1. Overview
What is Spatial Modeling?
Why do we care?

2. What’s the problem? The issues we need to solve are:
Getting larger spatially
Involving more complex data
Involving more data
Require special algorithms
Require meeting the needs of, and communicating with, much larger groups of people
These issues cannot be solved with traditional GIS analysis

3. What’s the solution? ArcGIS has limited ability to:
Manage complex datasets
Process large datasets
Create custom models
Run batch processes
Have to use ArcGIS appropriately, find other solutions to tough problems R
BlueSpray
Others…

4. Marine Spatial Planning
Over 100 raster layers
Millions of model runs
Years of work by teams of people
Multiple modeling packages
Maxent
Marxan
ArcGIS

5. STAC
Scientific, Technical Assessment and Reporting

6. Spatial Data Can be Big! MODIS: Landsat:
Entire earth at 250 meters resolution twice a day
Landsat:
Entire earth at 15/30 meters twice a month for over 30 years
DayMet: Daily Climate Predictions
LiDAR point clouds
And now we have UAVs!

7. Breaking it down “Type” of spatial data: Attributes/Measures:
Points
Polygon
Polyline
Rasters
Attributes/Measures:
Continuous, categorical measures
Dates
Descriptive text
Remotely sensed vs. Field data

8. Putting it Together Almost all spatial data has: Can also have:
Measures: occurrences, height, etc.
Spatial coordinates
Temporal information
Can also have:
2D, 3D, “4D”, or N-dimensions
Relational and/or hierarchical structure

9. How the data is stored Large files (to be avoided) Large sets of files
Relational databases (don’t put rasters in a database)
Distributed networks
Hierarchical storage

10. Spatial Modeling Spatial Model: Spatial Processing: Spatial Analysis:
Abstraction of something spatial
Typically on, or near, the earth’s surface
Spatial Processing:
Converting spatial data for a specific use
Spatial Analysis:
Analysis that uses spatial data
Spatial Simulation:
Models something that has or could occur spatially and temporally

11. Goals of Modeling Verifiable against the real world
Robust; repeatable and insensitive to parameter variance
Methods are transparent to modelers and stake holders
Simple to understand
Applicable to a real-world situation
Real world is within uncertainty bounds of the prediction

12. General Modeling Methods
Density:
Points (occurrences) -> Density surface
Interpolation:
Points with measured values -> Continuous Surface
Correlation/Regression:
Points with measured values & continuous covariant -> Continuous surface
Simulations:
Very general
Others…

13. Density
Find a density, abundance, concentration, of discrete occurrences
Examples:
Plants and animals
Disease
Crime
en.academic.ru

14. Density Methods Minimum Convex Polygon Kernel Density Estimates (KDE)
Wikipedia

15. Interpolation
Creates a raster with values for each pixel based on the proximity of sample points
Examples:
Climate layers from weather stations
Biomass from tree diameters (DBH)
Soil maps from pits
DEMs from points
Must have:
Autocorrelation

16. Interpolation: Methods
Kriging
Nearest-Neighbor
Bilinear
Spline
Bezier Surface
Natural Neighbor
Delaunay Triangulation
Inverse Distance Weighting (IDW)
Kernel Smoothing
Others…
Methods

17. Correlation/Regression
Variable being predicted is dependent on other variables (N-dimensional space)
Examples:
Habitat Suitability / Species Distributions
Fire potential
Land use change
Disease risk
Productivity

18. Correlation or Dependence
Systems of differential equations
Common Statistical Functions
Kernel functions
Bayesian Inference
Regression
Index Models
Trees
Neural Nets
“Graphical” techniques
Machine Learning Methods
Combinations of the above
Methods

19. Non-Linear Correlation
Several sets of (x, y) points, with the Pearson correlation coefficient of x and y for each set.
Wikipedia

20. Simulations
Use computer software to create a “simulation” for a general phenomenon
Examples:
Climate simulations
Population models
Disaster scenarios
Fire models
Shipping

21. Simulations
Cellular automaton
Agent-Based

22. Typical Spatial Models
Flood Planes
Potential Habitat/Species Distribution
Soil Erosion
Ice Extents
Climate Models
Oil Spill Extents
Bark Beetle Infestation
Geologic Layers
Flight Control Software

23. Atmospheric humidity on June 17, 1993, NASA

24. Model Characteristics
Stochastic or Deterministic
Transparent or “Black Box”
Simple or Complex
Rigorous or Lax
Applied or Theoretical
Internal or “External” Evaluation
Parametric or Non-Parametric

25. Software Correlation Interpolation Simulations Build your own! ArcGIS
R (GLM, GAM…)
Maxent
HyperNiche (NPMR)
BlueSpray
ENVI/IDL
Marxan
WinBugs (Bayes)
BioClim
GARP
Open Modeler
Interpolation
ArcGIS R
Simulations
Simple: ArcGIS
HexSim?
Logo?
NetLogo?
Build your own!
Java
C++
Python!

26. More Detailed Process Define the problem Investigate the topic
Gather, process, and analyze the data
Investigate and select methods
Find, evaluate, and select the software
Build, parameterize, and run the models
Evaluate the model and results
Along the way, document:
Assumptions
Uncertainties
Problems others have seen

27. Occam’s Razor
“other things being equal, a simpler explanation is better than a more complex one”

28. Additional Slides

29. Others Spatial Networks Finite Element Analysis Hydrology Simulations
Disaster Simulations

30. What is… A shapefile of zip code regions?
A text file of points of bird observations?
The PRISM Data?
GoogleEarth?
“Final Lab” from 270?
World of Warcraft?