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

By Zheng Xiang, Daniel R. Fesenmaier

This ebook offers innovative study at the improvement of analytics in shuttle and tourism. It introduces new conceptual frameworks and size instruments, in addition to purposes and case stories for vacation spot advertising and marketing and administration. it's divided into 5 elements: half one on shuttle call for analytics makes a speciality of conceptualizing and imposing commute call for modeling utilizing substantial information. It illustrates new how one can establish, generate and make the most of huge amounts of information in tourism call for forecasting and modeling. half makes a speciality of analytics in trip and daily life, featuring contemporary advancements in wearable desktops and physiological dimension units, and the results for our figuring out of on-the-go tourists and tourism layout. half 3 embraces tourism geoanalytics, correlating social media and geo-based information with tourism statistics. 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 few managerial difficulties. the ultimate half is a suite of case experiences utilizing web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging on-line reports within the inn undefined, and comparing vacation spot communications and industry intelligence with on-line resort experiences. The chapters during this part jointly describe quite a number various ways to realizing marketplace dynamics in tourism and hospitality.

Show description

Read or Download Analytics in Smart Tourism Design: Concepts and Methods PDF

Similar investing books

Ahead Of The Market - The Zacks Method for Spotting Stocks Early In Any Economy

Beat the professionals at their very own GameAll too usually, you find out about solid shares a long way too past due to benefit from the data. by the point you certainly purchase a inventory, specialist traders have already been there, acquired the inventory, pushed up the cost, and are only ready to dump it at an inflated expense. All that is approximately to alter.

Value-Based Power Trading: Using the Overlay Demand Curve to Pinpoint Trends & Predict Market Turns

Written through Donald Jones, the public sale industry worth thought (AMVT) procedure is going past industry Profile.

All About High-Frequency Trading (All About Series)

A close PRIMER ON contemporary such a lot refined AND arguable buying and selling procedure Unfair . . . significant . . . unlawful . . . inevitable. High-frequency buying and selling has been defined in lots of alternative ways, yet something is for sure--it has remodeled making an investment as we all know it. All approximately High-Frequency buying and selling examines the perform of deploying complex desktop algorithms to learn and interpret marketplace job, make trades, and pull in large profi ts―all inside of milliseconds.

How to Beat Wall Street: Everything You Need to Make Money in the Markets Plus! 20 Trading System Ideas

Comes with loose Amibroker buying and selling method code and over eighty extra spreadsheets of ancient information. All can downloaded unfastened from the JB Marwood web site with buy of the publication. Malcolm Gladwell claims the major to luck in any activity is the buildup of at the very least 10,000 hours of perform. JB Marwood has such event and has used it good of overdue, thoroughly predicting the ground in shares in 2009, the head in silver in 2011 and the head in gold in 2012.

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

Sample text

2008). Forecasting economic time series using targeted predictors. Journal of Econometrics, 146(2), 304–317. , Ryan, S. , & Yang, M. (2015). Machine learning methods for demand estimation. 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?

2013). Big data: A revolution that will transform how we live, work, and think. New York: Houghton Mifflin Harcourt. , Davenport, T. , & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 61–67. Predicting Tourist Demand Using Big Data 29 Meeker, W. , & Hong, Y. (2014). Reliability meets big data: Opportunities and challenges. Quality Engineering, 26(1), 102–116. Ng, E. C. (2012). Forecasting US recessions with various risk factors and dynamic probit models.

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. If Uin is the utility of alternative i for tourist n, explained through the personal characteristic xn of individual n and through attribute zi of the same alternative i, then the utility function is expressed as U in ¼ αi þ xn βi þ zi γ i þ εin where αi is the utility constant, βi and γ i are the parameters that measure (respectively) the effects of characteristic xn of the individual and attribute zi on the utility of alternative i and εin is the error term.

Download PDF sample

Rated 4.95 of 5 – based on 22 votes