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Geotechnology
Not Your Grandfather’s Map
Joseph K. Berry
CSU Alumnus, MS in Business Management ’72 and PhD emphasizing Remote Sensing ‘76
W.M. Keck Scholar in Geosciences, University of Denver
Principal, Berry & Associates // Spatial Information Systems
1701 Lindenwood Drive, Fort Collins, CO 80524
Phone: (970) 215-0825 Email: [email protected]
Website at
www.innovativegis.com/basis
(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
Geographic Information
Systems
Global Positioning
System
Remote Sensing
GPS/GIS/RS
Where
Mapping involves
precise placement
(delineation) of
physical features
(graphic)
Descriptive
Mapping
is
What
Prescriptive
Modeling
Analysis involves
investigation of
spatial
relationships
(numerical)
(Berry)
(Nanotechnology)
Geotechnology
(Biotechnology)
“The Sizzle”
Descriptive
Mapping
GPS Navigation
Internet Mapping
Last Year
Desktop Mapping
Multimedia Mapping
… we created a
multimedia map
of Pingree Park
(Berry)
(Nanotechnology)
Geotechnology
(Biotechnology)
“The Science”
Prescriptive
Modeling
Grid-Based Map Analysis
Surface Modeling maps 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 Mining investigates the “numerical” relationships in mapped data…
 Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering)
 Predictive— relationships among maps (e.g., regression)
Spatial
Statistics
 Prescription— appropriate actions (e.g., decision rules; optimization)
Spatial Analysis investigates 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)
Surface Modeling (Density Surface — “counts”)
A value is stored at each
grid cell location indicates
“what is where”— for
Elevation
example, a set of elevation
values form the familiar terrain
surface we hike on.
…continuous Map Surface
(grid-based analysis frame)
A paradigm shift from traditional
discrete Map Features
comprised of
Map Stack
Points,
Lines,
Polygons.
…Map Surfaces are used to investigate
relationships within and among map layers
Hugag
Counts
Hugag Density Surface
Hugag Activity draped over Elevation
Hugag
2 Hugags
every 30 min
for 30 days
Discrete
Map Surface
Avg- 17.49
StDev= 14.99
Continuous
Map Surface
Most of the activity is on
the NE ridge in cover type 14
near steep slopes toward the river
(Berry)
Surface Modeling (Mapping the Variance)
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
Numeric Distribution — Average, Standard Deviation
Continuous Surface — Geographic Distribution
(Berry)
Spatial Interpolation (soil nutrient levels)
Spatial Interpolation maps the geographic distribution inherent in the data
Corn Field Phosphorous (P)
Data “Spikes”
IDW Surface
(Berry)
Comparing Spatial Interpolation Results
Comparison of the
IDW interpolated 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)
(Berry)
Spatial Data Mining
Interpolated Spatial Distribution
Phosphorous (P)
What spatial relationships
do you see?
…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)
Clustering Maps for Data Zones
COMPUTERS can “see” detailed patterns in multiple map variables
…groups of “floating balls” in data space identify locations in the field
with similar data patterns– data zones
(Berry)
The Precision Ag Process (Fertility example)
Steps 1–3)
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).
Prescription Map
On-the-Fly
Yield Map
Zone 3
“As-applied” maps
Intelligent Implements
Step 5)
Step 4)
Derived
Nutrient Maps
Zone 2
Zone 1
Variable Rate Application
The yield map 4) is analyzed in combination with
soil, terrain and other maps (independent map
variables) to derive a “Prescription Map” …
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)
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
Typical
How Typical
22.0
28.2
Average = 22.0
StDev = 18.7
Discrete
Spatial Object
Continuous
Spatial Distribution
(Generalized)
(Detailed)
Identifies the Central Tendency
Maps the Variance
(Berry)
Precision Conservation (compared to Precision Ag)
Precision Conservation
Precision Ag
(Farm, Watershed,… Focus)
(Individual Field Focus)
Wind Erosion
Chemicals
Soil
Erosion
Runoff
Terrain
Leaching
Leaching
Leaching
Soils
Yield
Potassium
3-dimensional
Interconnected Perspective
(Stewardship Focus)
CIR Image
2-dimensional
Isolated Perspective
(Production Focus)
(Berry)
(Nanotechnology)
Geotechnology
(Biotechnology)
“The Science”
Prescriptive
Modeling
Grid-Based Map Analysis
Surface Modeling maps 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 Mining investigates the “numerical” relationships in mapped data…
 Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering)
 Predictive— relationships among maps (e.g., regression)
Spatial
Statistics
 Prescription— appropriate actions (e.g., decision rules; optimization)
Spatial Analysis investigates 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 Analysis
(Berry)
Spatial Analysis (example procedures)
Slopemap
Elevation
…relative terrain
steepness
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
Simple Proximity to Roads
…not seen
Viewshed from Roads
…seen a lot
Visual Exposure from Roads
(Berry)
Calculating Slope and Flow (terrain analysis)
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
Elevation Surface
Flow (28,46) = 451 Paths
Total number of the steepest
downhill paths flowing into each
location
Flow map draped
on Elevation
Flow map
Deriving Erosion Potential (terrain modeling)
Erosion Potential
Slopemap
Slope_classes
Flowmap
Flow_classes
Flow/Slope
Erosion_potential
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
Calculating Effective Distance (variable-width buffer)
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”
Erosion_potential
Erosion Buffers
Effective Erosion Distance
Close
Streams
Far
Simple Buffer
Heavy/Steep
(far from stream)
Light/Gentle
(close)
Effective Distance
Variable-width Buffers
(Berry)
Conclusions
Where
Mapping involves
precise placement
(delineation) of
physical features
(graphic)
is
Descriptive
Mapping
Why
Multimedia
Mapping
What
Prescriptive
Modeling
and
So What
Analysis involves
investigation of
spatial
relationships
(numerical)
GIS
Modeling
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)
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/