Journal of Geographic Information System, 2011, 3, 50-61
doi:10.436/jgis.2011.31004 Published Online January 2011 (http://www.SciRP.org/journal/jgis)
Copyright © 2011 SciRes. JGIS
A GIS-Based Multicriteria Decision Analysis Approach for
Mapping Accessibility Patterns of Housing Development
Sites: A Case Study in Canmore, Alberta
Yunliang Meng1
, Jacek Malczewski2
, Soheil Boroushaki3
1
School of Social Work, York University, Toronto, Ontario, Canada
2,3
Department of Geography, The University of Western Ontario, London, Ontario, Canada
E-mail: yunliang@yorku.ca, jmalczew@uwo.ca, sboroush@uwo.ca
Received August 31, 2010; revised September 31, 2010; accepted November 19, 2010
Abstract
This paper presents a Geographic Information System (GIS) based multicriteria decision analysis approach
for mapping accessibility patterns of housing development sites in Canmore, Alberta. The approach involves
integrating two multicriteria decision methods (Analytical Hierarchy Process and Ordered Weighted Averaging)
in a raster GIS environment, and incorporating the linguistic quantifier concept as a method for obtaining
the order weights. The approach facilitates a wide range of location (decision) strategies to be generated
and examined. The aim of the study is to help the housing development authorities in addressing the
uncertainty involved in the decision making process, achieving a better understanding of the alternative accessibility
patterns. It also assists the authorities in evaluating and prioritizing the potential housing development
sites in terms of accessibility levels.
Keywords: Accessibility, AHP-OWA Procedures, GIS, Housing Development
1. Introduction
The accessibility to services, facilities and amenities is
an essential factor affecting evaluation of potential sites
for housing development [1,2]. In many regions, urban
plans ensure that individuals have some minimal levels
of accessibility to the public sector facilities, such as
schools, emergency services, and recreation amenities.
At the same time, an essential element of location strategy
for housing development is to avoid proximity to
noxious facilities (e.g., waste disposal sites, gas depots,
and chemical factories).
It is noted that the results of accessibility evaluation
depend on the definition of accessibility [3,4]. In this
paper, we adopt a definition proposed by Dalvi, who
defines accessibility as “the ease with which any land-use
activity can be reached from a particular location,
using a particular transportation system” [5]. The concept
of accessibility can be operationalized in terms of
the average and maximum distance [6-8]. The two
methods of measuring accessibility originate from a
normative perspective on location analysis, in which the
most often used objectives are: 1) to maximize the geographical
efficiency (or minimize total travel cost for
customers), and 2) to maximize the geographical equity
(or to minimize travel cost for the farthest customers).
The accessibility to public and private sector facilities
has often been analyzed using GIS-based approaches [4,
9-12].
This paper focuses on measuring of accessibility in
terms of the spatial distributions of salutary facilities
(e.g., the supply facilities such as hospitals, fire stations,
and schools) and noxious facilities (e.g., gas tanks and
heliport) relative to the locations of demand (the potential
housing development sites). The average and maximum
distance methods are employed to explore the
trade-off between geographical efficiency and equity
[6-8]. The average distance method measures the average
distance that residents living in a particular neighbourhood
have to travel to be served. The measure is primarily
concerned with the efficiency of spatial distribution
of facilities. The larger is the average distance to a facility,
the lower is the accessibility level to the facility. The
maximum distance method measures the distance that
neighbourhood residents have to reach the farthest facility
of interest. This approach attempts to minimize the
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51
longest travel distance to a facility in a given area. The
maximum distance method mainly addresses the issue of
equity in accessibility to salutary or noxious facilities. It
is also inversely related to the accessibility level to a
facility.
In the context of housing development, a location may
have good access to some salutary facilities (e.g.,
schools), but not to others (e.g., community centres) or
be close to noxious facilities. Therefore, housing developers
have to trade off the benefits and costs of having
access to different facilities. In this study, the land suitability
involves evaluation, classification and prioritization
of the potential housing development sites according
to their accessibility to various facilities. This type of
problems can be tackled using GIS-based Multicriteria
Decision Analysis (GIS-MCDA) procedures [13]. GISMCDA
can be thought of as a process that combines and
transforms geographical data regarding accessibility to
facilities and value judgments of decision maker(s) to
obtain accessibility patterns of potential housing development
sites. Central to GIS-MCDA is the aggregation
algorithms or decision rules [13]. In this study, two
MCDA decision rules-Analytical Hierarchy Process
(AHP) and the Ordered Weighted Averaging (OWA) are
used [14,15]. The main objectives of this paper are: 1)
to implement AHP-OWA procedures to map accessibility
patterns of housing development sites for determining
the land suitability, and 2) to generate several scenarios
to explore how the uncertainty in the decision makers’
judgments can affect the MCDA outcomes.
This paper is organized as follows. Section 2 gives a
review of the literature on the AHP, OWA, and AHPOWA
procedures. Section 3 presents a case study of
mapping accessibility patterns in the context of housing
development in Canmore, Alberta. Section 4 gives conclusions
regarding the capabilities of the GIS-MCDA
approach and findings.
2. AHP and OWA
2.1. AHP
Over the last decade or so, a number of MCDA decision
rules have been implemented in the GIS environment,
including Weighted Linear Combination (WLC) [16],
ideal point methods [17], concordance analysis [18], and
AHP [19]. AHP was originally developed in order to
generate a simple way to help people make complex decisions.
Later, the power and simplicity of AHP has led
to a widespread acceptance and usage of the method
[19-21].
The first step in AHP is to decompose a particular
problem into a hierarchy that consists of all essential
elements of the problem. In developing a hierarchy, the
top level is the ultimate goal of the decision analysis. The
hierarchy then descends from the general goal to the
more specific elements of the problem (e.g., objectives,
attributes, and alternatives). In this study, a simple
four-level hierarchical structure is developed (see Subsection
3.3).
The second step is to generate objective and attribute
weights using pairwise comparison procedure. The pairwise
comparison method employs an underlying scale
with odd values from 1 to 9 to rate the relative preferences
for two elements of the hierarchy. If there is a need,
then intermediate values (2, 4, 6, 8) between two adjacent
intensities can be used. The pairwise comparison
matrix has the following form: A = [apq]n×n, where apq is
the pairwise comparison rating for attribute p and attribute
q. The matrix A is reciprocal; that is aqp = apq
-1
, and all
its diagonal elements, apq = 1, for p = q. Given this reciprocal
property, only n(n-1)/2 actual pairwise comparisons
are needed for an n×n matrix. Once the pairwise
comparison matrix is obtained, the preferences are summarized
so that each element of the hierarchical structure
can be assigned a relative importance. This can be
achieved by computing a set of weights, wj = [w1, w2, …,
wn], where j = 1, 2…n. The computation of the weights
involves two steps: 1) the entries in the matrix A are
normalized (that is, each element of the matrix is divided
by the sum total of its column), and 2) the average value
of the normalized weights is computed by dividing the
sum of entries in each row of the normalized matrix by
the number of elements in that row.
Last step of AHP is to obtain the overall priority score
for each alternative. The overall priority score, Ri of the
ith alternative is calculated in Equation (1).
1
n
i j ij
j
R w x
=
=  (1)
where wj is the aggregated composite weights of objectives
and attribute weights. The weights are calculated by
the multiplications of the matrices of relative weights at
each level of hierarchy. xij is the standardized attribute
value for i-th alternative.
2.2. OWA
Although AHP is widely used, one of the major issues of
AHP is its inability to address the uncertainty in the decision
maker’s judgments [22]. To overcome the shortcomings
of the AHP, OWA is used to integrate AHP to
determine the best alternative. OWA is a class of multicriteria
aggregation operators [15]. It provides an extension
and generalization for two fundamental classes of
decision rules in GIS: the Boolean overlay operations
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52
and WLC procedures. OWA involves a new concept:
order weights (vj, j = 1, 2, …, n) which is different from
attribute weights (wj, j = 1, 2, …, n). The attribute weight
wj is assigned to j-th attribute map for all locations to
indicate the relative importance of the attribute according
to the decision maker’s preferences (see Subsection
2.1). The order weights are associated with the attribute
values on the location by location basis. They are assigned
to the i-th location’s attribute value in decreasing
order without considering from which attribute map the
value comes.
The critical element of the OWA procedure is the
method for obtaining the order weights. There are several
methods for obtaining the order weights [23]. This
study uses a fuzzy linguistic quantifier approach [24].
The concept of fuzzy quantifiers allows us to convert
natural language into formal mathematical formulations.
They can be represented as fuzzy subsets over the unit
interval with proportional fuzzy statements, such as: All
of the criteria should be satisfied (“All” for short), most
of the criteria should be satisfied (“Most”), many of the
criteria should be satisfied (“Many”), half of the criteria
should be satisfied (“Half”), some of the criteria should
be satisfied (“Some”), few of the criteria should be satisfied
(“Few”), and at least one of the criteria should be
satisfied (“At least one”). In this paper, the regular increasing
monotone quantifiers, Q, is used [23]. If Q is a
linguistic quantifier (e.g., “Most”), then it can be represented
as a fuzzy subset over the unit interval [0,1],
where for each p in the unit interval, the membership
grade Q(p) indicates the compatibility of p with the concept
denoted by Q. To identify the quantifier, we employ
one of the most often used methods for defining a parameterized
subset on the unit interval:
α
ppQ =)( , α
> 0 [25]. The order weights can be derived from attribute
weights using Equation (2) as follows [23,26]:
1
1 1
j j
j k k
k k
v u u
α α−
= =
   
= −   
   
  (2)
where uk is the reordered j-th attribute weight, wj; a
0 ≤ vj ≤ 1 and
1
1.
n
j
j
v
=
=
The parameter α is associated with a set of order
weights. By changing the parameter α, one can generate
different types of linguistic quantifiers and associated
order weights between two extreme cases of the “At least
one” and “All” quantifiers. With different sets of order
weights, one can generate a wide range of OWA operators,
including the most often used GIS-base map combination
procedures: WLC, Boolean overlay combination
“OR” and “AND”. The “AND” and “OR” operators represent
the extreme cases of OWA and they correspond to
the MIN (intersection) and MAX (union) operators, respectively
(see Table 1). The order weights are associated
with the measures of ORness and trade-off [27].
These measures take values in the interval from 0.0 to
1.0. ORness indicates the degree to which an OWA operator
is similar to the logical “OR” in terms of its combinatorial
behaviour. The trade-off measure can be interpreted
as the degree of the order weights dispersion.
Specifically, the degree to which the order weights are
evenly distributed across all attributes controls the level
of overall trade-off between attributes (criteria). The
greater is the equality among the weights, the greater is
the degree of the trade-off (see Table 1).
Given the attribute weights, wj, attribute values xij, and
the parameter α, the linguistic quantifier-guided OWA
can be defined using Equation (3) as follows [23,26]:
1
1 1 1
j jn
i k k ij
j k k
OWA u u z
α α−
= = =
    
= −         
   (3)
where i = 1, 2, …, m, zi1 ≥ zi2 ≥ …≥ zin is obtained by
reordering the attribute values xi1, xi2, …, xin.
Table 2 illustrates the OWA procedure for a given set
of the attribute, xij = (0.3, 0.1, 0.5, 0.7) and the attribute
weights, wj = (0.05, 0.1, 0.35, 0.5).
2.3. AHP-OWA Procedures
The two approaches, AHP and linguistic quantifierTable
1. Selected linguistic quantifiers and corresponding α parameters.
α Quantifier (Q) OWA weights (vj)
Combination procedure
Trade-off OR-ness Decision strategies
α→0 At least one v1 = 1; vj = 0 for others Logic “OR” (MAX) 0 1 Extremely optimistic
α = 0.1 Few * * * * Very optimistic
α = 0.5 Some * * * * Optimistic
α = 1 Half vj = 1/n for all j WLC 1 0.5 Neutral
α = 2 Many * * * * Pessimistic
α = 10 Most * * * * Very pessimistic
α→∞ All vn = 1; vj = 0 for others Logic “AND” (MIN) 0 0 Extremely pessimistic
Note: “*” is Case dependent (see Equation (2) and Table 2 for details)
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Table 2. An illustrative example for calculating OWA.
Quantifier (Q) j xij wj zij uj vj zij*vj OWAi
1 0.3 0.05 0.7 0.50 1.00 0.700
2 0.1 0.10 0.5 0.35 0.00 0.000
3 0.5 0.35 0.3 0.05 0.00 0.000
At least one
α = 0
4 0.7 0.50 0.1 0.10 0.00 0.000
0.700
1 0.3 0.05 0.7 0.50 0.93 0.651
2 0.1 0.10 0.5 0.35 0.05 0.025
3 0.5 0.35 0.3 0.05 0.01 0.003
Few
α = 0.1
4 0.7 0.50 0.1 0.10 0.01 0.001
0.680
1 0.3 0.05 0.7 0.50 0.71 0.497
2 0.1 0.10 0.5 0.35 0.21 0.105
3 0.5 0.35 0.3 0.05 0.03 0.009
Some
α = 0.5
4 0.7 0.50 0.1 0.10 0.05 0.005
0.616
1 0.3 0.05 0.7 0.50 0.50 0.350
2 0.1 0.10 0.5 0.35 0.35 0.175
3 0.5 0.35 0.3 0.05 0.05 0.015
Half
α = 1
4 0.7 0.50 0.1 0.10 0.10 0.010
0.550
1 0.3 0.05 0.7 0.50 0.25 0.175
2 0.1 0.10 0.5 0.35 0.47 0.235
3 0.5 0.35 0.3 0.05 0.09 0.027
Many
α = 2
4 0.7 0.50 0.1 0.10 0.19 0.019
0.456
1 0.3 0.05 0.7 0.50 0.00 0.000
2 0.1 0.10 0.5 0.35 0.20 0.100
3 0.5 0.35 0.3 0.05 0.15 0.045
Most
α = 10
4 0.7 0.50 0.1 0.10 0.65 0.065
0.210
1 0.3 0.05 0.7 0.50 0.00 0.000
2 0.1 0.10 0.5 0.35 0.00 0.000
3 0.5 0.35 0.3 0.05 0.00 0.000
All
α → ∞
4 0.7 0.50 0.1 0.10 1.00 0.050
0.050
guided OWA, have been integrated and implemented in
ArcGIS environment [28]. In the AHP-OWA procedures,
AHP is a global tool for building a hierarchical structure
of the spatial decision problem, analyzing the whole
process, and prioritizing each alternative. The prioritization
process in AHP uses a WLC to calculate the local
scores of each alternative. The linguistic quantifier-guided
OWA operators provide a general framework for making
a series of AHP local aggregations [29].
In AHP-OWA procedures, users are first asked to use
the AHP method to 1) construct the hierarchical structure,
and 2) obtain weights for objectives and attributes by
conducting pairwise comparisons. Then, linguistic quantifier-guided
OWA is used to support user’s decision
making. Three main steps are involved at this stage: 1)
specifying a linguistic quantifier Q, 2) generating a set of
order weights associated with Q, and 3) calculating the
overall score for each alternative using linguistic quantifier-guided
OWA (see Equation (3)).
3. Case study
3.1. Study area
Canmore, Alberta is located in the Canadian Rocky
Mountains, approximately 100 km west of Calgary and
20 km east of Banff (see Figure 1 and http://www. canmore.
ca).
The town is the government and business centre for
residents and employers in the Banff National Park,
Kananaskis Country, and the Bow Valley. In 2006,
Canmore, Alberta had a population of 16,000, including
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54
Figure 1. Study area.
about 5,000 non-permanent residents. The permanent
population growth decreased by 0.1%, while the
non-permanent population increased by 37.2% (see Fiure
2). The town is undergoing rapid change and growth
as a result of industrial tourism promotion and facility
development. The change and growth contributed to a
construction boom brought by the non-permanent residents.
As a result of growing population pressures, issues
of land use planning have become increasingly impor-
tant.
3.2. Data
The data used in the Canmore case study consist of two
Figure 2. Population growth in selected years in canmore,
alberta.
sets: 1) the data on the demand for various services, and
2) the data on the location of facilities supplying services.
The Local Delivery Units (LDU), the smallest postal
delivery zones, were used for identifying the spatial distribution
of population (demand). The centroids of LDU
areas were used as the demand points. The Future Development
Areas (FDA) were identified by the Planning
Department in Canmore (see Figure 1). FDAs contain 43
LDUs that are mainly distributed in the “Silver Trip”,
“Three Sisters”, and “Three Sisters Parkway” areas of
Canmore. The base map (Figure 1) shows the location of
30 existing facilities (see Table 3).
The facilities can be classified into two categories: 1)
salutary facilities (e.g., education, emergency and recreation
facilities), and 2) noxious facilities (e.g., flammable
and noisy facilities). This distinction is made on the basis
of the impact on the neighbouring communities brought
by the proximity to those facilities. The proximity to the
salutary facilities has a positive impact, while the proximity
to noxious facilities is considered as a negative
factor affecting the location of housing development.
Consequently, the concept of accessibility is operationalized
in the context of the different types of facilities.
The objective for salutary facilities is to maximize the
accessibility. In other words, the housing development
should be located as close as possible to such facilities.
Meanwhile, the accessibility to noxious facilities should
be minimized, so the housing development should be
located far away from such facilities.
3.3. Hierarchical Structure of the Problem
In order to evaluate the accessibility level of housing development
sites, the following objectives are consid ered:
1) accessibility to education facilities, 2) accessibility to
emergency facilities, 3) accessibility to leisure facilities,
4) accessibility to flammable facilities, and 5) accessibil
Table 3. Facilities in canmore, alberta.
Type of facilities Name and number of facilities
Total
Number
Education
facilities
Schools (5), Daycare center
(1), Library (1)
7
Emergency
facilities
Fire stations (2), Hospital (1) 3
Recreation
facilities
Golf courses (3), Campgrounds
(3), Community and
recreation centers (6)
12
Flammable
facilities
Gas tanks (4), Lumber yards
(2)
6
Noisy facilities
Pacific railway (1) , Heliport
(1)
2
Total 30
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55
ity to noisy facilities (see Figure 3). Each of the first
four objectives is measured by two attributes: the average
and maximum distance. The distance was measured
using the road network-based distance between the centroid
of LDUs within FDAs and the location of facilities.
The accessibility to noisy facilities was quantified by two
attributes: the distance (Euclidean distance) to the pacific
railway and heliport. The attribute map layers were developed
using ArcGIS [30]. Ten attribute map layers
have been generated and all of them have been converted
into 30m resolution raster data layers, which constitute
the input dataset for mapping the accessibility patterns of
housing development sites.
3.4. Attribute Map Layers
The AHP-OWA procedures require that the attributes be
represented in the form of standardized attribute map
layers. The score range method, which linearly transforms
the attribute values to standard values ranging
from 0 to 1, has been used to transform the 10 attribute
map layers into the standardized attribute map layers
[13]. Figure 4 shows the standardized attribute map lay-
ers.
3.5. Criterion weights
Given the standardized attribute map layers, one of the
key inputs for the AHP-OWA procedures is the set of
criterion weights; that is, the weights assigned to the objective
and attribute maps. The weights have been derived
using the pairwise comparison method (Saaty 1980;
see Subsection 2.1). This approach required an expert in
the Planning Department of the Town of Canmore to
provide his/her best judgments regarding the relative
importance of objectives and attributes. A questionnaire
was used to assist the expert to make his/her judgments.
The questionnaire contained the following information:
the definition of objective and attribute weights in the
context of MCDA, the scale for ratio judgment, and a set
of questions regarding the ratios of importance for pairs
of objectives or attributes. The scale in Table 4 was
used.
Given the 1-9 scale, a series of the following types of
questions were asked: what is the ratio of importance of
Table 4. Scale for pairwise comparisons [14].
Intensity of Importance Description
1 Equal importance
3
Moderate importance of one factor over
another
5 Strong or essential importance
7
Very strong or demonstrated impor-
tance
9 Extreme importance
2, 4, 6, 8 Intermediate values
Reciprocals Values for inverse comparison
Figure 3. Hierarchical structure of accessibility of housing development sites to existing facilities in canmore, alberta.
Y. L. MENG ET AL.
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56
Figure 4. Standardized attribute map layers.
C1 to C2? The ratio questions were asked at each of the
two levels of the hierarchy: the objective level (between
pairs of the objectives) and the attribute level (between
pairs of attributes associated with a given objective).
Throughout the evaluation process, the expert was given
the opportunity to re-examine the pairwise comparisons,
re-calculate weights and check the consistency of his
judgments.
After debate and careful analysis of the set of evaluation
criteria, the planner indicated the relative importance
of 5 objectives and 10 attributes by the pairwise comparisons
at each level of the hierarchy. The accessibility
to emergency facilities and leisure facilities are the two
most important objectives, followed by accessibility to
education facilities, flammable facilities and noisy facilities
(see Table 5).
The planner thought that the geographical efficiency
(average distance) and equity (maximum distance)
should be assigned equal weights of 0.5 in the process of
mapping accessibility patterns of housing development
sites. The planners also indicated that the distance to the
railroad is 4 times more important than the distance to
the heliport with respect to the accessibility to noisy facilities.
Consequently, the attribute weights of 0.8 and
0.2 were assigned to the railroad and heliport criteria,
respectively.
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57
Table 5. Pairwise comparison matrix and five objectives weights.
Education facilities
Emergency
facilities
Leisure
facilities
Flammable
facilities
Noisy facilities Weights
Education facilities 1 1/3 1/2 7 8 0.171
Emergency facilities 3 1 1 6 7 0.378
Leisure facilities 2 1 1 5 7 0.347
Flammable facilities 1/7 6 1/5 1 2 0.059
Noisy facilities 1/8 7 1/7 0.5 1 0.045
Note: The consistency ratio, CR = 0.076 < 0.1
3.6. Linguistic Quantifier-Guided OWA
Combination
Different outcomes can be generated by varying the linguistic
quantifiers in the AHP-OWA procedures (see Section
2.2). One can obtain a very large number of evaluation
outcomes by varying the α parameter associated with
the linguistic quantifiers. There are 7 linguistic quantifiers
associated with the goal and five objectives; thus, theoretically,
7(1+5) alternative evaluation scenarios can be
generated for this case study. In this paper, we limit the
analysis to a selection of five linguistic quantifiers:
“Many” is assigned to accessibility to education facilities,
“All” is assigned to accessibility to emergency facilities,
“Many” is assigned to accessibility to leisure facilities,
“Most” is assigned to accessibility to flammable facilities,
and “Half” is assigned to accessibility to noisy facilities.
Given the weights for objectives and attributes, and linguistic
quantifiers for all objectives, we applied selected
fuzzy linguistic quantifiers (“At least one”, “Few”,
“Some”, “Half”, “Many”, “Most” and “All”) for the goal
of the decision making to obtain a series of accessibility
evaluation outcomes (see Figure 5).
In other words, these alternative scenarios have been
developed under the assumption that only the linguistic
quantifier associated with the goal of the decision making
Figure 5. Accessibility patterns of housing development sites: The results of AHP-OWA procedures for selected linguistic
quantifiers.
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58
problem changes. The linguistic quantifiers correspond
to the following decision strategies: extremely optimistic,
very optimistic, less optimistic, neutral, less pessimistic,
very pessimistic, and extremely pessimistic (see Table 1).
As mentioned, the overall site suitability for housing
development is determined based on the accessibility
levels in this study. Consequently, the OWA values were
reclassified into four suitability based on “Equal Intervals”
(Table 6). The reclassified outcomes are displayed
in Figure 6.
3.7. Results
Comparison of corresponding maps in Figures 5, 6 indicates
that the increasing value of α corresponds to the
decreasing degree of optimism. This implies that gradually
lower and lower order weights are assigned to the
higher attribute values, while higher and higher order
weights are assigned to the lower attribute values at a
given location. As a result, the size of the areas suitable
for housing development gradually becomes smaller and
Table 6. Land suitability classification based on OWA values.
Class Description OWA values
S1 (highly suitable) Land has no significant limitations to the given type of use. 0.75 – 1
S2 (moderately suitable) Land has limitations which in aggregate are moderately severe for a given type of use. 0.5 – 0.75
S3 (marginally suitable) Land has limitations which in aggregate are severe for a given type of use. 0.25 – 0.5
N (Not suitable) Land is not suitable and has limitations that may be surmountable in time. 0 – 0.25
Figure 6. Site suitability for housing development: The reclassified results of AHP-OWA procedures for selected linguistic
quantifiers.
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59
smaller (see Figures 6 and 7).
The linguistic term “At least one” (α→0) represents an
extremely optimistic strategy. Under this strategy, the
decision maker is willing to take the highest risk while
identifying the best sites for housing development. This
scenario selects the highest possible value at each location.
In other words, the decision making can be based
on optimistic attitudes represented by the best possible
outcomes. Under this scenario, the results of AHP-OWA
procedures show that most of FDAs are at least moderately
suitable for housing development in terms of the
accessibility level. With α close to 0, 65% of the FDAs
are dominated by sites highly suitable for housing development
and 35% of the FDAs are moderately suitable
for housing development (see Figure 7). There are no
unsuitable sites at all.
The linguistic terms “Few” (α = 0.1) and “Some” (α =
0.5) correspond to a very optimistic strategy and optimistic
strategy, respectively. For α = 0.1, the results of
AHP-OWA procedures show that the “highly suitable”
class covers 55% of the FDAs and the “moderately suitable”
area has increased to 45% of the total. There are no
sites, which belongs to the other three classes. When α
increases to 0.5, the highly suitable is down to 11%, but
the class “moderately suitable” has greatly increased to
74%. Noticeably, 6% and 9% of the FDAs are under the
classes “marginally suitable” and “unsuitable”, respectively.
The two classes concentrate on the “Three Sister
Parkway” area. In other words, it is the only place characterized
by a low accessibility level when applying the
linguistic term “Some”.
The use of linguistic term “Half” (α = 1) means that
equal order weights are assigned to all criteria. This leads
to a neutral strategy. For this strategy, the highly suitable
FDAs account for 8% of the total and they are located in
the northwest part of the “Three Sisters” area (see Figure
6). The class “moderately suitable” has increased to 76%
of the FDAs. Land parcels classified as “moderately
suitable” are clustered in the “Silver Trips” area and the
rest of the “Three Sisters” area. The class “unsuitable”,
mainly located in the “Three Sister Parkways” area accounts
for 16% of the total.
The linguistic term “Many” (α = 2) and “Almost” (α =
10) represent a pessimistic strategy and a very pessimistic
strategy, respectively. For α = 2, the “highly suitable”
and “moderately suitable” classes have been squeezed
down to 5% and 47%, respectively. These two classes
are characterized by a high accessibility level. They are
located in the “Three Sisters” area when applying the
linguistic term of “Many”. The class “marginally suitable”
is also located in the “Silver Trips” area. It is characterized
by a large increase (33% of the FDAs) in comparison
with α = 1. The “unsuitable” class accounts for
almost the same proportion (15%) of the total and it is
still dominant in the “Three Sister Parkways” area. For α
= 10, the “highly suitable” and “moderately suitable”
areas are down to 0% and 13%, respectively. The classes
“unsuitable” and “marginally suitable” are characterized
by a large increase to 26% and 61% of the FDAs. They
are mostly located in the “Three Sister Parkway” and
“Silver Trips” areas, respectively.
When linguistic term “All” (α→∞) is applied, an extremely
pessimistic strategy is adopted (see Subsection
2.2). It represents the worst-case scenario. Under this scenario,
the suitability pattern for housing development is
composed of the worst possible outcomes. As expected the
results of AHP-OWA procedures show that a very small
area (6% of the FDAs), located in the south part of the
“Three Sister” area, is characterized by a moderate suitability
for housing development in terms of the accessibility
level. There are no highly suitable sites for housing
development (see Figure 6). Noticeably, there is a large
increase of the areas categorized as “unsuitable” (for α→∞)
at the expense of declining the “marginally suitable” areas
(for α = 10). For the extremely pessimistic strategy, the
“unsuitable” class is the dominant one. It accounts for
59% of the total. The “unsuitable” and “marginally suitable”
classes together account for 94% of the FDAs.
4. Conclusions
This study has presented the application of AHP-OWA
Figure 7. Areas (%) of suitability classes derived from the seven resultant scenario maps.
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60
procedures for mapping accessibility patterns of potential
housing development sites in Canmore, Alberta. The
method provides a mechanism to generate a wide range
of decision strategies or evaluation scenarios by incorporating
the linguistic terms with the associated α parameters.
The AHP-OWA procedures incorporate uncertainty
of expert and decision maker’ opinions regarding
the evaluation criteria and their weights, and provide a
mechanism for guiding them through the multi-criteria
combination procedures. Several alternative scenarios of
site suitability for housing development have been developed
in this study. They show how the decision
maker’s attitude towards the uncertainty involved in land
suitability decision-making process can influence the
outcomes. It should be emphasized that the AHP-OWA
procedure does not aim at determining a single “optimal”
scenario. The procedure recommends sites under different
decision strategies could be considered as the priority
areas for housing development according to the level of
attitudes towards risk (e.g., optimistic, pessimistic, and
neutral). The other key capability of AHP-OWA procedure
is that it is particularly useful for experts and decision
makers to interact with and analyze all possible alternative
scenarios. Consequently, it facilitates a better
understanding of the alternative suitability patterns and
may lead to adopt a strategy for housing development
that planning authorities would never consider using traditional
land suitability analysis methods.
Finally, it should be noted that the selection of criteria
was largely limited due to data availability. The study
has been based on accessibility to a variety of facilities
that affect the site suitability prioritization for housing
development. Therefore, it is recognized that this research
only provides preliminary results for further assessment
of land suitability in the context of housing
development.
5. Acknowledgements
This research was supported by the GEOIDE Network
(Project: HSS-DSS-17) of the Networks of Centers of
Excellence. The authors would like to thank Gary Buxton,
senior manager of the Planning and Engineering
Department at the Town of Canmore, for his support in
data preparation, criteria selection, and pairwise com-
parison.
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