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Presentation by Joseph K. Berry W.M. Keck Scholar in Geosciences, University of Denver Principal, Berry & Associates // Spatial Information Systems 2000.

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Presentation on theme: "Presentation by Joseph K. Berry W.M. Keck Scholar in Geosciences, University of Denver Principal, Berry & Associates // Spatial Information Systems 2000."— Presentation transcript:

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2 Presentation by Joseph K. Berry W.M. Keck Scholar in Geosciences, University of Denver Principal, Berry & Associates // Spatial Information Systems 2000 S. College Ave, Suite 300, Fort Collins, CO Phone: (970) Website at Mapping Geotechnology A Brief History and Probable Future of Geotechnology Association of American Geographers Great Plains – Rocky Mountain Division Annual Meeting — September 28 and 29, 2007 — University of Denver

3 Historical Setting and GIS Evolution Spatial Database Management links computer mapping with database capabilities (80s) Computer Mapping automates the cartographic process (70s) Multimedia Mapping full integration of GIS, Internet and visualization technologies (00s) Manual Mapping for 8,000 years (Berry) Map Analysis representation of relationships within and among mapped data (90s) The US Department of Labor identifies Geotechnology as one of the “three most important emerging and evolving fields” of the 21 st century (along with Biotechnology and Nanotechnology) …focus of this presentation

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

5 MAP Analysis Framework (Raster, Continuous) Click on… Zoom Pan Rotate Display ShadingManager Continuous, regular grid cells (objects) Points, Lines, Polygons and Surfaces : --, --, --, --, --, 2438, --, --, --, --, --, :GridTable GridAnalysis …calculate a slope map and drape on the elevation surface (Berry) (click for MC_basocs.exe and MC_slope_drain.exe demos) MC_basocs.exeMC_slope_drain.exeMC_basocs.exeMC_slope_drain.exe

6 Map Analysis Evolution (90s, Revolution) Traditional GIS Points, Lines, Polygons Points, Lines, Polygons Discrete Objects Discrete Objects Mapping and Geo-query Mapping and Geo-query Forest Inventory Map 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) Spatial Analysis Cells, Surfaces Cells, Surfaces Continuous Geographic Space Continuous Geographic Space Contextual Spatial Relationships Contextual Spatial Relationships Slope Map Surface (Berry)

7 Travel-Time for Our Store to Everywhere OUR STORE …close to the store (blue) (Berry)

8 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)

9 Travel-Time Surfaces (Our Store & Competitor #4) Blue tones indicate locations that are close to a store (estimated twelve minute drive or less). The green through red tones form a bowl-like surface with larger travel-time values identifying locations that are farther away. Our Store Competitor (Berry)

10 Competition Map (Store #111 & 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 Store #111 advantage; red Competitor #4 advantage Our Store Competitor Negative Positive Our Advantage Competitors (Berry)

11 Map 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 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= 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) (Berry)

12 Map Analysis Geographic Space Discrete Spatial Object Spatially Generalized 22.0 Continuous Spatial Distribution Spatially Detailed GeoExploration vs. GeoScience Adjacent Parcels Desktop Mapping Data Space Average = 22.0 StDev = 18.7 Field Data Standard Normal Curve Desktop Mapping graphically links generalized statistics to discrete spatial objects (Points, Lines, Polygons)— non-spatial analysis (GeoExploration) Map Analysis map-ematically relates patterns within and among continuous spatial distributions (Map Surfaces)— spatial analysis and statistics (GeoScience) (Berry)

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 (digital slide show SSTAT) SSTAT (Berry)

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? Phosphorous (P) Multivariate Analysis (Berry)

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

16 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) Prescription Map Step 5) Zone 1 Zone 3 Zone 2 Step 6) Variable Rate Application Cyber-Farmer, Circa 1992 (Berry)

17 Map Analysis representation of relationships within and among mapped data (90s) l Spatial Analysis (Contextual) l Spatial Statistics (Numerical) Map-ematics Multimedia Mapping full integration of GIS, Internet and visualization technologies (00s) l Map Delivery/Devices l Map Display l Visualization l Geospatial Multimedia Knock-your-socks-off Spatial Database Management links computer mapping with database capabilities (80s) Computer Mapping automates the cartographic process (70s) Recall that in the beginning we had… Mapping and Geo-query (Berry)

18 Multimedia Mapping (00s,) 3) Visualization Rendered Scenes 1) Map Delivery/Devices GPS/GIS Enabled Devices and Internet Mapping Part 2 (Berry) 2) Map Display Interactive Maps Animated Maps 4) Geospatial Multimedia Maps with Integrated Photos Photos Video Video Audio Audio Text Text Data Data

19 3-D Visualization Approaches (Mega-Trend #2) Image Draping -- is an established technique in GIS. Draping a topographic or thematic map onto a 3-D terrain surface is effective but relies on abstract colors, shading and symbols. SportsTracker (MapTrek, 9/98) “Map Abstraction” (Berry)

20 Landscape Visualization (Rendering Technique) “Laying the Carpet” Step 1) 3-D Terrain Surface Step 2) Polygon Containers Step 3) Surface Texture Step 4) Tree Objects Step 5) Final Composition Step 6) Atmospheric Effects “Pouring the Trees” (Berry)

21 Visualizing Landscape Impacts (GIS Rendering) (Berry)

22 Visualizing Landscape Impacts (Clear cut) (Berry)

23 Visualizing Landscape Impacts (Water retention cut) (Berry)

24 Summer (diseased) Winter After Snowfall (ski run) Visualizing Landscape Conditions After Fire Before Fire (Berry) …changing the landscape’s carpet and objects to simulate different conditions Rendered Scenes

25 When (time) When (time) Where (X,Y) Where (X,Y) GPS Unit GPS Unit Digital Camera Digital Camera What (picture) What (picture) When (time) When (time) Geospatial Multimedia (Mega-Trend #4) …take pictures with a digital camera or video recorder while carrying a GPS with ‘track logging’ then link the Lat/Lon with each picture. (Berry) Export to HTML and post to Internet Pictures are “posted and linked” to a map

26 Google Earth (Killer App of 2005) Vessel for Mapped Data — has brought Geotechnology to the masses; not a GIS but digests map data for 3D display with satellite imagery of the globe as backdrop (Berry) Geo-taggedPhotos …and/or import GIS data layers into Google Earth (Wildfire Risk) (click for GE_EstesPark_Photos.avi demo) GE_EstesPark_Photos.avi

27 Philosopher’s Progression of Understanding— Data (all facts) Data (all facts) Information (facts within a context) Information (facts within a context) Knowledge (interrelationships among relevant facts) Knowledge (interrelationships among relevant facts) Wisdom (actionable knowledge) Wisdom (actionable knowledge) GIS Utility and Understanding … GeoExploration emphasizes tools for data access and visualization (general user) … GeoScience emphasizes tools for spatial reasoning and understanding of geographic patterns and relationships (application specialist) Mapping focus Data/Structure and Analysis focus

28 A Peek at the Bleeding Edge (2010 and beyond) Multimedia Mapping (2000s) Revisit Analytics (2020s) Revisit Geo-reference (2010s) GIS Modeling (1990s) Computer Mapping (1970s) Spatial dB Mgt (1980s) The Early Years Contemporary GIS Future Directions Mapping focus Data/Structure focus Analysis focus (Berry)

29 Traditional Geographic Referencing (Cartesian) Cartesian Coordinate System (X, Y, and Z) Discrete Spatial Objects (vector) — Point (X,Y) as fundamental unit Discrete Spatial Objects (vector) — Point (X,Y) as fundamental unit Continuous Surfaces (grid) — Cell (Col,Row) as fundamental unit Continuous Surfaces (grid) — Cell (Col,Row) as fundamental unit (Berry)

30 Alternative Geographic Referencing (Polyhedral) Nested Hexagons Nested Hexagons (2D hexagon grid ) (2D hexagon grid ) Solid Volumes Solid Volumes (3D polyhedral grid) (3D polyhedral grid) Geo-referencing (2010) (Berry) X,Y,A Map Layer X,Y,Z,A Volume Re-tooling Analytics (…and beyond)...new geo-referencing and data structures (X,Y,Z geography plus Attribute values) will spawn a host of new analytic algorithms (e.g., 3D flows and cycles)

31 Where Have We Been… Computer Mapping (70s) — Spatial Database Management (80s) Geo-referencing (2010s) — Re-tooling Analytics (2020s) Multimedia Mapping full integration of GIS, Internet and visualization technologies (2000s) l Map Delivery/Devices — Internet & Devices l 3D Visualization — Draping & Virtual Reality l Map Display — Interactive & Animated Maps l Multimedia Mapping — GPS/Photos & Video l Google Earth — New Vessel for Mapped Data Map Analysis representation of relationships within and among mapped data (1990s) l Spatial Analysis — “contextual” relationships l Spatial Statistics — “numerical” relationships online papers, materials, books and software online papers, materials, books and software


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