1. Spatial Simulations
Wolves hunting Caribou

2. Simulation A “model” that is a simulation of a past or potential event
Typically the models are not considered general (simpler models may be)
Relies on knowledge of the mechanisms behind the processes that created the event
"3DiTeams percuss chest". Licensed under CC BY-SA 3.0 via Wikipedia -

3. Simulations are Used In:
Volcanic eruption processes
Species population dynamics
Disease propagation
Flood dynamics
Social dynamics
Earthquakes
Oil spills
Land slides
NASA

4. Validation? Past Events: Future Events: Best case:
Can ground-truth based but how generalizable are they?
Future Events:
How to ground-truth?
Best case:
Model based on past events, ground-truth, then extend into the future carefully

5. Civil Engineering
Civil engineering is based on what has worked in the past
New structures are built based on:
Understanding of materials
Books of “margins of error” based on what has worked and not worked in the past
Simulations of potential scenarios

6. Tacoma Narrows Bridge http://www.youtube.com/watch?v=j-zczJXSxnw
After the Tacoma narrows bridge collapsed, all suspension bridges had to be checked for harmonic oscillations against the typical winds in the area
Today, this is just one of the simulations that are used to test structures in different situations.

7. Simulation Models NASA’s Perpetual Ocean NASA Simulation of aerosols:
NASA Simulation of aerosols:

8. Animations / Simulations
Tsunamis:
Tsunami in a city – Blender
Another for fun
Tsunami Floods City 2 - Blender Simulation
NOAA Tsunami Animation
Asteroid Impact Simulation

9. When to simulate? Completely hypothetic scenarios Really minimal data
Temporal process -> compelling animations
The process is believed to be well understood (simulations are typically mechanistic)
When the problem can be simplified enough to run on available hardware!
Educational

10. Methods
Agent-Based
Cellular automaton

11. Agent Based Models Agent: Typically a point
Agent:
Typically a point
Has “attributes”: health, size, age, sex, etc.
Behaves independently
Moves, feeds, breeds, dies
Can “interact” with other agents
Can “interact” with its envrionment

12. Environmental Science
Spatially Explicit Individually Based Models (SEIBM)
Each “object” in the model represents one individual
Spatially Explicit Population Based Models (SEPBM)
Each “object” represents N individuals

13. Simple Model All Agents Predator Prey X Y Hunger Health Pred 1 Prey 1

14. How it works Move agents Agent interactions Update attributes Prey
Hunger
Birth
Death
Loop for a period of time

15. Movement Each agent has an x, y coordinate
Moves to a new position based on:
Random movement
Directed movement
Terrain
Forces: wind, water, slope
Random
Directed
Lagrangian Movement

16. “Walking” Random Walk “Directed Walk” Lévy flight foraging hypothesis
Brownian Motion: pseudo-random movement of particles when interacting with other particles
“Directed Walk”
Movement toward a resource
Lévy flight foraging hypothesis
Line lengths drawn from a “heavy tailed” distribution

17. Interactions Agents interact with each other:
Breed
Feed
Interact with distance < some minimum
Agents interact with the environment:
Feed on grass

18. Real Interactions Are Complex

19. Agents Update Attributes
Hunger/Health go down without food
Birth happens at some cycle if conditions are correct
Death
If Hunger/Health are too high/low
Age > maximum
Conditions too harsh
Also can:
Grow
Learn
Bloom, senesce

20. Life Cycle
Birth
Youth
Adult
Death

21. Model of Riverine Fish
Fig. 1. Conceptual diagram of a spatially explicit individual-based model of shovelnose sturgeon (SEIBM-1D SNS ). Boxes indicate life stages of shovelnose sturgeon in the model. Italicized texts indicate individual-level processes that facilitate the transition to the next life stage in the model. Diamond shapes indicate environmental drivers (inputs) of the individual-level processes in the model. Detailed description of how the environmental drivers in fl uence each process of the model is provided in Appendix A (Supplementary material). 
Goto et. al, 2015, Spatiotemporal variation in flow-dependent recruitment of long-lived riverine fish: Model development and evaluation

22. Individually Based Models
Polytechnic University of Catalan - Crowds
Princeton’s migration studies
Agent Based Traffic Models

23. Cellular Automata Monitor what is in each “cell” Typically:
Each raster has the number of individuals of one type (or amount of available veg)
Can also include:
Land cover, barriers, water vs. land, etc.
Difficulty to cross area
Open vs. protected areas

24. Tools NetLogo HexSim MASON Multi-Agent Simulation Toolkit Repast
Programming!
Python
Java R?
Books: “Agent-Based Models of Geographical Systems”

25. Python SEIBM Very simple model Includes 2 classes:
Animal (prey and predators)
Veg (grass)

26. SEIBM – Main Script Imports: tkinter, time, random, Veg, Animal
Setup the GUI
Initialize animal objects in an array
Loop forever:
Update each object
Redraw the window
Let Python process events (mouse clicks)
Sleep for a bit

27. Others… HTML 5 Based Simulation Solutions? For others, see: EVE (game)
Insight Maker?
For others, see:
Wikipedia