2. GIS Technology in Transition Moving Maps to Mapped Data, Spatial Analysis and Beyond Presented by Joseph K. Berry GIS is more different than it is similar to traditional mapping and data analysis Berry & Associates // Spatial Information Systems 2000 South College Ave, Suite 300, Fort Collins, CO 80525 Phone: (970) 215-0825 Email: jberry@innovativegis.com …visit our website at www.innovativegis.com/basis 2003 Northwest GIS User Group Meeting September 16, 2003 – Skamania Lodge, Stevenson, Washington

3. Traditional Mapping manually drafted map Historical Setting and GIS Evolution Computer Mapping automates the cartographic process (70s) automates the cartographic process (70s) Spatial Database Management links computer mapping techniques with links computer mapping techniques with traditional database capabilities (80s) traditional database capabilities (80s) GIS Modeling representation of relationships within representation of relationships within and among mapped data (90s) and among mapped data (90s) (Berry)

4. (Berry) Indelix, www.idelix.com www.idelix.com Map Display Where is What …and Wow Connectivity and Map Delivery SDT, www.spatialdatatech.com www.spatialdatatech.com WHAT -- database WHERE – Digital Map Mapped data can be queried by interacting with the map (where) or database (what) Mapped data can be queried by interacting with the map (where) or database (what) Query Builder 1) Select forest type Aspen, SP1= Aw 2) Select tall Aspen stands, Height > 20m

5. Where is What and Wow to… Why and So What (Berry) Vector-based processing provides Mapping and Geo-Query capabilities that repackage existing spatial data as reports and displays Discrete Objects Descriptive Mapping WHERE IS WHAT Grid-basedprocessing provides Map Analysis capabilities that derive new information on relationships within and among mapped data Continuous Surfaces Prescriptive Mapping WHY AND SO WHAT

6. Simple Erosion Model (Berry) …a Command Macro Language consists of a graphical interface for entering, editing, executing, documenting, storing and retrieving a GIS Model …GIS Modeling involves logical sequencing of map analysis operations Script Logic

7. Variable-Width Buffer (Sediment loading) Simple Buffer (Berry) Effectively far away, though right near a stream …how can that be? …what about different soils? …what about roughness? …or time of year?

8. Characterizing Slope (and Aspect) A digital terrain surface is formed by assigning an elevation value to each cell in an analysis grid. The “slant” of the terrain at any location can be calculated– inclination of a plane fitted to the elevation values of the immediate vicinity. Micro Terrain Analysis Calculation of slope considers the arrangement and magnitude of elevation differences “Map-ematics” (Berry) (See Map Analysis, “Topic 11” for more information) Map AnalysisMap Analysis Characterizing Surface Flow The relative amount of water passing through each grid cell is determined by simulating a drop of water landing in each cell and proceeding downhill by the steepest path. The number of paths crossing each location identifies the total uphill confluence.

9. Map Analysis Data Mining investigates the “numerical” relationships in mapped data… Descriptive — aggregate statistics (e.g., average/stdev, similarity, clustering) Descriptive — aggregate statistics (e.g., average/stdev, similarity, clustering) Predictive — relationships among maps (e.g., regression) Predictive — relationships among maps (e.g., regression) Prescriptive — appropriate actions (e.g., optimization) Prescriptive — appropriate actions (e.g., optimization) Surface Modeling maps the spatial distribution and pattern of point data… Map Generalization — characterizes spatial trends (e.g., titled plane) Map Generalization — characterizes spatial trends (e.g., titled plane) Spatial Interpolation — deriving spatial distributions (e.g., IDW, Krig) Spatial Interpolation — deriving spatial distributions (e.g., IDW, Krig) Other — roving window/facets (e.g., density surface; tessellation) Other — roving window/facets (e.g., density surface; tessellation) Spatial Analysis investigates the “contextual” relationships in mapped data… Reclassify — reassigning map values (position; value; size, shape; contiguity) Reclassify — reassigning map values (position; value; size, shape; contiguity) Overlay — map overlay (point-by-point; region-wide; map-wide) Overlay — map overlay (point-by-point; region-wide; map-wide) Distance — proximity and connectivity (movement; optimal paths; visibility) Distance — proximity and connectivity (movement; optimal paths; visibility) Neighbors — ”roving windows” (slope/aspect; diversity; anomaly) Neighbors — ”roving windows” (slope/aspect; diversity; anomaly) (Berry)

10. Map Analysis Data Mining investigates the “numerical” relationships in mapped data… Descriptive — aggregate statistics (e.g., average/stdev, similarity, clustering) Descriptive — aggregate statistics (e.g., average/stdev, similarity, clustering) Predictive — relationships among maps (e.g., regression) Predictive — relationships among maps (e.g., regression) Prescriptive — appropriate actions (e.g., optimization) Prescriptive — appropriate actions (e.g., optimization) Surface Modeling maps the spatial distribution and pattern of point data… Map Generalization — characterizes spatial trends (e.g., titled plane) Map Generalization — characterizes spatial trends (e.g., titled plane) Spatial Interpolation — deriving spatial distributions (e.g., IDW, Krig) Spatial Interpolation — deriving spatial distributions (e.g., IDW, Krig) Other — roving window/facets (e.g., density surface; tessellation) Other — roving window/facets (e.g., density surface; tessellation) Spatial Analysis investigates the “contextual” relationships in mapped data… Reclassify — reassigning map values (position; value; size, shape; contiguity) Reclassify — reassigning map values (position; value; size, shape; contiguity) Overlay — map overlay (point-by-point; region-wide; map-wide) Overlay — map overlay (point-by-point; region-wide; map-wide) Distance — proximity and connectivity (movement; optimal paths; visibility) Distance — proximity and connectivity (movement; optimal paths; visibility) Neighbors — ”roving windows” (slope/aspect; diversity; anomaly) Neighbors — ”roving windows” (slope/aspect; diversity; anomaly) (Berry)

11. Spatial Interpolation (Geographic Distribution) (Berry) “Surface Modeling” 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 by the spatial pattern of the field samples …nearby things are more alike than distant things

12. Mapping the Variance Spatial Statistics seeks to map the variance Spatial Interpolation is similar to throwing a blanket over the “data spikes” to conforming to the geographic pattern of the data. (Berry) Non-Spatial statistics seeks the “typical” condition and applies uniformly throughout geographic space-- AVERAGE

13. Map Analysis Data Mining investigates the “numerical” relationships in mapped data… Descriptive — aggregate statistics (e.g., average/stdev, similarity, clustering) Descriptive — aggregate statistics (e.g., average/stdev, similarity, clustering) Predictive — relationships among maps (e.g., regression) Predictive — relationships among maps (e.g., regression) Prescriptive — appropriate actions (e.g., optimization) Prescriptive — appropriate actions (e.g., optimization) Surface Modeling maps the spatial distribution and pattern of point data… Map Generalization — characterizes spatial trends (e.g., titled plane) Map Generalization — characterizes spatial trends (e.g., titled plane) Spatial Interpolation — deriving spatial distributions (e.g., IDW, Krig) Spatial Interpolation — deriving spatial distributions (e.g., IDW, Krig) Other — roving window/facets (e.g., density surface; tessellation) Other — roving window/facets (e.g., density surface; tessellation) Spatial Analysis investigates the “contextual” relationships in mapped data… Reclassify — reassigning map values (position; value; size, shape; contiguity) Reclassify — reassigning map values (position; value; size, shape; contiguity) Overlay — map overlay (point-by-point; region-wide; map-wide) Overlay — map overlay (point-by-point; region-wide; map-wide) Distance — proximity and connectivity (movement; optimal paths; visibility) Distance — proximity and connectivity (movement; optimal paths; visibility) Neighbors — ”roving windows” (slope/aspect; diversity; anomaly) Neighbors — ”roving windows” (slope/aspect; diversity; anomaly) (Berry)

14. Visualizing Spatial Relationships (Berry) What spatial relationships do you see? …do relatively high levels of P often occur with high levels of K and N? …how often? …where? Interpolated Spatial Distribution Phosphorous (P)

15. Clustering Maps (Berry) …groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones

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

17. Spatial Data Mining …making sense out of a map stack (Berry) 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

18. Precision Ag Precision Conservation Leaching Chemicals Runoff Soil Erosion Wind Erosion Precision Ag to Precision Conservation From a Field perspective to Watershed, Landscape and Ecosystem perspective (Berry) SURFACE MODELING SPATIAL DATA MINING Isolated Perspective 2-dimensional Interconnected Perspective 3-dimensional SPATIAL ANALYSIS

19. Map Analysis Data Mining investigates the “numerical” relationships in mapped data… Descriptive — aggregate statistics (e.g., average/stdev, similarity, clustering) Descriptive — aggregate statistics (e.g., average/stdev, similarity, clustering) Predictive — relationships among maps (e.g., regression) Predictive — relationships among maps (e.g., regression) Prescriptive — appropriate actions (e.g., optimization) Prescriptive — appropriate actions (e.g., optimization) Surface Modeling maps the spatial distribution and pattern of point data… Map Generalization — characterizes spatial trends (e.g., titled plane) Map Generalization — characterizes spatial trends (e.g., titled plane) Spatial Interpolation — deriving spatial distributions (e.g., IDW, Krig) Spatial Interpolation — deriving spatial distributions (e.g., IDW, Krig) Other — roving window/facets (e.g., density surface; tessellation) Other — roving window/facets (e.g., density surface; tessellation) Spatial Analysis investigates the “contextual” relationships in mapped data… Reclassify — reassigning map values (position; value; size, shape; contiguity) Reclassify — reassigning map values (position; value; size, shape; contiguity) Overlay — map overlay (point-by-point; region-wide; map-wide) Overlay — map overlay (point-by-point; region-wide; map-wide) Distance — proximity and connectivity (movement; optimal paths; visibility) Distance — proximity and connectivity (movement; optimal paths; visibility) Neighbors — ”roving windows” (slope/aspect; diversity; anomaly) Neighbors — ”roving windows” (slope/aspect; diversity; anomaly) (Berry)

20. Elevation Surface Overland Flow Model 1) The Pipeline is positioned on the Elevation surface 1) Pipeline 2) Flow from Spill Points along the pipeline are simulated X 2) Spill Point #1 3) High Consequence Areas (HCA) are identified 3) HCA 4) A Report is written identifying flow paths that cross HCA areas X HCA Impact 4) Report 5) Overland flow is halted when Flowing Water is encountered (Channel Flow Model) 5) Flowing Water Spill Migration Modeling (Berry)

21. Types of Surface Flows (Berry) Common sense suggests that “water flows downhill” however the corollary is “…but not always the same way.”

22. Characterizing Overland Flow and Quantity (Berry) Intervening terrain and conditions form Flow Impedance and Quantity maps that are used to estimate flow time and retention

23. Simulating Different Product Types (Berry) Flow Velocity is a function of— Specific Gravity (p), Viscosity (n) and Depth (h) of product Slope Angle (spatial variable computed for each grid cell) Physical properties combine with terrain/conditions to model the flow of different product types

24. Characterizing Impacted Areas (Berry) Flows from spill 1, 2 and 3 The minimum time for flows from all spills… Drinking water HCA Impacted portion of the Drinking water HCA characterizes the impact for the High Consequence Areas characterizes the impact for the High Consequence Areas

25. Modeling Stream Channel Flow (Berry) Channel Flow Model 1) Channel Flow Time 0 hr 7.3 hr 8.4 hr 9.6 hr 10.8 hr 10.1 hr 13.1 hr 11.2 hr 13.6 hr 1) Channel Flow times along stream network segments are added Base Point 2) Overland Flow time and quantity at entry is noted X.14.12.27.12.25.72.78 X Overland Flow (2.5 hours) 2) Overland Flow Entry Time X = 12.10 +.36 = 12.46 hr away from Base Point 11.2 hr 13.1 hr 3) Impacted High Consequence Areas (HCA) are identified In = 11.46 hr Out = 9.86 hr HCA 3) Impacted HCA Times HCA 4) Report is written identifying flow paths that cross HCA areas 4) Report of Impacted HCA’s 2.5 + (12.46 -11.46) = 3.5 hours total

26. Modeling Customer Flow (Berry) …customer flow along a road network is similar to water flowing in a stream channel …a Travel-time Map identifies the time to travel from anywhere to a store

27. Competition Analysis (Berry) … travel-time surfaces for two different stores … can be compared for relative travel-time advantage

28. Transmission Line Siting Model Criteria – the transmission line route should… Avoid areas of high housing density Avoid areas of high housing density Avoid areas that are far from roads Avoid areas that are far from roads Avoid areas within or near sensitive areas Avoid areas within or near sensitive areas Avoid areas of high visual exposure to houses Avoid areas of high visual exposure to housesHousesRoads Sensitive Areas Houses Elevation Goal – identify the best route for an electric transmission line that considers various criteria for minimizing adverse impacts. Existing Powerline ProposedSubstation (Berry)

29. AVOID AREAS OF HIGH VISUAL EXPOSURE TO HOUSES Visual Exposure levels (0-40 times seen) are translated into values indicating relative cost (1=low to 9=high) for siting a transmission line at every location in the project area. Step 1. Visual Exposure levels (0-40 times seen) are translated into values indicating relative cost (1=low to 9=high) for siting a transmission line at every location in the project area. HOUSESELEVATIONVISUALEXPOSURE TO HOUSES DISCRETECOSTMAP Routing and Optimal Paths (Berry) ACCUMULATEDCOSTSURFACEEXISTINGPOWERLINE(START) Step 2. Accumulated Cost from the existing powerline to all other locations is generated based on the Discrete Cost map. MOSTPREFERREDROUTEPROPOSEDSUBSTATION(END) Step 3. The steepest downhill path from the Substation over the Accumulated Cost surface identifies the “least cost path”— Most Preferred Route avoiding areas of high visual exposure

30. Considering Multiple Criteria (Berry) HOUSING DENSITY AVOID AREAS OF HIGH HOUSING DENSITY ROADS PROXIMITY TO ROADS AVOID AREAS THAT ARE FAR FROM ROADS SENSITIVE AREAS PROXIMITY TO SENSITIVE AREAS AVOID AREAS IN OR NEAR SENSITIVE AREAS VISUAL EXPOSURE TO HOUSES AVOID AREAS OF HIGH VISUAL EXPOSURE AVERAGE COST STARTING LOCATION ACCUMULATION SURFACE ENDING LOCATION MOST PREFERRED ROUTE AVOID AREAS OF HIGH HOUSING DENSITY AVOID AREAS THAT ARE FAR FROM ROADS AVOID AREAS IN OR NEAR SENSITIVE AREAS HOUSING AVOID AREAS OF HIGH VISUAL EXPOSURE START END Base Maps Derived Maps Cost/Avoidance Maps AVG_COST ACUMM_COST BEST_ROUTE Criteria – the transmission line route should avoid… Areas of high housing density Areas of high housing density Areas that are far from roads Areas that are far from roads Areas within or near sensitive areas Areas within or near sensitive areas Areas of high visual exposure to houses Areas of high visual exposure to houses ELEVATION Step 2 Accumulated Cost Step 3 Steepest Path Step 3 Discrete Cost

31. Considering Multiple Criteria (Berry) AVOID AREAS OF HIGH HOUSING DENSITY AVOID AREAS THAT ARE FAR FROM ROADS AVOID AREAS IN OR NEAR SENSITIVE AREAS AVOID AREAS OF HIGH VISUAL EXPOSURE START END Base Maps Derived Maps Cost/Avoidance Maps AVG_COST ACUMM_COST BEST_ROUTE Criteria – the transmission line route should avoid… Areas of high housing density Areas of high housing density Areas that are far from roads Areas that are far from roads Areas within or near sensitive areas Areas within or near sensitive areas Areas of high visual exposure to houses Areas of high visual exposure to houses Step 2 Accumulated Cost Step 3 Steepest Path Step 3 Steepest Path

32. Step 1 Discrete Preference Map … identifies the relative preference of locating a transmission line at any location throughout a project area considering multiple criteria Least Most Preferred …average of the four individual preference maps (Berry)

33. Step 2 Accumulated Preference Map … identifies the preference to construct the preferred transmission line from a starting location to everywhere in a project area Splash Algorithm – like tossing a stick into a pond with waves emanating out and accumulating costs as the wave front moves (Berry)

34. Step 3 Most Preferred Route … the steepest downhill path over the accumulated preference surface identifies the most preferred route — minimizes areas to avoid Preferred Route (Berry)

35. Siting Model Flowchart (Model Logic) Model logic is captured in a flowchart where the boxes represent maps and lines identify processing steps leading to a spatial solution Avoid areas of… High Housing Density Far from Roads In or Near Sensitive Areas High Visual Exposure RankingsWeights …but what is high housing density and how important is it? …etc? (Berry)

36. Calibrating Map Layers (Relative Preferences) Model calibration refers to establishing a consistent scale from 1 (most preferred) to 9 (least preferred) for rating each map layer 1 for 0 to 5 houses …group consensus is that low housing density is most preferred The Delphi Process is used to achieve consensus among group participants. It is a structured method involving iterative use of anonymous questionnaires and controlled feedback with statistical aggregation of group response. (Berry)

37. Weighting Map Layers (Relative Importance) Model weighting establishes the relative importance among map layers (model criteria) on a multiplicative scale The Analytical Hierarchy Process (AHP) establishes relative importance among by mathematically summarizing paired comparisons of map layers’ importance. HD * 10.38 R * 3.23 SA * 1.00 VE * 10.64 …group consensus is that housing density is very important (10.38 times more important than sensitive areas) (Berry)

38. Generating Alternate Routes (changing weights) The model is run using three different sets of weights for the map layers— …to generate three alternative routes (draped over Elevation) (Berry)

39. Transitioning Beyond Mapping (Berry) Where is What and Wow mapping, geo-query, delivery and display… Data Mining investigates the “numerical” relationships in mapped data… Surface Modeling maps the spatial distribution and pattern of point data… Spatial Analysis investigates the “contextual” relationships in mapped data…

40. (Berry) GIS technology is transitioning from Where is What and Wow …to Why and So What …for more importation online, see GIS Technology in Transition …we’ve covered a lot, any questions?