Overview What is Spatial Modeling? Why do we care?

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

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…

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

STAC Scientific, Technical Assessment and Reporting

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!

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

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

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

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

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

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…

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

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

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

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

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

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

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

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

Simulations Cellular automaton Agent-Based

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

Atmospheric humidity on June 17, 1993, NASA

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

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!

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

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

Additional Slides

Others Spatial Networks Finite Element Analysis Hydrology Simulations Disaster Simulations

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?