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Overview

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

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Overview

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Presentation Transcript

  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

  6. 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

  7. Breaking it down • “Type” of spatial data: • 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: • 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 • Distributed networks • Hierarchical storage

  10. 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

  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

  12. 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…

  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…

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

  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

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

  23. 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?

  24. 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

  25. Atmospheric humidity on June 17, 1993, NASA

  26. 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

  27. 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++ • C#

  28. 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

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

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