Introductory%20Workshop%20on%20

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Introductory Workshop on Grid-based Map
Analysis and Modeling Applying Raster Analysis
in a Vector World From Points, Lines and
Polygons to Continuous Geographic Decision Space
 
Presentation by Joseph K. Berry
W.M. Keck Scholar in Geosciences, University of
Denver Principal, Berry Associates // Spatial
Information Systems2000 S. College Ave, Suite
300, Fort Collins, CO 80525Phone [phone]
Email jberry_at_innovativegis.com Website
at www.innovativegis.com/basis
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Where Are Headed?
PowerPoint posted at www.innovativegis.com/basis
/Present/SWUG09/SWUG09_miniWorkshop.ppt
(Berry)
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(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)
The Spatial Triad
Where is What
Why and So What
Map Analysis provides tools for investigating
spatial patterns and relationships
(Berry)
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History/Evolution of Map Analysis
(Berry)
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Desktop Mapping Framework (Vector)
(Berry)
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MAP Analysis Framework (Raster, Continuous)
(Berry)
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Underlying Concepts (continuous geographic space)
The Analysis Frame provides consistent
parceling needed for map analysis and extends
discrete point, line and polygon features to
continuous Map Surfaces
(See Beyond Mapping III, Topic 18,
Understanding Grid-based Data, www.innovativegis.c
om/basis )
(Berry)
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Calculating Slope and Flow (Terrain analysis)
(Berry)
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Deriving Erosion Potential (Terrain modeling)
Erosion Potential
(Berry)
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Calculating Effective Distance (Variable-width
buffer)
(See Beyond Mapping III, Topic 13, Creating
Variable-Width Buffers, www.innovativegis.com/basi
s )
(See Beyond Mapping III, Topic 24, Overview of
Spatial Analysis and Statistics,
www.innovativegis.com/basis )
(Berry)
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Map Analysis Evolution (SA and SS)
(Berry)
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Classes of Spatial Analysis Operators
(Geographic)
(Berry)
http//www.innovativegis.com/basis/Papers/Other/GI
SmodelingFramework/
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Evaluating Habitat Suitability
(See Beyond Mapping III, Topic 23,
Reclassifying and Overlaying Maps,
www.innovativegis.com/basis )
(Berry)
(See Beyond Mapping III, Topic 23, Suitability
Modeling, www.innovativegis.com/basis )
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Conveying Suitability Model Logic
(Berry)
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Extending Model Criteria
gentle slopes
Slope Preference Bad 1 to 9 Good
Slope
Elevation
southerly aspects
Habitat Rating Bad 1 to 9 Good
Aspect Preference Bad 1 to 9 Good
Aspect
Elevation
lower elevations
Additional criteria can be added
Elevation Preference Bad 1 to 9 Good
Elevation

• Hugags would prefer to be in/near forested areas

• Hugags would prefer to be near water

(Berry)
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Reclassify Overlay Techniques (Exercise)
(Berry)
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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)
(Berry)
http//www.innovativegis.com/basis/Papers/Other/GI
SmodelingFramework/
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Establishing Distance and Connectivity
(digital slide show DIST2)
(Berry)
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Simple Effective Proximity Comparisons
Simple Proximity as the crow flies
Effective Proximity as the crow walks
(See Beyond Mapping III, Topic 25, Calculating
Effective Distance, www.innovativegis.com/basis )
(Berry)
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Distance Techniques (Exercise)
(Berry)
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Generating an Effective Travel-time Buffer

1. superimposition of an analysis grid over the area
   of interest
2. burns the store location into its corresponding
   grid cell
3. burnsprimary and residential streets are
   identified
4. travel-time buffer derived from the two grid
   layers

(Berry)
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Travel-Time Waves
(Berry)
(See Beyond Mapping III, Topic 14, Deriving and
Using Travel-Time Maps, www.innovativegis.com/basi
s )
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Travel-Time Connectivity
(Berry)
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Accumulation Surface Analysis (customer
travel-time)
(See Beyond Mapping III, Topic 17, Applying
Surface Analysis, www.innovativegis.com/basis )
(Berry)
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Analysis Frame as Primary Key
(See Beyond Mapping III, Topic 28, Spatial Data
Mining in Geo-business, www.innovativegis.com/basi
s )
(Berry)
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Variable-Width Buffers (Simple/uphill proximity)
Simple Buffer as-the-crow-flies proximity to
the road no absolute or relative barriers are
considered
(Berry)
(See Beyond Mapping III, Topic 13, Creating
Variable-Width Buffers, www.innovativegis.com/basi
s )
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Establishing Visual Connectivity
Radiate analogous to a searchlight casting its
beam light onto the landscape Simply
viewshed Completely number of viewers that
see each location Weighted viewer cell value is
added
like Spread, it starts somewhere (starter cell)
and moves through geographic space by steps (wave
front) assigning a 1 (seen) to locations with
tangents larger than the previous ones
(See Beyond Mapping III, Topic 15, Deriving and
Using Visual Exposure Maps, www.innovativegis.com/
basis )
(Berry)
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Calculating Visual Exposure ( Times Seen)
(Berry)
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Visual Exposure from Extended Features
A visual exposure map identifies how many times
each location is seen from an extended eyeball
composed of numerous viewer locations (road
network)
(Berry)
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Weighted Visual Exposure (Sum of Viewer Weights)
Different road types are weighted by the relative
number of cars per unit of time the total
number of cars replaces the number of times
seen for each grid location
(See Beyond Mapping III, Topic 15, Deriving and
Using Visual Exposure Maps, www.innovativegis.com/
basis )
(Berry)
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Visual Exposure Connectivity (Exercise)

• RADIATE Ranch OVER ELEVATION TO 35
• AT 5 SIMPLY FOR Ranch_viewshed
• RADIATE Roads OVER ELEVATION TO 35
• AT 5 SIMPLY FOR Roads_viewshed
• RADIATE Roads OVER ELEVATION TO 35
• AT 5 COMPLETELY
• FOR Roads_VExposure
• RADIATE Housing OVER ELEVATION
• TO 35 WEIGHTED
• FOR housing_WeightedVE
• SLICE Housing_WeightedVE INTO 4
• FOR Housing_VE_Index
• SLICE Roads_VExposure INTO 4
• FOR Roads_VE_Index
• ANALYZE Housing_VE_Index
• WITH Roads_VE_Index Mean
• FOR RH_VE_Index_avg

(Berry)
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Variable-Width Buffers (Line-of-sight)
(Berry)
(See Beyond Mapping III, Topic 13, Creating
Variable-Width Buffers, www.innovativegis.com/basi
s )
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Neighborhood Operations (Exercise)
(See Beyond Mapping III, Topic 26, Assessing
Spatially Defined Neighborhoods,
www.innovativegis.com/basis )
(Berry)
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Map Analysis Evolution (SA and SS)
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Classes of Spatial Statistics Operators (Numeric)
all Spatial Analysis involves generating new map
values (numbers) as a mathematical or statistical
function of the values on another map layer(s
(Berry)
http//www.innovativegis.com/basis/Papers/Other/GI
SmodelingFramework/
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GeoExploration vs. GeoScience
Maps are numbers first, pictures later
(See Beyond Mapping III, Epilog, Technical and
Cultural Shifts in the GIS Paradigm,
www.innovativegis.com/basis )
(Berry)
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Point Density Analysis
Point Density analysis identifies the total
number of customers within a specified distance
of each grid location
(Berry)
(See Beyond Mapping III, Epilog, Technical and
Cultural Shifts in the GIS Paradigm,
www.innovativegis.com/basis )
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Identifying Unusually High Density
(See Beyond Mapping III, Topic 26, Spatial
Data Mining in Geo-business, www.innovativegis.com
/basis)
(Berry)
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Spatial Interpolation (Smoothing the Variability)
The iterative smoothing process is similar to
slapping a big chunk of modelers 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
(click for digital slide show SStat2)
repeated smoothing slowly erodes the data
surface to a flat plane AVERAGE
(Berry)
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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?
(Berry)
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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
(See Beyond Mapping III, Topic 16,
Characterizing Spatial Patterns and
Relationships, www.innovativegis.com/basis)
(Berry)
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Identifying Map Similarity
the relative data distance between the
comparison points data pattern and those of all
other map locations form a Similarity Index
the green tones indicate field locations with
fairly similar P, K and N levels red tones
indicate dissimilar areas
(Berry)
(See Beyond Mapping III, Topic 16,
Characterizing Spatial Patterns and
Relationships, www.innovativegis.com/basis)
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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
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)
(Berry)
(See Beyond Mapping III, Topic 16,
Characterizing Spatial Patterns and
Relationships, www.innovativegis.com/basis)
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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
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).
(Berry)
(See Beyond Mapping III, Topic 16,
Characterizing Spatial Patterns and
Relationships, www.innovativegis.com/basis)
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Spatial Data Mining

• Precision Farming is just one example of applying
  spatial statistics and data mining techniques

(Berry)
(See Beyond Mapping III, Topic 28, Spatial
Data Mining in Geo-business, www.innovativegis.com
/basis)
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Spatial Statistics (Exercise)
(Berry)
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Where Have We Been?

• Mapping (70s), Managing (80s) and Modeling (90s)
  and Multimedia Mapping (00s)
• Vector (discrete objects composed of irregular
  Point, Line and Polygon
• features Descriptive
  Mapping)
• Raster/Grid (continuous uniform Surfaces
  Prescriptive Modeling)

(Berry)
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Evolution of Map Analysis
(Berry)
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Future Directions in Map Analysis (2010 and
beyond)
Future Directions
Contemporary GIS
The Early Years
(See Beyond Mapping III, Topic 27, GIS
Evolution and Future Trends, www.innovativegis.com
/basis)
(Berry)
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Revamping Geographic Referencing
The Cartesian Coordinate System based on
rectangular grid referencing will be replaced by
Hexagon and Dodecahedron referencing
(See Beyond Mapping III, Introduction, GIS
Softwares Changing Roles, www.innovativegis.com/b
asis)
(Berry)
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Revamping Map Analysis Capabilities
Spatial Statistics 1) uncertainty and error
propagation handing for all analytical
processing 2) CART, Induction and Neural
Network techniques replace traditional
multivariate data analysis and 3) grid-cell
will become the primary key for database
referencing /analysis
Map Display Vectorbased systems will be used
for traditional mapping and 2D graphics
Grid-based systems will be used for 3D
visualization
Geo-referencing and Data Structure advances will
lead to revised/new Spatial Analysis (e.g.,
dynamic effective distance) and Spatial
Statistics techniques (e.g., CART)
(Berry)
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Whats Next? (References and Homework)
Joseph K. Berry, jberry_at_innovativegis.com