1. Workshop on GIS Modeling (Part 3)
Introduction to GIS Modeling Week 9 — Spatial Data Mining GEOG 3110 –University of Denver
Presented by Joseph K. Berry
W. M. Keck Scholar, Department of Geography, University of Denver
Basic Descriptive Statistics and its GIS Expression:
Normalizing maps; Mapping spatial dependency
Linking Numeric and Geographic Patterns:
Map comparison; Similarity maps; Clustering mapped data;
Investigating map correlation; Developing prediction models; Assessing prediction results
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

2. Workshop on GIS Modeling (Part 3)
Kicking for the Finish
Workshop on GIS Modeling (Part 3)
Exercise #9 — to tailor your work to your interests, you can choose to not complete this standard exercise, however in lieu of the exercise you will submit a short paper (4-8 pages) on a GIS modeling topic of your choice. Due 12:00 midnight, Thursday, March 10th.
Optional Exercises — you can turn in these exercises for extra credit anytime before 5:00 pm, Tuesday, March 15th.
Final Exam Study Questions — covering weeks 7-10, Spatial Statistics and Future Directions; the exam is optional and can only improve your grade. You are encouraged to study together and exchange insights about answering the questions.
…at least three-fourths of the exam will be taken directly (verbatim) from the list of study questions. The format will be similar to the last exam, with questions from Terminology, Procedures and Basic Concepts, How Things Work and Mini-Exercises.
…study questions for Exam 2 are posted on the class website now; send me an if a question needs further explanation and I will post the clarification.
Final Exam — Covers material from weeks 7 (GIS Modeling), 8 (Surface Modeling), 9 (Spatial Data Mining) and 10 (Future Directions)
…exam posted on the class website by 10:00 am, Thursday, March 10th and must be completed by
8:00 am, Monday, March 14th.
… at the end of the last class I will be handing out a CD with all of the class material—
sort of a “graduation present” that will keep you GIS-ing for years
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

3. Concepts in GIS -- Topic #1
An Analytic Framework for GIS Modeling
See
(Last week) Surface Modelling operations involve creating continuous spatial distributions from point sampled data (univariate).
(This week) Spatial Data Mining operations involve characterizing numerical patterns and relationships among mapped data (multivariate).
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

4. Basic Concepts in Statistics (Standard Normal Curve)
Workshop on GIS Modeling (Part 3)
Basic Concepts in Statistics (Standard Normal Curve)
See Beyond Mapping III , Topic 7, Linking Data Space and Geographic Space
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

5. Basic Concepts in Statistics (SN_Curve Shape)
Workshop on GIS Modeling (Part 3)
Basic Concepts in Statistics (SN_Curve Shape)
Kurtosis …shape
(positive= peaked; negative= flat)
See Beyond Mapping III , Topic 7, Linking Data Space and Geographic Space
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

6. Basic Concepts in Statistics (SN_Curve Shape continued)
Workshop on GIS Modeling (Part 3)
Basic Concepts in Statistics (SN_Curve Shape continued)
…Multi-modal
…Skewness
(positive= right;
negative= left)
See Beyond Mapping III , Topic 7, Linking Data Space and Geographic Space
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

7. Workshop on GIS Modeling (Part 3)
Preprocessing Mapped Data (Preprocessing Types 1-3)
Preprocessing involves conversion of raw data into consistent units that accurately represent mapped conditions (4 considerations)
Calibration 1 — “tweaking” the values… sort of like a slight turn on a bathroom scale to alter the reading to what you know is your ‘true weight’
Translation 2 — converts map
values into appropriate units for
analysis, such as feet into meters
or bushels per acre (measure of
volume) into tons per hectare
(measure of mass)
Adjustment/Correction 3 —
dramatically changes the
data, such as post processing
GPS coordinates and/or Mass Flow Lag adjustment
Antenna Offset
GPS Fix Delay
Overlap and Multiple Passes
Mass Flow Lag and Mixing
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

8. Workshop on GIS Modeling (Part 3)
Normalizing Mapped Data (4th type of preprocessing)
Workshop on GIS Modeling (Part 3)
Normalization — involves standardization of a data set, usually for comparison among different types of data…
Goal …Norm_GOAL = (mapValue / 250 ) * 100
…Norm_ = ((mapValue – min) * 100) / (max – min) + 0
SNV …Norm_SNV = ((mapValue - mean) / stdev) * 100
Since normalization involves scalar mathematics (constants), the pattern of the numeric distribution (histogram) and the spatial distribution (map) do not change
…same relative distributions
Norm_GOAL = (Yield_Vol / 250 ) * 100
…generates a standardized map based on a yield goal of 250 bushels/acre. This map can be used in analysis with other goal-normalized maps, even from different crops
“apples and oranges to mixed fruit scale”
Key Concept
Note: the generalized rescaling equation is…
  Normalize a data set to a fixed range of Rmin to Rmax = (((X-Dmin) * (Rmax – Rmin)) / (Dmax – Dmin)) + Rmin
…where Rmin and Rmax is the minimum and maximum values for the rescaled range, Dmin and Dmax is the minimum and maximum values for the input data and X is any value in the data set to be rescaled.
See Beyond Mapping III , Topic 18, Understanding Grid-based Data
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

9. Workshop on GIS Modeling (Part 3)
Assessing Localized Variation
Workshop on GIS Modeling (Part 3)
Question 1 – Visual Map Analysis
(Spatial and Numeric distributions)
The “Scan” operation moves a window around the yield map and calculates the Coefficient of Variation with a 2-cell radius of each location
Scan Yield_Volume
Coffvar
Within 2
For Yield_Coffvar
Where,
Coffvar = Stdev/mean *100
…higher values indicate areas
with more localized variability
CoffVar= (StDev/Mean) * 100
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

10. Workshop on GIS Modeling (Part 3)
Data Proximity/Buffer Stratification
…proximity to field edge
…Proximity map identifies the distance from point, line or polygon features to all other locations
Edge effects
“Sweet Spot” (interior)
…Stratification partitions the data (numeric) or the project area (spatial) into logical groups—
…Yield map
…unusually high yield
> Average + 1Stdev
…proximity to high yield areas
“High Yield”
vicinity
Far :
Close
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

11. Workshop on GIS Modeling (Part 3)
Summarizing Map Regions (template/data)
…creates a map summarizing values from a data map (Phosphorous levels) that coincide with the categories of a template map (Soil types)
BIB
Phosphorous
levels
Soil
Types
Soil Type Ve
VdC
BIB
BIA
TuC
HvB
Pavg
15.0
12.8
11.2
14.6
10.5
11.3
…average phosphorous level for each soil type
…average P-level for each soil unit
(clump first before COMPOSITE)
Overall BIA
Pavg = 14.6
15.5
13.6
8.6
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

12. Workshop on GIS Modeling (Part 3)
Comparing Discrete Maps (Multivariate analysis)
High
Medium
Low
Thematic Categorization
…we often represent continuous spatial data (map surfaces) as a set of discrete polygons
Which classified map is correct?
How similar are the three maps?
Spatial Precision
(Where — boundaries)
of Points, Lines and Areas (polygons) is a primary concern of GIS, but we are often less concerned with
Thematic Accuracy
(What — map values)
See Beyond Mapping III , Topic 10, Analyzing Map Similarity and Zoning
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

13. Workshop on GIS Modeling (Part 3)
Comparing Discrete Maps
Workshop on GIS Modeling (Part 3)
Two ways to compare Discrete Maps…
Coincidence Summary
Proximal Alignment
…Coincidence Summary generates a cross-tabular listing of the intersection of two maps.
Table Interpretation
Diagonal (Same)
Off-diagonal (Above/Below)
Percentages (% Same)
Overall Percentage
(( )/1950)*100= 83%
(( )/1950)*100= 68%
Raster versus Vector
693
See Beyond Mapping III , Topic 10, Analyzing Map Similarity and Zoning
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

14. Workshop on GIS Modeling (Part 3)
Comparing Discrete Maps
Workshop on GIS Modeling (Part 3)
Two ways to compare Discrete Maps…
Coincidence Summary
Proximal Alignment
…Coincidence Summary generates a cross-tabular listing of the intersection of two maps.
Table Interpretation
Diagonal (Same)
Off-diagonal (Above/Below)
Percentages (% Same)
Overall Percentage
(( )/1950)*100= 83%
(( )/1950)*100= 68%
Raster versus Vector
Question 2
Map2: Med = 626; (297/626) *100= 47 percent matched
= 1621; (1621/1950) *100= 83 percent matched
Map1
Map2
Map1
= 1335; (1335/1950) *100= 68 percent matched
Map3: Med = 912; (297/912) *100= 36 percent matched
Map3
See Beyond Mapping III , Topic 10, Analyzing Map Similarity and Zoning
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

15. Coincidence Table (idealized conditions)
Workshop on GIS Modeling (Part 3)
Coincidence Table (idealized conditions)
208
208/208
= 100%
Low
Med
High
624/624
Total
…PERFECT COINCIDENCE where all of the increasing ordinal steps are matched (diagonal) and there is no mismatches (off-diagonal).
Overall coincidence is 624/624 = 100% found by the sum of the diagonal elements (matches); the other totals indicate percent agreement by category on each map
Diagonal elements in the map comparison matrix identify agreement (matches) between two progressive ordinal maps
Off-diagonal elements in the map comparison matrix identify disagreement (miss-matches) between two progressive ordinal maps 69 70
69/208
= 33%
Low
Med
High
208/624
Total
…EQUALLY BALANCED matches and mismatches where there is no pattern relationship
Overall coincidence = 33%
partially matched and mismatched
104
0/208
= 0%
Low
Med
High
0/624
Total
…ALL MISMATCHES where there is an opposite relationship
Overall coincidence = 0%
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

16. Workshop on GIS Modeling (Part 3)
Comparing Discrete Maps
Workshop on GIS Modeling (Part 3)
Two ways to compare Discrete Maps…
Coincident Summary
Proximal Alignment
Proximity_Map1_Category1 * Binary_Map3_Category1
…non-zero values identify changes and how far away
…Proximal Alignment isolates a category on one of the maps, generates its proximity, then identifies the proximity values that align with the same category on the other map.
Table Interpretation
Zeros (Agreement)
Values (> Disagreement)
PA Index (average)
See Beyond Mapping III , Topic 10, Analyzing Map Similarity and Zoning
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

17. Workshop on GIS Modeling (Part 3)
Comparing Map Surfaces (Statistical Tests)
Workshop on GIS Modeling (Part 3)
Three ways to compare Map Surfaces…
Statistical Tests
Percent Difference
Surface Configuration
…Statistical Tests compare one set of cell values to that of another based on the differences in the distributions of the data— 1) data sets (partition or coincidence; continuous or sampled) 2) statistical procedure (t-Test, f-Test, etc.)
Box-and-whisker
graphs
See Beyond Mapping III , Topic 10, Analyzing Map Similarity and Zoning
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

18. Workshop on GIS Modeling (Part 3)
Comparing Map Surfaces (%Difference)
Workshop on GIS Modeling (Part 3)
Three ways to compare Map Surfaces…
Statistical Tests
Percent Difference
Surface Configuration
…Percent Difference capitalizes on the spatial arrangement of the values by comparing the values at each map location— %Difference Map, %Difference Table
Question 3
See Beyond Mapping III , Topic 10, Analyzing Map Similarity and Zoning
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

19. Workshop on GIS Modeling (Part 3)
Comparing Map Surfaces (Surface Configuration)
Three ways to compare Map Surfaces…
Statistical Tests
Percent Difference
Surface Configuration
…Surface Configuration capitalizes on the spatial arrangement of the values by comparing the localized trend in the values — Slope Map, Aspect Map, Surface Configuration Index
See Beyond Mapping III , Topic 10, Analyzing Map Similarity and Zoning
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

20. Workshop on GIS Modeling (Part 3)
Comparing Map Surfaces (Temporal Difference)
1997_Yield_Volume
- 1998_Yield_Volume
= Yield_Diff
Map Variables… map values within an analysis grid can be mathematically and statistically analyzed
…green indicates areas of increased production
…yellow indicates minimal change
…red indicates decreased production
(Berry)
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

21. Data Analysis (establishing relationships)
Workshop on GIS Modeling (Part 3)
Data Analysis (establishing relationships)
On-farm studies, such as seed hybrid performance, can be conducted using actual farm conditions…
…management action recommendations are based on local relationships instead of
Experiment Station research hundreds of miles away
…is radically changing research and management practices in agriculture and
numerous other fields from business to epidemiology and natural resources
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

22. Workshop on GIS Modeling (Part 3)
Spatial Dependency
Spatial Variable Dependence — what occurs at a location in geographic space is related to:
the conditions of that variable at nearby locations, termed Spatial Autocorrelation (intra-variable dependence)
the conditions of other variables at that location, termed Spatial Correlation (inter-variable dependence)
Map Stack– relationships among maps are investigated by aligning grid maps with a common configuration… #cols/rows, cell size and geo-reference.
Data Shishkebab– each map represents a variable, each grid space a case and each value a measurement with all of the rights, privileges, and responsibilities of non-spatial mathematical , numerical and statistical analysis
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

23. Visualizing Spatial Relationships
Workshop on GIS Modeling (Part 3)
Visualizing Spatial Relationships
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?
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

24. Workshop on GIS Modeling (Part 3)
Identifying Unusually High Measurements
…isolate areas with mean + 1 StDev (tail of normal curve)
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

25. Workshop on GIS Modeling (Part 3)
Level Slicing
…simply multiply the two maps to identify joint coincidence
1*1=1 coincidence (any 0 results in zero)
Question 4
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

26. Workshop on GIS Modeling (Part 3)
Multivariate Data Space
…sum of a binary progression (1, 2 ,4 8, 16, etc.) provides
level slice solutions for many map layers
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

27. Workshop on GIS Modeling (Part 3)
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
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

28. Workshop on GIS Modeling (Part 3)
Identifying Map Similarity
Question 5
…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 Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

29. Clustering Maps for Data Zones
Workshop on GIS Modeling (Part 3)
Question 6
…groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones
…a map stack is a spatially organized set of numbers
…fertilization rates vary for the different clusters “on-the-fly”
Variable Rate Application
Cyber-Farmer, Circa 1992
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

30. Workshop on GIS Modeling (Part 3)
Assessing Clustering Results
…Clustering results can be roughly evaluated using basic statistics
Average, Standard Deviation, Minimum and Maximum values within each cluster are calculated. Ideally the averages between the two clusters would be radically different and the standard deviations small—large difference between groups and small differences within groups.
Standard Statistical Tests
of two data sets
Box and Whisker Plots
to visualize differences
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

31. How Clustering Works (IsoData algorithm)
Workshop on GIS Modeling (Part 3)
How Clustering Works (IsoData algorithm)
The scatter plot shows Height versus Weight data that might have been collected in your old geometry class
The data distance to each weight/height measurement pair is calculated and the point is assigned to the closest arbitrary cluster center
The average X,Y coordinates of the assigned students is calculated and used to reposition the cluster centers
Repeat data distances, cluster assignments and repositioning until no change in cluster membership (centers do not move)
(Berry)
See Beyond Mapping III , Topic 7, Linking Data Space and Geographic Space
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

32. Spatial Data Mining (The Big Picture)
Workshop on GIS Modeling (Part 3)
Spatial Data Mining (The Big Picture)
…making sense out of a map stack
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
See Beyond Mapping III , Topic 16, Characterizing Patterns and Relationships
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

33. Concepts in GIS -- Topic #1
An Analytic Framework for GIS Modeling
This Week
Spatial Data Mining operations involve characterizing numerical patterns and relationships among mapped data.
See
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

34. Workshop on GIS Modeling (Part 3)
Regression (conceptual approach)
A line is “fitted” in data space that balances the data so the differences from the points to the line (residuals) for all the points are minimized
and the sum of the differences is zero…
…the equation of the regression line is used to predict the
“Dependent” variable (Y axis) using one or more “Independent” variables (X axis)
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

35. Workshop on GIS Modeling (Part 3)
Evaluating Prediction Maps (non-spatial)
Non-spatial …R-squared value looks at the deviations from the regression line; data patterns about the regression line
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

36. Workshop on GIS Modeling (Part 3)
Map Variables
The Dependent Map variable is the one that you want to predict…
…derive from
customer data
Question 7
…from a set of existing or easily measured Independent Map variables
See Beyond Mapping III , Topic 28, Spatial Data Mining in Geo-Business
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

37. Workshop on GIS Modeling (Part 3)
Map Regression Results (Bivariate)
Scatter plots and regression equations relating Loan Density
to three candidate driving variables (Housing Density, Value and Age)
Loans= fn( Housing Density )
Loans= fn( Home value )
Loans= fn( Home Age )
Question 8
The “R-squared index” provides a general measure of how good the predictions ought to be—
40%, 46% indicates a moderately weak predictors; 23% indicates a very weak predictor
(R-squared index = 100% indicates a perfect predictor; 0% indicates an equation with no predictive capabilities)
See Beyond Mapping III , Topic 28, Spatial Data Mining in Geo-Business
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

38. Workshop on GIS Modeling (Part 3)
Generating a Multivariate Regression
…a regression equation using all three independent map variables using
multiple linear regression is used to generate a prediction map
Question 9
See Beyond Mapping III , Topic 28, Spatial Data Mining in Geo-Business
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

39. Workshop on GIS Modeling (Part 3)
Evaluating Regression Results (multiple linear)
Optional Question 9-1
…a regression equation using all three independent map variables using multiple linear regression is used to generate a prediction map
…that is compared to the actual dependent variable data — Error Surface
See Beyond Mapping III , Topic 28, Spatial Data Mining in Geo-Business
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

40. Workshop on GIS Modeling (Part 3)
Using the Error Map to Stratify
One way to improve the predictions, however, is to stratify the data set by breaking it into groups of similar characteristics …and then generating separate regressions
…generate a different regression for each of the stratified areas– red, yellow and green
…other stratification techniques include indigenous knowledge,
level-slicing and clustering
See Beyond Mapping III , Topic 28, Spatial Data Mining in Geo-Business
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

41. Concepts in GIS -- Topic #1
An Analytic Framework for GIS Modeling
This Week
Spatial Data Mining operations involve characterizing numerical patterns and relationships among mapped data.
See
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

42. Workshop on GIS Modeling (Part 3)
Prescriptive Mapping
Four primary types of applied spatial models:
Suitability— mapping preferences (e.g., Habitat and Routing)
Economic— mapping financial interactions (e.g., Combat Zone and Sales Propensity)
Physical— mapping landscape interactions (e.g., Terrain Analysis and Sediment Loading)
Mathematical/Statistical— mapping numerical relationships…
Descriptive math/stat models summarize existing mapped data
(e.g., Standard Normal Variable Map for Unusual Conditions and Clustering for Data Zones)
Predictive math/stat models develop equations relating mapped data
(e.g., Map Regression for Equity Loan Prediction and Probability of Product Sales )
Prescriptive math/stat models identify management actions based on descriptive/predictive relationships (e.g., Retail Marketing and Precision Ag)…
Phosphorous (P)
Discrete Actions: If <condition(s)> Then <Action(s)>
If P is 0-4 ppm, then apply 50 lbs P2O5/Acre
If P is 4-8 ppm, then apply 30 lbs P2O5/Acre
If P is 8-12 ppm, then apply 15 lbs P2O5/Acre
If P is >12 ppm, then apply 0 lbs P2O5/Acre 50
12 more P
P2O5/ 30 15
Continuous Actions: Equation defining action(s)
Negative linear equation of the form: y = aX
Negative exponential equation of the form: y = e-x 50
12 more P
P2O5/
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

43. Workshop on GIS Modeling (Part 3)
Grid-Based Map Analysis
Spatial analysis investigates the “contextual” relationships in mapped data…
Reclassify— reassigning map values (position; value; size, shape; contiguity)
Overlay— map overlay (point-by-point; region-wide)
Distance— proximity and connectivity (movement; optimal paths; visibility)
Neighbors— ”roving windows” (slope/aspect; diversity; anomaly)
...Whew!!!
Surface modeling maps the spatial distribution and pattern of point data…
Density Analysis— count/sum of points within a local window
Spatial Interpolation— weighted average of points within a local window
Map Generalization— fits mathematical relationship to all of the point data
Spatial data mining investigates the “numerical” relationships in mapped data…
Descriptive— summary statistics, comparison, classification (e.g., clustering)
Predictive— math/stat relationships among map layers (e.g., regression)
Prescriptive— appropriate actions (e.g., optimization)
(Berry)
Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.