Graphic Sociology

Partnership-driven infrastructure project financing | Creating a formula for success from existing projects

Visualizing Finance

For almost a year I have been working at the Center on Law and Public Finance, a center based at New York University’s Institute for Public Knowledge, which is currently dedicated to research on American infrastructure. Infrastructure has a halo of geeky coolness about it that is a combination of the tinkerers desire to figure out “How Things Work” ala David MacCaulay and the awe of beholding massive public works projects like the Hoover Dam, the Tappan Zee bridge, and New York City’s monumental water delivery system.

Tappan Zee Bridge | ABC Local

Right now, though, the debate in DC and in various states is about how we can pay for the upgrades and extensions of our infrastructure that are badly needed. It’s also about just what counts as infrastructure. We define infrastructure as physical, regulatory, bureaucratic, and behavioral assemblages that are durable over time. This is a fairly academic definition, but it allows for the inclusion of not only of bridges, roads, ports, and mass transit, but also of things like suicide prevention hotlines, manufacturing plants, and educational institutions. Once we broaden the definition to include a more realistic, inclusive set of infrastructures that underpin civic, commercial, and social life, the challenge of explaining how we might pay for these projects gets even harder.

For our most recent report, “Partnership-driven Growth: A bipartisan way forward”, I tried to develop a flexible strategy for demonstrating the reliance on partnerships of monetary and non-monetary support that come together to meet the specific needs of particular projects, while following a loose template adopted by many infrastructure projects. Since infrastructure generally benefits many constituencies, including civic society, the most common successful infrastructure funding is like a collage. Often, successful projects draw on a modest amount of federal support, either in the form of loans, loan guarantees, or (matching) grants. These federal dollars are good at acting as funding anchors (and votes of confidence) which tend to smooth the way for states, local governments, and private investors to commit their own funds and support to the projects.

One of the things I wanted to emphasize with the graphic was that though each project presents a unique ‘flower’, there is a general formula for success. Nobody is out there re-inventing the wheel with respect to financing vehicles even though it might sometimes feel like that for local governments, states, and private investors who haven’t built many financing vehicles. I was also trying to find a way to indicate that not all support for infrastructure projects is monetary support. Sometimes support comes from a willingness to change a zoning law or to create a partnership with a local university where the business, design, or engineering school dedicates time and effort to overcoming challenges within the infrastructure or business plan.

The page you see at the top of the post was the frontis-page for a section in the report that looked at a number of case studies. Each case study contained the same “flower” from the frontispiece with a lengthier description of just how much of which kinds of funding were involved. I’ve included the relevant page of the Tesla case study below, just to demonstrate how the design was developed within the report. I wanted the frontis-page to this section to give readers pause – they had just made it through about 10 pages of prose – and to help them connect individual projects back to a general ‘formula for success’. Hence, I repeated the flower form from the frontis-page in each of the case studies, hoping that a little repetition would help to cement key concepts.

Tesla Fremont partnership project case study

Infrastructure banks

Politically, the reason it is important to understand how infrastructure financing works when it is successful, is that both at the national level and within particular states, lawmakers are considering establishing infrastructure financing authorities (hereafter referred to as infrastructure banks). The exact dimensions of these banks are still being hashed out. Will they fund only certain sectors of infrastructure like transit, energy, and manufacturing or should they include social infrastructure, too? Will they use revenues generated by some of the stronger infrastructure sectors to help support those sectors that are less likely to be self-sufficient? Or, should each project be responsible only for its own bottom line? Since infrastructure has a long time horizon, what is the best way to set up lifecycle-aware financing structures?

Electric Vehicle Charging infrastructure schematic | Schneider Electric

Our current work tries to build a baseline of understanding so that decision makers, including voters, will have a framework within which to advocate properly for their own interests while keeping an open mind about the visionary possibilities of infrastructure banks. This discussion needs to be much bigger than one that only responds to the “we’re in a recession, let’s find a rapid cash infusion from the private sector” frame. A new bank could do much more than that. It could be time to reconsider agency structures and break down silos; it could be time to reexamine the way infrastructure necessary for commerce relates to private sector revenues; it could be time to recognize synergies between sectors that make more sense now than they did in the past (the energy sector and private automotive transportation have something different to say to each other as more cars are electric, for instance. Social infrastructure and broadband supporters have a different conversation now that so many people turn to the internet for social services and broader social support).

There will be more to come in this series. The conversation is just getting started.

Criticism welcome

As always when I present my own work, I invite criticism. Readers of this blog have been generous (and civil) with their comments in the past and I am quite grateful to have such a thoughtful readership.

References

Likosky, Michael and Norén, Laura. (January 2012) “Partnership Driven Growth: A bipartisan way forward” [Report] Center on Law and Public Finance, Institute for Public Knowledge, New York University.

Spring Counter | John Maeda

What works

John Maeda (now head honcho at RISD, formerly of MIT’s Media Lab) designed this simple interactive graphic in 2006 while contemplating the cyclical nature of life during the still grey days of a New England winter. His visualization shows the number of springs men can expect to have if they live an average life span for men in their country. Users input their age and select their country. The flowers in color are those in the user’s future; the ones in grey represent the past. Simple. Elegant. An infographic haiku.

What needs work

I have a slightly sunnier view of the past than does Maeda, perhaps. I think I would have colored both the past and present flowers, just used different schemes. Maybe it’s the social scientist in me, but I believe our past and future both provide the context for our present. Perhaps some past years have been grey, but the territory of the past is not generally a cemetery.

References

Maeda, John. (2006) Life Counter. Interactive web-based graphic.

See also:
Maeda, John. (2006) The Laws of Simplicity. Cambridge, MA: MIT Press.

Who visits occupywallst.org? | Harrison Schultz and Hector R. Cordero-Guzman

What works

The graphic above was constructed using 5,006 surveys filled out by people who visited occupywallst.org. Here’s what the survey found:

Gender
Men 61%
Women 37.5%
Other 1.5%

Age
45 y/o 32%

Race/Ethnicity
White 81.4%
Black, African American 1.6%
Hispanic 6.8%
Asian 2.8%
Other 7.6%

Education
H.S. or less 9.9%
College 60.7%
Grad. School 29.4%

Annual Income
$50,000 30.1%

Employment
Unemployed 12.3%
Part-time 19.9%
Full-time 47%
Full-time student 10%
Other 10.7%

Politics
Support the protest 93%
———————
Republican 2.4%
Democrat 27.4%
Independent 70.7%

What needs work

I have two issues. First, I think the graphic is beautiful but functionally useless. It is nearly impossible to get any intuitive sense of anything at a glance. The circular shape forces the categories to come in the order of their popularity which is not always the most logical order. Look at the income data. That should come in order of least income to most income, but it doesn’t (why would anyone put incremental numerical data out of order?). The rounded sections of wedges are also nearly impossible to intuitively compare to one another in size, so I cannot figure out what the functional value of displaying demographic data in this modified pie chart is. In summary, it appears that the information part of the information graphic did not win the contest between aesthetics and utility. Remember: there should not be a contest between aesthetics and utility in the first place.

My second concern with this graphic is its overall reliability. The FastCompany article it accompanies is titled, “Who is Occupy Wall Street”. That title more than implies that this survey of visitors to a particular website associated with the movement – but not THE official website of the movement (there isn’t one) – accurately represent the protesters on the ground. I don’t think that the professor and his partner who conducted the surveys would make such grand claims.

References

Captain, Sean. (2 November 2011) Who is Occupy Wall Street? FastCompany.

Jess3. (2 November 2011) Who is Occupy Wall Street? [information graphic] FastCompany.

Time and Newsweek Circulation Figures | Graphic by Laura Norén

Newsweek and Time Circulation Figures | Graphic by Yolanda Cuomo

Which one works?

These two graphics portray some of the same information – household income, median age, audience and circulation – though the first one does not break down information between genders. Though it probably goes without saying, I like the one I designed best. The second one has some tantalizing shapes – I applaud the visual appeal – but it does nothing to aid people’s eyes as they try to compare relative sizes between the salient categories. I also happen to think it is easier to understand the complexity of the difference between audience and circulation with the textual explanation provided in the first one. I find the white-font-on-dark-background of the Time and Newsweek labels hard to read (it’s also a known graphic design no-no, especially with a small font size like this. It is easier for the human eye to grok the contrast with dark text on a light background than with light text on a dark background).

From a sociological perspective, comparing the readership of Time and Newsweek not only to each other but also to national averages provides a much deeper sense of context. The second graphic was built from the first though I never had a chance to meet with any of the writing or design team to understand why the national averages were removed.

There are other elements I dislike in the second one. I dislike, for instance, the need to repeat certain elements of text over and over again: “readers per copy” and “Total adult population” and even the “Time” and “Newsweek” headings. One of my closest friends and colleagues spends a lot of his time writing code. The best lesson I have learned from him is that where elements or actions have to be repeated over and over, there is inefficiency in the system. A better design is possible.

I would love to hear from my readers on this comparison. Am I suffering from too much ego investment in the graphic I made? Is the second graphic an improvement on the first? If so, how?

References

Norén, Laura. (2010) “Appendix: Data and Methods” in first draft of Dill, Nandi and Telesca, Jen Imagining Emergencies. [Information graphic].

Cuomo, Yolanda. (2011) “Readership Data Time and Newsweek 2008″ in final draft of Dill, Nandi and Telesca, Jen Imagining Emergencies. [Information graphic].

Original Version of the bar graph

How is the scoring system determined?

British researchers affiliated with the Independent Scientific Committee on Drugs met for a one day workshop and constructed a composite scoring system to determine which drugs are most harmful both to individuals and to society collectively. Scores can range from 0 – 100. Authors David Nutt, Leslie King and Lawrence Phillips found that,

heroin, crack cocaine, and metamfetamine were the most harmful drugs to individuals (part scores 34, 37, and 32, respectively), whereas alcohol, heroin, and crack cocaine were the most harmful to others (46, 21, and 17, respectively). Overall, alcohol was the most harmful drug (overall harm score 72), with heroin (55) and crack cocaine (54) in second and third places.

The full list of factors that were included in the composite score are here:

• Mortality
• Damage
• Dependence
• Impairment of mental functioning
• Loss of tangibles
• Loss of relationships
• Injuries to others
• Crime increase
• Environmental degradation
• Family breakdowns
• International turmoil
• Economic cost
• Loss of community cohesion and reputation

Though it is possible to go into an explanation of how each of these was measured and subsequently combined to produce the composite scores, I am going to leave that discussion to the authors of the original study. There’s an overview graph below and the full article Drug Harms in the UK: A multi-criteria decision analysis is at the Lancet.

Composite scores showing contributions from harm to individuals and harm to society

What can be done?

I found it interesting that there was no attempt made to distinguish between legal and illegal drugs. Yes, of course, some drugs are not clearly legal or illegal. They are legal when prescribed and supervised by a doctor but illegal when used off-label or outside the medical authority system (like anabolic steroids, methadone, and marijuana in California). I assumed that most methadone users are under some kind of supervision but that most anabolic steroid users are using the steroids off-label (ie illegally). You can quibble with my choices below. The point here is that I found the graph to have more context if the legality issue was visually inscribed into it.

Photoshopped version of graph that highlights legal drugs

There are age limits and places where it’s illegal to smoke or drink, but for the most part everyone will be able to use alcohol and tobacco legally for most of their lives. Methadone is probably being used legally in most cases. That’s why I shaded those bars grey. I am not expert on methadone, but I see that it is much less harmful to users and to society than heroin, the drug it stands in for, so I guess if this were the only data I had to make a decision about continuing methadone treatment programs, I would keep them going. I would also call for close scrutiny of methadone programs. Something is clearly not working as well as it could be.

As for alcohol and tobacco…well…it’s hard to argue *for* the continuing legality of alcohol. How large do detriments to society have to be to trigger additional control mechanisms? The authors of the study noted that alcohol is part of society and it isn’t going anywhere. I agree. Prohibition was a failed experiment in this country and I’m not suggested we try it again. However, I would like to reopen the debate about how the negative impacts of alcohol can be alleviated. I recommend that all new cars must have breathalyzers in them. If the driver cannot blow a legal sample, the car won’t start. Yes, people could game that system by having their friends blow for them, but often one’s friends are also drunk. And hopefully, friends really wouldn’t let their friends drive drunk. Once upon a time, seatbelts were considered extraneous and seatbelt laws were considered constraints upon American’s rights to freedom and the pursuit of happiness. Well, when a drunk driver kills one of your family members, you might decide that the sudden loss of your mother or son or niece puts a much bigger crimp in your pursuit of happiness than a breathalyzer in your car ever would have. Will breathalyzers make cars cost more? Probably. But the cost of dealing with car accidents caused by drunken driving, even when they aren’t fatal, is absorbed by random individuals who happened to be in the wrong place/time as well as tax payers who pay to repair guard rails, subsidize public hospitals and EMTs, pay cops’ salaries, and so on.

References

Nutt, David J, Leslie A King, and Lawrence D Phillips. (6 November 2010) “Drug harms in the UK: a multicriteria decision analysis” The Lancet, Vol 376(9752): 1558 – 1565.

Reading, Writing, and Earning Money | GOOD Transparency Blog

What works

Nothing is working for me with this graphic except possibly the few places where the designers offered detailed information about a particular location’s high school graduation ranking, college graduation ranking, and income ranking. But that’s being generous.

What needs work

Horrible use of a map. Maps should only be used where there is good reason to believe the information being conveyed is tied closely to geography. This information is not tied closely to geography though it might be tied closely to states. But states need not always be represented as geographical entities. Often, they are political entities and their particular geography is not salient.

The math that led to the graphic flattens important details and renders this a useless graphic. What I believe the designers did was something like this:

• They took all of their numbers and turned them into some scale between 0 and 100%
• Then they decided to represent each of the three variables with pure Cyan, Magenta, or Yellow. The higher the state scored on the scale from 0-100, the more saturated the color value.
• Then they gave each county a combined score by building new colors from mixing the values of the previous three. Higher scoring states ended up with more saturated colors. Basically, higher scoring states started to approach black. States that scored high on just one vector ended up having a clearer, lighter color profile.

Here’s the big problem with this. It was hard for me to explain to my MIT-educated friend so I’m not sure this is going to make sense the first time ’round. Representing everything on a scale from 0-100 is a slide towards obfuscation. The graduation rates are both unadulterated rates. The income data represents un-scaled median incomes. I appreciate that they are not scaled, but I have a hard time adding 65% with $45,000. That’s some troubled math. At least in the monochrome maps we know what we’re looking at before the three variables get added up.

A grave sin was committed when the numbers for these three different variables were added up. Now, of course, it wasn’t the numbers that were added up. It was the color values of each of the three separate data points that were added up. Additive color seems to be something that does not send up a red flag. I can guarantee you that if they had presented something – a table or graph – where they had ended up adding values from high school graduation, college graduation, and income, red flags would have been flying. Why? Well, maybe you’re starting to catch my drift, but I’ll help you by spelling it out. What happens when the colors are added is a clear violation of the ‘apples to apples’ rule. Comparisons do not work unless you are sure you are comparing like things. Graduation rates are not like income. They are two different kinds of numbers – one is a rate the other is either a linear value or a log-linear value. Either way, they cannot be added up and still make sense. It’s no surprise that the graphic ends up looking like an incomprehensible slurry of a gray area.

References

GOOD and Gregory Hubacek. (March 2011) Reading, Writing, and Earning Money in GOOD Transparency Blog.

Reading Suggestion

In case you missed it over the weekend, the New York Times ran a story about information graphics and the people who use them to communicate with the public. Unsurprisingly, Hans Rosling of Gapminder in Sweden – one of the new heroic figures in infographics – was the man in the picture and the first to be quoted. Rosling deserves the attention – gapminder had fairly humble origins and has grown because it draws from sound data, it is free to use, and it does a predictably good job of providing a visual overview of country level comparisons over time. Natasha Singer, the journalist who wrote the article, also interviewed Professor Ben Schneiderman of the Human-Computer Interaction Lab at the University of Maryland and Jim Bartoo of the Hive Group. And that’s where the article obliquely addressed the growing divide between infographics that are meant to be serious, complex, and complete and those that are meant to be beautiful and compelling, but user-directed. This second sort of infographic is the sort of thing that gets accused of being ‘info-porn’ and often covers information that is of dubious social value. Do we really care about celebrity’s twitter usage patterns? Is that as important as the work Hans Rosling does? What can the academic side of information graphics makers learn from the commercial side?

The article has a slightly different take on these questions,

The fact that serious software companies are now tree mapping the pop charts is a sign that data visualization is no longer just a useful tool for researchers and corporations. It’s also an entertainment and marketing vehicle.

but it’s clear that there are some divisions within the world of infographics that are worth considering more seriously. Nobody ever claimed that all writing is of the same species or that everything on TV is trying to do the same thing. Documentaries are not like sit coms which are not like dramas which are not like soap operas…but then again, they can all be found on TV and thus have some common elements. It’s no surprise that there is a wide variety of infographics out there with distinct goals.

Figuring out just how each type fits into the information ecology and changes the expectations about the entire range of infographics is worthwhile. When graphic designers started to take infographics seriously, it raised the bar for social scientists who were trying to communicate with information graphics. No longer was a chunky bar graph going to look sophisticated. It might look so generic and grade-school that it would reflect poorly on the overall quality of the argument.

References

Singer, Natasha. (2 April 2011) When the data struts its stuff. New York Times, Business Day Section: Slipstream.

Hans Rosling. Gapminder.org Hans Rosling is also a frequent TED Talks presenter.

Jim Bartoo. Hive Group.

Ben Schneiderman. Human Computer Interaction Lab at the University of Maryland.

Hillman, Dan [Director and Producer] | Rosling, Hans [Presenter] (7 December 2010 was first broadcast date) The Joy of Stats BBC. [Documentary] 60 minutes.
In the US you can stream The Joy of Stats from Hans Rosling’s gapminder.org website. Perhaps this works in other countries as well, but I haven’t had a chance to test it.

American Recovery Infrastructure Spending: Energy, Transport, Rail, Water, Broadband

American Infrastructure Spending

Over spring break I worked with Michael Likosky of the Center on Law and Public Finance to put together a couple of graphics for a project whose aim is to help lawmakers figure out how best to fund American infrastructure projects going forward. He gave me the data and I produced the graphics. We looked at the way the recovery act (and a few other small programs funding infrastructure in the recent past) have added up to a total infrastructure funding package under the Obama administration. Most of the funding has been part of the Recovery Act though it is spread across the Department of Treasury, Department of Energy, and various other governmental agencies.

We had started with spending across five sectors: energy, transport, rail, water, and broadband. Since rail is highly controversial and some of the states wouldn’t accept their rail funding, we decided to drop rail. We dropped broadband, too, because it isn’t included in either of the bills that are currently being considered (see here for an explanation of both bills) and that’s how we ended up with spending across only energy, transport, and water. But only the full five-sector version is part of this blog, since the three-sector version is considered controversial.

Keep in mind that the rail funding dramatically skewed per state allotments even though it was controversial from the beginning. The broadband funding is the smallest chunk overall but ended up being spread more evenly across states. This also turned out to be true when it came to water. Smaller total budget, more even spending across states.

I had tried to come up with a way to compare the amount states received in each category with some figure representing what they would have gotten had the funds just been split evenly. But this turned out to be nearly impossible. This is infrastructure spending, so it isn’t a matter of simply looking at spending on a per capita basis. Or a GDP basis. States that have more square miles often need to spend a bit more on infrastructure just to cover the territory (this is why rural states spend more on telephone service)…then again, sometimes this isn’t true because it’s cheaper to build some kinds of infrastructure in relatively less-developed areas than in, say, dense urban areas (it’s very difficult to rehab water mains in New York given the density of the built environment, subway tunnels, buried gas and electric lines, sewers, anti-terrorism security measures and who knows what all else comes into play).

I realize that what isn’t working here is that there is no good way to figure out whether a state is getting about what they should get or more or less…I did try to find a way to figure it out, but I couldn’t find a way to compute what any given state ought to have received though I am still interested in coming up with some standardized scale.

What works

I made this graphic so I am too biased to tell you what works and what doesn’t work*. This is where the blogosphere comes in. It’s your turn to tell ME what works and what fails.

How is it that Texas always gets so much funding for everything? Even if we were to look per capita (which is not the wisest move when looking at infrastructure projects) Texas would still be up towards the top of the list. And if we looked per square mile of state territory, it would also be up towards the top of the list. It’s a cliche to say that everything’s bigger in Texas, but in this case, the cliche holds: even infrastructure funding is bigger in Texas.

*I happen to know that Arkansas is incorrect in the five-sector version.

A recent (well, 2010 so not *that* recent) report from the UNDP traces the history of information graphics as tools for the promotion of public health. Illustrious crusaders from the yesteryear of public health like John Snow and Florence Nightingale developed some of the earliest ‘infographics’ in service of their public health goals. I’ll post more on that portion of the report later this week. But for now, I’d like to discuss the bulk of the report which was dedicated to the decisions that César Hidalgo (Professor at MIT’s Media Lab, Student at Harvard’s Center for International Development, Associate Professor at Northeastern’s School of Art and Design) made as he developed an appropriate information graphic to represent country level data generated by the Human Development Index. (See also: Measure of America’s Human Development Index graphics for the US only and the Graphic Sociology post about them).

The graphic below this is not intended to be a graphic. It is the basic formula upon which the Human Development Index (HDI) is based. The HDI is a single number that represents a composite score that takes contributions from educational, income, and health measures (which are themselves composite scores). The authors first came up with a simple, almost graphical representation of the relationship between the contributing factors that’s a sort of formula/graphic hybrid. Many social scientists would stop here and move on to the writing of the report, content to let a table with country-level data do the reporting for them.

Basic Human Development Index Relationship | César Hidalgo

HDI Spline Tree

From this hybrid between a formula and a graphic, Hidalgo developed the spline tree you see below. It shares some aspects with the basic formula above, that much is visually clear, but already the lengths and colors of the components are taking on meaning, allowing each country/year combination to produce a tree that is distinct from other trees, but similar enough to be comparable.

The HDI Tree - Spline Design | César Hidalgo

One of designer’s common strengths/weaknesses is the inability to stop designing. Design is never done because the design has reached some obvious and agreed-upon level of perfection. Usually design is deemed ‘done’ when the deadline rolls around. It would appear that Hidalgo was ahead of schedule and decided to go for another iteration, coming up with the diamond tree you see below. Though, as you’ll also see, he did not completely abandon the spline tree. It shows up again.

HDI Diamond Tree

The diamond HDI tree takes an area-based approach, one that is easier to understand visually at first glance than the spline tree. With the spline tree approach, the challenge is that the viewer needs to visually compare the lengths of lines that are not parallel to one another to gain full comprehension. Granted, one might most often compare the lengths of lines that ARE parallel to one another because viewers might mostly be comparing one country to the next. But that isn’t always the case. And even that is not as easy as comparing the areas in the diamond tree approach.

The Human Development Tree - Diamond Tree | César Hidalgo

The rules for the HDI diamond tree (and I’m quoting Hidalgo and team here) are as follows:

* The height of the tree trunk is proportional to the total value of the HDI
* The side of the tree branches are proportional to each sub-indicator
* The branches are ordered in increasing order from left to right
* The color of the trunk is the average color of the components

All together

And here is one country’s worth of Diamond and Spline trees, represented over time. This is where I think the two tree graphics – and the diamond tree in particular – work their magic best. Human eyes are good at doing comparison’s in this sort of way. The trees are more or less the same thing over and over again so this repetitive presentation allows the eye to pick out the relatively small changes over time, especially as they aggregate from one year to the next.

HDI in Rwanda 1970-2005 | César Hidalgo

Pan-Africa

With the last graphic in the series, you can see what it would look like to present the entire continent of Africa, by country, in two different years. It’s a little tough to fit a properly sized graphic into the format of the blog. I encourage you to click through to the full report in the references where you can see a much better version of the final graphic.

Human Development in Africa by country, 1970-2005 | César Hidalgo

Kudos

My biggest applause goes out to the Hidalgo team for abandoning the use of any map at all. This graphic should prove the point that just because one is faced with country level data – something that seems geographical in nature – one should not feel that they must use a map. A map would not have added anything to this information and it probably would have precluded the development of the tree concepts that are working pretty well.

References

Hidalgo, César A. (2010) Graphical Statistical Methods for the Representation of the Human Development Index and its Components [Research Paper] United Nations Development Program.

In theory there is an interactive portal for comparing any two HDI Diamond Trees of your choosing but I was not able to get it to work in Firefox. Worked like a charm in Safari and Chrome.

For ongoing comments on these graphics see: The HDI Tree: A visual representation at “Let’s Talk Human Development” a website published by the United Nations Development Project.