1. Basic Spatial Analysis

2. This process involves systems and systems thinking

4. Many times a picture of a system process or steps is worth a thousand words

5. Take out a blank sheet of paper Diagram/Draw the steps you took to get up today and come to school Try and have at least 5 steps; Such as: turn off alarm, get out of bed, get dressed……..

6. Spatial dataanalysis Input-> spatial operation -> output

7. Spatial dataanalysis Input-> spatial operation -> output

8. Input Scope Local – “point” to “point” Neighborhood – adjacent regions have input Global – the entire input data layer may influence output

9. Spatial dataanalysis Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as: Selection Reclassification Dissolving Buffering Overlay Cartographic Modeling (a combination of the above)

10. Spatial Selection Identifying features based on spatial criteria Adjacency, connectivity, containment, arrangement

11. Spatial Selection Identifying features based on spatial criteria Adjacency, connectivity, containment, arrangement

12. Selection based on spatial and non-spatial attributes

13. Adjacency depends on the algorithm used (the same is true for all spatial operations)

14. Spatial dataanalysis Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as: Selection Reclassification Dissolving Buffering Overlay Cartographic Modeling (a combination of the above)

15. Reclassification An assignment of a class or value based on the attributes or geography o an object Example: Parcels Reclassified By size Spatial data analysis: f

16. Spatial data analysis: Reclassification

17. Reclassification: defining categories

18. Spatial data analysis: reclassification defining categories

19. Spatial dataanalysis Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as: Selection Reclassification Dissolving Buffering Overlay Cartographic Modeling (a combination of the above)

20. Spatial data analysis :dissolve A function whose primary purpose is to combine like features within a data layer. Adjacent polygons may have identical values. Dissolve removes or “dissolves away” the common boundary. Used prior to applying area-based selection in spatial analysis

22. Dissolve: often used after reclassification

23. Boundaries are removed when they separate states with the same value. The dissolve attribute is_west. Dissolve: often used after reclassification

24. Spatial dataanalysis Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as: Selection Reclassification Dissolving Buffering Overlay Cartographic Modeling (a combination of the above)

25. Buffering and other Proximity Functions

28. Raster buffer is an array of distances

30. Mechanics of Point and Line Buffering

31. Buffering Variants: point buffer examples

33. Regions in Buffering – inside, outside, enclosed

34. Spatial data analysis Reclassification Dissolving Buffering Overlay Cartographic Modeling (a combination of the above)

35. Spatial Analysis: Overlay Combination of different data layers Both spatial and attribute data is combined Requires that data layers use a common coordinate system A new data layer is created

40. Overlay Raster Overlay Typically applied to nominal or ordinal data Cell by cell process which results in the combination of the two input layers Pay attention to the the number of possible combinations that may be possible and understand the effect on the output layer

42. Feature numbers increase in overlay

43. Vector Overlay Topology is likely to be different Vector overlays often identifies line intersection points automatically. Intersecting lines are split anda node placed at the intersection point Topology must be recreated for later processing Any type of vector may be overlain with any other type Output typically takes the lowest dimension of the inputs For example: Point on Polygon results in a point

44. Ambiguous result Un- ambiguous result

45. note null or flag values null or flag values

46. Vector Overlay (common ways applied) CLIP INTERSECTION UNION

47. CLIPCLIP Cookie cutter approach Bounding polygon defines the clipped second layer Neither the bounding polygon attributes nor geographic(spatial data) are included in the outputlayer

49. INTERSECTION Combines data from both layers but only for the bounding area (Bounding polygon also defines the output layer Data from both layers are combined Data outside the bounding layer (1 st layer) is discarded) Order of intersection is important (A toBorBtoA)

51. UNION Includes all data from both the bounding and data layers New polygons are formed by the combinations of the coordinate data from each layer

53. Why do buffering and vector overlay often take so long? Because a time consuming line intersection test must be performed for all lines in the data layers Then, inside vs. outside regions must be identified for all new polygons

54. BABA Does polygon A intersect/overlap/overlay polygon B?

55. We must check each line in one data layer against every other line in the second data layer to see if they intersect (x2,y2) Remember each line is composed of a linked set of straight line segments defined by a vertex or a node at each end (x1,y1) We can use the equation for a line, plus the coordinates at the endpoints to define the line, and use algebra and logic to see if the lines intersect Equation of a line: y = m * X + b

56. 10, 12 7,4 2,1 9,2 Line Equation y = m 1 x + b 1 Line Equation y = m 2 x + b 2 Line Intersection Calculations m 1 = slope = (12-1)/(10-2) = 1.375 b 1 = y - m 1 x = 12 - 10 * 1.375 = - 1.75 y = 1.375 * x - 1.75 m 2 = slope = (4-2)/(7-9) = -1 b 2 = y - m 2 x = 4 - (-1) * 7 = 11 y = -1 * x + 11 1) Calculate Equation Parameters

57. 2) Find Intersection Point Y =1.375 * x -1.75y = -1 * x +11 Set y values equal y =1.375 * x -1.75 = -1 * x +11 (1.375 + 1) * x = 11 + 1.75 x = 12.75/2.375 = 5.37 y = 1.375 * 5.37 –1.75 = 5.63 Potential Intersection Point atx = 5.37, y = 5.63

58. 3) Verify Intersection: Is it Within the Boxes 10,12 2, 12, 19,2 7,4 Test X: is 5.37 > 2 and < 10 is 5.37 > 7 and < 9 Test Y: is 5.63 > 7 and < 9 is 5.63 > 2 and < 4 Answer:No the lines do not intersect Yes No No

59. Finding the Interior: Is a point inside a polygon? Potential pointPotential point Algorithm : count line crossings to outside of convex hull, if they is an odd number the point is inside, if even num ber, point outside n= 2, out Potential point n= 4, out n= 1, in n= 3, in Algorithm: Pick a direction (East (right) in the example) Count line crossings to the outside of convex hull (shaded polygon) If odd number then the point is inside If even, the point is outside Finding the interior: Is a point inside a polygon (shaded) ?

60. Vector Overlay Common features in Vector overlays create “Slivers” or “Sliver polygons” A common feature in both layers. The problem is that each definition is very subtly different (different time, source, materials) so the polygons don’t line up. They can only be seen a very large display scale but can represent over half the output polygons. They take very little space but affect analytical results.

62. Methodsto reduce/remove slivers: Redefine the common boundaries with highest coordinate accuracy and replace them in all layers before overlay Manually identify and remove Use snap distance during overlay

63. 6565

64. 6666

65. 6767 3 blobs - overlap

66. 6868 3 blobs - before a union

67. 6969 3 blobs - after a union

68. 7070 Flowcharts - workflow diagram

69. 7171 Problem - Identify major watersheds that include counties withhigh corn production ( > 10,000,000 bushels/year) What doweneed tosolve this problem?

70. 7272

71. 7373

72. 7474

73. 7575 NASS Tables: Corn Production Data by County (note FIPS!)

74. 7 6 County Boundary Files from National Map

75. 7777 Condition Tables, Join

76. 7878 Reclass

77. 7979 Web Search for “Watersheds Download Data Shapefile Geodatabase,” then some sifting led to:

78. 8080

79. 8181

80. 8282 Finest Grain HUCs, Too Detailed

81. 8 3 Dissolve to HUC4

82. 8484 Spatial Select with High Production Counties

83. 8585 Results Pane - Left Click on Geoprocessing, Select Results Tip #1 - Your Processing Trail

84. 8 6 Your happy (or sordid) past revealed

85. 8 7 Tip #2 - Customized Toolboxes 1: R-click on ArcToolbox, L-click Add Toolbox 2: Navigate to your project directory, and L-click on the new toolbox icon 3: type in the name you’d like, with a.tbx extension

86. 8888 4: In ArcToolbox, L-click, hold, and drag onto new toolbox

87. 8989 Tip #3: Batch Jobs Open ArcCatalog, then Toolboxes, then System Toolboxes

88. 9090 Navigate to the tool you wish to use, left click on it, and select Batch in the dropdown menu

89. Network Models A set of connected features, often termed centers. Centers are connected to at least on and possibly many network links Links form a network that may have attributes that effect the flow (transit cost) Types of problems include route selection, resource and territory allocation and traffic modeling

92. Network Elements Links - “Conduits” for movement Intersections - Link joins Stops - Sources/sinks where resources can enter or exit the network Centers - node locations which may receive or provide resources. Attributes for total amount of resource supplied toor taken from a center, e.g., total water capacity for a reservoir Barriers - nodes which prevent flow through links, or links with infinite impedance

93. Network Elements Impedance - a resistance to flow through a link. Impedance may be directionally dependent Impedance is assumed to be uniform over the link. Impedance = 5 min/mile Impedance = 10 min/mile

94. Network Elements Turns - Allowable redirections at intersections/nodes.Often controlled by a turning impedance, energy or time spent in performing a turn - set to infinity for “illegal” turns allowed turns in a stream network

96. Route selection example

97. Find the least cost path?

99. Route selection example