GIS Modeling Week 1 — Overview GEOG 3110 –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 Presented by Joseph K. Berry W. M. Keck Scholar, Department of Geography, University of Denver

(Nanotechnology) Geotechnology (Biotechnology) GPS/GIS/RS Modeling involves analysis of spatial relationships and patterns (numerical analysis) PrescriptiveModeling Mapping involves precise placement (delineation) of physical features (graphical inventory) DescriptiveMapping 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) Why So What and What If… Global Positioning System (location and navigation) Geographic Information Systems (map and analyze) Where is What (Berry) Remote Sensing (measure and classify) The Spatial Triad

Historical Setting and GIS Evolution Computer Mapping …automates the cartographic process (70s) Map Analysis …representation of relationships within and among mapped data (90s) 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. Where is What Wow!!! …did you see that …but the last four decades have radically changed the very nature of maps and how they are used— the very nature of maps and how they are used— Where Spatial Database Management …links computer mapping with database capabilities (80s) … the focus of this GIS Modeling course Why, So What and What If… (Berry) …the 2010s await characterization Multimedia Mapping (Geo-web) …full integration of GIS, Internet and visualization technologies (00s)

Descriptive Mapping Framework (Vector, Discrete) Mapping Select Theme Zoom Pan InfoToolThemeTable Distance : Object ID X,YX,YX,Y : Feature Species etc. Feature Species etc. : : : : Object ID Aw : : : :SpatialTableAttributeTable Discrete, irregular map features (objects) Points, Lines and Areas (Berry) QueryBuilder …identify tall aspen stands Big …over 400,000m 2 (40ha)? Geo-query

Map Analysis Framework (Raster, Continuous) Click on… Zoom Pan Rotate Display ShadingManager Continuous, regular grid cells (objects) Points, Lines, Areas and Surfaces : --, --, --, --, --, 2438, --, --, --, --, --, : Grid Table GridAnalysis …calculate a slope map and drape on the elevation surface Map Stack (Berry)

Course Description and Syllabus (Berry) Grading Topics and Schedule Basic Concepts Spatial Analysis GIS Modeling Spatial Statistics Future Directions

…Required Reading … occasional in-class questions on required reading Course Textbook Textbook and Companion CD-ROM 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)

Links to Class Materials (Class Webpage) 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) Class folder in GIS lab …updated on Thursdays before class The GIS Modeling course’s main page contains links to course Administrative Materials and Readings, Lectures, and Homework assignments

Geotechnology Geotechnology – one of the three “mega-technologies” for the 21 st Century (the other two are Nanotechnology and Biotechnology, U.S. Department of Labor ) 70s Computer Mapping (Automated Cartography) 80s Spatial Database Management (Mapping and Geo-query) Map Analysis 90s Map Analysis (Spatial Relationships and Patterns) Global Positioning System (Location and Navigation) Remote Sensing (Measure and Classify) Geographic Information Systems Geographic Information Systems (Map and Analyze) History/Evolution of Map Analysis Spatial Statistics Spatial Statistics (Numerical context) Surface Modeling (point data to continuous spatial distributions Spatial Data Mining (interrelationships within and among map layers) Spatial Analysis 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) Framework Paper Organizational Structure of this Course (Berry)

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= 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) Mapped Data Analysis Evolution (Revolution) 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 Elevation(Surface) (Berry)

Elevation Surface (Berry) Calculating Slope and Flow (map analysis) Inclination of a fitted plane to a location and its eight surrounding elevation values (Neighbors) Total number of the steepest downhill paths flowing into each location Total number of the steepest downhill paths flowing into each location (Distance) Slope (47,64) = 33.23% Slope map draped on Elevation Slope map Flow (28,46) = 451 Paths Flow map draped on Elevation Flow map

Erosion Potential Flowmap Slopemap Erosion_potential But all buffer-feet are not the same… (slope/flow Erosion_potential) …reach farther in areas of high erosion potential  Erosion_potentialFlow/Slope Slope_classes Flow_classes Deriving Erosion Potential & Buffers Protective Buffers (Berry) Simple Buffer StreamsSimple Buffer …distance is “as the crow flies” Reclassify Overlay Reclassify …High erosion on steep slopes with heavy flows

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” “as the crow walks” Calculating Effective Distance (variable-width buffers) (Berry) Effective Buffers (digital slide show VBuff) VBuff Effective Erosion Distance CloseFar Heavy/Steep (far from stream) Light/Gentle (close) Simple Buffers Effective Buffers

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 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— —sort of a “map-ematics” for analyzing spatial relationships and patterns— GIS Toolbox (Geographic Context) (Berry)

Proximity operations involve measuring distance and connectivity among map locations– both “simple and effective distance” Neighborhood operations involve characterizing mapped data within the vicinity of each map location– “roving windows” 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— —sort of a “map-ematics” for analyzing spatial relationships and patterns— GIS Toolbox (Geographic Context) (Berry)

Relative scale: 1 =.05 minutes Travel-Time for Our Store to Everywhere OUR STORE …close to the store (blue) A store’s Travelshed identifies the relative driving time from every location to the store— …analogous to a “watershed” …analogous to a “watershed” (Berry)

Travel-Time for Competitor Stores Ocean 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. Our Store (#111) Ocean Competitor 1 Ocean Competitor 2 Ocean Competitor 3 Ocean Competitor 4 Ocean Competitor 5 (Berry)

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)

Competition Map (Our Store & Competitor #4) 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 Our Store Competitor Negative Positive Our Advantage Competitors (See Location, Location: Retail Sales Competition Analysis, (See Location, Location: Retail Sales Competition Analysis,

Mapped Data Analysis Evolution (Revolution) (Berry) Geographic Context Numeric Context …after a brief break in thought Exercise #1 Exercise #0 Setup Logistics Who are we? (class photo; books; break) …then… just to make sure you are comfortable with Homework Exercises  …and then on to Spatial Statistics

Setting Up and Using Class Data Moving MapCalc Data to your personal workspace 1)Right click on Start at the bottom left of your screen (Task Bar) 2)Select Windows Explorer 3)Locate your personal workspace as directed by the instructor (Z: drive) 4)Create a new folder in your workspace called …\GISmodeling 5)In the new folder create a sub-folder …\GISmodeling\MapCalc Data 6)Browse to the …\GEOG3110 class directory (I: drive) 7)Highlight all of the files/folders MapCalc Data folder on the class directory and select Copy 8)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\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\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) …\GISmodeling\Week2\ (contains all of the data, scripts, screen grabs, etc. developed for week 2) …etc. …etc. Example Exercise …download Exer0.doc to your …\GISmodeling\Week1\ folder and complete under the instructor’s guidance (Berry)

Exercise #0 (dry run) Use MapCalc to generate a 3D display of the Elevation surface … …in Lattice display format… …use Snagit to capture plot and paste into document …change to Grid display format… …use Snagit to capture plot and paste into document …briefly describe the differences you see between Lattice and Grid displays … the document to me …briefly describe the differences you see between Lattice and Grid displays … the document to me (Berry)

Homework Exercise #1 Download Exercise #1— “Links to Homework,” right-click on “Exer1.doc” and choose “Save” to download …and then access the exercise in Word Confirm Homework Teams— the class will be divided into teams containing two to three members Question #1 Model Criteria #1 Model Criteria #2 Analysis Levels #3 Derived Maps #4 Calibrated Maps #6Masking #7FancyDisplay Optional Questions #1-1 and #1-2 #5AnalyzeCommand Complete the exercise: Due next week Thursday 5:00 pm (7 days) (…slippage possible if requested by noon)

…rows represent Model Criteria GIS Modeling Framework (Model Criteria) Where are the best places for a campground? (Berry)

GIS Modeling Framework (Analysis Levels) …columns represent Analysis Levels …column transitions represent Processing Approaches Slope Spread Radiate Orient Renumber Analyze …map analysis operations are sequenced on map variables to implement the model’s logic “Algorithm”“Calibration”“Weighting” BaseDerived Interpreted Modeled (Berry)

Campground Suitability Model (Macro script) …the map analysis logic ingrained in the flowchart is translated into a logical series of map analysis commands (MapCalc) Derive (Algorithm) Gentle slopes Gentle slopes Near roads Near roads Near water Near water Good views Good views Westerly Westerly Interpret (Calibrate) Combine(Weight)Mask(Constraints) Tutor25_Campground Script (See “Short description of the Campground model” and “Helpful hints in Running MapCalc” in the Dialog section of the Class Webpage) (See “Short description of the Campground model” and “Helpful hints in Running MapCalc” in the Dialog section of the Class Webpage)(Berry)

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= 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) Mapped Data Analysis Evolution (Revolution) 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 EffectiveDistance(Surface) (Berry)

Spatial Data Mining operations involve characterizing numerical patterns and relationships within and among mapped data Classes of Spatial Statistics Operators (Spatial Statistics) Surface Modeling operations involve creating continuous spatial distributions from point sampled data …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— —sort of a “map-ematics” for analyzing spatial relationships and patterns— GIS Toolbox (Numeric Context) (Berry)

GeoExploration vs. GeoScience 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”,, ) (See Beyond Mapping III, “Epilog”, Technical and Cultural Shifts in the GIS Paradigm, )(Berry)

Point Density Analysis 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”,, ) (See Beyond Mapping III, “Epilog”, Technical and Cultural Shifts in the GIS Paradigm, )(Berry)

Identifying Unusually High Density 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, (See Beyond Mapping III, “Topic 26”, Spatial Data Mining in Geo-business,

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 = …repeated smoothing slowly “erodes” the data surface to a flat plane = AVERAGE (digital slide show SStat2) SStat2 (Berry)

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? Phosphorous (P) Geographic Distribution Multivariate Analysis— each map layer is a Multivariate Analysis— each map layer is a continuous variable with all of the math/stat continuous 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” (Berry)

Calculating Data Distance …an n-dimensional plot depicts the multivariate distribution— the distance between points determines the relative similarity in data patterns 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, (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships,

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 …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, (See Beyond Mapping III, “Topic 7”, Linking Data Space and Geographic Space, (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships, (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships,

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). Farm dB Step 4) Map Analysis On-the-Fly Yield Map Steps 1) – 3) Step 6) Variable Rate Application Cyber-Farmer, Circa 1992 Prescription Map Step 5) (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships, (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships,

Spatial Data Mining Precision Farming is just one example of applying spatial statistics and data mining techniques 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 (See Beyond Mapping III, “Topic 28”, Spatial Data Mining in Geo-business, (See Beyond Mapping III, “Topic 28”, Spatial Data Mining in Geo-business,

(Nanotechnology) Geotechnology (Biotechnology) GPS/GIS/RS Mapping involves precise placement (delineation) of physical features (graphical inventory) DescriptiveMapping 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) PrescriptiveModeling Why So What and What If (Berry) “Big Picture” take-home 