観光スポットとルート推薦のためのユーザ適応型旅行プラン生成アルゴリズム

早大学位記番号:新8428早稲田大

A Big Data Analytics Method for Tourist Behaviour Analysis

© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed

A Big Data Analytics Method for Tourist Behaviour Analysis

© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed

Spatial relational learning and foraging in cotton-top tamarins

Spatial relationalleaming can be defined as the use of the spatial (geometric) relationship between two or more cues (landmarks) in order to locate additional points in space (O'Keefe and Nadel, 1979). An internal spatial representation enables an animal to compute novel locations and travel routes from familiar landmarks and routes (Dyer, 1993). A spatial representation is an internal construct mediating between perceived stimuli in the environment and the behaviour of the animal (Tolman, 1948). In this type of spatial representation the information encoded must be isomorphic with the physical environment such that the geometric relations of distance, angle and direction are maintained or can be computed from the stored information (Gallistel, 1990). A series of spatial and foraging task experiments were conducted to investigate the utilisation of spatial relational learning as a spatial strategy available to cotton-top tamarins (Sag uinus oedipus oedipus). The apparatus used was an 8x8 matrix of holes set in an upright wooden board to allow for the manipulation of visual cues and hidden food items such that the spatial configuration of cues and food could be transformed (translated or rotated) with respect to the perimeter of the board. The definitive test of spatial relational learning was whether the monkeys relied upon the spatial relationship between the visual cues to locate the position of the hidden food items. In a control experiment testing for differential use of perceptual information the results showed that if given the choice, tamarins relied on visual over olfactory cues in a foraging task. Callitrichids typically depend on olfactory communication in socio-sexual contexts so it was unusual that olfaction did not also play a significant role in foraging. In the first spatial learning experiment, the tamarins were found to rely on the three visually presented cues to locate the eleven hidden food items. However, their performance was not very accurate. In the next experiment the task was simplified so that the types of spatial strategies the monkeys were using to solve the foraging task could be clearly identified. In this experiment, only two visual cues were presented on either end of a line of four hidden food items. Once the monkeys were trained to these cues, the cues and food were translated and/or rotated on the board. Data from the beginning and middle of each testing session were used in the final analysis: in a previous analysis it was found that the monkeys initially searched the baited holes in the beginning of a testing session and thereafter predominantly searched unbaited holes. This suggests that they followed a win-stay/lose-shift foraging strategy, a finding that is supported by other studies of tamarins in captivity (Menzel and Juno, 1982) and the wild (Garber, 1989). The results also showed that the monkeys were searching predominately between the cues and not outside or around of them, indicating that they were locating the hidden food by using the spatial relationship between the visual cues. This provides evidence for the utilisation of spatial relational learning as a foraging strategy by cotton-top tamarins and the existence of complex internal spatial representations. Further studies are suggested to test captive monkeys' spatial relational capabilities and their foraging strategies. In addition, comparative and field studies are outlined that would provide information regarding New World monkeys' spatial learning abilities, neurophysiological organisation and the evolution of complex computational processes

Harnessing big data to inform tourism destination management organizations

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn the last few years, Portugal has been witnessing a rapid growth of tourism, which reflects positively in many aspects, especially in what regards economic factors. Although, it also leads to a number of challenges, all of them difficult to quantify: tourist congestions, loss of city identity, degradation of patrimony, etc. It is important to ensure that the required foundations and tools to understand and efficiently manage tourism flows exist, both in the city-level and country-level. This thesis studies the potential of Big data to inform destination management organizations. To do so, three sources of Big data are discussed: Telecom, Social media and Airbnb data. This is done through the demonstration and analysis of a set of visualizations and tools, as well as a discussion of applications and recommendations for challenges that have been identified in the market. The study begins with a background information section, where both global and local trends in tourism will be analyzed, as well as the factors that affect tourism and consequences of the latter. As a way to analyze the growth of tourism in Portugal and provide prototypes of important tools for the development of data driven tourism policy making, Airbnb and telecom data are analyzed using a network science approach to visualize country-wide tourist circulation and presents a model to retrieve and analyze social media. In order to compare the results from the Airbnb analysis, data regarding the Portuguese hotel industry is used as control data

Plant ecology meets animal cognition: impacts of animal memory on seed dispersal

We propose that an understanding of animal learning and memory is critical to predicting the impacts of animals on plant populations through processes such as seed dispersal, pollination and herbivory. Focussing on endozoochory, we review the evidence that animal memory plays a role in seed dispersal, and present a model which allows us to explore the fundamental consequences of memory for this process. We demonstrate that decision-making by animals based on their previous experiences has the potential to determine which plants are visited, which fruits are selected to be eaten from the plant and where seeds are subsequently deposited, as well as being an important determinant of animal survival. Collectively, these results suggest that the impact of animal learning and memory on seed dispersal is likely to be extremely important, although to date our understanding of these processes suffers from a conspicuous lack of empirical support. This is partly because of the difficulty of conducting appropriate experiments but is also the result of limited interaction between plant ecologists and those who work on animal cognition

PICES Press, Vol. 15, No. 2, July 2007

Contents [Individual sections are downloadable from the official URL link listed below]: PICES Science in 2007 (pdf, 0.1 Mb) 2007 Wooster Award (pdf, 0.1 Mb) FUTURE - A milestone reached but our task is not done (pdf, < 0.1 Mb) International symposium on "Reproductive and Recruitment Processes of Exploited Marine Fish Stocks" (pdf, 0.1 Mb) Recent results of the micronekton sampling inter-calibration experiment (pdf, 0.1 Mb) 2007 PICES workshop on "Measuring and monitoring primary productivity in the North Pacific" (pdf, 0.1 Mb) 2007 Harmful Algal Bloom Section annual workshop events (pdf, 0.1 Mb) A global approach for recovery and sustainability of marine resources in Large Marine Ecosystems (pdf, 0.3 Mb) Highlights of the PICES Sixteenth Annual Meeting (pdf, 0.4 Mb) Ocean acidification of the North Pacific Ocean (pdf, 0.3 Mb) Workshop on NE Pacific Coastal Ecosystems (2008 Call for Salmon Survival Forecasts) (pdf, 0.1 Mb) The state of the western North Pacific in the first half of 2007 (pdf, 0.4 Mb) PICES Calendar (pdf, 0.4 Mb) The Bering Sea: Current status and recent events (pdf, 0.3 Mb) PICES Interns (pdf, 0.3 Mb) Recent trends in waters of the subarctic NE Pacific (pdf, 0.3 Mb) Election results at PICES (pdf, 0.2 Mb) A new PICES award for monitoring and data management activities (pdf, < 0.1 Mb

Maintaining orientation within route following tasks : a developmental approach

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