Critique charts can develop visualizations in many different aspects. Visualizations are now more popular than before. These days the computer makes creating graphs easier and quicker than years ago. With this development, it can be seen that graphs are everywhere and about everything. The increasing of charts makes some charts not follow the right roles and scientific basis for building graphs. This article critique two charts from The New York Times in specific terms; the first chart is about the relationship between the country’s income and the total fast-food price paid by people. The other graph evaluates the readability of the privacy policies of 150 popular websites and applications. For each graph, this paper discusses this graph’s main goal, the data that make up the graph, the target audiences, strengths and weaknesses points, ways to enhance the chart, and the reason for importing this chart to critique.

FIRST GRAPH

 

This scatterplot represents the change in wealth and the total fast-food price paid by customers in percentage from 2010 – to 2015 in 50 countries. Both are quantitative variables—this graph is guided for those who want to study and know about the relationship between income and fast-food price change over time.
There are three different types of countries listed in the diagram lower middle income, upper middle income, and high income. It can be noticed that a country with a more significant revenue rise would have greater fast-food businesses. Nevertheless, we cannot conclude that increasing wealth improves fast-food sales because other circumstances may affect wealth, sales, or both. This graph has four types of data dimensions as follows:

• • Country Name (N) – Position (X and Y)
  • Fast Food Sales (Q) – Position (Y)
  • Wealth (Q) – Position (X)
  • Country_Wealth_class (N) – Color.

This graph’s main task is to compare the portion of wealth and the percentage of total fast-food prices paid. The people who created this chart divided it into four sections to include negative values to include more data. Also, the three-class of countries in the graph can compare.

It can be said that the graph partially serves the expected goal. We can compare some countries with each other, which are labeled, but the other not labeled countries do not know what they are, so we cannot compare them with others. Besides, if we want to compare the data per continent, that would not be possible because we don’t know all countries’ names involved in this chart.

This part will discuss the strengths and weaknesses of this chart. This graph has three strengths. The first strength is the wide variety of scaling, which is different than the standard charts that start its axis from zero. It begins at -80% for the x-axis and from -40% for the y-axis. The wide range of scaling provides more data to examine and compare. However, it could cause some confusion for people who are not used to reading graphs. The second strength is that the chart uses four axes. Also, this is not standard, but in this case, the additional axes are required to indicate the cause of negative numbers written in the axes. In addition, the axis labels are beneficial to understanding the graph fast and making it clear, which makes the chart accomplish effectiveness and expressiveness. Lastly, contrasting colors are used in the graph to make it more expressive. The chart designer uses three colors to indicate the countries’ income class: red, orange, and light blue. These different colors make the chart expressive better because the reader of this chart can quickly identify each country by its income class. Which makes this graph achieve a vital property to be a right graph. This chart’s three strengths are the wide variety of scales, well-labeled axes, and the contrasting color used in the graph.

This graph has three weaknesses that will be discussed in the following sentences about the weakness points. The initial foible is that the sample used is insufficient to compare and make decisions. This graph shows 50 countries, which is a small sample of countries. This is maybe misleading for those who want to understand the graph subject, or it was created in this way to deliver specific kinds of information. The second weak point is that the chart is not appropriate to rely on because many points are not labeled. If we want to get accurate results or do research, this chart will not help build reliable results even if it is from a reliable source. There is necessary data not apparent in the graph, like the country’s name. There are just 18 labels, but what about the other 32? The final weakness, the graph focuses on one kind of class more than others: it can be noticed that the lower-middle-income countries’ label is in bold font style, whereas the others are in regular font style. Besides, the lower-middle-income countries were colored dark red. Dark colors between light colors make the chart reader focus on these countries more than other countries, which are in light colors. People tend to focus on the bigger fonts and different colors more than other elements.

Moreover, making the lower-middle-income countries in bold font and red color returns us to the first point, which is may this graph mislead the readers to get specific kinds of information in different ways. According to Mackinlay J.D. (2007), charts can drive great insight and lack it. The three weak points in this chart are there is not enough sample to be examined, a small amount of points is labeled, and the graph focuses on a specific category more than others, using different font styles and colors.

This graph can be enhanced by turning the classes into shapes instead of colors. In addition, countries are distinguished by colors, which makes them easier to identify and easy to compare.

I imported this chart because it compares two popular things relative to people’s lives. Fast-food restaurants are how much people get and how much they spend, especially on a controversial issue. Also, it discusses this issue between different countries that have different cultures. I don’t like the disability to identify other countries that were not labeled in the graph.

SECOND GRAPH

 

This scatterplot shows the difficulty of reading and the time spent reading the privacy policies for different companies’ websites and phone applications. This graph’s target audiences are those interested in reading privacy policies and who are using the internet and social media.
The chart contains the evaluations for privacy policies for most 150 famous phone applications, websites used by people using the internet, and five books. The chart shows three different readability levels: high school reading level, college reading level, and professional career reading level. Furthermore, there are four types of data dimensions in this chart:

• Company_Name (N) – Position (X and Y)
• Read_Time (Q) – Position (X)
• Readability_Score (Q) – Position (Y)
• Reading_Level (N) – Position (Y).

This chart’s primary goal is to compare and categorize these applications and websites through privacy policies’ readability levels. The illustration serves the intended purpose partially. It has distributed the names on the diagram in the right way, which makes it comparable. However, it could serve the goal better if the chart was more interactive. In his book Readings in information visualization, Mackinlay, J.D. (2007) says that using vision influences an excellent visual representation by making it interactive.

The chart has three strengths:

1. The diversity of companies listed in the chart is very varied. There are many categories like news, entertainment, technology, and social media companies. This diversity makes the chart better compare the readability of the privacy policies by category.
2. The graph categorized levels by the readability score because the readability score is unitless; the people who created this chart ranked the reading levels by readability score. It can be easy for readers to understand each score’s weight, making the illustration more expressive.
3. The chart achieves a high quality of effectiveness by placing the main graph elements in the right way, for example, the axis titles.

These titles are written to be clear and direct, which makes the chart more understandable and readable. The strengths that were discussed are the variety of companies type listed in the graph were good to compare, the categories that are used in readability levels, the benefit of understanding the readability scores, and achieving the effectiveness by writing clear axis titles.

On the other hand, the graph has three weaknesses. Firstly, the readability score is unitless that not based on a clear scale. It would be better if the people who created this chart indicated the readability score formula used to measure the score to be clear for the readers and for those who want to analyze this graph to get results on a clear base. Secondly, not all points are labeled, which make the comparison hard. Many famous websites and applications were not listed in the chart, like Apple, Google, YouTube, Instagram, and WhatsApp. These are important and popular websites and phone applications, but they did not record them in the chart. The lack of these data makes the analyses of the chart not reliable. Finally, the reading time reflects only the first chapter of each privacy policy. Reading the first chapter does not reflect the real-time of reading the whole document, leading to misleading and inaccurate results. Many factors determine the reading time, like the number of chapters and word count per document. The reading time measurement needs to be more accurate to get accurate results that can rely on. This weakness leads to a lack of expressiveness. According to Jock Mackinlay (1986), expressiveness principles determine whether a graphical language can reveal the wanted information. This chart’s weaknesses are that the y-axis contains unitless values, much important data is not labeled in the chart, and the reading time is not accurate.

The New York Times makes this graph in the black color, but they can make it more interactive by adding colors and coloring the website and application’s names by their category. If each of these categories were in a different color, that would be better even with those unlabeled dots. That would make the graph more comparable to the current.

I liked this chart because it represents data not popular to know about, even if it needs some work to make it better for those who want to use this graph for scientific purposes, as mentioned in the weaknesses part.