It is no secret that a majority of the United States thinks poorly of President Donald Trump, especially given his handling of the ongoing COVID-19 pandemic among other issues. But what do the nation’s overseas allies think of him? A Pew Research report published on September 15, 2020 details how the United States’ image waned on the global stage since Trump took office, and the numbers are damning.
South Korea has the highest approval rating of the United States’ image out of thirteen nations surveyed, yet it holds little confidence in President Trump’s ability to lead the U.S. Fourteen points behind South Korea is Italy with a 45% approval rating, while Belgium holds the least favorable view of the United States and the lowest confidence in Trump.
Lollipop charts like this one are a visually-appealing alternative to column or bar charts because they cut down on the amount of space the chart ink takes up; instead, they focus on the data points themselves and showing how they rank among other categories.
Lollipop Charts, like the name infers, look like little lollipops and is used as a visual infographic. Like many other charts, lollipop graphs are used to compare different variables of data. A lot like a bar graph lollipop charts compare the size and ending points of the data set.
As you can see from both charts above, which I made using Microsoft Excel, lollipop charts can be vertical or horizontal depending on the user’s preference. The pros of using a lollipop chart as opposed to a bar graph are that it puts focus on the end data points rather than the whole graph and it allows for less visual distractions. Colors can still be used but in a minimal way that does not make a chart seem overwhelming. Lollipop charts also allow for sharing a lot of data without losing its visual aesthetics.
While looking at tutorials on how to make a lollipop chart and the different tools available, I came across an article. This article titled “Lollipop charts, Why & how, Story Telling with Lollipops” written by Dario Weitz, is a great resource as to why and how to use a lollipop chart. The author touches base on the pro’s, con’s, and even the similarities of using this specific graph.
When looking at a comparison infographic, sometimes it’s difficult to determine the standard for comparing two or more things. One solution is to apply a benchmark to the chart, but there are many ways to go about it.
Benchmark comparisons in the news
As someone who has a professional interest in the natural sciences, it saddens me to look at the news and discover that not everyone in the United States trusts scientists to do the right thing. More often than not, the media cites misinformation and alleged government propaganda as sources for such distrust. However, I do sometimes wonder what opinion people from other countries have of scientists. Recently, I read a Pew Research Center report about the scientific community’s relationship to the general public, and I was surprised that people in countries like India and the Netherlands have far more trust in their nation’s scientists than the American public has in ours.
On the other end of the spectrum, I found myself wondering why nations like Japan and South Korea are skeptical of scientists. They are among the world’s leaders in technological advances, yet the people don’t seem to trust scientists to do the right thing. When compared to median values, the United States exceeds the benchmark, if only by a slight margin, which gives me hope that the vocal minority that distrusts scientists will one day see their error and learn to trust science.
Another way of showing a benchmark is by comparing positive and negative values to determine favorable perspectives. With the United States presidential election fast approaching, the public is more divided than ever on whether or not this year’s elections will be run well. It is likely that a divide in partisan politics and factors like the current COVID-19 pandemic and ongoing police brutality protests are responsible for this immense discourse.
Based on Pew Research Center’s data, I estimate that conservative-leaning voters believe that the election will be handled well. On the other hand, liberal-leaning voters believe that this year’s election will run into a series of blunders or be subject to manipulative interference. As someone who wants to see Donald Trump and his alleged enablers voted out for the sake of the rest of the nation, I am hoping for the best but preparing for the worst.
What about comparisons related to topics I enjoy?
Even when I am not catching up on the news, I still enjoy looking at comparisons for the things I love. For example, I’ve been playing Square Enix’s Final Fantasy XIV Online (shorthand: “FFXIV”) for about a year, and have a strong interest in joining the small community of players who spend most of their time on high-end or difficult pieces of content. When a new piece of content comes out, one of the first places I go to look for information is FF Logs. Their tools analyze combat data and give players a sense of their overall performance and where they can improve. They also have statistical charts that show performance averages for each of the game’s 17 combat classes (jobs). For example, one of the most difficult “raid dungeons” currently in the game goes by the name of “Eden’s Verse: Refulgence.” (shorthand: “E8”). Raids in FFXIV typically have two modes of difficulty, normal and “savage,” with the exception of specially-themed “ultimate” raids. The community typically pays attention to statistics and ranking for savage raids. In the first few weeks of release, Eden’s Verse: Refulgence (Savage) (shorthand: E8S) proved to be a difficult challenge for most raid groups.
A box plot comparing the minimum, lower quartile, median, upper quartile, and maximum damage per second (DPS) for all 17 jobs in one of Final Fantasy XIV Online’s more difficult fights. Source: FF Logs.
Combat jobs in FFXIV are divided into three major roles: tanks, healers, and damage per second (DPS). The box plot chart above shows an aggregate ranking for job performance in E8S over the course of two weeks across all percentiles. DPS jobs typically put out far greater damage than tanks or healers, with magic-based DPS jobs having the highest damage output. The only exception to that rule is the samurai job, which is typically described as being a selfish DPS job that brings little party support to a fight as it deals heavy hits at melee range.
Viewed from a different perspective– say, comparing the maximum and minimum percentiles for E8S damage– certain trends seem to appear among the 17 jobs, and it reflects in how well the player performs in the fight. The best players in the world generally fare much better and clear the raid faster than players who barely have a grasp of their jobs or exhibit poor performance.
A chart comparing the damage per second (DPS) output for E8S, in the maximum and minimum percentiles, across all 17 jobs in Final Fantasy XIV Online (job abbreviations are used). The jobs I play the most are highlighted in color. Source: FF Logs.
Compared to others who play the same jobs as me (color-highlighted in the chart above), I would say that I am somewhere between the two extremes. However, in the case of the job I play the most, red mage, I tend to lean toward the lower end of the spectrum. I recall running a four-person dungeon once with some of my friends a few months before the COVID-19 pandemic exploded. One of them, who went in with a tank job, had a third-party tool open to analyze everyone’s damage output in real-time (which is against the developer’s terms of service, but Square Enix doesn’t crack down on third-party tools if players don’t discuss them publicly in-game), and once the program crunched the numbers, he uploaded the data to FF Logs and sent me a link to the log over Discord. As I looked it over, he laughed in voice chat and likely shook his head at how low my damage output was. Another one of my friends, a healer who did significantly better than me, pointed out that at least I knew my spellcasting priority rotation as a red mage and the random black mage who got matched with our three-player party did less damage than me. I didn’t care that my damage output was sub-par, though. I was happy just to run content with my friends.
Most people look at infographics without actually knowing that they are and how much they have actually been beneficial to us. Little did we know that ever graph or pie chart or even an advertisement was an example of an infographic. They are a quick and easy way to get the point across to the reader without confusing them or spitting a whole bunch of information at them.
There are a whole bunch of cool tools and websites that can help make your infographics exciting and will help draw attention to your readers.
Visme, which is actually a multi purpose site that allows you to make presentations, infographics, advertisements and much more.
Easel.ly, on the other hand, can be used strictly for infographics. They have different templates that cater to different types of audiences and themes that you can choose from.
PiktoChart is really good if you need some quick graphs or charts done.
Other tips for infographics:
Always make sure to make it clear for all types of readers and listeners.
Make it simple. The whole purpose of infographics are to make hard information and graphs easy to understand.
It is okay to have black spaces! The point is to make it visually easy to look at. If you have way too much going it could easily distract those with short attention spans.
Grab their attention. Similar to an essay, you are. going to have to hook the readers in, usually with a title or some kind of visual
Data is everywhere. It’s been said ad nauseam, but advances in technology at scientific methodology have made it possible to collect and display data more efficiently. The major problem, however, is making sense of a set of data. Often, people ask, “What do these numbers mean,” especially when attempting to compare two or more numbers. Thankfully, data visualization makes it easier to comprehend the data and its message. The only problem is figuring out how to tell a story by comparing numbers using data visualization.
Prior to the emergence of the COVID-19 pandemic, I rarely pay attention to the political climate in the United States, opting instead to focus on my academics or escape to virtual worlds via computer gaming. Yet deep down, I’ve always wondered if others like me do pay attention to politics and other news topics, especially where a presidential election is concerned.
How interested have people been in previous elections?
Pulling data from Google Trends, I went ahead to see how interested Americans have been in previous elections since the year 2000. However, the data only goes as far back as 2004, since Google implemented search interest trend analysis around that time.
A chart depicting search interest for the U.S. presidential election since 2004. Source: Google Trends.
There are four significant peaks in search interest (shown in the chart above), and those spikes occurred in November of each general election year– 2004, 2008, 2012, and 2016– when the polls opened to the public on Election Day. In 2004, George W. Bush won his second term as President, though the search interest pales compared to 2016’s peak. That year, businessman Donald Trump ran for office and won the Electoral College vote to become the 45th president of the United States despite losing the popular vote to Hillary Clinton. Initially, in March of that year, Americans thought someone like him wouldn’t be able to secure the presidency. I recall seeing memes on the internet emerging around when he announced his candidacy but thought nothing else of it until he won the election. Based on the data from previous years, I can guess that search interest will spike again this year around Election Day. With issues such as COVID-19 and social unrest in the wake of George Floyd’s death at the hands of police officers, the search interest peak among U.S. voters will most likely dwarf that of 2016’s data.
When the search interest data is viewed another way, say on a map of the nation, it’s evident that some states had a stronger interest in election results than others.
A regional chart depicting election search interest by state. Source: Google Trends.
Some regions on the map, such as the District of Columbia and smaller states like New Jersey, do not have data labels tied to them due to graphing limitations in Excel. Still, Google Trends does show that the District of Columbia has the highest volume of interest when it comes to the presidential election. After that, New Jersey comes in second on a top-five comparison chart, followed by North Dakota, Maine, and Alaska.
The data provided by Google trends gives a general idea of how interested Americans are in general elections. But what do those numbers look like among demographics, and what do Americans look for in a candidate?
Breaking down election interest among demographics
Moving forward, I decided to pay a visit to the Pew Research Center’s website, which holds a repository of data trends for this year’s presidential election. The topics covered range from election-related social movements to hot-button issues such as COVID-19 and police reform.
One issue that Donald Trump has made a fuss about for the past four years is voter fraud, but it seems that more Americans are paying attention to those claims, especially where fraud resulting in Trump’s re-election is concerned. President Trump has allegedly made moves to attempt to block mail-in voting, a move that health experts say is more pandemic-friendly than in-person voting. Political analysts believe that if Americans are encouraged to vote by mail, the results could spell the end of Donald Trump’s presidency. Moreover, there is media documentation of President Trump crying foul on mail-in voting as a means of committing voter fraud.
However, the Pew Research Center’s data shows that it’s mostly Republican voters who share President Trump’s sentiment about voter fraud. Democrats, on the other hand, do not seem to be concerned about the issue. Furthermore, the research shows that voter preference in news sources may influence political opinion. The disclaimer states that Republican voters tend to watch Fox News or listen to radio talk shows, while Democrats prefer to get their news from MSNBC and the New York Times.
Political divide based on news source is one matter facing the nation this election year, but influence from conspiracy theorists and foreign powers is quite another. Misinformation propagated by groups such as QAnon and Russian government-backed “troll farms” has led to confusion about the election, even paranoia, and cases of domestic terrorism.
Roughly 47% of registered American voters have heard about QAnon. Among those who have heard o the conspiracy theory group, 40% of Republican-leaning voters believe that QAnon’s claims chalk up to being a good thing. On the other hand, 90% of Democratic-leaning voters believe that the misinformation QAnon spreads is bad for the country. Moreover, QAnon isn’t the only roadblock in getting clear-cut, unbiased information about the election. Since Donald Trump’s election, numerous reports have been published exposing negative influence on the United States’ affairs from global powers such as Russia. Many of those reports center on online “troll farms” funded and backed by the Kremlin. Their purpose is to flood social media sites such as Facebook with misleading information about Trump’s opponents in the hopes that their false claims would be shared by unsuspecting and uneducated users. Even worse, many Americans fear that hackers supported by foreign governments will alter the election results or create other forms of chaos that would impact the election.
Two years ago, 67% of Americans in both parties believed that Russia would attempt to influence the U.S. elections in some capacity, with 50% of Republicans believing that Russia would interfere with the election compared to 80% of Democrats who expect it. The Pew Research Center asked the same question twice this year amid the COVID-19 pandemic, and the results show an increased concern in election interference by the Russians or other foreign countries. 75% of Americans reported in August that they are concerned that Russia would try to interfere with the election, likely in favor of Donald Trump; 62% of Republicans and 88% of Democrats said the chance of foreign interference occurring would be possible.
What surprised me the most was a report on Catholic voters. According to the Pew Research Center, Catholics in the United States are “split down the middle politically,” with 48% leaning toward Republican policy and 47% leaning toward the Democratic approach. Furthermore, Catholics are swing voters: in 2016, 52% of Catholics voted for Donald Trump versus 44% who voted for Hillary Clinton, while the percentage clocked in at 54% to 45% in favor of Barack Obama when he ran against John McCain in 2008.
As someone who grew up in a Catholic household that often voted Republican, I can attest to how my parents’ beliefs shaped mine, at least until I arrived at an age where I decided to forge my fate. Once I became a registered voter, I turned my back on voting Republican vs. Democrat, opting to support independent candidates despite not knowing anything about politics. That was the case until this year when COVID-19 took the world by surprise and changed everything. Still, I carried on and shut politics out of my mind, once more focusing on my studies and learning more about the virus. Then a report came out exposing Donald Trump for covering up the seriousness of the pandemic. I had a feeling he was downplaying the issue to keep the American public from panicking. Still, the fact that he made moves to politicize the plague for his benefit infuriated me to no end. It was then that I knew that he had to go, for the sake of the nation’s health.
Conclusion
Data visualization can help voters make informed decisions on candidates and national issues and reflect trends in voting patterns. It is especially apparent in the partisan divide between Republican and Democrat voters today, though it doesn’t have to be that way. Regardless of your political beliefs, it is imperative to know that every voice counts, including yours. Now more than ever, the fate of the United States rests in her people’s hands. Whether you choose to go in-person to the polls or submit your ballot by mail…
Each day, data is being collected all across the world. There are hundreds upon thousands of different topics such as psychology, healthcare, and consumer analysis, many of which are free to access. This can be extremely useful for future projects or if you are just looking to expand your knowledge on the comparison of these numbers. Today, I have listed a couple great examples of free datasets that can be used for any project as well as some websites to access even more datasets.
Mall Customers Dataset
Found on https://data-flair.training/blogs/machine-learning-datasets/
This free dataset focuses on the collection of people at the mall. This is not limited to one specific subject such as age but gender, annual income, spending score are all included. This would be a great dataset to use for the comparison of males and females. Potential projects are also listed here which can be extremely helpful for those wanting to create an infographic but maybe do not know where to start.
2. Heart Disease Dataset
Found on https://www.cdc.gov/nchs/fastats/heart-disease.htm
For those who are interested in anything related to health, the site listed above is a great resource for this topic. These include datasets relating to mortality, morbidity, and disease. All of which contain multiple pages of data collection.
3. Website: Piktochart Blog
Found from https://piktochart.com/blog/100-data-sets/
This is a great website that lists 50 different datasets that are mostly free to access. These categories include finance, internet, etc. (see picture above). With all of these options, you will be able to choose which one works best for you.
4. The World Bank
Found on https://data.worldbank.org/
With the ability to search any topic you want, the possibilities are endless. The site also contains other features such as a Microdata Library, the ability to open a Data Catalog, Finances, Projects & Operations and more. This is another great website if you are looking to find more free datasets but maybe don’t know where to start.
Conclusion
Overall, there are thousands of datasets online. Some being free, while others requiring a subscription. However, with these free resources there are plenty of options to choose from. The ability to compare numbers of a particular topic whether this is gender, age, ethnicity, or countries is very important and useful in society. The possibilities and projects available are endless.
This week I had the pleasure of exploring VizHealth.org, a data visualization site for the medical field run by the University of Michigan. My initial impressions on the site were mixed at best, as I was not sure how useful a site categorizing visualizations specific to the medical field would be to other areas. I am happy to say that this impression was completely untrue and I will most likely use VizHealth as a reference for any future infographic projects.
A Brief Synopsis
The opening page of VizHealth
VizHealth serves as a database detailing a wide variety of infographic examples and their potential uses. The two main features of the site are its gallery of infographic examples and, more importantly, a search wizard for finding infographic designs. Given the wide range of designs and searchable tags the site presents, they thankfully provide a simple but concise guide on how to navigate the site and attribute any of designs for your own use.
The VizHealth Wizard
Serving as the main attraction of the site, the VizHealth wizard is an easy to use tool that allows you to quickly search for specific infographic designs based on a variety of criteria.
When I first viewed the wizard, I was surprised at how simplified the search criteria were. The eight primary goals were easy to understand while also being as non-specific as possible so that you could apply them other datasets that were not medical related.
Selecting a graphic style also provided a short example of how the described graphic would be used and poses several questions that might help you confirm whether or not you choose the correct category. I found this to be a very useful addition as I had difficulty fully understanding the difference between the second and third to last options on the list. The filters for exact numbers and basic concepts were also useful features for determining graphic styles based on whether the graphic is meant for the general public or a more professional audience.
The Graphics Gallery
While I found the Wizard to be helpful overall if you are looking to design an infographic, I was much more interested in the actual graphics browser that accompanies it. This browser displayed about 50 different styles of graphics that all feel under the purpose categories that the Wizard describes. While there were quite a few styles that I was familiar with, there were also significant number that were new to me or that I had never really considered to be infographics.
For example, we all have most likely seen this table at a doctor’s office and I had never realized it might be considered an appropriate way of displaying data. A variety of the presented graphics directly address this issue of determining if a graphic is appropriate for conveying certain types of data and the impacts they have had historically.
The “How did it do?” tab is the perfect addition to the site, as it allowed me to learn more about how well these different styles performed and any issues that arose from their use.
Final Thoughts
Overall, VizHealth seems to be perfect tool to get a wide-scale idea of the various styles of infographics that have been used in the past and provides fantastic information on how effective each is at their prescribed role. The Wizard is a great jumping off point for designing infographics when you are unsure of what kinds of visualizations are appropriate for your data set. I will definitely be using this site as a resource for when I have to
When it comes to a person’s health, having the right data can make a world of difference. But sometimes it’s hard to make sense of the data when it isn’t condensed. What does it mean when the doctor says their patient’s blood pressure is X-over-Y? Will this medication do more harm than good? What should a patient to do reduce their risk factors?
This week, I paid a visit to Visualizing Health (VizHealth), a data visualization style guide specifically designed for communicating health data. Going into this project, my main concern was COVID-19, so I hoped that they had at least some content related to the coronavirus.
First Impressions
The home page for VizHealth.
When I arrived at the VizHealth home page, the first thing I noticed was the University of Michigan logo. I moved my mouse over it and the Robert Wood Johnson Foundation logo, but there were no links to either program. Other than that, the page itself had a simple design with a limited color palette and several key links.
About VizHealth
VizHealth results from a collaborative effort between the Robert Wood Johnson Foundation and the University of Michigan Center for Health Communications Research. Their goal is to better communicate data for a variety of health issues through numerous scientific studies. In turn, the VizHealth team hopes that the graphics they designed will provide inspiration to other health data visualization designers who want to tell a different story with their data.
At the top of the “About VizHealth” page, they include a brief video detailing their mission and methods:
At the core of the program is the data visualization wizard, where users answer a few questions to help them select the right graphic for what they want to convey to their audience. Then, there’s the visualization gallery, which contains a variety of graphics depicting different design concepts. Finally, VizHealth’s resources page includes a sample risk calculator and a short comic detailing two real-world applications of health visualization.
The Wizard (of VizHealth)
The first tool on the site is the Data Visualization wizard. Two questions greeted me when I opened the wizard: “What is your primary goal for communicating this specific piece of risk information?” and “Do people need to know or remember exact risk numbers or is it enough for them to get the basic idea (the gist) about the risk?”. Put in simpler terms, the wizard asked me for the purpose of my data and how much of said data I wish to convey to my audience.
For the first question, the wizard gives explanation tooltips for each choice to help users decide what story they want to tell with their data. For example, the second option details risk awareness, and they use a measles outbreak as their source. They also ask the user to consider if they meant to tell a different story; clicking the second option in the tooltip redirects them to another choice. Once I’ve made my selection, the site brought me to a confirmation page where I can continue onto the visualization gallery (discussed below) or change my answers.
Visualization Gallery
VizHealth’s visualization gallery holds 54 pieces of data visualization examples for users to browse, filter, and examine. Immediately, I found myself checking off filters to sort through the examples provided, though I was somewhat disappointed when I saw that there was no option to display COVID-19-related visualizations.
The design I chose pulled information from a 2014 measles statistic report. It compared annual measles cases reported from Brooklyn to nationwide measles cases.
Using this as a design template, I decided to pull monthly COVID-19 statistics for New Jersey and compared them to the national cases reported. As of September 24, COVID-19 cases are slightly increasing when compared to numbers from the previous month. One possible explanation for the increase could be attributed to Labor Day crowds and people disregarding CDC guidelines for social distancing and wearing masks.
This image is based on original research and designs from Visualizing Health, a collaborative project between the University of Michigan and the Robert Wood Johnson Foundation made publicly available via Creative Commons license. Our use of this image should not be considered an endorsement from Visualizing Health. For more information, visit www.vizhealth.org.
Resources from VizHealth
Finally, VizHealth provides a list of resources on how to use their content as well as some interactive tools. The first thing I saw was a comic depicting a diabetes researcher, Melanie, as a sort of data-visualization superhero with an illustrator fox sidekick. It was rather cheesy but it also showed how health visualization can help ordinary people better understand their risk factors.
While on the subject of risk factors, VizHealth also has a risk factor calculator that users can interact with to determine what daily habits impact their health.
I had my father, a casual smoker at age 70, enter some answers into the calculator. He thought the design of the graphic was neat, but when I told him that it meant that he was 30% at risk of developing cardiovascular disease, he simply said to me, “Well, there’s a 70% chance I won’t get cardiovascular disease.” Needless to say, I insisted that he consult a doctor about his risk factors, specifically his smoking habits.
My father’s results from entering information into the VizHealth risk calculator.
VizHealth also provides a link to an icon array generator (depicted below) that users can interact with.
Conclusion
Looking around VizHealth and pulling inspiration from the visualizations provided was a fun activity, but I think the program can truly benefit from including more issues such as mental health and COVID-19. Even more so, it would also be interesting to see more interactive tools added to the site, like a free-to-use template generator. If there are any other health issues that arise, I’ll be sure to use VizHealth to learn more.
One of the best tools for quick and easy infographics I have found so far is Canva Basic and Canva Pro. Canva Basic is a free subscription with over free 100+ design types, along with thousands of free templates, free photos, fonts, and storage. On the other hand, Canva Pro is a $9.95/month subscription, which comes with 450k+ premium templates, 1000 GB storage, background remover, premium stock images, logos and many more.
Canva Infographic Example
Personally, I have been using this tool for over 6 months now to operate various social media accounts for one of the student organizations. Through this tool, I have created various infographics, IG stories, posts, and flyers just in a matter of a few minutes. And, the best part about this tool is that it can be found in the Apple app store and can be used through the phone. Hence, this is a super easy but satisfactory tool for Infographics. Below I also provided a design I created for Earth Day to post on student organizations IG posts.
The following video provides insightful knowledge into what a dataset is along with several concrete examples.
Finding Datasets Using Scientific Research:
Upon navigating to the Connecticut Data Collaborative website & downloading the “Marijuana Use” CSV, you will be shown results to a study conducted to determine the usage of marijuana in individuals aged twelve years and older. Upon first glance this may seem like an overwhelming amount of information. Fret not, there are actually many different possible datasets within this study! Below you will see there is also a sample of the data included in this study.
Location: Where would you like your dataset to take place? For this example your options would be Connecticut, Eastern Region, North Central Region, Northeast, Northwestern Region, South Central Region, Southwest Region & United States.
Time: Over what time span would you like your data set to take place? The possibilities within this example would be 2004 through 2006, 2006 through 2008, 2008 through 2010, 2010 through 2012, 2012 through 2014 & 2014 through 2016. You could also choose to measure a span of four years (such as 2006 through 2010) or even the entire span of the study (2004 through 2016).
Age: How old do you want the participants in your observed dataset to be? Possibilities within this example are 12 to 17, 18 to 25, over 17 & over 25.
It is important to understand how to select the appropriate dataset(s) from scientific research as if you do not, it is likely that you will select a dataset that is not the best suited for your topic at hand. Always be sure to have a clear and concise understanding of what it is you would like to present to you target audience.
