Category: viz reviews

The data presentation that I recently saw was by “thawraecon”, an Instagram page that was created at the start of the October revolution in Lebanon. The page’s purpose is to provide data-driven economic insights that explain how past governments, ministries, and policies brought the country to its current economic collapse, while emphasizing the urgent need for change, growth, and development. 

The presentation is targeted particularly to Lebanese citizens but also to non-Lebanese citizens who are interested in acquiring knowledge and information on Lebanon’s economy. Given the purpose of the page, which is to share economic facts that are easy to understand, I think the presentation does a good job at highlighting the poor state of Lebanon’s road network. On the first page, the main statistic, “85% of Lebanon’s road network is in fair or poor condition”, is clearly the center of the story given the size of the font used. The second page is insightful in terms of breaking down the level of urgency for the different type of roads. The schematic is clear and sophisticated as it uses broken/not broken blocks to symbolize the proportion of roads that are in good/poor condition. 

One way I think this presentation could have been more helpful would be the breakdown of the road network by geographic area as such a breakdown could help identify which improvement in road infrastructure would lead to the highest return on investment in terms of GDP for Lebanon in the least amount of time. 

I have a PhD in virology, so it’s been interesting to see how information about the novel coronavirus (nCoV) is being presented. There’s a lot that we still don’t know about nCoV, which means people have been creating projections with varied estimates of how contagious the virus is as well as estimates of case fatality rates (CFRs). There is a lot of fear-mongering going on about the outbreak, with people only showing the projections based on worst case scenarios for CFR and infectivity. At the same time, it would be naive to underestimate nCoV, and individuals calculating its impact using the lowest transmission and CFR estimates are also not super helpful.

Which is why I like this CFR vs infectivity figure from the NYTimes. It shows the estimates for nCoV (reddish square) while also demonstrating how it compares to previous Coronavirus outbreaks (SARS in 2002, MERS in 2012) as well as other viruses. This figure illustrates two things pretty effectively: (1) epidemiologists are still at the information gathering phase, and (2) the CFRs for nCoV are below those for SARS and MERS.

This graph is part of a larger information packet from the Times, all of which add more nuance to the nCoV situation. I think most readers will have a bit more peace of mind and after reading the packet and especially after seeing this graph. My one quibble is using a log scale for CFR. It’s a great solution for showing super spread-out data, but it ends up placing the nCoV square towards the middle of the graph, making it seem like the CFR is much higher than < 3%, especially to readers who might not be familiar with log scales.

Link to NYTimes Page: Click here

This data presentation compares statistics about the Wuhan Coronavirus to other viral outbreaks to communicate that the Wuhan coronavirus is less dangerous than its public perception. The graphic displays cases reported in time since the initial report and a timeline of major response markers such as confirmation of the virus by WHO and gene sequencing. In addition, the graphic introduces a measure called the reproduction number, which is related to how contagious the disease is. Based on the color scheme and quantitative quality of this data, I think that the intended audience is people with higher education in STEM fields. The dark colors are reminiscent of technological displays or science-based marketing. In addition, the emphasis on numbered data and the statement about media attention implies that the intended audience takes a skeptical approach to words and is more convinced by statistics. The data was a little difficult to interpret without the guidance of explanatory text, but was understandable after some analysis. On a separate note, I really appreciated the flow of the bolded white text which created a one-sentence summary of the poster. In my opinion, the presentation was effective as it convinced me that the media has exacerbated public concern about the virus. However I will note that I am definitely a subsection of the intended audience, and this presentation of data may not convince other viewers.

This article uses multiple visuals conveying Olympic medals won by countries; the first visual provides an overview of total medals per country or region, while the visuals that follow disaggregate this information by sport. The visuals are striking in that they convey a lot of information – the longer you engage with it, the more you discover. The first visual gives a timeline; the locations of Olympics; the years in which Olympics were not operating; years in which select countries were not participating; and a sense of trends related to both medals by country and total medals awarded at the Olympics. On the sport-disaggregated charts, you can also hover your mouse over the image to see exactly how many of each type of medal any given country won, in any given year. I am impressed by the amount of information conveyed within a visual. However, as a reader, I do not think that the visuals are easily accessible. It took my sometime and reading to understand the visuals and discover its dimensions. I believe that this is because the author of the visuals attempted too many dimensions in lieu of simple and well-labeled visuals. There are notable aspects that could be improved – for example, the y axis is un-labeled and should be for clarity; the wave patterns in the chart would be easier to read if they were aligned at the bottom. Regardless, I think communicating too much information is the largest pitfall of this visual – and that aside from the Olympic-history enthusiast or data visuals/storytelling analyst, the message of the graphics will be lost to audiences.

https://www.nytimes.com/interactive/2016/08/08/sports/olympics/history-olympic-dominance-charts.html