© 2005, Joseph K. Berry—permission to copy granted Figure P-1. Spatial Analysis and Spatial Statistics are extensions of traditional ways of analyzing mapped data. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 1-1. Calculating the total number of houses within a specified distance of each map location generates a housing density surface. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 1-2. Spatial Data Mining techniques can be used to derive predictive models of the relationships among mapped data. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 1-3. Map Analysis techniques can be used to identify suitable places for a management activity. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 2-1. Grid-based data can be displayed in 2D/3D lattice or grid forms. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 2-2. Contour lines are delineated by connecting interpolated points of constant elevation along the lattice frame. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure D display “pushes-up” the grid or lattice reference frame to the relative height of the stored map values. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 2-4. Map values are characterized from two broad perspectives—numeric and geographic—then further refined by specific data types. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 2-5. Discrete and Continuous map types combine the numeric and geographic characteristics of mapped data. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 2-6. Comparison of original and goal normalized data. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 2-7. The map values at each grid location form a single record in the exported table. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 2-8. A vector-based system can store continuous geographic space as a pseudo-grid. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 2-9. A grid-based system stores a long list of map values that are implicitly linked to an analysis frame superimposed over an area. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure A map stack of individual grid layers can be stored as separate files or in a multi-grid table. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure An Equal Ranges contour map of surface data. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Equal Count and +/- 1 Standard Deviation contour maps. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Comparison of different 2D contour displays. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 3-1. An iterative processing environment, analogous to basic math, is used to derive new map variables. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 3-2. Areas of meadow and forest on a COVERTYPE map can be reclassified to isolate large areas of open water. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 3-3. A sequence of reclassification operations (renumber, clump, size and renumber) can be used to isolate large water bodies from a cover type map. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 3-4. Point-by point overlaying operations summarize the coincidence of two or more maps, such as assigning a unique value identifying the COVERTYPE and SLOPE_CLASS conditions at each location. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 3-5. Category-wide overlay operations summarize the spatial coincidence of map categories, such as generating the average SLOPE for each COVERTYPE category. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 3-6. A cross-tab table statistically summarizes the coincidence among the categories on two maps. (299/625 * 100= 47.84) Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 3-7. The Hugag prefers low elevations, gentle slopes and southerly aspects. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 3-8. Binary maps representing Hugag habitat preferences are coded as 1= good and 0= bad. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 3-9. The binary habitat maps are multiplied together to create a Binary Suitability map (good or bad) or added together to create a Ranking Suitability map (bad, marginal, better or best). Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure The sum of a binary progression of values on individual map layers results in a unique value for each combination. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure A Rating Suitability map is derived by averaging a series of “graded” maps representing individual habitat criteria. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 4-1. Viewshed of all surface water locations. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 4-2. Example calculations for determining visual connectivity. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 4-3. Visual screens that block line-of-sight connections can be introduced. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 4-4. Identifying the “viewshed” of the road network. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 4-5. Calculating simple and weighted visual exposure. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 4-6. Determining the visual exposure/impact of alternative power line routes. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 4-7. Calculating visual exposure for two proposed power lines. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 4-8. Determining visual impact on local residents. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 4-9. Determining visible portions of a proposed power line. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Calculating a viewshed map. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Calculating a visual exposure map. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Calculating a visual vulnerability map. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Visual connectivity to a map feature (Profile Rock) identifies the number of times each location sees the extended feature. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure An aesthetic map determines the relative attractiveness of views from a location by considering the weighted visual exposure to pretty and ugly places. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Weighted visual exposure map for an ongoing visual assessment in a national recreation area. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 5-1. A Simple Proximity Buffer identifies the distance to roads throughout the buffered area. Note that the buffer extends into the ocean—an inappropriate “reach” for terrestrial applications. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 5-2. The figure on the left clips the simple buffer to represent only land areas. The figure on the right uses the elevation surface to identify only areas that are uphill from the roads. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 5-3. The “viewshed” of the road network forms a variable-width, line-of-sight buffer. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 5-4. A “visual exposure” map identifies the number of times each map location is visually connected to an extended map feature. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 5-5. A “noise buffer” considers distance as well as line-of-sight connectivity. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 5-6. Examples of Variable-Width and Line-of-Sight Buffers. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 5-7. Development of Effective-Distance Buffers for Hiking and off-road travel. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 5-8. Comprehensive Travel-Time Maps for Hiking and Off-Road Movement. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 6-1. Maps of Cover Type and Roads are combined and reclassified for relative and absolute barriers to hiking. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 6-2. The hiking-time surface identifies the estimated time to hike from the Ranch to any other location in the area. The protruding plateaus identify inaccessible areas (absolute barriers) and are considered infinitely far away. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 6-3. The steepest downhill path from a location (Cabin) identifies the “best” route between that location and the starter location (Ranch). Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 6-4. Hiking friction based on Cover Type and Roads is updated by terrain slope with steeper locations increasing hiking friction. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 6-5. Hiking movement can be based on the time it takes move throughout a study area, or a less traditional consideration of the relative scenic beauty encountered through movement. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 6-6. The “best” routes between the Cabin and the Ranch can be compared by hiking time and scenic beauty. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 6-7. Map of surface flow confluence. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure D, 3-D and draped displays of surface flow confluence. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure 6-9. Spill mitigation for pipelines identifies high consequence areas that could be impacted if a spill occurs anywhere along a pipeline. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Overland flow can be characterized as both distance traveled and elapsed time. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Effective downhill proximity from a pipeline can be mapped as a variable-width buffer. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Surface flow takes the steepest downhill path whenever possible but spreads out in flat areas and pools in depressions. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Surface inclination and liquid type determine the type of surface flow—path, sheet or flat. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Pooling of surface flow occurs when depressions are encountered. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Overland flow is calculated as a series of time steps traveling downhill over an elevation surface. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Flow velocity is dependent on the type of liquid and the steepness of the terrain. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure An Impact Buffer identifies the minimum flow time to reach any location in the impacted area—areas that are effectively close to the pipeline. Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Identification of High Consequence Areas (HCAs) impacted by a simulated spill is automatically made when a flow path encounters an HCA Analyzing Geo-Spatial Resource Data – text figures

© 2005, Joseph K. Berry—permission to copy granted Figure Channel flow identifies the elapsed time from the entry point of overland flow to high consequence areas impacted by surface water.