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 Digital Elevation Model (DEM); Basic surface modeling (interpolation) concepts; Assessing interpolation results

3. Class Logistics and Schedule Berry Exercise #6 (mini-project) — you will form your own teams (1 to 3 members) and tackle one of five projects; we will discuss the project “opportunities” in great detail later in class …assigned tonight Thursday, February 12 and final report due Sunday, February 21 by 5:00pm …assigned tonight Thursday, February 12 and final report due Sunday, February 21 by 5:00pm Exercises #7 and #8 — 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. You will form your own 1-3 member teams. 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. What should we do about submitting “large” mini-Project Reports …??? No Exercise Week 7 — a moment for “a dance of celebration” Blue Light Special …after lecture, in-house advising on mini-projects (the Doctor is in) 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 13 and must be completed by 5:00 pm Wednesday February 17

4. Spatial Data vs. Spatial Information From GIS as a Toolbox enabling display and geo-query to a Sandbox for developing, communicating, interacting and evaluating solutions to complex spatial problems— …from Where is What to So What, Why and What If (Berry) (digital slide show BB-BK) BB-BK Tropical Resources Institute Yale University — 1988 Yale University — 1988 Compaq II Portable Computer Summagraphics Bit Pad Digitizer Infusing Stakeholder Perspectives Spatial Reasoning and Dialogue

5. 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

6. 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

7. 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

8. Characterizing Major Terrain Features (Berry) (digital slide show TerrainFeatures ) TerrainFeatures

9. Modeling Forest Access (Berry) (digital slide show ForestAccess) ForestAccess

10. Is Technology Ahead of Science? 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? Are geographic distributions a natural extension Are geographic distributions a natural extension of numerical distributions? of numerical distributions? 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? (Berry)

11. 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

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

13. 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

14. 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”

15. 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)

16. …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

17. 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