Last call for Tourism: Digital transformation or dead Trends analysis (2000-2020) using R for Data Visualization

Introduction

The dynamics in the global tourism sector shows that before the year 2000, the global tourism business was doing very well. However, there is a dramatic drop in the tourism related activities that led to loss in revenue as well as the contribution of tourism revenue to the global GDP. In essence, at the start of the year 2020, it appeared that the effect of COVID-19 pandemic was turning the world of tourism business upside down. As a result, the global tourism operations suddenly ran down to a halt (Spencer et al, 2021). Following the most recent data, the estimated tourism revenue for the year 2020 was expected to grow by approximately $150B, but in reality, it was slashed by approximately $315B. This was a direct adverse effect of the COVID – 19 pandemic, a significant drop by 40% decrease.

Analysis

Following the available data obtained from the online data bank (https://www.statista.com/), three datasets have been collected and analyzed. These include the travel leisure expenditure from the year 2000 to 2019, contribution of tourism revenue to the global GDP from 2006 to 2018, and finally, the loss in revenue in various countries between 2019 and 2020 (Lock, (2021). The analysis was done using R Studio coding for visualization and using datasets well-organized in text data (.csv).

Results

Contribution of Tourism Revenue to the Global GDP

Global tourism revenue contributes to the global GDP as shown in figure 1 below.

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Figure 1: Contribution of Tourism Revenue to GDP (Billions of US$)

It is observed that there is a constant growth of revenue from the year 2006 to 2018, except in the year 2009, when there was a slight reduction in the contribution and in 2015. The same contribution of revenue to GDP is presented in figure 2 below, in a line graph to demonstrate the trend of changes in the contribution values across the years.

Figure 2: Variation of Revenue Contribution to GDP

Still on revenue, a linear regression line was plotted using the data as shown in figure 3 below.

?Figure 3: Linear Regression (Revenue Contribution vs. Years)

The result was that there is a positive correlation between the years and the contribution of the tourism revenues to the GDP. The regression line can be used to predict the value of revenue contribution for the subsequent years assuming the linear relationship is maintained.

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?Tourism Travel Leisure Spending

The second element of data analysis was the amount of money that tourists spent on leisure travels. This data as analyzed for the period between 2000 and 2020.

Figure 4: Leisure Travel Expenses

The results in this test were consistent with the result of contribution of tourism revenues to the global GDP. There was a steady increase in the leisure spending across all the years except with the slight reduction in 2009 and 2015. In 2020, there was a sudden large drop from $4692 billion to $2373 billion. This is attributed to the inception of COVID-19 pandemic. The trend of tourist leisure spending is better demonstrated in the line graph in figure 5 below.

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Figure 5: Trend of Tourism Leisure Spending

To predict the possible leisure spending for the future, the regression line in figure 6 below shows a positive correlation between the leisure spending and the years, in spite of the presence of COVID – 19 pandemic.

?Figure 6: Linear Regression (Tourist Leisure Travel Spending vs. Years)

Total Contribution of Tourism Revenue to the Global GDP

The total global tourism revenue contributes to the global GDP as shown in figure 7 below.

Figure 7: Total Contribution of Tourism Revenue to GDP (Billions of US$)

It is observed that there was a constant growth of the total revenue contribution to the GDP from the year 2006 to 2018, except in the year 2009 and 2015, when the total revenue contribution to GDP slightly reduced. The trend of total contribution of revenue to GDP is presented in figure 8 below, in a line graph.

Figure 8: Variation of Total Revenue Contribution to GDP

Still on the total revenue contribution, the prediction of the contribution for the subsequent years can be done using the linear regression line as shown in figure 9 below.

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?Figure 9: Linear Regression (Total Revenue Contribution vs. Years)

The regression was that there is a positive correlation between the years and the total revenue contribution to the GDP.

Revenue Losses Due to COVID-19

There were many revenue losses at the end of the year 2020, due to COVID-19, that affected the tourism industry in many countries globally. The analysis of the revenue losses is shown in figure 10 below, with only a few countries with the highest losses.

Figure 10: Countries with the Top Losses Globally (Million US$)

The analysis shoes that the US was the most affected country globally, with $147,245 Million.

The contemporary global market industry exhibits a lot of potential in the tourism industry in spite of the effect of COVID 19. This is due to the leveraging on various strategies to attract tourists from all over the world. This notion is attributed to the high volume of revenue countries normally obtain from the tourists as well as the spill effects of tourist, which includes creation of employment among local citizens who are employed in tourist hotels and travel companies as well as those with businesses involving selling cultural elements to the tourists.

Discussion

From the analysis of the data given, the global tourism industry has the potential of growth and contribution to the global GDP.?However, the sudden drop in 2020 and the sharp reduction in the revenue for each country is a direct effect of the COVID-19 pandemic. There is therefore, need for reforms of bilateral relationships between the countries to mitigate the economic effect of the pandemic by reducing the costs of tourism activities such as travels, leisure activities and hotel services (Sigala, 2020) and a tremendous digital transformation implementing across cities the most significant, revolutionary and valuable technology introduced the last 20 years- high bandwidth wireless networks and a whole range of top cloud based apps and platforms-. Without those networks automatization and digitization, neither the cloud, smartphone, nor AI/ML would be much of a value. And that′s what happens whenever you retailer, or public administration force to citizenship to use obsolete hardware solutions or non-digital solutions from the past. Tourism is obviously affected by a number of factors such as personal fears, risk of insecurity, cross-cultural conflicts and unfavorable bilateral relations. From the data analysis in this article, the US as a country has suffered massive revenue loss of revenue. The US therefore, requires serious attention to recover its tourism business.

Conclusion

In conclusion, there is reasonable evidence to support the fact that the tourism industry has a potential for future growth in spite of the existence of the global COVID – 19 effects. The need for personal leisure attracts more tourists to various destinations globally. Additionally, success in the fight of COVID – 19 will obviously favor tourism industry boosted by digital transformation and take it back on the original track of 2019 before the sudden drop was realized and before massive revenue losses occurred in many countries. Additionally, there is need for an effective analysis of the number of tourists visiting the various destinations across the years. The challenge also was the unavailability of data on the population of tourists. It is time for investors to support companies commanding a profound automatization by all means firmly putting the user experience at the core of all the stages of the value chain.

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References

Lock, S. (2021). Global tourism industry - statistics & facts. Travel, Tourism & Hospitality, 2021. Available At: https://www.statista.com/topics/962/global-tourism/.

Sigala, M. (2020). "Tourism and COVID-19: Impacts and Implications for Advancing and Resetting Industry and Research". Journal of Business Research.

Spencer, A., Tarlow, P. E., Gowreesunkar, V. G., Maingi, S. W., Roy, H., Micera, R., ... & Lane, W. (2021). Tourism Destination Management in a Post-Pandemic Context, New York, Emerald.

This is my first article using R and coding and brain.

Annex:

Code?

> Data <- read.csv("C:/Users/USER/Desktop/DATA/Contribution.csv")

> View(Data)

> Year<-Data$Characteristic

> ContToGDP<-Data$Direct_Contribution

> TotContToGDP<-Data$Total_Contribution

> model <- lm(TotContToGDP ~ Year, data = Data)

> summary(model)

> summary(Data)

Result

?Characteristic Direct_Contribution Total_Contribution

?Min.?:2006?Min.?:1629????Min.?:5160???

?1st Qu.:2009?1st Qu.:1916????1st Qu.:6147???

?Median :2012?Median :2256????Median :7263???

?Mean?:2012?Mean?:2217????Mean?:7116???

?3rd Qu.:2016?3rd Qu.:2386????3rd Qu.:7669???

?Max.?:2019?Max.?:2893????Max.?:9258

Call:

lm(formula = TotContToGDP ~ Year, data = Data)

Residuals:

??Min??1Q Median??3Q??Max?

-441.6 -269.1?128.9?180.3?397.9?

Coefficients:

???????Estimate Std. Error t value Pr(>|t|)??

(Intercept) -553734.36?38940.02?-14.22 7.15e-09 ***

Year??????278.68???19.35?14.40 6.18e-09 ***

---

Signif. codes:?0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

eResidual standard error: 291.8 on 12 degrees of freedom

Multiple R-squared:?0.9453, Adjusted R-squared:?0.9408?

F-statistic: 207.4 on 1 and 12 DF,?p-value: 6.178e-09

From the regression, the coefficient of correlation between Year and the total revenue contribution is 278.68 and the intercept is -553734.36. In this case, the predictive model is:

Total_Contribution = 278.68 * Year - 553734.36

Using this model, it is possible to predict the Total_Contribution for 2021, 2022 and any other future year as shown below.

Year 2021: Total_Contribution = 278.68 * 2021 - 553734.36 = 9477.92

Year 2022: Total_Contribution = 278.68 * 2022 - 553734.36 = 9756.6