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Overview

Overview. What is “Big Data”? 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

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Overview

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Presentation Transcript

  1. Overview • What is “Big Data”? • 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

  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

  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. Big Data • MODIS: • Entire earth at 250 meters resolution twice a day • Landsat: • Entire earth at 15/30 meters twice a month for 26 years • Human Genome • Global Biodiversity Information Facility (GBIF) • Climate models

  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. How the data is stored • Large files (to be avoided) • Large sets of files • Relational databases • Distributed networks • Hierarchical storage

  9. 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

  10. Goals of Modeling • Verifiable against the real world • Robust; repeatable and insensitive to parameter variance • Transparent to modelers and stake holders • Simple to understand • Applicable to a real-world situation

  11. Modeling Techniques • Interpolation: • Creates a raster with values for each pixel based on the proximity of sample points • Example: Climate layers from weather stations • Correlation: • Variable being predicted is dependent on other variables (N-dimensional space) • Habitat Suitability / Species Distributions • Others…

  12. Interpolation • Kriging • Nearest-Neighbor • Bilinear • Bezier Surface • Delaunay Triangulation • Inverse Distance Weighting (IDW) • Natural Neighbor • Spline • Others…

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

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

  15. Others • Simple Representations • Cellular automaton • Agent-Based / Simulations

  16. What is… • A shapefile of zip code regions? • A text file of points of bird observations? • The PRISM Data? • GoogleEarth? • Pika Model from Geo 565? • World of Warcraft?

  17. 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

  18. Atmospheric humidity on June 17, 1993, NASA

  19. Model Characteristics • Stochastic or Deterministic • Transparent or “Black Box” • Simple or Complex • Rigorous or Lax • Applied or Theoretical • Internal or “External” Evaluation

  20. Software • Correlation • ArcGIS • R (GLM, GAM…) • Maxent • HyperNiche (NPMR) • BlueSpray (HEMI) • ENVI/IDL • Marxan • WinBugs (Bayes) • BioClim • GARP • Open Modeler • Interpolation • ArcGIS • R • Others • Simple: ArcGIS • OpenSource? • Logo? • NetLogo? • Build your own! • Java • C++ • C#

  21. More Detailed Process • Define the problem • 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

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

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