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Chapter 15: Simulation, Modeling, and Spatial Decision Systems

Simulation, modeling, and spatial decision systems

Spatial models help people reason about possible worlds: where floodwater may go, how traffic may shift, where habitat may persist, which sites are suitable, and how risk changes under scenarios.

Learning Goals

  • Distinguish spatial analysis, models, simulations, and decision systems.
  • Understand suitability analysis, network optimization, agent-based models, and scenario planning.
  • Validate models and communicate uncertainty.
  • Operationalize spatial models in software products.

Theory

A spatial model is a simplified representation of a system. A simulation evolves a model through time or repeated scenarios. A decision system connects models to choices, constraints, tradeoffs, and human judgment.

Useful models are transparent about assumptions. They do not eliminate judgment; they structure it.

Inspection digital twins add another layer: a captured scene becomes the visual context for decisions about maintenance, safety, repair priority, and risk. Gaussian Splatting can provide a photorealistic navigable scene for reviewers, while traditional GIS layers, asset records, sensor streams, simulations, and work orders provide the authoritative decision context.

Math

Important math includes weighted overlay, multi-criteria decision analysis, graph optimization, cellular automata, agent-based rules, hydrologic flow, Monte Carlo simulation, calibration, sensitivity analysis, and uncertainty propagation.

Key computation patterns:

weighted suitability:
score(location) = sum(i=1..n) weight_i * normalized_criterion_i(location)

constraint:
eligible(location) =
  not in_exclusion_zone(location)
  and slope(location) <= max_slope
  and distance_to_road(location) <= max_distance

Monte Carlo estimate:
result = mean(simulation_run_1, simulation_run_2, ..., simulation_run_n)

inspection change score:
inspection_change_score(asset) =
  w_geometry * geometric_residual(asset)
  + w_image * photometric_residual(asset)
  + w_risk * risk_score(asset)

See also: Math and Algorithms Reference

Tools of the Trade

  • GIS model builders and notebooks.
  • Python, R, NumPy, SciPy, Mesa, NetworkX.
  • Hydrologic and environmental models.
  • pgRouting, OR-Tools, spatial optimization libraries.
  • Scenario dashboards and digital twins.
  • Field inspection digital twins: drone/mobile capture, photogrammetry, LiDAR, Gaussian Splatting viewers, asset management systems, work order systems, and dashboard review tools.

Examples of Real-World Solutions

  • A conservation team prioritizes land protection using habitat, cost, connectivity, and climate resilience.
  • A city evaluates growth scenarios under zoning and transit constraints.
  • A flood model estimates exposure under storm events.
  • A logistics system optimizes facility placement and routes.
  • An infrastructure team compares a baseline Gaussian Splat scene with a new field capture, links observations to asset IDs, and uses risk scoring to decide which defects require immediate inspection, repair, or monitoring.

Working Practice Examples

  1. Build a suitability model using three raster or vector criteria.
  2. Run the model with different weights and compare outputs.
  3. Build a simple agent-based model of movement across a grid.
  4. Write a validation plan for a spatial risk model.
  5. Design a maintenance decision workflow that combines a Gaussian Splat site capture, asset inventory, defect labels, field notes, and a risk model.

Common Failure Modes

  • Hiding subjective weights inside a model.
  • No sensitivity analysis.
  • Calibrating to one place and applying everywhere.
  • Confusing model precision with real-world certainty.
  • Presenting scenario outputs as predictions.
  • Letting photorealistic inspection scenes create false confidence when coverage, measurement accuracy, or asset linkage is incomplete.

Works Cited

Batty, Michael. The New Science of Cities. MIT Press, 2013.

Malczewski, Jacek. GIS and Multicriteria Decision Analysis. Wiley, 1999.

Kerbl, Bernhard, et al. "3D Gaussian Splatting for Real-Time Radiance Field Rendering." ACM Transactions on Graphics, vol. 42, no. 4, 2023, article 139. https://doi.org/10.1145/3592433.

O'Sullivan, David, and George L. W. Perry. Spatial Simulation: Exploring Pattern and Process. Wiley-Blackwell, 2013.

Wegener, Michael. "Operational Urban Models: State of the Art." Journal of the American Planning Association, vol. 60, no. 1, 1994, pp. 17-29.

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