Introduction to GIS Modeling Week 7 — GIS Modeling Examples GEOG 3110 –University of Denver Presented by Joseph K. Berry W. M. Keck Scholar, Department.

Introduction to GIS ModelingIntroduction to GIS Modeling Week 7 — GIS Modeling ExamplesWeek 7 — GIS Modeling Examples

GEOG 3110 –University of DenverGEOG 3110 –University of Denver

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

Example Real-World Projects; Introduction to Spatial Example Real-World Projects; Introduction to Spatial Statistics; mini-Project Working SessionStatistics; mini-Project Working Session

Class Logistics and ScheduleClass Logistics and Schedule

BerryBerry

Exercises #8 and #9Exercises #8 and #9 — you can tailor to your interests by — you can tailor to your interests by choosing to not complete either or both choosing to not complete either or both of these of these standard exercises; in lieu of an exercise, however, you must submit a standard exercises; in lieu of an exercise, however, you must submit a short paper (4-8 pagesshort paper (4-8 pages) on a GIS modeling ) on a GIS modeling topic of your own choosing. I need to know your choices by next Wednesday as I will topic of your own choosing. I need to know your choices by next Wednesday as I will form new teams for exercises form new teams for exercises #8 and #9#8 and #9..

Final Exam Final Exam — to lighten the load at the end of the term, — to lighten the load at the end of the term, you can choose to forego the final examyou can choose to forego the final exam; you will ; you will receive your average grade for all work to date. receive your average grade for all work to date. Optional ExercisesOptional Exercises can be turned in through finals week. can be turned in through finals week.

Submit via two emails, one with report Submit via two emails, one with report BodyBody attached and the other with attached and the other with AppendixAppendix attached attached

No Exercise Week 7No Exercise Week 7 — a moment for “a dance of celebration”— a moment for “a dance of celebration”

Blue Light Special Blue Light Special …after lecture, in-house advising on mini-projects …after lecture, in-house advising on mini-projects ((the Doctor is in)the Doctor is in)

Map Analysis EvolutionMap Analysis Evolution (Revolution)(Revolution)

(Berry)(Berry)

Spatial AnalysisSpatial Analysis

• Cells, Surfaces Cells, Surfaces

• Continuous Geographic SpaceContinuous Geographic Space

• ContextualContextual Spatial Relationships Spatial Relationships

StoreStoreTravel-TimeTravel-Time

(Surface)(Surface)

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 ppmStDevStDev= 15.5= 15.5

Spatial StatisticsSpatial Statistics

• Map of Variance Map of Variance (gradient)(gradient)

• Spatial DistributionSpatial Distribution

• NumericalNumerical Spatial Relationships Spatial Relationships

Spatial Spatial DistributionDistribution(Surface)(Surface)

Traditional GISTraditional GIS

• Points, Lines, PolygonsPoints, Lines, Polygons

• Discrete ObjectsDiscrete Objects

• Mapping and Geo-queryMapping and Geo-query

Forest Inventory Forest Inventory MapMap

……past six weekspast six weeks

BP Pipeline Routing BP Pipeline Routing (Global Model)(Global Model)

(Berry)

The simulation is queued for processing then displayed as the The simulation is queued for processing then displayed as the Optimal Optimal RouteRoute (blue line) and 1% (blue line) and 1% Optimal CorridorOptimal Corridor (cross-hatched) (cross-hatched)

1% Corridor

Fort Collins

San DiegoOptimal

Path

4% Corridor

FC

SD

(digital slide show (digital slide show BP_Pipeline_routing))

Increased population growth into the Increased population growth into the wildland/urban interface raises the wildland/urban interface raises the threat of disaster…threat of disaster…

Modeling Wildfire RiskModeling Wildfire Risk

(Berry)

(digital slide show (digital slide show Wildfire Risk Modeling))

……a practical method is a practical method is needed to needed to identify areas identify areas

most likely to be impacted most likely to be impacted by wildfireby wildfire so effective so effective

pre-treatment, pre-treatment, suppression and recovery suppression and recovery plans can be developedplans can be developed

Is Technology Ahead of Science?Is Technology Ahead of Science?

• Are Are geographic distributionsgeographic distributions a natural extension a natural extension of numerical distributions? of numerical distributions?

(Berry)(Berry)

• Is the "Is the "scientific methodscientific method" relevant in the " relevant in the data-rich age of knowledge engineering? data-rich age of knowledge engineering?

• Is the "Is the "random thingrandom thing" pertinent in deriving " pertinent in deriving mapped data? mapped data?

• Can Can spatial dependenciesspatial dependencies be modeled? be modeled?

• How can commercialHow can commercial “on-site studies “on-site studies" " augment traditional research?augment traditional research?

““Maps as Data”Maps as Data”

Map Analysis EvolutionMap Analysis Evolution (Revolution)(Revolution)

(Berry)(Berry)

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

• ContextualContextual Spatial Relationships Spatial Relationships

StoreStoreTravel-TimeTravel-Time

(Surface)(Surface)

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 ppmStDevStDev= 15.5= 15.5

Spatial StatisticsSpatial Statistics

• Map of Variance Map of Variance (gradient)(gradient)

• Spatial DistributionSpatial Distribution

• NumericalNumerical Spatial Relationships Spatial Relationships

Spatial Spatial DistributionDistribution(Surface)(Surface)

……next weeknext week

GeoExploration GeoExploration vs.vs. GeoScience GeoScience

(Berry)(Berry)

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 (Map Surfaces)—distributions (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 ))

Spatial Interpolation Spatial Interpolation (Spatial Distribution)(Spatial Distribution)

The “iterative smoothing” process is similar to slapping a big chunk of 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 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 samples …valleys implied in the original field samples …mapping the Variancemapping the Variance

……repeated repeated smoothing smoothing slowly “erodes” slowly “erodes” the data surface the data surface to a flat planeto a flat plane= = AVERAGEAVERAGE

(Berry)(Berry)(digital slide show SSTAT)(digital slide show SSTAT)

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?

(Berry)(Berry)

Phosphorous (P)

Geographic Distribution

Multivariate AnalysisMultivariate Analysis— each map layer is a — each map layer is a continuous map variablecontinuous map variable with all of the math/stat 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”

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

(Berry)(Berry)

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 data largest data distancedistance from the from the comparison point comparison point (low, low, medium)(low, low, 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)

……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 zonesdata zones

Spatial Data Mining

Geographic Space

Relatively low responses in P, K and N

Relatively high responses in P, K and N

Clustered DataZones

Map surfaces are clustered to identify data pattern groups

Clustering MapsClustering Maps

(Berry)(Berry)

Data Space

……other techniques, such as Level Slicing, Similarity and Map Regression, other techniques, such as Level Slicing, Similarity and Map Regression, can be used to discover relationships among map layers can be used to discover relationships among map layers

……map-ematics/statisticsmap-ematics/statistics

The Precision Ag ProcessThe 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).

(Berry)(Berry)

Farm dBFarm dBStep 4)Step 4)

Map AnalysisMap Analysis

On-the-Fly On-the-Fly Yield MapYield Map

Steps 1) – 3)Steps 1) – 3)

Prescription MapPrescription Map

Step 5)Step 5)

Zone 1

Zone 3

Zone 2

Step 6)Step 6)

Variable Rate ApplicationVariable Rate Application

Cyber-Farmer, Circa 1992Cyber-Farmer, Circa 1992

……in-house advising on mini-projectsin-house advising on mini-projects

(Berry)(Berry)

Two teams working on Pipeline Spill MigrationTwo teams working on Geo-business Analysis

One team on Emergency ResponseOne team on Hugag Habitat

…deleted Spatial Analysis “enrichment” slide sets

(digital slide show TerrainFeatures )(digital slide show TerrainFeatures )(digital slide show ForestAccess)(digital slide show ForestAccess)