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GIS Modeling
Week 1 — Overview
GEOG 3110 –University of Denver
Presented by
Joseph K. Berry
W. M. Keck Scholar, Department of Geography, 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
(Nanotechnology)
Geotechnology
(Biotechnology)
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)
Geographic Information
Systems (map and analyze)
Global Positioning
System (location and navigation)
Remote Sensing
(measure and classify)
GPS/GIS/RS
The Spatial Triad
Mapping involves
precise placement
(delineation) of
physical features
(graphical inventory)
Where
Descriptive
Mapping
Why
is
What
Prescriptive
Modeling
So What and What
Modeling involves
analysis of spatial
relationships and
patterns
(numerical analysis)
(Berry)
Historical Setting and GIS Evolution
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.
…but the last four decades have radically changed
the very nature of maps and how they are used—
Computer Mapping
…automates the
cartographic process (70s)
Where
Spatial Database Management
…links
computer mapping with database capabilities (80s)
Where is What
… GIS Modeling course
Map Analysis
…representation of relationships
within and among mapped data (90s)
Why, So What and What If…
Multimedia Mapping
…full integration of
GIS, Internet and visualization technologies (00s)
Wow!!! …did you see that
(Berry)
Desktop Mapping Framework (Vector, Discrete)
Click on…
Select Theme
Zoom Pan
Info
Tool
Theme
Table
Distance
Spatial
Table
:
Object ID
X,Y
X,Y
X,Y
:
Query
Builder
…identify tall
aspen stands
Attribute
Table
Feature
:
Object ID
:
Big …over 400,000m2 (40ha)?
Species
:
Aw
:
etc.
Discrete, irregular map features (objects)
Points, Lines and Areas
(Berry)
Manual GIS (Geo-query circa 1950)
1)
Spatial Table
and written description/data in the center
Hole
15
Notch
14
#11
Index Card with series of numbered holes around the edge
13
12
10
2)
a particular characteristic (attribute), such as #11 notch =
Douglas fir timber type
3)
9
8
Special Punch was used to notch-out the hole assigned to
7
(spatial objects)
Where
Pass a long Needle through the stack of cards and lift…
Hole
6
Cards pulled up…
5
… DO NOT have characteristic
4
3
2
1
Query Tray holds all of the index
cards for a project area
Notch
What
Data Table
(attribute records)
Cards falling down…
4)
… HAVE characteristic
Repeat using the search
results sub-set for more characteristics
5)
Card ID# identifies the timber stand polygons from the
search and the appropriate locations are shaded—
…a “Database-entry Geo-query”
(Berry)
Map Analysis Framework (Raster, Continuous)
Click on…
Zoom Pan Rotate
Display
Shading
Manager
Grid
Analysis
Map Stack
…calculate a
slope map and
drape on the
elevation surface
Grid Table
Continuous, regular grid cells (objects)
Points, Lines, Areas and Surfaces
:
--, --, --, --,
--, --, --, --,
--, --, --, --,
--, 2438, --,
--, --, --, --,
:
(Berry)
Course Description and Syllabus
www.innovativegis.com/basis/Courses/GMcourse11/
Who are we?
…self-introductions …break …class photo
Class website Email dialog Student Statements
Topics and Schedule
Basic Concepts
Spatial Analysis
GIS Modeling
Spatial Statistics
Future Directions
(Berry)
Textbook and Companion CD-ROM
Course Textbook
…Required
Reading
…pop quizzes
and in-class
questions on
required reading
…Other Reading
CD Materials
Access Default.htm
…to view & install materials
Online
…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)
(Berry)
Links to Class Materials (Class Webpage)
Class folder in GIS lab
http://www.innovativegis.com/basis/Courses/GMcourse10/
The GIS Modeling course’s main page
contains links to course Administrative
Materials and Readings, Lectures, and
Homework assignments
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)
History/Evolution of Map Analysis
http://www.innovativegis.com/basis/Papers/Other/GISmodelingFramework/
Geotechnology – one of the three “mega-technologies” for the 21st Century
(the other two are Nanotechnology and Biotechnology, U.S. Department of Labor)
Global Positioning System (Location and Navigation)
Remote Sensing (Measure and Classify)
Geographic Information Systems (Map and Analyze)
70s Computer
Mapping (Automated Cartography)
80s Spatial Database Management (Mapping and Geo-query)
90s Map Analysis (Spatial Relationships and Patterns)
Organizational
Spatial Analysis
(Geographical context)
Framework Paper
Structure of
this Course
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)
Spatial Statistics
(Numerical context)
Surface Modeling (point data to continuous spatial distributions
Spatial Data Mining (interrelationships within and among map layers)
(Berry)
Mapped Data Analysis Evolution (Revolution)
Traditional GIS
Spatial Analysis
Elevation
(Surface)
Forest Inventory
Map
• Points, Lines, Polygons
• Cells, Surfaces
• Discrete Objects
• Continuous Geographic Space
• Mapping and Geo-query
• Contextual Spatial Relationships
Traditional Statistics
Spatial Statistics
Spatial
Distribution
(Surface)
Minimum= 5.4 ppm
Maximum= 103.0 ppm
Mean= 22.4 ppm
StDEV= 15.5
• Mean, StDev (Normal Curve)
• Map of Variance (gradient)
• Central Tendency
• Spatial Distribution
• Typical Response (scalar)
• Numerical Spatial Relationships
(Berry)
Calculating Slope and Flow (map analysis)
Inclination of a fitted
plane to a location and
its eight surrounding
elevation values
Slope (47,64) = 33.23%
(Neighbors)
Slope map draped
on Elevation
Slope map
Elevation Surface
Flow (28,46) = 451 Paths
Total number of the steepest
downhill paths flowing into each
location (Distance)
(Berry)
Flow map draped
on Elevation
Flow map
Deriving Erosion Potential & Buffers
Slope_classes
Reclassify
Flow/Slope
Erosion_potential
Reclassify
Slopemap
Overlay
Reclassify
Erosion Potential
Flowmap
Flow_classes
Protective Buffers
But all buffer-feet are not the same…
…reach farther in
areas of high erosion
potential
(slope/flow Erosion_potential)
Streams
Simple Buffer
Erosion_potential
Simple Buffer
(Berry)
Calculating Effective Distance (variable-width buffers)
Distance away from the streams is a
function of the erosion potential (Flow/Slope
Distance
Erosion_potential
Class) with intervening heavy flow and steep
slopes computed as effectively closer than
simple distance— “as the crow walks”
Erosion Buffers
Effective Erosion Distance
Streams
Close
Far
Simple Buffer
Heavy/Steep
(far from stream)
Light/Gentle
(close)
Effective Buffers
(digital slide show VBuff)
(Berry)
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—
(Geographic Context)
GIS Toolbox
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
(Berry)
Classes of Spatial Analysis Operators
(Geographic)
…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—
(Geographic Context)
GIS Toolbox
Proximity operations involve
measuring distance and connectivity
among map locations
Neighborhood operations involve
characterizing mapped data within
the vicinity of map locations
(Berry)
Travel-Time for Our Store to Everywhere
A store’s Travelshed identifies the relative driving
time from every location to the store—
…analogous to a “watershed”
Relative scale:
1 = .05 minutes
OUR STORE …close to the store (blue)
(Berry)
Travel-Time for Competitor Stores
Ocean
Ocean
Competitor 1
Our Store (#111)
Competitor 3
Ocean
Ocean
Competitor 2
Competitor 4
Ocean
Competitor 5
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.
(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
Competitor
Our Advantage
Positive
Our Store
Negative
Competitors
(See Location, Location, Location: Retail Sales Competition Analysis, www.innovativegis.com/basis/present/GW06_retail/GW06_Retail.htm)
(Berry)
Mapped Data Analysis Evolution (Revolution)
Logistics
…break
…using MapCalc and Snagit
for Lab #1
…on to Spatial Statistics
(Berry)
Setting Up and Using Class Data
Moving MapCalc Data to your personal workspace
1)
2)
3)
4)
5)
6)
7)
Right click on Start at the bottom left of your screen (Task Bar)
Select Windows Explorer
Locate your personal workspace as directed by the instructor (Z: drive)
Create a new folder in your workspace called …\GISmodeling
In the new folder create a sub-folder …\GISmodeling\MapCalc Data
Browse to the …\GEOG3110 class directory (I: drive)
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\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)
…etc.
Example Exercise
…download Exer0.doc to your …\GISmodeling\Week1\ folder
and complete under the instructor’s guidance
(Berry)
GIS Modeling Framework (Model Criteria)
…rows represent Model Criteria
(Berry)
GIS Modeling Framework (Analysis Levels)
…columns represent Analysis Levels
…column transitions represent Processing Approaches
“Weighting”
…analytic operations
are sequenced on
map variables to
implement the
model’s logic
Renumber
Slope
Analyze
Renumber
Spread
Modeled
Renumber
Spread
Renumber
Radiate
Orient
Base
“Calibration”
Renumber
“Algorithm”
Derived
Interpreted
(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)
Tutor25_Campground Script
Derive (Algorithm)
Gentle slopes
Near roads
Near water
Good views
Westerly
Interpret
(Calibrate)
Combine
(Weight)
Mask
(Constraints)
(See “Short description of the Campground model” and “Helpful hints in Running MapCalc” in the Email Dialog section of the Class Webpage)
(Berry)
Homework Exercise #1
Question
Confirm Homework Team— the
#1 Model Criteria
class will be divided into teams
containing two to three members
#2 Analysis Levels
Download Exercise #1— “Links
#3 Derived
Maps
to Homework,” right-click on
“Exer1.doc” and choose “Save” to
download …and then access the
exercise in Word
#4 Calibrated
Maps
Complete the exercise:
#5
Analyze
Command
#6
Masking
Due next week Thursday 5:00 pm (7 days)
(…slippage possible if requested by noon)
#7
Fancy
Display
Optional Questions #1-1 and #1-2
(Berry)
Mapped Data Analysis Evolution (Revolution)
Traditional GIS
Spatial Analysis
Effective
Distance
(Surface)
Forest Inventory
Map
• Points, Lines, Polygons
• Cells, Surfaces
• Discrete Objects
• Continuous Geographic Space
• Mapping and Geo-query
• Contextual Spatial Relationships
Traditional Statistics
Spatial Statistics
Spatial
Distribution
(Surface)
Minimum= 5.4 ppm
Maximum= 103.0 ppm
Mean= 22.4 ppm
StDEV= 15.5
• Mean, StDev (Normal Curve)
• Map of Variance (gradient)
• Central Tendency
• Spatial Distribution
• Typical Response (scalar)
• Numerical Spatial Relationships
(Berry)
Classes of Spatial Statistics Operators (Spatial Statistics)
…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—
(Numeric Context)
GIS Toolbox
Surface Modeling operations
involve creating continuous spatial
distributions from point sampled data
Spatial Data Mining operations
involve characterizing numerical
patterns and relationships within and
among mapped data
(Berry)
GeoExploration vs. GeoScience
“Maps are numbers first, pictures later”
Desktop Mapping graphically links generalized statistics to discrete spatial objects
(Points, Lines, Polygons)— non-spatial analysis (GeoExploration)
Desktop Mapping
Map Analysis
X, Y, Value
Data Space
Field
Data
Geographic Space
Standard Normal Curve
Point
Sampled
Data
(Numeric Distribution)
Average = 22.0
StDev = 18.7
40.7 …not a problem
Discrete
Spatial Object
22.0
Spatially
Generalized
(Geographic Distribution)
High Pocket
Continuous
Spatial Distribution
Spatially
Detailed
Discovery of sub-area…
Adjacent
Parcels
Map Analysis map-ematically relates patterns within and among continuous spatial
distributions (Map Surfaces)— spatial analysis and statistics (GeoScience)
(See Beyond Mapping III, “Epilog”, Technical and Cultural Shifts in the GIS Paradigm, www.innovativegis.com/basis )
(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”, Technical and Cultural Shifts in the GIS Paradigm, www.innovativegis.com/basis )
(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, www.innovativegis.com/basis)
(Berry)
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
= AVERAGE
(digital slide show SStat2)
(Berry)
Visualizing Spatial Relationships
Phosphorous (P)
Geographic Distribution
What spatial relationships
do you SEE?
…do relatively high levels
of P often occur with high
levels of K and N?
…how often?
…where?
“Maps are numbers first, pictures later”
Multivariate Analysis— each map layer is a
continuous variable with all of the math/stat
“rights, privileges and responsibilities” therewith …simply “spatially organized “ sets of numbers (matrix)
(Berry)
Calculating Data Distance
…an n-dimensional plot depicts the multivariate distribution—
the distance between points determines the relative similarity in data patterns
Pythagorean
Theorem
2D Data Space:
Dist = SQRT (a2 + b2)
3D Data Space:
Dist = SQRT (a2 + b2 + c2)
…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, www.innovativegis.com/basis)
(Berry)
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
…a map stack is a spatially organized set of numbers
…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, www.innovativegis.com/basis)
(See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships, www.innovativegis.com/basis)
(Berry)
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
yield variation every few feet. This map is
Steps 1) – 3)
4) combined with soil, terrain and other maps to
derive 5) a “Prescription Map” that is used to
6) adjust fertilization levels every few feet
in the field (variable rate application).
On-the-Fly
Yield Map
Step 4)
Map Analysis
Farm dB
Cyber-Farmer, Circa 1992
Prescription Map
Variable Rate Application
Step 5)
Step 6)
(Berry)
(See Beyond Mapping III, “Topic 16”, Characterizing Spatial Patterns and Relationships, www.innovativegis.com/basis)
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, www.innovativegis.com/basis)
(Berry)
(Nanotechnology)
Geotechnology
(Biotechnology)
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)
Geographic Information
Systems (map and analyze)
Global Positioning
System (location and navigation)
Remote Sensing
(measure and classify)
GPS/GIS/RS
The Spatial Triad
Mapping involves
precise placement
(delineation) of
physical features
(graphical inventory)
Where
Descriptive
Mapping
Why
is
What
Prescriptive
Modeling
So What and What If
Modeling involves
analysis of spatial
relationships and
patterns
(numerical analysis)
(Berry)