Introduction to GIS Modeling Week 7 — GIS Modeling Examples GEOG 3110 –University of Denver

1. Presented byJoseph K. Berry W. M. Keck Scholar, Department of Geography, University of Denver Introduction to GIS ModelingWeek 7 — GIS Modeling ExamplesGEOG 3110 –University of Denver Example Real-World Projects; Introduction to Spatial Statistics; mini-Project Working Session

2. Class Logistics and Schedule Midterm Study Questions (hopefully you are participating in a study group) Midterm Exam…you will download and take the 2-hour exam online (honor system) sometime between 8:00 am Friday February 13 and must be completed by 5:00 pm Wednesday February 17 Blue Light Special …20 minutes of Instructor “Help” on midterm study question “toughies “ 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 Sunday, February 20 by 5:00pm Submit via two emails, one with report Body attached and the other with Appendix attached Blue Light Special …after lecture, in-house advising on mini-projects (the Doctor is in) No Exercise Week 7— a moment for “a dance of celebration” 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. Final Exam — to lighten the load at the end of the term, you can choose to forego the final exam; you will receive your average grade for all work to date. Optional Exercises can be turned in through finals week. Berry

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

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

5. Increased population growth into the wildland/urban interface raises the threat of disaster… …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 (digital slide show Wildfire Risk Modeling) Modeling Wildfire Risk (Berry)

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

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

8. GeoExplorationvs.GeoScience Desktop Mapping Data Space Field Data Map Analysis Geographic Space Standard Normal Curve Average = 22.0 StDev = 18.7 22.0 “Maps are numbers first, pictures later” Desktop Mappinggraphically links generalized statistics to discrete spatial objects (Points, Lines, Polygons)—non-spatial analysis (GeoExploration) X, Y, Value Point Sampled Data (Numeric Distribution) (Geographic Distribution) 40.7…not a problem Discrete Spatial Object Continuous Spatial Distribution High Pocket Spatially Generalized Spatially Detailed Discovery of sub-area… Adjacent Parcels Map Analysismap-ematically relates patterns within and among continuous spatial distributions (Map Surfaces)—spatial analysis and statistics (GeoScience) (See Beyond Mapping III, “Epilog”, Technical and Cultural Shifts in the GIS Paradigm, www.innovativegis.com/basis) (Berry)

9. 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 (digital slide show SSTAT) (Berry)

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

11. Calculating Data Distance …an n-dimensional plot depicts the multivariate distribution— the distance between points determines the relative similarity in data patterns Pythagorean Theorem 2D Data Space: Dist = SQRT (a2 + b2) 3D Data Space: Dist = SQRT (a2 + b2 + c2) …expandable to N-space …this response pattern (high, high, medium) is the least similar point as it has thelargest data distance from the comparison point (low, low, medium) (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships, www.innovativegis.com/basis) (Berry)

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

13. The Precision Ag Process(Fertility example) Steps 1) – 3) On-the-Fly Yield Map Map Analysis Step 4) Cyber-Farmer, Circa 1992 Farm dB Zone 3 Zone 2 Zone 1 Variable Rate Application Prescription Map Step 6) Step 5) 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 yield variation every few feet. This map is 4) combined with soil, terrain and other maps to derive 5) a “Prescription Map” that is used to 6) adjust fertilization levels every few feet in the field (variable rate application). (Berry)

14. …in-house advising on mini-projects Two teams working on Pipeline Spill Migration Two teams working on Geo-business Analysis One team on Emergency Response One team on Hugag Habitat …deleted Spatial Analysis “enrichment” slide sets (digital slide show ForestAccess) (digital slide show TerrainFeatures ) (Berry)