Transcript An Entity Relationship Model of Wildlife Habitat Relations

An Entity Relationship Model of
Wildlife Habitat Associations
Robert A. Deitner
Kenneth G. Boykin
New Mexico Cooperative Fish & Wildlife Research Unit
New Mexico State University
Southwest Regional GAP
Arizona, Colorado, Nevada, New Mexico, Utah
US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology
Outline
•Introduction /Why?
•Description of Entity
Relationship (ER) models
•Description of Wildlife
Habitat Associations
(WHR)
•ER model of WHR(s)
Scalability
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More pixels
More animals
More attribute maps
More suitability measures
More people involved
More speed
Entity-Relationship Modeling
• Entity
– Class of facts that are described by a consistent
set of attributes. The basic building block
• Attributes
– Specific quality of an entity (may in itself be an
entity)
• Relations
– Description of the association between entities
– Cardinality, modality
Characteristics of ER modeling
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Abstract method of modeling data
Graphical in nature
Independent of analysis
Beginning of a well designed database
– Guarantee of “working”
– Standards exist (SQL)
– scalable solution
Wildlife Habitat Relations (WHR)
Any rule based model that predicts habitat quality
based on a set of landscape attributes.
Wildlife Habitat Relations are used to create maps of
habitat suitability. Usually by “overlay” of
landscape attributes.
Example
Suitable habitat for my favorite species occurs
in the Rio Grande Basin at elevations over
1800 meters and less than 2400 meters.
Breeding habitat occurs up to 2000 meters
on South facing slopes.
A WHR is analogous to a paragraph
Clause a: “Suitable habitat for my favorite species occurs
in the Rio Grande basin”
Clause b: “{Suitable habitat my favorite species occurs}
between 1800 and 2400 meters”
Clause c: “Breeding habitat {for my favorite species}
occurs up to 2000 meters”
Clause d: “{Breeding habitat for my favorite species}
occurs on south facing slopes
A WHR Has Two Major Entities
• Clause: The relationship between a single
attribute and its suitability to a particular
taxon.
• Statement: An expression that contains the
rules for combining multiple clauses into a
single habitat prediction.
Clause Taxonomy
Four types of clauses based on the nature of the
inputs (attribute) and outputs (Suitability measure)
Categorical
Polygons (pixels) labeled
a,b,f, and h are considered
suitable habitat.
Classification
Polygons (pixels) between
100 and 200 are considered
suitable habitat.
Score
Polygons (pixels) labeled
a,b,f, and h are given a
score of 50.
Numerical Classification
Polygons (pixels) between
100 and 200 are given a
score of 50.
Statements Combine Clauses
Using a Decision Matrix
“and” decision matrix / (“or” decision matrix)
Not Suitable
Suitable
Breeding
Not Suitable
Not Suitable Not Suitable
(Not Suitable) (Suitable)
Not Suitable
Suitable
Not Suitable
(Suitable)
Not Suitable
(Breeding)
Suitable
(Breeding)
Suitable
(Breeding)
Breeding
Suitable
(Suitable)
Suitable
(Breeding)
Example as algebraic expression
SmartOverlay($[Or matrix],
Smartoverlay($[and matrix],
$[clause a],
$[clause b]),
Smartoverlay($[and matrix],
$[clause a],
$[clause c],
$[clause d])
)
USGS
HUCS
Restrictive
Overlay
Alliance
Liberal
Overlay
Ecol.
Systems
Result
Wildlife Habitat Relationship
Taxon
Attribute
Metadata
Results
Crosswalk
details
Statement
Clause
Classification
details
Score details
Citation
The End
Literature
Numerical
Classification