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Lincoln

Lincoln. NAACP. Darwin. GIS. www.casa.ucl.ac.uk/gistimeline. Longley et al. Chapters 14, 6. Spatial Analysis. answer questions, support decisions, and reveal patterns. Spatial Analysis. all of the transformations, manipulations, and methods Data ----> Information ---> Understanding

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Lincoln

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  1. Lincoln NAACP Darwin GIS www.casa.ucl.ac.uk/gistimeline

  2. Longley et al. Chapters 14, 6 Spatial Analysis

  3. answer questions, support decisions, and reveal patterns Spatial Analysis • all of the transformations, manipulations, and methods • Data ----> Information ---> Understanding • ”…a set of methods whose results change when the locations of the objects being analyzed change."

  4. calculating the average income for a group of people? calculating the center of the United States population? Which is Spatial Analysis?

  5. Types of Spatial Analysis • Queries and reasoning • Measurements • Aspects of geographic data, length, area, etc. • Transformations • New data, raster to vector, geometric rules • Descriptive summaries • Essence of data in 1 or 2 parameters • Optimization - ideal locations, routes • Hypothesis testing - sample to entire pop.

  6. Spatial Search (Query):Gateway to Spatial Analysis (Reasoning) • Overlay is a spatial retrieval operation that is equivalent to an attribute join. • Buffering is a spatial retrieval around points, lines, or areas based on distance.

  7. Image courtesy of K. Foote/M. Lynch, UT-Austin Overlay

  8. Raster Overlay 0 1

  9. Overlay like an attribute join

  10. Types of overlay operations • Union • Intersect • Identity • Max • Min Etc.

  11. Union • computes the geometric intersection of two polygon coverages. All polygons from both coverages will be split at their intersections and preserved in the output coverage.

  12. within 25 miles of a city OR within 25 miles of a major river. Union

  13. Intersect • computes the geometric intersection of two coverages. Only those features in the area common to both coverages will be preserved in the output coverage.

  14. Intersect within 25 miles of a city AND within 25 miles of a major river.

  15. Identity • computes the geometric intersection of two coverages. All features of the input coverage, as well as those features of the identity coverage that overlap the input coverage, are preserved in the output coverage.

  16. Intersect Identity

  17. Identity Portion of the major city buffer WITHIN the major river buffer Intersect Union within 25 miles of a city OR within 25 miles of a major river. within 25 miles of a city AND within 25 miles of a major river.

  18. Buffer

  19. Identity

  20. Map Algebra Map Algebra Compared with RAINFALL 1990 RAINFALL 1991 MAX RAINFALL 1990-’91

  21. 2 Analysis Examples from ArcGIS • (1) Interpolation - soil samples on a farm [transformation] • (2) Location Analysis - coffee shops & customers [optimization]

  22. "a set of methods whose results change when the locations of the objects being analyzed change" • (1) Interpolation - soil samples on a farm • (2) Location Analysis - coffee shops & customers

  23. Soil Samples of Farm Area w/ Interpolation

  24. Interpolate samples, then query to find pH > 7Farmer needs to treat these areas w/ammonium sulfate GIS Analysis Model

  25. GIS Lanslide Susceptibility Model in ArcGIS 9 Model Builder (Lab 6)

  26. Choose Interpolation Parameters

  27. IDW Interpolation

  28. pH surface [pH surface] > 7 Instead of hillshade, use raster calculator

  29. Result: areas that farmer should treat w/ammonium sulfate to lower the pH to 7 so that soil is balanced

  30. The Farm • Size = ~5.35 acres (233,046 sq ft. or 21,650 sq m) • Combined size of new treatment areas = ~0.145 acres (6,338 sq ft or 588 sq m) • Ammonium sulfate @ $50.00 per acre • Treat whole field - $267.50 • Treat only where needed - $7.25 • Crop yield and treatment maps over time

  31. "a set of methods whose results change when the locations of the objects being analyzed change" • (1) Interpolation - soil samples on a farm • (2) Location Analysis - coffee shops & customers

  32. Best location for new Beanery w/ location analysis ( distance & proxmity )

  33. Marketing questions • Too close to existing shops? • Similar characteristics to existing locations? • Where are the competitors? • Where are the customers? • Where are the customers that are spending the most money?

  34. Shops w/in 1 mile will compete for customersPotential shops > 1 mile away GIS Analysis Model

  35. Straight line distance function

  36. Result: yellow/orange = close to shopspurple/blue = farther away

  37. Spending Density Function, Customer Spending

  38. Result: Dark blues are greatest density of customer spending

  39. Spending density ([Distance to Shops] > 5280) & ([Spending density] > .02) Find areas 1 mile from an existing shop that are also in a high spending density customer area

  40. Result: Best locations for a new Beaneryw/ proximity to an interstate highway, zoning concerns, income levels, population density, age, etc.

  41. Web Site of the Week

  42. Visualization & Spatial Analysis:An Example from The Districthttp://dusk.geo.orst.edu/gis/district.html

  43. Spatial Analysis Handout • On course web site • Overlays (union, intersect, identity) • Buffering • Map Algebra • Clipping and Masking • Recoding • Many others! “Spatial Madness” Article!Spatial analysis of NCAA basketball tournament

  44. Uncertainty in the Conception, Measurement, and Representation of Geographic Phenomena • Previous examples assumed it didn’t exist • Conception of Geographic Phenomena • Spatial Uncertainty - objects do NOT have a discrete, well-defined extent • Wetlands or soil boundary? • Oil spill? pollutants or damage? • Attributes - human interp. may differ

  45. Uncertainty in Conception • Vagueness - criteria to define an object not clear • What constitutes a wetland? • An oak woodland means how many oaks? • Seafloor ages/habitats • What does a grade of “A” really mean??

  46. Uncertainty in Conception Ambiguity - y used for x when x is missing Direct indicators: salinity (x) or species (y) Indirect more ambiguous Wetlands (y) of species diversity (x)?? Figure courtesy of Jay Austin, Ctr. For Coastal Physical Oceanography, Old Dominion U.

  47. Uncertainty in Conception • Regionalization problems • What combination of characteristics defines a zone? • Weighting for composites? • Size threshold for zone? • Fuzzy vs. sharp

  48. Uncertainty in Measurement • Physical measurement error • Mt. Everest is 8,850 +/- 5 m • Dynamic earth makes stable measurements difficult • Seismic motion • Wobbling of Earth’s axis • Wind and waves at sea!

  49. Uncertainty in Measurement • Digitizing error, e.g., • Undershoots • Overshoots • “Gafs”

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