PRELIMINARY APPLICATION OF TOURISM 4.0 DATA ANALYTICS IN ODESSA CITY REVEALS CHALLENGES AND OPPORTUNITIES FOR SUSTAINABLE TOURISM DEVELOPMENT
Notice bibliographique
Résumé
Topicality. Modern tourism is recognized as one of the most important commercial activities globally. In 2019, it generated 10% of the total employment and represented a share of 10.4% of global GDP. The tourism sector in Ukraine as a whole saw slow development after independence in 1991. However, the introduction of a visa-free regime in 2005 boosted the country’s global image and visitor numbers. In 2013, Ukraine was visited by over 26 million tourists, primarily from Eastern Europe, but also from Western Europe, USA, Israel and Canada. However, tourism can impose a number of negative economic, social and environmental impacts on the destination and its region.Aims and tasks. Unfortunately, there remains a persistent gap between the tourism sector (both regulators and operators) and the appreciation and use of emerging technologies such as those applied in Tourism 4.0 that can improve its economic efficiency and environmental sustainability. The general objective of the research reported here was to test the current level and effectiveness of Tourism 4.0 technologies (or more specifically High-Performance Data Analytics - HPDA) under the conditions of a large urban coastal destination with a highly diverse economy that is not solely dependent on tourism. Data collection for the TIM took place between August 2020 and May 2021, in collaboration with the Odessa City Council Department for Culture and Tourism. Overall, 295 questions were addressed. The data could be quantitative (amount of electricity or water used per day), or more subjective expert opinion (whether and when the city suffers from traffic congestion or satisfaction of residents with levels of incoming tourists). The data was also quality controlled and labelled according to its accuracy, type (digital or analogue) and frequency of collection. The target baseline year was 2019, with data from 2018 and 2017 obtained where available for trend analysis. In addition, expert estimations of values for 2020 were also made in order to forecast future demands. Data sources fell into three broad categories: (i) government agencies at regional or national level, including the State Statistics Service of Ukraine and Ministry of Justice; (ii) private enterprises; and (3) civil society organisations.Research results. Among the results, it was found that power consumption did not significantly increase during the summer as a result of increased visitors. In addition, the revenue directly generated from a tourist tax levied on registered accommodation providers in 2019 amounted to Euro 393,100, which was only 0.11% of the city’s total budget of Euro 344,947,580. The amount contributed indirectly from other service providers (catering, entertainment, retail, transport etc) through employment and profit taxes was unknown, as was the amount lost in the informal economy.Conclusion. Through a better understanding of current patterns of tourist visits, visitor demographics and revenues, infrastructure use, resource consumption and stakeholder collaboration, the study aimed to spur innovative touristic services and policies tailored to the local challenges and opportunities. While useful insights were obtained from the TIM analysis, it proved impossible at the present time to create a robust overall model of the tourism sector in the city owing to numerous issues concerning the availability and quality of the data needed.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».