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A GEOGRAPHIC INFORMATION SYSTEM (GIS) AND MULTI-CRITERIAANALYSIS FOR SUSTAINABLE TOURISM PLANNING
MANSIR AMINU
A project submitted in fulfillment of the
requirements for the award of the degree of
Master of Science (Planning-Information Technology)
FACULTY OF BUILT ENVIRONMENT
UNIVERSITI TEKNOLOGI MALAYSIA
April, 2007
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We hereby declare that we have read this project report and in
our opinion this project report is sufficient in terms of scope and
quality for the award of the degree of Master of Science
(Planning-Information Technology)
Signature : ______________________ Name of Supervisor I : Prof. Dr. Ahris Bin Yaakup
Date : ______________________
Signature : ________________________________________
Name of Supervisor II : Assoc. Prof. Dr. Ahmad Nazri B. Muhamad Ludin
Date : ________________________________________
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DECLARATION
I declare that this project report entitled A Geographic Information System (GIS)
and Multi-Criteria Analysis for Sustainable Tourism Planning , is the result of myown research except as cited in the references. The project report has not been accepted
for any degree and not concurrently submitted in candidature of any other degree.
Signature : ________________________
Name of Student : Mansir Aminu__________
Date : 27 th April, 2007_________
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This project is dedicated to the entire members of my family
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ACKNOWLEDGEMENT
I wish to express my profound gratitude to the Almighty Allah for his blessing
and guidance throughout my masters programme. My appreciation goes to my parents
whose support and affection can never be quantified. I would like to seize this
opportunity in thanking my brothers Abdullahi, Ibrahim and Nasiru for their financialand moral support all through my stay here, may Allah continue to guide and bless them.
My sincere gratitude goes to my supervisors Prof. Dr. Ahris Bin Yaakup and
Assoc. Prof. Dr. Ahmad Nazri B. Muhamad Ludin for their constructive criticisms,
patience and understanding that facilitated me through all phases of my study. I am also
indebted to all my lecturers and non-teaching staff that have contributed in the course of
writing this project. Finally, I want to thank all my friends and well wishers who directly
or indirectly played a role towards the completion of my study.
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ABSTRACT
The need for a sustainable approach in tourism development is very often
addressed among the academia, the authorities and the stakeholders, as well as the
apparent need for tools which will guide the decision environment in evaluation and
planning. This project aims to identify conservation and compatible areas for tourismdevelopment in Johor Ramsar site, using spatial modeling in Geographic Information
System (GIS). The study describes a methodological approach based on the integrated
use of Geographic Information System (GIS) and Multi Criteria Decision Model
(MCDM) to identify nature conservation and development priorities among the wetland
areas. A set of criteria were defined to evaluate wetlands biodiversity conservation and
development; the criteria include tree age class, harvesting season, size of endangered
fauna, habitats proximity to natural land use/ land cover, habitat area and water quality.Having defined the criteria, the next step was selecting suitable indicators and variables
to measure the selected criteria. Subsequently the criteria were evaluated from
conservation and tourism development point of view. These criteria were then ranked
using the pair wise comparison technique of multi criteria analysis (MCA) and the
results integrated into GIS. Several conservation scenarios are generated so as to
simulate different evaluation perspectives. The scenarios are then compared to highlight
the most feasible and to propose a conservation and development strategy for the
wetlands area. The generation and comparison of conservation and development
scenarios highlighted the critical issues of the decision problem, i.e. the wetland
ecosystems whose conservation and development relevance is most sensitive to changes
in the evaluation perspective. This study represents an important contribution to
effective decision-making because it allows one to gradually narrow down a problem.
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TABLE OF CONTENT
Page
Declaration ii
Dedication iii
Acknowledgement iv
Abstract v
Abstrak vi Table of Content vii
List of Tables xiiList of Figures xiii
CHAPTER 1 INTRODUCTION
1.1 Background 1
1.2 Statement of research problem 3
1.3 Aim of the study 5
1.4 Objectives of the study 5
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1.5 Significance of the study 5
1.6 Scope of study & methodology 7
1.7 Limitations of the study 10
CHAPTER 2 GIS and Decision Support Systems in Sustainable Tourism
2.1 Concept of sustainable tourism 11
2.2 Wetlands assessment 14
2.3 Spatial modeling environments 18
2.4 Geographic Information System (GIS) in sustainable 22Tourism planning
2.5 Multi Criteria Decision Making and Natural resources 25Management
2.6 Multi criteria decision making (MCDM) 27
2.6.1 Multiple criteria decision making an overview 27
2.6.2 Multi-criteria decision making and GIS 30
2.6.2.1 Evaluation criteria 32
2.6.2.2 Criterion maps 35
2.6.2.3 Criterion standardization 36
2.6.2.4 Assigning weights 38
2.6.2.5 Decision rules 44
2.6.2.6 Error assessment 45
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CHAPTER 3 Wetlands Assessment using multi-criteria decision model
3.1 The study area 48
3.1.1 Pulau Kukup 48
3.1.2 Sungai Pulai 50
3.1.3 Tanjung Piai 51
3.2 Data collection 54
3.3 Database development for wetland assessment 54
3.3.1 Data layers for the study 56 3.3.1.1 Land use 56
3.3.1.2 Harvesting 57
3.3.1.3 Endangered Species 59
3.3.1.4 Tree age class 60
3.3.1.5 Management 61
3.3.1.6 Pulai River 62
3.3.1.7 Habitat area 64
3.4 Evaluating existing developments to the wetlands 66
3.4.1 Threat analysis 66
3.4.1.1 Port of Tanjung Pelepas (PTP) 66
3.4.1.2 Tenaga Nasional Power Transmission lines (PTL) 68through the Sungai Pulai
3.4.2 Tourism issues 69
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3.5 Main steps of the approach 70
3.5.1 Definition of criteria 71
3.5.2 Evaluation of conservation and development criteria 723.5.3 Multi criteria analysis and priority ranking 79
3.5.3.1 Pairwise comparison method 79
3.5.4 Generation and analysis of conservation/ development 98scenarios
3.5.4.1 Tourism development scenario 1 98
3.5.4.2 Tourism development scenario 2 100
3.5.4.3 Economic development scenario 100
3.5.4.4 Conservation scenarios 102
CHAPTER 4 WETLANDS ASSESSMENT AND RESULT
4.1 Introduction 105
4.2 Wetlands conservation 107
4.2.1.1 Habitat area 107
4.2.1.2 Endangered fauna 108
4.2.1.3 Wetlands proximity to natural land cover 110
4.2.1.4 Tree age class 112
4.2.1.5 Harvesting season 114
4.2.1.6 Water quality 115
4.2.1.7 Conversion of data layers 118
4.2.1.8 Reclassification of data layers 119
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4.2.2 Conservation scenarios 119
4.2.2.1 Raster calculations of the data layers 120
4.2.2.2 Comparison of conservation scenarios 132
4.3 Wetlands Development 134
4.3.1 Tourism development 135
4.3.1.1 Habitat area 136
4.3.1.2 Threatened fauna 137
4.3.1.3 Habitats proximity to natural land cover 139
4.3.1.4 Water quality 141
4.3.2 Economic development 144
4.3.2.1 Tree age class 145
4.3.2.2 Harvesting season 146
4.3.2.3 Water quality 148
4.3.3 Comparison of development scenarios 150
4.4 Comparison of conservation and development scenarios 153
CHAPTER 5 CONCLUSION AND FUTURE RESEARCH
5.1 Conclusion 157
5.2 Future research 161
REFERENCES
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LIST OF TABLES
Table No Page
Table 2.1: Example of straight rank weighting procedure 39Table 2.2: Assessing weights by ratio estimation procedure 40
Table 2.3: Illustration of pairwise comparison method 41
Table 3.1: Data inventory for the project 55
Table 3.2: Water quality parameters of Pulai River sampling stations 63
Table 3.3: Study criteria and indicators 73
Table 3.4: Illustration of pairwise comparison method 81
Table 3.5: Tourism development criteria and indicators 99
Table 3.6: Economic development criteria and indicators 101
Table 3.7: Conservation criteria and indicators 102
Table 4.1: Water quality Sub-index 116
Table 4.2: Comparison of conservation scenarios (%) 133
Table 4.3: Comparison of development scenarios (%) 151
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LIST OF FIGURES
Figure No Page
Figure 1.1 : Conceptual framework of the study 9
Figure 2.1 : A general model of MCDM (after Jankowski 1995) 29
Figure 2.2 : Spatial multicriteria evaluation 32
Figure 2.3 : Spatial multicriteria analysis in GIS after Malczewski (1999), 34modified.
Figure 2.4 : Score range procedure in GIS 38
Figure 2.5 : The General Structure of the Super matrix 43
Figure 2.6 : Simple additive weighting method performed in GIS on raster 45data
Figure 3.1 : Study area 49
Figure 3.2 : Land use map 56
Figure 3.3 : Harvesting schedule 58
Figure 3.4 : Endangered species 59
Figure 3.5 : Tree age class 61
Figure 3.6 : Management 62
Figure 3.7 : Pulai River 63
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Figure 3.8 : Species habitat 65
Figure 3.9 : Schematic research approach 71
Figure 3.10: Steps in pairwise comparison method 82
Figure 3.11: Tourism development suitability model 99
Figure 3.12: Economic development model 101
Figure 3.13: Wetlands conservation model 103
Figure 4.1 : Habitat area (reclassified) 108
Figure 4.2 : Endangered fauna (reclassified) 109
Figure 4.3 : Multiple ring buffer 110
Figure 4.4 : Habitats proximity to upland/ natural land cover 111(reclassified)
Figure 4.5 : Habitats proximity to upland/ natural land cover 112(enlarged area)
Figure 4.6 : Tree age class (reclassified) 113
Figure 4.7 : Harvesting (reclassified) 115
Figure 4.8 : Water quality (reclassified) 117
Figure 4.9 : Spatial analyst (Features to Raster) 118
Figure 4.10: Spatial analyst (Reclassify) 119
Figure 4.11: Raster calculations 120
Figure 4.12: Conservation model 121
Figure 4.13: scenario 1 (Conservation) 122
Figure 4.14: Scenario 2 (Conservation) 124
Figure 4.15: Scenario 3 (Conservation) 125
Figure 4.16: Scenario 4 (Conservation) 127
Figure 4.17: Scenario 5 (Conservation) 129
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Figure 4.18: Scenario 6 (Conservation) 130
Figure 4.19: Comparison of conservation scenarios 132
Figure 4.20: Tourism development model 136
Figure 4.21: Habitat area (reclassified) 137
Figure 4.22: Endangered fauna (reclassified) 138
Figure 4.23: Habitats proximity to upland/ natural land cover 139(reclassified)
Figure 4.24: Habitats proximity to upland/ natural land cover 140(enlarged area)
Figure 4.25: Water quality (reclassified) 141
Figure 4.26: Scenario 1 (Tourism development) 142
Figure 4.27: Scenario 2 (Tourism development) 143
Figure 4.28: Economic development model 145
Figure 4.29: Tree age class (reclassified) 146
Figure 4.30: Harvesting (reclassified) 147
Figure 4.31: Water quality 148
Figure 4.32: Scenario 3 (Economic development) 149
Figure 4.33: Comparison of development scenarios 151
Figure 4.34: Comparison of Conservation and development scenarios 153
Figure 4.35: Schematic description of activities 156
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CHAPTER 1
INTRODUCTION
1.1 Background
The proliferation of mass tourism over the last 50 years has often occurred with
little concern for environmental and cultural protection. As outlined by Inskeep (1991)
the coastal resorts of the Mediterranean and tourism development in the Caribbean bearwitness to this uncontrolled planning and development process. Most of the tourism
destinations in developing countries, try to make the best out of this, taking everything
out of the environment and causing damage to their land that sometimes can be
permanent.
Throng tourism has been responsible for the destruction of valuable wetlands and
threatening water supplies in the Mediterranean (World Wildlife Fund, 2005). It warns
an expected boom over the next 20 years, with tourist numbers set to reach 655 million
people annually by 2025, will strain supplies further. France, Greece, Italy and Spain
have already lost half of their original wetland areas. In the case of Spain, tourism
expansion near Donana National Park can be seen to compete with the park's wetlands
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for already scarce resources. It is further stated that resorts planned on the Moulouya
estuary in Morocco could further threaten the endangered monk seal and the slender-
billed curlew, one of the rarest birds in Europe. These problems have been responsible
for pollution, shrinkage of wetlands and the tapping of non-renewable groundwater in
some regions (World Wildlife Fund, 2005).
Not only do they use up their natural resources to support the growing tourism
industry, but they also deprive local population of what is rightfully theirs. Yet, all they
do is taking without putting much back in. Unless appropriate action is taken, continued
growth of tourism will further damage such ecosystems with serious consequences in
sustaining long term development and human well being.
Most significantly, however, tourism planning processes have lacked the refined
modeling and simulation tools now available to predict potential outcomes from the
medium to long term. Similarly, the authorities in charge have lacked tools that can
provide them with value-added information that is information about remote locations
and unexploited potentials.
Geographic Information System (GIS) are valuable instruments to resource
managers in identifying "hot spots" or problem areas needing immediate work, and
allow experimentation with various management approaches to working with those
resources, without risking those resources in experimentation. Decision support systems,
ecosystem modeling, and resource assessment allow users to put GIS data bases to their
full use for individualized applications or research studies. GIS is now recognized
widely as a valuable tool for managing, analyzing, and displaying large volumes of
diverse data pertinent to many local and regional planning activities. Its use in
environmental planning is rapidly increasing. Tourism is an activity highly dependent on
environmental resources. Hence, the strength of sustainable tourism planning can be
enhanced by GIS applications.
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1.2 Statement of research problem
Wetland ecosystems are often mistakenly undervalued. Few people realize therange of products derived from freshwater habitats such as wetlands - food such as fish,
rice and cranberries, medicinal plants, peat for fuel and gardens, poles for building
materials, and grasses and reeds for making mats and baskets and thatching houses.
These complex habitats act as giant sponges, absorbing rainfall and slowly releasing it
over time. Wetlands are like highly efficient sewage treatment works, absorbing
chemicals, filtering pollutants and sediments, breaking down suspended solids and
neutralizing harmful bacteria (World Wildlife Fund, 2005).
Yet half of the world's wetlands have already been destroyed in the past 100
years alone (World Wildlife Fund, 2005). Conversion of swamps, marshes, lakes and
floodplains for large-scale irrigated agriculture, ill-planned housing and industrial
schemes, toxic pollutants from industrial waste and agricultural run-off high in nitrogen
and phosphorous pose some of the main threats to wetlands. Among threatened species
are several river dolphins, manatees, fish, amphibians, birds and plants. In addition, alien
'invasive' species brought from ecosystems in foreign lands disrupt functions in native
ecosystems. Africa alone spends about US$60 million annually to control aquatic
invasive species (World Wildlife Fund, 2005).
Johor wetland reflects an extraordinary diversity of Malaysia: a region of lakes,
mangroves, and woodlands. Owing to a variety of habitats with fascinating landscape,
the wetlands support an incredibly high species biodiversity with a high level of
endemism. It has been a major source of attraction to visitors from all over the world.
However, tourism development is taking place rapidly in this sensitive wetlands
environment with modest concern on the environment. For example the threats faced by
the Sungai Pulai mangrove forest around the Port Tanjung Pelapas (PTP) area, it is
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alarming to note that the site is surrounded by development, which has encroached into
the locale; in addition to this is the continuous logging of its forest in an unsustainable
manner. Rapid and unsustainable development of these wetlands and the river basins
especially the construction of a new port at the river estuary represent a direct impact on
the wetland ecosystem, causing coastal erosion, water pollution and natural habitat
destruction from associated dredging and reclamation works and traffic which has led to
the disruption of natural hydrological cycles.
The degradation and loss of wetlands and their biodiversity has imposed major
economic and social losses; and costs to the human populations of these river basins.
Thus, appropriate protection and management of the wetlands is essential to enable theseecosystems to survive and continue to provide important goods and services to the local
communities. The main threat to Pulau Kukup comes from the agricultural activities in
the straits, coupled with unplanned tourism, hunting, and water activities.
In view of these problems spatial modeling and Geographic Information System
(GIS) can be regarded as powerful tools that facilitate mapping of wetland conditions,
which is useful in varied monitoring and assessment capacities. More importantly, the
predictive capability of modeling provides a rigorous statistical framework for directing
management and conservation activities by enabling characterization of wetland
structure at any point on the landscape. Spatial (environmental) data can be used to
explore conflicts, examine impacts and assist decision-making. Impact assessment and
simulation are increasingly important to tourism development in wetland areas, and GIS
can play a role in examining the suitability of locations for proposed developments,
identifying conflicting interests and modeling relationships. Systematic evaluation of
environmental impact is often hindered by information deficiencies. GIS seems
particularly suited to this task.
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1.3 Aim of the study
The study aims to identify conservation and compatible areas for tourismdevelopment in Johor Ramsar site, using spatial modeling of Geographic Information
System (GIS) and Multi Criteria Decision Model (MCDM).
1.4 Objectives of the study
1. To study the concept and principles to sustainable tourism/ wetland assessment,
environmental modeling and multi criteria evaluation.
2. To identify suitable areas for tourism and economic development in Ramsar site.
3. To conserve unsuitable areas for tourism development in Ramsar site.
4. To develop a GIS and multi criteria evaluation model for the conservation anddevelopment of Ramsar site.
1.4 Significance of the study
The study area comprises of Johor wetlands that have been declared as wetlandsof international importance at the Ramsar convention, namely Sungai Pulai, Tanjung
Piai and Pulau Kukup; all in Southern Johor State not far from Singapore, particularly
rich in mangroves and inter-tidal mudflats. These coastal and estuarine sites support a
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large number of species, notably vulnerable and threatened species, and provide both
livelihoods and important functions for the local population.
These study areas are chosen because of their ecological significance, serving a
source of food and water, a place for recreation, education and science and most
importantly, a home for the many plants and animals which need wetlands to survive. As
well as providing a buffer against coastal erosion, storm surges and flooding; they also
provide breeding and roosting sites for migratory birds and local water birds. Wetland
plants shelter many animals and birds and are vital for the survival of many threatened
species. Information on the location and conservation value of existing wetlands is
valuable for anyone, particularly those who are involved in coastal activities including
management, recreation and living on the coast.
These study sites are selected among others in view of the problems they face
despite their declaration as wetlands of international significance at the Ramsar
convention. The Convention on Wetlands, signed in Ramsar, Iran, in 1971, is an
intergovernmental treaty which provides the framework for national action andinternational cooperation for the conservation and wise use of wetlands and their
resources. There are presently 154 Contracting Parties to the Convention, with 1650
wetland sites, totaling 149.6 million hectares, designated for inclusion in the Ramsar List
of Wetlands of International Importance.
Study will attempt to utilize spatial modeling tools in GIS software, which can be
used for tourism development and conservation in the wetland areas. The use of GIS in
sustainable tourism development and planning demands the development of indicators
of sustainable tourism. This study will be carried out because most previous research
have only focused on identifying potentials of the area with regard to tourism, without
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looking at its environmental effects. On the other hand a significant number of preceding
researches have tended to use the conventional methods of planning and evaluation.
Therefore, Geographic Information System (GIS) application in this respect will be of significant benefit. Since, most environmental planning problems can be shown to
have spatial or geographical characteristics and tend to be increasingly multi-
dimensional and complex, it is likely that such a project could be more accurately
managed using the techniques and tools found in a GIS environment.
The study intends to apply GIS tools and techniques to bring significant value in
tourism planning; (a) emphasis remote localities or situations where tourism
development is only at the consideration stage and (b) where issues of sustainability are
on the planning agenda because the environment remains largely unprotected. The result
of this research will aid in exploiting hidden potentials for tourism development, also it
will help in preventing conflict between environmentally sensitive areas and the areas to
be developed for tourism. Moreover the authorities will be able to monitor
developmental activities, to ensure compliance. This in the long run will ensure a
sustainable tourism development.
1.6 Scope of study & methodology
The study will focus only on the physical assessment of the wetlands i.e
biodiversity value of the study area using spatial modeling techniques and Multi CriteriaDecision Model (MCDM). It will centre on identifying potential tourism areas and areas
that needs to be conserved in the wetland area. This study is to understand how GIS can
be used to identify potential areas for tourism development; at the same time locating
environmentally sensitive areas that needs to be conserved.
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Considering the project objectives, the methodology will be looked at from two
perspectives i.e conservation and development. The data collection procedure will
mainly be based on secondary sources with partial primary investigation of the study
sites. The data collected will be processed by the use of Multi Criteria Decision Making
model (MCDM) and Geographic Information System (GIS).
In order to assess the relevance for wetlands conservation and development, a set
of evaluation criteria will be selected and suitable indicators to measure the selected
criteria. These criteria will be represented inform of data layers, representing different
needs for conservation and development. Subsequently the criteria will be evaluated by
reclassifying the data layers; they will be evaluated from conservation point of view byconsidering areas of high biodiversity as most relevant for conservation and low
biodiversity areas most appropriate for development. This will be computed by using
typical functionalities of raster-based GIS; such as distance operators, conversion and
reclassification functions. The GIS package ArcGIS 9.0 will be used because it is
provided with tools for analysis and transformation of raster data.
Pair wise comparison method of Multi Criteria Evaluation will be used in order
to support solution of a decision problem by evaluating possible alternatives from
different perspectives. The pair wise comparison will be developed in Microsoft Excel
and results transferred into ArcGIS framework. Alternatives to be evaluated and ranked
will be represented by different criterion maps. As different criteria are usually
characterized by different importance levels, the subsequent step of MCE will be the
prioritization of the criteria by means of pair wise technique; which allows for the
comparison of two criteria at a time. This can be achieved through the assignment of a
weight to each criterion that indicates its importance relatively to the other criteria under
consideration. Conservation and development scenarios will be generated, with each
scenario representing the best solution to decision problem, according to the assessment
perspective adopted. Map scenarios reflecting the opinion of different experts or
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stakeholders involved will be compared using the Boolean overlay approach of GIS, in
order to highlight the robustness of the solution and support decision making (Figure
1.1)
Feed back
Figure 1.1: Conceptual framework of the study
Aim andob ectives
Setting-up ofcriteria and parameters
Databasedesign and
development
Modeldevelopment
Wetlandsdevelopment
model
Wetlandsconservation
model
Assessment ofconservation and
developmentscenarios
Conservationand development
scenarios
Issues and problems
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1.7 Limitations of the study
This project will be restricted to identifying potential tourism and conservation
areas only and will not be dealing with other aspects of tourism as; travel cost,
perception, definition of wilderness and other principles inherent to sustainable tourism.
Also the study will dependent on secondary data, with partial primary investigation of
the study sites.
Another limitation is in the technique to be used in data analysis. This technique
(pair wise comparison method) has the capacity of comparing only two criterias at atime. Also the highly subjective nature of preference weights and rapid elicitation of the
method can lead to questions of validity. Moreover problems with inconsistencies in
preferences between objectives sometimes arise.
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an indefinite period of time" (Butler, 1993). The definition of sustainable tourism
development is quite different and more elusive; as it is a relatively recent concept
whose definitions win continue to evolve. Yet, a number of notions advanced by the
World Commission on Environment and Development (WCED) contribute to the
definition.
Inskeep (1991) thought of sustainable tourism development as "meeting the
needs of present tourism and host regions while protecting and enhancing opportunity
for the future". Sustainable tourism development involves management of all resources
in such a way that "economic, social and aesthetic needs are fulfilled while maintaining
cultural integrity, essential ecological processes, biological diversity and life supportsystems". It involves the minimization of negative impacts and the maximization of
positive impacts. Yet, while sustainable tourism may therefore be regarded as a form of
sustainable development as well as vehicle for achieving the latter, there is not as direct
a relationship between the two terms as might be expected. The Brundtland Report,
curiously, makes no mention of tourism even though the latter had already attained
megasector status by the mid 1980s. This neglect was evident several years later in the
agenda 21 strategy document that emerged from the seminal Rio Earth Summit in 1992,
which made only few incidental references to tourism as both a cause and potential
ameliorator of environmental and social problems (UNCED, 1992).
Budowskis (1976) defines sustainable tourism as tourism that wisely uses and
conserves resources in order to maintain their long-term viability. Butler (1993) believed
that a working definition of sustainable development in the context of tourism could be
taken as tourism which remains viable over an indefinite period and does not degrade or
alter the environment (human and physical) in which it exists to such a degree that it
prohibits the successful development and well-being of other activities and processes".
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The concept of tourism sustainability points to the need for better spatial,
environmental, and economic balance of tourism development, requiring new integrative
public-private approaches and policies in the future. When the principle of sustainability
is applied to new tourism development, it would mean that coastal hotels would not
pollute their water bodies with raw sewage, that hillside resort will not incite soil
erosion, and that sites of fragile and rare vegetation or wildlife would not be used for
tourism except as scenery and interpretation. Tourist businesses can benefit by land use
decision making that offers long-range protection of resources. Only by accepting such
responsibility will tourism be assured a continuing quality future. Some of the
guidelines, approaches and principles to sustainable tourism development include;
Tourism should provide real opportunities to reduce poverty; create quality employment
to the community residents and stimulate regional development. Prospects for economicdevelopment and employment should be enhanced while maintaining protection of the
environment. Linkage between the local businesses and tourism should be established.
This is aimed at improving the quality of life in local communities.
Tourism should also conserve the natural and cultural assets; it should guarantee
the protection of nature, local and the indigenous cultures. The relationship between
tourism and the environment, both natural and cultural, must be managed so that it is
sustainable in the long term. Tourism should enhance and complement the unique
natural and cultural features of its area. It should provide mechanisms to preserve
threatened areas that could protect wildlife; and also preserve the historic heritage,
authentic culture and traditions. In addition, tourism should ensure that the local or
regional plans contain a set of development guidelines for the sustainable use of natural
resources and land; and are consistent with overall objectives of sustainable
development. These plans should establish a code of practice for tourism at all levels;
national, regional, and local, based on internationally accepted standards. Guidelines for
tourism operations, impact assessment, monitoring of cumulative impacts, and limits to
acceptable change should be established and.
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Tourism should minimize the pollution of air, water, land and the generation of
waste by tourism enterprises and visitors. This is about outputs from the tourism sector,
minimizing pollution in the interests of both the global and the local environment. Some
key issues for tourism include promoting less polluting forms of transport as well as
minimizing and controlling discharges of sewage into sensitive environments. Integrated
management approaches should be used to carry out restoration programmes effectively
in areas that have been damaged or degraded by past activities.
2.2 Wetlands assessment
In physical geography , a wetland is an environment at the interface between truly
terrestrial ecosystems and truly aquatic systems making them different from each yet
highly dependent on both (Mitsch & Gosselink, 1986). In essence, wetlands are
ecotones . Wetlands are typically highly productive habitats , often hosting considerable
biodiversity and endemism . In many locations such as the United Kingdom and USA
they are the subject of conservation efforts and Biodiversity Action Plans . The United
States Army Corps of Engineers and the Environmental Protection Agency (1987)
jointly define wetlands as: Those areas that are inundated or saturated by surface or
ground water at a frequency and duration sufficient to support, and that under normal
circumstances do support, a prevalence of vegetation typically adapted for life in
saturated soil conditions. Wetlands generally include swamps, marshes, bogs, and
similar areas.
In the 1970s, a growing number of scientists, ecologists, and conservationists
began to articulate the values of wetlands. During the last three decades, dozens of
international, national, and state wetland related policies, agreements, and initiatives
were brought into effect. Actions like the Convention on Wetlands, signed in Ramsar,
Iran, in 1971, which is an intergovernmental treaty which provides the framework for
http://en.wikipedia.org/wiki/Geographyhttp://en.wikipedia.org/wiki/Terrestrial_ecoregionhttp://en.wikipedia.org/wiki/Ecosystemshttp://en.wikipedia.org/wiki/Aquatic_habitathttp://en.wikipedia.org/wiki/Ecotoneshttp://en.wikipedia.org/wiki/Habitat_%28ecology%29http://en.wikipedia.org/wiki/Biodiversityhttp://en.wikipedia.org/wiki/Endemismhttp://en.wikipedia.org/wiki/United_Kingdomhttp://en.wikipedia.org/wiki/USAhttp://en.wikipedia.org/wiki/Conservationhttp://en.wikipedia.org/wiki/Biodiversity_Action_Planhttp://en.wikipedia.org/wiki/United_States_Army_Corps_of_Engineershttp://en.wikipedia.org/wiki/United_States_Army_Corps_of_Engineershttp://en.wikipedia.org/wiki/Environmental_Protection_Agencyhttp://en.wikipedia.org/wiki/Environmental_Protection_Agencyhttp://en.wikipedia.org/wiki/United_States_Army_Corps_of_Engineershttp://en.wikipedia.org/wiki/United_States_Army_Corps_of_Engineershttp://en.wikipedia.org/wiki/Biodiversity_Action_Planhttp://en.wikipedia.org/wiki/Conservationhttp://en.wikipedia.org/wiki/USAhttp://en.wikipedia.org/wiki/United_Kingdomhttp://en.wikipedia.org/wiki/Endemismhttp://en.wikipedia.org/wiki/Biodiversityhttp://en.wikipedia.org/wiki/Habitat_%28ecology%29http://en.wikipedia.org/wiki/Ecotoneshttp://en.wikipedia.org/wiki/Aquatic_habitathttp://en.wikipedia.org/wiki/Ecosystemshttp://en.wikipedia.org/wiki/Terrestrial_ecoregionhttp://en.wikipedia.org/wiki/Geography -
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photographic data and collect additional data, from which the wetlands are ultimately
mapped. The inventory further classifies wetlands by type based on substrate or soil
type, dominant hydrologic regime, vegetation community and aquatic habitat type,
among other things (USFWS, 1996). NWI maps are not intended to provide wetland
boundaries for regulatory purposes, but rather to provide information to the public about
the possible locations and types of wetlands in a given geographic area. Information
arising from the National Wetlands Inventory indicates that the United States has lost
over half of the wetlands which historically existed in the lower 48 states, most
frequently as a result of drainage for agriculture (Dahl 1990). The development of
inventory data is a type of assessment which provides information identifying the
locations, areal extent and types of wetlands existing within a landscape. The term
assessment, however, as it is most commonly used, implies a more detailed evaluation ofhow a specific wetland or range of wetlands functions. Assessment may also involve an
evaluation of the condition, or ecological integrity, of the wetland system.
In discussing wetland assessment, it is often discussed in terms of wetland
functions and wetland values. Wetland functions are defined as physical, chemical, or
biological processes occurring within wetland systems. Wetland values are attributes of
wetlands which are perceived as valuable to society. Wetland functions are therefore
able to be more objectively assessed or measured, while wetland values are inherently
subjective and may be difficult to assess. Nevertheless, decision making is a valuative
process and consequently must consider wetland values in weighing decision
alternatives and consequences. Consideration of wetland value is often indirectly
imbedded in the assessment process as well, because the choice of which functions to
assess is often made based on the perception of which wetland functions are most
important.
There are a wide variety of applications for which information on wetland
function and condition may be used. The most common uses of assessment have been:
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1) The evaluation of wetlands proposed for fill development; 2) Evaluation of impacts
for planning purposes; 3) Evaluation of wetland restoration potential for conservation
programs; 4) Determining wildlife habitat potential for properties proposed for
acquisition for wildlife management purposes, or where changes in land management
are proposed to occur.
In response to the desire to achieve the goal of no net loss of wetland function,
there have been over forty different methods developed in the last decade alone which
are designed to assess wetlands (Bartoldus, 1999). They range in level of rigor from
those based on ad hoc consensus among professionals to more sophisticated peer-
reviewed mechanistic models. Consequently, these techniques differ greatly in the levelof detail, objectivity and repeatability of the results. There is also considerable
variability in the range of wetland functions that are considered by any given technique.
Some methodologies are narrowly focused and may only consider a single or a small
related group of functions such as fish habitat, bird habitat, wildlife habitat, flood
storage, etc (USFWS, 1996); others look at a broader range of wetlands functions
concurrently, such as flood storage capacity, sediment stabilization, nutrient uptake,
primary production export, fish and wildlife habitat (Adamus et al. 1987, Bartoldus ,
1999). Some of these techniques have components to consider wetland values as well as
functions. Because wetlands are such complex systems, however, there is no single
technique, no matter how comprehensive, which can evaluate all functions performed by
a given wetland. Generally speaking, assessment methods fall into approximately four
general types of approaches:
1. Inventory and classification . These are objective techniques which describe the areal
extent and/or types of wetlands within a given landscape. This includes such information
as the National Wetland Inventory maps.
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2. Rapid Assessment Protocols . These are mostly low-cost techniques in which the data
necessary to perform the assessment may be gathered in a short period of time. Rapid
assessment protocols tend to focus mostly on single wetlands or small populations of
wetlands. The results are likely to be either completely qualitative, or involve a large
extent of subjective (best professional judgment) information.
3. Data-driven Assessment Methods . These are usually expensive to develop, often
model based, but provide a high degree of reproducibility. The results often have
predictive value.
4. Bio-indicators/Indices of Biotic Integrity. These techniques involve a selected set of
variables, which are measured across wetland types. The variables may be evaluatedseparately, or used to develop multi-metric indices, which can be used to measure the
condition or ecological integrity of a wetland and can be used as environmental triggers
to identify long-term changes.
However, these methods have lacked the predictive capability of spatial
modeling in GIS. Spatial modeling provides a rigorous statistical framework for
directing management and conservation activities by enabling characterization of
wetland structure at any point on the landscape. Spatial (environmental) data can be used
to explore conflicts, examine impacts and assist decision-making.
2.3 Spatial modeling environments
In general, a spatial modeling environment may be thought of as an integrated set
of software tools providing the computer facilities needed to develop and execute
spatially explicit simulations and display model results. These integrated environments
have been designed to support modeling efforts of groups engaged in activities as varied
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in scope as global climate change research, watershed management, and urban planning.
Various approaches have been undertaken to integrate spatial modeling with GISs.
These approaches have been described relative to intensity of coupling, as well as degree
of modeling flexibility Albrecht et al . (1997). A number of these efforts have resulted in
methods for modeling environmental processes such as forest dynamics and hydrologic
processes. Other developments have introduced graphical user interfaces with sliders to
modify weightings within models. While these method allows exploration of alternative
scenarios, they are domain specific and do not support generic spatial model
development.
Other approaches to spatial modeling and GIS integration have required users towrite code in a formal programming language or assisted users to specify model
structure either through guided question and answer sessions Robertson et al. (1991) or
using pseudo-English to generate code (Lowes and Walker, 1995). Albrecht et al.
(1997), in pointing out limitations of these approaches, have noted that they tend to be
domain-specific, require users to learn a specific programming language, may be
difficult to follow through model implementation, and importantly, do not support
creative conceptual model development.
Another approach to integrating spatial modeling and GIS is diagrammatic, that
is, spatial models are represented as process flow diagrams that graphically illustrate
relationships among input data, geo-processing functions, and output or derived data.
Applications of this approach range from image analysis (ERDAS IMAGINE
Professional 8.4, Spatial Modeler) to static cartographic modeling (Virtual GIS or VGIS
prototype described by Albrecht et al., (1997), and ESRI's ModelBuilder in the Spatial
Analyst 2.0 extension to ArcView GIS) to dynamic simulation modeling (Spatial
Modeling Environment, SME). This approach has a number of advantages. First, these
types of flow diagrams frequently appear in various disciplines and therefore represent a
common conceptual framework. In fact, such flow charts are a standard process-oriented
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tool in visual programming Chang et al., (1990). Process flow diagrams make
relationships among model elements apparent and model behavior easy to follow and
explain to others. This is a powerful advantage for non-GIS model developers, as well as
stakeholders and decision-makers, as they engage in exploring and solving
environmental problems.
Lately, spatial modeling and GIS have become popular as assessment tools in
many disciplines such as environmental protection, watershed management, wetland
evaluation and land use changes; which sometimes integrate the workings of the above
methods. GIS technology was initially developed as a tool for spatial data storage,
retrieval, manipulation and display, and now more and more powerful analyticalfunctions have been built into commercial GIS software to perform much of its general
spatial analysis as well as data management tasks. One of the most persistent and
pervasive words in the field of GIS is integration. Indeed, the ability of GIS to
integrate diverse information is frequently cited as its major defining attribute, and its
major source of power and flexibility in meeting user needs. The analytical module in
many of the specific areas such as, environmental modeling, wetland functional
assessment, ecological and economic impacts of agricultural policy, must be developed
and then integrated into GIS (Drayton et al. 1996). A system with this type of function
and analytical module falls into the category of Decision Support System (DSS).
Decision makers are increasingly turning to GIS to assist them with solving complex
spatial problems. Spatial Decision Support Systems (SDSS) are explicitly designed to
support a decision research process. SDSS provides a framework for integrating
database management systems with analytical models, graphical display, tabular
reporting capabilities and expert knowledge of decision makers. The concepts and
technologies of DSS and SDSS are still evolving (Densham, 1991; Power, 2003).
Many recent works raise the crucial question of decision-aid within GIS
(Malczewski 1999). Most if not all of these works have come to the conclusion that GIS
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by itself can not be an efficient decision-aid tool and they have recommended the
combination between GIS and a form of decision aid. The long-term objective of such
integration is to develop a Spatial Decision Support System (SDSS). What really makes
the difference between a SDSS and a traditional decision support system (DSS) is the
particular nature of the geographic data considered in different spatial problems. In
addition, traditional DSS are designed primarily for solving structured and simple
problems which make them non practicable for complex spatial problems. Since the end
of the 1980s, several researchers have oriented their works towards the extension of
traditional DSS to SDSS that support spatially-related problems (Densham 1991;
Jankowski 1994; Malczewski 1999). This requires adding to conventional DSS a range
of specific techniques and functionalities used especially to manage spatial data. These
additional capacities enable the SDSS to (Densham 1991): acquire and manage thespatial data; represent the structure of geographical objects and their spatial relations;
diffuse the results of the user queries and SDSS analysis according to different spatial
forms including maps, graphs, etc., and; perform an effective spatial analysis by the use
of specific techniques.
In spite of their power in handling the first three operations, GIS are particularly
limited tools in the fourth one. Moreover, even if the GIS can be used in spatial problem
definition, they fail to support the ultimate and most important phase of the general
decision-making process concerning the selection of an appropriate alternative. To
achieve this requirement, other evaluation techniques instead of optimization or cost-
benefit analysis ones are needed. Undoubtedly, these evaluation techniques should be
based on Multi Criteria Decision Model (MCDM) in GIS.
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2.4 Geographic Information System (GIS) in sustainable tourism planning
Although GIS is rarely discussed in the context of tourism, its wider use by
planners concerned with environmental issues and resource management is now well
established (Berry, 1991; Robinson, 1992). One of the earliest applications of GIS in
tourism planning is discussed by Berry (1991) in the US Virgin Islands. GIS was used to
define conservation and recreation areas and determine the best locations for
development. Best locations were determined according to engineering, aesthetics, and
environmental constraints. Similarly, Boyd and Butler (1993) demonstrated the
application of GIS in the identification of areas suitable for ecotourism in Northern
Ontario, Canada. At first, a resource inventory and a list of ecotourism criteria weredeveloped. At a next stage GIS techniques were used to measure the ranking of different
sites according to the set criteria and therefore identify those with the best potential.
Minagawa & Tanaka (1998) used GIS to locate areas suitable for tourism development
at Lombok Island in Indonesia. The main objective was to propose a methodology for
GIS based tourism planning. Using map overlay and multi-criteria evaluation a number
of potential sites for tourism development was identified. Beedasy and Whyatt (1999)
developed a GIS based decision support system for sound spatial planning for tourism in
Mauritius. Given the space limitation of Mauritius, the increasing tourist demand and the
need to consider alternative sites in order to avoid further deterioration of existing tourist
zones, a spatial decision support system was developed to support tourism planning. GIS
technology was considered as the appropriate platform for such a system because it can
integrate both qualitative and quantitative information, it can provide a visual display of
results thus permitting an easy and efficient appraisal of results, and can communicate
information to all interested parties becoming thus a participatory and exploratory tool.
Williams et al ., (1996) also used GIS to record and analyze tourism resource
inventory information in British Columbia, Canada. He developed a tourism capability
map which indicates areas of high, moderate, and low capability for specific tourism
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activities. Ribiero de Costa (1996) used GIS to create a map of tourism potential in the
Mediterranean area of Europe. Carver (1995) used GIS to describe the development of a
wilderness continuum map showing areas designated as wilderness in the UK and its
use to identify areas of potential risk from recreational development. Bahaire and Elliott-
White (1999) provided a brief description of various applications of GIS in tourism
planning in the United Kingdom. These applications included data integration and
management (for example data on tourism destination types and accommodation),
landscape resource inventory, designation of tourist areas in terms of use levels, tourism
suitability analysis, and pre and post-tourism visual impact analysis. The overall
conclusion is that GIS is an efficient and effective means of helping the various
stakeholders examine the implications of land-use decisions in tourism development.
GIS has also been used to analyze tourism related issues such as the perception
and definition of wilderness (Kliskey & Kearsley, 1993; Carver, 1997), countryside
management (Haines- Young et al ., 1994) and travel costs (Bateman et al. 1996).
Another early example of the use of GIS in tourism is provided by Binz & Wildi (cited
in Heywood et al . 1994 who modeled the effect of increased tourist development in the
Davos Valley in Switzerland; based on scenario analysis. However, more recent
publications (Elliott-White & Finn, 1998) suggest a growing interest in GIS applications
in tourism. GIS applications are now common place in the utilities, land information and
planning. Tourism growth is intensifying an often stretched and overloaded tourism
infrastructure and is itself threatened by local and environmental pressure groups. GIS
can be an effective tool in the design and monitoring of sustainable.
GIS can be used to identify areas or zones which should be undisturbed by
tourism or any kind of development. Gribb (1991) describes the planning effort that took
place at the Grayrocks Reservoir in Wyoming, US. The aim was to come up with a
recreation development plan that would contribute at the same time to environmental
conservation of the Reservoir. McAdam (1994) reported the case of a GIS prototype
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application developed for monitoring the impacts resulting from the increasing number
of trekking and special interest tourists in a remote region in Nepal. Shackley (1997)
within her involvement in regional and site tourism management issues newly opened to
visitors, Himalayan Kingdom of Lo (Mustang), Nepal, suggested the development of a
GIS based spatially-referenced multimedia cultural archive. This archive, with data
collected at an early stage of tourism development, would serve to monitor possible
change through time.
Dietvorst (1995) used a survey based time-space analysis at a theme park in the
Netherlands, to better understand visitors preferences for the various attractions of the
park. A GIS was used for the analysis of the coherence between the various attractions
and other elements of the park. Findings were then used for a more balanced diffusion of
visitor streams and a better routing system. Van der Knaap (1999) used GIS to
understand the use of the physical environment by tourists in order to promote
sustainable tourism development. Bishop and Gimblett (2000) presented the use of
spatial information systems, spatial modeling and virtual reality in recreation planning.
Using rule-driven autonomous agents moving in a GIS-based landscape, the movement
patterns of the visitors can be simulated. In this way it is argued that better management
of the recreational area is achieved through the effective management of recreationists
behavior; a case study was conducted at Broken Arrow Canyon, Arizona.
Tourism destinations are usually characterized by three different landscape
features: points, lines, and polygons. Point features are individual tourist attractions, for
example, a campground in a park, or a historic site along the highway. Streams and
coastal beaches often follow a linear pattern, while habitat location or natural parks arecharacteristics of a polygon feature. These locational attributes are essential to a
Geographic Information System. It is apparent that GIS has tremendous potential for
application in sustainable tourism.
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However, due to the general lack of databases and inconsistencies in data, its
applications are limited. For example, there is very little site-specific information about
suitability of sites for conservation or tourism development, sources of visitors origin
and destination, travel motivation, spatial patterns of recreation and tourism use, visitor
expenditure patterns and levels of use and impacts- all of which are suitable application
areas of GIS. So far, applications of GIS in tourism has been limited to recreational
facility inventory, tourism-based land management, visitor impact assessment,
recreation-wildlife conflicts, mapping wilderness perceptions and tourism information
management system.
2.5 Multi Criteria Decision Making and Natural resources Management
Rapid socioeconomic improvements driven by increased income and wealth have
increased the demand for ecosystem services, such as aesthetic enjoyment and
recreation. Nature-based tourism is an important income source in many countries and
having a pristine environment is paramount for its success. Planning and management of
natural areas are inherently difficult because of the multiple attributes of nature-based
tourism, and conflicts between use and conservation of those areas. Management of
nature-based tourism and natural areas should control use patterns and implement
resource protection practices that maintain the quality of visitor experiences without
denigrating ecological, cultural, and social values (Figgis 1993). The emergence of the
concept of sustainable development in the 1980s was a reflection of the failure to
safeguard ecosystem values from population and economic growth. Sustainable resource
management requires maintaining environmental quality and ecological integrity for
future generations.
The management of wetlands needs to be changed in order to improve their
quality and ensure that economic development does not degrade their health. Wetlands
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perform a variety of critical functions in maintaining healthy river systems, and have
ecological, hydrologic, and economic value (Herath 2004). They improve water quality,
replenish groundwater, retain floodwater, provide habitat for a diversity of plants and
animals, trap sediment, reduce nutrients, and remove contaminants. Such critical
ecosystem services of wetlands are lost when wetlands are converted to other uses
and/or degraded. Stakeholder perceptions of river ecosystems and wetlands need to be
changed through education and intervention strategies.
Improving decision making for human and natural resource management requires
consideration of a multitude of non-economic objectives, such as biodiversity,
ecological integrity, and recreation potential. When ecosystems become degraded, the provision of ecosystem services is impaired. There are limits to the changes that
ecosystems can undergo and still remain productive. Decision making related to the
sustainable use of natural resources involves important tradeoffs because increasing one
benefit typically decreases other benefits. For example, converting a natural forest to a
plantation forest increases timber output, but reduces wildlife habitat in the remaining
forest compared to the untouched forest. Furthermore, the values of environmental
attributes, such as biodiversity, cannot be properly measured using monetary criteria;
appropriate non-monetary criteria need to be developed.
Methods that facilitate better management and policy decisions must account for
the variation in stakeholders preferences for attributes, and conflicting stakeholder
interests and values. As the complexity of decisions increases, it becomes more difficult
for decision makers to identify a management alternative that maximizes all decision
criteria. This difficulty has increased the demand for more sophisticated analytical
methods that consider the myriad of attributes of decision outcomes and differences in
stakeholders preferences for those attributes. The neoclassical economic approach
based on maximization of a single objective (i.e., utility for consumers and profit for
businesses) has limited applicability in multi-attribute decision problems in natural
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resource management (Joubert et al. 1997). Over the past two decades, considerable
attention has been focused on developing and using multi-criteria decision making
(MCDA) techniques to identify optimal alternatives for managing natural resources.
The foregoing discussion highlights the difficulties of natural resource planning
and management when there are a multitude of heterogeneous stakeholders, objectives,
goals, and expectations, and stakeholder conflicts. Planning requires a multi-objective
approach that leads to well conceived and acceptable management alternatives and
expands the ability to make decisions in complex natural resource management settings.
It also requires analytical methods that examine tradeoffs, consider multiple political,
economic, environmental, and social dimensions, reduce conflicts, and incorporate theserealities in an optimizing framework.
MCDA techniques have emerged as a major approach for solving natural
resource management problems and integrating the environmental, social, and economic
values and preferences of stakeholders while overcoming the difficulties in monetizing
intrinsically non-monetary attributes. Quantifying the value of ecosystem services in a
non-monetary manner is a key element in MCDA (Martinez-Alier et al. 1999; Munda,
2000).
2.6 Multi criteria decision making (MCDM)
2.6.1 Multiple criteria decision making an overview
Multicriteria decision making (MCDM) is a term including multiple attribute
decision making (MADM) and multiple objective decision making (MODM). MADM is
applied when a choice out of a set of discrete actions is to be made. In MODM, it is
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assumed that the best solution can be found anywhere in the feasible alternatives space,
and therefore is perceived as continuous decision problem. MADM is often referred as
multicriteria analysis (MCA) or multicriteria evaluation (MCE). Instead, MODM is
more close to Pareto optimum searching with use of mathematical programming
techniques (Jankowski 1995, Malczewski 1999). Here, the term multicriteria decision
making is used in reference to multiple attribute decision-making and the other
expressions are used as equivalents. The main objective of MCDM is to assist the
decision-maker in selecting the best alternative from the number of feasible choice-
alternatives under the presence of multiple [decision] criteria and diverse criterion
priorities. Every MCDM technique has common procedure steps, which are called a
general model (after Jankowski 1995). This procedure includes the following actions
(Figure 2.1):
1. Deriving a set of alternatives
2. Deriving a set of criteria
3. Estimating impact of each alternative on every criterion to get criterion scores
4. Formulating the decision table with use of the discrete alternatives, criteria and
criterion scores.
5. Specifying decision-makers (DM) preferences in the form of criterion weights
6. Aggregating the data from the decision table in order to rank the alternatives (simple
and multiple aggregation functions)
7. Performing sensitivity analysis in order to deal with imprecision, uncertainty, and
inaccuracy of the results
8. Making the final recommendation in the form of either one alternative, reduced
number of several good alternatives, or a ranking of alternatives from best to worst.
All the MCDM techniques are based on the above presented general model.
However, division can be made for compensatory and non-compensatory methods. The
compensatory methods can be further subdivided into additive and ideal point
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techniques, where the first includes e.g. weighted summation, concordance analysis and
Analytical Hierarchy Process and the latter, Technique for Order Preference by
Similarity to Ideal Point (TOPSIS), Aspiration-level Interactive Method (AIM) and
Multi-Dimensional Scaling (MDS). Non-compensatory techniques are for example
dominance, conjunctive, disjunctive and lexicographic techniques. Two of the most
popular techniques will be discussed here. Good summary of the MCDM techniques and
its choice strategy is given by Jankowski (1995); Voogd (1983) provides a
comprehensive theoretical background.
Figure 2.1: A general model of MCDM (after Jankowski 1995)
All additive methods, being compensatory techniques, are based on the
standardized criterion scores, which can be then compared and added. Standardization
allows comparison of criterion scores within one alternative, to come into some kind of
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trade-off when poor performance of the alternative under one criterion can be
compensated by a high performance under another criterion. Total score for each
alternative is achieved by multiplying criterion score with its appropriate weight and
adding all weighted scores. Weighted summation technique, being a basic form of
additive methods, can be written down in the matrix algebra as follows:
Where:Si is a total score for alternative i,Cji is a criterion score for alternative i and criterion jWj is criterion weight.
The weighted summation allows for evaluation and ordering of all alternatives
based on the criteria preferences by decision-makers. However, there are techniques
which allow setting preferences to both criteria and criterion scores. Second technique,
Analytical Hierarchy Process (AHP) uses a hierarchical structure of criteria and both
additive transformation function and pairwise comparison of criteria to establish
criterion weights Jankowski (1995).
2.6.2 Multi-criteria decision making and GIS
GIS has good capabilities of handling spatial problems, and as such can be used
to support spatial decision-making. Solving a complex multiple criteria problem without
spatial analytical and visualization tools would be computationally difficult, if not
impossible Jones (1997). Multicriteria decision making techniques, as stand alone tools,
have been computerized and nowadays there is much software to use. However, it is not
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common that such software is capable to handle spatial problem in the form of maps.
There exist two strategies: loose and tight, for coupling of GIS with MCDM techniques
Jankowski (1995). The loose coupling relies on a file exchange mechanism which
enables communication with the two types of software. Separate tasks are performed in
either of software. GIS is used for performing land suitability analysis, selecting a set of
criteria and their scores in order to export the decision table into MCDM program. The
MCDM module is used for executing multicriteria evaluation and the result is
transferred again into the GIS for display. The tight coupling strategy instead, is realized
by a common interface and common database for GIS and MCDM. This in fact means
that the multicriteria evaluation functions are embedded into the GIS software. The
advantage is that all necessary functions are on place and troublesome data exchange is
avoided. However, not every proprietary GIS have developed such a facility in its basicversion. There is example of IDRISI, which employs pairwise comparison and Analytic
Hierarchy Process to evaluate weight scores (Clark Labs). Another software Spans, by
Tydac Technologies, has inbuilt weighted overlay functions, which are similar to
weighted summation MCE technique Carver (1991). The ESRI software provides a
cartographic modeling tool called Model Builder, which is capable to handle similar
decision problems, hence requires some initial input of work. Generally speaking,
multicriteria evaluation with use of GIS can be done in two stages, (i) survey and (ii)
preliminary site identification. In the first step, the area is screened for feasible
alternatives using deterministic decision criteria. Here, all the sites, which meet all the
exclusion criteria (constraints) simultaneously, are identified and taken away from the
analysis. This stage is sometimes referred as suitability analysis, traditionally performed
by manual map overlay, further revolutionized by GIS digital maps.
The second stage, called preliminary site identification, is operationalized by
MCE techniques. First, secondary siting factors are elaborated and then weighted
according to their importance. The second stage allows handling multiple objective
problems Carver (1991); Jankowski (1995). Multiple criteria overlay was proposed by
McHarg (1969) who suggested identifying physical, economic and environmental
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criteria in order to assure social and economic feasibility of the project. The complexity
of the decision problem determines whether binary or multiple values overlay technique
is used (Figure 2.2a and b.). In geographic analysis, most commonly used operations are
AND and OR (Boolean), which correspond to spatial intersection and union. If the
decision factors have different levels of importance, weighted overlay should be used
(Figure 2.2). However, special scores aggregation procedure is required to achieve
meaningful results Jones (1997).
Source : McHarg (1969)
Figure 2.2: Spatial multicriteria evaluation: a) binary overlaying; b) multiple
values overlaying; c) multiple values weighted overlay
2.6.2.1 Evaluation criteria
An evaluation criterion is a term used to encompass both objectives and
attributes of multicriteria decision problem Malczewski (1999). Other authors refer them
as decision criteria or factors and scores respectively Voogd (1983); Carver (1991). The
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objectives describe the desirable state of a geographical space. They formulate the
criteria that need to be fulfilled in order to make the right decision by minimizing or
maximizing some variables. The attributes, on the other hand, contain measures used
to assess the level of achievement of the criterion by each alternative. Evaluation criteria
are presented in GIS as thematic maps or data layers. It is required that decision
attributes fulfill several requirements. Firstly, they need to be measurable, which implies
that it should be easy to assign numerical values that correctly asses the references to or
the level of achievement of the objective. Secondly, an attribute should clearly indicate
to what degree the objective is achieved, which is unambiguous and understandable for
decision maker. This is called comprehensiveness of an attribute. Furthermore a set of
attributes should be operational. If the attribute is understandable for the decision maker,
he/she can correctly describe relation between the attribute and a level of achievement ofthe overall objective than it can be used meaningfully in the decision-making process. A
set of attributes should also be complete, which means that it covers all aspects of a
decision problem. The set of attributes should be minimal, which form the smallest
possible set that completely describes the decision problem. No redundancy means that
consequences of valuation of decision influence only one attribute. The test of
coefficient of correlation can be used for every pair of attributes to test for no
redundancy. Lastly the set of attributes should be decomposable. It is true if evaluation
of the attributes in the decision process can be simplified into few smaller decisions.
Usually evaluation criteria form a hierarchical structure Malczewski (1999).
Selecting a proper set of evaluation criteria can be done by means of literature
study, analytical studies or survey of opinions. Literature can be found with some
authors providing literature review of criteria evaluation to a specific spatial decision
problem. Governmental agencies and governmental publications can provide guidelines
for selection of evaluation criteria. Another method is to recognize objectives from
governmental or other documents and review relevant literature to identify attributes
associated with every objective. Analytical studies can be performed for example by
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system modeling. Opinions survey is aimed at people affected by decision or a group of
experts, where several formalized techniques exist Malczewski (1999).
Figure 2.3: Spatial multicriteria analysis in GIS after Malczewski (1999), modified
A set of objectives and attributes used for a specific decision is affected by data
availability. It may not be feasible to obtain required information for the ideal set of
attributes designed for a specific objective, or data may not exist. The choice of
attributes is also limited by cost and time of gathering the data. It must be a trade-off
between the accuracy of prediction and cost and time required. An example is taken
from the case study considering location of a water transmission line, where six pipeline
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corridor alternatives are evaluated. The criteria were, among others: total cost of route,
amount of public right-of-way, area of wetlands and length of streams falling inside each
corridor. All of the cited criteria have natural measured scale, dollars, acres and meters
respectively. The decision table would have rows representing the alternatives, columns
representing the criteria and fields for criterion scores. The field values are derived from
spatial analysis. Another table is constructed to weight every criterion and then the total
score for each alternative calculated (Jankowski and Richard 1994). Another example of
criteria could be geology, land use type, land acquisition cost, buildings, conservation,
etc. certain type of behavior is assigned to each of them.
2.6.2.2 Criterion maps
Criterion maps form an output of evaluation criteria identification phase. This
follows after input of data into GIS (acquisition, reformatting, georeferencing, compiling
and documenting relevant data) stored in graphical and tabular form, manipulated and
analyzed to obtain desired information. Usually, with help of various GIS techniques a
base map over the study area is created and used to produce several criterion maps. Each
criterion is represented at a map as a layer in GIS environment. Every map represents
one criterion and can be called a thematic layer or data layer. They represent in what
way the attributes are distributed in space and how they fulfill the achieving of the
objective. In other words, a layer represents a set of alternative locations for a decision.
The alternatives are divided into several classes or are assigned values to represent the
level of preference of the alternative upon given criterion. This is a kind if internal
relation within a layer between alternative locations in respect to the attribute. In this
way one visualizes more and less desirable alternatives. The attributes need to be
measured in certain scale, which reflects its variability. The scale can be classified as
qualitative or quantitative. For example, soil types and vegetation types are expressed in
qualitative scale, while precipitation level in a quantitative measure. Scales can be
natural or constructed. The natural scale is a scale expressed in objective units, for
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example in km or in quantity per square km. The constructed scale is a subject of
personal judgment e.g. landscape aesthetic, ranked witch numbers or assigned linguistic
scale. Another issue is raised for direct and proxy scales. The direct scale measures
directly the level of achievement of an objective. If the objective is a cost of building a
road, the direct scale would map sites with respect to cost associated with building a
road there. The proxy scale is used when the attribute for specific criterion is not
obvious and should be measured indirectly. Different techniques are used to generate
various types of criterion maps scales.
2.6.2.3 Criterion standardization
As far as criteria and the criterion maps have different scales of measurement,
they can not be compared by their raw scores. In order to allow comparability, which is
essential to multicriteria evaluation, the criterion maps should be standardized.
Basically, linear and nonlinear standardization procedures exist. If it concerns
deterministic maps, where each alternative is related to a single value, linear scale
transformation methods are most frequently used. Two linear methods will be described
below: maximum score procedure and score range procedure. Other standardization
methods, including probabilistic and fuzzy relationships, are described thoroughly by
Malczewski (1999). Maximum score procedure is one of the linear scale transformation
methods. It uses a simple formula, which divides each raw score by the maximum value
of a given criterion Malczewski (1999):
xij = xij / xmax j
where xij is the standardized score for the ith object (feasible alternative / location) and
the jth attribute, xij is the raw score of this object and xmax j is the maximum score of
the jth attribute. The standardized scores range from 0 to 1. A benefit criterion is a
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criterion which should be maximized. For example, the larger the raw score the better
the performance. However, if the criterion should be minimized formula
xij = 1 xij / xmaxj
Should be used; such criterion is referred as cost criterion.
The advantage of the straight transformation is that it is proportional and relative
order of magnitude remains the same. For example 23/45 = 0.511/1 = 0.511 and 5/23 =
0.111/0.511 = 0.217. The disadvantage is that, when the scores are larger than zero the
standardized minimal score will not equal zero. This may make interpretation of leastattractive alternative difficult Malczewski (1999). The best alternative is always scored
1. The alternative method is score range procedure which is calculated by formula:
xij = xij xj min / xj max xjmin
For benefit criteria, and
xij = xj max xij / xj max xj min
for cost criteria. Factor xj min is the minimum score of the jth attribute, xj max is the
maximum score for the jth attribute, and xj max xj min is the range of given criterion.
The range of scores is from 0 to 1, the worst standardized score is always equal 0 and the
best equals 1. Unlike the maximum score procedures, the score range procedure does not
preserve proportional changes in the outcome. Linear scale transformation can be used
for example to standardize the proximity map Malczewski (1999). Such defined
standardization procedures can be easily transformed to fit raster-based GIS data model.
Figure 2.4 shows the example of score range procedure.
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Source : Malczewski (1999) Figure 2.4: Score range procedure in GIS
2.5.2.4 Assigning weights
Criterion weights are usually determined in the consultation process with
decision makers (DM) which results in ratio value assigned to each criterion map. They
reflect the relative preference of one criterion over another. In such a case, they can be
expressed in a cardinal vector of normalized criterion preferences:
w = (w1, w2, , wj) and 0
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maximum threshold) or desired aspiration levels Jankowski (1995). The second
approach is more preferable in formulating location constraints. The task of assigning
weights (deciding the importance of each factor) is usually performed outside GIS
software; unless such a mo