GIS Modeling Week 1 — Overview GEOG 3110 –University of Denver Concepts in GIS -- Topic #1 GIS Modeling Week 1 — Overview GEOG 3110 –University of Denver Presented by Joseph K. Berry W. M. Keck Scholar, Department of Geography, University of Denver Course overview; GIS mapping, management and modeling; Discrete (map objects) vs. continuous (map surfaces); Linking data and geographic distributions; Framework for map-ematical processing Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

(Nanotechnology) Geotechnology (Biotechnology) Keynote Address, ESRI SWUG 2009 Geotechnology is one of the three "mega technologies" for the 21st century and promises to forever change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications (U.S. Department of Labor) Global Positioning System (location and navigation) Geographic Information Systems (map and analyze) Remote Sensing (measure and classify) GPS/GIS/RS The Spatial Triad Where is What Mapping involves precise placement (delineation) of physical features (graphical inventory) Descriptive Mapping Modeling involves analysis of spatial relationships and patterns (numerical analysis) Prescriptive Modeling Why So What and What (Berry)

Keynote Address, ESRI SWUG 2009 Historical Setting and GIS Evolution Manual Mapping for 8,000+ years We have been mapping for thousands of years with the primary of navigation through unfamiliar terrain and seas, with emphasis on precise placement of physical features. …but the last four decades have radically changed the very nature of maps and how they are used— Computer Mapping …automates the cartographic process (70s) Where Spatial Database Management …links computer mapping with database capabilities (80s) Where is What … GIS Modeling course Map Analysis …representation of relationships within and among mapped data (90s) Why, So What and What If… Multimedia Mapping …full integration of GIS, Internet and visualization technologies (00s) Wow!!! …did you see that (Berry) Joseph K. Berry, all rights reserved

Keynote Address, ESRI SWUG 2009 Desktop Mapping Framework (Vector, Discrete) Click on… Select Theme Zoom Pan Info Tool Theme Table Distance Query Builder …identify tall aspen stands Big …over 400,000m2 (40ha)? : Object ID X,Y Feature Species etc. : : Object ID Aw Spatial Table Attribute Discrete, irregular map features (objects) Points, Lines and Areas (Berry) Joseph K. Berry, all rights reserved

Manual GIS (Geo-query circa 1950) 1) Index Card with series of numbered holes around the edge and written description/data in the center Data Table (attribute records) Spatial Table (spatial objects) What Hole Index card (tray) 1 2 3 4 5 6 7 8 9 10 2) Special Punch was used to notch-out the hole assigned to a particular characteristic (attribute), such as #11 notch = Douglas fir timber type Notch #11 12 13 14 15 Timber Stand Map (wall) Where 3) Pass a long Needle through the stack of cards and lift… Cards pulled up… Hole … DO NOT have characteristic Query Tray holds all of the index cards for a project area 5) Card ID# identifies the timber stand polygons from the search and the appropriate locations are shaded— …a “Database-entry Geo-query” #57 #57 Cards falling down… … HAVE characteristic Notch 4) Repeat using the search results sub-set for more characteristics (Berry)

Map Analysis Framework (Raster, Continuous) Click on… Zoom Pan Rotate Display Shading Manager Grid Analysis …calculate a slope map and drape on the elevation surface Map Stack 2D contour map of elevation default display (Tutor25 dB)– polygons of 200-foot interval range Click Layer Mesh button– analysis grid superimposed; note elevation values interpolated “on-the-fly” as more than one elevation value in a grid cell Click Use Cells button– switches to grid display type; each cell shows the elevation value stored at that location; note single value per cell Click 3D Toggle button– switches to 3D Grid display Press Zoom In button– drag a rectangular portion of the top of the mountain to enlarge; move cursor to display values Press Zoom Out button– click and drag (down) to resize to a smaller plot Press Reset View button– resets to the default plot Double-click on the map– pops up Data drill down window; move cursor around and note the “stack” of map values for different locations Press Use Cells button– switches to 3D wireframe plot Click Map Analysis button– select Neighbors class and Slope function; enter “SLOPE Elevation Fitted FOR Slopemap”; move cursor around to inspect slope values… red (gentle) to green (steep) From the Main menu, Windows  Elevation– to restore the 3D Elevation display From the Main menu, Map  Overlay  Slopemap– to graphically overlay the Slopemap on the Elevation surface; note that the gentle areas (red) and the steep areas (green) align with the correct terrain features depicted on the surface …minimize MapCalc and proceed to the next slide Continuous, regular grid cells (objects) Points, Lines, Areas and Surfaces : --, --, --, --, --, 2438, --, Grid Table (Berry) Joseph K. Berry, all rights reserved

Concepts in GIS -- Topic #1 Course Description and Syllabus www.innovativegis.com/basis/Courses/GMcourse11/ Who are we? …self-introductions …break …class photo Class website Email dialog Student Statements Topics and Schedule Basic Concepts Spatial Analysis GIS Modeling Spatial Statistics Future Directions (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Concepts in GIS -- Topic #1 Textbook and Companion CD-ROM …Required Reading …pop quizzes and in-class questions on required reading Course Textbook CD Materials …Further Reading Recommended/ Optional …Text Figure slide set (color) …Optional Exercises at end of each topic …Example Applications …MapCalc software, data, tutorials and manual …Surfer software, sample data and tutorials …SnagIt software (recommended) Access Default.htm …to view & install materials …Other Reading Online (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Concepts in GIS -- Topic #1 Links to Class Materials (Class Webpage) http://www.innovativegis.com/basis/Courses/GMcourse10/ Class folder in GIS lab The GIS Modeling course’s main page contains links to course Administrative Materials and Readings, Lectures, and Homework assignments Links to Reading Assignments — required readings are from the course Text with some Recommended and Optional readings on the CD and posted online Links to Lecture Notes — lecture slide sets are posted Wednesdays by 5:00pm; available in the GIS Lab Thursdays by 12:00noon Links to Homework Assignments — exercise templates are downloaded then completed in teams and submitted to class Dropbox Links to Software — all of the software/data used in the class are on the class CD or available for download (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

History/Evolution of Map Analysis Geotechnology – one of the three “mega-technologies” for the 21st Century (the other two are Nanotechnology and Biotechnology, U.S. Department of Labor)   http://www.innovativegis.com/basis/Papers/Other/GISmodelingFramework/ 70s Computer Mapping (Automated Cartography) 80s Spatial Database Management (Mapping and Geo-query) 90s Map Analysis (Spatial Relationships and Patterns) Global Positioning System (Location and Navigation) Remote Sensing (Measure and Classify) Geographic Information Systems (Map and Analyze)  Framework Paper Organizational Structure of this Course Spatial Analysis (Geographical context) Reclassify (single map layer; no new spatial information) Overlay (coincidence of two or more map layers; new spatial information) Proximity (simple/effective distance and connectivity; new spatial information) Neighbors (roving window summaries of local vicinity; new spatial information) Spatial Statistics (Numerical context) Surface Modeling (point data to continuous spatial distributions Spatial Data Mining (interrelationships within and among map layers) (Berry)

Concepts in GIS -- Topic #1 Mapped Data Analysis Evolution (Revolution) Traditional GIS Points, Lines, Polygons Discrete Objects Mapping and Geo-query Forest Inventory Map Spatial Analysis Cells, Surfaces Continuous Geographic Space Contextual Spatial Relationships Elevation (Surface) Traditional Statistics Mean, StDev (Normal Curve) Central Tendency Typical Response (scalar) Minimum= 5.4 ppm Maximum= 103.0 ppm Mean= 22.4 ppm StDEV= 15.5 Spatial Statistics Map of Variance (gradient) Spatial Distribution Numerical Spatial Relationships (Surface) (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Calculating Slope and Flow (map analysis) Concepts in GIS -- Topic #1 Calculating Slope and Flow (map analysis) Elevation Surface (Berry) Inclination of a fitted plane to a location and its eight surrounding elevation values (Neighbors) Slope (47,64) = 33.23% Slope map draped on Elevation Slope map Total number of the steepest downhill paths flowing into each location (Distance) Flow (28,46) = 451 Paths Flow map draped on Elevation Flow map Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Concepts in GIS -- Topic #1 Deriving Erosion Potential & Buffers Concepts in GIS -- Topic #1 Erosion Potential Flowmap Slopemap Slope_classes Flow_classes Flow/Slope Erosion_potential Reclassify Overlay Erosion_potential But all buffer-feet are not the same… (slope/flow Erosion_potential) …reach farther in areas of high erosion potential Protective Buffers Simple Buffer Streams (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Concepts in GIS -- Topic #1 Calculating Effective Distance (variable-width buffers) Erosion_potential Streams Erosion Buffers Distance Distance away from the streams is a function of the erosion potential (Flow/Slope Class) with intervening heavy flow and steep slopes computed as effectively closer than simple distance— “as the crow walks” Effective Buffers (digital slide show VBuff) Effective Erosion Distance Close Far Heavy/Steep (far from stream) Light/Gentle (close) Simple Buffer (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Classes of Spatial Analysis Operators mini-Workshop on Map Analysis and Modeling Classes of Spatial Analysis Operators …all Spatial Analysis involves generating new map values (numbers) as a mathematical or statistical function of the values on another map layer(s) —sort of a “map-ematics” for analyzing spatial relationships and patterns— GIS Toolbox (Geographic Context) Reclassify operations involve reassigning map values to reflect new information about existing map features on a single map layer Overlay operations involve characterizing the spatial coincidence of mapped data on two or more map layers (Berry) Joseph K. Berry, all rights reserved

Classes of Spatial Analysis Operators (Geographic) mini-Workshop on Map Analysis and Modeling Classes of Spatial Analysis Operators (Geographic) …all Spatial Analysis involves generating new map values (numbers) as a mathematical or statistical function of the values on another map layer(s) —sort of a “map-ematics” for analyzing spatial relationships and patterns— GIS Toolbox (Geographic Context) Proximity operations involve measuring distance and connectivity among map locations Neighborhood operations involve characterizing mapped data within the vicinity of map locations (Berry) Joseph K. Berry, all rights reserved

Travel-Time for Our Store to Everywhere Keynote Address, ESRI SWUG 2009 Travel-Time for Our Store to Everywhere A store’s Travelshed identifies the relative driving time from every location to the store— …analogous to a “watershed” Relative scale: 1 = .05 minutes OUR STORE …close to the store (blue) (Berry) Joseph K. Berry, all rights reserved

Travel-Time for Competitor Stores Keynote Address, ESRI SWUG 2009 Ocean Competitor 1 Ocean Competitor 2 Ocean Our Store (#111) Ocean Competitor 3 Ocean Competitor 4 Ocean Competitor 5 Travel-Time maps from several stores treating highway travel as four times faster than city streets. Blue tones indicate locations that are close to a store (estimated twelve minute drive or less). Customer data can be appended with travel-time distances and analyzed for spatial relationships in sales and demographic factors. (Berry) Joseph K. Berry, all rights reserved

Travel-Time Surfaces (Our Store & Competitor #4) Keynote Address, ESRI SWUG 2009 Travel-Time Surfaces (Our Store & Competitor #4) Blue tones indicate locations that are close to a store (estimated twelve minute drive or less). Increasingly warmer tones form a bowl-like surface with larger travel-time values identifying locations that are farther away. Our Store Competitor (Berry) Joseph K. Berry, all rights reserved

Competition Map (Our Store & Competitor #4) Keynote Address, ESRI SWUG 2009 The travel-time surfaces for two stores can be compared (subtracted) to identify the relative access advantages throughout the project area. Zero values indicate the same travel-time to both stores (equidistant travel-time) …yellow tones identifying the Combat Zone ; green Our Store advantage; red Competitor #4 advantage Competitor Our Advantage Positive Negative Our Store Competitors (See Location, Location, Location: Retail Sales Competition Analysis, www.innovativegis.com/basis/present/GW06_retail/GW06_Retail.htm) (Berry) Joseph K. Berry, all rights reserved

Concepts in GIS -- Topic #1 Mapped Data Analysis Evolution (Revolution) Logistics …break …using MapCalc and Snagit for Lab #1 …on to Spatial Statistics (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Concepts in GIS -- Topic #1 Setting Up and Using Class Data Moving MapCalc Data to your personal workspace Right click on Start at the bottom left of your screen (Task Bar) Select Windows Explorer Locate your personal workspace as directed by the instructor (Z: drive) Create a new folder in your workspace called …\GISmodeling In the new folder create a sub-folder …\GISmodeling\MapCalc Data Browse to the …\GEOG3110 class directory (I: drive) Highlight all of the files/folders MapCalc Data folder on the class directory and select Copy Go to your new …\GISmodeling\MapCalc Data sub-folder and Paste the MapCalc Data files Suggested folder organization …\GISmodeling\MapCalc Data\ (…just created folder containing MapCalc base data) …\GISmodeling\Week1\ (contains all of the data, scripts, screen grabs, etc. developed for week 1) …\GISmodeling\Week2\ (contains all of the data, scripts, screen grabs, etc. developed for week 2) …etc. Example Exercise …download Exer0.doc to your …\GISmodeling\Week1\ folder and complete under the instructor’s guidance (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Concepts in GIS -- Topic #1 GIS Modeling Framework (Model Criteria) …rows represent Model Criteria (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Concepts in GIS -- Topic #1 GIS Modeling Framework (Analysis Levels) …columns represent Analysis Levels …column transitions represent Processing Approaches “Algorithm” “Calibration” “Weighting” Slope Spread Radiate Orient Renumber Analyze …analytic operations are sequenced on map variables to implement the model’s logic Modeled Base Derived Interpreted (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Campground Suitability Model (Macro script) Concepts in GIS -- Topic #1 Campground Suitability Model (Macro script) …the map analysis logic ingrained in the flowchart is translated into a logical series of map analysis commands (MapCalc) Tutor25_Campground Script Derive (Algorithm) Gentle slopes Near roads Near water Good views Westerly Interpret (Calibrate) Combine (Weight) Mask (Constraints) (See “Short description of the Campground model” and “Helpful hints in Running MapCalc” in the Email Dialog section of the Class Webpage) (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Concepts in GIS -- Topic #1 Homework Exercise #1 Concepts in GIS -- Topic #1 Question #1 Model Criteria #2 Analysis Levels #3 Derived Maps #4 Calibrated #6 Masking #7 Fancy Display Optional Questions #1-1 and #1-2 #5 Analyze Command Confirm Homework Team— the class will be divided into teams containing two to three members Download Exercise #1— “Links to Homework,” right-click on “Exer1.doc” and choose “Save” to download …and then access the exercise in Word Complete the exercise: Due next week Thursday 5:00 pm (7 days) (…slippage possible if requested by noon) (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Concepts in GIS -- Topic #1 Mapped Data Analysis Evolution (Revolution) Traditional GIS Points, Lines, Polygons Discrete Objects Mapping and Geo-query Forest Inventory Map Spatial Analysis Cells, Surfaces Continuous Geographic Space Contextual Spatial Relationships Effective Distance (Surface) Traditional Statistics Mean, StDev (Normal Curve) Central Tendency Typical Response (scalar) Minimum= 5.4 ppm Maximum= 103.0 ppm Mean= 22.4 ppm StDEV= 15.5 Spatial Statistics Map of Variance (gradient) Spatial Distribution Numerical Spatial Relationships (Surface) (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

Classes of Spatial Statistics Operators (Spatial Statistics) mini-Workshop on Map Analysis and Modeling Classes of Spatial Statistics Operators (Spatial Statistics) …all Spatial Analysis involves generating new map values (numbers) as a mathematical or statistical function of the values on another map layer(s) —sort of a “map-ematics” for analyzing spatial relationships and patterns— GIS Toolbox (Numeric Context) Surface Modeling operations involve creating continuous spatial distributions from point sampled data Spatial Data Mining operations involve characterizing numerical patterns and relationships within and among mapped data (Berry) Joseph K. Berry, all rights reserved

GeoExploration vs. GeoScience “Maps are numbers first, pictures later” 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 (Numeric Distribution) 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) Continuous Spatial Distribution Discrete Spatial Object 22.0 Spatially Generalized Detailed 40.7 …not a problem Adjacent Parcels High Pocket Discovery of sub-area… (See Beyond Mapping III, “Epilog”, Technical and Cultural Shifts in the GIS Paradigm, www.innovativegis.com/basis ) (Berry)

Point Density Analysis mini-Workshop on Map Analysis and Modeling Point Density analysis identifies the total number of customers within a specified distance of each grid location Roving Window (count) (See Beyond Mapping III, “Epilog”, Technical and Cultural Shifts in the GIS Paradigm, www.innovativegis.com/basis ) (Berry) Joseph K. Berry, all rights reserved

Identifying Unusually High Density mini-Workshop on Map Analysis and Modeling High Customer Density pockets are identified as more than one standard deviation above the mean Unusually high customer density (>1 Stdev) (See Beyond Mapping III, “Topic 26”, Spatial Data Mining in Geo-business, www.innovativegis.com/basis) (Berry) Joseph K. Berry, all rights reserved

Concepts in GIS -- Topic #1 Spatial Interpolation (Smoothing the Variability) 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 …repeated smoothing slowly “erodes” the data surface to a flat plane = AVERAGE (digital slide show SStat2) (Berry) Joseph K. Berry, BA_SIS, Inc. All Rights Reserved.

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

mini-Workshop on Map Analysis and Modeling 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 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) (Berry) Joseph K. Berry, all rights reserved

Clustering Maps for Data Zones mini-Workshop on Map Analysis and Modeling Clustering Maps for Data Zones Groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones or Clusters …a map stack is a spatially organized set of numbers …data distances are minimized within a group (intra-cluster distance) and maximized between groups (inter-cluster distance) using an optimization procedure (See Beyond Mapping III, “Topic 7”, Linking Data Space and Geographic Space, www.innovativegis.com/basis) (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships, www.innovativegis.com/basis) (Berry) Joseph K. Berry, all rights reserved

mini-Workshop on Map Analysis and Modeling The Precision Ag Process (Fertility example) mini-Workshop on Map Analysis and Modeling 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). On-the-Fly Yield Map Steps 1) – 3) Farm dB Step 4) Map Analysis Prescription Map Step 5) Step 6) Variable Rate Application Cyber-Farmer, Circa 1992 (Berry) (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships, www.innovativegis.com/basis) Joseph K. Berry, all rights reserved

mini-Workshop on Map Analysis and Modeling Spatial Data Mining 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 Precision Farming is just one example of applying spatial statistics and data mining techniques (See Beyond Mapping III, “Topic 28”, Spatial Data Mining in Geo-business, www.innovativegis.com/basis) (Berry) Joseph K. Berry, all rights reserved

(Nanotechnology) Geotechnology (Biotechnology) Keynote Address, ESRI SWUG 2009 (Nanotechnology) Geotechnology (Biotechnology) GPS/GIS/RS Mapping involves precise placement (delineation) of physical features (graphical inventory) Descriptive Mapping Geotechnology is one of the three "mega technologies" for the 21st century and promises to forever change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications (U.S. Department of Labor) Global Positioning System (location and navigation) Geographic Information Systems (map and analyze) Where is What Remote Sensing (measure and classify) The Spatial Triad Modeling involves analysis of spatial relationships and patterns (numerical analysis) Prescriptive Modeling Why So What and What If (Berry)