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Spatial Data Mining Practical Approaches for Analyzing Relationships Within and Among Maps. “GIS technology is rapidly moving beyond mapping and spatial database management to analytical capabilities that assess spatial relationships within decision-making contexts” (JKB).

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Spatial data mining practical approaches for analyzing relationships within and among maps
Spatial Data MiningPractical Approaches for Analyzing Relationships Within and Among Maps

“GIS technology is rapidly moving beyond mapping and spatial database management to analytical capabilities that assess spatial relationships within decision-making contexts”(JKB)

Presented byJoseph K. Berry

W.M. Keck Scholar in Geosciences, University of Denver

Berry & Associates // Spatial Information Systems2000 S. College Ave, Suite 300, Fort Collins, CO 80525Phone: (970) 215-0825 Email: jberry@innovativegis.com…visit our Website atwww.innovativegis.com/basis


Visually comparing maps
Visually Comparing Maps

I bet you've seen and heard it a thousand times before  a speaker waves a laser pointer at a couple of maps and says something like "see how similar the patterns are."

But just how similar are the maps?

…what proportion has the same classification?

…where are they different?

…where are they the same?

and of course, your response should be objective and repeatable

(Berry)



Coincidence summary results table 1
Coincidence Summary Results (Table 1)

(Table 1)

(Berry)


Proximal alignment results table 2
Proximal Alignment Results (Table 2)

(Table 2)

(Berry)



Statistical test results table 3
Statistical Test Results (Table 3)

…Statistical Tests of entire surface or partitioned areas

(Table 3)

(Berry)


Percent difference results table 4
Percent Difference Results (Table 4)

…Percent Difference between two map surfaces

(Table 4)

(Berry)


Surface configuration results table 5
Surface Configuration Results (Table 5)

The two superimposed maps at the left side of figure show the normalized differences in the slope and aspect angles (dark red being very different). The map of the overall differences in surface configuration (Sur_Fig Index) is the average of the two maps.

(Table 4)

Note that over half of the map area is classified as low difference (0-20) suggesting that the two surface maps align fairly well overall.

(Berry)


Visualizing spatial relationships

Phosphorous (P)

Visualizing Spatial Relationships

Interpolated Spatial Distribution

What spatial relationships do you see?

…do relatively high levels of P often occur with high levels of K and N?

…how often?

…where?

(Berry)


Spatial data mining practical approaches for analyzing relationships within and among maps

Calculating Data Distance

…an n-dimensional plot depicts the multivariate distribution; the distance between points determines the relative similarity in data patterns

…the closest floating ball is the least similar (largest data distance) from the comparison point

(Berry)


Spatial data mining practical approaches for analyzing relationships within and among maps

Identifying Map Similarity

…the relative data distance between the comparison point’s data pattern and those of all other map locations form a Similarity Index

The green tones indicate field locations with fairly similar P, K and N levels; red tones indicate dissimilar areas.

(See Map Analysis, “Topic 16, Calculating Map Similarity” for more information)

(Berry)


Clustering maps for data zones

…a map stack is a spatially organized set of numbers

…groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones

(Cyber-Farmer, Circa 1990)

Variable Rate Application

Clustering Maps for Data Zones

…fertilization rates vary for the different clusters “on-the-fly”

(Berry)



Spatial data mining practical approaches for analyzing relationships within and among maps

Map Surface Correlation/Regression

Histogram/Map View--Data Space(joint magnitude of values)

are linked toGeographic Space(position of values)

(Berry)


Spatial data mining practical approaches for analyzing relationships within and among maps

Creating Prediction Models (Scatter Plot)

…aScatter Plot identifies the “joint condition” at each map location; the trend in the plot forms a prediction equation

(Berry)


Spatial data mining practical approaches for analyzing relationships within and among maps

Deriving a Predictive Index (NDVI)

…an index combining the Red and NIR maps can be used to generate a better predictive model

Normalized Difference Vegetation IndexNDVI= ((NIR – Red) / (NIR + Red))

for the sample grid locationNDVI= ((121-14.7) / (121 + 14.7))= 106.3 / 135.7= .783

(Berry)


Spatial data mining practical approaches for analyzing relationships within and among maps

Evaluating Prediction Maps (Spatial error analysis)

…the regression equation is evaluated and the predicted map is compared to the actual measurements to generate an error map

Error = Predicted - Actual

for the sample grid locationYest = 55 + (180 * .783) = 196 …error is 196 – 218 = 22 bu/ac

Exercise #8c, page 30– Create a regression model relating Yield and NDVI

Note that the average error is 2.62 and 67% of the predictions are within +/- 20 bu/ac

Also, most of the error is concentrated along the edge of the field

(See Map Analysis, “Topic 16, Predicting Maps” for more information)

(Berry)


Spatial data mining practical approaches for analyzing relationships within and among maps

Stratifying by Error Zones

Other ways to stratify mapped data—

1) Geographic Zones, such as proximity to the field edge; 2) Dependent Map Zones, such as areas of low, medium and high yield; 3) Data Zones, such as areas of similar soil nutrient levels; and 4) Correlated Map Zones, such as micro terrain features identifying small ridges and depressions.

Stratifying Maps for Better Predictions

The Error Zones map is used as a template to identify the NDVI and Yield values used to calculate three separate prediction equations.

A Composite Prediction map is created by applying the equations to the NDVI data respecting the template map zones.

(See Map Analysis, “Topic 16, Stratifying Maps for Better Predictions” for more information)

(Berry)


Spatial data mining practical approaches for analyzing relationships within and among maps

Actual

Yield

Whole Field

Prediction

Error Map

Stratified

Prediction

Error Map for

Stratified Prediction

none

80%

none

Assessing Prediction Results

(Berry)


Spatial data mining practical approaches for analyzing relationships within and among maps

The Precision Ag Process(Fertility example)

Steps 1)–3)

Step 5)

Prescription Map

On-the-Fly

Yield Map

Map Analysis

Step 6)

Cyber-Farmer, Circa 1992

…come a long ways baby

Step 4)

Farm dB

Zone 3

Zone 2

Zone 1

Variable Rate Application

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. This map 4) is combined with soil, terrain and other

maps to derive a 5) “Prescription Map” that is used to

6) adjust fertilization levels every few feet in the field.

(Berry)


Spatial data mining

Mapped data that exhibits high spatial dependency create strong prediction functions. As in traditional statistical analysis, spatial relationships can be used to predict outcomes

…the difference is that spatial statistics

predicts where responses will be high or low

Spatial Data Mining

…making sense out of a map stack

(Berry)


Spatial data mining practical approaches for analyzing relationships within and among maps1
Spatial Data MiningPractical Approaches for Analyzing Relationships Within and Among Maps

Presented byJoseph K. Berry

W.M. Keck Scholar in Geosciences, University of Denver

Berry & Associates // Spatial Information Systems2000 S. College Ave, Suite 300, Fort Collins, CO 80525Phone: (970) 215-0825 Email: jberry@innovativegis.com…visit our Website atwww.innovativegis.com/basis