A Geographic Information System (Gis) and Multi-criteria Analysis for Sustainable Tourism Planning

8/13/2019 A Geographic Information System (Gis) and Multi-criteria Analysis for Sustainable Tourism Planning

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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


4.2.1.2 Endangered fauna 108

4.2.1.3 Wetlands proximity to natural land cover 110


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


4.3.1.2 Threatened fauna 137

4.3.1.3 Habitats proximity to natural land cover 139


4.3.2 Economic development 144


4.3.2.2 Harvesting season 146


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





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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

A Geographic Information System (Gis) and Multi-criteria Analysis for Sustainable Tourism Planning

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