Grid-based%20Map%20Analysis%20Techniques%20and%20Modeling%20Workshop

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Grid-based Map Analysis Techniques and Modeling
Workshop
Part 1 Maps as Data Part 2 Surface
Modeling Part 3 Spatial Data Mining Part 4
Spatial Analysis Suitability mapping Measuring
effective distance/connectivity Visual exposure
analysis Analyzing landscape structure Characteriz
ing terrain features Part 5 GIS Modeling
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Grid-Based Map Analysis

• Surface Modeling maps the spatial distribution
  and pattern of point data
• Map Generalization characterizes spatial trends
  (e.g., titled plane)
• Spatial Interpolation deriving spatial
  distributions (e.g., IDW, Krig)
• Other roving window/facets (e.g., density
  surface tessellation)

• Data Mining investigates the numerical
  relationships in mapped data
• Descriptive aggregate statistics (e.g.,
  average/stdev, similarity, clustering)
• Predictive relationships among maps (e.g.,
  regression)
• Prescriptive appropriate actions (e.g.,
  optimization)

• Spatial Analysis investigates the contextual
  relationships in mapped data
• Reclassify reassigning map values (position
  value size, shape contiguity)
• Overlay map overlay (point-by-point
  region-wide map-wide)
• Distance proximity and connectivity (movement
  optimal paths visibility)
• Neighbors roving windows (slope/aspect
  diversity anomaly)

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3
Evaluating Habitat Suitability
Assumptions Hugags like gentle slopes,
southerly aspects and lower elevations
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4
Conveying Suitability Model Logic
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(See map Analysis, Topic 22 for more
information)
5
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

• Hugags are 10 times more concerned with slope,
  forest and water criteria than aspect and
  elevation

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6
Establishing Distance and Connectivity
(digital slide show DIST)
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Grid-based Simple Proximity Surfaces
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Calculating Effective Distance (Demo)
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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. burns primary and residential streets are
   identified
4. travel-time buffer derived from the two grid
   layers

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Travel-Time Connectivity
increasing distance from a point forms
bowl-shaped accumulation surface steepest
downhill path identifies the optimal path wave
front that got there first.
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11
Accumulation Surface Analysis
(See Map Analysis, Topic 5 and Topic 17 for
more information)
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Establishing Visual Connectivity
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13
Calculating Visual Exposure ( Times Seen)
Visual exposure identifies how many times each
map location is seen from a set of viewer
locations
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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)
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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 Map Analysis, Topic 15, Deriving and Using
Visual Exposure Maps for more information)
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Calculating Visual Exposure (Demo)
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Real-World Visual Analysis
Weighted visual exposure map for an ongoing
visual assessment in a national recreation area
the project developed visual vulnerability maps
from the reservoir in the center of the park and
a major highway running through the park. In
addition, aesthetic maps were generated based on
visual exposure to pretty and ugly places in the
park
(Senior Honors Thesis by University of Denver
Geography student Chris Martin, 2003)
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18
Neighborhood Techniques (Covertype Diversity Map)
a DIVERSITY map indicates the number of
different map values (categories) that occur
within a window e.g., cover types As the
window is enlarged, the diversity generally
increases
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Neighbor Techniques (Demo)

• SCAN Covertype diversity within 3 for
  Cover_diversity3
• SCAN Slope coffvar with 2 for Roughness
• SCAN Housing total with 5 for Housing_density
• RENUMBER Housing_density for High_hdensity
• assign 0 to 0 thru 15 assign 1 to 15 thru 50
• COMPOSITE Districts with Housing_density average
• for Districts_HDavg

Housing Density by Districts Average housing
density for each district
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Neighborhood Variability
(See MapCalc Applications, Assessing Cover Type
Diversity and Delineating Core Area and
Assessing Covertype Diversity for more
information)
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Spatial Analysis of Landscape Structure
Area Metrics (6), Patch Density, Size and
Variability Metrics (5), Edge Metrics (8), Shape
Metrics (8), Core Area Metrics (15), Nearest
Neighbor Metrics (6), Diversity Metrics (9),
Contagion and Interspersion Metrics (2) 59
individual indices
(US Forest Service
1995 Report PNW-GTR-351)

• For example,
• Area Metrics
• Area per patch
• Shape Metrics
• Shape Index per patch
• Edge Metrics
• Edge Contrast per patch

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22
Grid-Based Map Analysis

• Surface Modeling maps the spatial distribution
  and pattern of point data
• Map Generalization characterizes spatial trends
  (e.g., titled plane)
• Spatial Interpolation deriving spatial
  distributions (e.g., IDW, Krig)
• Other roving window/facets (e.g., density
  surface tessellation)

...Whew!!!

• Data Mining investigates the numerical
  relationships in mapped data
• Descriptive aggregate statistics (e.g.,
  average/stdev, similarity, clustering)
• Predictive relationships among maps (e.g.,
  regression)
• Prescriptive appropriate actions (e.g.,
  optimization)

• Spatial Analysis investigates the contextual
  relationships in mapped data
• Reclassify reassigning map values (position
  value size, shape contiguity)
• Overlay map overlay (point-by-point
  region-wide map-wide)
• Distance proximity and connectivity (movement
  optimal paths visibility)
• Neighbors roving windows (slope/aspect
  diversity anomaly)

(Berry)