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GIS ModelingGIS ModelingWeek 1 — OverviewWeek 1 — Overview
GEOG 3110 –University of DenverGEOG 3110 –University of Denver
Course overviewCourse overviewGIS mapping, management and modelingGIS mapping, management and modeling
Discrete Discrete (map objects)(map objects) vs. Continuous vs. Continuous (map surfaces)(map surfaces)
Linking data and geographic distributionsLinking data and geographic distributionsFramework for map-ematical processingFramework for map-ematical processing
Presented byPresented by Joseph K. BerryJoseph K. BerryW. M. Keck Scholar, Department of Geography, University W. M. Keck Scholar, Department of Geography, University
of Denverof Denver
http://www.innovativegis.com/basis/Courses/GMcourse13/http://www.innovativegis.com/basis/Courses/GMcourse13/
(Nanotechnology)(Nanotechnology) GeotechnologyGeotechnology (Biotechnology)(Biotechnology)
GPS/GIS/RSGPS/GIS/RS
ModelingModeling involvesinvolves analysisanalysis of spatial of spatial relationships and relationships and
patterns patterns
((numerical analysisnumerical analysis) )
Prescriptive Prescriptive ModelingModeling
MappingMapping involves involves precise placement precise placement
(delineation) of (delineation) of physical featuresphysical features
((graphical inventorygraphical inventory))
Descriptive Descriptive MappingMapping
GeotechnologyGeotechnology is one of the three "mega technologies" for the 21st century and is one of the three "mega technologies" for the 21st century and promises to promises to forever change how we conceptualize, utilize and visualize forever change how we conceptualize, utilize and visualize
spatial relationships in scientific research and commercial applications spatial relationships in scientific research and commercial applications (U.S. Department of Labor)(U.S. Department of Labor)
WhyWhy So WhatSo What and and What IfWhat If……
Global Positioning Global Positioning System System (location and navigation)(location and navigation)
Geographic Information Geographic Information Systems Systems (map and analyze)(map and analyze)
WhereWhere isis WhatWhat
(Berry)(Berry)
Remote SensingRemote Sensing(measure and classify)(measure and classify)
The Spatial TriadThe Spatial Triad
Historical Setting and GIS EvolutionHistorical Setting and GIS Evolution
Computer MappingComputer Mapping …automates the …automates the cartographic process cartographic process ((70s70s) )
Map AnalysisMap Analysis …representation of relationships …representation of relationships within and among mapped data within and among mapped data ((90s90s) )
Manual Mapping for 8,000+ yearsManual Mapping for 8,000+ yearsWe have been mapping for thousands of years with the We have been mapping for thousands of years with the
primary of primary of navigationnavigation through unfamiliar terrain and seas, through unfamiliar terrain and seas, with emphasis on with emphasis on precise placementprecise placement of physical features. of physical features.
Where is What
Wow!!! …did you see that
……but the last four decades have but the last four decades have radically changedradically changed the very nature of maps and how they are used— the very nature of maps and how they are used—
Where
Spatial Database ManagementSpatial Database Management …links …links computer mapping with database capabilities computer mapping with database capabilities ((80s80s) )
… … the focus of this GIS Modeling coursethe focus of this GIS Modeling course
Why, So What and What If…
(Berry)(Berry)…the 2010s await characterization
Multimedia MappingMultimedia Mapping (Geo-web) (Geo-web) …full …full integration of GIS, Internet and visualization technologies integration of GIS, Internet and visualization technologies ((00s00s))
Descriptive Mapping FrameworkDescriptive Mapping Framework (Vector, (Vector, DiscreteDiscrete))
MappingMapping
Select ThemeSelect Theme Zoom PanZoom PanInfo Info ToolTool
ThemeThemeTableTable DistanceDistance
::Object IDObject IDX,YX,YX,YX,YX,YX,Y ::
FeatureFeature SpeciesSpecies etc.etc. : :: :Object ID AwObject ID Aw : :: :
Spatial Spatial TableTable
AttributeAttributeTableTable
Discrete, irregular map features (Discrete, irregular map features (objectsobjects))
PointsPoints, , LinesLines and and AreasAreas(Berry)(Berry)
QueryQueryBuilderBuilder
……identify tall identify tall aspen standsaspen stands
Big …over 400,000m2 (40ha)?
Geo-queryGeo-query
Map Analysis Framework Map Analysis Framework (Raster, (Raster, ContinuousContinuous))
Click on…Click on…Zoom Pan RotateZoom Pan Rotate DisplayDisplay
ShadingShadingManagerManager
Continuous, regular grid cells (Continuous, regular grid cells (objectsobjects))
PointsPoints, , LinesLines, , AreasAreas and and SurfacesSurfaces
::--, --, --, --,--, --, --, --,--, --, --, --,--, --, --, --,--, --, --, --,--, --, --, --,--, --, 24382438, --,, --,--, --, --, --,--, --, --, --,::
Grid TableGrid Table
GridGridAnalysisAnalysis
……calculate a calculate a slopeslope map and map and drape on the drape on the elevation surfaceelevation surface
Map StackMap Stack
(Berry)(Berry)
www.innovativegis.com/basis/Courses/www.innovativegis.com/basis/Courses/GMcourse13/GMcourse13/
Course Description and SyllabusCourse Description and Syllabus
(Berry)(Berry)
GradingGrading
Topics and ScheduleTopics and Schedule
Basic ConceptsBasic Concepts
Spatial AnalysisSpatial Analysis
GIS ModelingGIS Modeling
Spatial StatisticsSpatial StatisticsFuture DirectionsFuture Directions
……Required Required ReadingReading
……occasional occasional in-class questions in-class questions on required on required readingreading
Course TextbookCourse Textbook
Textbook and Companion CD-ROMTextbook and Companion CD-ROM
CD MaterialsCD Materials
……Further ReadingFurther Reading Recommended/OptionalRecommended/Optional
……Text FigureText Figure slide set slide set (color)(color)
……Optional ExercisesOptional Exercises at end of each topicat end of each topic
……Example ApplicationsExample Applications……MapCalcMapCalc software, data, tutorials and manual software, data, tutorials and manual
……SurferSurfer software, sample data and tutorials software, sample data and tutorials
……SnagIt SnagIt softwaresoftware (recommended)(recommended)
Access Access Default.htmDefault.htm……to view & install materialsto view & install materials
……Other ReadingOther Reading OnlineOnline
(Berry)(Berry)
Links to Class MaterialsLinks to Class Materials (Class Webpage)(Class Webpage)
Links to Links to ReadingReading Assignments Assignments — — required readings are from the course Text required readings are from the course Text with some Recommended and Optional readings on the CD and posted onlinewith some Recommended and Optional readings on the CD and posted online
Links to Links to LectureLecture Notes Notes — — lecture slide sets are posted Wednesdays by 5:00pm; lecture slide sets are posted Wednesdays by 5:00pm; available in the GIS Lab Thursdays by 12:00noonavailable in the GIS Lab Thursdays by 12:00noon
Links to Links to HomeworkHomework Assignments Assignments — — exercise templates are downloaded then exercise templates are downloaded then completed in teams and submitted to class Dropboxcompleted in teams and submitted to class Dropbox
Links to Links to Software Software — — all of the software/data used in the class are on the class CD all of the software/data used in the class are on the class CD or available for downloador available for download
(Berry)(Berry)
http://www.innovativegis.com/basis/Courses/GMcourse13/http://www.innovativegis.com/basis/Courses/GMcourse13/
Class folder in GIS labClass folder in GIS lab ……updated on Thursdays before classupdated on Thursdays before class
The GIS Modeling course’s main page The GIS Modeling course’s main page contains links to course Administrative contains links to course Administrative Materials and Readings, Lectures, and Materials and Readings, Lectures, and
Homework assignmentsHomework assignments
GeotechnologyGeotechnology – one of the three “mega-technologies” for the 21st 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)
90s Map Analysis Map Analysis (Spatial Relationships and Patterns)
Global Positioning System (Location and Navigation)
Remote Sensing (Measure and Classify)
Geographic Information SystemsGeographic Information Systems (Map and Analyze)
History/Evolution of Map AnalysisHistory/Evolution of Map Analysis
Spatial StatisticsSpatial Statistics (Numerical context)
Surface Modeling (point data to continuous spatial distributions
Spatial Data Mining (interrelationships within and among map layers)
Spatial AnalysisSpatial 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)
http://www.innovativegis.com/basis/Papers/Other/http://www.innovativegis.com/basis/Papers/Other/GISmodelingFrameworkGISmodelingFramework/ /
Framework Paper
Organizational Structure of this Course
(Berry)(Berry)
Traditional StatisticsTraditional Statistics
• Mean, StDev (Normal Curve)Mean, StDev (Normal Curve)
• Central TendencyCentral Tendency
• Typical Response (scalar) Typical Response (scalar)
Minimum= 5.4 ppmMinimum= 5.4 ppmMaximum= 103.0 ppmMaximum= 103.0 ppm
Mean= 22.4 ppmMean= 22.4 ppmStDEV= 15.5StDEV= 15.5
Spatial StatisticsSpatial Statistics
• Map of Variance Map of Variance (gradient)(gradient)
• Spatial DistributionSpatial Distribution
• Numerical Spatial RelationshipsNumerical Spatial Relationships
Spatial Spatial DistributionDistribution(Surface)(Surface)
Mapped Data Analysis EvolutionMapped Data Analysis Evolution (Revolution)(Revolution)
Traditional GISTraditional GIS
• Points, Lines, PolygonsPoints, Lines, Polygons
• Discrete ObjectsDiscrete Objects
• Mapping and Geo-queryMapping and Geo-query
Forest Inventory Forest Inventory MapMap
Spatial AnalysisSpatial Analysis
• Cells, Surfaces Cells, Surfaces
• Continuous Geographic SpaceContinuous Geographic Space
• Contextual Spatial RelationshipsContextual Spatial Relationships
ElevationElevation(Surface)(Surface)
(Berry)(Berry)
Elevation Surface
(Berry)
Calculating Slope and FlowCalculating Slope and Flow (map analysis)(map analysis)
Inclination of a fitted Inclination of a fitted plane to a location and plane to a location and its eight surrounding its eight surrounding elevation valueselevation values(Neighbors)
Total number of the steepest Total number of the steepest downhill paths flowing into each downhill paths flowing into each location location (Distance)
Slope Slope (47,64)(47,64) = 33.23% = 33.23%
Slope map draped on Elevation
Slope map
Flow Flow (28,46)(28,46) = 451 Paths = 451 Paths
Flow map draped on Elevation
Flow map
Erosion PotentialErosion 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/SlopeSlope_classes
Flow_classes
Deriving Erosion Potential & BuffersDeriving Erosion Potential & Buffers
Protective BuffersProtective Buffers
(Berry)Simple Buffer
Streams Simple Buffer
…distance is “as the crow flies”
Re
cla
ss
ify
Re
cla
ss
ify
Ov
erl
ay
Re
cla
ss
ify
…High erosion on steep slopes with heavy flows
Erosion_potential
Streams
Erosion Buffers
Dis
tan
ce
Distance away from the streams is a Distance away from the streams is a function of the erosion potentialfunction of the erosion potential (Flow/Slope (Flow/Slope Class) with intervening heavy flow and steep Class) with intervening heavy flow and steep slopes computed as slopes computed as effectively closereffectively closer than than simple distance— simple distance— ““as the crow walks”as the crow walks”
Calculating Effective DistanceCalculating Effective Distance (variable-width buffers)(variable-width buffers)
(Berry)(Berry)
Effective BuffersEffective Buffers
(digital slide show (digital slide show VBuff))
Effective Erosion DistanceEffective Erosion Distance
CloseClose FarFar
Heavy/Steep(far from stream)
Light/Gentle(close)
Simple BuffersSimple Buffers
Effective BuffersEffective Buffers
ReclassifyReclassify operations involve operations involve reassigning map valuesreassigning map values to reflect new to reflect new information about existing map information about existing map features on a features on a singlesingle map layer map layer
OverlayOverlay operations involve operations involve characterizing the characterizing the spatial coincidencespatial coincidence of of mapped data on mapped data on two or moretwo or more map layers map layers
Classes of Spatial Analysis OperatorsClasses of Spatial Analysis Operators……all Spatial Analysis involves generating all Spatial Analysis involves generating new map valuesnew map values (numbers) as a (numbers) as a mathematical or statistical function mathematical or statistical function of the values on another map layer(s)of the values on another map layer(s)
— —sort of a “sort of a “map-ematicsmap-ematics” for analyzing spatial relationships and patterns—” for analyzing spatial relationships and patterns—
GIS Toolbox(Geographic Context)(Geographic Context)
(Berry)(Berry)
ProximityProximity operations involve operations involve measuring distance and connectivitymeasuring distance and connectivity among map locations– both “simple among map locations– both “simple and effective distance”and effective distance”
NeighborhoodNeighborhood operations operations involve characterizing mapped data involve characterizing mapped data
within the vicinitywithin the vicinity of each map of each map location– “roving windows”location– “roving windows”
Classes of Spatial Analysis OperatorsClasses of Spatial Analysis Operators……all Spatial Analysis involves generating all Spatial Analysis involves generating new map valuesnew map values (numbers) as a (numbers) as a mathematical or statistical function mathematical or statistical function of the values on another map layer(s)of the values on another map layer(s)
— —sort of a “sort of a “map-ematicsmap-ematics” for analyzing spatial relationships and patterns—” for analyzing spatial relationships and patterns—
GIS Toolbox(Geographic Context)(Geographic Context)
(Berry)(Berry)
Relative scale:1 = .05 minutes
Travel-Time for Our Store to EverywhereTravel-Time for Our Store to Everywhere
OUR STORE …close to the store (blue)
A store’s A store’s TravelshedTravelshed identifies the relative driving identifies the relative driving time from every location to the store— time from every location to the store— ……analogous to a “watershed”analogous to a “watershed”
(Berry)(Berry)
Travel-Time for Competitor StoresTravel-Time for Competitor Stores
Ocean
Travel-Time maps from several stores Travel-Time maps from several stores treating highway travel as four times faster than city streets.treating highway travel as four times faster than city streets.
Blue tones indicate locations that are close to a store (estimated Blue tones indicate locations that are close to a store (estimated twelve minute drive or lesstwelve minute drive or less). Customer data can ). Customer data can be appended with travel-time distances and analyzed for spatial relationships in sales and demographic factors.be appended with travel-time distances and analyzed for spatial relationships in sales and demographic factors.
Our Store (#111)Our Store (#111)
Ocean
Competitor 1Competitor 1
Ocean
Competitor 2Competitor 2
Ocean
Competitor 3Competitor 3
Ocean
Competitor 4Competitor 4
Ocean
Competitor 5Competitor 5
(Berry)(Berry)
Travel-Time SurfacesTravel-Time Surfaces (Our Store & Competitor #4)(Our Store & Competitor #4)
Blue tones indicate locations that are close to a store (estimated twelve minute drive Blue tones indicate locations that are close to a store (estimated twelve minute drive or less). Increasingly warmer tones form a or less). Increasingly warmer tones form a bowl-like surfacebowl-like surface
with larger travel-time values identifying locations that are farther away. with larger travel-time values identifying locations that are farther away.
Our Store Competitor
(Berry)(Berry)
Competition MapCompetition Map (Our Store & Competitor #4)(Our Store & Competitor #4)
The travel-time surfaces for two stores can be compared (subtracted) to identify the The travel-time surfaces for two stores can be compared (subtracted) to identify the relative access advantagesrelative access advantages throughout the project area. throughout the project area.
Zero values indicate the same travel-time to both stores (equidistant travel-time) Zero values indicate the same travel-time to both stores (equidistant travel-time) ……yellow tones identifying the yellow tones identifying the Combat ZoneCombat Zone ; green Our Store advantage; red Competitor #4 advantage ; green Our Store advantage; red Competitor #4 advantage
Our Store
Competitor
Negative
Positive
Our Advantage
Competitors
(See (See Location, Location, Location: Retail Sales Competition AnalysisLocation, Location, Location: Retail Sales Competition Analysis, www.innovativegis.com/basis/present/GW06_retail/GW06_Retail.htm), www.innovativegis.com/basis/present/GW06_retail/GW06_Retail.htm)(Berry)(Berry)
Mapped Data Analysis EvolutionMapped Data Analysis Evolution (Revolution)(Revolution)
(Berry)(Berry)
Geographic ContextGeographic Context
Numeric ContextNumeric Context
……after a brief after a brief break in thoughtbreak in thought
Exercise #1
Exercise #0
Setup
LogisticsWho 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 Setting Up and Using Class Data Moving MapCalc Data to your personal workspaceMoving MapCalc Data to your personal workspace 1)1) Right click on Right click on StartStart at the bottom left of your screen (Task Bar) at the bottom left of your screen (Task Bar)2)2) Select Select Windows ExplorerWindows Explorer3)3) Locate your Locate your personal workspacepersonal workspace as directed by the instructor (Z: drive) as directed by the instructor (Z: drive)4)4) Create a new folder in your workspace calledCreate a new folder in your workspace called …\GISmodeling …\GISmodeling5)5) In the new folder create a sub-folder …In the new folder create a sub-folder …\GISmodeling\MapCalc Data\GISmodeling\MapCalc Data6)6) Browse to the Browse to the …\GEOG3110…\GEOG3110 class directory (I: drive) class directory (I: drive) 7)7) Highlight all ofHighlight all of the files/folders MapCalc Data folder on the class directory the files/folders MapCalc Data folder on the class directory
and select and select Copy Copy 8)8) Go to your new …Go to your new …\GISmodeling\MapCalc Data \GISmodeling\MapCalc Data sub-folder and sub-folder and Paste Paste the the
MapCalc Data filesMapCalc Data files
Suggested folder organizationSuggested folder organization ……\GISmodeling\MapCalc Data\\GISmodeling\MapCalc Data\ (…just created folder containing MapCalc base data)(…just created folder containing MapCalc base data)
……\GISmodeling\Week1\\GISmodeling\Week1\ (contains all of the data, scripts, screen grabs, etc. developed for week 1)(contains all of the data, scripts, screen grabs, etc. developed for week 1)
……\GISmodeling\Week2\\GISmodeling\Week2\ (contains all of the data, scripts, screen grabs, etc. developed for week 2) (contains all of the data, scripts, screen grabs, etc. developed for week 2)
……etc.etc.Example ExerciseExample Exercise …download …download Exer0.docExer0.doc to your to your …\GISmodeling\Week1\…\GISmodeling\Week1\ folder folder
and complete under the instructor’s guidance and complete under the instructor’s guidance (Berry)(Berry)
Exercise #0Exercise #0 (dry run)(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
…email the document to me
…briefly describe the differences you see between Lattice and Grid displays
…email the document to me
(Berry)(Berry)
Homework Exercise #1Homework Exercise #1
Download Exercise #1— Download Exercise #1— “Links “Links to Homework,” right-click on to Homework,” right-click on “Exer1.doc” and choose “Save” to “Exer1.doc” and choose “Save” to download …and then access the download …and then access the exercise in Wordexercise in Word
Confirm Homework Teams— Confirm Homework Teams— the class will be divided into teams the class will be divided into teams containing two to three memberscontaining two to three members
QuestionQuestion
#1 #1 Model Model CriteriaCriteria
#2 Analysis #2 Analysis LevelsLevels
#3 Derived #3 Derived MapsMaps
#4 Calibrated #4 Calibrated MapsMaps
#6#6MaskingMasking
#7#7FancyFancy
DisplayDisplay
Optional Questions #1-1 and #1-2Optional Questions #1-1 and #1-2
#5#5AnalyzeAnalyze
CommandCommandComplete the exerciseComplete the exercise::
Due next week Due next week Thursday 5:00 pmThursday 5:00 pm (7 days)(7 days)(…slippage possible if (…slippage possible if requested by noonrequested by noon))
www.innovativegis.com/basis/Senarios/Campground.htm
(Berry)(Berry)
……rowsrows represent represent Model CriteriaModel Criteria
GIS Modeling FrameworkGIS Modeling Framework (Model Criteria)(Model Criteria)
Where are the best places for a campground?Where are the best places for a campground?
(Berry)(Berry)
GIS Modeling FrameworkGIS Modeling Framework (Analysis Levels)(Analysis Levels)
……columnscolumns represent represent Analysis LevelsAnalysis Levels……column transitions represent column transitions represent Processing ApproachesProcessing Approaches
Slo
pe
Sp
rea
dS
pre
ad
Rad
iate
Ori
en
t
Ren
um
be
r
Ren
um
be
r
Ren
um
be
r
Ren
um
be
r
Ren
um
be
r
An
aly
ze
…map analysis operations are
sequenced on map variables to
implement the model’s logic
“Algorithm” “Calibration” “Weighting”
Base Derived Interpreted
Modeled
(Berry)(Berry)
Campground Suitability ModelCampground Suitability Model (Macro script)(Macro script)
……the the map analysis logic map analysis logic ingrained in the flowchart is ingrained in the flowchart is translated into a translated into a logical series of map analysis commandslogical series of map analysis commands (MapCalc)(MapCalc)
Derive Derive (Algorithm)(Algorithm)
Gentle slopesGentle slopes
Near roadsNear roads
Near waterNear water
Good viewsGood views
WesterlyWesterly
Interpret Interpret (Calibrate)(Calibrate)
CombineCombine(Weight)(Weight)
MaskMask(Constraints)(Constraints)
Tutor25_Campground ScriptTutor25_Campground Script
(See “(See “Short description of the Campground modelShort description of the Campground model” and “” and “Helpful hints in Running MapCalcHelpful hints in Running MapCalc” in the ” in the Email DialogEmail Dialog section of the Class Webpage) section of the Class Webpage)(Berry)(Berry)
Traditional StatisticsTraditional Statistics
• Mean, StDev (Normal Curve)Mean, StDev (Normal Curve)
• Central TendencyCentral Tendency
• Typical Response (scalar) Typical Response (scalar)
Minimum= 5.4 ppmMinimum= 5.4 ppmMaximum= 103.0 ppmMaximum= 103.0 ppm
Mean= 22.4 ppmMean= 22.4 ppmStDEV= 15.5StDEV= 15.5
Spatial StatisticsSpatial Statistics
• Map of Variance Map of Variance (gradient)(gradient)
• Spatial DistributionSpatial Distribution
• Numerical Spatial RelationshipsNumerical Spatial Relationships
Spatial Spatial DistributionDistribution(Surface)(Surface)
Mapped Data Analysis EvolutionMapped Data Analysis Evolution (Revolution)(Revolution)
Traditional GISTraditional GIS
• Points, Lines, PolygonsPoints, Lines, Polygons
• Discrete ObjectsDiscrete Objects
• Mapping and Geo-queryMapping and Geo-query
Forest Inventory Forest Inventory MapMap
Spatial AnalysisSpatial Analysis
• Cells, Surfaces Cells, Surfaces
• Continuous Geographic SpaceContinuous Geographic Space
• Contextual Spatial RelationshipsContextual Spatial Relationships
Effective Effective DistanceDistance(Surface)(Surface)
(Berry)(Berry)
Spatial Data MiningSpatial Data Mining operations operations involve characterizing numerical involve characterizing numerical
patterns and relationships within and patterns and relationships within and among mapped dataamong mapped data
Classes of Spatial Statistics Operators Classes of Spatial Statistics Operators (Spatial Statistics)(Spatial Statistics)
Surface ModelingSurface Modeling operations operations involve creating continuous spatial involve creating continuous spatial distributions from point sampled datadistributions from point sampled data
……all Spatial Analysis involves generating all Spatial Analysis involves generating new map valuesnew map values (numbers) as a (numbers) as a mathematical or statistical function mathematical or statistical function of the values on another map layer(s)of the values on another map layer(s)
— —sort of a “sort of a “map-ematicsmap-ematics” for analyzing spatial relationships and patterns—” for analyzing spatial relationships and patterns—
GIS Toolbox(Numeric Context)(Numeric Context)
(Berry)(Berry)
GeoExplorationGeoExploration vs.vs. GeoScienceGeoScience
ContinuousSpatial Distribution
DiscreteSpatial Object
Map AnalysisGeographic Space
Map AnalysisMap Analysis map-ematically relates patterns within and among continuous spatial map-ematically relates patterns within and among continuous spatial
distributions distributions (Map Surfaces)— (Map Surfaces)— spatial analysis and statistics spatial analysis and statistics ((GeoScienceGeoScience))
(Geographic Distribution)
Average = 22.0StDev = 18.7
Desktop MappingData Space Field
Data
Standard Normal Curve
Desktop MappingDesktop Mapping graphically links generalized statistics to discrete spatial objects graphically links generalized statistics to discrete spatial objects
(Points, Lines, Polygons)—(Points, Lines, Polygons)— non-spatial analysis non-spatial analysis ((GeoExplorationGeoExploration))
X, Y, Value
PointSampled
Data
(Numeric Distribution)
““Maps are numbers first, pictures later”Maps are numbers first, pictures later”
22.0Spatially
GeneralizedSpatiallyDetailed
40.7 …not a problem
AdjacentParcels
High Pocket
Discovery of sub-area…
(See Beyond Mapping III, “Epilog”, (See Beyond Mapping III, “Epilog”, Technical and Cultural Shifts in the GIS Paradigm, www.innovativegis.com/basis, www.innovativegis.com/basis )) (Berry)(Berry)
Point Density AnalysisPoint Density AnalysisPoint Density Point Density analysis identifies the analysis identifies the total number of customers total number of customers within within
a specified distance of each grid locationa 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, www.innovativegis.com/basis, www.innovativegis.com/basis )) (Berry)(Berry)
Identifying Unusually High DensityIdentifying Unusually High Density
High Customer Density High Customer Density pockets are identified as pockets are identified as more than one standard deviation above the meanmore than one standard deviation above the mean
Unusually high customer density(>1 Stdev)
(See Beyond Mapping III, “Topic 26”, (See Beyond Mapping III, “Topic 26”, Spatial Data Mining in Geo-businessSpatial Data Mining in Geo-business, www.innovativegis.com/basis), www.innovativegis.com/basis)(Berry)(Berry)
Spatial Interpolation Spatial Interpolation (Smoothing the Variability)(Smoothing the Variability)
The “The “iterative smoothingiterative smoothing” process is similar to slapping a big chunk of ” process is similar to slapping a big chunk of modeler’s clay over the “data spikes,” then taking a knife and cutting away modeler’s clay over the “data spikes,” then taking a knife and cutting away
the excess to leave a the excess to leave a continuous surfacecontinuous surface that encapsulates the peaks and that encapsulates the peaks and valleys implied in the original field samplesvalleys implied in the original field samples
……repeated repeated smoothing smoothing slowly “erodes” slowly “erodes” the data surface the data surface to a flat planeto a flat plane= = AVERAGE
(digital slide show SStat2)(digital slide show SStat2)
(Berry)(Berry)
Visualizing Spatial RelationshipsVisualizing Spatial Relationships
What spatial relationships What spatial relationships do you do you SEESEE??
……do relatively high levels do relatively high levels of P often occur with high of P often occur with high levels of K and N?levels of K and N?
……how often?how often?
……where?where?
Phosphorous (P)
Geographic Distribution
Multivariate AnalysisMultivariate Analysis— each — each map layermap layer is a 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”Maps are numbers first, pictures later”
(Berry)(Berry)
Calculating Data DistanceCalculating Data Distance
……an n-dimensional plot depicts the multivariate distribution—an n-dimensional plot depicts the multivariate distribution—the the distance between pointsdistance between points determines the relative similarity in data patterns determines the relative similarity in data patterns
Pythagorean Pythagorean Theorem Theorem
2D Data Space:2D Data Space:
Dist = SQRT (aDist = SQRT (a22 + b + b22))
3D Data Space:3D Data Space:
Dist = SQRT (aDist = SQRT (a22 + b + b22 + c + c22))
……expandable to N-spaceexpandable to N-space
……this response this response pattern pattern (high, high, (high, high,
medium) medium) is the is the least least similarsimilar point as it point as it has thehas the largest largest data distancedata distance from from the comparison the comparison point point (low, low, (low, low, medium)medium)
(See Beyond Mapping III, “Topic 16”, (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and RelationshipsCharacterizing Spatial Patterns and Relationships, www.innovativegis.com/basis), www.innovativegis.com/basis) (Berry)(Berry)
Clustering Maps for Data ZonesClustering Maps for Data Zones
Groups of “floating balls” in data space identify locations in the field Groups of “floating balls” in data space identify locations in the field with similar data patterns– with similar data patterns– data zones data zones or or ClustersClusters
……data distancesdata distances are minimized within a group (intra-cluster distance) and are minimized within a group (intra-cluster distance) and maximized between groups (inter-cluster distance) using an optimization proceduremaximized between groups (inter-cluster distance) using an optimization procedure
(See Beyond Mapping III, “Topic 7”, (See Beyond Mapping III, “Topic 7”, Linking Data Space and Geographic SpaceLinking Data Space and Geographic Space, www.innovativegis.com/basis), www.innovativegis.com/basis)(See Beyond Mapping III, “Topic 16”, (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and RelationshipsCharacterizing Spatial Patterns and Relationships, www.innovativegis.com/basis), www.innovativegis.com/basis) (Berry)(Berry)
The Precision Ag Process The Precision Ag Process (Fertility example)(Fertility example)
As a combine moves through a field it As a combine moves through a field it 1)1) uses GPS to check its location then uses GPS to check its location then 2)2) checks the yield at that location to checks the yield at that location to 3)3) create a continuous map of the create a continuous map of the yield variation every few feet. This map isyield variation every few feet. This map is 4)4) combined with soil, terrain and other maps to combined with soil, terrain and other maps to derive derive 5)5) a “Prescription Map” that is used to a “Prescription Map” that is used to 6)6) adjust fertilization levels every few feet adjust fertilization levels every few feet in the field (variable rate application).in the field (variable rate application).
Farm dBFarm dBStep 4)Step 4)
Map AnalysisMap Analysis
On-the-Fly On-the-Fly Yield MapYield Map
Steps 1) – 3)Steps 1) – 3)
Step 6)Step 6)
Variable Rate ApplicationVariable Rate Application
Cyber-Farmer, Circa 1992Cyber-Farmer, Circa 1992
Prescription MapPrescription Map
Step 5)Step 5)
(See Beyond Mapping III, “Topic 16”, (See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and RelationshipsCharacterizing Spatial Patterns and Relationships, www.innovativegis.com/basis), www.innovativegis.com/basis) (Berry)(Berry)
Spatial Data MiningSpatial Data Mining
Precision Farming is just one example of applying Precision Farming is just one example of applying spatial statistics and data mining techniquesspatial statistics and data mining techniques
Mapped data that Mapped data that exhibits high exhibits high spatial spatial dependencydependency create create strong prediction strong prediction functions. As in functions. As in traditional statistical traditional statistical analysis, spatial analysis, spatial relationships can be relationships can be used to predict used to predict outcomesoutcomes
……the difference is the difference is that spatial statisticsthat spatial statisticspredicts predicts wherewhere responses will be responses will be high or lowhigh or low
(See Beyond Mapping III, “Topic 28”, (See Beyond Mapping III, “Topic 28”, Spatial Data Mining in Geo-businessSpatial Data Mining in Geo-business, www.innovativegis.com/basis), www.innovativegis.com/basis) (Berry)(Berry)
(Nanotechnology)(Nanotechnology) GeotechnologyGeotechnology (Biotechnology)(Biotechnology)
GPS/GIS/RSGPS/GIS/RS
MappingMapping involves involves precise placement precise placement
(delineation) of (delineation) of physical featuresphysical features
((graphical inventorygraphical inventory))
Descriptive Descriptive MappingMapping
GeotechnologyGeotechnology is one of the three "mega technologies" for the 21st century and is one of the three "mega technologies" for the 21st century and promises to promises to forever change how we conceptualize, utilize and visualize forever change how we conceptualize, utilize and visualize
spatial relationships in scientific research and commercial applications spatial relationships in scientific research and commercial applications (U.S. Department of Labor)(U.S. Department of Labor)
Global Positioning Global Positioning System System (location and navigation)(location and navigation)
Geographic Information Geographic Information Systems Systems (map and analyze)(map and analyze)
WhereWhere isis WhatWhat
Remote SensingRemote Sensing(measure and classify)(measure and classify)
The Spatial TriadThe Spatial Triad
ModelingModeling involvesinvolves analysisanalysis of spatial of spatial relationships and relationships and
patterns patterns
((numerical analysisnumerical analysis) )
Prescriptive Prescriptive ModelingModeling
WhyWhy So WhatSo What and and What IfWhat If
(Berry)(Berry)
““Big Picture” take-home Big Picture” take-home
