Category: viz reviews

Data presentation blog post by Eugenio Zuccarelli

In a world filled with buzzwords ranging from Internet of Things to AI and Edge Computing, navigating the innovation space can be quite tricky.

One aspect shared by mostly all the innovative technologies though, is the general trend they follow, starting from low key discoveries, until reaching maturity and widespread use.

Gartner, a global research and advisory firm, developed a general framework that captures all innovative technologies and maps them into a specific framework.

This framework, called Hype Cycle for Emerging Technologies, shows the general trend followed by technologies over time, mapping society’s expectations towards each innovation.

In particular, the chart lists all current technologies on x-y axes, where the position determines the general expectation over time. The innovations start from an “Innovation Trigger”, propelling them to peak hype, then to a minimum point for then coming back again at a more moderate pace, reaching stable progress.

By showing such information in a conceptual form, rather than being data-heavy, the Hype Cycle can be used to reach any audience. Indeed, the simple and relatable concepts expressed by the chart can be understood by any person with knowledge of the “common buzzwords”.

However, the chart can be even more useful for technical people, with a strong understanding of the technology environment that can benefit from using or investing in the technology.

Overall, the Hype Cycle aims at giving context to emotionally-inflated concepts such as AI, IoT and Cloud Computing. These concepts are usually able to capture the interest of the masses, but then showing similar patterns of disillusionment. Here, Gartner’s cycle successfully clarifies that some technologies are still in the early stages and overinflated, while other technologies have almost reached maturity, and are ready to become part of our daily lives.

https://www.wsj.com/graphics/new-space-race/

This scrolling data story shows a dataset on satellites orbiting the Earth, with the goal of highlighting the magnitude of the business opportunity and impact of recent space exploration developments. The main dataset is from the U.S. Space Surveillance Network, which contains details about each satellite orbiting the Earth, including the country of origin, purpose, and type (government/commercial). The data is visualized in both charts and in a model of Earth with its surrounding space.

The data is augmented by details on possible business opportunities, including satellite-based internet services such as SpaceX’s Starlink…

… as well as satellite imagery services, which is forecasted to be a growing industry in the coming years.

The purpose of the article is to inform the Wall Street Journal’s typical audience, largely business and finance-focused professionals, of the economic and business implications of current trends in space exploration and satellite launches. It also tries to educate the audience about some basics of orbital satellites, including the key government/commercial players, satellite and orbital types, and the risk of space debris.

The story’s bar charts and simple data visualizations very effectively show some of the key trends relevant to the industry: the shift from government to commercial, the rise of China as a significant player, the decreasing cost of launching payloads, and the increasing economic opportunity in satellite imagery. The visualization of satellite clouds in orbit is also an impactful one, emphasizing the magnitude and scale of the topic. While some orbits and satellite densities are exaggerated compared to actual sizes, the visualization serves its purpose well.

The story also does an effective job of visualizing the business opportunities of two specific companies, SpaceX and Planet Labs. However, from a storytelling perspective, it does not transition into these two cases very effectively. A visual demonstrating the presumed dominance of these two players in their respective industries may have better justified their inclusion.

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The Opportunity Atlas is an interactive data tool that visualizes and spatializes children’s outcomes in the U.S., defined by a slew of metrics, in their adulthood. To test out this tool, I zoomed in on my hometown Hixson, TN to see how children who grew up there fared later in life. For example, children from Hixson, TN who grew up in the poorest of households are likely to see little income mobility, though they fare better than their counterparts in neighboring census tract.  

The project aims to bring to life the multitude of factors that determine socio-economic mobility in the U.S. The tool allows users to test their own assumptions on income inequality, race, gender, place and space in the land of eternal promise. The Atlas draws on anonymized U.S. Decennial Census data from 2000 and 2010, Federal income tax return data, and the American Communities Surveys (ACS) data from 2005-2015 all combined to create one large dataset covering nearly the entire U.S. population.

The Atlas is accessible to the general public, but complex enough to appeal to social science students and scholars. Users can select pre-made stories to explore specific topics such as the effect of moving from one neighborhood to another in Chicago. Those interested in using the tool to answer their own questions can customize the map using panels to select outcomes of interest and neighborhood and demographic characteristics. Data can also be downloaded for those seeking to conduct their own research. 

 The Atlas, at large, is quite effective in achieving its goal. Aesthetically, the maps are pleasing with census tracts painted in pleasant pastels. The pre-made stories walk novice users through a series of maps and graphs that answer address issues faced by cities like Chicago and Detroit, using data to draw insights on complex urban issues such as the neighborhood effect.  The tool assumes a basic proficiency with statistics and can be difficult to use beyond its basic offerings.

Maria Popova has always been one of my favorite writers and I found this graphic on her blog brainpickings.com. The graphic shows sleep habits and literary output for various famous writers, ordered starting with Honore de Balzac who woke up at 1am and ending with Charles Bukowski who would get up at 12pm. It packs a good amount of information into a small icon for each writer – name, lifespan, wake-up time, number of books or collections published separate by category, and awards won. A lot of the information is encoded by colors, mini bar charts and symbols that are unlabeled, which saves space but does require the reader to look at and understand the key provided. All the information included in the chart seems to have been deliberate and relevant to the question for example, writer lifespan was included for context since “length of a writing career influences the volume of literary output” as explained at the top of the chart. The chart leads the eye very clearly, with a line going through the authors presented and a meaningful ordering. The illustrations of the authors make the chart more engaging and remind the reader that there are individual humans behind the data. There’s also visual reinforcement of one of the major datasets – wake-up time – since different times appear at different points around the circle enclosing each writer’s portrait. This does mean there is a good amount of blank space in the figure since each writer’s information is centered for consistency but there needs to be a wide spacing to handle the bar charts of productivity pointing in different directions. I do think this chart is not as effective seen on a large scale – it is meant to be viewed as a poster, but it’s hard to see anything viewing the entire chart in a browser window for example. It’s harder to see overall trends since the individual information is too small when the chart is viewed as a whole. It’s only when you zoom in on a specific writer that the data starts to come into focus. This chart seems to be aimed at people who are interested in literature and potentially those who are interested in optimizing their daily routines for productivity. For people who are willing to spend some time and zoom in on the writers – or maybe just look at their favorite writers – this chart is a fun look into how famous writers lived their lives and a chance to consider how much wake-up time could affect literary productivity.

The graphic shows the Iowa Caucus results that were last updated Feb. 6, 2020, 12:11 a.m. ET.

The aim of the graphic is to give details about how the candidates are doing in different parts of the state at the most recent count.  I believe it is effectively showing the key areas are for specific candidates. It is slightly unclear who has an overall majority however the graphics are ordered with the candidate with the most votes showing first, but this is secondary to the bright pictures that show the spread over the state.

The audience is people who pay attention to politics, as people who don’t are likely to only read headlines and leaderboards, especially before the votes have been completely counted.

The interesting part about live-updating graphics is that they need to tell a story even as the data used is continuously changing. When choosing scales on static data, it is easier to select those which highlight specific points or accurately give the details required.

In this graphic, I presume that there is a ratio to translate the number of votes to circle size. It is possible that they used estimates of voter turnout to determine the ratio before results came in, in which case there is a secondary source of data that went into creating this graphic. If this is the case, choosing ratios too high would create circles that are too small and similar in size to be meaningful. Choosing a ratio too small would create circles that are too large and overlapping. On the other hand, if the ratio was dynamic, a person continuously checking the graphic could see changes that imply that candidates have lost/gained votes as the ratio adapts to the data.