BUILDING BETTER AGENTSStatistical and Spatial Analysis of Tourist Movement Data

451-450 Research ProjectAlice O’Connor

SupervisorsDr. Andre Zerger, Department of Geomatics

Bob Itami, GeoDimensions

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Acknowledgements

Many people helped directly, and indirectly, with this research. I would like to thank, in no particularorder, Bob Itami (GeoDimensions), Claire Burton & Dino Zanon (Parks Victoria), Wayne Astill, Dr.Kathryn Williams, Hamish Webb, Steve Wealands & Dr. Andre Zerger. More specifically...

• Steve Wealands for being a great help and for his technical and moral support duringfieldwork,

• Wayne Astill for the ALGE Timing System provision and training• Hamish Webb and Kathryn Williams for providing another dimension to the project

and• Andre Zerger for ongoing enthusiasm and support throughout the year, never tiring of my

endless questions and being a pleasure to work with.

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Title of thesis: Building Better Agents: Statistical and spatial analysis of individualtourist behavior to develop a typology for agent-based recreationmodelling.

Student Name: Alice Nairne O'Connor

Student Number: 56712

Course of Study for which the Thesis/Project Report is to be Submitted:451-450 Research Project

Supervisors: Dr. Andre Zerger, Department of Geomatics, University of Melbourne & BobItami, Director, GeoDimensions

in association with: Dr. Kathryn Williams & Hamish Webb (ResearchStudent), ILFA, University of Melbourneand Claire Burton & Dino Zanon (Team Leader, Visitor Research) ParksVictoria

Study Site: Twelve Apostles site, Port Campbell National Park, Victoria, Australia

Research Hypothesis:"Tourist typologies for agent-based models of human behavior innational parks can be developed using individual-based trackingtechnologies and the subsequent identification of spatial patterns fromthis data."

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TABLE OF CONTENTS

ABSTRACT..........................................................................................................................................7

1 INTRODUCTION........................................................................................................................2

1.1 OVERVIEW ..............................................................................................................................21.2 TOURISM.................................................................................................................................3

1.2.1 Tourism Trends ..............................................................................................................31.2.2 Sustainable Tourism ......................................................................................................41.2.3 Tourists ..........................................................................................................................41.2.4 Summary ........................................................................................................................5

1.3 RECREATION PLANNING & RESOURCE MANAGEMENT ...........................................................61.4 AGENT-BASED SIMULATION AND RBSIM ...............................................................................7

2 LITERATURE REVIEW ...........................................................................................................9

2.1 RECREATION PLANNING & MANAGEMENT .............................................................................92.1.1 Overview ........................................................................................................................92.1.2 Review............................................................................................................................9

2.2 AGENT-BASED SIMULATION..................................................................................................112.2.1 Overview ......................................................................................................................112.2.2 Applications of Agent-Based Simulation .....................................................................112.2.3 Agent-Based Simulation for recreation management ..................................................12

2.3 TRACKING TECHNOLOGIES ...................................................................................................142.4 TOURIST TYPOLOGIES...........................................................................................................162.5 CONCLUSIONS.......................................................................................................................17

3 STUDY SITE..............................................................................................................................18

3.1 INTRODUCTION .....................................................................................................................183.2 SITE DESCRIPTION ................................................................................................................19

4 METHODOLOGY ....................................................................................................................22

4.1 OVERVIEW ............................................................................................................................224.2 STUDY SITE -RECONNAISSANCE AND PRELIMINARY GIS DEVELOPMENT.............................224.3 AGENT-BASED SIMULATION - FAMILIARISATION .................................................................224.4 DATA CAPTURE REQUIREMENTS, TECHNOLOGY AND DESIGN..............................................23

4.4.1 Overview ......................................................................................................................234.4.2 Requirements ...............................................................................................................234.4.3 Technology...................................................................................................................234.4.4 Data Capture Design ...................................................................................................244.4.5 Research Questions: ....................................................................................................27

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4.5 DATA CAPTURE - INDIVIDUAL MOVEMENT DATA ................................................................274.5.1 ALGE Timing System Data Capture ............................................................................27

4.6 ANCILLARY DATA CAPTURE - PATH DIMENSIONS AND TRAFFIC COUNT ..............................284.6.1 Demographic Data Capture ........................................................................................284.6.2 Ancillary Tourist Number Data Capture .....................................................................284.6.3 Path Areas....................................................................................................................284.6.4 Traffic Counts ..............................................................................................................294.6.5 Database Design..........................................................................................................29

4.7 DATA REDUCTION ................................................................................................................304.8 ANALYSING GEO-TEMPORAL DATA. ......................................................................................31

4.8.1 Overview ......................................................................................................................314.8.2 Database queries .........................................................................................................314.8.3 Basic Statistical Analysis .............................................................................................324.8.4 Ordination Analysis .....................................................................................................324.8.5 Research Questions......................................................................................................33

4.9 SPATIAL VISUALISATION OF DATA .......................................................................................344.9.1 Overview ......................................................................................................................344.9.2 Map Objects and Visual Basic for Visualisation .........................................................344.9.3 Generation of Continuous visitor locations from discrete sensor data. ......................35

5 RESULTS ...................................................................................................................................37

5.1 OVERVIEW ............................................................................................................................375.2 ALGE DATA ........................................................................................................................375.3 TRAFFIC COUNT DATA AND VISITOR ESTIMATES .................................................................375.4 EXAMINING ALL THE VARIABLES ..........................................................................................405.5 EXAMINING TIME..................................................................................................................415.6 REGRESSION IN EXCEL..........................................................................................................435.7 ORDINATION .........................................................................................................................435.8 ANALYSIS OF SEQUENCES.....................................................................................................455.9 VISUALISATION OF TRACKING DATA ....................................................................................485.10 DEMOGRAPHIC DATA............................................................................................................485.11 SUMMARY OF RESULTS.........................................................................................................49

6 DISCUSSION OF RESULTS ...................................................................................................50

6.1 OVERVIEW ............................................................................................................................506.2 SUMMARY OF FINDINGS........................................................................................................506.3 LIMITATIONS.........................................................................................................................51

7 CONCLUSIONS ........................................................................................................................52

7.1 OVERVIEW ............................................................................................................................527.2 FURTHER ANALYSIS OF TWELVE APOSTLES DATA ...............................................................52

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7.3 IMPLEMENTATION OF TOURIST TYPOLOGIES IN RBSIM2 .......................................................527.4 INTEGRATION WITH ADDITIONAL DATA.................................................................................53

PROJECT DIARY AND TIMELINE..............................................................................................54

SELECTED REFERENCES AND BIBLIOGRAPHY ..................................................................57

APPENDICES CD .............................................................................................................................60

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TABLE OF FIGURES

FIGURE 2.1 TOURISM PLANNING APPROACH: PHYSICAL & SPATIAL (HALL & PAGE, 1999)...............9FIGURE 3.2 TWELVE APOSTLES CLIFF STACKS (WEALANDS, 2002).................................................18FIGURE 3.3 TWELVE APOSTLES SITE LOCATION (PARKS VICTORIA, 1998) ......................................19FIGURE 3.4 TWELVE APOSTLES STUDY SITE - AERIAL VIEW ...........................................................20FIGURE 3.5 MAIN VIEWING PLATFORM (ITAMI, 2002) .....................................................................21FIGURE 4.1 ALGE TIMING SYSTEM .................................................................................................24FIGURE 4.2 ALGE ANKLE BANDS....................................................................................................25FIGURE 4.5 DEMOGRAPHIC DATA RECORDED ..................................................................................28FIGURE 4.6 DATA COLLECTION POINT..............................................................................................29FIGURE 4.7 DATABASE DESIGN ........................................................................................................30FIGURE 5.8 TOURIST SEQUENCES IN DATABASE ...............................................................................31FIGURE 5.9 POSSIBLE PATHS OF TRAVEL BETWEEN ALGE SENSOR PADS .........................................31FIGURE 4.10 INTERPOLATION OF CONTINUOUS DATA.......................................................................35FIGURE 5.1 DAY VISITOR ESTIMATES AND FIELDWORK DATES........................................................38FIGURE 5.2 HOURLY VISITOR ESTIMATES ON FIELDWORK DATES....................................................39FIGURE 5.3 PROPORTION OF PEOPLE TRACKED.................................................................................39FIGURE 5.4 TOTAL PEOPLE ON SITE COMPARED TO TIME SPENT ON VIEWING PLATFORM ...............40FIGURE 5.5 GRAPHS TO VISUALISE MOVEMENT PATTERNS..............................................................41FIGURE 5.6 TOTAL TIME SPENT ON SITE...........................................................................................41FIGURE 5.7 TIME SPENT BETWEEN SENSOR PADS ............................................................................42FIGURE 5.8 TIME SPENT BETWEEN SENSOR PADS ............................................................................42FIGURE 5.10 ABSENCE OF CLUSTERING IN TIME DATA.....................................................................44FIGURE 5.11 APPARENT CLUSTERING IN TIME DATA .......................................................................45FIGURE 5.13 TYPES OF MOVEMENT PATTERN IDENTIFIED AT SITE...................................................46FIGURE 5.14 TIME SPENT ON SITE BY PEOPLE EXHIBITING DIFFERENT MOVEMENT PATTERNS ......47FIGURE 5.15 PROPORTIONS OF PEOPLE EXHIBITING DIFFERENT MOVEMENT PATTERNS..................47FIGURE 5.16 PROPORTIONS OF PEOPLE EXHIBITING DIFFERENT MOVEMENT PATTERNS ON SEPARATE

DAYS ................................................................................................................................................48

ABSTRACT

The aim of this research was: To develop a typology of tourists via the geo-statistical analysisand visualisation of individual human movement data within a day-use recreational facility.

The research hoped to determine whether tourist typologies can be developed by trackingindividual people moving around at a tourist destination. Spatial tracking devices were usedto obtain detailed geo-temporal data of tourist movement at the Twelve Apostles, PortCampbell National Park. Subsequent analysis, using Excel, MS Access and PC-Ord, andvisualisation using Visual Basic and MapObjects, helped to identify statistical and spatialpatterns in the data. Identification of patterns in the data allowed for the classification of anumber of tourist typologies. The typologies were based on similarity between people withrespect to an assortment of variables. The study raised a number of important questions abouthow human behavior, specifically movement patterns within a network of walking paths, canbe meaningfully classified.

Valid micro-scale tourist typologies will help to improve agent-based simulations of tourismscenarios and assessment of management decisions at the site where they are generated, andpossibly at other similar sites.

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1 Introduction

1.1 Overview

This research involved tracking tourists at the Twelve Apostles site, Port Campbell NationalPark, Victoria, and developing tourist typologies from the data. Suitable technology wasneeded to track the tourists at the site. A range of statistical and spatial analysis wasperformed on the tracking data to determine if any common patterns of movement existed.Animated spatial visualisation of the data was also investigated as an alternative to statisticalanalysis for identifying movement patterns. The aim of developing accurate tourist typologiesis to improve agent-based simulation of tourism scenarios. Agent-based simulation is atechnology which is being used as a decision support tool for recreation managers to betterplan the development of tourist destinations in the face of increasing tourist numbers.

This chapter will discuss the changes in tourism trends that have lead to the need for thisresearch and cover previous efforts to develop typologies of tourists. It will also address theissues and complexities facing recreation planning and introduce the concept of agent-basedsimulations as a planning and management tool.

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1.2 Tourism

1.2.1 Tourism Trends

Tourism has been a popular activity for a number of centuries. It contributes a large amountof money to the global economy and accounts for millions of jobs worldwide. While theexact extent of the tourism industry is difficult to define, the significance of its financialcontribution to communities and economies internationally is well realised. This makestourism of primary concern to governments, the private sector, academics and the public.

There is a continuing increase in global tourism. Over 650 million people are travelinginternationally on an annual basis and this figure is expected to rise to 1,600 million in 20years time (Holden 2000). Aside from slight changes in tourism patterns as a result of theSeptember 11 disaster 2001, there has generally been an increase both in the number ofpeople visiting tourist destinations across the world and the distances which tourists aretraveling from their home countries. This trend of growing tourist numbers follows theoverall increase in population. The growth is further fuelled by an increase in the leisure timeavailable to many people and the increased popularity of tourism as a consumer good.

In Australia, the total number of overseas visitors has increased from around 2 million in1991 to greater than 4 million in 1999. The main purpose of the journey in 1999 was'holiday', as opposed to 'work' or other reasons, for 2.29 of the 4 million visitors (Bureau ofTourism Research, 1999). These figures indicate that tourism to Australia is following theglobal trend and increasing.

Along with the overall increase in tourism, there has been an increase in the range ofactivities in which people are partaking. More specifically there has been a shift towardsmore 'natural' experiences, fitting in with the concept of sustainable tourism. Theseexperiences are being catered for by the growing eco-tourism sector. The shift in peoples'preferred tourism activities can be seen as part of the shift to 'alternative tourism' which ismotivated both by environmental awareness and the desire to undertake tourist activitiesaway from the mainstream.

As tourism trends continue to change, we must better understand the nature of touristactivities, to reduce the impact of increasing tourist activities on the environment.Understanding tourist behavior better will help to develop tools to help us effectively andefficiently manage our resources and improve the tourism experience.

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1.2.2 Sustainable Tourism

Tourism involves human interaction with a broad range of environments, from national parksand beaches to archeological site and cities. These tourist interactions have noted effects andimpacts on these environments. As the modern environmentalist movement continues togrow, as it has done since the late 1960s, the effects of tourism on the environment are ofincreasing interest to a range of parties. The environment is seen by many as somethingprecious which we must preserve and protect. The environment can also be seen as an assetessential to tourism. Given the economic contribution of the tourism industry it is acceptedthat the environment must be preserved to protect the industry. For these reasons, bothenvironmentalists and private sector, along with academics and governments have a commongoal in understanding the impacts tourists have in environments and in preserving thoseenvironments.

Along with the increase in environmental awareness over past decades, has come a generalshift towards 'sustainable development'. Sustainable development is development which isconsidered economically, socially and environmentally sustainable. Sustainable developmentis being considered in many industries, including tourism. Sustainable tourism has becomethe focus of many stakeholders who realise that the conservation and preservation of physicaland cultural environments is integral to the tourism industry. Since the Rio Earth Summit(1992), there has been as steady increase in awareness of the importance of environmentalissues for travel and tourism. Governments, industry and academia around the world haveundertaken research and implemented actions to try to ensure that travel does not impactadversely on the natural, human or built environment (World Travel & Tourism Council,2002).

1.2.3 Tourists

There are a wide range of tourist types that behave in different ways and have differentpreferred experiences. Demographics, culture, lifestyle, level of education, beliefs andattitudes are all believed to influence tourist choices and behavior (Holden 2000). People visitplaces which provide favorable perceptions (Holden 2000) but this perception varies fromperson to person, changes over time and can be difficult to predict. "The fact that tourists aredifferent means that their interaction with the cultural and physical environments of theplaces they visit will vary." (Holden 2000, p40). There have been a number of macro-scaletourist typologies developed over the years. These typologies generally categorise touristsbased on broad characteristics, such as income and world views, which researchers such asCohen (1972) believe define certain aspects of tourist behavior. Typologies have attempted todescribe tourist behavior for marketing and other purposes but there is little understanding ofactual tourist behavior or movement at particular destinations on the micro-scale.

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1.2.4 Summary

As tourism increases, tourist behavior diversifies and, more importantly, tourists tend towardswanting to spend more time enjoying unspoilt, natural environments, there are a number ofnew tasks required to manage the resources sustainably and provide the desired experiences.An effort needs to be made to understand how and why people wish to interact with naturalenvironments. Once the nature of tourist behavior at particular tourist destinations is betterunderstood it will be possible to model the effects of further increases in tourism at thosedestinations. This will help to manage and develop these resources and ensure that people canenjoy natural environments for tourist purposes well into the future.

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1.3 Recreation Planning & Resource Management

The increase in tourism and shift towards sustainable development has created increasingpressure for efficient, effective recreation planning & resource management. There are a largenumber of recreation environments, from beaches to snow fields and national parks, all withspecific planning needs. As the utilisation of these environments increases and facilities arepushed to their limits, there is a real need for good planning. It is no longer acceptable toimplement 'band-aid' solutions to capacity problems. Prospective planning strategies need tocater for projected tourism trends and to be implemented only once their validity has beenanalysed.

National Park management is becoming an increasingly multifaceted problem. As with manytourist activities, there are a growing number of national park visitors and more diversifiedpark uses are arising. These factors have created a need for effective and efficient decisionsupport tools to assist park managers to administer resources, assess planning decisions, caterfor an increased range of recreators, avoid user conflicts and minimise negative impacts onthe environment. Growing environmental awareness and recreation activity participation hasmade solutions to park management problems an issue that is being prioritised bygovernment, academics and recreational managers alike.

Traditionally, user surveys, simple traffic count methods and manager expertise have beenthe main sources of information for assessing national park user experiences, crowding,movement patterns and impacts. As park usage increases, management decisions becomemore complex, the variety of user types and other factors are creating situations which arealmost impossible for a manager to predict (Itami & Gimblett 2000). As a way of dealingwith the increasing complexity of the problem, agent-based simulation models are being usedto help model the human usage of parks to assist managers to model current situations andpredict the effect of management decisions (Bishop and Gimblett, 1998, Gimblett et al. 1996,Gimblett et al. 2000, Itami & Gimblett 2000, Itami et al. 2002.)

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1.4 Agent-Based Simulation and RBSim

Agent-based modelling and simulations are technologies which have been of integral interestto Geographic Information Systems (GIS), simulation, visualisation, management and othercomputer science fields since the 1990s. While research to date suggests that agent-basedsimulations will provide significant advancement in a range of disciplines, there are stilllimitations in using agents to represent complex beings, such as humans as they are an'inherently static representation of spatial patterns, though many of the processes they areused to model are quite dynamic' (Fik 1997). This research will address the issue ofmodelling human behavior. This the next step in agent-based modelling and simulation oftourist behavior.

The purpose of agent-based simulation in tourism research is to predict consequences ofmanagement decisions on visitor flows and analyse encounters within a defined network inan outdoor recreation setting (Itami & Gimblett 2000). Simulators do this using autonomousagents which move about in a virtual landscape, such as a network of paths stored in a GIS.An autonomous agent is a computer simulation which is based on concepts from ArtificialLife (Langton 1996) research. An agent-base simulation is 'a system situated within, and apart of, an environment that senses that environment and acts on it, over time, in pursuit of itsown agenda and so as to effect what it senses in the future' (Franklin & Graesser 1996, p1).Agents in simulation make decisions based on other agents and factors within the system. Asan example, some agents within a recreation simulation of a national park might choose awalking track based on the fact that it has other agents on it, preferring to spend time inpopulated areas, while others may avoid proximity to busy areas and choose to be moreadventurous.

Shechter and Lucas (1978) described simulation for recreation management as a replacementof the 'trial and error' method of planning. In the past, due to lack of analytical tools, trail anderror was the way in which a lot of recreation planning proceeded. Simulations provide amore effective, and less costly solution to planning problems. Shechter (1978) stresses thatsimulations give probable results of levels of use and aid decision making processes, but it isstill the responsibility of managers to evaluate results given by simulators and make theactual decisions.

The purpose of agent-based simulation is not to replicate human behavior per se, but ratherthe expectation is that groups of interacting artificial agents can adequately simulate thebehavior of a unit, such as an army platoon or a group of tourists (Garson 2001). Gimblett etal (2000) found that using agents to represent individuals or parties, incorporating GIS torepresent the environment and utilizing intelligent agent technology in modelling, presents a

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number of important advantages for recreation modelling and management. Advantages ofsimulating tourist groups could include being able to:

• evaluate the cascading effects of the flow of visitors though a sequence of sites• estimate the effects of increasing visitor flows• assess whether designed capacities for parking, visitor centers, roads etc can

accommodate projected visitor numbers• plan for conflicts between different recreation modes• determine impacts on the environment• ascertain seasonal effects, such as day length, on design

(Gimblett and Itami, 2000)

The agents used in recreation simulations to date, have been programmed with way-findinglogic that determines what decisions they make as a simulation is run. In the past a range oftypical trips have been defined and agent behavior has been based on user feedback and otherfield data. While there is potential for agent-based simulation to become an integral part ofpark management decision making, a particular concern of systems developed to date is thelack of sufficient tests to confirm the veracity of trip projections provided by simulationmodels (Gimblett et al. 2000). The further development and implementation of thistechnology requires 'realistic' agents and typical trips to be developed to ensure the validity ofsimulation applications. This research attempts to overcome limitations caused byincomplete, subjective data by attempting to build agent typologies using more detailed, real,tourist movement data.

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2 Literature Review

2.1 Recreation Planning & Management

2.1.1 Overview

There is little doubt that the expectations, requirements and nature of recreation planning andmanagement have changed over the past decade. Parks Victoria (2002) state that managersare now required to provide for a range or recreation activities in different settings and mustconsider how well each environment will cope with each use. It is these new planning needs,in response to diversified uses and growing concern about the environment, that haveprompted the need for this research. Parks Victoria state that recreation planning aims toachieve a balance among resources, community demands and management objectives. Thisresearch aims to further our understanding of tourist behavior in a way which can improvedecision support tools to help to meet these aims.

2.1.2 Review

Planning for tourism has traditionally focused on land-use zoning, site development, thepresentation of natural tourist features (Getz 1987, in Hall and Page, 1999). Hall and Page(1999) note that in recent years tourism planning has adapted and expanded to includebroader environmental and socio-cultural concerns. They note a number of spatial andphysical approaches to modern tourism planning (Table 2.1). The range of spatial factorsassociated with recreation planning make it a discipline that can benefit from technologiessuch as GIS. Future tourism planning, whether it be on global, local or other scales shouldutilise the ability of GIS and associated Spatial Decision Support Systems (SDSS) tosimultaneously analyse a number of spatial phenomenon.

Figure 2.1 Tourism planning approach: Physical & Spatial (Hall & Page, 1999)

Ryan (1991) discusses recreational management techniques in terms of macro and micro-techniques. Micro-techniques relate to the management of flows of people within a tourist

Physical/Spatial

TOS (tourism opportunity spectrum)

destination life cycles

perceptual studies

development defined in environmental termspreservation of genetic diversity

manipulating travel patterns and visitor flows

physical carrying capacity

visitor managementconcentration or dispersal of visitors

perceptions of natural environment

environmental conservation

tourism as a spatial and regional phenomenon

tourism as a resource user

wilderness and national park management

designation of environmentally sensitive areas

Some examples of related methods

Some examples of related models

regional planning

spatial patterns and processesphysical impactsresort morphologyLAC (limits of acceptable change)

ROS (recreational opportunity spectrum)

ecological basis to development

ecological studiesenvironmental impact assessment

Planning Tradition Underlying Assumptions and related attitudes

Definition of the tourism planning problem

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zone. Ryan identifies a number of micro-techniques such as restricting entry to a site, signposting, planning of paths, and changing access points. Ryan also identifies the majorproblem in site management being that of changing patterns of established behavior. Furtherstudies in the area of recreation site management should attempt to qualify and quantify theprecise nature of use of tourism sites. This will give valuable insights into whether sites arebeing used as previously hypothesised. If patterns of actual behavior and usage can be wellunderstood and modelled, future planning will benefit.

Ryan (1999, p129) notes that:'...the management of tourist and recreational areas is not simply the technical one ofassessing visitor numbers, planning footpaths, calculating the number of car spaces.The tourist areas carry with them a heritage and a received perception of roles andfunctions. It is this aspect of the tourist site life cycle that perhaps needs examination.'

It is insufficient to assess tourism usage via simple counts and surveys, as has been done inthe past. There is a need for a more detailed spatial usage data that will provide a betterunderstanding of the complex nature of user patterns.

Hall and Page (1999) note that studies examining the socio-economic characteristics ofrecreators have been common but that there have been gaps in the examination of touristbehavior. Hall and Page (1999) includes discussion of the research done by Glyptis (1981)into the spatial distribution of site use by recreators at a 242 ha area of grassland in ruralUnited Kingdom. Glyptis' study involved using dispersion maps, to locate visitordistributions in time and space, and traditional social survey methods to analyse visitorbehavior. From this a range of distribution maps were developed. This study resulted in noteddifferences between the behavior of local users and others. Additionally, nearest neighboranalysis was used to ascertain levels of tolerance of proximity to other groups,acknowledging that this tolerance may be a cultural factor that may vary. There is a need fordetailed research examining tourist movement along restricted path networks.

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2.2 Agent-based Simulation

2.2.1 Overview

Agents and multi-agent systems are one of the most prominent and attractive technologies incomputer science. Agent-based simulations have been used in an increasing range ofapplications. They are a promising technology for analysing problems and designing systems.While there is general excitement about the potential applications of agent-based simulations,it is widely acknowledged that advances are required in agent architectures, theories anddesign techniques (AAMAS, 2002). Research into using agent-based simulations forrecreation planning should acknowledge these deficiencies in argent architectures to date andcontribute to the advance the theories behind intelligent agents and their programming.

2.2.2 Applications of Agent-Based Simulation

The applications of agent-based simulations are diverse and include modelling of traffic andpedestrian systems. Automobile traffic modelers model their traffic macroscopically ascontinuous flows or as individual vehicles. In recent years, traffic simulation has turnedtowards discrete event and individual-based models. Increases in computing power have leadto more interest in exploring the dynamic nature of automobile traffic. Tourism researchshould follow these trends mentioned in Box (1998) and take advantage of developments inspatial modelling algorithms, and computer power, to simulate the complex individual humanmovement patterns.

TRANSIMS a transport modelling simulation, models the behavior, movement andinteraction of up to 200,000 individual travelers (Haklay et al. 2001). The agents individualbehavior is derived from their social-economic profile. Haklay et al. (2001) use socio-economic data to populate their model STREETS under the premise that this will create avariety of agents whose behavior is likely to be different from one another. If spatial patternsare detected in the tourist movement data obtained in future research, they should becompared to socio-economic or demographic data to determine to what extent these factorsactually contribute to people's behavior. This will help to validate or reject some of the waysin which tourist agents have programmed to date.

Haklay et al. (2001) suggest that past approaches to modelling pedestrian movement haverarely been successful at a street or building scale. They blame this on an absence of adequatedata at the required level of detail and some of the underlying assumptions. They suggest thatpast attempts have been suited to modelling general patterns of movement but not themovement of individuals.

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Haklay et al. (2001) also identify conflict in arguments about what the main generator ofpedestrian movement is. Some argue that it is the street or path network itself that determinesthe patterns of movement. Haklay et al. (2001) suggest that it is actually the location ofattractions or site of interest on these networks that govern pedestrian flow, for examplearguing that a street configuration can remain the same, but if all the shops close down, thepedestrian patterns will change. Examination of whether the attractions of the study sitethemselves, or the presence of other tourists (i.e. crowding) are major determinants in theflow of pedestrians is required.

2.2.3 Agent-Based Simulation for recreation management

Agent-based simulation for national park management is a developing field of research. Anumber of projects have been undertaken, applying the technology to a range of parkscenarios. These projects have reported successful preliminary progress but acknowledge theneed for more 'realistic' agents (Gimblett et al. 2000, Bishop & Gimblett 1998). It has beensuggested that the next step in this research is to verify agent-based modelling at a particulartourism site .

Bishop & Gimblett (1998) state that effective management depends on knowing what visitorsare seeking, how they will behave and how behavior may be modified by the presence ofothers, or by particular management strategies. Some of this information can be gathered bytraditional visitor surveys. Management of visitor behavior however, depends upon modelswhich allow for interaction between visitors, allow for changes in movement patterns or timeallocations. In response to this need for more detailed user data, research in this field shouldbe based on data which can model the complexities of visitor behavior accurately.

RMIT University is currently undertaking a project into patterns of tourist use at PortCampbell National Park (RMIT, 2001). They have acknowledged the lack of data pertainingto tourist behavior, such as duration of visits and speed of movement through the park. Theyalso found that where such data does exist it is not in a form from which individual behaviorsequences can be ascertained, and, as such meaningful behavior-based classifications cannotbe performed. RMIT aim to develop a typology of visitors using annotated maps (given totourists to complete with details such as times and feelings at locations) and GPS. There is aneed for research which can fill the gap in tourist behavior data by obtaining detailed spatialtracking data of the tourists. While annotated maps may provide interesting insight into someaspects of tourist behavior, it may be subjective data, of interest to sociological studies oftourist behavior but inappropriate for spatial analysis. GPS receivers can capture detailedtracking data of tourists but as they are intrusive devices, their presence may alter touristbehavior. Care needs to be taken in selecting technologies and methods of obtaining datasuitable for analysis of spatial patterns.

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Previous agent-based simulations of recreation environments have used a range of methodsfor capturing tourist behavior. The simulation developed for Grand Canyon RiverManagement by Gimblett and Daniel (2000) based its tourist behavior, or 'typical trips' ondetailed diaries/itineraries filled out by tourists and guides. Gimblett and Daniel acknowledgethat their database was not adequate for specification of the complex spatial processes whichoccur at the destination. Trip 'profiles' were generated from the diaries itineraries and wereused to generate the behavioral rules for the agents in the simulation. Research to datesuggest that trip diaries and similar methods are not sufficient to capture the spatial nature oftourist behavior. With this in mind the next step in this research might be to use purely spatialdata, such as the location of a person at any given time, to generate typologies or 'typicaltrips' as Gimblett refers to them.

It has been said that if agent-based modelling can be validated in particular tourism sites thenthe potential exists for extension to a more complete spatial decision support system (SDSS)(Bishop & Gimblett 1998) for recreation planning. If a tourist typologies can be developedfrom spatial data patterns of those tourists' movement, and verified in some way, it willincrease the potential of using agent-based models for recreation planning at many sites.

Research to date relies on a number of assumptions surrounding the validity of usertypologies and the concepts of typical trips, which are based on the premise that visitors havecommon patterns of use (Itami et al. 2002). This research aims to determine the extent ofpatterns of use and ascertain whether meaningful user typologies can be developed.

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2.3 Tracking Technologies

Research has been conducted into tracking and simulating spatial movement for a range ofpurposes and on a range of scales (Haklay et. al, 2001). Human and animal tracking is ofinterest to many, and as such a number of technologies and algorithms exist in this discipline.Some technologies focus on counting numbers, or tracking individuals. More recently, effortsto track multiple people or animals have been developed.

Past research has involved tracking human movement* for purposes such as securitymonitoring, evacuation procedures and pedestrian detection (Zhao and Thorpe 2000). Arange of these methods rely on using video cameras. Some of these methods fall short ofcounting pedestrians due to non-predictable trajectories which are difficult to detect. Othermethods, such as Heikkila and Silven's (unknown), are able to determine counts ofpedestrians passing the camera, trajectories and velocities. Other researchers, includingHaritaoglu et. al (1998) and Bregler and Malik (1998) have developed ways to overcomeproblems caused by shadows, occlusions and illumination changes to successfully trackpeople using real-time stereo video footage. Most of this research has involved trackinghumans from video cameras viewing humans as they pass the field of view. In terms oftourist tracking, where the path taken by each tourist through the entire site is of interest,these methods would be cumbersome and expensive, requiring a number of video cameras,and a lot of post processing to obtain a workable data set. Privacy issues also need to beconsidered when visually tracking people.

Pers and Kovacic (2001) note the lack of research into the accuracy of human trackingmethods. Their research involved tracking people in the partially controlled environment of asporting field. By capturing the video footage from above the sporting ground in stereo,accurate locations for players were able to be obtained. For a small area, such as a basketballcourt, this method is workable. The images can be rectified and accurate positions of allplayers can be obtained. For an outdoor recreation site, with a much greater area, this sort ofanalysis, while appropriate on many levels, will not be possible until satellite imageresolutions and repeat cycles greatly improve.

Haklay et. al (2001) state that for the purpose of programming simulations, physical countsand time lapse photography methods have been used in the past to track individuals. Theyused 'physical' counts, recording the number of people passing a set point, to get an idea of

* This paper will use the terms 'human movement' and 'pedestrian movement' interchangeably to refer to the

movement of a human from one place to another, generally by walking, rather than the specific movement oflimbs or the head.

15

flow though the network. They suggest that paths of individuals movement can be derivedfrom the time lapse photography. Knowing the precise time of people arriving and leavingpoints of interest, travel speeds, directions traveled, sequence of locations visited and howmany people act in certain ways will contribute to creating more intelligent models. Whilethis could be done by manually identifying people in footage from different cameras andinferring paths between the camera stations it should be possible to do using spatial trackingmethods which will provide paths of individuals movement for all people without theequipment and post processing that time lapse photography would require.

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2.4 Tourist Typologies

A range of tourist typologies have been generated by different researchers. These typologiesgenerally help to describe groups of tourists with specific purposes, such as marketing, inmind.

Tourism Victoria has identified ten macro-scale tourist groups types (Parks Victoria, 2002).These typologies are based on a range of factors including demographics, lifestyle andattitudes. They range from 'Socially Aware', who are social issues oriented,politically/community active, arts and culture, top jobs, wealth managers to 'TraditionallyFamily Life' who are retired middle Australia, family focused lives, cautious of new things,passive income investors

These divisions help in the marketing of tourism by private and government sectors. They areways of describing tourist spending behavior and can help ascertain the destinations peoplewill choose. They do not describe how the tourist will behave physically once they are at aparticular destination. Factors such as the speed of tourist travel, whether they will avoidcrowds or queue for attractions and other spatial aspects which are of importance forrecreation planning, are not yet well understood. Nor are they described by existing touristtypologies. Determination of the spatial behavior of tourists may or may not be related toexisting typologies, but should be more relevant for recreation planning.

Ryan (1991) covers a range of psychological motivations for tourism (developed by Cohenand Taylor 1976, Crompton 1979 and Mathieson and Wall, 1982) and some profiles of touristtypes (Cohen 1972). These tourist profiles vary from 'The Organised Mass Tourists', who arethe least adventurous and make few decisions about their holiday to 'The drifters, who willshun contact with other tourists and identify with the local community'. These definitionsgive us some indication about how tourists might act. They differentiate between people whowill disembark a tourist bus, take a photo, and re-embark and people who will explore thedestination more thoroughly. Ryan (1991) acknowledges that while Cohen's categorisationcreates easily recognised types, it is not know if they actually reflect the complexities oftourist behavior. What is required for recreation planning, is verification of how touristsactually act in certain sites.

Shechter and Lucas (1978) developed an agent-based simulation of a wildernessenvironment. The agents within their simulation had user characteristics based on party type,either walking or horseback riding and party size, either small, medium or large. Where eachparty type made different choices depending on the period of day and type of week withrespect to their:

• likelihood of selecting a certain route

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• speed of travel (randomly generated)• time spent at camps• reactions to visual encounters

Shechter and Lucas (1978) acknowledge that it might be useful to define several classes ofparties not only by these type-size categories, but also by their speed. More verification ofwhat factors influence group and individual behavior in wilderness environments is neededbefore characteristics for simulations can be meaningfully defined.

2.5 Conclusions

Existing models of tourist behavior have been based on insufficient or inappropriate data setsand as such have failed to predict future patterns of use and assess planning decisions with anacceptable degree of accuracy. This poses the question of whether simulations can predictfurther patterns if they are based on more detailed data initially. There are also criticisms ofthe validity of using agents to model intelligent life forms. Longitudinal studies are requiredto test the accuracy to which models can predict future recreation usage. These models shouldbe based on detailed usage data.

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3 Study Site

3.1 Introduction

The Twelve Apostles site is a part of the Port Campbell National Park, which is located onthe Great Ocean Road, in Western Victoria, near the township of Port Campbell.

The Twelve Apostles are a series of high cliff stacks resulting from the weathering of the softlimestone coastline (Figure 3.2). The cliff stacks have been formed as a result of geologicalprocesses which have been occurring over many million years (Parks Victoria, 1999). Thesite is surrounded by other similarly spectacular geology such as Lochard George and GibsonSteps. (Figure 3.3).

Figure 3.2 Twelve Apostles Cliff Stacks (Wealands, 2002)

Port Campbell national park and the Great Ocean form one of Victoria's most popular touristregions, being the most visited area after Melbourne. The Twelve Apostles, along with theLoch Ard Gorge are considered to be the most significant attractions within this region. Thepark attracts an increasing number of visitors each year. Around 2,100,000 visitors wererecorded in 2001-2 and 2,800,000 are predicted for 2006-7 (Parks Victoria, 1998).

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Figure 3.3 Twelve Apostles Site Location (Parks Victoria, 1998)

3.2 Site Description

The Twelve Apostles site comprises a car park, visitor center and landscaped walkways(Figure 3.4). There is no water level access from this site which is perched around 60m abovethe sea level. The site has been developed to accommodate the large numbers of tourists(700-6000 per day depending on season). The site is a day-use facility, which ispredominantly used for short visits (less than an hour) and has no overnight facilities. Themain tourist activities at the site are walking and photography. The site has a simple networkof paths totaling approximately 600 meters in length.

A number of deficiencies in the current site design have been identified. These include heavyuse of the main viewing platform (Figures 3.4 & 3.5) in peak times. Other issues includeunacceptable levels crowding on stairways within the site. During summer and autumn carpark facilities, walking tracks and lookouts often have their capacities exceeded creating parkmanagement issues (Parks Victoria, 1998).

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Figure 3.4 Twelve Apostles Study Site - Aerial View

The Twelve Apostles site has been developed substantially in the past 3 years in response tothe increase in visitor numbers. Developments have included wheelchair access and otherfacilities but the site is still over used at times. Due to the importance of the site, both as asignificant tourist site for Victoria and as an important source of income for surroundingtownships, it is essential for the site to be planned and managed carefully in the future. ParksVictoria's management strategies for the site include:

• establishing programs to monitor visitor numbers and visitor satisfaction and trafficcirculation as a basis for future research and planning of visitor facilities.

• developing models to predict likely impacts on visitor satisfaction and the naturalenvironment of alternative visitor management strategies.

To date counts of traffic entering the site's car park and basic assumptions about userbehavior have been the most sophisticated data used for site planning.

As mentioned, the main viewing platform at the Twelve Apostles is identified as beingheavily used. This platform is accessed by wooden walkways and stairs in one direction(Figure 3.5). The stairs are the first opportunity for visitors to view the Twelve Apostles andtake photographs. Itami (2002) states that where stairways have access to views there shouldbe room for pedestrians to stop and look at the view while allowing others to pass. This is notthe case at the at this site and the stairs causes a bottleneck in visitor flow. The walkways andplatforms were designed to meet Level of Service1 standards but their use has proved them to

1 'Levels Of Service' are levels of space (m2), flow rate (ped/min/m) and speed (m/s) recommended by the HighwayCapacity Manual (Itami, 2002)

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be undersized. Itami (2002) states that this is an example of service standards needing to bereconsidered when used in outdoor recreation contexts.

Figure 3.5 Main Viewing Platform (Itami, 2002)

The way in which the Twelve Apostles site is currently used could not have previously beenpredicted. The bottlenecks and crowding were unexpected. Further development at the siteshould utilise advances in technology to assess planning decisions more thoroughly.Obtaining detailed visitor numbers, average duration of stays, tourist behavior, patterns ofuse, growth estimates and other information should assist the management of the site andimprove tourist experience. As well as being a major issue at Port Campbell, crowding issuesare likely to have consequences at other sites owing to the steady increase in tourist activity.

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4 Methodology

4.1 Overview

This research involved a range of field work, data reduction and analysis. A number ofquestions were addressed, which involved the following stages:

• Study site - reconnaissance and preliminary GIS development• Agent-based simulation - familiarisation• Data capture requirements, technology and design - individual movement data• Data capture - individual movement data• Ancillary data capture - path dimensions and traffic counts• Data reduction - individual movement data• Database design - individual movement data• Data analysis - individual movement data and traffic count data• Visualisation - individual movement data

4.2 Study Site -Reconnaissance and Preliminary GIS Development

Initially, to gain an appreciation of the nature of the Twelve Apostles locale a number of sitevisits were undertaken and a GIS of the site was developed. The site visits involved visuallymonitoring the flow of tourists through the site and roughly mapping the path and facilitylayout. This formed an invaluable reconnaissance for the project. Following the site visits,spatial data such as path and road networks and aerial photographs were obtained. A GIScontaining this data was developed with which preliminary spatial analysis, such as length ofpaths and possible sequences of movement through the network, was carried out.

4.3 Agent-Based Simulation - Familiarisation

The next stage was to become familiar with agent-based simulation, particularly RBSim2

software. The main purpose of this research was to determine whether agent behavior couldbe improved via observing and analysing individual movement data. As such, it wasnecessary to become familiar with the nature and design of RBSim. It was important tounderstand how the simulation functioned and more specifically what information the agentswere currently programmed with. This would allow for more informed data capture andanalysis for this research.

RBSim programs agents with a series of rules. Agent-types are based on what sort of sitesthey value i.e.: historical or natural, and determine what speeds they traveled at and whatdecisions they make. For example, some agents will queue and wait to see an attraction if it is

2 RBSim is an agent-based Simulation software developed by GeoDimensions Pty Ltd

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already full, while others would move on and return later when it was quieter. Behavioralfactors of interest for improving the intelligence of the agents would be:

• the speeds at which people travel on different terrain• the influences on tourists decisions' to chose one path over another one• the time different people spend at different sites• the sequence people visit sites in

These factors were kept in mind when collecting and analysing tourist movement data.

4.4 Data Capture Requirements, Technology and Design

4.4.1 Overview

A method for capturing the movement data, and a decision on the technology to use wasrequired at an early stage in the research. Site trips, analysis of aerial images and GIS datawere influential in developing the data capture methodology.

4.4.2 Requirements

The main aim was to obtain spatial data of tourist movement through the site. There were anumber of technologies that could fulfill this need. A second requirement was to obtain a richdata set, on which a large amount of analysis could be performed. System cost, data types andusability were other factors that needed to be considered.

4.4.3 Technology

4.4.3.1. Global Positioning System (GPS)

Handheld Global Positioning System (GPS) receivers could have supplied continuous touristmovement data. It is believed that the obtainable accuracy and spatial nature of the datawould have been sufficient for the requirements of this research, although it may have beenoverly complex to analyse. Conversely, it was believed that the amount of data that could becaptured using the limited (due to availability and cost) number of GPS receivers would beinsufficient for the subsequent analysis required. GPS receivers are quite obtrusive and mayhave altered the behavior of tourists.

4.4.3.2. ALGE Timing System

Another spatial tracking technology, the ALGE timing system , which provides discrete data,was selected for the research. The ALGE timing system (TdC 8000 or equivalent) is systemcommonly used in sporting events which can record the identity and time-stamp ofindividuals at a range of discrete locations using detectors and unique wristbands. Thissystem comprises sensors, which are fixed at locations of interest, data loggers (Figure 4.1)and receivers (with unique codes), which are fixed to people's ankles (Figure 4.2). Thissystems then logs the time and ID every time a person crosses the sensor. The system differsfrom some other tracking methods in that it logs a unique ID for each person. This provides a

24

more meaningful data set than other methods such as Infrared counters which just count thenumber of people to pass a point. It provides similar data to video tracking but without thepost processing required to differentiate between individuals. The ALGE system, which istypically used for Triathlons as a means of timing competitors through the various stages ofthe event, is well suited to the need of this research.

Figure 4.1 ALGE Timing System

4.4.4 Data Capture Design

Using the site's path networks, a design for placement of the six ALGE sensors wasdeveloped. The aim of the sensors is to capture all movement through the site. Initially it wasproposed that the sensors be placed at each of the path intersections. Analysis of movementflows though networks (Zijpp & Tebaldi, 1997) use counts at network nodes. Their analysisassumes no travel-time complications and considers only the number of items between eachpair of nodes at specific time intervals. Placement of sensors at path intersections wouldcapture all possible movement combinations along the arcs (Appendix 1.1). This wouldallow for continuous paths of movement to be inferred later, based on time, distance between

25

each sensor and a constant speed assumption. The station where bands would be handed outand collected would act as an additional sensor, or node, as band ID and time would berecorded for each person at this location on arrival and departure.

Figure 4.2 ALGE Ankle Bands

Due to other factors of interest in the movement patterns, aside from just the order of pointsvisited, it was proposed that a sensor be placed either side of the main viewing platform, toobtain quantative data on the number of people on the platform at any one time. Effectivelymaking the platform another arc in the network, with a node on either side. Information formthis set-up could aid in determining whether crowding in this area effected the movementpatterns of visitors elsewhere in the park. This lead to a new design for sensor locations beingdeveloped (Appendix 1.2).

Owing to technical problems on the field work dates, the number of sensors available was 5and not 6. This lead to a reassessment of the sensor placement design. The final design wasdeveloped to capture all movement patterns in addition to the number of tourists on the mainviewing platform, using 5 sensors (Figure 4.3, Appendix 1.3).

26

%U

%U

%U

%U

%U

%UHighway

Cliff EdgeCliff Edge

Cliff Edge

0 - Base

1 - Boardwalk Entrance

4 - Path Intersection

5 - Path Intersection

2 & 3 - Viewing Platform

20 0 20 Meters

N

Prepared by: Alice O'Connor

Figure 4.3 ALGE Sensor Pad Proposed Locations

27

4.4.5 Research Questions:

The questions to be answered in this part of the research were:• Can a plausible methodology for tracking individual human movement at the study

site be developed?• Which of the available data capture methods, ALGE timing system or GPS, provides

data most appropriate to the research being undertaken?.

4.5 Data Capture - Individual Movement Data

Data capture involved using the ALGE timing system to capture the bulk of the data onwhich the research was based. Field work took place over three days from 18/07/2002 to20/07/2002. Five ALGE receivers and 300 ankle bands were used. Hand held GPS receiverswere used by research staff, rather than tourists, to provide additional tracking data whichwas used as a point of comparison to the ALGE timing system data.

4.5.1 ALGE Timing System Data Capture

Sensors were placed in their proposed locations (Figure 4.3). Their relative positions, at pathintersections, were noted and stored in the GIS. Analysis of movement patterns would bebased on knowing which path segments tourists used and for how long. For this reasonprecise locations of sensors was not a major concern for this research.

At the commencement of each data collection period, the times for all the data loggers weresynchronized. They were set 00:00:00 and the actual time (EST) was recorded. There were 5data collection periods, namely Thursday AM, Thursday PM, Friday AM, Friday PM andSaturday AM. The data was downloaded after each of these periods to avoid data loss,recharge batteries and run system checks. This meant that there were breaks in the datacollection for the middle of each day.

Figure 4.4 ALGE Timing System Data

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ALGE timing data was produced by the data logger at each sensor pad. This gives data in theform: ID, Time, (Figure 4.4) where the ID is that of the ankle band. Five text files per datacollection period were produced, with the exception of the Saturday when sensor 2 was notfunctioning(Appendix 2)

4.6 Ancillary Data Capture - Path dimensions and Traffic Count

4.6.1 Demographic Data Capture

Visitors were approached on entering the park and asked whether they were willing toparticipate in the study. They were provided with plain language statements describing theresearch project. Willing participants were fitted with ALGE ankle bands. A few basicattributes were recorded for each participant (Figure 4.5). An arival time and departure timewere also recorded here, making the point effectively another sensor (Figure 4.6, full versionAppendix 3).

Figure 4.5 Demographic Data Recorded

4.6.2 Ancillary Tourist Number Data Capture

It was hoped that low resolution video cameras in locations of interest would provideancillary data for verification of interpolated tourist numbers and other comparisons at laterstages in the analysis. Unfortunately ethics clearance was not granted for this and instead aresearch staff member documented approximate crowds in areas of interest at 1 minuteintervals.

4.6.3 Path Areas

Path areas were determined by a basic survey of the site. Lengths and widths of the pathsegments were measured using a stylon tape and pacing. This data was obtained to attributethe path networks within the GIS. This information could allow for analysis of crowdingdensities within the site if required (Appendix 4).

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Figure 4.6 Data Collection Point

4.6.4 Traffic Counts

Traffic count data was obtained from Parks Victoria (Appendix 5). This data consisted ofvehicle numbers entering the site's car park in hourly blocks and covered a 12 month period.From this data, using the estimate of 3.116 people (Parks Victoria) per vehicle, total numberof people on site could be estimated and yearly visitor trends could be predicted.

4.6.5 Database Design

A relational database was developed to store the demographic and spatial data. The databasewould be queried to determine time between sensors, average time spent in certain areas,sequence of pads visited and other patterns or factors of interest. The data was divided up intothree main tables and some ancillary data (Figure 4.7). Including a 'Visitor Table', containinga unique Id for each visitor, their ankle band ID and their demographic data as recordedduring the field work. A 'Pad Table', containing a unique ID for each sensor pad and theeasting and northing of the pad, as extracted from the GIS. And a 'Tracking Table', whichcontained the person ID, ankle band ID, pad ID and time at each pad. This design allowed forefficient querying of the data, extraction of data for external software such as Excel and PC-Ord and required minimum manipulation of the original ALGE dataset.

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Figure 4.7 Database Design

4.7 Data Reduction

The ALGE data text files were converted into a suitable format for the database using Excel(Appendix 6). Dates were added to the files to differentiate between the three data collectiondays and real-times were generated to replace the relative times which ALGE produces.These files were then imported into the database (Appendix 7).

All ALGE data files were combined using append queries and then. Using a query based onankle band ID and time, a unique Person ID (from the demographic data) was added to thefile. This information was needed as the ankle bands did not provide unique identification.Only 300 ankle bands were utilised during the field work for 800 plus participants and somebands were used two or three times. The ankle band ID and time, did however provide uniqueidentification, as only one person could use an ankle band at any one time.

The time data collected on arrival and departure from the study site was incorporated into thedata set. This involved adding the 'Time In' and 'Time Out' information, which was loggedwith the demographic data (Figure 4.5) to the data set. These times were added with padnumbers 0 and 6 respectively. Even though these times were both recorded at the samegeographical location, allocating these numbers would allow for easy identification of entiretrips at a later stage. Once the AGLE files were combined into a database table and uniqueIDs were added, this created a table which, when sorted by Person ID, then time, would givethe sequence of pads visited by each individual (Figure 5.8).

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Figure 5.8 Tourist Sequences in Database

4.8 Analysing geo-temporal data.

4.8.1 Overview

Tracking data from the ALGE timing system data was converted into MS Access tables in theformat mentioned above. This data was queried and examined for spatial movement patterns.Data was exported for exploratory data analysis in Excel and ordination in PC-Ord. Thisanalysis determined factors such as: where visitors come from, where visitors go, what kindsof visitors or groups exist, types of visitor activities, duration of visitor activities and allowedfor the identification of common patterns occurring between users. The desired result of thisanalysis was to determine 'typical trips' and identify some simple typologies.

4.8.2 Database queries

Initially basic queries were run on the data to yield time between pads and data for individualtrips. A number of variables pertaining to visitor movement were generated. These includedtotal time spent on site, number of pads visited, sequence of pads visited and time spent incertain areas of the site.

Data was generated and graphed for the 19 different possible combinations of sensor pads(Figure 5.9).

Figure 5.9 Possible paths of travel between ALGE sensor pads

S e n s o r P a d ID 0 1 2 3 4 5 6

0

1

2

3

4

5

6

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Note that it was possible to travel from sensor 5 to sensor 5 due to it being the last pad beforea path extending out to the point (Figure 4.3). The time between crossing pad 5 the first andsecond time was inferred to be the time spent out on this point.

4.8.3 Basic Statistical Analysis

Ways of visualising trips using graphs were experimented with in Excel. Preliminary graphsand tables incorporated unique individual IDs, time and pad IDs. Following this moredetailed statistical analysis was undertaken.

Time between sensors was graphed as a preliminary point of comparison between visitors'movement (Appendix 8).. Analysis of time between pads for individuals aimed to determinewhat ranges of travel times or speeds existed for different path segments. Statistical analysisat this level gave the overall distribution of times spent between pads but didn't differentiatebetween individuals. This analysis could give an initial idea of whether the tourists werebehaving similarly or whether a number of usage patterns were emerging. Bimodaldistributions in histograms, for example, would suggest that two distinct patterns of use wereoccurring.

The total time spent on site was computed for each person surveyed. This information is ofparticular interest to planning and is the first detailed data of this nature obtained for the site.Traffic counts are obtained at the site year round, from which crowd estimates are derived,but this information involves arrival rates only, and doesn't give information on actual timespent on site.

Classification algorithms were also run on the spatial data. In Excel, R-squared values(square of the Pearson product moment correlation coefficient through data points) weregenerated to determine if there was correlation between the time different people werespending between pads. The aim of this was to determine if there were common movementpatterns. For example, some people might spend little time walking to the viewing platforms,then reside there for most of the time, while others might explore the whole site, spendingequal time is all areas. Alternatively the same people might spend extended periods of time atdifferent viewing areas. If these patterns did exist one point of interest was to see if peoplewith common movement patterns also had common demographic descriptors.

4.8.4 Ordination Analysis

Ordination of the data was carried out in PC-Ord software. The ordination technique usedwas Sorensen (Bray-Curtis). Ordination is typically used to determine similarity betweenecological communities. A number of variables, such as foliage cover, amount of sand,

33

presence of algae or many others are provided for a number of sites and the ordinationclusters the locations based on similarity between variables.

In terms of tourist tracking, the same technique was used, but variables such as time spentbetween sensor pad & time spent on site were utilised. The premise behind using ordinationfor recreation behavior analysis was that there might be 'clusters' of tourist types, with similarspatial behavior. If clusters were found they could contribute to agent-behavior programming,their demographic data could also be investigated to determine if people with common spatialbehavior had common ages, modes of transport or group types.

4.8.5 Research Questions

The main research questions lie in this area. They include:• Which methods for analysing spatial similarity, clustering and patterns can best be

applied to human recreation movement data?• Can meaningful recreation user typologies and typical trips be extracted from spatial

data tracking individual movement?

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4.9 Spatial Visualisation of Data

4.9.1 Overview

Patterns defining park user typologies were explored using database queries, statisticalanalysis and ordination of the tracking data. An important question was whether spatialvisualisation of the data could also reveal patterns in visitor movement. Following thestatistical analysis of the discrete sensor data visual patterns in the data were analysed.

4.9.2 Map Objects and Visual Basic for Visualisation

A Map Objects application, developed in Visual Basic by Andre Zerger (University ofMelbourne) was adapted to visualise the tracking data. The application plots a point basedon X and Y coordinates and time resulting from an SQL query of a database (Figure 4.11).

Figure 4.11 AgentView Visualisation Program

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4.9.3 Generation of Continuous visitor locations from discrete sensor data.

To visualise the ALGE tracking data, continuous representation of visitor movement wasgenerated. This representation was 'continuous' in the sense that it inferred the location ofvisitors in between the sensors, so that their 'movement' could be visualised. The pointsgenerated were never truly continuous but could be viewed at a range of time steps, creatingmovement information that appears 'continuous' for all intents and purposes. Thisrepresentation made the following assumptions:

people moved at a steady pace between sensors the path between sensors could be generalised as a straight line, or number of

straight linesThese assumptions were believed sufficient to represent the data in question.

The algorithm to generate the locations between sensors was developed using the followinginformation: At time t, for visitor v

• the last sensor passed over Sa(xa,ya)• the next sensor that will be passed over Sb(xb,yb)• the travel time between Sa and Sb tab• the change in x value from Sa to Sb ∆xab• the change in y value from Sa to Sb ∆yab• the distance in meters from Sa to Sb dab=√(∆xab2+∆yab2)Pythagoras• the velocity from Sa to Sb vab=dab/tab• the bearing of the line from Sa to Sb bab= arctan(∆xab/∆yab)• the x coordinate at time t xt=xa+vabt(sin(bab))• the y coordinate at time t yt=ya+vabt(cos(bab))

These equations required the sensor coordinates to be reprojected from longitudes andlatitudes. This was done using a Transverse Mercator Projection using the AustralianSpheroid.

Figure 4.10 Interpolation of Continuous Data

Sb(xa,ya)

Sb(xb,yb

∆yabdab

∆xab

bab (xt,yt)

N

36

Visual Basic code was written to generate coordinates between the sensors for the entire dataset at a given time increment using the above equations. (Appendix 9). These coordinateswere then stored in the database and queried via the visualisation program. The visualisationprogram allowed viewers to observe the actual behavior at the site over the three days of fieldwork.

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5 Results

5.1 Overview

A range of results were achieved by this research. Statistical and spatial analysis revealedpatterns of use and typologies were derived from the spatial movement data at the site. Thearrival at these typologies or 'typical trips' and the effectiveness of various analysis methodsfor finding spatial patterns will be discussed in this chapter. The identification of usertypologies begs the question of whether the typologies are typical of most sites or whetherbehavior is unique to this site. The implications of this will be discussed in the next chapter.

5.2 ALGE Data

The dataset obtained using the ALGE timing system was an appropriate format for therequired analysis. It was unobtrusive and as such is believed not to have overly altered visitorbehavior. Having counts of people at path nodes allowed for inference of the number ofpeople on each section of path at given times. This was useful for the determination ofmovement patterns. The system was also favored by the park rangers at the site who preferredthe tracking devices used not to have a high visual impact. A side result of theunobtrusiveness of the ALGE timing system was that some visitors failed to walk over thesensor pads. This resulted in a reduction of comparable data as reliable movement patternscould not be derived for those people who missed sensor pads. This issue will be coveredfurther in the following chapter.

The data capture design was adequate to infer tourist movement patterns at the site, in mostcases, it was clear where people had traveled and how long they had spent there. Ideallyadditional sensor pads could be used which would reduce the effects of people failing to passover sensor pads and allow for a more complete and detailed dataset.

It is possible that the location of ALGE sensors within the study site could have concealedpatterns in tourists' travel times. This will be discussed in greater length in the 'AnalysingTime' section of this chapter.

5.3 Traffic Count Data and Visitor Estimates

The tourist numbers inferred from traffic count estimates gave a range of additionalinformation. The visitor estimates derived from traffic counts relied on the assumption of3.116 people per vehicle, as advised by Parks Victoria. The accuracy of this figure is notcritical. For the most part relative crowds, such as peak and quiet times, rather than exactnumbers are of interest.

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The first information that the traffic count data revealed was that the time of year at which thefieldwork was undertaken, July 2002, had considerably lower visitor numbers than peaktimes (Figure 5.1). This means that behavior resulting from excessive crowding may not havebeen exhibited of the field work days, and as such not recorded by this research.

Figure 5.1 Day Visitor Estimates and Fieldwork Dates

Secondly, the traffic count data showed the daily trends in arrival to the site (Figure 5.2). Thisinformation was compared to the number of people tracked by this research to gain anestimate of the proportion of the population which were sampled in this research (Figure 5.3).It appears from the data (Figure 5.3) that generally more than half the people on site werebeing tracked, but the accuracy of this proportion cannot be ascertained. While an average of3.116 people vehicle is assumed an acceptable estimate over extended periods of time, if in agiven hour only large or small groups arrived at the sight, it would fail to account for this.

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Figure 5.2 Hourly Visitor Estimates on Fieldwork Dates

Figure 5.3 Proportion of People Tracked

This estimates of visitor numbers also allowed for the exploration into the effects crowdinghad on visitor behavior. Assessment of crowding effects included plotting the estimated totalnumber of people on site (in hourly increments) against average time spent on site, or inspecific regions, in that hour. This aimed to determine if people were spending less time, forexample, on the viewing platform, when the site was more crowded (Figure 5.4). It was

40

found no correlation existed on the study days between total estimated people on site andtime spent in certain areas. The afore mentioned problems with using the traffic count data onan hourly scale and the time of year may have effected these results, as such it cannot beconclusively claimed that crowding does not effect user behavior.

Figure 5.4 Total People on Site Compared to Time Spent on Viewing Platform

5.4 Examining all the variables

The initial attempts to find patterns in the data involved graphic and tabulating the trackingdata. This could be done in ways where the shape of the graph, or the data, represented thegeo-temporal patterns in a meaningful way. For example, the different shapes in Figure 5.5,when checked against the visitor's details revealed that the similar shapes, represented peopletraveling together. Graphing data presented a range of visuals of the movement patterns butthey were generally difficult to digest for the entire dataset. While Figure 5.5 gavemeaningful and interesting representation of 10 people's behavior, it would be difficult toanalyse the same information for 800+ visitors. It was apparent at this stage of the researchthat to extract meaningful information about movement patterns, more effective ways ofanalysing the whole data set were needed.

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Figure 5.5 Graphs to Visualise Movement Patterns

5.5 Examining Time

The distribution of time spent on site by visitors was analysed. Identifying common use andoutliers are both important for programming agents. Knowing the distribution of times spenton site could contribute to more informed agent rules than available previously. Most peoplewere found to be spending similar times at the site, but there was a broad overall range oftimes occurring (Figure 5.6)..

Figure 5.6 Total Time Spent on Site

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In addition to knowing how long people spend on site, more detailed information, pertainingto how long people spent on certain paths was of interest. This information is a feature of datafrom the ALGE timing system and was a previously unavailable. The results of analysing thetime spent by visitors on the various paths were as follows. Some regions were seen to havetwo distinct travel time groups (Figure 5.7). This indicates that some people simply walkedalong the path segment while others stopped there for extended periods of time.

Figure 5.7 Time Spent Between Sensor Pads

Figure 5.8 Time Spent Between Sensor Pads

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The distribution of times spent in other sections of the site showed that most people werefollowing similar behavior patterns (Figure 5.8). The basic statistical analysis of the trackingdata discussed in this section would be possible with simple count data and does not utilisethe extra information, of individual IDs, that ALGE provides. To incorporate thisinformation, individual times could be queried from the data base and compared to thehistograms set to see whether individuals were following the overall trend or acting asoutliers. Due to the large number of tourists tracked, this proved to be a cumbersome way ofsearching for movement patterns.

5.6 Regression in Excel

Correlation coefficients were generated for the data of time spent between different pads.This aimed to see if individuals were displaying common patterns in terms of time. Thiswould help to identify behavior specific to people, rather than behavior of all tourists as awhole, which was done in the previous section. Regression analysis of this data revealed littlecorrelation between individuals in terms of their travel times throughout the site (Figure 5.9).This was surprising as it suggested that people were behaving quite randomly in terms oftravel time in different sections. It is possible that the location of sensors were dividing thepark into regions which were not significant in terms of travel time, and as such any patternsthat may have exist were hidden due to aggregation.. This is a common problem with spatialdata, such as administrative boundaries, which can conceal variation by aggregation.

Correlation between times spent in various areas of the study site by visitors.

0to1 1to2 2to3 3to5 5to5 5to4 4to6

0to1 0.0962

1to2 0.0012

2to3 0.0316

3to5 0.0013 0.0092

5to5 0.0018 0.0023

5to4 0.0450 0.0257

4to6 0.0134

Figure 5.9 Correlation Between Time Spent in Various Areas of the Study Site

5.7 Ordination

Ordination is a form of multiple-regression and results here were similar to those obtained viasimple regressions in Excel. Results from ordinations indicated that there was no clusteringbased on their travel times. There was variation in the times but no distinct clusters wereapparent. Figure 5.10, is an ordination for the variables; total time spent on site, time spent onthe platform and time spent on the point shows. It depicts an absence of any clustering in the

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data set but also reveals a correlation between times spent on the point and time spent on site.The correlation between these two variables was 0.68 (for 553 people).

Figure 5.10 Absence of Clustering in Time Data

When ordinations were run on datasets which included the time spent on the point past sensorpad 5, a small degree of clustering occurred (Figure 5.11). These ordinations resulted in asubgroup, which, when investigated, consisted of people who spent very little time in thisarea, less that a minute. About 10 people fell into this subgroup. Given the number was smallit was easy to extract all their trip information from the database and further investigate theirbehavior. The group numbers for these people were extracted along with the movementpatterns of any other people in their groups. Analysing this information made it apparent thatthe people falling into this sub-category, of people spending very small amounts of time pastpad 5, were not actually spending a short period of time in this area, but that they had crossedthe pad twice on arrival and failed to cross over the sensor pad on their return. This wasinferred from the information that they were with their other group members at all other timesand their fellow group members had stayed out past this point longer and from the fact thatthere was 'missing' time before they reached any other pads. These clusters were deemed tobe artifacts of erroneous data and were not considered for division of tourist movement typesfor this research.

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Figure 5.11 Apparent Clustering in Time Data

5.8 Analysis of Sequences

Following the discovery that time alone was not separating the tourists at the TwelveApostles site into meaningful groups, sequence and locations of paths visited were examined.This involved querying the database for the various possible sequences of paths visited at thesite. This analysis was divided first into which areas were visited by different people, thenmore specifically, the order in which they visited those areas. This analysis is of interest forplanning, in terms of ascertaining high use areas, and in terms of agent-based simulation hasimportant implications to path choices and typical trips at the site. The queries to determinesequences included such things as; find all people who visited the viewing platform, thenvisited the point (Pad Sequence: 0, 1, 2, 3, 5, 5, 4 ,6), or find all people who visited the point,then visited the viewing platform (Pad Sequence: 0, 4, 5, 5, 3, 2, 1 ,6). This was carried outfor common movement sequences (Figure 5.12).

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Figure 5.12 Possible Sequences of Travel Within Twelve Apostles Study Site

This querying of possible path combinations generated a number of mutually exclusivemovement categories or 'typical trips' (Figure 5.13 & 5.13).

Figure 5.13 Types of Movement Pattern Identified at Site

The total time spent on site for each of these sequence categories was plotted and comparedto each other and to the total time spent on site for the entire population (Figure 5.14). Thefindings of this were that the distribution of time spent on site for each type were similar toeach other and to the entire population. Confirming the earlier findings that time spent onsite, or in areas of the site, did not seem to contribute to any distinctive groups.

TYPE 1 TYPE 2 TYPE 3 TYPE 4 TYPE 5

O utgoing paths R eturn paths01 1601 12 21 1601 12 23 32 21 1601 12 23 34 4601 12 23 34 45 41 1601 12 23 34 45 55 54 4601 12 23 35 54 53 32 21 1601 12 23 35 55 54 53 32 2104 53 32 21 1604 4504 45 5501 14 4501 14 45 55

P ossible S equences of T ravel W ithin T w elve A postles S tudy S ite

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Figure 5.14 Time Spent on Site by People Exhibiting Different Movement Patterns

The proportions of people exhibiting each 'type' of behavior were greatly varied (Figure5.15). It can be seen that the majority of people exhibited Type 1 behavior, which muchsmaller proportions followed the other sequences.

Figure 5.15 Proportions of People Exhibiting Different Movement Patterns

The distribution of the typologies derived from this research, based on sequence, wereexamined on a daily level to determine if these 'types' of behavior are co-occurring or if theyare a result of temporal factors, for example, type 3 behavior have only have occurred one onday. The proportions of behavior types being exhibited on each day were quite similar(Figure 5.16).

This division of tourist behavior into typologies based on sequence of paths or areas visitedand associated times should contribute to simulation and subsequent site planning at theTwelve Apostles site. They also have possible implications for simulation at other similarsites.

Category No. People Avg. Minutes σTYPE 1 419 22 6TYPE 2 250 27 8TYPE 3 31 23 6TYPE 4 87 25 12other 37 23 10All 813 23 7

TOURIST TYPOLOGIESbased on sequence and areas

visited

TYPE 1

51%TYPE

230%

TYPE 3

4%

TYPE 4

11%other4%

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Figure 5.16 Proportions of People Exhibiting Different Movement Patterns on Separate Days

5.9 Visualisation of Tracking Data

The visualisation tool developed assisted in identifying movement patterns. In running ananimation of the data, it could be easily seen that most of the visitors were moving aroundthe park in one order while a few took different paths. Speed was also viewable in thevisualisation, which was run at 5 times real time. Speed was harder to analyse visually. It wasapparent if people moved particularly quickly or slowly, but it was difficult to determine ifthe same people were moving quickly all the time or changed speeds as all the visitors wererepresented in the same way. The results of the visualisation confirmed those of the statisticalanalysis, in terms of both sequences and times.

5.10 Demographic Data

At various stages of the analysis, variables such as time and sequence were compared to thebasic demographic data. This was in an attempt to ascertain if, for example, people exhibitingType 3 behavior were mostly traveling in groups, or people spending very long times in allareas of the park were elderly. No obvious correlation was found between any of thesevariables.

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5.11 Summary of Results

The main questions addressed in this research were:1 Can a plausible methodology for tracking individual human movement at the study

site be developed?2 Which of the available data capture methods, ALGE timing system or GPS, provides

data most appropriate to the research being undertaken?.3 Which methods for analysing spatial similarity, clustering and patterns can best be

applied to human recreation movement data?4 Can meaningful recreation user typologies and typical trips be extracted from spatial

data tracking individual movement?

The findings were:1 Placement of sensor pads at path intersections allowed for thorough data capture and

analysis and interpolation of movement patterns but may have concealed variation intravel times due to aggregation.

2 The ALGE timing system was more suitable than GPS in terms of cost, evasivenessand data volume provided.

3 Database queries and visualisation revealed the most significant patterns in the touristmovement data.

4 Meaningful typologies were developed based on sequence of paths visited. Time wasnot seen to be a differentiating factor between individual movement patterns.

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6 Discussion of Results

6.1 Overview

Previous agents may have been adequately programmed to simulate usage at some sites.Comparison with previous agents at the Twelve Apostles demonstrated that some patterns ofuse were not being modelled. Time, for example, did not play the role expected and differentgroup types did not have distinguishably different travel times. This could be a result of thelocation of the timing sensors but is more likely to be caused by the simplicity and size of thesite. This finding, that simple small sites may not bring out differences in travel timesbetween individuals is an important discovery for agent programming.

This research identified movement patterns at the study site which will contribute to theimprovement of recreation agent behavior. The identification of these patterns was possiblebecause of the individual movement patterns captured by the ALGE timing system.

Following initial analysis based predominantly on travel times it seemed that no distincttypologies existed at the site, that none were exhibited on the days of the fieldwork, or thatthe data resulting from the location of ALGE sensors did not reveal these patterns. The aim ofthe research was to examine the spatial movement of visitors to the site and identify anypatterns if they did exist. Subsequent analysis of data based on sequence of movement didreveal distinct 'typical trips'.

6.2 Summary of Findings

The analysis in this research allowed for the identification of the following importantfindings.

Travel times alone did not divide the tourists at the Twelve Apostles site into distinguishablegroups. This may have been due to the placement of the ALGE sensors or the size of the site.There was however a significant correlation between time spent on site and time spent on thepoint at the study site which has important implications for future planning. There was noapparent relationship between number of people on site (crowding) and time people spent onsite. This could be investigated further at busier times.

Tourist behavior at the Twelve Apostles site can be divided into distinguishable groups basedon movement sequences. These sequences could contribute to agent programming forrecreation simulation at the site. These sequence types were exhibited with similarproportions on each of the three field work days. This is a reassuring outcome and if it can beassumed that the fieldwork days were 'typical' days, in terms of visitation, then these resultscould be used to simulate behavior at the site year round.

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Animated visualisation of the data was useful for identifying usage patterns. Assuming thatthe patterns exhibited during the field work were typical of the rest of the year, thevisualisation tool itself could be useful for future planning of the site. This tool was notdeveloped until after much of the statistical analysis was performed. It would be suggestedfor future studies that it be developed early in the analysis as it gave valuable insight into userpatterns.

The findings of this research will contribute to improvement of agent behavior forsimulations at the study site and potentially at other sites. The finding will also assistplanning at the Twelve Apostles site, both directly and by improving future simulations.

6.3 Limitations

The dataset obtained had some deficiencies. These were generally due to tourists not passingover some of the sensor pads on site. This happened due to the unobtrusive nature of theAGLE timing system. This factor was an advantage of the system, as it hopefully didn't altertourist behavior dramatically. A more obvious system may have reminded people to pass overthe sensor pads but may have altered tourist behavior if they were constantly aware of beingtracked.

The result of tourists missing pads was that their actual path taken was not recorded. Some ofthese errors were easily detected. If someone was recorded at pad 1 at time step x and at pad3 at time step y it could be inferred that they passed pad 2 at some time between x and y.Alternatively if someone was recorded at pad 1 at time step x and at pad 5 at time step y, itwould be difficult to infer whether they had traveled via points 2 and 3 or via point 4. In thesecases, when examined manually, it could be observed where fellow group members traveledand if it seemed they were together at all other times it could possibly be inferred that theperson followed the same path as the other group members. Alternatively inferring this sortof information could alter the data and hide the fact that some groups may not travel together,thus degrading the database. If a more complete dataset was required gaps could be filledmethodically noting any assumptions made.

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7 Conclusions

7.1 Overview

The aim of this research was to determine whether a typology of tourists could be developedvia the geo-statistical analysis and visualisation of individual human movement data within aday-use recreational facility. This aim was achieved.

Previously agents have been programmed with times of travel and path choices based onsimple or subjective data, such as people counts and user surveys. Simulations which haveused these agents have been useful at visualising and analysing possible outcomes ofplanning decisions and increases in usage but their validity has generally been unknown.

The ALGE timing system generated data which allowed for spatial analysis not previouslyavailable at the Twelve Apostles, and analysis that is significant in general for developmentof better agents for recreation simulation.

7.2 Further Analysis of Twelve Apostles Data

The scope of analysis that could be performed on the spatio-temporal tracking data obtainedin this analysis is very broad. Patterns in sequences, time and inferred speed were analysed inthis research. There is a variety of analysis that was not pursued in this research that could beinvestigated. These include raster analysis of densities on paths, or crowding, and morethorough analysis of the existing data set. More detailed analysis could occur at a finertemporal scale such as PM visitors. This would help to ascertain if any trends are beinghidden by aggregating the entire data set but it would also reduce the amount of data beinganalysed.

The results for this research could be considered for agent-based simulation at other similarsites and the methods developed here could be considered for developing tourist movementtypologies at a wider variety of sites. Carrying out a similar study at a different site couldhelp to answer the question of whether the behavior patterns exhibited at the Twelve Apostlesare exhibited at other sites or whether they are a product of the site alone. This begs a veryimportant question of whether recreator behavior is an attribute of the individual or a productof their environment.

7.3 Implementation of tourist typologies in RBSim2

The user typologies developed in this research can be used to program new agents for RBSimsoftware. These new agents could be used to run simulations of the site to assess the effectsof crowding and predict the effects of management decisions more accurately. The results of

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this will determine whether the methodology and research undertaken by this research couldbe implemented at other sites in the future.

7.4 Integration with additional data.

A number of further research questions arise from this research. They include furtherintegration of human behavioral research with spatial data modelling techniques to furtherascertain the socio-economic and demographic influence on tourist behavior.

Further research will take place by other parties3 collaborated with this research and willinvolve integration or user typologies with behavioral data.

3 Dr. Kathryn Williams & Hamish Webb (Research Student), Environmental Psychology, University of Melbourne

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PROJECT DIARY AND TIMELINE

March21/03/2002 Project Meeting, Zerger, Itami, Williams and Webb22/03/2002 Research Hypothesis Submitted27/03/2002 RBSim Demonstration, Itami and WebbCommenced Literature Review

April14/04/2002 Site Trip - Preliminary Reconnaissance17/04/2002 Parks Victoria Meeting, Burton, Williams and Webb24/04/2002 Structural Analysis of Research Papers SubmittedContinued Literature Review

May15/05/2002 Parks Victoria RBSim Demo and Site Visit16/05/2002 Parks Victoria RBSim Demo and Site Visit17/05/2002 Parks Victoria RBSim Demo and Site Visit31/05/2002 Research Proposal Submitted31/05/2002 Preliminary Web site PostedCommenced Familiarisation with MapObjects and Visual BasicCommenced Database Design for GIS and existing spatial dataContinued Literature Review

June21/06/2002 Site Trip - Detailed Assessment of Site Layout22/06/2002 Site Trip - Tracking MethodologyCommenced Development of Methodology for Data AnalysisCommenced Preliminary analysis using sample dataCommenced Database design for spatial dataCommenced Database design for temporal dataCommenced Methodology for data collectionContinued Familiarisation with MapObjects and Visual BasicContinued Database Design for GIS and existing spatial dataConcluded Literature Review

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July17/07/2002 Site Trip - Data Capture18/07/2002 Site Trip - Data Capture19/07/2002 Site Trip - Data Capture20/07/2002 Site Trip - Data CaptureCommenced Data post processingCommenced Compilation of Final Report

AugustConcluded Data post processingContinued Spatial Data mining and analysisContinued Compilation of Final Report

SeptemberConcluded Spatial Data mining and analysisCommenced Development of User TypologiesCommenced Assessment of ResultsContinued Compilation of Final Report

October23/10/2002 Presentation of ResearchConcluded Development of User TypologiesConcluded Assessment of ResultsConcluded Compilation of Final Report

November01/11/2002 Project submission

OngoingIntegration of findings with Agent-Based ModelsIntegration of findings with study undertaken by Williams and Webb on perceived crowdingat the study site

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SELECTED REFERENCES AND BIBLIOGRAPHY

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Fik, T. J. (1997). The Geography of Economic Development: Regional Changes, GlobalChallenges. College Custom Series. McGraw-Hill, New York, 1 edition.

Franklin, S. & A. Graesser (1996). Is in an Agent, of just a program?: A Taxonomy forAutonomous Agents, Proceeding of the Third International Workshop on Agent Theories,Architectures, and Languages, Springer Verlag, [Online], Available,http://www.msci.memphis.edu/~franklin/AgentProg.html [2002, May 2]

Garson, D. (2001). Social Agent Simulation,[Online], Availablehttp://www2.chass.ncsu.edu/garson/pa765/agentsim.htm [2002, June 1].

Gimblett, R., B. Durnota, and B. Itami (1996). Spatially-Explicit Autonomous Agents forModelling Recreation Use in Complex Wilderness Landscapes, Complexity International,Vol. 3, [Online], Available http://www.csu.edu.au/ci/vol03/gimetal3/gimetal3.html [2002,May 15].

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Gimblett, R., T. Daniel, and C. Roberts (2000). Grand Canyon River Management:Simulating Rafting the Colorado River through Grand Canyon National Park Using SpatiallyExplicit Intelligent Agents, 4th International Conference on Integrating GIS andEnvironmental Modeling (GIS/EM4): Problems, Prospects and Research Needs Proceedings,Banff, Alberta, Canada.

Haklay, M., D. O'Sullivan & M. Thurstain-Goodwin (2001). "So go down town": simulatingpedestrian movement in town centers. Center for Advanced Spatial Analysis, UniversityCollege London, United Kingdom

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Haritaoglu, I., D Harwood & L. Davis (1998). W4S: A Real Time System for Detecting andTracking People in 2.5D, European Conference on Computer Vision, 1998

Heikkila, J. & O. Silven (1997). Counting and tracking people from image sequences.[Online], Available http://www.ee.oulu.fi/monitor/Monitor.html [2002, Sep 17].

Holden, A. (2000). Environment and Tourism, Routledge, London

Itami, R. (2002). Estimating Capacities for Pedestrian Walkways and Viewing Platforms, AReport to Parks Victoria. Geodimensions, Brunswick, Australia

Itami, R. M., and Gimblett, H. R. (2000). Intelligent Recreation Agents in a Virtual GISWorld, Complex Systems 2000 Conference Proceedings, University of Otago, Dunedin, NewZealand.

Itami, R., R., Raulings, G. MacLaren, K. Hirst, R. Gimblett, D. Zanon, and P. Chladek(2002). RBSim 2: Simulating the complex interactions between human movement and theoutdoor recreation environment, Monitoring and Management of Visitor Flows inRecreational and Protected Areas, Institute for Landscape Architecture and LandscapeManagement Proceedings, University of Agricultural Sciences, Vienna, Austria.

Langton, C. G., editor (1996). Artificial Life: an Overview, Cambridge Massachusetts. MITPress.

Parks Victoria (1998). National Park and Bay of Islands Management Plan, NationalLibrary of Australia, Cataloguing-in-Publication entry. Parks Victoria, Kew, Victoria

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Parks Victoria (1999). Port Campbell National Park. Geology [Online], Available,http://www.12apostlesnatpark.org/_12/geology.htm [2002, Jun 30]

Parks Victoria (2002). Tourism in Parks. [Online], Available,http://www.parkseducation.org/tourism_parks/da3.htm [2002, Sep 17]

Pers, J. & S. Kovacic (2001). Tracking People in Sport: Making use of Partially ControlledEnvironment. Faculty of Electrical Engineering, University of Ljubljana, Slovenia

Ryan, C. (1991). Recreational Tourism, Routledge, London

RMIT University (2001). Port Campbell National Park: Patterns of Use, RMIT SpecialResearch Project Fund Application

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Zerger A., K. Williams, D. Zanon (2001). Building Better Agents: Linking Behavioral andSpatial Data for Recreation Planning, The University of Melbourne, Research Project GrantApplication.

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APPENDICES CD

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PROJECT WEBSITE2002 http://webraft.its.unimelb.edu.au/451450/students/aliceno/pub/public_html/2003 http://www.geom.unimelb.edu.au/research/index.htmlEMAIL aliceno@hotmail.com