutting Support
Methods and Tools
This section gathers the practical resources that support the whole project. It is not a separate linear book. It is the shared tool bench for readers moving between the core book, topic libraries, and the laboratory.
GIS resources
Workflow support
Quantitative methods
Shared tool bench
Current Resource Clusters
Methods Route
This Section Should Teach How To Judge Models, Not Just How To Build Them
This section is where the book’s cross-cutting discipline lives: testing models honestly, choosing the right model family, carrying uncertainty forward, and making cleaner inferential claims instead of bluffing confidence.
1
Validate
Learn to separate fit, validation, residual analysis, sensitivity, and uncertainty reporting.
2
Choose
Learn how to match questions to analytical, GIS, statistical, or simulation model families.
3
Quantify uncertainty
Learn when to move from point estimates to ranges, scenario bands, Monte Carlo runs, and ensembles.
4
Simulate
Learn when evolving systems call for cellular automata, agents, or stock-and-flow feedback models.
5
Decide
Learn how objectives, constraints, tradeoffs, and optimization turn model outputs into choices.
6
Infer
Learn to update beliefs with evidence and distinguish predictive association from causal claims.
How to Choose a Model
When to use analytical equations, GIS workflows, statistical learning, or simulation.
Uncertainty Propagation and Ensembles
Scenario bands, Monte Carlo thinking, and how uncertain inputs create output ranges.
Simulation Paradigms
When to use cellular automata, agent-based models, or aggregate feedback models.
Decision Models and Optimization
Objectives, constraints, tradeoffs, and how to support choices under geographic limits.
Bayesian Updating and Geographic Inference
Priors, likelihoods, posteriors, and why updated uncertainty is often more honest than one fixed estimate.
Causal Inference for Geographic Questions
Confounding, counterfactuals, and research design for place-based claims that aim to explain rather than merely predict.
Getting Started
Environment setup, reading equations, units, maps, and uncertainty create the calmest entry into the whole project.
Spatial Analysis Toolkit
Spatial predicates, overlays, raster methods, visibility, and watersheds provide the practical GIS backbone used across the series.
Remote Sensing Methods
Observation geometry, sensor logic, and specialist sensing methods support both the core environmental chapters and the remote sensing library.
Machine Learning and Statistical Methods
Regression, dimensionality reduction, Bayesian thinking, and inference-heavy chapters belong here when you need later-stage quantitative support.
Laboratory Workflow
The laboratory turns methods into investigations, giving the project a place for advanced practice instead of only linear reading.
What This Section Should Become
Over time, this section can grow into the place for:
- GIS resource pages
- modelling workflow primers
- data handling patterns
- software and setup guides
- quantitative method explainers
- reusable practical notes that should not be trapped inside one branch
- inference and research-design chapters that support causal and probabilistic reasoning across the whole project