2. Introduction to GIS Modeling Week 7 — GIS Modeling Examples GEOG 3110 –University of Denver Presented by Joseph K. Berry W. M. Keck Scholar, Department of Geography, University of Denver Example Real-World Projects; Introduction to Spatial Statistics (revisited); mini-Project Working Session

3. Class Logistics and Schedule Berry Exercise #6 (mini-project) — you will form your own teams (1 to 4 members) and tackle one of the eight projects; we will discuss the project “opportunities” in great detail later in class …assigned tonight Thursday, February 11 and final report due Monday, February 20 by 5:00pm …assigned tonight Thursday, February 11 and final report due Monday, February 20 by 5:00pm Exercises #8 and #9 — you can tailor to your interests by choosing to not complete either or both of these standard exercises; in lieu of an exercise, however, you must submit a short paper (4-8 pages) on a GIS modeling topic of your own choosing. I need to know your choices by next Wednesday as I will form new teams for exercises #8 and #9. Submit via two emails, one with report Body attached and the other with Appendix attached No Exercise Week 7 — a moment for “a dance of celebration” Midterm Study Questions (hopefully you are participating in a study group) Blue Light Special …20 minutes of Instructor “Help” on midterm study question “toughies “ Midterm Exam …you will download and take the 2-hour exam online (honor system) sometime between 8:00 am Friday February 10 and must be completed by 5:00 pm Wednesday February 15

4. Map Analysis Evolution (Revolution) (Berry) Spatial Analysis Cells, Surfaces Cells, Surfaces Continuous Geographic Space Continuous Geographic Space Contextual Spatial Relationships Contextual Spatial Relationships StoreTravel-Time(Surface) Traditional Statistics Mean, StDev (Normal Curve) Mean, StDev (Normal Curve) Central Tendency Central Tendency Typical Response (scalar) Typical Response (scalar) Minimum= 5.4 ppm Maximum= 103.0 ppm Mean= 22.4 ppm StDev= 15.5 Spatial Statistics Map of Variance (gradient) Map of Variance (gradient) Spatial Distribution Spatial Distribution Numerical Spatial Relationships Numerical Spatial Relationships Spatial Distribution (Surface) Traditional GIS Points, Lines, Polygons Points, Lines, Polygons Discrete Objects Discrete Objects Mapping and Geo-query Mapping and Geo-query Forest Inventory Map …past six weeks

5. BP Pipeline Routing (Global Model) (Berry) The simulation is queued for processing then displayed as the Optimal Route (blue line) and 1% Optimal Corridor (cross-hatched) 1% Corridor Fort Collins San Diego Optimal Path 4% Corridor FC SD (digital slide show BP_Pipeline_routing) BP_Pipeline_routing

6. Increased population growth into the wildland/urban interface raises the threat of disaster… Modeling Wildfire Risk (Berry) (digital slide show Wildfire Risk Modeling) Wildfire Risk ModelingWildfire Risk Modeling …a practical method is needed to identify areas most likely to be impacted by wildfire so effective pre-treatment, suppression and recovery plans can be developed

7. Modeling Retail Competition (Berry) (digital slide show Combat Zone)Combat Zone

8. Is Technology Ahead of Science? Are geographic distributions a natural extension Are geographic distributions a natural extension of numerical distributions? of numerical distributions? (Berry) Is the "scientific method" relevant in the Is the "scientific method" relevant in the data-rich age of knowledge engineering? data-rich age of knowledge engineering? Is the "random thing" pertinent in deriving Is the "random thing" pertinent in deriving mapped data? mapped data? Can spatial dependencies be modeled? Can spatial dependencies be modeled? How can commercial “on-site studies" How can commercial “on-site studies" augment traditional research? augment traditional research? “Maps as Data”

9. Map Analysis Evolution (Revolution) (Berry) Traditional GIS Points, Lines, Polygons Points, Lines, Polygons Discrete Objects Discrete Objects Mapping and Geo-query Mapping and Geo-query Forest Inventory Map Spatial Analysis Cells, Surfaces Cells, Surfaces Continuous Geographic Space Continuous Geographic Space Contextual Spatial Relationships Contextual Spatial Relationships StoreTravel-Time(Surface) Traditional Statistics Mean, StDev (Normal Curve) Mean, StDev (Normal Curve) Central Tendency Central Tendency Typical Response (scalar) Typical Response (scalar) Minimum= 5.4 ppm Maximum= 103.0 ppm Mean= 22.4 ppm StDev= 15.5 Spatial Statistics Map of Variance (gradient) Map of Variance (gradient) Spatial Distribution Spatial Distribution Numerical Spatial Relationships Numerical Spatial Relationships Spatial Distribution (Surface) …next week

10. Spatial Statistics Operations – Numerical Context Map Analysis Toolbox Grid Map Layers GIS and Map-ematical Perspectives (SA) Berry Basic Descriptive Statistics (Min, Max, Median, Mean, StDev, etc.) Basic Classification (Reclassify, Binary/Ranking/Rating Suitability) Unique Map Descriptive Statistics (Roving Window Summaries) Map Comparison (Joint Coincidence, Statistical Tests) Surface Modeling (Density Analysis, Spatial Interpolation) Advanced Classification (Map Similarity, Maximum Likelihood, Clustering) Predictive Statistics (Map Correlation/Regression, Data Mining Engines) Map-ematical Perspective: Surface Modeling (Density Analysis, Spatial Interpolation, Map Generalization) Spatial Data Mining (Descriptive, Predictive, Prescriptive) GIS Perspective:

11. Map-ematical Perspective (Examples) Berry Discrete Point Map Keystone concept is… “Spatial Autocorrelation” In Data Space, a standard normal curve can be fitted to the histogram of the map surface data to identify the “typical value” (Average)– fits a Curve. Data Space Standard Normal Curve Average = 22.9 StDev = 18.7 Numeric Distribution Geographic Space 22.9 + 1StDev (41.6) -1StDev (4.2) …lots of NE locations exceed +1Stdev In Geographic Space, this typical value forms a horizontal plane implying the average is everywhere. X= 22.9 …click anywhere on the map surface and the corresponding histogram pillar is highlighted …click anywhere on the histogram and all map locations in that range are highlighted Continuous Map Surface Geographic Distribution Surface Modeling techniques are used to derive a continuous map surface from discrete point data– fits a Surface to the data. Inverse Distance Weighted (IDW) spatial interpolation assigned distance- weighted average of sample points

12. Spatial Interpolation (Spatial 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 original field samples …mapping the Variance …repeated smoothing slowly “erodes” the data surface to a flat plane = AVERAGE (Berry) (digital slide show SSTAT) SSTAT

13. Visualizing Spatial Relationships 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) Phosphorous (P) Geographic Distribution Multivariate Analysis — each map layer is a Multivariate Analysis — each map layer is a continuous map variable with all of the math/stat continuous map variable with all of the math/stat “rights, privileges and responsibilities” therewith …simply “spatially organized “ sets of numbers (matrix) “rights, privileges and responsibilities” therewith …simply “spatially organized “ sets of numbers (matrix) “Maps are numbers first, pictures later”

14. Calculating Data Distance …an n-dimensional plot depicts the multivariate distribution— the distance between points determines the relative similarity in data patterns (Berry) PythagoreanTheorem 2D Data Space: Dist = SQRT (a 2 + b 2 ) Dist = SQRT (a 2 + b 2 ) 3D Data Space: Dist = SQRT (a 2 + b 2 + c 2 ) Dist = SQRT (a 2 + b 2 + c 2 ) …expandable to N-space …this response pattern (high, high, medium) is the least similar point as it has the largest data distance from the comparison point (low, low, medium) (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships, www.innovativegis.com/basis) (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships, www.innovativegis.com/basis) Actual data in JMP

15. …groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones Spatial Data Mining Geographic Space Relatively low responses in P, K and N Relatively high responses in P, K and N Clustered Data Zones Map surfaces are clustered to identify data pattern groups Clustering Maps (Berry) Data Space …other techniques, such as Level Slicing, Similarity and Map Regression, can be used to discover relationships among map layers …map-ematics/statistics

16. The Precision Ag Process (Fertility example) As a combine moves through a field it 1) uses GPS to check its location then 2) checks the yield at that location to 3) create a continuous map of the 2) checks the yield at that location to 3) create a continuous map of the yield variation every few feet. This map is yield variation every few feet. This map is 4) combined with soil, terrain and other maps to 4) combined with soil, terrain and other maps to derive 5) a “Prescription Map” that is used to derive 5) a “Prescription Map” that is used to 6) adjust fertilization levels every few feet 6) adjust fertilization levels every few feet in the field (variable rate application). in the field (variable rate application). (Berry) Farm dB Step 4) Map Analysis On-the-Fly Yield Map Steps 1) – 3) Prescription Map Step 5) Zone 1 Zone 3 Zone 2 Step 6) Variable Rate Application Cyber-Farmer, Circa 1992

17. Who is doing what… Alicia and Michael are working on the Landslide Susceptibility Project Paulina and Graham are working on the Visual Exposure to Timber Harvesting Project Rob and Courtney are working on the Hugag Habitat Project Sharon and Mingming are working on the Wildfire Risk Analysis Project …mini-projects working session (Berry) …deleted Spatial Analysis “enrichment” slide sets (Optional) (digital slide show ForestAccess) ForestAccess Topic 29Topic 29 – Spatial Modeling in Natural Resources (digital slide show TerrainFeatures ) TerrainFeatures Topic 11Topic 11 – Characterizing Micro Terrain Features