This project studies the travel and tourism population around the world from 2007 to 2015 using the visualizations created in Tableau Public. The report covers the creation of different visualization types including a line chart, a bar chart, and a map. These visualizations clearly depict the change of the population that arrive at or depart a country within a time span of 9 years. The goal was not only to look at the changes but also to figure out some major factors or events that were playing a role on affecting people’s traveling decisions. Moreover, this time-series analysis categorizes countries into different income groups to analyze the relationship between one’s income level and travel willingness.

Example Visualizations

Maternal Risk around the World (2015)

（Source: https://public.tableau.com/en-us/s/gallery/maternal-risk-around-world?gallery=votd)

The first example I found was a study on the probability of maternal death in developing regions in 2015. The author founded out that the maternal death rate in developing regions was a lot higher than in developed regions (1:150 vs 1:4900). A comparison between the two regions were illustrated in light blue and dark blue, and was created in a complete world map, as shown on the top left corner of the visualization. A close-up look of the developing regions was the main focus of this visualization. Regions were assigned with different colors on the map for easy distinctions. Circles were drawn in different sizes to represent the maternal death rate for each region. The larger a circle is, the higher the maternal death rate is for that region. In my visualization, I would like to see what regions/countries have the largest and the smallest population of departures, arrivals, or both, using different circle sizes on the map.

Wildlife Population (2017 – 2022)

(Source: http://www.excel-easy.com/examples/line-chart.html)

The second example that inspired me was a simple line graph. This visualization clearly depicts the estimated amount of three kind of wild animals from 2017 to 2022. Time measurement is placed on the x-axis and the amount is placed on the y-axis. Each line represents an animal, and the trends of the amount going up or down are illustrated in the visualization. Inspired by the line graph, I would like to view the change of population of the world or a certain country that are traveling over time. In this way, we can look at some historical points that had affected people’s traveling decisions and estimate on the future trends.

Iran Missile Launches (2006 – 2017)

(Source: https://public.tableau.com/en-us/s/gallery/iran-missile-launches?gallery=votd)

The third example that inspired me was a visualization that depicts the missile launches in Iran from 2006 to 2017. Similar to the previous examples, this is also a time-series visualization that places the time measurement on the x-axis. The number of missiles launched was placed on the y-axis. However, in this example there is a comparison between missiles that were launched in the same year using a bar chart. Each bar represents one quarter of the year, and each year has four bars. One missile was illustrated as one rectangle, and the total number of missiles launched in the same quarter were clearly shown by the number of rectangles. Similar to this bar chart, I would like to compare the arrivals and departures in each year for all countries or a certain country. My graph will have the same layout as this example where each year has two columns, one showing arrivals and the other showing departures, and the number arrivals and departures will be placed in rows.

Materials

To clean up the data set I sourced from World Bank (http://wdi.worldbank.org/table/6.14) , I used OpenRefine to transpose the columns into rows and downloaded as CSV file. The file was then imported into Tableau Public for further groupings and visualizations. A dashboard was also created to summarize the findings.

https://public.tableau.com/shared/8MD3MS84C?:display_count=yes

Methods

After downloading the original data source in the CSV format, I used OpenRefine to shape the data by transposing the years in columns to rows so that each row has only one value, which was the number of arrival or departure for a certain country in a certain year. To add the complexity of the visualization, I also extracted the data points of ‘income group’ and ‘region’ from a metadata sheet and added them into the csv file. The file was then imported into Tableau Public for visualization creation. To look at the number of traveling of each country on a world map, I placed the countries in the sheet and Tableau automatically recognized them and generated a world map. Then I placed the number of arrivals and departures onto the map and selected to represent the data in size. Tableau generated the data into circles with different sizes. Each circle represents one country, and the bigger the circle is, the number of arrivals and departures is higher. Lastly, I placed income group onto the map as well, represented in colors. Each income group was assigned a color, and these colors were applied on the circles.

For the second visualization, I would like to see a line graph that shows the overall trends of people traveling over time. I placed the year measurement on the x-axis and the number of arrivals and departures on the y-axis. Tableau generated a line graph for me that depicts the change of people traveling for the whole world from year 2007 to 2015. In order to view the trend by country, I assigned the country variable as a filter so that the visualization could be read by selecting wanted country/countries from the filter on the right-hand side. Last thing I wanted to do was a comparison between arrivals and departures in each year. To achieve this goal, I placed the year variable and the category (arrivals and departures were shown in separate columns) on the x-axis. The year variable was set to be discrete so that it could be read as a category but not a numeric value. The number of arrivals and departures was then placed on the y-axis so that it could illustrate the columns with different heights. To compare the trend between arrivals and departures for all countries or a certain country, I created another visualization which I simply placed the year on the x-axis and the number of travelers on the y-axis. A graph with two lines were created, representing the arrivals and the departures over time. The country variable was also assigned as a filter. By selecting a country, the lines representing the trends of arrivals and departures of that country over time could be easily read.

Discussion

After creating a few graphs in Tableau, the following visualizations were closely reviewed and discussed for a deeper understanding of the topic.

In the first visualization above, a world map with different sizes of circles were created to illustrate the number of departures and arrivals for each country. Each circle was also assigned with a color to represent the income level of the country. From the visualization, it can be clearly understood that countries with larger circles had more people traveling outside and visitors coming in. These countries are mostly countries in Europe and North American, such as France, Germany, UK, or US. One common character for these countries are that they all belong to the high income group. China and Russia also have a large population of people traveling in and out and they are grouped into the upper middle income level. Besides, countries with the smallest circles are mostly located in Africa and are grouped into the low income level. Therefore, countries that are in a higher income group are likely to have more people traveling abroad and have more visitors coming in. In the opposite, countries that are in the lower income group tend to have the least traveling population.

Next, I would like to review the overall trend of people traveling from different income level groups. The line graph below clearly shows the change from 2007 to 2015.

As shown in the graph, there is a steady increase for the number of people traveling from countries in the high and upper high income levels. For countries in the lower middle and low income groups, there were not many changes in regard to numbers of arrivals and departures. An interesting point is a downward trend on the high income country line between 2008 and 2009, when the global financial crisis occurred. Therefore, we can conclude that the tourism industry was largely affected by the financial crisis and a lot less people were traveling during that time period.

The third visualization I wanted to look into was a comparison between the number of arrivals and departures in a year. From the bar chart below, we can see that from 2007 to 2010, there were more people traveling abroad from the United States. But from 2011 to 2017, there were more visitors coming into the country.

United States

In order to investigate the trend of number of people traveling in and out of the United States, a line graph was also created. From the graph below, the time point when there were less people departing the country is more clearly represented. which was around the first quarter of 2010. However, if we look at each line as a whole, we can see the impact the financial crisis has brought to other countries, as the number of arrivals to the US dropped to the lowest point at the end of 2008. While the number of American people traveling abroad was going downward in a much steadier trend, which means the financial crisis hasn’t really had much impact on Americans’ traveling situations.

United States

Future Directions

Tableau Public is a powerful visualization creation tool which is suitable for beginners. It only requires a short learning curve for the users to master how to navigate and create their own visualizations so that users could focus more on the data rather than learning a new tool. For my first time experience using Tableau, I created the map, the line graph, and the bar chart. In the future, I would like to try out more visualization types with different datasets, such as the heat map, the pie chart, the area chart, etc. In regard to the topic, I am interested in diving into the visualization deeper to find out more interesting facts and events that would affect the tourism industry, such as the financial crisis event explained in the report.