Report

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 Spatial Data Can be Big! • MODIS: – Entire earth at 250 meters resolution twice a day • Landsat: – Entire earth at 15/30 meters twice a month for 26 years • DayMet: Daily Climate Predictions • LiDAR point clouds Breaking it down • “Type” of spatial data: – 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: – 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 Distributed networks Hierarchical storage Spatial Modeling • Spatial Model: – 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: – 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… Correlation • 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 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 – – – – – – – – – – – ArcGIS R (GLM, GAM…) Maxent HyperNiche (NPMR) BlueSpray ENVI/IDL Marxan WinBugs (Bayes) BioClim GARP Open Modeler • Interpolation – ArcGIS –R • Simulations – – – – Simple: ArcGIS OpenSource? 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?