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Determination of High Water Mark (HWM) is important for identifying the landward extent of the ocean. The delineation of HWM is required for cadastral boundary definition, land use and infrastructure development along the foreshore and for planning associated with climate change adaptation. Currently, apart from medium to long term traditional tidal observation methods and subjective interpretation, only limited scientific methods have been developed to determine HWM accurately. The aim of this paper is to develop a systematic method to determine HWM based on the HWM indicators, wave runup distribution and the spatial continuity theory (See Figure 1). A range of HWM indicators are used to determine HWM in this paper including the Mean Higher High Water (MHHW); suggested heights determined by Landgate experienced surveyors in the field (Landgate) and the engineer in Department of Transport (DoT); Highest Tide Records (HTR) derived from tidal records, and natural features on the beach such as sudden change of beach slope (SCoS) and the High Water Line (HWL, boundary between wet sand and dry sand). In order to extract these feature indicator lines, Object-Oriented Image Analysis (OOIA) is used in this research. The heights of HWM indicators are then derived from Digital Elevation Model (DEM) to determine HWM indicator line.To determine HWM based on these indicators for cadastral boundary definition purpose, we develop a method derived from a semivariogram model of wave runup probability. The wave runup probability is defined as the probability that sea water will reach the indicator line in 8 years. Generally the higher the wave runup probability, the more likely sea water reaches the indicator. Cross sections are set up at every 50 meters in the study area and intersect with calculated indicator lines. The wave runup probability was calculated for each intersection point. A semivariogram model of these wave runup probabilities is used to understand spatial continuity

Pedestrian vehicle crashes are a vital traffic safety concern. About 12% of all people who die on the roads in Western Australia are pedestrians. This paper will investigate the changes of characteristics of drivers and pedestrian associated with pedestrian vehicle crashes over the time of a day. A two step cluster algorithm will be adopted to group time of pedestrian vehicle crashes into time intervals. Then the Expectation Maximisation (EM) algorithm will be used to detect segments of drivers and pedestrians for each time interval. The information about target groups of pedestrians and drivers will be useful for developing practical education and enforcement strategies and to improve the design of the pedestrian environment on safety.

Tourist movement is a complex process. It can be modelled from a number of different perspectives; for example, Tourism, Geography, Economics, Mathematics, Computer Sciences and Psychology. This paper aims to establish a sound methodology, using Semi-Markov processes to model the spatial and temporal movement of tourists. The objective is to understand, predict, control for, and optimise the decisions made by tourists in their choice of attractions. Semi-Markov processes have a Markov chain and a renewal process embedded within their structure, and as such, can be used to provide a wide variety of practical models. One of the outcomes of this approach is the introduction of a measure to assess the attractiveness of particular tourist attractions based on spatial and temporal interactions between the attractions. A case study conducted at Phillip Island Nature Park, Victoria, Australia is used to validate the model. The study’s results support the proposed model’s efficiency. The results are also practical and useful for assisting park managers and tourist operators with park planning and marketing decisions: for example, knowing which attractions are the most popular, how long tourists will spend at any one site; and what the likely routes are that they will follow.

Managing the diversity of student backgrounds in today's complex higher education classrooms is a challenging task for lecturers. This paper reports on action research involving a complex nested group of units in Geographic Information Science (GIS) delivered to a diverse student body, including international and domestic students from different disciplines studying in on-campus, online and distance education modes. GIS is a technology-dependent discipline of relevance to a wide variety of other disciplines including, for example land management, urban development or health sciences, to facilitate land-use planning, map disease distribution, analyse spatial patterns of crime or model urban growth. The concern of the lecturer was that adaptation of the learning activities necessary for accommodating flexible modes of delivery and student backgrounds would impact on student assessment outcomes. The research findings indicate that while there were some significant differences in assessment outcomes associated with student backgrounds and discipline, no differences could be ascribed to, for example, language background of students. This paper discusses the study and seeks explanations for the results. The implications for teaching and further action research with this unit in order to sustain student numbers and teaching and learning standards in the future are also presented.

Wayfinding is an important aspect that should be considered by tourist park managers when allocating resources and facilities to aid tourists navigating their way through a park. This paper discusses the influence of individual differences such as age, gender, travel group, and familiarity with the environment towards the wayfinding decision making process and physical movement. A case study was conducted where respondents’ movements were traced using GPS receivers to look at the movement patterns and at the end, they were asked to complete a questionnaire particularly to determine the individual differences. The spatial and attribute data were analysed using ESRI ArcGIS Tracking Analyst and ET Geowizards. SPSS 17 were used for statistical purposes. As a result, in terms of decision making, specific landmarks and strategies were noticed for specified age, gender, travel group, and familiarity with the environment. Furthermore, correlations were found between gender and familiarity with the environment with the physical movement such as direction, distance, and arrival time. This paper highlights the need for tourist managers to understand that tourists use different methods of wayfinding and produce different results based on different individual differences and that management should provide complementary materials to assist in wayfinding.

The aim of the paper is to ascertain if tourist wayfinding behaviours correlate with individual differences. Individual differences in this study refer to age, gender, type of travel group and familiarity with the environment. The specific tourist wayfinding behaviours of in this paper are utilising landmarks and wayfinding strategies.. The methods used to identify the individual differences are Pearson Chi-square test and Odds ratio. Pearson Chi-square test is used to identify the significant differences and Odds ratio measures strength of association. A case study was conducted at the Koala Conservation Centre at Phillip Island Nature Park, Victoria, Australia. Differences in wayfinding behaviours between gender, age group, type of travel group and level of familiarity with the environment are identified. Females tend to follow a crowd and are more likely to use wayfinding strategies such as Least Time, First Noticed, and Different from Previous Route Taken than males. Males are more likely than females to use Vegetation Types and Track Surfaces as their wayfinding landmarks, and they prefer Most Scenic wayfinding strategies.When age is considered, the middle aged tourist group tends to find its destination based on a Shortest Path strategy while younger tourists prefer First Notice wayfinding strategies. Furthermore, tourists who are familiar with the environment are more likely than others to navigate using Shortest Path and Few Turns wayfinding strategies. Understanding individual differences among tourist wayfinding behaviours can be beneficial in developing wayfinding systems/devices that can assist tourists as they move from attraction to attraction within a tourist site. In addition, this information will be useful in park or urban design. In the future, we compare individual differences in tourist wayfinding behaviours with tourists’ physical movements as tracked by GPS. The key question is how the individual differences of tourist wayfinding behaviours in

Tourist movement is a complex process, but it provides very useful information for park managers and tourist operators. This paper aims to establish a sound methodology for modelling the spatial and temporal movement of tourists, with the objectives of understanding, predicting, controlling and optimising the decisions made by them as they go about choosing the attractions they want to visit. Tourist movements, in this paper, are modelled as discrete processes between specific tourist locations, which could be located some distance apart. A Semi-Markov process has a Markov chain and a renewal process embedded within its structure. Therefore, the Semi-Markov chain can be used to understand the interaction of tourists with attractions as a sequence of movements over time, rather than their interaction with individual attractions. The following two assumptions, which underlie the Semi--Markov process, make it an especially ideal tool for modeling movements of tourists: The probability that a tourist will visit a particular attraction depends only on the most recent attraction that was visited by that tourist. The distribution of the time spent at each attraction is dependent on both that attraction and the next attraction that is visited.One of the outcomes of this approach is a measure which assesses the attractiveness of particular tourist attractions based on spatial and temporal interactions between the attractions. Two assumptions, based on the assumptions of the Semi--Markov process, are derived for assessing attractiveness of tourist attractions. The more tourists visit an attraction, the more attractive it is. A transition probability matrix is developed for estimating the probability that a tourist will visit a particular attraction based on the first assumption. The longer tourists stay at an attraction, the more attractive it is. A mean time transition matrix is calculated, based on the second assumption, to estimate the time spent at each attraction. The at

Data collection can become an overwhelmingtask for the simulation modeler and,indeed, can become the single most importantfactor that restricts the development and ultimateuse of that model. This chapter serves as aresource for the simulation modeler to assist indetermining what data is required and whattechniques and technologies are currently available.The chapter discusses the data requiredfor describing the spatial and-temporal movementof visitors. A review of the techniques forthe tracking and counting of visitor movements atvarious spatial scales will be undertaken andeach method will be assessed for its suitability asa mechanism for acquiring data for simulationmodeling. These techniques are summarizedassessing their advantages and disadvantagesand suitable corresponding applications.

The aim of this paper is to identify suitable tracking techniques for the spatio-temporal movement behaviour of tourists in a natural environment for various applications. Several techniques at both a micro and macro scale are compared and reviewed. Each review includes a summary of advantages and disadvantages for the techniques and identifies suitable applications for each technique. Only tracking techniques will be reviewed here. The types of tracking techniques discussed includes observation and interviews, self-administered questionnaires, GPS tracking, PDA tracking and Location-Based Services (LBS), virtual questionnaires, mobile phone tracking and closed circuit television tracking. The literature review has provided guidance in choosing suitable tracking techniques for a research project that aims to model the spatio-temporal movement patterns of tourists visiting Phillip Island. This paper will provide an outline of this research project including the practical suitability of each chosen tracking technique for the Phillip Island case study.

People perceive, think, and behave differently at various spatial and temporal scales. Spatiotemporal modelling of tourist movements considers how people move about or why they exhibit certain movement behaviours. Research into spatio-temporal movements of tourists can be studied from a number of different aspects. Psychologists, for example, are concerned with understanding the cognitive aspects of why people move along particular pathways in preference to alternative pathways. Geographers and tourism researchers are more interested in how people move around particular locations and model what is observed in a visitors’ movement. However in developing simulation models that can be used to emulate tourism movement the issue of scale of movement in both time and space needs to be well understood. It is too simplistic to just apply the same model from one situation to another without thinking about the issues relating to scale. This paper discusses the issues of temporal and spatial scale for the modelling of tourist movements in terms of definition of movement, movement tracking techniques, data acquisition, data analysis, and the transition between the scales using spatio-temporal “zooming theory”.These findings have important implications when developing agent models. The paper first discusses issues relating to measuring, modelling and analysing movement behaviour at two distinct scales, namely the macro and micro level. From this initial discussion the paper then applies techniques discussed to a specific study location at Phillip Island in Victoria. The first scale examined is the macro level which covers the whole of Phillip Island. At the micro scale tourist movement behaviour is examined for a specific geographic location, the Koala Conservation Centre. Modelling the spatio-temporal movement of tourists at the macro level aims to represent the general travel patterns of a variety of tourist types. However movements of tourists modelled at the micro level reli

AlKahtani, S. J. H., Xia, J. C., Veenendaaland, B., Caulfield, C., & Hughes, M. (2015). Building a conceptual framework for determining individual differences of accessibility to tourist attractions. Tourism Management Perspectives, 16, 28-42. Available here

Chen Z., Xia J.C., Irawan B., Caulfied C. (2014). Development of location-based services for recommending departure stations to park and ride users. Transportation Research Part C: Emerging Technologies, 48, 256-268. Available here