PRELIMINARY APPLICATION OF TOURISM 4.0 DATA ANALYTICS IN ODESSA CITY REVEALS CHALLENGES AND OPPORTUNITIES FOR SUSTAINABLE TOURISM DEVELOPMENT
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Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it