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.

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.

Figure 5. Average Salaries in New York City | Report 12, Office of the New York State Comptroller, Thomas DiNapoli

What works

This may not be the worldest most attractive graphic, but it makes its point: financial workers have much, much higher annual income than the rest of us and the gap is growing over time. The text of the New York State Comptroller’s report said the same thing in words.

Wages (including bonuses) paid to securities industry employees who work in New York City grew by 13.7 percent in 2010, to $58.4 billion. Nonetheless, wages remained below the record paid in 2007 ($73.9 billion), reflecting job losses. In 2010, the securities industry accounted for 23.5 percent of all wages paid in the private sector even though it accounted for only 5.3 percent of all private sector jobs. In 2007, the industry accounted for 28.2 percent of private sector wages.

In 2010, the average salary in the securities industry in New York City grew by 16.1 percent to $361,330 (see Figure 5), which was 5.5 times higher than the average salary in the rest of the private sector ($66,120). In 1981, the average salary in the securities industry was only twice as high as in all other private sector jobs.

You be the judge. I think the graphic leaves a greater impact than the text alone. The two together are striking. Maybe we should…occupy Wall Street to demand a decrease in inequality?

The short report has a few more interesting graphs. First, they throw together a quick graph of Wall Street bonuses. These bonuses are tied to performance and so big that they often represent more than a finance worker’s annual salary. As you can see, they took a dip, but they didn’t disappear even though the US economy is still not great.

Wall Street Bonuses | New York State Comptroller's Report No. 12, 2011

The other interesting metric the report contains is a compensation-to-earnings ratio graph, which is the right context for this discussion. Bankers often defend their large salaries and even larger bonuses by pointing out how much money they have made for their banks. I agree with the bankers that this is the place to look. The question should not be: “How much are individual bankers making?” Rather, it should be, “How much does the banking sector make and is that the way we as a society want to distribute our surplus, primarily to banks and bankers through processes of financialization?”

Ratio of banker's (and insurer's) compensation-to-net-revenues | New York State Comptroller's Report No. 12, 2011

What needs work

The graphs are not attractive and the first one reads as cluttered. I generally go with line graphs for this kind of trend data to cut down on the clutter impact, something I have repeated again and again so I won’t hammer on that point too much. I like the information behind these graphs so I am not going to swat at them too much. Excel is not a graphic design tool for graphs; I have occasionally made some sweet tables with it.

I’m glad the report put these data points into graphs, glad that the report is available during the discussions brought on by the OccupyWallStreet crowd, and glad that the New York State Comtroller’s office rolled right on ahead with the release of some fairly damning evidence against the status quo.

Want more?

Another Society Pages blog, Thick Culture, ran a post including graphs that deal with the compensation and wealth differentials between the tippy-top echelon of financiers and the rest of us at Tax Gordon Gekko.

References

DiNapoli, Thomas and Bleiwas, Kenneth. (October 2011) “The Securities Industry in New York City” Report No. 12, Office of the State Comptroller.

See also: A blog I wrote – Americans estimate our wealth distribution and fail. Horribly. using a Dan Ariely graphic about how bad Americans are at estimating the distribution of wealth in this country. Teaser: we think it is much more equitable than it actually is.

The most popular blog post of all time on Graphic Sociology: Champagne Glass Distribution of Wealth

US Map Iconographic

Graphic Sociology is back this week

After a summer hiatus, Graphic Sociology is coming back this week.

This graphic map of the US is just one of the interesting graphics I tucked into my digital knapsack of visual concepts over the summer. Regular readers know that I think maps are overused. There are good times to put them into use, and I have some solid examples coming up this week. But for now…

Graphic Map

The reason I posted this map is to spark a debate about the use of maps more generally. The graphic above is something I adapted from the blog Fuck Yeah Cartography (hey, I don’t name these blogs). The frame up there is something I basically “traced” and then messed around with so feel free to borrow my version if you like.

I thought about maps for communicating about sociological data and realized that I don’t generally see a reason to be cartographically accurate. An iconographic depiction of the US map does pretty much the same work as a depiction that had all the nooks and crannies and bends in the rivers carefully plotted out.

See subway maps:

Boston/Cambridge area T map

Massimo Vignelli 1972 Map of New York City's Subways

Subway maps are often better without anything more than the names of the stops and maybe a river (Boston’s map does not show the Charles River; New York’s map does show bodies of water).

Readers out in the blogosphere, do you agree that when maps are used to represent social science data they don’t usually have to be much more than iconographic? Do they have to have exact political boundaries? Does using an iconographic version help make the often-arbitrary quality of political boundaries more obvious?

For sure using an iconographic version is cleaner and therefore a bit easier to swallow all in one visual gulp. Are there other graphical advantages? Pedagogical advantages?

Coming Soon

I’ll post examples from around the interwebs the day after tomorrow, but for today, please ruminate on whether or not exact topography and political boundaries and all sorts of other things that could be represented well on maps have a place on maps that are used to show social science data.

New York mapped by geotagged photos

Just thought this was cool

This map of New York was created by Eric Fisher. He gathered the geotags of the photos uploaded to flickr. The colors work like this: blue photos were taken by locals (deemed to be local because they had taken pictures in the same location over an extended period of time), red indicates photos taken by tourists (people taking photos outside of their frequent-photo-taking-zone), and the yellow ones were indeterminate (taken by people who hadn’t uploaded any photos in the previous 30 days though we guess they might be tourists because they may be the kind of people who only take photos while on vacation).

I like the aesthetic and the method so that’s why I decided to share.

Greenhouse gas emissions | World Resource Institue and Google's Public Data Explorer

What works

The World Resource Institute has partnered with Google to create an interactive portal for creating visualizations based on publicly available data. Google has been in the business of doing this sort of thing at least since the time they acquired Trendalyzer from Scottish-based gapminder.org in 2007. To be sure, gapminder.org is still a going concern of its own and IBM also offers free web-based visualization services through their Many Eyes program.

The focus of the trendalyzer is to show change over time and they succeed in making it quite easy to watch panel data change over time.

What needs work

BUT…I find that this particular graphic is a great example of a misleading reliance on time as the key ‘context’ variable. So the graphic above breaks down greenhouse gas emissions by US state over the course of the year. If you have already clicked over to the World Resource Institute and watched the animation of these bars pumping up and down (more up than down) and trading places with each other over time, you will surely have been fascinated. I watched it three times in a row. But I was stuck wondering what the take away was meant to be. Clearly, there is the first order take away that the bars pretty much grow over time, they do not shrink. If I were the World Resource Institute, getting that message out would be important to me. But I would hope for more than just the bullhorn approach, “More is BAD! More is BAD!” which is kind of how this hits me at the moment.

One of the biggest problems with this graphic is: not all US states are the same size. Of course Texas emits more greenhouse gases than most states – many more people live there than in, say, Kentucky, Iowa, Oregon, etc. But the World Resource Institute chose to display per capita emissions with the bubble approach (which has almost no redeeming value in my opinion because I cannot even see half of the bubbles. Maybe they all could have been reduced by half or more? And maybe instead of going with colors on a spectrum, the worst could have been red, the best could have been green, and most everyone else could have been some shade of grey? It’s just not possible to hold 50 changing variables in your active cognitive space at once. Reducing it to three variables – the good, the bad and the mediocre – could actually increase retention and pattern recognition.)

Greenhouse Gas Emissions Per Capita, US 2007 | World Resource Institute and Google

But back to the bar graph at the top. For the purposes of greenhouse gas emissions, it makes the most sense to interpret size as population not square miles, so that’s what I am going to do. In an attempt to be helpful, I threw together a bar graph of the top 10 most populous US states (using 2009 population estimates) in good old Excel. Note that our friend Texas is not the most populous state by about 12 million people – that is a lot of people. California is the biggest and they emit way less than Texas. New York is the third most populous state and we emit far less than our proportional share would suggest. Let’s hope it stays that way because I already find it unpleasant to breathe the air in Manhattan (admittedly, that could be due to many causes besides greenhouse gas emissions).

Most populous US states by size

My suggestion here is clear: prepare a bar graph per state, per capita. And, yes, I would want to see how that changes over time. I would probably watch the animation six times instead of three times. My fantasy is that we could compare not necessarily by state, because that is in many ways arbitrary, but by personal habits. Say we get the most extreme environmentalists – vegan, freegan, won’t even take motorized public transportation, never flies, prefers candles to compact fluorescents, has a composting toilet – to the somewhat average person who has a car but not an SUV, eats meat but not every day, does not pay more for organic food – to the extreme non-environmentalist who owns three houses, drives in an Escalade or something of that nature, flies internationally at least four times a year, pays extra for organic food (but at restaurants), and sends clothes to the dry cleaners twice a week. But that would probably result in a graphic best described as “info-porn”, enticing and exciting but intellectually vacuous.

Summary

The WRI is on to something with their Google partnership. My favorite of their early work is this line graph that does a better job of telling the emissions story than any data broken down by state.

Greenhouse Gas Origins | World Resource Institute and Google

But the other great thing about the new partnership is that they ask for suggestions and set up a google group to manage the roll-out and incorporate nay-sayers like myself.

“By pairing [the Climate Analysis Indicators Tool] CAIT data with Google’s tools, there are new possibilities for people everywhere to take part in using sound data to tell stories that frame environmental problems and solutions. In the future, we hope to include additional data sets that can tell even more stories through Google’s visualization tools.

Suggestions for what you would like to see, or have a question about CAIT-U.S. data? Let us know here or join the conversation at http://groups.google.com/group/climate-analysis-indicators-tool.”

Manhattan traffic patterns | Wired Magazine June 2010

What works

The tendency with geographical data is to try to find a way to portray everything on a map. Surely, there is a map up there, and many people will recognize that the area is Manhattan instantly by looking at the map before they read it in the title. That’s a nice thing about maps – they transcend language and bad captioning to some degree. However, much of the detail is not to be found in the map. The map just shows us where congestion tends to occur, but it doesn’t tell us when we can expect these areas to be congested or just what “congested” means. In Manhattan, the average speed is under 10 mph so does congested mean less than 5 mph? Or what?

But if we look at the other graphs and charts it is a veritable jackpot of traffic information, at least at the collective level. I wouldn’t try to use this collection of information to plan your route through the city unless, of course, this collection of information causes you to take the subway instead of driving.

I hate pie graphs (as in the “Proportion of Miles Traveled”), but I am sympathetic to the triangulated pie graphs in the “Vehicle Distribution” graphic. At least it is visually easier to calculate the volume of a true triangle than a rounded off triangle. So if you find that you have to go with a pie graph, emulate the triangulated version found here and your viewers will come away with a better understanding of the information you are attempting to convey. I was surprised at how many people take taxis to get to work. But I am even more surprised at how many fewer trips there are on weekends. Fewer than half of those made on an average weekday.

Anecdotal evidence warning: When I first moved to Manhattan, I remember sitting in the car for two hours to drive around the block. There was a street fair nearby (not on any of the sides of the block traversed in this trip) and that seemed to slow everything to a standstill.

What needs work

I would have found a way to combine the average speed and the delays and associated costs. Clearly, the two are related – lower average speed must mean more delays. I had a little trouble understanding the delays and associated costs without the text from the article. If the speed and costs had been integrated into a single graphic instead of split into two (with a big pie graph in between), I think the link between speed, delays, and costs would have started to become more intuitive.

Here’s an excerpt from that section for the curious:

“In the end, Komanoff found that every car entering the CBD causes an average of 3.23 person-hours of delays. Multiply that by $39.53–a weighted average of vehicles’ time value within and outside the CBD–and it turns out that the average weekday vehicle journey costs other New Yorkers $128 in lost time.”

For more on how that was calculated, you’ll have to read the article. But the bottom line came down to a proposed $16 toll to enter Manhattan below 60th Street. It’s about what drivers in central London pay and the proceeds would go to bolster public transportation. Such an idea – known as congestion pricing – was proposed by the Bloomberg administration but voted down in 2008.

References

Salmon, Felix. (June 2010) “The Traffic Cop.” in Wired Magazine [infographic by Pitch Interactive].

Bonanos, Christopher. (17 December 2007) “Fare Enough” New York Magazine.

Conquest of Pestilence | Courtesty of New York City Dept. of Health via Glaeser

What works

Ah, old-timey graphics. What works here is that this graphic reveals how far we’ve come, I think. The purpose is to show what percentage of New York City’s population died, annually. We can see the trend jumps around a bit – infectious diseases cycle through, sanitation improvements are made, the demographics of the population change – but mostly trends downwards. I like the inclusion of information about deadly diseases though I wouldn’t have just stuck labels on the peaks. The labels here clutter up the graphic territory and do not leave any room for adding other kinds of helpful trendlines and so on like that.

What needs work

Of course, there is not nearly enough context to make proper sense of this information. The implication is that the general downward trend is due to public health improvements, so of course the spikes are all labeled with diseases. I do not dispute that people were dying from cholera or typhus, I just want to hear more about what might have been causing people to LIVE (rather than just seeing what was causing them to DIE). What about demographic changes that shifted the population towards and then away from a preponderance of new immigrants? From young babies to slightly older people (who used to be at risk of death more than children and adults)? What of other changes (like, say, improvement in building codes that made the Triangle Shirt Waist Fire an anomaly rather than one of many similar situations)? What about income levels? The assumption is that as income rises, death rates drop, but I’d like to see that represented because it’s unclear just how rising income is linked to public health measures. Are we healthier because our increased contributions to the general fund (through taxes) go to support public health? Or is there simply something about being richer – either as individuals or as a collective – that leads to better health independent of the direct funding of public health?

More to come on Time Lines

I’m working on timelines this week but I want to create something new rather than just talking about existing ones which is going to take me some time. It will be a group effort, I strongly encourage you to send in your favorite time lines, your least favorite time lines, and comments about the time line I put together once I’ve got it posted.

Thanks much.

References

Glaeser, Edward. (22 June 2010) The Health of the Cities in The New York Times, Economix blog.

New York City Department of Public Health. [the image]

Constituency Chart

What works

What Terri Chiao and Deborah Grossberg Katz from Columbia University’s GSAPP design school have done is come up with a way to represent percentages using a flow-chart. Not only is it creative in the sense that this sort of data rarely gets displayed this way, but it helps turn the data into a narrative. In order to figure it out, the viewer quite literally has to reconstruct a story that sounded something like this in my head: “The population they are concerned about has 40% of people already experiencing homelessness with another 60% at risk of homelessness. The folks who are already homeless are the only ones living on the street, but really, 75% of already homeless people live in shelters. As for the at-risk-of-homelessness people, 60% live with family or friends. Twenty-five percent of the at-risk population owns their homes … why, then, are they at risk of homelessness? Both the at-risk and already homeless groups have far more families than single folks. And what does it mean to be homeless in jail/prison? That you aren’t sure where you will go when you exit? Somehow I feel like that could describe a lot of the prison population. And what about half-way houses? Those still exist, right?”

The flow-chart concept is not typically used to describe the breakdown of percentages and what works here is that it forces the viewer to walk through the narrative. As a pedagogical maneuver, it’s quite successful. Because of the way the information is presented, it invites questions in a way that a pie chart or a bar graph may not. It’s also a little harder to interpret. Graphics that invite questions often are a bit more challenging to ingest, not quite so perfectly sealed as other more common strategies might appear.

What needs work

I spent a good deal of time looking at this chart trying to figure out what the blue means. I still don’t understand what the blue means.

I also would like to see on the graphic some explanation of how they determined who was at risk of being homeless. Because when I got to the section of the flow-chart that showed how many of the at-risk population owned their homes, I began to get confused. By ‘own home’ do they not mean actually owning the home, but renting it or paying a mortgage on it? And if they do mean that folks actually own their homes outright, how can they be at risk of homelessness? Is the home about to be seized by eminent domain to make way for Atlantic Yards? At risk of being condemned (I hope NYC doesn’t have so many properties at risk of condemnation)? I’m sure if the makers of the graphic ever find their way to this page they will be upset because ‘at-riskness’ is described in the paper. But in life online, stuffing a little more text into the graphic is often a good idea because cheap folks like me will take the graphic out of context and whatever isn’t included will be lost. In this case, though, all is not lost. First, you can visit the blog on which I found this lovely graphic and get the whole story. But if you aren’t ready for all that, note that the authors define those who are at risk of homelessness as anyone who has spent some time in a shelter in the past year, regardless of whether they happened to have been homeless at the time of the survey.

Bonus Graphic

They also included the graphic below. I still don’t know what the blue means. This graphic does make it easier to understand that being truly homeless appears to mean running out of friends and family who have homes to share. Because none of the truly homeless live with family and friends. It’s also clear from both graphics that most homeless people are not visibly homeless. The folks you might see sleeping on the train or the street 1) may not be homeless, they could be sleeping away from home for reasons unrelated to homelessness per se and 2) if they are homeless, they may be quite different from the rest of the homeless population. They’re more likely to be single adults than families and more likely to be men than women.

Public/Private: Project Sites

References

Chiao, Terri and Grossberg Katz, Deborah. (11 November 2009) “Public/Private: Rethinking Design for the Homeless” at Urban Omnibus.