Analytics in smart tourism design : concepts and methods
This 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. It is divided into five parts: Part one on travel demand a...
Saved in:
| Other Authors | , |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Switzerland :
Springer,
[2017]
|
| Series | Tourism on the verge.
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9783319442631 9783319442624 |
| ISSN | 2366-262X |
| Physical Description | 1 online resource (xvi, 307 pages) |
Cover
| LEADER | 00000cam a2200000Ii 4500 | ||
|---|---|---|---|
| 001 | 97856 | ||
| 003 | CZ-ZlUTB | ||
| 005 | 20251008101103.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 161014s2017 sz ob 000 0 eng d | ||
| 040 | |a N$T |b eng |e rda |e pn |c N$T |d IDEBK |d EBLCP |d N$T |d YDX |d OCLCF |d UAB |d MERUC |d VT2 |d IOG |d ESU |d JBG |d IAD |d ICW |d ICN |d OTZ |d OCLCQ |d IAS |d VLB |d JG0 |d U3W |d CAUOI |d OCLCQ |d KSU |d AU@ |d UKMGB |d OCLCO |d BRX |d OL$ |d OCLCQ |d ERF |d UKAHL |d OCLCQ | ||
| 020 | |a 9783319442631 |q (electronic bk.) | ||
| 020 | |z 9783319442624 | ||
| 035 | |a (OCoLC)960701651 |z (OCoLC)960838734 |z (OCoLC)962786957 |z (OCoLC)965355959 |z (OCoLC)966257343 | ||
| 245 | 0 | 0 | |a Analytics in smart tourism design : |b concepts and methods / |c Zheng Xiang, Daniel R. Fesenmaier, editors. |
| 264 | 1 | |a Switzerland : |b Springer, |c [2017] | |
| 300 | |a 1 online resource (xvi, 307 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a počítač |b c |2 rdamedia | ||
| 338 | |a online zdroj |b cr |2 rdacarrier | ||
| 490 | 1 | |a Tourism on the verge, |x 2366-262X | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Acknowledgments; Contents; List of Contributors; Analytics in Tourism Design; 1 Introduction; 2 Foundations of Big Data Analytics; 3 Analytics in Tourism Design: Needs and Opportunities; 4 Directions for Research; References; Part I: Travel Demand Analytics; Predicting Tourist Demand Using Big Data; 1 Introduction; 2 What Is Tourism Big Data?; 3 Advantages of Using Big Data in Tourism; 4 Characteristics of Tourism Big Data; 5 Benefits of Big Data to Tourism Businesses; 5.1 Consumer Behavior; 5.2 Feedback Mechanisms; 6 How to Use Big Data in Tourism Forecasting | |
| 505 | 8 | |a 6.1 Capturing Big Data for Tourism Forecasting7 Selecting and Shrinking Big Data; 8 A Framework for Predicting Tourism Demand Using Big Data; 9 Conclusions; References; Travel Demand Modeling with Behavioral Data; 1 Introduction; 2 Empirical Results; 2.1 Heterogeneity in Tourists; 2.2 Choice Set; 2.3 Information Hierarchy; 3 Research Avenues; 4 Conclusions; References; Part II: Analytics in Everyday Life and Travel; Measuring Human Senses and the Touristic Experience: Methods and Applications; 1 Introduction; 2 Senses and Tourism Research; 3 Psychophysiological Foundations of Senses | |
| 505 | 8 | |a 4 Senses and Related Research4.1 Vision; 4.2 Hearing; 4.3 Smell; 4.4 Taste; 4.5 Touch; 4.6 Other Somatosensory Modalities: Movement, Temperature, and Pain; 5 Capturing Travelerś Senses: Challenges and Possible Solutions; 6 Conclusions; References; The Quantified Traveler: Implications for Smart Tourism Development; 1 Introduction; 2 Emergence of the Quantified Traveler and Wearable Technologies; 2.1 The Quantified Traveler and Context-Awareness; 2.2 The Quantified Traveler and Ordinary Life; 3 The Quantified Traveler and Smart Tourism Development; 4 Conclusion; References | |
| 505 | 8 | |a Part III: Tourism GeoanalyticsGeospatial Analytics for Park and Protected Land Visitor Reservation Data; 1 Introduction; 2 Working with PPL Reservation Data Sets; 2.1 Lessons from Private Sector Tourism; 2.2 Preprocessing and Enriching PPL Reservation Data; 2.2.1 Enrichment from Visitor Origin Geography; 2.2.2 Enrichment of PPL Destinations Attributes; 2.3 Data Reduction and Geographic Data Mining; 2.4 Utilizing Information Generated from Geographic Data Mining; 2.5 Geovisualization for Pattern Interpretation of PPL Demand Populations | |
| 505 | 8 | |a 2.6 Geovisualization for Pattern Interpretation of PPL Destinations3 U.S. Federally Managed PPL Reservation Data Set Example; 4 Geovisualizations for U.S. Federally Managed PPLs; 5 The Past, Present and Future of U.S. Federally Managed PPL Reservation Data; 6 Conclusions; References; GIS Monitoring of Traveler Flows Based on Big Data; 1 Introduction; 2 Literature Review; 3 Tourist Flow Analysis; 4 GIS Analysis of Tourist Flows; 5 Methodology; 6 Data Description; 7 Results; 8 Conclusions; References; Part IV: Web and Social Media Analytics: Concepts and Methods | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a This 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. It is divided into five parts: Part one on travel demand analytics focuses on conceptualizing and implementing travel demand modeling using big data. It illustrates new ways to identify, generate and utilize large quantities of data in tourism demand forecasting and modeling. Part two focuses on analytics in travel and everyday life, presenting recent developments in wearable computers and physiological measurement devices, and the implications for our understanding of on-the-go travelers and tourism design. Part three embraces tourism geoanalytics, correlating social media and geo-based data with tourism statistics. Part four discusses web-based and social media analytics and presents the latest developments in utilizing user-generated content on the Internet to understand a number of managerial problems. The final part is a collection of case studies using web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging online reviews in the hotel industry, and evaluating destination communications and market intelligence with online hotel reviews. The chapters in this section collectively describe a range of different approaches to understanding market dynamics in tourism and hospitality. | ||
| 590 | |a SpringerLink |b Springer Complete eBooks | ||
| 650 | 0 | |a Tourism. | |
| 650 | 0 | |a Web usage mining. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Xiang, Zheng, |e editor. | |
| 700 | 1 | |a Fesenmaier, Daniel R., |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Analytics in Smart Tourism Design. |d [Place of publication not identified] : Springer Verlag 2016 |z 9783319442624 |w (OCoLC)953709276 |
| 830 | 0 | |a Tourism on the verge. | |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-3-319-44263-1 |
| 992 | |c NTK-SpringerBM | ||
| 999 | |c 97856 |d 97856 | ||
| 993 | |x NEPOSILAT |y EIZ | ||