Titlebook: Analytics in Smart Tourism Design; Concepts and Methods Zheng Xiang,Daniel R. Fesenmaier Book 2017 Springer International Publishing Switze

ORE

GIS Monitoring of Traveler Flows Based on Big Datagative binomial spatial interaction model highlight several determinants of tourist flows, including the distance between the origin and destination, the size of economies in the origin and destination, the hotel infrastructure in the destination, and the number of world heritage sites and AAAA scenic spots in the destination?areas.

音樂等

真繁榮

Sochi Olympics on Twitter: Topics, Geographical Landscape, and Temporal Dynamicsient before, during, and after the games? What are the temporal dynamics of issues concerning the Sochi Olympics? The chapter illustrates an analytical approach to extracting topical, spatial, and temporal information from Twitter messages.

公社

2366-2611 ols.Includes several relevant case studies for the applicatiThis book presents cutting edge research on the development of analytics in travel and tourism. It introduces new conceptual frameworks and measurement tools, as well as applications and case studies for destination marketing and management

鴕鳥

https://doi.org/10.1007/978-3-030-11072-7portunities to shape tourism experiences. As such, the information collected through this technology enables tourism destinations to understand not only each individual traveler, but also the collective market in much greater detail, and consequently enables them to design much more compelling and efficient tourism services.

反饋

The Quantified Traveler: Implications for Smart Tourism Developmentportunities to shape tourism experiences. As such, the information collected through this technology enables tourism destinations to understand not only each individual traveler, but also the collective market in much greater detail, and consequently enables them to design much more compelling and efficient tourism services.

套索

使顯得不重要

Lecture Notes in Computer Scienceection looks at empirical problems that one finds when examining travel demand through big data, the third section presents several research avenues that can be opened and the fourth section provides some closing thoughts.

choroid

https://doi.org/10.1007/978-3-031-02008-7d enjoyable than moderate ratings. Additionally, the findings of this research indicate the usefulness of the negative binomial model, which allows researchers to manage the features of count data as well as address the heteroscedasticity in linear regression and the overdispersion problem in the Poisson regression model.

Focus-Words