Introduction to GIS Modeling Week 7 GIS

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

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 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: 00 pm What should we do about submitting “large” mini-Project Reports …? ? ? 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 #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. Berry

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 (digital slide show BB-BK) So What, Why and What If Spatial Reasoning and Dialogue Tropical Resources Institute Yale University — 1988 Infusing Stakeholder Perspectives Compaq II Portable Computer Summagraphics Bit Pad Digitizer (Berry)

Map Analysis Evolution (Revolution) Traditional GIS Spatial Analysis …past six weeks Forest Inventory Map • Points, Lines, Polygons • Discrete Objects • Mapping and Geo-query Traditional Statistics Minimum= 5. 4 ppm Maximum= 103. 0 ppm Mean= 22. 4 ppm St. Dev= 15. 5 St. Dev= • Mean, St. Dev (Normal Curve) • Central Tendency • Typical Response (scalar) Store Travel-Time (Surface) • Cells, Surfaces • Continuous Geographic Space • Contextual Spatial Relationships Spatial Statistics Spatial Distribution (Surface) • Map of Variance (gradient) • Spatial Distribution • Numerical Spatial Relationships (Berry)

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) FC Fort Collins 4% Corridor SD 1% Corridor San Diego (digital slide show BP_Pipeline_routing) Optimal Path (Berry)

Modeling Wildfire Risk 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) (Berry)

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

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

Is Technology Ahead of Science? • 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)

Map Analysis Evolution (Revolution) Traditional GIS Forest Inventory Map • Points, Lines, Polygons • Discrete Objects • Mapping and Geo-query Traditional Statistics Spatial Analysis Store Travel-Time (Surface) • Cells, Surfaces • Continuous Geographic Space • Contextual Spatial Relationships Spatial Statistics …next week Minimum= 5. 4 ppm Maximum= 103. 0 ppm Mean= 22. 4 ppm St. Dev= 15. 5 St. Dev= • Mean, St. Dev (Normal Curve) • Central Tendency • Typical Response (scalar) Spatial Distribution (Surface) • Map of Variance (gradient) • Spatial Distribution • Numerical Spatial Relationships (Berry)

Geo. Exploration vs. Geo. Science “Maps are numbers first, pictures later” Desktop Mapping graphically links generalized statistics to discrete spatial objects (Points, Lines, Polygons)— non-spatial analysis (Geo. Exploration) Desktop Mapping Map Analysis X, Y, Value Data Space Field Data Geographic Space Standard Normal Curve Point Sampled Data (Numeric Distribution) Average = 22. 0 St. Dev = 18. 7 40. 7 …not a problem Discrete Spatial Object 22. 0 Spatially Generalized (Geographic Distribution) High Pocket Continuous Spatial Distribution Spatially Detailed Discovery of sub-area… Adjacent Parcels Map Analysis map-ematically relates patterns within and among continuous spatial distributions (Map Surfaces)— spatial analysis and statistics (Geo. Science) (See Beyond Mapping III, “Epilog”, Technical and Cultural Shifts in the GIS Paradigm, www. innovativegis. com/basis ) , (Berry)

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)

Visualizing Spatial Relationships Phosphorous (P) 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)

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

Clustering Maps …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 Data Space Clustered Data Zones …other techniques, such as Level Slicing, Similarity and Map Regression, can be used to discover relationships among map layers …map-ematics/statistics (Berry)

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 yield variation every few feet. This map is Steps 1) – 3) 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). On-the-Fly Yield Map Farm d. B Step 4) Map Analysis Zone 3 Cyber-Farmer, Circa 1992 Zone 1 Prescription Map Variable Rate Application Step 5) Step 6) (Berry)