{"id":645,"date":"2021-11-03T17:50:25","date_gmt":"2021-11-03T17:50:25","guid":{"rendered":"https:\/\/pressbooks.ulib.csuohio.edu\/understanding-literacy-in-our-lives\/?post_type=chapter&p=645"},"modified":"2021-11-03T18:00:23","modified_gmt":"2021-11-03T18:00:23","slug":"645","status":"publish","type":"chapter","link":"https:\/\/pressbooks.ulib.csuohio.edu\/understanding-literacy-in-our-lives\/chapter\/645\/","title":{"rendered":"6.1.1 Three questions to ask yourself next time you see a graph, chart or map"},"content":{"raw":"

<\/h1>\r\n
White House Coronavirus Task Force members reference a misleading chart in a press briefing.\r\nAP Photo\/Alex Brandon<\/a><\/span><\/figcaption><\/figure>\r\nCarson MacPherson-Krutsky<\/a>, Boise State University<\/a><\/em><\/span>\r\n\r\nSince the days of painting on cave walls, people have been representing information through figures and images. Nowadays, data visualization experts know that presenting information visually<\/a> helps people better understand<\/a> complicated data<\/a>. The problem is that data visualizations can also leave you with the wrong idea \u2013 whether the images are sloppily made or intentionally misleading.\r\n\r\nTake for example the bar graph presented at an April 6 press briefing<\/a> by members of the White House Coronavirus Task Force. It\u2019s titled \u201cCOVID-19 testing in the U.S.\u201d and illustrates almost 2 million coronavirus tests completed up to that point. President Trump used the graph to support his assertion that testing was \u201cgoing up at a rapid rate<\/a>.\u201d Based on this graphic many viewers likely took away the same conclusion \u2013 but it is incorrect.\r\n\r\nThe graph shows the total cumulative number of tests performed over months, not the number of new tests each day.\r\n\r\n[embed]https:\/\/datawrapper.dwcdn.net\/pG025\/2\/[\/embed]\r\n\r\nWhen you graph the number of new tests by date, you can see the number of COVID-19 tests performed between March and April did increase through time, but not rapidly. This instance is one of many when important information was not properly understood or well communicated.\r\n\r\nAs a researcher of hazard and risk communication<\/a>, I think a lot about how people interpret the charts, graphs and maps<\/a> they encounter daily.\r\n\r\nWhether they show COVID-19 cases, global warming trends, high-risk tsunami zones, or utility usage, being able to correctly assess and interpret figures allows you to make informed decisions. Unfortunately, not all figures are created equal.\r\n\r\nIf you can spot a figure\u2019s pitfalls you can avoid the bad ones. Consider the following three key questions the next time you see a graph, map or other data visual so you can confidently decide what to do with that new nugget of information.\r\n

What is this figure trying to tell me?<\/h2>\r\nStart by reading the title, looking at the labels and checking the caption. If these are not available \u2013 be very wary. Labels will be on the horizontal and vertical axes on graphs or in a legend on maps. People often overlook them, but this information is crucial for putting everything you see in the visualization into context.\r\n\r\nLook at the units of measure \u2013 are they in days or years, Celsius or Fahrenheit, counts, age, or what? Are they evenly spaced along the axis? Many of the recent COVID-19 cumulative case graphs use a logarithmic scale, where the the intervals along the vertical axis are not equally spaced. This creates confusion for people<\/a> unfamiliar with this format.\r\n\r\n[embed]https:\/\/www.msnbc.com\/msnbc\/embedded-video\/mmvo80534597724[\/embed]\r\n
A March 12 broadcast of \u2018The Rachel Maddow Show\u2019 included a graph with unlabeled numbers and a tricky horizontal axis.<\/span><\/figcaption><\/figure>\r\nFor instance, a graph from \u201cThe Rachel Maddow Show\u201d on MSNBC<\/a>, showed coronavirus cases in the United States between Jan. 21 and March 11. The x-axis units on the horizontal are time (in a month-day format) and the y-axis units on the vertical are presumably cumulative case counts, though it does not specify.\r\n\r\nThe main issue with this graph is that the time periods between consecutive dates are uneven.\r\n\r\n[embed]https:\/\/datawrapper.dwcdn.net\/yzUp1\/5\/[\/embed]\r\n\r\nIn a revised graph, with dates properly spaced through time, and coronavirus diagnoses plotted as a line graph, you can see more clearly what exponential growth<\/a> in the rate of infection really looks like. It took the first 30 days to add 33 cases, but only the last four to add 584 cases.\r\n\r\nWhat may seem like a slight difference could help people understand how quickly exponential growth can go sky high and maybe change how they perceive the importance of curbing it.\r\n

How are color, shape, size and perspective used?<\/h2>\r\nColor plays an important role<\/a> in how people interpret information. Color choices can make you notice particular patterns or draw your eye to certain aspects of a graphic.\r\n
\"\"
Oregon landslide susceptibility.<\/span>\r\nOregon Department of Geology and Mineral Industries<\/span><\/span><\/figcaption><\/figure>\r\nConsider two maps depicting landslide susceptibility, which are exactly the same except for reversed color schemes. Your eye may be be drawn to darker shades, intuitively seeing those areas as at higher risk. After looking at the legend, which color order do you think best represents the information? By paying attention to how color is used<\/a>, you can better understand how it influences what stands out to you and what you perceive.\r\n\r\nShape, size and orientation of features can also influence how you interpret a figure<\/a>.\r\n
\"confusing<\/a>
What industries employ Coloradans?<\/span>\r\nHemispheres<\/a><\/span><\/figcaption><\/figure>\r\nPie charts, like this one showing employment breakdown for a region, are notoriously difficult to parse. Notice how hard it is to pull out which employment category is highest or how they rank. The pie chart\u2019s wedges are not organized by size, there are too many categories (11!), the 3D perspective distorts the wedge sizes, and some wedges are separate from others making size comparisons almost impossible.\r\n\r\n[embed]https:\/\/datawrapper.dwcdn.net\/yCDTo\/2\/[\/embed]\r\n\r\nA bar chart is a better option for an informative display and helps show which industries people are employed in.\r\n

Where do the data come from?<\/h2>\r\n
\"screen<\/a>
Survey posted on \u2018Lou Dobbs Tonight,\u2019 requesting viewers vote on Twitter about Trump\u2019s performance.<\/span>\r\nFox Business Network<\/a><\/span><\/figcaption><\/figure>\r\nThe source of data matters in terms of quality and reliability. This is especially true for partisan or politicized data. If the data are collected from a group that isn\u2019t a good approximation of the population as a whole, then it may be biased.\r\n\r\nFor example, on March 18, Fox Business Network host Lou Dobbs polled his audience with the question \u201cHow would you grade President Trump\u2019s leadership in the nation\u2019s fight against the Wuhan Virus?\u201d\r\n
<\/div>\r\nImagine if only Republicans were asked this question and how the results would compare if only Democrats were asked. In this case, respondents were part of a self-selecting group who already chose to watch Dobbs\u2019 show. The poll can only tell you about that group\u2019s opinions, not people in the U.S. generally, for instance.\r\n\r\n[Get facts about coronavirus and the latest research.<\/em> Sign up for The Conversation\u2019s newsletter.<\/a>]\r\n\r\nThen consider that Dobbs provided only positive responses in his multiple choice options \u2013 \u201csuperb, great or very good\u201d \u2013 and it is clear that this data has a bias.\r\n\r\nSpotting bias and improper data collection methods allows you to decide which information is trustworthy.\r\n

Think through what you see<\/h2>\r\nDuring this pandemic, information is emerging hour by hour. Media consumers are inundated with facts, charts, graphs and maps every day. If you can take a moment to ask yourself a few questions about what you see in these data visualizations, you may walk away with a completely different conclusion than you might have had at first glance.\"The\r\n\r\nCarson MacPherson-Krutsky<\/a>, PhD Candidate in Geosciences, Boise State University<\/a><\/em><\/span>\r\n\r\nThis article is republished from The Conversation<\/a> under a Creative Commons license. Read the original article<\/a>.","rendered":"

<\/h1>\n
\"image\"
White House Coronavirus Task Force members reference a misleading chart in a press briefing.
\nAP Photo\/Alex Brandon<\/a><\/span><\/figcaption><\/figure>\n

Carson MacPherson-Krutsky<\/a>, Boise State University<\/a><\/em><\/p>\n

Since the days of painting on cave walls, people have been representing information through figures and images. Nowadays, data visualization experts know that presenting information visually<\/a> helps people better understand<\/a> complicated data<\/a>. The problem is that data visualizations can also leave you with the wrong idea \u2013 whether the images are sloppily made or intentionally misleading.<\/p>\n

Take for example the bar graph presented at an April 6 press briefing<\/a> by members of the White House Coronavirus Task Force. It\u2019s titled \u201cCOVID-19 testing in the U.S.\u201d and illustrates almost 2 million coronavirus tests completed up to that point. President Trump used the graph to support his assertion that testing was \u201cgoing up at a rapid rate<\/a>.\u201d Based on this graphic many viewers likely took away the same conclusion \u2013 but it is incorrect.<\/p>\n

The graph shows the total cumulative number of tests performed over months, not the number of new tests each day.<\/p>\n