Transcript Sieve mapping

Lecture 9.
Land suitability modelling
Outline:
– Introduction
– Multi-criteria evaluation (MCE)
– Multi-objective land allocation (MOLA)
– Examples
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Introduction
• Land is a scarce resource
– essential to make best possible use
– identifying suitability for:
 agriculture
 forestry
 recreation
 housing
 etc.
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Sieve mapping
• Early methods
– Ian McHarg (1969) Design with Nature
 tracing
paper overlays
 landscape architecture and facilities location
– Bibby & Mackney (1969) Land use capability
classification
 tracing
paper overlays
 optimal agricultural land use mapping
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GIS approaches
• Sieve mapping using:
– polygon overlay (Boolean logic)
– cartographic modelling
– Example uses:
 nuclear
waste disposal site location
 highway routing
 land suitability mapping
 etc.
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Question…
• What problems or limitations are there
with the sieve mapping approach?
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Multi-criteria evaluation
• Basic MCE theory:
– “Investigate a number of choice possibilities in
the light of multiple criteria and conflicting
objectives” (Voogd, 1983)
– generate rankings of choice alternatives
 simple
linear programming algorithms
 multi-objective optimisation
 multi-dimensionality of planning problems
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Principles of MCE
• Methodology
– construct evaluation matrix…
S11…..SI1
S =
.
.
S1J…..SIJ
– standardisation (normalisation) of criterion
scores
– evaluation of matrix using MCE algorithms
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MCE techniques
• Many techniques
– most developed for evaluating small matrices
– suitability for large (GIS) matrices?
 layers
= criterion scores
 cells or polygons = choice alternatives
– incorporation of levels of importance (weights)
– Incorporation of constraint maps
– e.g. ideal point analysis, weighted linear
summation, hierarchical optimisation, etc.
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Example: weighted linear summation
Map 1
Map 2
Map 3
Map 4
Standardise
User
weights
Evaluation
matrix
MCE routine
Output
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Multi-objective land allocation
• Basic MOLA theory:
– procedure for solving multi-objective land
allocation problems for cases with conflicting
objectives
 based
on information from set of suitability maps
 one map for each objective
 relative weights assigned to objectives
 amount of area to be assigned to each land use
– determines compromise solution that attempts
to maximize suitability of lands for each
objective given weights assigned
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Principles of MOLA
• Methodology
– construct ranked suitability maps for each
objective using MCE
– decide on relative objective weights and area
tolerances
– evaluate conflict demands on limited land via
iterative process
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Example: protected areas
• Multi-layered system in Britain:
– National Parks, Areas of Outstanding Natural
Beauty, Heritage Coasts, Special Areas of
Conservation, Special Protection Areas, Sites of
Special Scientific Interest, Nature Reserves,
Ramsar Sites, National and Community Forests,
Environmentally Sensitive Areas, National
Scenic Areas, Regional Parks, Common Land,
and Less Favoured Areas
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Protected areas in Britain
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Identifying “wilderness” areas
• Wilderness Britain?
– continuum of environmental modification from
“paved to the primeval” (Nash, 1981)
– the “Wilderness Continuum” concept
– measurable and mappable?
 remoteness
from settlement
 remoteness from mechanised access
 apparent naturalness (lack of human artefacts)
 biophysical naturalness (ecological integrity)
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Factor maps
Apparent naturalness Biophysical naturalness
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Remoteness from
mechanised access
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Remoteness from
settlement
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Possible solutions
Stressing naturalness
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Equally weighted
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Stressing remoteness
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Wild and city park output
Wild park with & without existing
protected areas constraint
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City park with & without existing
protected areas constraint
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MOLA Results: wild park vs city park
Suitability for wild park
Suitability for city park
MOLA results (yellow = wild park, red = city park, blue = constraints
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Conclusions
• Few GIS packages provide MCE
functionality (e.g. Idrisi32)
• Most GIS provide facilities for building MCE
analyses (e.g. Arc/Info GRID)
• Important method for:
– site and route selection
– land suitability modelling
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Practical
• MCE in GRID
• Task: Locate suitable sites for a wind farm in the
Yorkshire Wolds using MCE
• Data: The following datasets are provided…
– Digital elevation model (50m resolution 1:50,000 OS
Panorama data)
– Contour data (10m interval 1:50,000 OS Panorama data)
– ITE land cover map (25m resolution)
– Population data (200m resolution)
– Roads (1:250,000 Meridian data)
– Wind speed data
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Practical
• Steps:
1. Decide on criterion/factors required
(including any constraints)
2. Pre-process factor and constraint maps
(including standardisation of factor maps)
3. Decide on factor weights
4. Build and run MCE model
5. Display results
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Practical
• Experience with building and running MCE
models in Arc/Info GRID
• Familiarity with MCE techniques and data
requirements
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Next week…
• Spatial Decision Support Systems
– principles and theory
– examples
– online SDSS
• Practical: Siting radioactive waste disposal
facilities using web-based SDSS
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