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Master Thesis Submitted within the UNIGIS MSc programme at Interfaculty Department of Geoinformatics from Salzburg University

Identifying priority 'freedom camping' campsite areas for decision support on donations redistribution in the Kaikoura region, New Zealand by

Maria del Carmen Flood MSC. 01633542 A Thesis submitted in partial fulfilment of the requirements of the Degree of Master of Science (Geographical Information Science & Systems)-MSc (GIS)

Auckland-New Zealand. March 13, 2019

Compromiso de ciencia Por medio del presente documento, incluyendo mi firma personal, certifico y aseguro que, mi tesis es completamente el resultado de mi propio trabajo. He citado todas las fuentes que he usado en mi tesis y en todos los casos he indicado su origen.

Maria del Carmen Flood Moro Auckland, New Zealand. March 13, 2019.

Abstract The main objective of this study was to evaluate the suitability and identify the high priority areas for Freedom Camping in the Kaikoura region of New Zealand. To achieve this aim, AHP and GIS-based weighted overlay methods were adopted. AHP was used to determine the weights of the factors involved, and ArcGIS 10.6 was used to calculate and map the Freedom camping areas. In pursuit of minimum environmental effects, this investigation identifies five factors to evaluate the suitability of areas for freedom camping: natural environment condition, landscape condition, infrastructure condition and carrying capacity.

The outcome of this study was the Freedom Camping priority areas map for fundings redistribution. This research not only provides a theoretical guide for the identification of Freedom Camping areas but also provides a scientific workflow to evaluate the appropriateness of those areas. The result shows a first approach to the identification of Freedom Camping areas that can be revised, improved and became more accurate. These ones can also become operable for camping infrastructure development and also useful for managers and planners working in local governments as well as investors.

Table of Contents 1

INTRODUCTION .......................................................................................................................... 8 1.1 BACKGROUND ....................................................................................................................... 8 1.2 OBJECTIVES AND RESEARCH QUESTIONS.............................................................................. 8 1.2.1

General objetive ......................................................................................................... 8

1.2.2

Specific Objective ....................................................................................................... 9

1.2.3

Research questions .................................................................................................... 9

1.3 HYPOTHESIS .......................................................................................................................... 9 1.4 JUSTIFICATION....................................................................................................................... 9 1.5 Scope ................................................................................................................................... 11 2

LITERATURE REVIEW ................................................................................................................ 11 2.1 Theoretical Frame ............................................................................................................... 11 2.1.1

Tourism in the present days. .................................................................................... 11

2.1.2

Freedom camping tourism in New Zealand ............................................................. 13

2.1.3

GIS for tourism. ........................................................................................................ 17

2.2 METHODOLOGICAL FRAME ................................................................................................. 22 2.2.1

Multi-Criteria-Analysis (MSC) ................................................................................... 22

2.2.1.1 2.2.2 3

Multi-Criteria-Evaluation (MCE) .......................................................... 22

Analytical Hierarchical Process (AHP) ...................................................................... 23

METHODOLOGY ....................................................................................................................... 27 3.1 STUDY AREA ........................................................................................................................ 27 3.2 GEOGRAPHIC DATA ............................................................................................................. 29 3.3 METHODOLOGY FLOW CHART ............................................................................................ 32 3.3.1

Data normalization ................................................................................................... 34

3.3.1.1 3.3.2

AHP evaluation ......................................................................................................... 35

3.3.2.1

Experts Survey and AHP matrix ............................................................ 35

3.3.2.2

Consistency Check ................................................................................. 44

3.3.3

4

Identification of Decision Criteria.......................................................... 34

Weighted Overlay..................................................................................................... 47

3.3.3.1

Factor Layers Creation. .......................................................................... 47

3.3.3.2

Constraint Layers ................................................................................... 49

3.3.3.3

Zonification and favourable factors -unfavourable factors . .................. 52

3.3.3.4

Site Suitability map. ............................................................................... 52

RESULTS .................................................................................................................................... 53 4.1 Landscape Condition Suitability Map .................................................................................. 53

4.2 Sustainable carrying capacity suitability map ..................................................................... 54 4.3 Services Condition Suitability map ...................................................................................... 55 4.4 Natural Environment suitability map. ................................................................................. 56 4.5 Infrastructure Condition map. ............................................................................................ 57 4.6 Freedom camping suitability areas in the Kaikoura region ................................................ 60 5

Conclusion. ............................................................................................................................... 64

Figures Index Figure 1: http://www.mbie.govt.nz/info-services/sectors-industries/tourism...................... 13 Figure 2: http://www.mbie.govt.nz/infoservices/sectors-industries/tourism ....................... 14 Figure 3: Mocenni C, 2017, Note AHP, p2. Recovered from: http://www.dii.unisi.it/~mocenni/Note_AHP.pdf ................................................................ 25 Figure 4: Mocenni C, 2017, Note AHP, p2. Recovered from: http://www.dii.unisi.it/~mocenni/Note_AHP.pdf ................................................................ 25 Figure 5:Mocenni C, 2017, Note AHP, p2. Recovered from: http://www.dii.unisi.it/~mocenni/Note_AHP.pdf ................................................................ 26 Figure 6: Mocenni C, 2017, Note AHP, p2. Recovered from: http://www.dii.unisi.it/~mocenni/Note_AHP.pdf ................................................................ 26 Figure 7: Mocenni C, 2017, Note AHP, p4. Recovered from: http://www.dii.unisi.it/~mocenni/Note_AHP.pdf ................................................................ 26 Figure 8:Own Source ........................................................................................................... 28 Figure 9:Methodology flowchart. Own Source ................................................................... 33 Figure 10:AHP Survey. Own-Source .................................................................................. 38 Figure 11:Saaty fundamental scale.(Saaty, 1987) ................................................................ 39 Figure 12: Klaus D. Goepe , AHP calculator software BPMSG, recovered from: https://bpmsg.com/academic/ahp_calc.php ......................................................................... 40 Figure 13: Klaus D. Goepe , AHP calculator software BPMSG, recovered from: https://bpmsg.com/academic/ahp_calc.php ......................................................................... 40 Figure 14: Klaus D. Goepe , AHP calculator software BPMSG, recovered from: https://bpmsg.com/academic/ahp_calc.php ......................................................................... 41 Figure 15: Constraint Layers Map.Own Source .................................................................. 50 Figure 16 Constraints Mask. Own Source ........................................................................... 51 Figure 17:Landscape Condition Suitability Map. Own-Source. ......................................... 53 Figure 18:Carrying Capacity priority areas in the Kaikoura region map.Own Source ....... 54 Figure 19:Services Condition Suitability Map.Own Source. .............................................. 55 Figure 20:Natural Environment Suitability Map. Own Source. .......................................... 56 Figure 21:Infrastructure Condition Suitability Map.Own Source ....................................... 57 Figure 22:Freedom Camping Priority Areas Map. Own Source ......................................... 60 Figure 23 High Priority Freedom Camping Areas.Own Source .......................................... 61 Figure 24 Hight Priority hectars graph. Own Source ......................................................... 62 Figure 25:Final Map graph. Own Source. ........................................................................... 62

Tables Index Table 1: Geopraphic data (own source) ............................................................................... 29 Table 2:Factors, Criteria and Constraints. Own Source ...................................................... 31 Table 3: Criteria Layers weights.Own Source ..................................................................... 41 Table 4: AHP matrix. Own Source ...................................................................................... 42 Table 5 Original Factor Matrix. Own Source ...................................................................... 43 Table 6: Normalized AHP matrix.Own Source. .................................................................. 43 Table 7 Eigenvector matrix criteria .Own Source ............................................................... 44 Table 8:CR calculation. Own Source. ................................................................................. 45 Table 9:AHP Matrix with adjustments.Own Source ........................................................... 46 Table 10:New Normalized Matrix.Own Source .................................................................. 46 Table 11:CR calculation.Own Source .................................................................................. 47 Table 12:Factor Layers Description. Own Source............................................................... 47 Table 13:Factor Layer Weights. Own Source. ..................................................................... 49 Table 14 Priority hectares per factor layer. Own Source. .................................................... 58 Table 15 Suitability 1hectares percentage. Own Source ..................................................... 59 Table 16:High priority areas Ha proportion. Own Source................................................... 63

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INTRODUCTION

1.1 BACKGROUND Camping in New Zealand is a popular activity for both residents and for some of the two million foreign tourists arriving every year. A very common camping practice is known as Freedom camping, which refers to camping done in a location without facilities and is not a designated campground. This is allowed in most public areas of New Zealand under certain conditions. Many councils are struggling to manage the impact of this increase on infrastructure, services, environment and community amenity.

The Kaikoura region is a coastal seaside tourist town and a very popular destination for local and international tourists. Recently some councils had restricted freedom camping because they could not sustain maintenance costs, and four sites in Central Otago and Taranaki had already been closed. In consequence a freedom camping app call CamperMate (which has 50,000 users a day and owned by the country´s largest campervan rental company), it is asking users to make voluntary online donations to cover running costs. Currently, it is asking campers staying at Meatworks site beside a popular surf break to donate through the app to the Kaikoura District Council. A new Freedom Camping model where a koha or donation culture is meant to be tested in order to implement it in some areas where the current model is fairly broken. These donations were described as a way of getting councils to consider an alternative approach to the upkeep of freedom campsites and prove there is an alternative to further closures. If the trial is successful, the donations scheme could be extended to other councils and sites (Cropp Amanda, 2018) .

In this context, this investigation looks to generate a zoning map using GIS tools, in order to show the best suitable areas for freedom camping that can help identify and prioritize fundings and donations distribution.

1.2 OBJECTIVES AND RESEARCH QUESTIONS. A new Freedom Camping model where a koha or donation culture is meant to be tested in order to implement it in some areas where the current model is fairly broken. These donations were described as a way of getting councils to consider an alternative approach to the upkeep of freedom campsites and prove there is an alternative to further closures.

1.2.1 General Objective Identifying priority 'freedom camping' areas in the Kaikoura region, New Zealand where 8

government fundings and donations should be distributed first.

1.2.2 Specific Objective •

Identifying the most suitable areas for ´freedom camping’ campsites in the Kaikoura region, considering each influencing variable: Natural Environment, Landscape Conditions, Infrastructure Condition, Services Condition and Carrying capacity.

1.2.3 Research Questions Principal: Which are the priority ´freedom camping´ areas in the Kaikoura region, New Zealand’ where government fundings and donations should be distributed first?

Specific: •

What percentage of the Kaikoura region is suitable for freedom camping, in general?

•

Which are the most suitable ´freedom camping´ areas in the Kaikoura region for each influencing variables: Natural Environment, Landscape Conditions, Infrastructure Condition, Services Condition and Carrying capacity?

1.3 HYPOTHESIS The described general situation leads to the hypothesis that there are areas of high value for Kaikoura local Council to take into account for funds redistribution related to ‘freedom camping’.

1.4 JUSTIFICATION This investigation was done in collaboration with Geozone company and Kaikoura´s local council. Considering potential impacts, prioritizing the most suitable locations for freedom camping is crucial not only for the Kaikoura council´s decision support in fundings and donations distribution but it is also a reasonable way for: Reducing environmental impacts: A strategical redistribution of funds and donations to campgrounds located within a short distance to basic services such as dumps stations or public toilets can help reduce environmental and social impacts generated by waste and mass tourism. This is a very important aspect not only in terms of preservation but also in regard to New Zealand Tourism brand which led the country to had the chance to become one of the most successful destination branding campaigns. Since its colonial development, NZ has been promoted as a scenic wonderland, blessed with pristine environments and a unique landscape. More recently, this imagery was reflected by the 100% Pure New Zealand destination branding and marketing campaign and the prominent appearance of the country’s 9

landscapes in the Lord of the Rings and The Hobbit movies. Those representations of NZ created an image of a country more committed to protecting the environment than other developed nations («Origins, Success of 100% Pure New Zealand Destination Brand Campaign», 2016).

Reducing social impacts: In the same way having a funds redistribution based on strategic location can help to reduce negative local social impact such as noise, littering, incorrect wastewater disposal and defecation common. Social and cultural impacts of tourism are the way in which tourism is contributing to changes in value systems, individual behavior, family relationships, collective lifestyle, moral conduct, creative expressions, traditional ceremonies and community organization. In other words, they are the effects on the people of host communities of their direct and indirect associations with tourists.

Continuous Economic growth: Tourism is one of the few industries in the world that is steadfastly growing, therefore it is important to allow this continued economic growth to help raise economic incomes, however, it is even more important that this can be done while guaranteeing environment protection and avoiding negative social issues to emerge.

Improving sustainable tourism development: ´´With the right policies, tourism can contribute to gender equality, ecosystems and biodiversity conservation and natural and cultural heritage protection, as well as offering many other solutions to several issues that our world faces today´´(United Nations World Tourism Organization -UNWTO-, 2016). As a result of the explained statements above, this investigation will improve tourism sustainability, a subject that is being considered as key in the political agendas, such as the one created UNWTO in 2017.

Innovation: Nowadays we have access to several IT solutions for tourism planning, however, professionals related to this industry have not make the most of this tool in order to find better decision support solutions.

And last but not least, if the results are found to be useful to the Council objectives, there will be a possibility to do investigations for other regional councils that have to deal with the same issues.

Taking everything into account this investigation could signify a starting point of a new mindset towards the importance of the use of GIS tools in tourism planning and could be used as a reference for future related investigations or projects.

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1.5 SCOPE The main objective of this investigation is, as previously stated, to identify priority 'freedom camping' areas in the Kaikoura region in relation to the natural environment condition, restricted areas and spatial accessibility to services and infrastructure, for funding’s and donations redistribution. This information will considerably help the local council to find temporary solutions to a problematic issue that has been increasing along the years, until an intelligent and democratic final decision could be establish, not only for this region but to all the country. In this sense, this practical solution could be copied and implemented in all the other regions in order to obtain the same benefits.

1. The research will show how fundings prioritization can be a user-friendly process, that can be performed on any scale considered necessary. This is particularly important for the national government, which invests considerable amounts of funds in tourism-related issues, especially to areas that really require it.

There are different limitations to this analysis, related mainly with geodata availability and the subjective reasons of the tourists for staying in specific areas.

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LITERATURE REVIEW

2.1 THEORICAL FRAME 2.1.1 Tourism in the present days.

An increasing number of destinations around the world had opened to tourism and had invested in this Industry, which makes this sector a key for the socio-economic progress, through the creation of employment, enterprises, incomes generated from exportation, and the infrastructure execution

´´A growing number of destinations around the world had open and invested in the tourism market, making from it a key sector for socio-economical progress, through the creation of employment, enterprises and exports income generation, infrastructure execution’’.(World Tourism Organization (UNWTO), 2017, p. 2)

Nowadays tourism has become one of the fastest growing economic sectors in the world.

Just to have an idea, tourists international arrivals around the globe had changed from 25 million in 1950 to 278 million in 1980, 674 million in 2000 and 1,235 million in 2016 (World 11

Tourism Organization (World Tourism Organization (UNWTO), 2017). Moreover, this industry has practically overcome other important economic sectors such as petrol export and therefore represents one of the most important income sources. Such is the importance that UNTWO, is currently helping destinations to improve their sustainability, and it is also insisting in the fact that many developing countries can become benefited especially by sustainable tourism (World Tourism Organization (UNWTO), 2017).

Sustainable Tourism can be defined as: "Tourism that takes full account of its current and future economic, social and environmental impacts, addressing the needs of visitors, the industry, the environment and host communities" (UNWTO, 2017).

This important potential that the tourism industry has been previously evaluated in 2011 at the Undeveloped countries Tourism Declaration in the Canarias Island, where it was established that tourism could help to improve these countries to participate in the global economy, reduce poverty and gain socioeconomic progress. However, the WTO says that the way that tourism can contribute to the society is not only by focusing on sustainability but also on ethics and fighting poverty (CALVET, 2011).

From the 1992 Rio summit, the sustainable development ideology influence in the global economy has become relevant and people have started to comprehend that tourism development also needs to start following sustainability´s path. On the other hand, as this industry is considered essential for most of the counties economies, tourism planning improvement is becoming more important in government's agenda (Xie Hongyong & Shi Xi, 2010). Sustainability is understood as ´´The development that accomplishes the needs of the present without compromising the ability to accomplish the needs of future generations´´(Development, 1987).

As a consequence of this important issues and in the 2030 sustainability development agenda context, year 2017 has been established as the international sustainable tourism year, which main goal has been to create awareness to the different stakeholders and public in general about the huge benefits that this kind of tourism can develop in a way that everyone involved make tourism a positive change tool (OMT, 2017).

For a long time, tourism development has been a synonym of promotion and economic development, however, nowadays, we can observe that unplanned tourism development, has a very close connection with tourist dissatisfaction and resources negative impact. Until now, tourism planning hasn't been any further than being able to identify spatial entities that form 12

part of the destination tourism infrastructure, however nowadays planning needs more than ever the addition of the sustainability aspect.

2.1.2 Freedom camping tourism in New Zealand Tourism is New Zealand's largest export industry in terms of foreign exchange earnings. It directly employs 7.5 percent of the New Zealand workforce and it has the potential to improve the economies of communities around the country. Kaikoura, one of New Zealand´s most important regions, is a seaside settlement is the most northern district in the Canterbury region located on the east coast of the South Island where freedom camping became very popular as a result of the great natural sceneries and tourism activities. The importance of the tourism industry in the country, can be demonstrated in figures 1 and 2.

Figure 1: http://www.mbie.govt.nz/info-services/sectors-industries/tourism

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Figure 2: http://www.mbie.govt.nz/infoservices/sectorsindustries/tourism

´´According to the International Visitor Survey (IVS), an average of 60,000 international visitors per year freedom camped for at least one night during their stay in New Zealand in the 2013-2015 periods. In 2015, 12,882 international visitors reported using freedom camping as their main form of accommodation during their stay. This is equivalent to 0.4% of all international visitors to New Zealand in 2015´´ («Review of camping opportunities in New Zealand: Report to the Minister of Conservation», 2006) Freedom camp means to camp (other than at a camping ground) within 200 m of a motor vehicle accessible area or the mean low-water springs line of any sea or harbour, or on or within 200 m of a formed road or a Great Walks Track, using 1 or more of the following: a tent or other temporary structure, a caravan: a car, campervan, house truck, or other motor vehicle. In this Act, freedom camping does not include the following activities: temporary and short-term parking of a motor vehicle: recreational activities commonly known as daytrip excursions: resting or sleeping at the roadside in a caravan or motor vehicle to avoid driver fatigue (Freedom camping Act 2011).

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´´The Department of Internal Affairs’ (DIA) Situational Analysis Document noted that demand for camping from international visitors as a form of accommodation is growing, with a 79% increase in campervan hires 2012-2015, and a 67% increase in visitors using paid camping or caravan accommodation during the same period´´ («Review of camping opportunities in New Zealand: Report to the Minister of Conservation», 2006). ´´Freedom campers generally come from New Zealand, Australia or Europe (particularly Germany). Caldicott's 2014 study in Australia noted that freedom camping is very much a cultural norm in European countries such as France and Germany´´ («Review of camping opportunities in New Zealand: Report to the Minister of Conservation», 2006) Nevertheless, nowadays this freedom campers are not being well informed. As it is indeed legal to stay for free at many places but only if you have the right set-up. This means that they will need a certified self-contained vehicle, with an on-board toilet as well as adequate water and waste disposal facilities (Cropp Amanda, 2018).

Issues with freedom camping have been reported throughout the country, though it is apparent that it is a greater issue in some areas than others. The 2011 Freedom Camping Bill Regulatory Impact Assessment noted that specific environmental conditions (e.g. good weather, scenic beauty, and absence of crowds) and personal recreation goals (e.g. enjoying the outdoors, peace and quiet) are associated with satisfying experiences, whereas opposite characteristics were associated with dissatisfying experiences. This explains why some campers do not want to camp in commercial holiday parks (to avoid crowds), and why some areas are more popular than others (scenic beauty).

In terms of domestic visitors, which reported that freedom camping was part of New Zealand identity, with coastal campgrounds, in particular, being "part of the Kiwi tradition and are closely connected with idealized visions of childhood and family life´´ («Review of camping opportunities in New Zealand: Report to the Minister of Conservation», 2006).

For some visitors, freedom camping is simply a matter of convenience, allowing them to sleep in close proximity to the entry point for their activity of choice (e.g. surfing, hunting); as an opportunity to rest during their travels (e.g. those following cycling or walking trails); or on occasions where staying in commercial campsites may not necessarily be possible (e.g. visitors being stranded by inclement weather or earthquake damage) («Review of camping opportunities in New Zealand: Report to the Minister of Conservation», 2006).

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Perhaps the biggest complaint about freedom camping in recent years is around the impact that freedom campers have on the environment where they stay, with complaints about noise, littering, incorrect wastewater disposal and defecation common («Review of camping opportunities in New Zealand: Report to the Minister of Conservation», 2006) Campgrounds are not just wild places where tourists have short rests during their journeys but are also small communities where people live, entertain and communicate. Thus, from this perspective, they not only provide affordable public access to nature as physical sites but also possess sociological and psychological significance. However, considerable evidence has illustrated the environmental effects resulting from activities around campgrounds, such as decrease of vegetation cover, degradation of soil, damage to living trees, and disturbance on wildlife communities. Although negative impacts on the environment caused by camping cannot be eliminated, many of them can easily be avoided.

Where freedom camping is permitted, the Act gives council specific powers to impose fines for offences relating to damage of freedom camping areas, structures, flora and fauna, and the depositing of waste (including of the most personal kind)(«Working group on freedom camping may ease tensions», 2018).

Taking everything into account, in response to the described situation, the council is committed to finding solutions that manage the impact of badly-behaved campers whilst encouraging responsible campers to visit and enjoy our District. In addition, tourism (including freedom camping) is an important part of the local economy. Councils will work forward on finding solutions that continue to encourage visitors to the District whilst managing their impacts more effectively. A very important recently action, executed by recently by the government was the donation of $8.5 million for freedom camping infrastructure (Cropp Amanda, 2018).

The funding has been made available in response to recommendations in a report from the Responsible Camping Working Groups forming a Responsible Camping Working Group to address freedom camping in Kaikōura (Cropp Amanda, 2018).

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2.1.3 GIS for tourism.

The proliferation of mass tourism over the last 50 years has often occurred with little concern for environmental or cultural protection. As an example, we can mention the Mediterranean and Caribbean tourism development, where it is not difficult to see this uncontrolled planning and development process. The reason of this, has been founded on either short-term management strategies or an ignorance of more sustainable planning. Tourism planning processes have lacked the refined modelling and simulation tools, however these ones are now available to predict potential outcomes for the medium to long term. This problem continues today despite the emergence of sophisticated information technology, like GIS (Mcadam, 1999).

The first steps towards the use of GIS in the tourism industry can be found in the 1990s, however, tourism was an emergent industry and didn´t count with the technology and development necessary at the present. “An early example of the use of GIS in tourism is provided by Binz & Wildi (cited in Heywood et al., 1994 who modelled the effect of increased tourist development in the Davos Valley in Switzerland; based on scenario analysis. However, some publications (Oppermann, 1996; Elliott-White & Finn, 1998; Herandez et al., 1997) suggest a growing interest in GIS applications in tourism. GIS applications have been common place in the utilities (Mahoney, 1991), land information (Dale, 1991) and planning (Cowen & Shirley, 1991). Tourism growth is exacerbating an often stretched and overloaded tourism infrastructure (Cleverdon, 1993) and is itself threatened by local and environmental pressure groups� (Bahaire & Elliott-White, 1999). GIS offers big opportunities for the development of modern tourism applications involving apps.

Some of the registered cases where GIS has been used to improve sustainable tourism development in the country that we can mention, are in first place the investigations and projects done by Hasse and Milne. The particular focus of their study was on the potential use of GIS for community involvement in tourism. They have explored how participatory approaches and geographical information systems can be blended to provide a framework that can facilitate a better understanding of attitudes towards tourism and enhance participation and stakeholder Interaction in tourism planning. They did this by focusing in the case of Marahau, a small community in New Zealand which lies at the gateway to the 17

Abel Tasman National Park, one of New Zealands tourism icons. Their community was facing considerable changes that impacted their quality of life (Hasse & Milne, 2005). There where multiple stakeholders involved in deciding the future of this town, and they suggested that PAGIS has the potential to play an important role in enhancing sustainable tourism development outcomes.

To date GIS applications in tourism, have focused mainly on smaller scales, for example in relation to producing a recreational facility inventory, tourism-based land management and visitor-impact assessment. A general lack of tourism databases and/or data inconsistencies have limited the application of GIS to tourism analysis and planning. Nowadays, we can say that within the tourism industry, GIS can have different applications (Natalia Giordano, 2009), such as: •

Tourism inventories: These ones are used to offer organized and structure and information over parameters of interest for planners and tourism promoters. This one generally includes information regarding natural resources, infrastructure, demography and cultural heritage among others. GIS capacity to store, to integrate, manipulate and visualize data can become very useful for this type of inventories.

An example of this, has been the study done by Regil-Garcia, et al.from UAEM Toluca, Mexico, regarding the analysis of the possibilities of recreational tourism resources of the National Park of Nevado de Toluca, which is considered one of the most important protected natural areas (Franco-Maass, Osorio-García, Nava-Bernal, & Regil-García, 2009). The National park showed an increase of natural resources depletion, therefore it required sustainable alternatives for rural communities. development. The analysis considered the inventory of 19 potential tourism resources. This analysis was carried out by using the MCE method.

•

Establish suitable locations: Through basic geographic variables analysis, it is possible to establish a place potential to become a tourist destination.

There are several studies and projects related to this subject that have been analysed.

Below I mention some of the most relevant: The research done by T. Fung and F. K.-K. Wong, regarding the use of IKONOS imagery and a geographic information system, for rational planning of ecotourist activities and conservation measures (Fung & Wong, 2007). Through image 18

classification of IKONOS and integrated with ecological data, essential habitats could be mapped, and a spatial database was also established. Suitable sites for recreational activities (including camping, heritage visiting, hiking and snorkelling) and important ecological habitats where both be identified using multiple criteria evaluation techniques.

The SIGTUR developed by José Manuel Sánchez Martín. The main purpose of the application was the planning of rural tourism in the province of Caceres, Spain (Montero Lobo, 1999). The aim was the localization of the most suitable places for these rural lodgings. For this purpose, he carried out a multi-criteria analysis.

Finally, the analysis done by Curiong Wang, et al. about the Campgrounds Suitability Evaluation Using GIS-based Multiple Criteria Decision Analysis, which is the most related research to the present Thesis. For this purpose, a suitability evaluation was done in order to select the most appropriate areas for building campgrounds in Kuerdening, China (Cuirong, Zhaoping, Huaxian, Fang, & Wenjin, 2016).

•

Tourism impact measurement: Related to the discovery of patterns which means parameters monitoring selected in time and space instead predict potential impacts.

In the case of sustainable tourism development where environmental, social and economic information is pillars, it is required a GIS that allows to integrate and manage this different type of information. GIS has the valuable ability to integrate heterogeneous information, which facilitates key indicators monitoring.

An example to be mention is the study done by Davide Geneletti and Dorje Dawa regarding assessing the adverse environmental impacts of tourism, and in particular of trekking-related activities, in Ladakh, Indian Himalaya (Geneletti & Dawa, 2009).

The proposed approach is based on the use of Geographical Information System (GIS) modelling and remote sensing imageries to cope with the lack of data that affect the region.

•

Visitors flow monitoring: Directly related to GIS routing tools. It is used for timespace tourism behaviour analysis.

There are several studies in this matter, a very interesting one is the one done by Rodolfo Baggioa, and Miriam Scaglione. They have developed a Strategic Visitor 19

Flows (SVF) analysis using mobile data (Baggio & Scaglione, 2017). The aim of the research is to show empirical evidence of SVF in the Fribourg region in Switzerland by exploiting mobile phone data.

We can also mention the project done by Marise Safwat George, Christina Albert Rayed for Building a GIS Surveillance Conceptual Model for Protecting Tourists in Egypt (George & Rayed, 2017). He proposes a GIS model to provide security and safety that helping tourists to be in safe. The proposed model will allow tourists to use the proposed GIS model to locate the nearest police station or the touristic place’s guards’ office, informing them with their location and the kind of threat. The model can help tourists to determine the shortest and safest roads to reach their destination.

Here in New Zealand case we can mention in the project runned by the New Zealand Ministry of Tourism to provide public agencies with information on past, present and future tourism demand at a sufficiently refined geographic level to make infrastructure-related decisions. Their assumption was that a better understanding of the spatial distribution of tourism growth and impacts on publicly provided infrastructure can facilitate informed decision-making on where to invest and where to adopt proactive policy, planning and resource allocation practices. For this, they have created the Tourist Flow Model (TFM) which consist of an underlying statistical model of tourist movements and a GIS front end that allows the user to view the data spatially.

•

Decision Support: There are several GIS tools that could be used as a decision support system as it can provide useful information in different ways, such as tables, maps, graphs, statistics, etc.

An example of this we can mentioned the decision-support system developed by Jaishree Beedasyl and Duncan Whyatt for tourism planning in the Mauritius island (Beedasy & Whyatt, 1999). Here Tourism is the third largest economic sector of the country and a limitation of space and the island’s vulnerable ecosystem, warrants a rational approach to tourism development. The main problems have been to manipulate and integrate all the factors affecting tourism planning and to match spatial data with their relevant attributes. A Spatial Decision Support System (SDSS) 20

for sustainable tourism planning was therefore proposed, this included a GIS as its core component.

A very interesting and more recent use of the GIS in the tourism sector for decision support, was the Geographic Tourism Analysis Information System (SIGTUR) created by the Tourism Studies Institute (IET) in 1996. This one started with the essential idea of establishing methodologies and a general background analysis of the spatial influence in the Tourism Industry. This system was finished and already working by 1997. The system was able to achieve the following goals: Spatial treatment of statistical information, tourism thematic maps creation, creation of environmental and terrain indicators, identification of the tourism areas (Montero, 1999).

Taking everything into account we can say that GIS contributes to decision support, by providing new valuable information, specially because one of the main problems is the missing of data in the tourism industry, as well as the difficulty in making cross analysis or comparisons, which can allow discovering new relevant information in an economic, social or environmental level. GIS is an instrument that it is useful not only to get a proper approach to a specific issue but also it has an enormous utility for administration and of heritage resources valorisation. Tourism management together with GIS can provide strategic opportunities, economic development, wealth redistribution, and global equality tools (Jovanović & Njeguš, 2008). Therefore, is possible to conclude that GIS clearly enhances sustainable tourism, which is the "tourism that takes full account of its current and future economic, social and environmental impacts, addressing the needs of visitors, the industry, the environment and host communities" «World Tourism Day, 27 September», (2017)

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2.2 METHODOLOGICAL FRAME 2.2.1 Multi-Criteria-Analysis (MSC) Multicriteria analysis (MCA) is an analysis based on a group of techniques that allow analysing different options or choices by considering multiple criteria and priorities. This methodology emerged in 1960 as an operative investigation tool, where it is important to look after the best decision while maximizing economical functions. (Sergio et al.,2009) MCA is a method that integrates scientific knowledge to multiple criteria problems in a simple and easy way. This methodology involves criteria and multiple objectives evaluation with the final scope of identifying acceptable alternatives. Its main objective is the one-off finding solutions but the one of building or creating something able to help or transform the different criteria that belong to decision-making. (Beedasy & Whyatt, 1999).

MCA can be integrated to GIS by combining geographic data with multicriteria decisionmaking models, creating maps showing the available options rankings. This creates spatial systems for decision-making that allow these ones to be clear, as the maps can show where the alternative options are located, criteria differences for each option and an options ranking. This kind of systems have already been used worldwide for a variety of issues such as environmental management, regional and urban planning, natural disasters monitoring, etc. (Graymore, Anne, & J. Richards, 2009) 2.2.1.1 Multi-Criteria-Evaluation (MCE) The combination of GIS and MCA is a process that changes and combines geographical data and value judgements (the decision-makers’ preferences) to obtain information, helpful for decision making. Within the MCA we can find two different kind of methods: Multicriteria Evaluation (MCE), which involves a specific single objective and Multi-Objective Evaluation, which analyses multiple objectives (Estoque, 2011). The MCE analysis implicates six steps: 1) Define the goal. This one should be specific, measurable, attainable, relevant and time-bound 2) Determine the criteria, which is set of guidelines or requirements used as basis for a decision. These serve to limit the alternatives under consideration element or feature that represents limitations or restrictions; area that is not preferred in any way or considered unsuitable. protected area, water body, etc. (Estoque, 2011).

22

3) These criteria impose strong negative opportunities in the selection of areas for the identified land use. Consequently, they inform of us of where the particular land use under consideration should not be located. These criteria serve to limit or exclude areas from the final suitability.

4) Standardize the factors. These are criteria that enhance or detract from the suitability of a specific alternative for the activity under consideration. i.e. distance to road (near = most suitable; far = least suitable) This step sets the suitability values of the factors to a common scale to make comparisons possible. Decision makers have to decide based on their knowledge and fair judgement, which function should be used for each criteria.

5) Determine the weight of each factor. In this step, several methods can be used: (a) the Ranking method, which ranks the factors with 1, 2, and 3, where 1 is the least important while 3 is the most important; (b) the rating method that rates the factors using percentiles. Ranking and ratings are usually converted to numerical weights on a scale of 0 to 1, with overall summation of 1 (normalization); (c) the Pairwise comparison, which is a matrix where each criterion is compared with the other criteria, relative to its importance on a scale from 1 to 9. In this case, weights are also expressed in numerical weights that sum up to 1(Khwanruthai Bunruamkaew & Yuji Murayama, 2011).

6) Aggregate the criteria. This can be done by applying the GIS raster calculator or the weighted overlay tool 7) Verify the result. The result needs to get verified by implementing some specific formulas in order to check the accuracy of the result.

2.2.2 Analytical Hierarchical Process (AHP) Fortunately, in the last years, improvements had been generated in GIS capabilities, which means that nowadays we can count on more user-friendly MCA tools that could be used from any GIS software. (Graymore et al., 2009).

On the other hand, we can identify a problem with this kind of tool, the fact that it is difficult to establish values judgments weights, as these involve decision-making agents to dictate subjectively the importance ranking or level for each criteria (Graymore et al., 2009). In 23

order to know the strategic policies development for each place, taking the priority levels into account, it is necessary a method that can establish weights based on professionals previous knowledge, so that the results obtained can be the most accurate as possible. In this sense, the most frequently used method is known as an Analytical Hierarchical Process (AHP), which was created by Saaty in 1977, within the complex decision-making processes context. This logical framework allows a better understanding of complex decisions throughout the problem decomposition in a hierarchic structure. The AHP involves a pairwise comparison that shows out the importance and preferences depending on the objectives nature. (Beedasy & Whyatt, 1999).

The AHP can be implemented in three simple consecutive steps (Saaty, 1987): 1) Computing the vector of criteria weights.

2) Computing the matrix of option scores.

3) Ranking the options.

It is assumed that m evaluation criteria are considered, and n options are to be evaluated. Computing the vector of criteria weights in order to compute the weights for the different criteria, the AHP starts creating a pairwise comparison matrix A. The matrix A is a m×m real matrix, where m is the number of evaluation criteria considered. Each entry ajk of the matrix A represents1 the importance of the jth criterion relative to the kth criterion. If ajk > 1, then the jth criterion is more important than the kth criterion, while if ajk < 1, then the jth criterion is less important than the kth criterion. If two criteria have the same importance, then the entry ajk is 1. The relative importance between two criteria is measured according to a numerical scale from 1 to 9, as shown in Figure 3. The phrases in the “Interpretation” column of Table 1 are only suggestive and may be used to translate the decision maker’s qualitative evaluations of the relative importance between two criteria into numbers. It is also possible to assign intermediate values which do not correspond to a precise interpretation (Saaty, 1987).

24

Figure 3: Mocenni C, 2017, Note AHP, p2. Recovered from: https://bpmsg.com/academic/ahp_calc.php

Once the matrix A is built, it is possible to derive from A the normalized pairwise comparison matrix Anorm by making equal to 1 the sum of the entries on each column.

Finally, the criteria weight vector w (that is an m-dimensional column vector) is built by averaging the entries on each row of Anorm. Figure 4 illustrates the described formula:

Figure 4: Mocenni C, 2017, Note AHP, p2. Recovered from: http://www.dii.unisi.it/~mocenni/Note_AHP.pdf The AHP incorporates an effective technique for checking the consistency of the evaluations made by the decision maker when building each of the pairwise comparison matrices involved in the process, namely the matrix A and the matrices. The technique relies on the computation of a suitable consistency index, and will be described only for the matrix A. It is straightforward to adapt it to the case of the matrices by replacing A with, w with s(j), and m with n. The Consistency Index (CI) is obtained by first computing the scalar x as the average of the elements of the vector 25

whose jth element is the ratio of the jth element of the vector A¡w to the corresponding element of the vector w. Figure 5 shows the described formula:

Figure 5:Mocenni C, 2017, Note AHP, p2. Recovered from: http://www.dii.unisi.it/~mocenni/Note_AHP.pdf

A perfectly consistent decision maker should always obtain CI=0, but small values of inconsistency may be tolerated. Figure 6 shows the described formula:

Figure 6: Mocenni C, 2017, Note AHP, p2. Recovered from: http://www.dii.unisi.it/~mocenni/Note_AHP.pdf If the inconsistencies are tolerable, and a reliable result may be expected from the AHP. In (8) RI is the Random Index, i.e. the consistency index when the entries of A are completely random. The values of RI for small problems (m â&#x2030;¤ 10) are shown in figure 7.

Figure 7: Mocenni C, 2017, Note AHP, p4. Recovered from: http://www.dii.unisi.it/~mocenni/Note_AHP.pdf

26

3

METHODOLOGY

3.1 STUDY AREA The study area of this research is the Kaikoura district (shown in figure 8), a politically part of the Canterbury region, New Zealand. Kaikoura´s main town, has an estimated permanent resident population of 2,080 (Tourism Industry Aotearoa, 2018).

The Kaikoura Peninsula extends into the sea south of the town, and the resulting upwelling currents bring an abundance of marine life from the depths of the nearby Hikurangi Trench.

The town owes its origin to this effect since it developed as a center for the whaling industry.

The name Kaikōura means 'meal of crayfish' (kai – food/meal, kōura – crayfish) and the crayfish industry still plays a role in the economy of the region. However, Kaikoura has now become a popular tourist destination, mainly for whale watching and swimming with or near dolphins. There is also a large and readily observed colony of southern fur seals at the eastern edge of the town. At low tide, better viewing of the seals can be had as the ocean gives way to a rocky base which is easily navigable by foot for quite some distance (Baxter Andrew et al., 2008) It is also one of the best reasonably accessible places in the world to see open ocean seabirds such as albatrosses, petrels, and shearwaters, including the Huttonss sharewater which nests high in Kaikoura mountains. A strategic plan for the future of the Kaikoura coast is being developed by Te Korowai o te Tai o Marokura, the Kaikoura Coastal Guardians (Baxter Andrew et al., 2008) The town is located at the Seaword Kaikoura Mountains, a branch of the Southern Alps that come nearly to the sea at this point on the coast. Because of this, there are many walking tracks up and through the mountains (Baxter Andrew et al., 2008)

27

Figure 8:Own Source

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3.2 GEOGRAPHIC DATA The geodata used in the present work comes from several sources, all of which are in the projected coordinate systems WGS_1984_Web_Mercator_Auxiliary_Sphere, WKID: 3857 Authority: EPSG. The following Table 1 describes the type of data and its source: Table 1: Geopraphic data (own source) Data

Source

•

DEM for LiDAR data from the Kaikoura area captured in 2012.

•

Department of Conservation (DOC) campsites (shp).

•

Raster Query API, Catalog Service (CS-W), data.govt.nz Atom Feed.

•

DOC Public Conservation Areas Rights http://creativecommons.org/lice nses/by/3.0/nz/Identifier:https:/ /koordinates.com/layer/754/

•

Public conservation land (shp).

•

Freedom-camping prohibited areas (shp).

•

New Zealand populated places (shp).

•

Koordinates.com

•

New Zealand state highways centerlines (shp).

•

Koordinates.com

•

Koordinates.com

•

Kaikoura Lakes (shp).

•

Koordinates.com

•

Dump stations, Free campgrounds, interesting tips, public showers, public toilets, rubbish bins, user-locations, water (shp).

•

Koordinates.com

•

GeoZone.

29

GeoZone is a technology start-up that began life in 2013 data. They had assembled a database of 6,000 GPS coordinates which information was made available through a free iPhone/Android app called ‘CamperMate‘, that was launched in September 2011.

CamperMates main purpose is to show locations of things like public toilets, dump stations, campsites, hostels, and free Wi-Fi, and it allows users to geocache new, undiscovered locations and submit them directly onto the app. To evaluate the priority areas for council’s expenditure, multiple factors were considered: unfavourable factors (Constraint layers) and favourable factors (Factors layers).

The selected Factor layers where: Natural Environment, Landscape Condition, Infrastructure Condition, Services Condition and Carrying Capacity. Natural Environment is composed by two Criteria layers (Slope and Aspect) and it refers to the Natural Environment aspects that can affect a campground. In this sense, it is important to consider flat ground areas only in order to be able to pitch a tent. The available sunlight it was also another factor that was considered, taking into account the low average temperatures in New Zealand, especially during winter.

The Landscape Condition it is composed by two layers (Distance from Points of view and View-shed) and it refers to the Landscape Conditions that influence positively at the moment of choosing a campsite.

The Infrastructure Conditions is composed by four layers (Distance from Roads, Distance from Dump Stations, Distance from Publish Shower and Distance from Public Toilets.) and it refers to the distance from the necessary freedom camping infrastructure.

The Services Conditions is composed by two layers, water accessibility and distance to rubbish bins. This layer refers to the accessibility to basic Freedom Camping services in the area.

The Carrying Capacity is composed by one layer (User features) and is related to the negative socio-cultural impacts that may be caused due to overconcentration of tourism in a certain area.

The selected Constratint layers is composed by the non desirable areas, this areas are represented by different layers: rivers, lakes, swamps, non grasslands, residential areas, roads buffer, and South bay recreation reserve.

30

All these Factor (Criteria) and Constraint layers can be better understood in the following Table 2:

Table 2:Factors, Criteria and Constraints. Own Source Target Layers

Factor Layer

Criteria Layer

Constraint Layers

A1 Natural Environment B1 Slope.

B2 Aspect.

A2 Landscape condition B3 Distance from points of interest.

B4 View-shed.

-Rivers.

-Lake.

-Swamps.

-NonGrassland areas.

-Residential

A3Infrastructure

B5 Distance from Roads.

condition

B6 Distance from Dump areas.

-Roads Stations.

MCA Priority areas

B7 Distance from Public- buffer.

-South Bay Shower.

index for fundings redistribution.

B8 Distance from Public- Recreation.

Reserve.

Toilets.

(Tourists priorities scenario) A4 Services condition

B9 Water accesibility.

B10 Distance to Rubbish bins

A5 Carrying capacity

B11 Users features

31

3.3 METHODOLOGY FLOW CHART The method was done using an analytical structure, where several SIG techniques were used to elaborate an analysis.

The methodology was achieved by examining a number of individual criteria, assigning them relative levels of importance as a whole, and using a mathematical resultant model to identify the most suitable location. By adopting this site suitability method, it was possible to identify the criteria considered, clearly document the relative importance of one criterion over another, analyze the net outcome using a Geographic Information System, and then possibly revisit the mathematical relationships in this “decision model” (Salo, & Hamalainen, 1995). By revising the relative importance to identified criteria based upon the particular land under consideration, it was possible to generate “suitability maps” for each Factor, and then generate a final Freedom Camping areas suitability map.

To achieve this, all the criteria where assigned a “rank” denoting their relative levels of importance within the suitability study. These ranks where assigned as numeric values ranging from 1 to 10, with 1 reflecting a high level of importance and 10 reflecting a low level of importance. A similar scale of 1 to 10 is used to assign individual “weights” based on the proximal relationship to each particular feature type, taking a specific criteria into account, previusly used in the decision model (Ligmann-Zielinska, & Jankowski, 2008).

Collectively, the weights, multiplied by the rank, provide a suitability score that cumulatively is used to identify the most suitable locations displayed as a “Freedom Camping areas suitability map”.

The following flowchart in figure 9, explains the methodology that was applied in this research:

32

Figure 9:Methodology flowchart. Own Source

33

The following steps had been established for the investigation process: 3.3.1 Data normalization 3.3.1.1 Individual shapefiles and raster datasets layers creation and reclass steps 1) The Topo to Raster tool was used to create a 12.5m DEM of the area of interest using elevation data, elevation point, stream and contour. With this DEM it was possible to create a hillshade.

2) The Creation of a bounding box shapefile given the bounding box coordinates, that were used to create points that where afterwards projected into the correct coordinate system.

3) Once obtained both the hillshade and the bounding box shapefile, CLIP analysis tool was used in order to obtain the area that we need to analyse.

4) After that, a shapefile of the New Zealand boundaries within the bounding box was used as mask for the Extract by mask tool, in order to finally obtain the results for the continental area only. It is important to mention that all geoprocessing actions needed to be made before the Extract by mask operation in order to obtain more accurate results.

5) Finally, it was possible to start creating each Factor Layer and Constraint layers, by using the DEM.

3.3.1.1 Identification of Decision Criteria.

Next, it was necessary to determine the analysis decision criteria. The decision criteria for site selection was examined for assigning relative ranks and individual feature weights based on the land use type for which suitability is being examined (Pareta Kuldeep, s. f.).These was very

significant in the site selection and acted as key drivers in the selection of the geographic location. These criteria have a strong influence in the final suitability.

1) Less than 10° terrain Slopes. Slopes greater than 20° are extremely unsuited for construction because it may cause serious soil erosion (Cuirong, Zhaoping, Huaxian, Fang, & Wenjin, 2016). This criterion was used for the creation of the

34

Natural Environment A1, which contains the criteria layers Slope B1 and Aspect B2.

2) The terrain needed to beat least 100m away from roads. This was considered for the creation of the constrain layer Distance from Roads and is relevant in terms of security purposes. However, this distance was established randomly as there was no research available regarding this feature.

3) The result polygons to consider have to be larger than 400m2. This measurement has been determined as campsites occupy large areas. As there is no previous research done on respect to the most convenient measurement for a campground, this size has been establish by taking into account a reasonable size were several tents and infrastructure could fit.

4) Exclusion of recreation areas as determined by the Department of Conservation.

This criterion was considered for the creation of the Reserves constraint layer.

5) At least five meters away from water reserves. This criterion was considered for the creation of the Reserves Land layer. An average of five meters was established for the purpose of this investigation, as there is no previous and specific study related to the minimum best distance from water reserves for a campground.

6) Areas facing south and flat areas were considered to have the most suitable aspect (Cuirong et al., 2016). Aspect was used to measure the available sunlight.

3.3.2 AHP evaluation 3.3.2.1 Experts Survey and AHP matrix In first place, a survey was created in order to know the expertsâ&#x20AC;&#x2122; opinions and obtain a pairwise comparison matrix for the Factor Layers and Criteria Layers.

35

Survey results:

36

37

Figure 10:AHP Survey. Own-Source

38

The above figure ( see figure 10) shows the results of the AHP research method survey, wich were answered by James Imlach, a national policy and planning manager from the New Zealand Motor Caravan Association. The survey was designed following the AHP method principles.

The results obtained in the survey where converted into a numeric scale, considering Saaty´s fundamental scale (see figure 11). The scale, allows to make pairwise comparisons between different criteria. It does it by identifying the level of importance of one criteria over the other.

Figure 11:Saaty fundamental scale.(Saaty, 1987) In this step, the Criteria layer weights were established, in order to run the weighted overlay tool and create the Factor layers. This step was simple for the Natural Environment and Landscape Condition layer as each of their criteria was considered of equal importance; therefore, the assigned weight percentage for each of them was proportional to the number of criteria (50%). In the case of the carrying capacity, these ones were composed by only one criteria, which is User Features (CamperMate app users geolocalizations); therefore there was no need of using the weighted overlay tool. For the Infrastructure Condition and the 39

Services Condition, it was necessary to use the AHP method, as there where different values for each criteria. In this analysis the online AHP calculator software BPMSG developed by Klaus D. Goepel was used. The following figures 12, 13 and 14 illustrate the AHP procedures by using the the AHP online tool for each different criteria .

Figure 12: Klaus D. Goepe , AHP calculator software BPMSG, recovered from: https://bpmsg.com/academic/ahp_calc.php

Figure 13: Klaus D. Goepe , AHP calculator software BPMSG, recovered from: https://bpmsg.com/academic/ahp_calc.php

40

Figure 14: Klaus D. Goepe , AHP calculator software BPMSG, recovered from: https://bpmsg.com/academic/ahp_calc.php In the three cases, the consistency calculated by the online tool indicates that is correct.

Therefore the next step was to use the indicated weights (see Table 3) in the weighted overlay tool in order to create each Factor Layer.

Table 3: Criteria Layers weights.Own Source

With all the created layers, the first task was the one of identifying weights for each Factor Layer, and the AHP method was used for this purpose. However, in this instance, a manual procedure has been done instead of using the online BPMSG, due to consistency errors 41

developed in previous different attempts (see chapter 3.3.2.2 below).

The following steps explains how the AHP method was applied: 1) The first step was to create the AHP matrix with the obtained survey results converted into numeric rating.

Table 4: AHP matrix. Own Source Natural Environment Landscape Condition Infraestructure Condition Services Condition Carrying Capacity

Natural Environment

1

Landscape Condition

3

Infraestructure Condition

1 1/5

Services Condition

3

Carrying Capacity

1

Total

1/3

1/3

5 1/5

1

1/5

3

1 8.2

5

2.73

5

1 1

1/3 1

5

3 1/3

19

1/5

7

1 6.2

As we can see in the above Table 4, the AHP weights were written in each box indicating the comparison between each pair of criteria regarding the importance that they have for the Thesis objective.

42

2) After creating the AHP matrix, it was possible to create the normalized matrix.

The first step was to convert the fraction numbers into decimals (Table 5) in order to obtain the total sum for each column.

Table 5 Original Factor Matrix. Own Source

Table 6: Normalized AHP matrix.Own Source.

Normalized matrix.

0.12

0.12

0.26

0.04

0.16

0.36

0.36

0.26

0.71

0.16

0.02

0.07

0.05

0.04

0.03

0.36

0.07

0.15

0.14

0.48

0.12

0.36

0.26

0.04

0.16

This matrix shown in Table 6 has been created by dividing the obtained weights of the Original Factor Matrix, by the total sum of their corresponding column.

43

3) The normalized matrix helped to calculate the eigenvector matrix criteria (Table

7), which indicates the weights:

Table 7 Eigenvector matrix criteria .Own Source

The above table has been created calculating the average for each row from the normalized matrix. As a result, it is possible to obtain a Vector matrix that indicates the weight for each criteria. The following weights had been used for each related layer in order to execute the weighted overlay tool: 0.14 for the Natural Environment, 0.37 for the Landscape Condition, 0.24 for the Services Condition and 0.188 for the Carrying Capacity.

3.3.2.2

Consistency Check

When many pairwise comparisons are performed, some inconsistencies may typically arise, therefore, a consistency check has been performed. This procedure is important in order to see that the gathered information is coherent. For this purpose, the ratio consistency has been calculated. As the following Table 8 shows, several previous calculations where needed:

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Table 8:CR calculation. Own Source.

Once the normalized matrix is obtained, the first step is to calculate the Average Matrix; this is done by obtaining the average of each row of the normalized matrix. The following step is to calculate the total row vector. This one was calculated by multiplying the Original Matrix shown in Table 3 by the Average Matrix. The third step was to calculate Coefficient; this has been done by dividing the total row Vector by the Average Matrix. Once this one was obtained, it was possible to calculate the Lambda Max represented as λ, which was needed for the calculation of the Consistency Index (CI). This was done by doing λ minus the matrix range, divided by 2. Finally, it was possible to calculate the Ratio Consistency (CR), which was done by dividing the CI by the Random Consistency of the Matrix.

As we can observe, the CR exceeds by far the maximum percentage for the consistency which is 10%. Regarding the AHP method, this percentage indicates the maximum value allowed for the CR when the matrix range is >=5.In this case, as shown in table 1, the matrix range is 5 by 5. Therefore that was the percentage taken into account in order to check the consistency. Following adjustments were needed in order to obtain the desired consistency.: Table 9 shows the new matrix with the adjustments made inside the blue cells. The adjustments were done based on Saaty´s procedure for consistency improvements, which basically consists in checking the matrix and identifying the problem. These problems are usually due to mistakes done by the specialist or also because of appreciation problems (related to the Transitiviy and Proportions), like in the present case. The tool used to detect the values that needed adjustments, was the AHP web calculator BPMSG.

45

Table 9:AHP Matrix with adjustments.Own Source

As in the procedure previously described, the following step is to create the normalized matrix. The obtained table is shown below in table 10:

Table 10:New Normalized Matrix.Own Source

Once the normalized matrix was created, it was possible to recalculate the CR (Table 11) in order to assure that the result is below the maximum allowed percentage. The calculation was done by establishing the same calculation process for table 8.

46

Table 11:CR calculation.Own Source

3.3.3 Weighted Overlay Suitability for Freedom Camping areas in the Kaikoura region, is modelled based on 5 Factor layers and a Constraint Mask. At this stage GIS techniques are integrated with the MCA method in order to obtain a Freedom Camping Priority areas map. This was possible thanks to the Weighted Overlay tool, that allows to join different layers and create a new one from them, taking all the weights into account.

3.3.3.1 Factor Layers Creation.

The first important step in this MCA process to create the Factor Layers. These ones were also mapped separately (see table 12) in order to show the variation and be able to answer to some of the investigation questions.

Table 12:Factor Layers Description. Own Source

Factor Layers Layer A1 A2 A3 A4 A5

Name Natural Environment Landscape condition Infrastructure condition Services condition Carrying capacity

Map name Reference Natural Environment Suitability Map Chapter 4.4 Landscape Condition Suitability Map Chapter 4.1 Infraestructure Condition Suitability Map Chapter 4.5 Services Condition Suitability map Chapter 4.3 Sustainable carrying capacity priority areas Chapter 4.2

These ones were created using the MCA method together with the GIS weighted overlay tool, based on their corresponding Criteria layers. The first step was the values reclassification for each layer, in order to match the ranking values and be able to use the weighted overlay tool. Each Criteria layer was previously created, taking into account the Analysis Criteria, using different ArcGIS Spatial Analysis tools.

47

A1 layer is composed by two influencing layers with equal importance, therefore, the weight percentage was of 50%. To calculate layer A1, a B1 Slope layer was created showing the most suitable aspect.

For calculating layer A2, a B3 layer was created by applying the Euclidean Distance tool to the Dem, and another B4 layer will be created applying the Viewshed tool. The viewshed was calculated by applying the view-shed calculation tool on the DEM data, based on the sea-coast line shapefile. Locations with better views of attractions received higher scores, and the scores were assigned along natural breaks. The weighted overlay tool was used to create the final layer, where the weights were as per the A1 layer, 50 % for each layer.

To calculate A3; B5, B6, B7 and B8 layers were created by applying Euclidean distance tool to the roads, dump stations, public showers and public toilets. The locations of scenic spots were collected in the field survey using a portable GPS by Geo Zone, and a buffer analysis was then applied in ArcGIS around the locations. The score increases as the distance to attractions decreases. The weighted overlay tool was used to create the final layer. The weights used for this, are the ones shown in figure 14, which are the ones obtained from the survey translated into numerical format, taking Saaty´s fundamental scale into account from figure 11.

For calculating the layer A4; B9 and B10 layers were first created by applying Euclidean distance tool to the water services and rubbish bins. The weighted overlay tool was used to create the final layer using the weights shown in figure 13. These weights were also established based on the results obtained on the survey and then translated into numbers by comparing it with Saaty´s scale.

Finally, layer A5 was created by applying the Euclidean distance tool to a buffer shapefile of the user features layer, which are the geolocalization points of the Campermate app clients gathered by Geozone company.

After all the Factor layers were created, it was possible to create a first approach of the Priority Freedom Camping areas suitability map by using the MCA method and the weighted overlay tool. The weights used, where the ones obtained in the survey. The following Table 48

13 shows in the first column the sum obtained from the normalized matrix from table 10; in the second column, the weights expressed in numbers and in the third column, the weights expressed in percentage.

Table 13:Factor Layer Weights. Own Source.

The above table shows that the most important layer is the Landscape Condition with a weight of 42.4% and being the Infrastructure Conditions the least important with a weight of 12.88%. The rest of the layers have a similar level of importance.

3.3.3.2

Constraint Layers

In order to continue with the creation of the Freedom Camping priority areas map, it was necessary to eliminate the unwanted or unsuitable areas. For this reason, different shapefiles considered as Constraint layers where chosen with the aim of creating a Constraint mask (see Figure 16).

The shapefiles are related to land conditions, land use, and accessibility. The mask was done by doing a merge of all the criteria layers involved (see Figure 15), such as Rivers, Lakes, Swamps, Non grassland areas shapefile (in order to obtain the grassland areas only), residential areas (in order to obtain the rural areas), a roads buffer layer to exclude areas next to the roads and the South Bay Recreation Reserve layer which is the only reserve are within the study area were is not allowed to freedom camp. All these shapefile layers were created considering the mentioned analysis criteria, therefore a 5-meter buffer was applied to the Lakes and Rivers layers and a 1 meter buffer to the Roads and where all obtained from open source data.

49

Figure 15: Constraint Layers Map.Own Source 50

Figure 16 Constraints Mask. Own Source 51

3.3.3.3

Zonification and favourable factors -unfavourable factors .

In the last step of the analysis, the mask obtained from the constraint layers were erased from the final layer obtained from the MCA method using the Factors layer. In other words, the mask (unfavorable factors or undesired areas) was erased from the final map obtained from the MCA method (favorable factors).In order to erase the mask layer from the final map obtained from the MCA process, the ‘Extract by mask’ tool of Arcgis was used. To be able to use this tool, the Factor layer weighted overlay process was converted into a polygon by using the tool ‘Raster to Polygon’ as the extract tool only does its processing between polygon layers. After this procedure, the Dissolve tool was applied to obtain more defined polygons instead of lots of lines, this was done in order to avoid problems during the processing.

3.3.3.4 Site Suitability map.

Finally, it was possible to obtain a final Suitability map with the Freedom Camping priority areas in the Kaikoura region (see Figure 22), with a suitability range value from 1 to 17, were 1 are the most suitable areas.

To be able to see values from 1 to 5 in more detail, the select by attributes tool was used in order to obtain a new layer with the main priority areas. These ones are represented in the High Priority Freedom Camping areas in the Kaikoura region shown in Figure 23. Moreover, by converting the obtained raster to a polygon and merging its table with the Parcels layers table, it was possible to calculate the hectares for each area. This calculation was done by creation a new field in the new table and then applying the calculate geometry tool.

However, there was still a need of getting rid of the non-desired areas due to the reduced squared meters; there were too many polygons which size was too small for a camping area.

Eliminating these areas was possible by using the selecting by attributes tool and extracting only the polygons above of 400 square meters.

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4

RESULTS

The results that are presented in this section refer to the study area within the Kaikoura region in New Zealand. Therefore, the cartography only displays that area, which has been analysed excluding the river, lakes swamps, non-grassland areas, residential areas and roads buffer.

4.1

Landscape Condition Suitability Map

This map (see Figure 17) shows the weighted overlay of the Euclidean distance from points of interest layer and the viewshed layer. As a result, is possible to see that there are two different types of priority suitability areas. In one hand, we can spot three hubs. The first one located in the centre of the study area, just next to the mountain range, the second one located in the north east by the sea, and a third one in the Kaikoura Peninsula, where most of the population is found. The second priority suitability area that can be detected, is within the hubs

mentioned above, which are specifically related to

the quality view.

Figure 17:Landscape Condition Suitability Map. Own-Source.

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4.2

Sustainable carrying capacity suitability map

This map (see figure 18) is a result of the Euclidean distance process layer from the user features buffer. We can clearly notice that the high value areas are those ones located at a far distance from the urban areas and roads. Therefore, the areas that are more able to support human activity are those ones located just by the mountain chain.

Figure 18:Carrying Capacity priority areas in the Kaikoura region map.Own Source

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4.3 Services Condition Suitability map This map (see Figure 19) is a result of the Distance to rubbish bins and water accessibility.

We can notice on the map that there are three main hubs of high suitability areas regarding the services conditions. As it was expected, these areas are located near the urban areas, places where is possible to have access to water and bins. These areas are located in the north east , south and south east in the Kaikoura Peninsula.

Figure 19:Services Condition Suitability Map.Own Source.

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4.4

Natural Environment suitability map.

This map (see figure 20) has been created by executing the weighted overlay tool of the Slope and Aspect layers. As a result, we can see that the high suitability values are located far from the mountain range: the further the distance, the more suitable it is. This is quite logical to deduce, as those areas are too steep for a camping location. Also, these areas are located in flat spots facing south; this is the reason why the main suitability area is in the south east area, near to the urban areas. These ones are based in flat areas as well due to its perfect condition for building.

Figure 20:Natural Environment Suitability Map. Own Source.

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4.5

Infrastructure Condition map.

This map (see figure 21) is a result of a combination of layers processed with the Euclidean distance tool. These layers are: Distance from roads, Distance from dump stations, Distance from Public Shower, Distance from public toilets. In this case, it is possible to notice three hubs for the high value suitability areas. One located in the centre near to the mountains, another one located in the Kaikoura peninsula and the third one in the south east by the sea.

These where the expected results as like in the Services Conditions maps, most of the infrastructure is locates mainly in the most populated areas or frequented by tourists.

Figure 21:Infrastructure Condition Suitability Map.Own Source

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The following Tables 13 and 14, indicate the hectares percentage of high suitability for each one of the influencing variables.

Table 14 Priority hectares per factor layer. Own Source.

Suitability 1 Ha Landscape Condition (values 1, 2 and 3)

Total Ha 38 32180

% 0.12

Carrying Capacity (Values 1.2.3 and 4)

3638

32180

11.31

Services Condition

2353

32180

7.31

390

32180

1.21

1995

32180

6.20

Natural Environment Infraestructure Condition

The above table presents information regarding the hectares percentage of the most suitable areas for each Factor Layer. For all the layers, except for the Landscape Condition Layer, the hectares were calculated from value 1 zones, which are considered the most important.

For the Landscape Conditions, also values 2 and 3 where calculates as the obtained polygons where too small and for the Carrying Capacity Layer values 2, 3 and 4 where also added for the same reason. It is important to mention that other zones were considered for the carrying capacity layer as well as the Landscape conditions layer because those layers are composed by very small polygons, if only value 1 was considered then the result wasnâ&#x20AC;&#x2122;t going to be proportional with the others. In the future it might be more efficient to create a merge of polygons in order to obtain more accurate and proportional results.

In order to calculate the hectares, the tool Raster to Polygon was firstly used. Once the Polygons were obtained, another field was added with the name of hectares which was used to calculate the hectares by using the tool Calculate Geometry. The following table 15, shows a pie chart with the percentages of the calculated hectares. Suitability 1 refers to the values that have been taken into account for this calculation.

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Table 15 Suitability 1hectares percentage. Own Source

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4.6

Freedom camping suitability areas in the Kaikoura region

Figure 22:Freedom Camping Priority Areas Map. Own Source Finally, the Freedom camping suitability map (see Figure 22) was obtained by doing a weighted overlay of the maps above. The results indicate that there are areas of high value for the Kaikoura Council to consider for fundings redistribution. It is possible to see very clearly that most of these places are located at the south east of the study area.

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The following map (see figure 23) shows in detail most of the suitability areas from range 1 to 5.

Figure 23 High Priority Freedom Camping Areas.Own Source

Taking the statistics shown in Figure 24 and 25 into account, it is possible to say that only the 4,5% of the territory corresponds to high value areas (see Table 15). Figure 24 shows the High Priority Freedom Camping areas map statistics calculated by Arcgis 10.6, specifically the ones related to the hectares column of the table data. The hectares where calculated by Arcgis, using the table data information together with the DEM layer. In the other hand, Figure 25, shows the statistics related to the total hectares from the Freedom Camping priority areas final map, therefore this graph provides information of the total hectares of our study area.

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Figure 24 Hight Priority hectars graph. Own Source

Figure 25:Final Map graph. Own Source.

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Table 16:High priority areas Ha proportion. Own Source

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5

Conclusion.

Tourism is a very complex activity, and therefore requires tools that aid in effective decision making to come to terms with the related economic, social, and environmental demands of sustainable tourism development. Applications of GIS in tourism and recreation planning illustrate that GIS is strong and effective tool that can help tourism planning and decisionmaking.

The present analysis looks at the Freedom Camping suitability areas in order to facilitate public findings redistribution in the Kaikoura region. It allowed to verify that it is possible to integrate and apply GIS with MCA and site suitability methods in order to address Freedom Camping related issues. The evaluation of the tool proved that it is a sensitive method of sustainability assessment. Its ability to show results could help managers prioritise certain areas for fundings redistributions, in terms of sustainability initiatives and other management actions, making it a useful decision support tool for progressing sustainability.

If used for repeated years, it will also be useful for monitoring and evaluation of the effectiveness of sustainability and planning strategies.

MCE is a sound tool for tourism planning, since it takes into consideration the different criteria that have a significant impact on the decisions. The application of MCE has successfully divided the study area into different levels of values by considering various factors and constraints. The MCE works by using Weighted linear combination which allows for the high performance of an alternative achieved on one or more criteria to compensate for the weak performance of other criteria. The weights were determined using an AHP analysis based on the different layers created and overall suitability maps were generated using ArcGIS 10.2.

The insight provided by the Landscape Conditions map (Figure 17) answers to the first question of this work related to the location of the most suitable freedom camping areas for each influencing variable, in this case the Landscape Condition. It shows that the main areas are near the main touristic points, one associated with the mountain trails, surfing and Kaikoura village. We can also add that the areas near the mountain and just by the sea of the study area have better View-sheds, in the first case because of its high elevation and in the second case, due to the the coast proximity. In this case, the considered Viewshed was related 64

to the coastline, however, for further studies other types of view perspectives can be considered, as for example a mountain view. It is important to take note that in the processing of this map, the Euclidean Distance tool was used, however would be interesting to investigate further about how to apply a Network Analysis tool that could consider the fastest route to get to the touristic areas. This method could be applied for all the created layers where the Euclidean Distance tool was used.

The information provided by the Sustainable carrying capacity suitability map (Figure 18) also answers to the first specific question of this work related to the location of the most suitable freedom camping areas for each influencing variable, in this case the carrying capacity suitability map. The map shows how the most suitable areas are located far from the populated areas and roads, therefore we can find suitability mainly in the mountain areas and also between the mountain and the roads. One of the reasons of these results is that most of the camper mate app users where located in the populated areas, touristic areas and roads.

As tourist activities are concentrated along specific zones along coastal areas, these densities exert heavy pressure on the environment and local infrastructure. In this case only the touristâ&#x20AC;&#x2122;s density that can be easily visualized was used as a technique to interpret the carrying capacity, however other methods can be applied for this matter. Tourist-host contact is another capacity indicator. sea links [Briguglio & Briguglio, 19961.

In the other hand, the information provided by the Services Condition Suitability map (Figure 19) shows the most suitable areas for the Services Condition variable. Similarly, to some of the previous maps, we can see 3 main areas to consider highly suitable, one in the north east, the second one in the Kaikoura Peninsula surroundings and the third smaller area in the south. Also, as some of the already described results, in this map the Euclidean distance tool was used, however it would be very interesting to analyse the Network Analysis options in order to know the areas with access to the fastest routes

In regard to the Natural Environment map (Figure 20), the map answers to the first specific question of this work related to the location of the most suitable freedom camping areas for the Natural Environment influencing variable. For the creation of this map, only the Slope and Aspect layers have been considered, however, for more precise results we could also add other criteria as Canopy Density.

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The same analysis done for the services condition can be apply for the Infraestructure Conditions map (Figure 21). As said before, the government is giving fundings for freedom camping infrastructure, therefore this kind of maps are becoming key for tourism planning.

Finally, the Freedom Camping Priority areas map shown in Figure 22 confirms the hypothesis that there are priority Freedom Camping areas in the Kaikoura region, to take into account for fundings redistribution, these ones are represented in red, specifically the areas represented in Figure 23. The maps shown in Figures 17,18,19,20 and 21, also answer to the specific questions of this Thesis, related to the location of the most suitable freedom camping areas for tourists for each influencing variable. These ones are shown in red in each one of the maps. In regards the most suitable areas for Freedom Camping in the Kaikoura region, this occupies the 4,5% of the study area, based on the results shown in Figure 24,25 and Table 15.

The most suitable tourism site identified according to the analysis are the ones located in the South East in the Kaikoura peninsula and in the middle east area by the coast as shown in the Freedom Camping suitability areas map.

1. It will be a great resource for the Kaikoura Council for different reasons. First and foremost, it would be useful for tourism promotion and information. As mentioned, before, One of the main reasons of the environmental, social and economic problems caused by tourists in the area is due to a bad spatial distribution. In order to address this issue, the council can provide up to date information to tourists regarding the places that gather the most important requirements, to be able to get a good quality Freedom Camping experience, and that can also guarantee sustainable tourism premises. It is important to highlight that these areas have been delimited considering the already existing infrastructure and services provided by the Kaikoura Council. At the beginning of the analysis, it was expected to evaluate the study area in order to find potential sites in need of new infrastructures and services for fundings redistribution, however during the process and evaluating the data, I consider to be more efficient to be able to provide information that can help enhance the performance of the already invested money infrastructure and services. This is the 66

reason why this is not a study that tries to find potential Freedom Camping touristic areas but already existing high value touristic areas in order to became more visible for tourists and improve maintenance. Maintenance can be done more effectively by knowing with precision the areas that are worth to consider a priority by considering relevant criteria stablished by professionals of the sector.

The main limitation of this research is that some important factors were not considered: for example, the safety condition and legal implications. The Safety condition would need contains three criteria: Geological Disaster, Threat of Dangerous Animals, and Forest Fire.

These can be calculated based on the DEM, slope and lithology. (ÂŤCuirong et al. - 2016 Campgrounds Suitability Evaluation Using GIS-based.pdfÂť, s. f.). In terms of the legal implications, it would be important to have a deeper comprehensive review of the relevant state laws, regulations and industry rules, related to the classification, choice of locations and layout requirements of campgrounds, consultations with experts in the fields of tourism, ecology, environment and geology and considerations of the actual situation in Kaikoura. .

The results can act as a guideline and support for Freedom Camping planning. Nowadays there are many studies related to MCA and its integration with GIS for tourism planning matters, however there are no studies found specifically to address Freedom Camping related issues. This is not a minor implication considering that some of the countries in the world where this type of camping is legal are Sweden, Norway, Scotland and Iceland, Finland, Mongolia and Turkey. To conclude we can say that the analysis can be considered pioneer in the subject and can be modified and improved to enhance Sustainable tourism by developing Freedom Camping management and planning in some of the most important tourism destinations in the world as well as contributing with a worldwide repositioned sector which has a relevant influence not only to tourism sustainability but also international economies and personal wellness.

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