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Geotechnology Not Your Grandfather’s Map

Geotechnology Not Your Grandfather’s Map.

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Geotechnology Not Your Grandfather’s Map

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  1. GeotechnologyNot Your Grandfather’s Map Joseph K. BerryCSU Alumnus,MS in Business Management ’72 and PhD emphasizing Remote Sensing ‘76W.M. Keck Scholar in Geosciences, University of DenverPrincipal, Berry & Associates // Spatial Information Systems1701 Lindenwood Drive, Fort Collins, CO 80524Phone: (970) 215-0825 Email: jberry@innovativegis.com Website at www.innovativegis.com/basis

  2. Geographic Information Systems Global Positioning System Remote Sensing Mapping involves precise placement (delineation) of physical features (graphic) Analysis involves investigation of spatial relationships (numerical) Descriptive Mapping Prescriptive Modeling (Nanotechnology) Geotechnology (Biotechnology) Geotechnology is one of the three "mega technologies" for the 21st century and promises to forever change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications GPS/GIS/RS WhereisWhat (Berry)

  3. “The Sizzle” Descriptive Mapping GPS Navigation Internet Mapping Last Year Desktop Mapping Multimedia Mapping … we created a multimedia map of Pingree Park (Nanotechnology) Geotechnology (Biotechnology) (Berry)

  4. “The Science” Prescriptive Modeling Spatial Statistics (Nanotechnology) Geotechnology (Biotechnology) Grid-Based Map Analysis • Surface Modelingmaps the spatial distribution and pattern of point data… • Map Generalization— characterizes spatial trends (tilted plane) • Spatial Interpolation— deriving spatial distributions (e.g. IDW, Krig) • Other— roving windows and facets (e.g., density surface; tessellation) • Spatial Data Mininginvestigates the “numerical” relationships in mapped data… • Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering) • Predictive— relationships among maps (e.g., regression) • Prescription— appropriate actions (e.g., decision rules; optimization) • Spatial Analysisinvestigates the “contextual” relationships in mapped data… • Reclassify— reassigns map values (position, value, size, shape, contiguity) • Overlay— map coincidence (point-by-point; region-wide; map-wide) • Distance— proximity and connection (movement; optimal paths; visibility) • Neighbors— roving windows (slope; aspect; diversity; anomaly) (Berry)

  5. Surface Modeling(Density Surface — “counts”) Elevation A value is stored at each grid cell location indicates “what is where”— for example, a set of elevation values form the familiar terrain surface we hike on. A paradigm shift from traditional discrete Map Features comprised of Points, Lines, Polygons. Map Stack …continuous Map Surface (grid-based analysis frame) …Map Surfaces are used to investigate relationships within and among map layers Hugag Counts Hugag Density Surface Hugag Activity draped over Elevation Hugag Discrete Map Surface Continuous Map Surface 2 Hugags every 30 min for 30 days Most of the activity is on the NE ridge in cover type 14 near steep slopes toward the river Avg- 17.49 StDev= 14.99 (Berry)

  6. Surface Modeling(Mapping the Variance) Numeric Distribution — Average, Standard Deviation Continuous Surface —Geographic Distribution The “iterative smoothing” process is similar to slapping a big chunk of modeler’s clay over the “data spikes,” then taking a knife and cutting away the excess to leave a continuous surface that encapsulates the Peaks and valleys implied in the field samples – Spatial Distribution (Berry)

  7. Spatial Interpolation maps the geographic distribution inherent in the data Corn Field Phosphorous (P) Data “Spikes” IDW Surface Spatial Interpolation(soil nutrient levels) (Berry)

  8. Comparison of the IDWinterpolated surface to the whole field average shows large differences in localized estimates (-16.6 to 80.4 ppm) Comparison of the IDW interpolated surface to the Krig interpolated surface shows small differences in localized estimates (-13.3 to 11.7 ppm) Comparing Spatial Interpolation Results (Berry)

  9. Interpolated Spatial Distribution Phosphorous (P) What spatial relationships do you see? Spatial Data Mining …do relatively high levels of P often occur with high levels of K and N? …how often? …where? HUMANS can “see” broad generalized patterns in a single map variable (Berry)

  10. COMPUTERS can “see” detailed patterns in multiple map variables Clustering Maps for Data Zones …groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones (Berry)

  11. The Precision Ag Process(Fertility example) As a combine moves through a field 1) it uses GPS to check its location then 2) checks the yield at that location to 3) create a continuous map of the yield variation every few feet (dependent map variable). Steps 1–3) “As-applied” maps On-the-Fly Yield Map Intelligent Implements Prescription Map Step 4) Step 5) Derived Nutrient Maps Zone 3 Zone 2 The yield map 4) is analyzed in combination with soil, terrain and other maps (independent map variables) to derive a “Prescription Map” … Variable Rate Application Zone 1 5) …that is used to adjust fertilization levels every few feet in the field (action). …more generally termed the Spatial Data Mining Process(e.g., Geo-Business application) (Berry)

  12. Data Analysis Perspectives (review)(Data vs. Geographic Space) Traditional Analysis Map Analysis (Data Space — Non-spatial Statistics) (Geographic Space — Spatial Statistics) Field Data Standard Normal Curve fit to the data Spatially Interpolated data Central Tendency Average = 22.0 StDev = 18.7 Typical How Typical Discrete Spatial Object (Generalized) Continuous Spatial Distribution (Detailed) 22.0 28.2 Identifies the Central Tendency Maps the Variance (Berry)

  13. Precision Conservation (Farm, Watershed,… Focus) Precision Ag (Individual Field Focus) Wind Erosion Chemicals SoilErosion Terrain Runoff Leaching Soils Leaching Leaching Yield Potassium 3-dimensional CIR Image (Stewardship Focus) (Production Focus) 2-dimensional Interconnected Perspective Isolated Perspective Precision Conservation(compared to Precision Ag) (Berry)

  14. “The Science” Prescriptive Modeling Grid-Based Map Analysis • Surface Modelingmaps the spatial distribution and pattern of point data… • Map Generalization— characterizes spatial trends (tilted plane) • Spatial Interpolation— deriving spatial distributions (e.g. IDW, Krig) • Other— roving windows and facets (e.g., density surface; tessellation) • Spatial Data Mininginvestigates the “numerical” relationships in mapped data… • Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering) • Predictive— relationships among maps (e.g., regression) • Prescription— appropriate actions (e.g., decision rules; optimization) • Spatial Analysisinvestigates the “contextual” relationships in mapped data… • Reclassify— reassigns map values (position, value, size, shape, contiguity) • Overlay— map coincidence (point-by-point; region-wide; map-wide) • Distance— proximity and connection (movement; optimal paths; visibility) • Neighbors— roving windows (slope; aspect; diversity; anomaly) Spatial Statistics Spatial Analysis (Nanotechnology) Geotechnology (Biotechnology) (Berry)

  15. Spatial Analysis(example procedures) Slopemap …relative terrain steepness Elevation Flowmap …relative amount of water …continuous Map Surface (grid-based analysis frame) Map Stack …whereas Spatial Statistics investigates Numerical Relationships, Spatial Analysis investigates Geographic Context Roads & Water …far from Roads …not seen …seen a lot Simple Proximity to Roads Viewshed from Roads Visual Exposure from Roads (Berry) (Berry)

  16. Inclination of a fitted plane to a location and its eight surrounding elevation values Slope (47,64) = 33.23% Slope map draped on Elevation Slopemap Flow (28,46) = 451 Paths Elevation Surface Total number of the steepest downhill paths flowing into each location Flow map draped on Elevation Flow map Calculating Slope and Flow(terrain analysis)

  17. Erosion_potential Slope_classes Flow/Slope Slopemap Flow_classes Flowmap Individual Map Analysis Operations But all buffer-feet are not the same… Need to reach farther under some conditions and not as far under others— common sense? Protect the stream Simple Buffer – fixed geographic reach Deriving Erosion Potential(terrain modeling) Erosion Potential

  18. Distance away from the streams is a function of the erosion potential (Flow/Slope Class) with intervening heavy flow and steep slopes computed as effectively closer than simple distance— “as the crow walks” Effective Erosion Distance Erosion Buffers Close Far Simple Buffer Heavy/Steep (far from stream) Erosion_potential Light/Gentle (close) Effective Distance Variable-width Buffers Streams Calculating Effective Distance(variable-width buffer) (Berry)

  19. WhereisWhat WhyandSo What Multimedia Mapping GIS Modeling Mapping involves precise placement (delineation) of physical features (graphic) Analysis involves investigation of spatial relationships (numerical) Descriptive Mapping Prescriptive Modeling Conclusions Geotechnology promises to forever change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications Remote Sensing, GPS, Internet Mapping, Desktop Mapping, Multimedia Mapping, Spatial Statistics and Spatial Analysis (Berry)

  20. Where to go from here… www.innovativegis.com GPS – Google Earth — and Beyond(# OSHR 1502 100 ) Thursdays October 9, 16, 23, 30 from 5:00 p.m. to 7:00 p.m. and Saturday field lab, October 25 from 9:00 a.m. to 1:00 p.m. Osher Lifelong Learning Institute Colorado State University, Division of Continuing Education Phone: 303-376-2618 Web Site: http://www.learn.colostate.edu/fortcollins/osher/

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