Zheng Xiang, Daniel R. Fesenmaier's Analytics in Smart Tourism Design: Concepts and Methods PDF

By Zheng Xiang, Daniel R. Fesenmaier

ISBN-10: 3319442627

ISBN-13: 9783319442624

ISBN-10: 3319442635

ISBN-13: 9783319442631

This ebook offers leading edge learn at the improvement of analytics in commute and tourism. It introduces new conceptual frameworks and dimension instruments, in addition to functions and case reports for vacation spot advertising and administration. it really is divided into 5 elements: half one on trip call for analytics specializes in conceptualizing and imposing shuttle call for modeling utilizing titanic info. It illustrates new how one can establish, generate and make the most of huge amounts of knowledge in tourism call for forecasting and modeling. half makes a speciality of analytics in shuttle and lifestyle, proposing fresh advancements in wearable desktops and physiological size units, and the consequences for our knowing of on-the-go tourists and tourism layout. half 3 embraces tourism geoanalytics, correlating social media and geo-based info with tourism records. half 4 discusses web-based and social media analytics and provides the most recent advancements in using user-generated content material on the net to appreciate a couple of managerial difficulties. the ultimate half is a set of case reviews utilizing web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging on-line experiences within the lodge undefined, and comparing vacation spot communications and industry intelligence with on-line inn studies. The chapters during this part jointly describe more than a few assorted ways to realizing industry dynamics in tourism and hospitality.

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Additional info for Analytics in Smart Tourism Design: Concepts and Methods

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However, data mining techniques always use static data as opposed to time series and are seldom used in tourism demand forecasting. When we turn to traditional forecasting methods for tourism demand forecasting with big data, the biggest problem is that the traditional forecasting tools cannot handle the size, speed, and complexity inherent in big data (Madden, 2012) even when it has been structured. In order to apply a traditional forecasting method to big data, we have to simplify the structured big data (Hassani & Silva, 2015).

With the alternative that is really chosen. The fact that an individual declares that he/she would like to go to Hawaii on his/her next summer holiday does not necessarily mean that he/she will go there in the end. Conversely, the Revealed Preferences Approach analyses the real choices made by tourists in order to obtain their preferences. In the example above, the individual reveals his/her preferences when, from a group of destination choices, he/she chooses and goes to Hawai. However, one of the weak points of the Revealed Preferences Approach derives from the fact that the estimation of preferences is made at a global sample level, which does not allow representation of individual level preferences.

American Economic Review, 105(5), 481–485. , & R€unstler, G. (2011). A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP. International Journal of Forecasting, 27(2), 333–346. Bangwayo-Skeete, P. , & Skeete, R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46, 454–464. Bates, J. , & Granger, C. W. (1969). The combination of forecasts. Or, 20(4), 451–468.

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Analytics in Smart Tourism Design: Concepts and Methods by Zheng Xiang, Daniel R. Fesenmaier

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