Recently, while researching how tourism in Panama has evolved since the global pandemic of 2020, I came across official reports analyzing the performance of tourism in Panama during 2024. One of these documents included a statistical summary presented to go over key metrics. While examining this report, I noticed some graphs that felt cluttered and underdeveloped, and it ocurred to me that it would be an interesting project to reimagine these graphs and share my process as I try to enhance its readability and effectiveness. That’s what i’ll be sharing on this post.

Table of Contents

1. Identifying the Structure of the Original Graph
2. Highlighting the Issues of the Original Graph
   1. Issue #1 – Lack of clear visual hierarchy
   2. Issue #2 – Overloaded data plot
   3. Issue #3 – Redundant explainer paragraph
3. Building a Solution that Adresses the Issues
4. Closing Thoughts

Identifying the Structure of the Original Graph

I’ve prepared a simple adaptation of the original graph to maintain a consistent color scheme and language in this post but you can see the original here. The graph we’ll be working with can be roughly split into 5 elements:

1. The Graph Title: It presents the subject matter for the graph. In this case, Visitor Arrivals per Point of Entry and Their Distribution.
2. Key Context Numbers: This line of text introduces the total number of international visitors observed during 2024 (2,229,989 Visitors) and the percentage difference relative to the number of visitors observed during 2023 (8.6%)
3. Data Plot: The data plot of the graph is organized in four rows that correspond to the relevant points of entry for international visitors and displays data over three columns: the first column lists the percentage difference relative to the number of visitors observed during 2024 vs. 2023 in each point of entry, the second column shows a horizontal bar that serves as a visual reference for the proportion of visitors arriving through each point of entry and a simple vector graphic to illustrate what the diferent points of entry are associated with, and the third columns shows labels for both the proportion presented with the bar plot and the name of the point of entry relevant to each row.
4. Explainer Paragraph: Short paragraph that goes over the data presented in the Key Context Number element in a verbose manner.
5. Source Notes: Provides further context as to what qualifies as an international visitor and it references the data source for this graph.

Now that we know what makes up our graph lets take a look at what can be improved

Highlighting the Issues of the Original Graph

Issue #1 – Lack of clear visual hierarchy

Most of the cultures of the western hemisphere and some of the eastern hemisphere have the ingrained habit of reading from left to right and top to bottom. With that in mind, the content of an effective graph intended for these audiences will be arranged so that the reader has all the relevant context as they read and ideally never has to backtrack or skip ahead to understand its content. The ways in which this graph disregards visual hierarchy range all the way from minor, like not aligning the Graph Title and Key Context Numbers to the left; to mild, as in not having the same two elements be obvious enough (say bolder letters, larger font size) that it wont be dismissed in favor of the data plot; to major, where the name of the point of entry for visitor arrivals is positioned after all the data points related to it instead of before, meaning readers have to skip ahead or backtrack to make sense of it.

Issue #2 – Overloaded data plot

The original data plot tries to present too much information at once, making it difficult to read intuitively. It resembles a table more than a graph, which is not ideal for visual communication. What is specially jarring to me is the first “column” that provides interesting data in the percent difference of arrivals vs. 2023 in each point of entry but it only exists as numbers in labels beacuse it has no corresponding visual component to provide a sense of scale. In addition to that, the small vector illustrations provide a small amount of context but they are not necesary to understand the data and we would be better off without them.

Issue #3 – Redundant explainer paragraph

The Explainer Paragraph repeats what is presented in the Key Context Numbers, but avoids providing any further context on the Data Plot which is where it might be needed the most. Specially in a report presented as a statistical summary a explainer paragraph should be used to help a reader understand what can be learned from our plot.

Building a Solution that Adresses the Issues

Issue #1 focuses on the visual hierarchy of the elements in the graph. Among other issues it highlights how the Graph Title and Key Context Numbers are presented in contradiction with human intuition. The following adjustments were made to counteract this.

• The Graph Title was changed for a simpler title with larger bolder letters and its closer to the left margin.
• Key Context Numbers were made larger to the the point they are impossible to miss. A brief description guides the reader and avoids the need to add an explainer paragragh about it later on.