I’ve previously written about the GSS data (see here and here for more details on the survey, and here for some trends beyond space exploration).   The 2012 updates below use preliminary data rather than the final release, so handle with care:

GSS survey trends show significant changes since 2008, confirming the trend first evidenced in 2010

Initial signs of an upward trend showed up in 2010′s “too little” responses, but were accompanied by a rise in “too much” responses as well, suggesting a polarization.   The “too much” trend has reversed itself in 2012, leading to an overall higher favorability rating stronger than at any point in the combined GSS (1973-2012) and Launius (1965-1971) history.

A slightly different rending shows the composite scores (note, “don’t know” responses are not included here):

100% view of survey responses, excluding “don’t know” answers

Finally, the GSS question variant which excludes the word “program” shows the same trends (“program” tends to yield slightly less favorable responses, presumably due to a baseline anti-government-spending sentiment):

Survey responses excluding the word “program”

Stay tuned for updates using the full weighted and quality controlled data when they are released…

I can’t say enough good things about this book. Beautiful and inspirational, it has the reassuring feature that a book on foundational principles of design should be exceptionally well designed itself. Each principle gets a two page spread with just the right depth of summary, narrative and citations on one side, and exceptionally well chosen examples on the other.

The principles bridge graphic design, industrial design and engineering and can greatly aid building a common language of design and better understanding and collaboration in organizations which contain these often not-well-married communities. While the principles themselves might range from pithy to thought provoking, the overall ensemble is very rewarding.

Hands down, 5 stars.

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As with some others on Goodreads, I found this book a little hard to rate, thinking it a “3.5″ and opting for a 4 star rating from an “E for Effort” standpoint. Part of this is high expectations on my part based on affinity for Silver’s FiveThirtyEight election prediction work.

The book is well researched and covers a nicely diverse array of example topics, including but not limited to economics, betting, sports, weather, climate, earthquakes and terrorism. The diversity keeps the interest going. A challenge here is that few of the examples were unfamiliar to me; ironically as the book is ultimately about Bayesian inference, there may be a little bit of a Bayesian thing going on relative to those most likely to buy/read and those most likely to have prior exposure and be left wanting more. The same thinking might suggest that the book is targeted more towards readers attracted by Silver’s political forecasts than those with a wonkish or professional interest in prediction itself.

For the latter, Silver redeems by offering something hard to find in similar popular literature, a high level synthesis across both realms and disciplines in prediction. A contrast with Kahneman’s Thinking, Fast and Slow and Surowiecki’s The Wisdom of Crowds (both of which Silver draws from) helps illustrate: While these two books are by no means peers (Kahneman’s represents a lifetime of scholarship, Surowiecki’s is more management faddish), as books, both suffer a bit from “the curse of knowledge” – the authors’ over familiarity with the often contradictory details leaves the reader rudderless on how to apply the findings in practice.

Silver, instead, takes a first step towards synthesis. This is welcome, although occasionally questions do arise about the formal correctness of mixing and matching themes and findings from very the different predictive methods (regression, classification, physics based modeling, simulation, etc) covered in the book. Absent a unifying framework to relate these methods (Silver is clearly an applied forecaster rather than a theoretician) the reader must rely on his claims to authority by experience (as well as the depth of research indicated by heavy citation) in trusting the synthesis and recommendations.

Overall, Silver ends up on the positive side of the trust ledger sheet, and even for readers already familiar with the topical examples, he provides enough additional color, as well as thought provoking commentary, to make it all worthwhile.

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Wow! This book is not only beautiful but really interesting. Far more than just a nostalgia coffee-table piece, the text is both interesting and thought-provoking.

I found the best chapters / collections to be those on the human form, and on modern art influences. In these not only were the graphics themselves stunning, but the narrative added significant depth. I was a smidge less interested in the chapters that spent more time writing about technical details – the content seemed too deep for a non-aerospace reader, but “already known” for insiders.

A surprising side to this book was how many of the advertisements are focused on recruiting, reflecting the early space age ramp up. To this end the ad copy itself is often very interesting as well.

I gather the author is hard at work on another book. Can’t wait!

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Jon Gertner’s “The Idea Factory” tells an important story about the history of many of the communications and information technology underpinnings of our current era. More importantly, it explores (indirectly and eventually) a major question of what is needed to make large basic and applied research labs successful. I’m glad I read this book, but can’t say I necessarily enjoyed reading it. As such I’m struggling with whether to rate 3 or 4 stars … if Goodreads allowed 3.5, that’d be it.

Growing up very close to Bell Labs’ Holmdel NJ facility, I was attracted to this book because of the place the Labs occupied in our local culture. If you were bright, technically oriented, and wanted a well-paying job, Bell Labs was the place to strive for. The invention/discovery of radio astronomy at Crawford Hill added to the mystique.

The book’s primary drawback (for me) is what feels like a 70/30 split on biography versus technology – I think it would have been a more interesting and engaging narrative had the biographies of a few famous players in the Labs’ long history been treated as embellishment, rather than primary narrative. (I say this regretfully as many of these individuals and their world-changing contributions deserve recognition, but as a book review, it makes for a challenging narrative structure). The reality is the connection between the sometimes (but not always) colorful individual personalities and the ideas and technologies they generated is often too thin; the researchers emerge as characters in a narrative without a strong or clear connection to the plot devices, so to speak. Claude Shannon, whose prescience in 1950′s communications/information theory was truly remarkable, is perhaps an exception.

In short, the secrets to success of the Bell Labs miracle, which generated an amazing number of globally transformational technologies, seem to fall into:

1 – access to the huge reservoirs of working capital that the AT&T monopoly and “cash cows” (local telephone companies) to fund large scale R&D

2 – protection from competition, insofar as it created an environment where fundamental / basic / applied research could be pursued as an end of itself (shielding researchers from having to seek funding), working towards 30-year (not product cycle driven) time horizons, and loosely organized and driven by :

3 – the “grand challenge” of emplacing and continually improving the nation’s telephone network, at its creation, the most complex “machine” (or at least system) ever considered

4 – the co-location of the basic/applied research function with a very large engineering and systems development capability, which could turn fundamental discoveries into applications

The pregnant question – treated only glancingly in the final 2-3 chapters – is whether such a construct is possible again in today’s world. The industrial lab model (at least at this scale) seems to have been supplanted by the venture/entrepreneurial distributed model, but as Gertner points out, this has a defect in that it sacrifices item #2 above, driving innovation to be more incremental and product driven, than transformational. It is unfortunate that this theme was not more deeply developed in the book, as well as a deeper treatment of the evolution of Bell Labs after the AT&T breakup of the 1980s; this is rushed through in single chapter, and yet it is the crux of one of the more thought provoking and relevant lines of thinking. Also only glancingly treated are whether information technology (for which the only giants capable of investing such R&D today – Apple, Facebook, Google, etc) is even the right domain, or whether the next grand challenges will arise in biology or energy.

As a long-time NASA employee, I can’t help but try and draw contrasts between the Bell Labs 4-point model summarized above, and how the Agency currently operates (recognizing that this is peripheral to the book review – but it does illustrate how the book’s concepts have relevance):

1 – At its face, the “capital sufficiency” test is met, with an $18B/year annual budget, NASA is exceedingly fortunate relative to government R&D agency peers. However, the budget is profoundly oversubscribed, and broken down (with significant administration and legislative branch ‘assistance’) into a number of stovepipes which do not intercommunicate or leverage resources well. As a result, basic and applied research are confoundingly “resource starved” at the lowest levels.

2 – Competition is a mixed bag. As a government agency, NASA must embrace competition among researchers as part of its fiduciary responsibilities to taxpayers. That said, competition between NASA ‘business units’, as well as the machinery of a grants process, tends to disfavor basic and applied research for its own sake, working to objectives with much longer time horizons than the needs of individual programs and program managers. The “luxury factor” AT&T was able to provide as a corporate culture – and management – decision isn’t within the purview of program managers to grant.

3 – While NASA faces many challenges in doing some of the most complex engineering and discovery possible, it (1) lacks the “grand challenge” incentive that drove emplacement of the national phone system, and (2) has for too many decades been hostage to warring philosophies – and politics – over purpose. It may be that until a truly globally critical function becomes apparent – energy or rare earth metal supply, planetary protection, space security – a driving purpose as vast, technologically provocative, economically relevant and complex as the phone system creation, may be elusive.

4 – This piece of the Bell Labs equation is present in NASA, co-location (at least at an Agency level) of a basic/applied research base with a vast capability in engineering development. Unfortunately, the capabilities are too stovepiped, with the research capabilities and centers too poorly integrated with the development capabilities and centers (as well as programmatic impedance towards making the transition). Of the four factors, this is the one that would be most readily addressed by changes in management approach (although by no means easy).

Coming back to the book review – I would say The Idea Factory is a “should read” for those interested in the topic of innovation or R&D rather than a “must read”. It is rewarding, but only with the investment of a good bit of energy, patience and passion for the subject.

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It probably takes a special sort of person to dive into an entire book about one statistical theory, but for those so-motivated, this one pays off.

The pro’s: The author has done a phenomenal job at capturing and richly detailing the very “large” personalities that have championed (or condemned) the use of Bayes’ Rule through the centuries, amidst a little-known and long-simmering war that has persisted between statistical Bayesians and frequentists since the concept was first brought forward. This is even more impressive as she is a journalist, rather than a statistician. McGrayne immerses the reader in what can only be called “lush” detail of the history, from personalities to global events.

The con’s: This a very dense text. Not dry in an academic sense, but a lot of material to consume. At times I had to summon extra reserves of motivation to proceed to the next chapter. The topic is also a difficult one to communicate solely through narrative – more than once I found myself wishing for just a little bit of math-by-the-way-of-example to help grasp the concepts. (With such, this could actually serve well as an educational vehicle). While already familiar with Bayes, the application in some of the historical examples was, for me, elusive.

Computing power has today made the Bayesian/frequentist conflict somewhat moot, and I found myself wishing for a little more exposition of Bayesian applications in the modern era. (To me, this is where the real excitement lies, if “excitement” is the correct term!)

Overall – if statistics, scientific inference, decision theory or machine learning excite you, this is probably a book to have under your belt. Reading the history of Bayesian vs frequentist wars triggered some good musing and reflection on the critical question of “how to make inferences when too little, rather than too much, data are at hand”.

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To cut to the chase … much to my surprise, I could actually track long term trends.  Shown at left are both my daily and weekly-averaged data for the last three months.  Even without the weekly averaging the ‘envelope’ of the raw data suggested trends.   (I also have a pretty good intuitive understanding of the related causes, but there’s only so much I’m going to dump onto a public blog!)

Since much of my mood is driven by what’s going on at work (both negative, on the frustration side, and positive, on the “things that feed me” side), I next wondered if there were any weekly trends buried in the data.   Switch the smoothing filter from 7-day to 3-day and … voila! :

There’s the expected weekly cycle, hiding out in the raw data.   While I hypothesized there might be such a signal, I never anticipated it would be that strong.   But in hindsight it makes sense, my work-weeks, while not routine, do tend to share some general characteristics week-to-week.  Switching to a distribution-based view of the raw scores, the story becomes, “Tell Me Why I Don’t Like Monday’s” (that’s an easy one actually; it’s all staff meetings, ‘sync tags’, etc, with very few opportunities to interact with people in motivating or inspiring ways – all activity, no energy).

(For stats junkies, the indented boxplots are a cool graph the Mac stats program Aabel generates … fatter boxes at a given score mean more occurrences; diamonds show the mean value, solid horizontal bars show the median value).

Anyway, back to the data.   After lousy-Mondays, I typically get to work a lot more with people on, well, actually creating stuff, rather than managing it.   Whether or not I’m doing it myself or just coaching, this is what fires me up.   Of course, then the week trudges along, energy gets drained, stress goes up, backlogs grow, opportunities for conflict increase, etc, leading to not-so-pleasant Fridays.   By the weekend, I’m mostly in “recharge” mode (“meh”), hovering more around neutral values.  Unfortunately the PANAS positive affect questions all seem to be skewed towards very “active” positive values (“determined”, “inspired”, “interested”, etc); there’s no way to get positive affect credit for things like “cozy”, “content”, “relaxed”, etc).

As a final exercise I took the weekly data and simplified it down to a simpler user’s guide; for any given day of the week, how likely is it that I’m going to have a good day (mood score > 60), a bad day (mood score < 40), or a “meh” day (40-60).   Arbitrary thresholds, I just picked ones that divvied up the data well.   And yes, these are now posted on my office door (how’s that for biofeedback?!)

On the “large and growing” side, there are few surprises; Colorado stands as the biggest “winner”, due to Lockheed Martin’s capture of the Orion / MPCV crew vehicle.  The trends of the solid rocket industry are largely captured in the Utah data; the recent rise reflects development of the 5-segment reusable boosters slated for the new Ares rockets, prior to their cancellation by the administration.  Florida’s rise over time is likely related to procurement of launch services from the EELV fleet of Delta and Atlas rockets, mostly for scientific missions.

Trends in NASA procurements, state-by-state, 3-year smoothed and in FY10 inflation-adjusted dollars, from 1999-2010

On the shrinking side, Louisiana’s decline is not surprising, as production of the Space Shuttle’s large external fuel tank slowed, then ceased.  The nearly $1B drop in Texas spending may be related to completion of the development of International Space Station modules and systems, in the earlier part of the last decade.

As notable as the ‘winners’ and ‘losers’ is the more than 40 dB (4 orders of magnitude) difference between states receiving the least, and the most, NASA direct spending.  (Emphasis on direct, since these data only reflect NASA “prime” and first order service contract procurements.   A web of subtier supplier industries provide services to these contractors, so the actual national distribution of NASA dollars would be slightly more uniform).

Here is the PDF version of the above graphic:  PieSlice_States

The visualization below shows the trends for selected major contractors from 1997-2010.   To construct the graphic, the following simplification were made:

• All data are converted to FY10 dollars; all inset numbers are in $M per year
• Previous 3-year smoothing is applied (e.g., the year 2000 data point is the average of the 1998, 1999 and 2000 point data)
• I have tracked industry mergers to the best of my ability and report only the “latest standing” prime contractor … an incomplete list follows the post
• I’ve excluded a number of primarily construction contractors, as well as many smaller contractors that were active in the late 90′s / early 00′s, but are not active today.

First, the procurement data broken out by field Center.  In all plots below, historical real year dollars have been inflation-adjusted to FY10 dollars, and each plotted year corresponds to the average of the previous five years’ procurement data (the data are smoothed).

5-year smoothed NASA procurements by field Center

The trends are a little more coherent if grouped by major field Center ‘type’, i.e. Human Space Flight (Marshall, Stennis, Kennedy, Johnson), Aeronautics/Research (Glenn, Ames, Langley, Dryden) or Science (Goddard, Jet Propulsion Lab):

5-year smoothed NASA procurements by field Center type

In this view, the slow but steady increase in “procurement share” of the science centers since the early 1980′s stands out.   Human spaceflight centers have accounted for a fairly stable 50-60% of Agency procurements since the early 1970′s.

Rendered as curves the trends are also apparent, modulated by the long term trends in the Agency’s budget:

5-year smoothed NASA procurements by field Center type

Finally – and less expectedly – the share of NASA’s spending that occurs as procurements has interesting trends.   Almost all of NASA’s budget occurs as procurements – i.e., its services are bought from industry, in the form of prime or support contracts.   This has been true since its inception.   The interesting trend occurs from 1990-present though, as the procurement percentage has steadily dropped from 90% to about 83%:

Share of NASA outlays accounted for by procurements

Since there was no 20-year acquisition policy which inclined towards insourcing over this period, the most likely explanation is that this reflects the steady increase in Agency fixed costs, relative to the total budget.   These fixed costs might include the costs of aging infrastructure, as well as utilities and civil servant salary expenses which may have increased faster than both inflation and the Agency’s top line budget.

To explore this question, I’ll start with a refresh and update of “Ask and You Shan’t Receive”, where I riffed a NYT “porcupine graphic” to illustrate the differences between Presidential budget requests, and actual Congressional appropriations, for the NASA budget.   I’ve added in some earlier budget years’ worth of data, and turned the crank for DoD as well.

First, an eye-opener from the defense data.   I’ve previously illustrated the magnitude of long term defense cyclicals, which are fairly large in actual, appropriated amounts.   Even more impressive (disturbing?) is the disconnect between administration / defense department long range (5-year) planning, and the actual spending amounts:

Administration 5-year budget planning forecasts (light blue) and actual congressionally-appropriated budget authority (dark blue) for the Department of Defense (OMB subfunction 051). All estimates are converted to FY12 dollars using composite defense deflators.

After 2003, part of the disconnect is attributable towards appropriating the wars in Iraq and Afghanistan outside the baseline DoD budget request, year to year.   However, even prior to that, it was not uncommon for the out-years of defense plans to be hundreds of billions of dollars out of bed with the eventual, actual appropriations.  That’s a lot of plans that never made it to fruition (yet somehow, the nation’s defense capability remained and remains intact and operational).   To call the defense planning and budgeting environment “volatile” would be an understatement of the highest order.

An update to the equivalent NASA data shows a much more “tempered” situation:

Administration 1-year (1978-1989), 3-year (1990-1995) or 5-year (1996-2012) budget plans (light blue), versus actual appropriated budget authority (dark blue). All estimates converted to FY12 dollars using GDP chained deflators.

With the exception of the 1990′s (begun with ambitious requests for Space Station Freedom and the Space Exploration Initiative; ended with aggressive plans for downsizing of NASA), there have been only comparatively minor differences between administration out-year planning and congressional appropriation.   This has certainly been the case through the 2000′s.

To drive home the point further, the same data are integrated over 3-year windows and overlaid for both DoD and NASA:

Further, NASA’s overall budget fluctuates significantly less than most other Federal agencies, year to year.   Comparatively speaking, NASA’s funding is the stablest in the Federal government:

Typical budget variability of various federal agencies and departments, calculated as the average, year-to-year, percentage deviation of appropriated budget authority, from the 1978-2011 mean budget authority.

We thus have a situation where:

• NASA’s actual year-to-year budget is the stablest in the Federal government
• Administrations and congresses tend to agree on the right “size” of the Agency in any given year, far more so than DoD (and by implication of the variability above, likely more than other agencies as well)
• NASA’s programmatic portfolio is nonetheless continually in disarray, both in planning and execution

The last bullet is particularly confounding.

Over the decades various blue ribbon panels have repeatedly exhorted that “NASA is asked to do too much with too little”, however, this rationale is likely true of every Federal agency.   The “finding” is both unsurprising (coming as it does from inside-the-bubble panels) and not particularly helpful; providing advocacy, not advice.

Others observe that since Challenger, NASA has found itself repeatedly trapped between severely divergent, and seemingly irreconcilable, ideological and political belief systems about the next steps in the development and exploration of space.  Aligned with powerful legislative and industrial stakeholder interests, the shuttlecock of civil space bounces back and forth.   Unfortunately, this again is true of most sectors of Federal government.   It suffices as a description for the situation, but fails as an excuse.

Some of this disarray must come home to the Agency itself to roost.  As an agency within the executive branch, it is NASA’s job to balance competing stakeholder interests, fluctuating budgets, midstream replanning, and future uncertainties.  This job is neither unique, nor more difficult than in any other branch of the Federal government that engages in long term development (rather than services or operations) programs.   Put simply, it goes with the turf.

What NASA needs first and foremost, I will argue, is neither new vision, nor the “right” 20-year plan, nor revolutionary innovation, nor bottoms-up reinvention.

Rather, it needs to continue making progress on a long term path towards disciplined management.

As a first step, the Agency desperately needs to re-establish the capability to formulate and implement coherent, integrated, and self-consistent budgets (briefly grasped during the early years of PPBE implementation, but lost more recently for a variety of reasons).   The challenge here will be to restore discipline, while not succumbing to the lure of central planning, since…

… as a second step, the Agency must develop the ability to adjust these budgets rapidly, and flexibly, in response to the winds of change on both sides of the Mall.  Currently, minor (a few percentage points) adjustments to NASA’s budget lead to crippling paralysis in the system.  This is an inevitable outcome of attempting to manage detailed implementation outcomes at too high a level (a trend which continues unabated), and pursuing too many conflicting agendas simultaneously.  Failures to delegate appropriate decisions to appropriate levels, or to manage by budget, are exacerbated by a field center culture which has evolved to treat the institution itself as an entitlement, rather than a tool.

At the end of the day, the budget data speak loud and clear:  Relative to other Agencies, NASA is blown by a light breeze in the eye of the Federal budget storm, not Cat-5 winds at landfall.   As an Agency it has enjoyed – and continues to enjoy – a level of budgetary stability almost unprecedented in the Federal government.  The “complexity” of its situation is ultimately tempered by that simple reality; rationales for failing to quickly adapt to change are few.  The budgetary “safe zone” has been ours to either use, or to abuse and lose.  On our current vector, the risk of the latter seems extremely high.

Selected References

OMB Historical Budget Tables (and Deflators)

Department of Defense Green Books

Older DoD Future Years Defense Plans (see Table 14)

Older SecDef Annual Reports

NASA Budget Estimates

More NASA Budget Data

The photographs below are mostly “obvious” targets but nature was very cooperative.   For the faded Pepsi sign shot, I was lucky enough to find reflected sunset/golden hour light, off of the glass facade of a large building across the street (this wall is eastward facing).  The Murray’s photo I snuck in just because it’s so wonderfully font-o-rrific.  Doors-to-nowhere are obligatory catches, and transformers are just plain cool.   The evening shot of the Mill museum and commercial loft spaces isn’t very well done technically, but captures how beautifully the city has merged old and new in the Mill district renovation.    Enjoy!

With a few (52) manual iterations of adjusting the offsets in the “Winter Wave” pre-programmed GoogleCorrelate search, I’ve arrived at the following visualization.  The outer ring denotes the “most peaked” term for each week of the year, the middle ring denotes the “second most peaked” term in the lists, and the inner ring denotes the “third most peaked” term.   Poking around on the actual GoogleCorrelate site, there are some really interesting nuggets buried deeper in the rankings, but with a flat graphic I’m limited by how much data I can cram in!

The real “Winter Wave” is almost a little comical.   After shopping for designer boots, the jet-setting crowd sets off transworld snowboarding, for which they need Nordica and Volkl ski equipment.   After the holidays, the fun is a little closer to home (hockey arenas), after which all this extreme exposure results in much more mundane concerns (oranges, presumably to help cure bronchiolitis).    Well, OK, that may be more of a coherent narrative than is justified by the data, but it’s nonetheless fun.

The February-April rat race of summer internships, summer programs, and finally summer camps makes me a little glad to be neither a kid, nor a parent, in the 21st century.

The summertime drill from May-June appears to be:

• Dress to kill (leather sandals, linen clothing, born sandals)
• Fix-it-up (awning, decking, cedar decking, deck railing)
• Keep-it-at-bay (Brush Hog, trumpet vine, johnson grass)

If you’re selling pre-packaged christmas musicals for kids, get them on the shelves by early September.   October and November are completely devoted to killing things with 4, rather than 6-8, legs (treestands, duck blinds, deer stands, upland hunting, waterfowl).

After which we go shopping for designer boots and begin it all over again…

The Data

The data are an annual survey of satellite service providers on market, regulatory and industry factors which either promote, or inhibit, the service sector’s demand for new satellite launches.   While the data are presented each year, I have never seen them actually trended anywhere.   Doing so provides some good evidence that the market may indeed by looking up for commercial launch providers, independent of the self-referential and squirrelly launch demand forecasts.

While the sample size is small (this year, 14 companies responded), the coherence of the results trended over time indicates that there is some value to the data.   Each of these companies were asked whether various factors had “significant negative”, “some negative”, “no effect”, “some positive” or “significant positive” impact on their plans to purchase and launch satellites.   The factors included the readiness of the launch and satellite service base (launch vehicle and satellite availability and reliability), the financial and regulatory environment (economic conditions, availability of financing and insurance, and ease of securing operating and export licenses), and finally structural changes in the overall market and competitive landscape (demand for services, competition from other service providers, competition from ground-based providers, and increasing lifetime of satellites).   Note that these are my three “meta-categories”, rather than COMSTAC’s.    Trended over time (and aggregated), the results are very informative:

The schema here is that dark red, orange, yellow, light green, dark green correspond to the percent of survey responses in the corresponding “significant negative impact” to “significant positive impact” categories above.  Even with a small survey sample (with variable participants from year to year), time trends are coherent and clear.   The self-reported perception of these large providers is that overall conditions have indeed improved significantly since 2003.

The Good and the Bad

What is most interesting is which factors have improved, and which are still the sticking points.   The readiness and availability of both launch and satellite services has evolved from being a primarily “no effect” issue in 2003 to a net positive factor in 2011.  This is strongly driven by improvement on the satellite side, although slow improvement on the launch side contributes as well.  In 2011 no respondent rated launch vehicle availability or reliability as a “significant negative” impact.

Rather, the remaining key issues all line up on the “business side” of the equation.  Financial and regulatory issues were ranked a significant burden in the early 2000′s.  Since then it appears that a supportive financing, insuring and licensing infrastructure is beginning to emerge.   The limiters in this category continue to be export licensing, as well as global economic conditions (and the closely linked category of financing availability).   Should the economic environment improve, and ITAR issues be addressed, this category could evolve to be at least “neutral”.

Inside the Fight Cage

Within the category I am calling “market changes”, the most notable trend is a steady increase in overall demand for satellite services, ranked strongly as a positive or significant positive factor.   The other factors in this category are structural within the industry and market, relating to competition, consolidation and innovation.   The overall picture is one in which demand for services is high (and increasing) but the viability of any given provider’s position to provide those services within the market competitively (and from space) might fluctuate.   When combined with innovation (increasing satellite lifetime, and hence downward pressure on demand for hardware and launch), this category overall remains a mixed bag of impact for launch providers.

The data seem to provide a clear and intuitively acceptable story of evolution within the sector over the last ten years.  Unfortunately, we lack critical validation data, since the survey in its current form was initiated after the collapse of the late-1990′s commercial services-and-launch bubble.   Reference data from a comparable questionnaire during that period would have gone a long way in helping to calibrate out unfounded optimism.   The best we can point to is the fact that many of the subfactors point towards reasonably objective “externalities”, such as licensing, financing and insurance availability.   Further confidence might be found in the sheer size of the market in play, on the services side – at least two orders of magnitude larger than that of the launch industry, as per the latest annual Space Report.   The idea is to tune in to the folks with the biggest stakes in the game (the space products, services and infrastructure survey respondents):

Whatever the details of the breakout (including fine details around commercial vs non-commercial launch) it is clear that the center of gravity in the space economy is on the demand (services) side, and it is that side that should be focused upon when assessing the climate for future launch activity (instead of the supply side, i.e. launch providers themselves).

Put simplistically: if the fluctuations of an existing $189B commercial space products and services market, and an existing $86B governmental services market, have been unable to cause more than minor fluctuations in an essentially flat rate of actual launches per year, it would be imprudent to assume that fundamentally new markets predicted by emergent launch providers are likely to change the overall demand picture dramatically (or at least quickly).

Kool-Aid vs Protein Drinks

This is not to say the outlook isn’t optimistic, but the optimism should stem from much more mundane (and sustainable, and believable) factors than anticipation of self-bootstrapping “airmail miracles” of new market generation.  (There, I’ve lost 50 “Fast Company” brownie points with one simple sentence.   So be it).   The gains reported in the surveys are much, much more prosaic.   Satellites (rather than launch vehicles) have crossed a reliability/availability threshold and are now on average seen as positive factors.  The operating environment (both financial and regulatory) is increasingly supportive (or at least, much less inhibitive) of a space sector economy.   Finally, demand for services as a positive factor is improving much more rapidly than negative structural changes within the competitive environment seem to be offsetting it.   All of these point towards strong fundamental (rather than speculative) causes for mid-to-long term improvement in demand for launch services.

Postscript

A final note on forecasting: eyeballing the composite survey trends above, versus the actual launch rate data for the last decade, it looks like a regression-type actual forecast model driven by the survey, or the survey plus recent actuals, might be possible.   This would be limited by the number of available data points (currently 8, from 2003-2010) but wouldn’t have to incorporate much skill to outperform the actual COMSTAC forecast, which I think (still to validate) has less skill than a “persistence forecasts”.   Stay tuned for a future post on this topic…

Two separate threads led me to the results below: (1) Experimenting with Google’s new experimental GoogleCorrelate tool, and (2) helping Marshall Space Flight Center‘s Summer and Minority Internship Programs improve their external communications.  GoogleCorrelate adds a new twist to the analytics mining provided by Google Insights; it allows “reverse engineering” to find which search terms most closely match pre-defined geographic patterns (e.g, state-by-state demographics), or pre-defined temporal patterns (e.g., an annual yearly cycle).

The time search, driven by a simple annual sine wave in Google’s Winter Wave example, identifies which terms are most likely to be searched with a pronounced annual peak.   By adding an offset value, the peak can be shifted in time.   Playing around with the tool, I was surprised to find how strongly summer internships, summer programs, etc, dominated the searches for all peaks centered in the January-to-March time frame:

Switching over to GoogleInsights allowed plotting and extraction of the actual annual data in CSV format (the first step in generating a composite).   To increase the data content, I extracted both “summer internship” and “summer internships” (plural):

After a little bit of smoothing, we get the average annual cycle of summer internship searching:

This is probably nothing that education specialists don’t already know, but may not known by our supporting communications staff.   The message I take away is clear: kids (or parents) start searching during and immediately after Christmas break, with peak interest the first two weeks in January.

From a communications perspective, any data which point to a more-than-doubling of target audience interest over a narrow window are of great interest.   The associated promotional campaign recommendations would include making sure updated supporting content or collateral is firmly in place before the holidays.   January would then  make an excellent month to reinforce interest and enthusiasm by highlighting the internship program in executive speeches, mobilization of intern program alumni or targeted college almuni already in the employee ranks, etc.  Even small slips in the communications campaign schedule could lead to significant gaps in reaching the “first wave” of internship seekers, who may also be the most motivated.

Including the “need factor” based on poverty rates and Kiva penetration helps offset this bias, and helps identify those MFI’s that may be “worth taking a chance on” in high-need, but higher-risk, countries.   Below is a revised regional ranking, in which I have also filtered out about a third of the MFIs with extremely low scores in any of the four metrics:

Kiva MFI partner rankings. Sub-factors include Interest Practices (green), Risk (yellow), Experience (blue), and Relative Need (red).

To simplify things, I’ve also recreated a “top 20 table” from the overall rankings.   In this table, any MFI with a score in the lower third of any of the four sub-factors has been filtered out.   In short, this table provides a way to pick Kiva loans such that your loan dollars go to the neediest regions of the world, while balancing your financial risk and rewarding institutions that keep their interest rates within (or better than) the norm for microloans.

The Top Twenty

The shots below are all from 2010, when I spent a few days in July traveling around the French province of Aveyron, thanks to a wonderful tip from a friend.

Wildflowers at Najac

This one is all about the color, since the good department of Aveyron apparently spared nothing from its palette in this little corner. A little better framing and focus might have improved the photo, but overall I am quite happy with it.

Through the ruins at Najac

This shot was actually taken with a very high zoom from the catwalk on the other side of the interior courtyard of the chateau. I am of two minds on the photograph itself - on one hand, I love the effect, and the depth of field seems perfect. On the other hand, the misalignment and partially out-of-frame content leaves a mild sense of incompleteness. What I can not tell is if that is a good thing or a bad thing! What the photo lacks in symmetry it may make up for in intrigue.

La Bastide des Fonts, near Cornus

Atop the ridge, this panorama is absolutely breathtaking. As a photo, it utterly and completely escaped me - and not for want of trying (I have a couple dozen failed versions in the reject bin). Try as I might, I was not able to capture the depth of the distance or the literal depth of the scarp and valley. The panoramic view worth capturing extends at least another photo frame to the left. By attempting to scrunch too much into the frame, I also sacrificed some lens distortion of the church itself. As a result, the photo only hints at the real majesty of this beautiful scene.

Atop the chateau at Belcastel

Belcastel is one of the many official "most beautiful villages in France" to be found in Aveyron. While the chateau has been significantly reworked and restored it is nonetheless unbelievably idyllic. This photo looks down upon the old village and church. I think the framing and symmetry work well, but I wasn't able to keep the backlighting from washing out.

Grasses in southeast Aveyron

These grasses had an absolutely beautiful, feather-like texture. Capturing their flagellations in the strong breeze was beyond my skill at the time, but could have made for a very nice photograph, one I really regret not capturing. Now I at least know enough about shutter speeds to tinker around next time!

Treed texture in Brousse-le-Chateau

Another texture capture (I am really drawn to textures ... hopefully that is not a sign of low level schizophrenia). The colors in this photograph struck me as very well balanced and contrasted, with colors leaning to the earthy side in both the trees and sky. As a feature of interest, it was also intriguing that this tree had not yet bloomed in late June.

 Along Le Cernon, near La Bastide-Pradines

I stopped the car on first sight of this house because of its amazing, well, green-ness. I think the photo did it justice.

Aveyron Blue

I would have been remiss had I not captured at least one of the beautiful cerulean shades from the bastides and villages of Aveyron. During my drive through southern France and then down to Costa Brava in Spain it seemed that each town (or at least region) had picked an "official" shade of blue. This photo captures how vibrantly the "town colors" jump out when contrasted against grey masonry.

Open for business?

I loved this photo as much for the Moulin Rouge-esque font, as for the very uncertainty answer to the question: "Is this hotel open and functioning, or derelict?" My guess is the former (the upstairs shutters seem too neatly arranged partially ajar).

Obligatory photo of the Milau Viaduct

There is no way I could match the most breathtaking photos of the graceful Milau Viaduct (be still my erstwhile civil engineering heart!), but hopefully this photo at least captures the spirit of how this beautiful structure blends with the landscape, treading "lightly" upon the valley and exhibiting a "paper thin" presence. Go to images.google.com and search for "Milau Viaduct" for the real eye-poppers.

The Freshness score is computed as in the original post; Freshness is a composite of the trending upwards (or downwards) in Bain Survey Utilization scores (1993-present), Google Books (1990-present) and Google Search Insights (2004-present).   The scores are normalized to percentile rank, 0-100.   Again, the old touchstone TQM shows that tools don’t have to be fresh to be effective, especially when honed towards their target niche applications.

Satisfaction is also computed as in the original post, it is a composite of the Bain average satisfaction score for the past 10 years, as well as the satisfaction trending direction (thus, “new entrants” which have low absolute satisfaction but are trending upwards, have a chance to stand out, while “new entrants” which arrive with a bang but with experience lose their luster, also get corrected).  Because this score composites both the mean and the trend, it is important to cross-compare against the absolute satisfaction score at the bottom of this post (as an example, even though strategic planning leads all tools in absolute satisfaction, it is ranked as “middle of the pack” in the Fad-O-Meter since its satisfaction trend is nonetheless slightly downward … we seem to have run out of ways to squeeze even further performance from this old touchstone tool, and may need new tools for today’s business environment).

Tools which focus on knowing “yourself”, your competitive environment and your customers occupy strong positions in both buzz and satisfaction (core competencies, vision statements, customer segmentation, benchmarking, scenario planning).

Open or collaborative innovation tops the freshness (hype) chart right now, but has yet to demonstrate itself in Bain satisfaction scores; the case is similar for supply chain management.   After many years, balanced scorecards maintain their buzz but are similarly lagging in generating satisfaction among executives.    Overall, tools which focus on structural or process change (or management) rank either in the middle of the pack, or as laggers, in satisfaction (TQM being a notable exception).

In the Bain 2×2 format (power-boosted to 3×3), here are 10-year composites for both usage and satisfaction (averaging helps bring out continuity across the biannual Bain surveys).   This helps separate (as Bain analysts note) “power tools” (high usage, high satisfaction), “niche tools” (low usage, high satisfaction), “blunt instruments” (high usage, low satisfaction) and “watch items” (low usage, low satisfaction).

If you’re just interested in executive satisfaction, here are the ranked 10-year average satisfaction scores (i.e., reading from right to left on the chart above):

Distribution of my loaned Kiva dollars

My default quick-and-dirty visualization workhorse is IBM ManyEyes – lacking in design elegance and configurability, but free, interactive and very versatile.  Whether in color density or bubble mode (click the link to try that out), I’m a little underwhelmed by the results.   I’ve only got 35 loans to work with, so the starting data are thin, but nonetheless, the graphic looks a little too spartan for my tastes.

Home-made Kiva loan dashboard using daytum.com

I’ve also tried a Kiva dashboard page using Nick Felton’s Daytum service (at right); slick but more of a curiosity than anything else.   (Nick and Ryan are now on their way over to work for Facebook, but I’m still holding out hope of WordPress-embeddable Daytum panes, and some point!)

Visualization-wise, my best results came by simply adding locations to GoogleMaps, along with judicious selection of a good contrast background (the terrain map).  This does (hopefully) a better job at pulling out my preference towards funding women borrowers, showing my interest in lending to Mongolia (spurred by both a visit there, and the admirable social lending practices of XacBank, including both microsavings reinvestment, and green loans) as well as several countries along the former Silk Roads (derivative Central Asian interest from the Mongolia thing, plus some empathy from the locals for surviving millenia of cross-cultural political intrigue).

Distribution of my Kiva loans, including gender

Of course, my own loans are of interest to, well, no one but me.   More interesting is the distribution of all Kiva microloans to date.  Most interesting is this distribution compared with the worldwide occurrence of poverty.   For the latter, I’ve pulled data from the World Bank, specifically, country-by-country percentages of population living on $2 (USD) per day, or less.   Combining that with raw population data converts percentages to poor population estimates – the upper panel in the graphic below:

Kiva need (low wage earning population) and supply penetration (dollars loaned per low wage earning population)

Clicking the graphic will take you to the live visualization at ManyEyes.org (where you can also visualize many other parameters).   In the lower panel above, color intensity shows where Kiva lending has made the “biggest dent” in various countries’ poorest populations (Kiva dollars loaned per “poor” individual).  The imbalance between microloan need (upper panel) and Kiva supply (lower panel) begins to emerge.  A bubble representation highlights this better than color depth, with the disconnect in Africa being most evident (inverse pattern from South America):

Kiva need (low wage earning population) and supply penetration (dollars loaned per low wage earner)

In the highest tier, Lebanon, Ukraine, Azerbaijan and Paraguay show the highest penetration … and it is very high.  The approximate equivalent* of more than better than 1 in 100 of these countries’ poorest people have been served by Kiva microloans.  Mongolia and a host of Central and South American countries come next (Nicaragua, Costa Rica, Ecuador, Peru, Bolivia) within equivalent penetration rates of 1-in-150 to 1-in-250.   In order, Armenia, El Salvador, Tajikistan, Cambodia, the Dominican Republic, Togo, Kyrgyzstan, Chile and Moldova round out uppermost tier, up to 1-in-1000 equivalent penetration.

Back to visualization – the ManyEyes shaded map is still less than satisfactory to me.   Rearranging the data as a treemap may communicate better; in the graphic below, countries are binned by region, the boxes are scaled by the absolute size of the <$2/day population, and the color intensity shows the total amount of money loaned through Kiva.

Kiva estimated need (box size) and supply (box color)

Personally, I think the treemap approach does a better job at communicating relative need and gaps.

Of course for lending “decision support”, I’m more interested in even more finely tuned assessment of need.  Comparing poverty rates (x-axis) with lending penetration (y-axis) and throwing in a bubble scaling effect to denote absolute magnitude, brings out some patterns.

Tiers of Kiva countries

This is helpful for understanding (many countries with high Kiva “penetration” have comparatively small absolute populations in poverty) but less useful for quick and dirty decision support when it comes time to make loans.    So instead, I converted both the absolute poverty incidence (blue below, from the $2/day data) and the Kiva penetration data (or rather, its inverse; green below), percentile ranked all the countries, and combined the two into one “need ranking”:

Ranking of Kiva relative "need", based on poor population size (blue) and Kiva penetration gap (green)

In practice, I’ll be comparing this with my MFI rankings from last November.   Part of the reason some countries are underserved is due to shakier partner institutions.

Finally, by binning the “need ranking” into high, medium and low categories, and returning to trusty old Google Maps, I can provide a (perhaps) clearer picture of Kiva’s penetration worldwide:

Kiva relative need; countries with lower poverty + higher Kiva penetration in green; countries with higher poverty + lower Kiva penetration in red

Simplistic, to be sure, but hopefully the analysis and visualization drive home the need for continued penetration of microfinance (whether Kiva or otherwise) into African and Southeast Asian markets.

My final caveat is that “need”, of course, continues to exist worldwide.   Personally, I will continue to lend to “green” countries above, as they have mature microfinance networks and, perhaps, “manageably” sized poorer populations, where microlending can make a dent.   However, I will also now go out of my way to look for reasonably decent MFI partners (based on my earlier rankings) in “red” countries, where the need is demonstrably greater.

Technical note: To estimate “equivalent penetration” statistics, i.e., “1 in 100 poor people served”, I have taken the inverse of (total dollar amount loaned to a country, divided by the estimated size of the population living on <$2/day, divided by the average size of a Kiva loan [$749]).   I.e., I estimate total loans per poor-capita, and take the inverse to get “1 in x” statistics.   I call this “equivalent” since actual loan sizes vary, since loan recipients are often repeat customers, once they pay back their initial loans, and since recipients don’t necessarily originate from the lowest tier of wage owners ($2/day is a proxy for overall microloan need).   Even with the uncertainties, I think it really highlights the power of Kiva.  The fact that countries that develop strong MFI partner networks can reach “1 in hundreds“, not “1 in millions“, type penetration, is both surprising and inspirational.

If you’re dabbling and think it might be useful (perhaps after reading the 72 page free short summary of the book available on the website), I’d still recommend it, though with the caveat that you should set your expectations based on a version 1.0 software release.

Before digging in, a couple of quibbles to go along with the release.  Both center around the fact that the iPad app release actually makes it almost harder to advocate for greater pickup and use of the Canvas tool.

1. The price – $29.99 is OK for the already-converted, power users, but it begs the question of why a “lite version” isn’t on the street as well.   Just in using the paper canvases, I have found many people who would probably be motivated to spend a few dollars to experiment with the canvas – but not $30.  The authors may be missing a customer segment.   I hope they consider a Light version or lower app costs once they recover their development costs.
2. The free downloadable canvas temporarily disappeared from the BMG website – but has reappeared now on wikipedia - so strike that quibble!   Huge props to the authors and developers for continuing to make the Canvas available with a Creative Commons license.

On to the review:

The iPad app interface is reasonably user friendly, and certainly supportive of individual, offline experimentation with business models.   However, as a substitute for printing a wall sized version of the canvas and working collaboratively through models, it is “not yet there”.   Even with an iPad2 and video mirroring to a projector, I just don’t see the app as an equal substitute – yet.   So my recommendation is to treat it as an individual / personal tool.   The experience is about as claustrophobic as one would expect taking an exercise intended for wall-size collaboration, down to iPad-size screen resolution, would be.   That’s not necessarily bad, just set your expectations right.

Making up for the interface limitations is the inclusion of a very nice underlying cost/revenue model.   The model is heavily biased towards “unit sales” type models, rather than services or levels of effort, but it can be retrofitted.   Extremely nice features include binning revenues according to different customer segments, value propositions, etc.   It appears that future versions will allow costs to be binned and sorted similarly, but that feature doesn’t seem to be available yet.    Adding an actual rubber-to-the-road calculation engine underneath the canvas itself is a real value add and worth paying for.   The interface to access the model isn’t seamless, but it is clever.

Critical (but incremental) features which would spice up a version 2 would include:

1. The ability to filter by assigned colors, to focus attention on individual value props or customer segments
2. PDF export
3. A native file format, so that canvases can be shared between users
4. Adding in the (latent?) feature to sort costs, not just revenues, by customers or value props
5. Adding an “annotation and markup layer” (arrows, highlight boxes, etc) to help show connectivity within models, as is done in the book

In this experiment I map the Library of Congress classification system through to its Dewey Decimal counterpart.  (For those playing along at home, Delicious Library adds DD numbers automatically upon import.  Sadly, though, LOC codes must be searched and added manually).   The classification bins in the network diagram below are scaled based on the number of books in each bin (width, height, and font size scaled by linear factors with the square root of book count), and the edge connections are similarly weighted by book count.

Pause for rationale:   Why?   Answer:  Why not?

Library of Congress codes are on the left here, Dewey on the right.   A couple of things jump out at me: for purposes of coarsely bucketing my “top-level” interests, the much-maligned Dewey system seems to do a better job (fewer buckets, better categories), although at the expense of granularity.

More interesting is that the process of network mapping (allowing the graph layout algorithm to rearrange both top and second layer categories, driven by the cross-system edge connections) yields to me what seems a more seamless “sequential” ordering of each top-level system.   For example, both systems nominally list Technology (mostly engineering) after Science, but for me, the societal element of technology/engineering clearly places it closer to the social science domain (or is this just my ex-civil-engineer’s sensibilities in action?).   The network-rearranged system captures this.

Network mapping also “repairs” some Dewey defects, moving History & Geography back up to a “proper” (LOC-driven) place in between Philosophy and Social Science.

Interestingly, the mapping places Literature and Language close to, and “after”, Science, rather than buried deeper in the social sciences.   Topologically I might envision an even better classification to be circular rather than linear in nature, with Language “wrapping around” to connect back up with Philosophy or Religion.

A similar exercise with a larger sample of books (unbiased by personal preference selection) might yield a very interesting “meta-classification” system (not that the world necessarily needs one!).

A more readable PDF version is available.

The Man Who Ran the Moon: James E. Webb, NASA, and the Secret History of Project Apollo

4 of 5 stars

Readers will find little new in “The Man Who Ran the Moon” (nothing in the way of a “secret history”), although its focus on an often-overlooked but central figure in the Apollo program – NASA Administrator James Webb – is a welcome supplement to the popular histories of the era. Webb’s views on the organization and management of the newborn agency are almost as interesting as his political dealings through the 1960s. The complex interplay between aerospace contractors and the Federal government is also given more exposure than conventional in popular texts.

A quick and easy read, “The Man Who Ran the Moon” is a worthwhile diversion for anyone interested in the history of NASA as an organization or the Apollo program itself, as well as public administration in general and Cold War-era beliefs about technocracies and their role in society.

Four Fish: The Future of the Last Wild Food

4 of 5 stars

I received this book as a gift from my brother, a far greater “afishionado” (groan) of all things finned than I. I’ll confess it sat idle for a while in the “medium priority” layer of my stack of reading material. It should have been higher.

Fitting comfortably in the “microhistory of a natural resource” new genre of books, “Four Fish” succeeds by drawing together and interweaving compelling personal, historical, economic and ecological narratives. Greenberg tackles difficult and tangled questions of sustainability of the oceans, but leaves readers with enough of a clear narrative and grasp of the issues to begin to form their own, informed, opinions. (This is no easy feat, and one that would have been fumbled by less competent authors).

One of the most interesting (and unexpected) turns is the treatment of aquaculture – domestication of wild species of fish – and its long term viability. The coverage is both balanced and deep. Greenberg also raises uncomfortable but compelling points about the disconnect between Western consumers’ faith in the power of the market, vs the realities of firm policy.

Even if you are only glancingly interested in the topic of “things with fins” (as I was) – this book is worth a try.

The Silk Road: Two Thousand Years in the Heart of Asia

3 of 5 stars

My interest in central Asia has been piqued since a recent homestay trip in Mongolia. Since then, the “-istan’s”, for me hidden behind the opacity of the Cold War for most of my life, have been a source of mild intrigue. Reading “The Silk Road” has added significant color – if not necessarily clarity – to my familiarity with the tangled history of the region.

The book is readable, if not necessarily fully accessible, to non-academics, presuming significant familiarity with the region and its peoples (both past and present). While not “lavishly” illustrated, it certainly draws from an eclectic sample of material which keeps the interest level up. Descriptions and details are rich. The historical narrative is far from linear, and overall the book would have benefitted from a greater use of maps than the single, stylized map in the front matter.   Quite honestly, if it had not be for the power outage, it would have taken me many more weeks to plod through this text, as I had struggled for three weeks’ worth of nightly reading to make it only halfway through.

I wouldn’t recommend this book as a casual read, although for fans of history – specifically multicultural and oft-overlooked corners of world history – it may be worth the plunge.

1. I wanted a test project to learn GraphViz and its related programming syntax for generating network diagrams.
2. I wanted to see how much semantic structure was built into my Delicious bookmarks, after two years and ~1190 entries worth of use.
3. I wanted to do the latter before Delicious gets transferred to its new owner and potentially gets dorked up.   (Yahoo’s utter apathy and neglect has at the very least been benign.  Although I am optimistic about AVOS…)

More specifically on #2, has enough information content found its way into my assigned tags to self-organize related keywords?  (I’m fascinated with the concept of folksonomies, even if a folksonomy technically requires more than one contributor.   

Garden-variety Wordle’s are easy enough to construct from Delicious bookmark tags, but are little more than toys, failing to reveal any linkage or semantic content.   (Other than the, I’m sure, accidentally fortuitous for me, choice of a Space Shuttle-type envelope for the “horizontal and alphabetical” tag ordering).   This makes for a nice bumper sticker, but not much else:

GraphViz (which happens to also have a very nice and convenient MacOS X interface) helps add back connectivity between tags.  Lots of connectivity:

The graphic is easier to penetrate in its PDF version.  In the diagram, I’ve connected pairs of tags that occur together in three or more bookmarks.  Tag bubbles are scaled (manually) by their overall frequency of occurrence.   The rest of the organization is done automatically by GraphViz in attempting to minimize the complexity (!) of the network’s edge (connection) layout.

Overall I’m neither disappointed nor ecstatic with the results.   There are nicely laid out “regions” on the diagram:  an “aerospace” zone in the middle-left, a “government” sector at lower-left, a “business” area at center-bottom, even a “shopping” nook at far-bottom.   Interestingly, the “travel” quarter got pulled up into the “aerospace and policy” regions, presumably due to multiple connections for  ”Europe” and “China” (which I have both travelled to, and also bookmark often from a space policy perspective).

Alternatively, there are limits to how much a complex, multidimensional semantic space can be “flattened” to 2-D.   The almost-lowest tier of tags sometimes has the appearance of a catch-all, and some tags have found themselves stranded there.

For a great discussion of folksonomies, check out Moritz Stefaner’s thesis - several years old but still an excellent read.

Below is a subset of the GraphViz code used to generate the network diagram.   It’s probably “kindergarten” in terms of GraphViz grammar level and any tips on improving the layout would be welcome.  Unfortunately, not much of this process was automated – scraping of the delicious bookmarks, scaling, and transcription to .gv format were a manual (although not painful) process.

digraph Delicious {
	graph [splines=true,overlap=false,concentrate=true,style="bold"]
	node [style=filled fillcolor=red]	

	"space" [shape=ellipse,regular=false,style=filled,fillcolor=green,height=24.5,width=73.5,fontsize=703.5];	
	"visualization" [shape=ellipse,regular=false,style=filled,fillcolor=green,height=22.36,width=67.1,fontsize=670.8];	
	"NASA" [shape=ellipse,regular=false,style=filled,fillcolor=green,height=20.82,width=62.45,fontsize=624.5];
	"travel" [shape=ellipse,regular=false,style=filled,fillcolor=green,height=16.832,width=50.4975,fontsize=504.975] ;

...
	"RP" [shape=ellipse,regular=false,style=filled,fillcolor=green,height=2.357,width=7.071,fontsize=70.71] ;
	"upper_stage" [shape=ellipse,regular=false,style=filled,fillcolor=green,height=2.357,width=7.071,fontsize=70.71] ;
	"vehicle" [shape=ellipse,regular=false,style=filled,fillcolor=green,height=2.357,width=7.071,fontsize=70.71] ;...
	space -> NASA [dir=none, weight=67];
	space -> launch [dir=none, weight=35];
	space -> policy [dir=none, weight=26];
	space -> science [dir=none, weight=26];

...
	booster -> reusable [dir=none, weight=2];
	booster -> RP [dir=none, weight=2];
	HLV -> Senate [dir=none, weight=2];
	HLV -> heavylift [dir=none, weight=2];

}

First, some background.   Each year since 1995, the FAA, which has responsibilities for regulation of commercial space launches, has issued 10-year forecasts of future launch vehicle demand for both geostationary/geosynchronous (GSO, typically communications) and non-geostationary (NGSO, typically low earth orbit) payloads.   These forecasts form the backbone of this analysis.

Second, some context.   The commercial space bubble of the 1990s, fueled by high expectations in the communications satellite arena, is well-known.   Those responsible for launch forecasts were also well aware of systematic optimism in their integration and run-out techniques and have worked systematically to reduce them.   My goal here is not to savage the hard-working folks at FAA, but to highlight what happens when models quietly diverge from reality, and to help create better educated “consumers” of the official forecasts.

This graphic comes from the 2010 FAA/COMSTAC forecast, and demonstrates FAA’s good faith in providing both forecast and actual data against which to validate model skill.   Included in this chart are total (GSO+NGSO) launch demand forecasts from 2000 forward only.   An appendix in the 2010 report also includes NGSO forecast vs actual data for 2004 forward only.   The systematic positive bias is plain to see, and has typically been remarked upon within the reports themselves.

However, this chart has some serious defects which very much get in the way of communicating the wide disparity between available forecasts in any given year, and “truth”.   They are almost certainly unintentional defects, but the net effect is significant:

1. Forecasts prior to 2000 have been eliminated from this chart, and prior to 2004 from the NGSO-only chart.   This has the effect of removing some of the most egregiously bad bubble-era forecasts from the composite (see below for the effects of the actual bubble-era data).   In fairness, the forecast model has not been stationary over that time frame, undergoing tweaking and evolution.   Also in fairness, the caption represents this graphic as “Commercial Space Transportation Forecasts” – i.e., the performance of the FAA office – not, “Forecast model version 3.x forecasts”.   The implication is that this is a graphic of COMSTAC’s historical performance, and as such, it should include all data.
2. In compiling this graph, FAA has excluded all of the “robust market” alternative scenarios included in old forecasts (a practice stopped in the mid-2000′s when it became obvious that there wasn’t a robust market in the immediate future).  By instead showing only the conservative forecast, it obscures the fact that consumers of the forecasts in the Y2K time frame had a very wide range (one could argue, uselessly wide) of forecasts to “choose from”.   Again, see below for the forecast data.
3. Visual sleight of hand with the 2010 forecast gives the illusion of greater skill than actually exists.   Up until 2009, all forecasts are represented on the chart by lines, and “actuals” by the blue sand chart wedge.   However, the 2010 forecast is shown by seamlessly extending the actuals sand chart, not by including an additional line.    The effect is that all of the old 2010-2020 forecast run-outs now appear to live “within” an actuals wedge, the grey wedge (which of course, is nothing of the sort, it’s just another forecast).   This hides the fact that prior to 2009, the forecasts almost never intersected with reality, instead, systematically overestimating demand.   This is either intentionally deceptive (unlikely) or really unfortunately bad chartsmanship (likely).
4. By visualizing the joint NGSO and GSO forecast, the graphic downplays the severe errors in the very badly modeled NGSO forecast, since almost all of the “skill” in the model is from the comparatively stable GSO market.

Again, given FAA’s transparency and ongoing efforts to improve their models, this is almost certainly due more to poor chartsmanship and the difficulty of tracking and integrating heterogeneous data over multiple decades, than any intent to deceive.   To get a clearer picture, I’ve created the graphic at right, which shows instead the probability distribution of FAA forecasts for each year, compared against the actuals.  The intent is to show the full range of forecasts “available to choose from” for potential consumers of the data.   In compiling the forecasts, I have treated the “baseline” and “robust” market historical forecasts as equally valid, since in reality, consumers of the historical forecasts had little basis to select between them.

In this graphic, the dashed line shows the median of all forecasts, the innermost dark grey band contains the “most central 33%” of forecasts (33%-67% percentiles), the next lightest grey band contains the “most central 50%” of forecasts (25%-75% percentiles), and the outer very light bands contain 90% and 100% of all forecasts, respectively.  (In short, the intensity of grey shading is intended to cue viewers towards the most frequently occurring forecast values).   This view provides a clearer view of what a “bubble” looks like in practice.   The compilation is troubling not only for revealing the excessive optimism, but also for calling into question the model’s skill in predicting any trends whatsoever.  Other than the initial rise from 1996-1997 (forecasted two years too early), there is little evidence that the forecasts as a whole actually capture any signal in the actual time series.   A simple flat runout of the previous year’s actual launch rate (known as a “persistence” forecast in weather circles) would likely have provided better overall skill as a model (I need to verify this, but eyeballing the graphic, I’m 99% confident of that outcome).

The situation gets worse if we split the forecast into its non-geostationary and geostationary components, below.   The bulk of the forecast “skill” is coming from the stable GSO market (at bottom), although even there the forecasts are optimistically biased, lying atop an essentially flat trend.   The NGSO forecast performance contains even less “skill” than the aggregate above.

So – why share this bad news?   The point most certainly isn’t to be cute and condemn the folks at FAA who work hard to forecast an extremely fragile and unstable emergent market – a thankless and “no-win” task, almost by definition.  Nor is it to make life any more difficult for the struggling commercial space sector.

The point is to help create educated consumers of the official data, and to heighten awareness of how critically dependent we are on the industry’s own self-reported future manifests, which are almost certainly optimistically skewed (since they do not allow for unexpected contingencies).    Indeed, in the aggregate, the 10-year forecasts actually have performed worse in the first three years of each forecast, than in the last seven years – one hypothesis could be that this is bias by overly optimistic near-term industry manifests.

FAA continues to work to improve its model and has been fully open in communicating its performance.   Until, however, the forecasts are consistently able to eliminate positive bias, and to demonstrate an ability to actually detect trend signals, at minimum a deep “discount factor” should be applied before using them for policy or commercial purposes.

The map below doesn’t contain any major surprises, but there are some interesting details.   The very well known Centers (JPL, Johnson and Kennedy) make comparatively less use of their name and acronym when tweeting.   In contrast, my own home center of Marshall, as well as Dryden in the California desert, seem to blare out “please don’t forget us” by insistently using and re-using our names.

Also evident is how some of the outreach / public interest activities at some centers (the Great Moonbuggy Race here at Marshall, as well as our meteor spotting network, and the TEDxNASA event at Langley) can come to dominate the tweetstreams.   This isn’t a bad thing – these are great educational and outreach events in their own right, and reflect good pairing of social media with publicly engaging topics – but Marshall’s core capabilities and mission, including the primary ways it delivers value to the American public, have little to do with student competitions and meteor showers.   It underscores the challenges of maintaining clear and coherent “corporate branding” transmitted through an increasingly diverse array of media vehicles, even more so when by a public sector organization.

A higher quality PDF version is available here:  YouAreWhatYouTweet .

Note that this isn’t the only tweeting by NASA; many NASA missions tweet on their own.   The tweets shown here are only those by NASA’s “stewards”, or field Centers.

As technical notes, in creating this graphic I used the visualization tool at Neoformix to search and extract tweet histories, filtered out the common words “space” and “NASA”, and limited scope to mission-related nouns (i.e., verbs, adjectives and generic words like “video” or “images” were removed).  For the most part, I consolidated word stubs / roots where appropriate in scaling the words to match frequency.

Also, I’ve used absolute word counts (not relative) and applied a uniform cutoff across all Centers.   This has the effect of making our friends at Glenn look a little anemic, an unfortunate side effect of being a little bit newer to the Twitter game.   When I update to version 2, they will likely have more content “above the cutoff level”.

As a bonus for my NASA compadres at other field Centers:

I switched careers from science to management in 2005, publishing my last journal paper in that year.   From 1993-2005 I published 11 journal papers as a first author, and participated as a co-author in 17 more.   In theory, that should leave a tidy trail of followup citations to determine if I’m fading from view.   Unfortunately, it looks like I’ll have to wait a little longer to find out.

The problem now is that the shelf life appears to be at least longer than the five years since I stopped publishing.   If I had continued writing (and collaborating), I would have expected the curves at right to rise much more than linearly (not exponentially, but more than linearly), due to a “composite citation” effect.   Instead, the curves are roughly linear, and haven’t yet begun to flatten out.

As an aside, interlinking of scientific journal citations across the internet is still in a fairly sorry and byzantine state.   There are a number of reasons, including that nonprofit professional societies that publish journals typically lack the resources to integrate across platforms with each other, or backfill legacy data, while commercial journal publishers lack the financial incentive.   All of which goes to say – it wasn’t easy to cobble the data above together across multiple earth science journal publishers (AMS, AGU, Elsevier, others), and before 2002, the data are pretty spotty.

Anyway – the impacts of disengaging from the scientific community are a little easier to see by looking at citation-per-year rates.    (At a first author citation rate of about 30 per year I certainly wasn’t on track to shake the foundations of the scientific establishment!)  The differences between first and co-authorship trends is instructive.

After 2005, a time when I made a pretty clean break with both publishing and the research community, the citation rate of my first author papers flatlined.  Eventually, the inevitable will happen and that trend will start declining … from which I’ll then be able to extrapolate my EYO (Estimated Year of Obscurity, of interest to absolutely nobody but me.)   It would certainly be nice if this were greater than my remaining life expectancy, but I’m not optimistic.   Then again, I didn’t get into earth science to jump on the fast track to immortality.

In contrast, the citation rates for papers I participated as a co-author on continues to rise.   That suggests one (or both) of two things:

1. The colleagues who were leads on these papers were systematically better at conducting and publishing more relevant and enduring research (certainly possible, although if I look at the citation rates paper by paper, it doesn’t seem to match the data).
2. The colleagues who were leads continued to engage, continually increasing their own visibility within the community as well as that of their publications, through self-citation, leading to even  greater awareness of publications and increasing citation rates.

Note that I’m only tracking citations for the controlled set of papers I was author or co-author on, not citations for all authors I participated with.   The short form is: my colleagues’ stock continues to rise, while mine has – as expected – stalled out.

To readers who have slogged this far, my thanks for enduring what, in retrospect, is an entry high in narcissism and low in protein content.

Unfortunately, I know that I don’t really take great photographs … what folks are reacting to, I think, is that I’m developing the ability to find and compose interesting photographs, but that’s a long way from the technical skills needed to really “close the deal”.   Maybe when I retire!   Until then, I’m building a good inventory of “things I would have done differently” from existing photographs.

The shots below are all from 2010, when I spent a few days in July traveling around the French province of Aveyron, thanks to a wonderful tip from a friend.   It was a “solo vacation” (my better half had gone off to Poland for a Peace Corps host family reunion), so I had a lot of unstructured time and opportunity to stop at a whim and dawdle with photography when something caught my eye.

I would highly recommend Aveyron to anyone interested in an off-the-beaten-path European vacation.  It is not well-known (and thus buffered from the worst side effects of tourism), yet is the French department with the most number of “Most Beautiful Villages” in the official registry.  ”Ridiculously beautiful” is the phrase I would have to use.   I split my visit between Aveyron, the Pyrenees, and Aragon and Costa Brava in Spain, but those are posts for another time.

Anyway – below are some of what I think are the best photographs I took in Aveyron, plus some commentary on why I found them interesting, as well as what I could have done better.  I would love to hear from “real” photographers, amateur or professional, on ways I could have improved these shots!

Fields of Color – Southwest Aveyron, near Sainte Eulalie de Cernon

This one is all about the contrast. The sharp clean lines and resulting "polygons" of color are absolutely beautiful to me. The splashes of lavender and isolated red wildflowers in the wheat field are added bonuses, and the wheat picks up the strong wind adding some dynamism. If I had been more patient, I might have waited for the cloud shadow over the grass to pass, although arguably that makes for a slightly more dynamic scene as well.

Along the Tarn River Valley

These tunnels may be a dime a dozen in the area, but they still make for great photographs. The over the top busyness of the signposts is an added bonus, providing interest and alluding to all the great stuff waiting in every direction. The tunnel and shadows add just a bit of mystery. The tunnel also seems to organically meld into the mountainside. On the downside, the angle of the shot and lighting arent good. I suspect I could have done better with the angle of the shadow, and the glare on the road signs brings out my one and only complaint with my Sony HX-1 camera (which I otherwise love) - it struggles with very high and sharp white/black contrast, adding a purplish tinge.

Milau Viaduct, Take 1

Ill have more on the Milau viaduct a little further down. For now, the focus is on how something as massive as the concrete pylons nonetheless manages to leave a "light touch" upon the natural landscape (by design). I took the shot from just-off-centerline of the road to suggest how the entire structure would "vanish" if seen boresight-on. I think the gentle curve of the roadway is very attractive, and the stair-stepping of the pylons helps bring depth to an otherwise "flat" landscape shot.

The Village and Chateau at Najac

There are better views of the beautiful château at Najac, but I shared this angle here because it brings in the very complex geography of the village, all lined up along a very narrow ridge. This particular angle would probably have benefitted from waiting for better lighting and a different street scene. I also think too much of the scene is in focus - I lost some of the depth, making the château seem closer than it is.

Monsieur le Bad-Ass (Belcastel)

There is nothing particularly remarkable about this shot at the château in Belcastel, except that it occurred to me that the silhouette and backlighting around the suit of armor seemed to slightly "bring it to life", giving it a slightly ominous and imminent presence. I think the effect is even more pronounced in the photograph. Improvement-wise, I could have done a better job with the angle in hiding and masking the glass window and its frame behind the arch. Together they rob the scene of its "authenticity" and subtly add back a museum-like flavor.

 Rooftop foliage in Brousse-le-Chateau

Im actually pretty happy with this one overall. Its not obvious from the photo (part of why I like it) but this is the roof of a small chapel near the château at Brousse-le-Chateau (my home base while in Aveyron). I was fascinated by the rooftop "ecosystem" evolving. The colors and lighting, I think, are good. A slightly different angle might have brought out the foreground pink flower better.

 The cellars of Albi

This is not the most aesthetically pleasing of photos (although the houses in the upper layers are quite pretty), but what intrigued me were the subterranean layers of doors and arches, plus their inaccessibility. As interest spots on the photograph they raise all sorts of questions ... who owns them? How do you get to them? Are they still in use? What were they used for? In retrospect this is a case where if I knew what I was doing - and had the time - I think I could have spent a good half day searching for the right angle, lighting and composition to bring out what intrigued me about the doors.

Royal fortress of Najac

Im actually quite happy with this shot as it stands. The slightly eerie lighting effect isnt HDR, or Photoshop tricks - all I did was boost the highlights a smidge in iPhoto to bring out the cloud texture. I love the upward angle and honestly I think perspective adds a lot over the "full frontal" approach most of the photos of the château that I can find. Overall this photo gives me distinct "Ninth Gate" vibes!

Impressionism … and frustration

This may seem a pretty picture but to me it is a source of significant frustration. I spent a long time in this (very large) field of wildflowers, hoping to capture the "impressionist" feeling, but at the end, it just kicked my butt - its beyond my skills as an amateur photographer. Youd think flowers would be easy! The problems (there are many) were: difficulty in finding the right angle and location to focus upon, the right perspective (I wanted to capture "millions of things", rather than close up on a few), the color balance (the field was not THAT green), and the fluttering of the paper-like red flowers in the breeze. I keep this shot around to remind me that finding something interesting is only the start, and at some point I really need to learn how to use a camera rather than just letting the auto-features do all the work. I think they betrayed me here!

Church outside the bastide at Villeneuve

I originally was drawn to this small church near Villeneuve because of the interesting "leaning church" effect. But I liked the photograph much more than the interesting tilt would warrant. It took me a while, but I think what Im attached to is the hodgepodge of architectural elements in play - some apparently "grafted on" to support the original structure. I get the distinct impression this church looked very different at some time in the past. I think this "implied" history is why I like the photo so much. I wish I could say I realized all that in real-time - instead, it just jumped out at me as something that needed capturing, without fully understanding why.

That’s all for now … I’ll post a few more next week.   Whether a photographer or not, please feel free to share some constructive criticism!   To me these are “nice”, but only 80% of the way there…

First, some context around advance technology imports and exports.  Recent data are available from the U.S. Census bureau, and are discussed in much better detail than here in the biannual NSF Science and Engineering Indicators series, typically Chapter 6.   (Minor hack tip: to access previous years, replace the path in the link above with “seind08″, “seind06″, etc.).  I’ve been able to find aggregate data back to 1989 and sector by sector data back to 2000.   As a technical note, in this as in all my posts I’ve inflation adjusted the historical data using GDP chained deflators (here, to convert to 2010 dollars).

The short form of the story would run: within the 10 industry sectors tracked as “advance technologies”, the U.S. has sustained vigorous exports, (although these are somewhat flat in constant dollars, which is troubling).  Meanwhile, imports have increased, leading to a net trade deficit with the rest of the world.  This is troubling more from the standpoint of where the U.S. economy is headed long term, rather than where it is now.  With technology sectors including aerospace, biotechnology, life sciences, advanced materials, electronics, optoelectronics, and others, the waterfront of potential new manufacturing sectors in which to excel is shrinking rapidly.  Sector by sector, the trade surplus / deficit data are instructive.

Aerospace and Weapons. Aerospace continues to lead, with strong emphasis on the “aero” side, as the space subsector surplus is declining rapidly (see my previous post).  The U.S. continues to benefit from the decades-long investments in this area from the 20th century.  Nonetheless, the engineering skill base which sustains this sector is fragile and extremely sensitive to disruption (or interruption); this goes for both disciplinary mechanical/aerospace engineering skills, and the more elusive systems engineering skills needed to coordinate the development of both aircraft and spacecraft.  The bulk of the exports in the chart at right are from the aviation sector; space and weapons comprise small but non-negligible shares of the total.

Advanced Materials and Flexible Manufacturing. The news here is also good (slight positive balance of trade) but the magnitude of this sector is much smaller.  As an engine which might crosscut other sectors, this is promising as it suggests the U.S. hasn’t yet exhausted its innovation potential, although that may be contingent on sustained investment into R&D in this area.  Unfortunately, the physical sciences and engineering have lagged in public sector national research investment in recent years – the subject of a future post.

Electronics and Optoelectronics. This is a large sector in which the surplus / deficit tide has turned in recent years.  The bulk of the decline comes from the opto-electronics side, although conventional electronics has declined in the last several years as well.  (I’ll have to remember this next time I’m caressing my iPad screen, or marveling at my Sony camera…)   If the global supply chain for those products is any indicator, it’s hard to see the U.S regaining ground in this area.

Biotechnology and Life Sciences. If there’s a story within a story to this post, it’s the performance of the biotech sector.  Not so much because there aren’t signs of progress (exports are increasing, and the balance looks like it might be on track to someday turn positive) but because of the truly massive public sector R&D investment in this area over the last 15 years, and corresponding disruption to the rest of the U.S. R&D portfolio.  Whether intentionally or unintentionally, by virtue of its spending priorities, the U.S. has “bet big” that this sector will be the defining technology for 21st century economies; more than half of the national nondefense R&D investment now goes to life sciences, in terms of magnitude; it has now exceeded Apollo Program level spending.   That will be the subject of a future post.  For now – it looks like the jury remains painfully out on whether the sector will pay off.

Information and Communications. This is code for computers, routers, videoconferencing, comsats, radars, etc.   Note that the vertical scale on this plot is twice that of the other detail breakouts.  This ship appears to have sailed, and comprises a big share of the increasing advanced technology trade deficit.  Indeed, if the Information and Communications sectors are excluded from the total, the storyline changes to a positive trade surplus (largely driven by aerospace), and a looming question:

What new sector will replace the United States’ declining market share in electronics and highly related technologies?  Will the biotech investment pay off?  Will the current national investment priorities and policies allow the aerospace sector to maintain its prominence as anchor of the advanced technology trade surplus?

Mandatory refresher training: these memeograms plot the year at which a given word or phrase peaks in frequency within the Google Books database.

If there’s a story to this graphic, it is that apparently there have been three “eras” of publishing on space-related topics: early in the space race (early 1960s), in the wake of Challenger (late 1980s) and millennial (turn of the century).  Perhaps as surprising as the peaks are the chasms in between them.   To be sure, space-related terms were still being published in these years, but almost no terms (old or new) reached their peak frequency during the 1970s or 1990s.

After a supernova. For reals.

On a more positive note, the flourishing of publishing on the space sciences (astrophysics and heliophysics shaded yellow, planetary science and astrobiology shaded blue) after the 1990′s is notable.   With the launch of the great observatories (Hubble, Compton, Spitzer, Chandra) and major planetary missions such as Galileo (Jupiter), Cassini (Saturn) and the Mars rovers, popular knowledge and attention apparently engaged accordingly.

But I digress pedantically.   Memeograms are meant to be fun, not to educate!   Check out the full image below, or download the PDF:   Memeogram_Space.

First a little calibration for those unfamiliar with how much actually gets spent around the world.   International tallies are not performed systematically every year, so the numbers at left are a little rough (if you can get your hands on a EuroConsult report, that’s a good start), but they give a fairly sound indication of the ranges of national annual spending (in billions of USD) over the years 2008-2011.  Fluctuations of 10-30% within those years are not uncommon, with one of the largest being India, which increased its civil space spending from approximately $900M to $1.5B over the last several years.

While total dollars count for a lot, it is also instructive to look at how much of a country’s economy, or government budget, is invested; this gives some indication of space as a relative priority.  By share of GDP, the U.S. lags behind Russia and the Ukraine, and leads our traditional partners of France, Japan, Germany and, by a much greater extent, Canada.  India’s recent increase places it in the “top tier” of space-investing nations, highlighting that while small, the country takes space seriously as a component of its future economy.

India’s investment stands out even further when expressed as a share of its total government budget.  As investment priorities go, and relative to its peers, India appears to be “off the fence” and fully committed.  Other emergent players such as China and South Korea are also rising in the charts; China likely occupies an even higher slot as the most recent estimates of its very opaque space program are now three years old, and probably understated.   Its recent construction wave of space-related infrastructure alone may be yet-to-be-tallied.

As the U.S. considers expanded global partnerships as a central tool in dealing with increased domestic discretionary budget pressure (or so we are told daily within NASA), it seems clear that it cannot afford to continue with an incoherent policy of engagement with emergent players in Eastern Europe and Asia indefinitely, particularly with respect to the key existing and emergent partners of Russia & Ukraine, Japan, India and China.   Particularly for the latter three, alignment with one may strain relations with the others.

A final note is while the accumulated budgets of major partners are not insubstantial, the portions of those budgets devoted to human space flight are.  The Japanese, Canadian and European portions lie between 10 and 20%, and seem to be declining.  While India has ambitious plans, its HSF spending is still a tiny fraction (1%) of ISRO’s overall budget.  NASA and ROSCOSMOS (the Russian space agency) are unique in spending significantly higher amounts on HSF (35-45%).   The point being: international coalitions as a solution to the costs of Beyond Earth Orbit exploration plans will take more than good intentions and breathless enticement to bring to fruition.   Not only would international space spending have to increase (bucking the current projections of flat spending), most participating countries would also have to significantly rebalance their investment portfolio priorities away from technology, launch and/or satellite activities, and towards HSF, in order to find meaningful funds to offset the costs of major human exploration campaigns.   It goes without saying that if the U.S. is to lead such a charge, it will require extremely coherent, concrete, and credible exploration policies, architectures, and plans.

Carbon Counting

I’m was mildly skeptical of the “carbon offsets included” (or at least curious), so I did some number crunching to calibrate.  By my bottoms-up calculations, my share of jet fuel for the trip from Atlanta to New York to Dubai to Malé (and back) corresponds to approximately 57,237 Megajoules of energy.   Various other sites cough up different estimates of my personal share.  Sustainable Travel International pegs my offset at 7.45 tons of CO2, which would be 115,353 Megajoules, while TerraPass‘ carbon footprint calculator spits out 4796 pounds (a mere 2.18 tons of CO2, or 32,380 Megajoules).   Clearly estimating my share of jet fuel consumption is an imprecise science.
More firmly (based on my actual, metered consumption), I used:

• about 62,187 Megajoules of energy in natural gas to heat my home from Dec-Feb 2010-2011
• about 60,940 Megajoules of energy in electricity to power and cool my home from Jul-Dec 2010
• about 46,977 Megajoules of energy in gasoline for an entire year of driving in 2010

Given the uncertainty range in my jet fuel estimate (32,000-115,000), I’m just going to say that my trip was about as energy intensive as any of these three.   Flying around the world turns out to be a pretty lousy thing to do if you’re conservation conscious.   In retrospect this shouldn’t be surprising … mile by mile and per capita, air travel is more efficient than automobile travel, but by comparatively small factors (2-4), rather than orders of magnitude.   So, flying halfway around the world and back ought to add up pretty quickly as an energy expense.   All of which goes to say that as eco-friendly and carbon-offsetting as Six Senses claimed to be, I strongly doubt they offset me anywhere near the full impact of my air travel there.

I’ll share more about experiments in lifelogging (hence the energy data above) in a future post.   For now, the final fun factoid is that:

I consume a barrel of oil every 8.9 days.

More shots are in my full photo gallery.   Wikipedia’s got a great rundown of stats and info on the half-mile high Burj Khalifa, in all its photogenic glory.   Travelers interested in similar experiences might consider Coruscant …

The cartoonish packaging and presentation of this book shouldn’t dissuade prospective readers, there actually is some meat. BMG’s slug ambitiously reads, “You’re holding a handbook for visionaries, game changers and challengers striving to defy outmoded business models and design tomorrow’s enterprises…” While this is a little of a breathless oversell, BMG contains good tools to work with.

Its primary offering is a very useful “Business Model Canvas” which can be used with organizations to help map out their existing (or planned) models, and from which brainstorming and tweaking can proceed. Working in the public sector, I’m most intrigued by this tool and its value in working with teams and managers who are more used to budget-driven management; BMG’s canvas can provide a simple and tractable way to help them think about other ways to think about resource flow and achieving their objectives.

Unfortunately BMG’s offering peaks with the Canvas itself, in the first third of the book. The remaining two thirds present simplistic and superficial treatments of tools to facilitate brainstorming, creative discussion, etc, as well as summaries of some nonconventional business models that have evolved over the last decade or two. There’s nothing really wrong with either of these, other than that anyone interested in the book have probably already been exposed to most of this subsequent content, and the treatment is usually too superficial to add much new value. Harmless, but can easily be skipped.

The authors (all 470 of them) deserve props for their creative approach towards the content, design, and delivery. The book is well worth the money and time investment to explore what it has to offer. Just keep your expectations limited…

View all my reviews

Long term budget planning is an interesting Federal dynamic.  The battle between administration priorities and legislative branch priorities is starkly evident when executive branch hopes and dreams (or sometimes, nightmares) in five year budget plans are compared with the actual funds appropriated by Congress each year.

5-Year administration NASA budget plans (grey) and actual appropriated funds (blue) (in FY11 dollars)

In the case of NASA, Congress has (over the last 15 years) been mostly a “restoring force”, keeping the NASA budget comparatively constant.   The variability in NASA’s budget is very small compared to that of other Federal agencies.

5-Year total budget request, and 5-year total actual appropriated funds

The other key information buried in the data is that “out-year” budget plans (those beyond the current year of appropriation and execution) actually mean very little.   This is something project and program managers are keenly aware of, especially in the defense and space industries, where initiatives (missions) span many years to develop and bring to completion.  While still a valuable tool in formulating the implementation of major programs, long term budget plans are as much a means of signaling broad intent from one side of the Mall to the other, as they are any real indication of future fiscal reality.   This goes as much for appropriated funds as requested funds:  consider the wide spread in the light blue lines above in any given year.

Let it not be said that steering Federal programs is easy…

To this end I turn to the Best Places to Work in Federal Government survey.  Some may argue that self-assessment is hardly an acid test, but I subscribe to the view that the troops in the trenches know when things are off the rails, and in the aggregate, their opinions count.  In the 2009 BPT survey (taken in Feb/Mar 2009, “in between” Administrations),  NASA achieved its highest scores (since the survey’s start in 2003) in the categories of:   Strategic Management, Teamwork, Effective Leadership, Performance-Based Rewards, and Training and Development.   This steady improvement would have been commendable in and of itself, but more importantly, NASA field centers stood out as leaders among all Federal agencies and subcomponents, often in the top 5% of all installations. Far from being a “dispirited” workforce as described recently by OSTP Director John Holdren, NASA’s field Centers have been – and continue to be – model workplaces in the Federal government.

The slideshow below steps through a number of the detailed findings of the BPTW survey.   Individual field centers have been assigned gold stars if they are in the top 5% of all Federal facilities (approximately 223), silver stars if they are in the top 10%, and bronze stars if they are in the top 25%.

Several detail findings are particularly instructive.  The training and development results include questions capturing whether employees are “given real opportunities to improve their skills”, which in the NASA context, I read as reflecting the opportunity to participate in real spaceflight projects, with real hands-on experience.   In this context it is not surprising that the “development Centers” (Marshall, Johnson, Goddard) and the “testing Center” (Stennis) score in the top 5%.   (My own home Center of Marshall scored 2nd out of all Federal agencies).  For an administration greatly concerned about STEM education, NASA’s ability not only to engage, but also to train, STEM workforces should not go un-noticed.

The series of questions related to effective leadership are also critical, as they go directly to culture issues alluded to in the Columbia Accident Investigation Board report.   Questions in this series address issues such as:

• Supervisors’ job performance
• Employee opportunities to demonstrate leadership skills
• Support for development and worthwhile feedback about performance
• Tolerance of arbitrary actions / favoritism
• “Whistleblower” comfort level
• Empowerment with respect to work processes
• Involvement in decisions affecting work
• Respect for senior leaders
• Amount of information provided by management
• Senior leadership honesty, integrity, ability to motivate

In general, the Human Space Flight and development Centers appear to have strong leadership cultures, at least relative to their NASA and Federal agency peers.   (From “street” experience I can vouch that the issues above are by no means perfect within today’s NASA, but by comparison with Federal peers, the HSF and Development Centers are clearly “best in class”).

A more sobering result is in the teamwork question.   While most of the Agency is still “best in class” here, (top 10% or higher), the trend data from 2007 to 2010 are ominous; the scores are down from 2-7% at almost all Centers from their 2007 levels.  It is hard not to ascribe this to the change in NASA direction, and the return of a planning mode in which Centers compete with each other for work, rather than cooperate towards shared goals.  With any luck the debate over the direction of future space policy will stabilize sooner, rather than later, stemming any further losses in this area.

Coming full circle to the caterwauling critics of the blogosphere:  it is conveniently easy to ignore the difficulty of forming and maintaining effective working structures and relationships in any large organization, whether public or private sector, and even more so in one as encumbered by the constraints of bureaucracy as the Federal government.  Achieving even these (self-assessed) measures of organizational excellence is no mean feat – as shown by the 200-odd Federal installations that fall below NASA field Centers in the rankings.   It is also easy to lose track of this during the day to day – certainly many of my days don’t always feel like gold-star models for others to learn from!

So, to my field Center colleagues, take a breath, reach around, pat yourselves on the back for the things we have accomplished, then roll up your sleeves and keep plugging along to make things even better.

Here is a link to the full set of charts in PDF format:   FedWork10_Neutral.

The graphic below sets the template for the detail plots below.   The solid grey curve shows the net support for social issue spending from the General Social Survey.   (In the example graph, I use the crude composite score from my previous GSS post, just for purposes of illustration.   Issue by issue scores will be used below.)   This curve shows [percentage of people who say too little is being spent] minus [percentage of people who say too much is being spent].

40 Years of Spending and Public Opinion Data

The green bars show (smoothed) year to year changes in Federal spending on various issues (in the example graph, it’s just the total budget).   If spending is increasing from one year to the next, the bars will be positive above the (invisible) y-axis above; if spending is decreasing, they will be negative.   Technically the bars represent changes in spending expressed as percentages of GDP, rather than absolute dollars, so more properly, they denote changes in Federal “investment priority”.

Now, a caveat.   Because of the mechanics of the three years it takes to prepare, request, and appropriate Federal budgets, as well as the two-year granularity of much of the General Social Survey data, it isn’t really possible to show cause and effect here.   The best we can say is whether the “coupled public opinion / spending system” is roughly tracking, or out of whack, in any given year.   Even with those data limitations, you’ll see some surprisingly coherent behavior below.

One final note:  to preserve design minimalism, I’ve followed Tufte rules and stripped the graphics down pretty heavily.   In all graphs below, the data use the same scaling.   The public opinion curve spans a dynamic range from +80% to -80%, and the year to year spending changes span a dynamic range of +20% to -20% of their then-year levels.

OK, on to the data.

Space Exploration

Space Exploration

I’ll start with Space Exploration, both because I’ve written about it before as well as the fact that the trends are spookily close.   In the GSS, public opinion is asked on the Space Exploration Program. For federal spending, I’ve shown the Space Flight Subfunction of the budget tables.  The signal is dominated by parts of NASA’s budget.  The storyline here is actually, I think, fairly straightforward: space exploration has never been a net positive spending support category, but the antipathy built up during Apollo era spending decreased during the 1970s.   Major successes (delivery of the Space Shuttle in 1981, and post-Challenger Return To Flight in 1988) were coincident with relative increases in space flight spending.  Conversely, the early 1990′s saw the Hubble mirror failure, GHWB’s unpopular and expensive exploration program, and the very public and bruising battles around the International Space Station.   Again, cause and effect are indeterminate, but it is clear that a period of falling public support overlapped with a period of falling national investment priority.   What is fairly remarkable is that consistently, periods of increasing support correspond to, at the very least, periods of reduction in the downward pressure on space spending, and very occasionally, to increases.   Since the overall trend (as share of GDP) is nonetheless negative, this seems to perhaps support ex-NASA-Administrator Mike Griffin’s “next fifty years” proposition, which is that space spending has been, and is likely to, remain more or less flat in absolute dollars (rather than in investment priority / share of GDP).

Defense

Defense

In this chart, public opinion is for Military, Armaments and Defense, and spending data are for the National Defense Superfunction of the budget tables.  The public opinion data for this category are fascinating: they tip positive after instances of direct acts of aggression against the U.S. (1980 – Iranian hostage crisis; 2001 – 9/11 attacks).   Although just as interestingly, the upward trend in support in both cases had been nearly a decade long.   While there is not coherence in phase between opinion and spending, overall, the patterns are more synchronized than contradictory.  What I think I see here is a consistent lag between spending changes and public opinion, probably related to the sheer size and inertia of defense program budgeting, appropriate and spending.

Health

Health

Public opinion here is for Improving and Protecting the Nation’s Health, and spending data are for the Health Function of the budget tables.   There’s certainly no question that (up until recently) Americans have been in violent support of health care spending.  Periods of comparatively modest increases in the strong “baseline” support definitely tracked with periods of increased national investment; conversely, declines in support corresponded to periods in which spending increases slowed, but rarely reversed.   The 2010 downturn in public support, presumably in response to the national dialogue on the subject, is unprecedented in the GSS historical record; it will be fascinating to see which way the investment trend heads.

Environment

The Environment

Here, the data are opinion data on Improving and Protecting the Environment, and spending data for the sum of the Pollution Control and Abatement Subfunction and the Conservation and Land Management Subfunction. Some of these patterns now look familiar: support for more spending increasing through the 1980s, peaking around 1990, followed by a fall in support along with (or after) periods of actual increased spending.   It would be tempting to speculate a cycle in which cuts in this area generate greater support for restored funding, which builds in intensity and eventually overturns the reductions and turns them into increases … after which support drops.   Now again, that would be reading too much into the data since we can’t correlate causality here, but … it’s not an impossible storyline.

Crime

Crime

The opinion data here are for Halting the Rising Crime Rate, while the spending data are for the Administration of Justice Function of the budget tables.  This is an instance where the public opinion trend may be out of sync with the spending trend; while overall strong, public opinion support for spending on crime has fallen since the early 1990s, while Federal spending on the justice system has continue to grow with almost no interruptions.   In fairness, it is difficult to make a direct intercomparison here, as most spending on crime is at the local, rather than Federal, level (the same will be true for Education, below).

Education

Education

The data here are for Improving the Nation’s Education System (public opinion) and the Elementary and Higher Education Subfunctions of the budget tables (spending).  As with crime, the trends do not closely track each other.  It is tempting to speculate that this is due to the local vs Federal issue; in both cases, both opinion and spending may be tracking broader policy debates around appropriate Federal roles, rather than showing a closely coupled system in which acknowledged Federal spending functions are in sync with and reflect public sentiment.  In both cases what is really needed is a long term trend of the national (not just Federal) public sector spending towards these issues.

International Aid

International Aid

Turning from the solidly popular to the manifestly unpopular, the data here track Foreign Aid (public sentiment) and the International Development Aid Subfunction (spending).  Here, the slight uptick in public support after 2000 (while still overwhelmingly net-negative) corresponds with significant increases in international development aid.

The Upshot

I have to say, overall these data have more hallmarks of a “better behaved system” than I was really expecting.  It will probably never be possible to untangle the hairball that is policy, public opinion, and spending from a causal perspective, but it is reassuring to see even indirect evidence that the system self-corrects in practice (spending), rather than just appearance (politics and elections).

Nostalgically it’s interesting to compare these top lists with the artists I was actually listening to, say, in 1980.   Not all have withstood the test of time.

It’s also interesting to me to see how my grad school years (1990-1995) put a significant dent in my musical spectrum, whether by virtue of time or finances.

Anyway, mostly just an exercise in datavis doodling to pass some time.   Hurrah for lifelogging.

For a quick recap, the GSS asks whether the government is spending “Too Much”, “Too Little”, or “About Right” on a variety of social issues.   In the graphs below, I’ve collapsed the data into a single ‘Spending Support’ variable by subtracting the “Too Much” percentage from the “Too Little” percentage for each issue.   (Some other minor processing and cleanup of the data is noted at the end of this post).

Notable features of the 2010 GSS:

• Solid and sustained support for Education spending (always perplexing given how little influence the Federal government has here)
• Significant downturn in net support for both Health and the Environment.   In both cases, it is hard to see the changes (especially when viewed historically) as anything other than major backlash against recent public policy initiatives in these areas.
• Recent decreases in support for spending on Urban Issues, Social Security, Racial Inequality, and Welfare, as well as International Aid. As fiscal austerity looms, social spending does not seem well favored.
• A somewhat surprising uptick in support for Defense spending – surprising in that the most recent defense downturn has only just started, and not yet cut very deeply.

For kicks, the graph at left also composites (adds) the “spending support” for all 15 issues (equally weighted).   This yields a crude proxy for the public “appetite for government” over the last 40 years.   The results seem to track intuitively, even down to the 1994 Republican congressional takeover and concomitant budget tightening focus.   It is notable that this year’s composite score is as low as the 1994 score.

Technical Notes:

• The sparklines above represent (Percent Responding “Too Little” minus Percent Responding “Too Much”) spending.
• All graph y axes above are normalized to span 100 percentage points.   The y=0 axis is offset but the dynamic range is preserved between graphs to allow accurate intercomparison.
• In years between biannual General Social Surveys, data have been interpolated.  (1979,1981,1992,1995,1997,1999,2001,2003,2005,2007,2009)
• Yearly data have been partially smoothed to highlight trends.   The smoothing kernel is:  ((0.5* [Year -1]) + (1.0*[Year]) + (0.5*[Year+1]))/2.0, and the first and last data points are unsmoothed.
• Data are from the Berkeley/SDA database and include the Berkeley compwt [Nadults, Oversamp, Formwt] weightings, except for 2010, which is comprised of raw data from NORC, and 2009, which is interpolated between 2008 and 2010.
• All data are from the Social Problem Spending (Variant 1) Group of Attitudinal Measures – National Problems.   Variant 2 / Version Y of some topics/questions are not included.

If the numbers hold, the change from 2008 to 2010 indicates an increasing “partisanship” around space exploration program support, with increases in the number of respondents who say too little is being spent, as well as increases in the number who say too much is being spent.   (The number of “baby bears” in the middle has decreased).   This would be consistent with the current highly polarized situation around national space policy and the cancellation of the Constellation moon/Mars program.   The net change (+0.7% per year) is essentially no different than for the preceding decade of slow-but-steady gains in support.

The focus of the increased antipathy towards the space program can be seen by plotting the “NATSPACY” variant of the GSS survey question, which asks only about spending on “space exploration” (no use of the word program).   Whether this is tied to the new space policy, or to the broader economic situation in the country, it is noteworthy that the “too little” respondents increased in 2010, suggesting that space exploration overall, while still a “niche” priority, is not losing mind-share.

Note:  The Berkeley interactive browser for the GSS (which also includes some weighting corrections) has not yet been updated with the 2010 data, so the numbers here are preliminary and reflect the unweighted counts.

For those just joining, these memeograms plot the year in which various words and phrases “peaked” in frequency in the database of all books scanned and stored by GoogleBooks.  The last few weeks have been devoted to ‘words of the workplace’.

There’s really no voice track that can do this justice … best to just download it, print it out, and try to connect the dots!   PDF file:   Memeogram_LaborAndCapital

First: a reminder that the U.S. aerospace industry still dominates the U.S. net exports (balance of trade) with the rest of the world, as manufacturing industries go.    This courtesy of the annual rack and stack of data from the Aerospace Industries Association.

The bulk of this positive balance (net exports) is the “aero” part of the picture, that is, aviation.   A small, but critical, additional part of the export picture is in the “space” part of the sector. A billion and change of space-related net exports a year is small compared to aircraft tens of billions of dollars of aircraft exports, but not trivial when compared with all of the industrial sectors in which the balance is negative.

What is less well known is that the space sector positive balance is steadily dwindling.   The trend is slow; ten-year averaging helps pull it out from the data.  The year the U.S. buys more satellites, rockets or missiles from the rest of the world than it sells (looking to be somewhere in the 2025-2030 timeframe, based on the current trends) will be a quiet but important page-turning event in history, perhaps more symbolic than strategic (we are after all net importers of far more strategic assets such as fossil fuels).

Still, the world, it is a’changing.

That said, the constant dollar spending is useful as well.   Clicking on the live chart at left and drilling into the subcategories, the long term trends in spending for, e.g., defense, health, and energy show important patterns to be aware of.   As well as, of course, net interest on the national debt…

Technical notes: Data for these charts come from outlay tables hist03z1, hist03z2, and deflator/GDP table hist10z1, at OMB’s historical budget data site.   I’ve used the GDP-chained price index for inflation adjustment.   For the President’s Budget Request 12, internal breakouts of DoD spending were not included for FY13-FY16, to complete the charts here, I have estimated by prorating the DoD requested topline for those years based on the FY12 internal distribution.

Whether prurient to the prudish, this memeogram might provide some new words to go on your candy hearts this year.   Enjoy!

For download, here is the PDF:  Memeogram_Valentines.

• Memeogram (meem-ey-oh-gram) [n]:   A graph composed of plotting the peak frequency vs time of peak frequency for keywords / ngrams / memes, with markers comprised of the words themselves.
• Plain english: A graph showing show the year at which individual words or phrases “peaked” in popularity (frequency of occurrence) in the database of all books scanned at GoogleBooks.

What is fascinating is how predictably the national appetite for such initiatives evolves, and how much “damping” is applied to both ramp-up, and spending cuts, through the political process.   The chart at right shows the year-to-year percent changes in defense discretionary spending.   (Note, the data beyond FY10 are from last year’s President’s Budget Request, thus are not yet “actuals”).    Even in the most fervent defense reduction climates, annual cuts peaked at -8%/year.

The historical pattern is probably very relevant right now, with much attention focused on the national discretionary budget, and with future defense plans contending with active engagements worldwide, dramatically rising operations costs, and already-planned recapitalization programs.   According to CBO,  the current Future Years Defense Program calls for a budget runout this decade which is comparatively “flat” compared to the deep downturn cycles of the 1970s and 1990s.   While there is no guarantee that history will repeat itself, it seems highly unlikely in the current climate that the national will to sustain investment at that level will persist.   The FY11 President’s Budget Request was possibly only the opening salvo.

The defense cyclicals stand in contrast to trends in the non-defense discretionary budget.   Even in inflation-adjusted dollars, years with actual, effective cuts to the non-defense discretionary top line have been few and far between.   Even “flat” years still trend up slightly.   How the nation treats non-defense discretionary spending in the coming decade – relative to defense spending, and mandatory entitlements – will be very interesting to watch.   This is an area which historically has been easy to put first up for reductions, but has been difficult to actually cut when specific federal services start to get taken off the table.

While the cycles above are already pretty clear, they jump out even further with a little “smoothing”.   By averaging over 10 years, the defense cycles begin to look almost “scientific” in nature.   While the conspiracy theorists might start hyperventilating about secret cabals, I think it is far more interesting to view this as emergent behavior of a highly complex social system with many, many built in self-regulating and negative feedback loops.

I’ll do a quick update to these graphs, as well as my ManyEyes sand chart of federal spending, late next week after the President’s FY12 Budget Request announcement on February 14.

A longstanding source of expert advice is the National Academies Space Studies Board.   Since NASA’s founding, the Academies have provided over 25,000 pages of advice (not all of it necessarily guaranteed to be either affordable, or internally consistent … though recent studies are making improvements in those areas).   As the graph at right shows, this advice has steadily increased in volume over the years.   (A factor of two from the 1990s forward seems to be more than can be explained simply by the efficiencies provided by desktop publishing).  8500 pages of “encouragement” during the 2001-2010 decade is voluminous by any standard.

It’s not just a matter of NASA getting more money to “advise upon”, either.   When adjusted for budget (in inflation-adjusted FY10 billions of dollars), both the volume and “intensity” of advice-giving has nearly doubled in the last decade.

In addition to the NRC, NASA has, for most of its history, also had no less than 10 different external advisory boards (“FACA committees“).   Until quite recently with the reorganization and consolidation  of the NASA Advisory Council, most of these boards had no obligation to provide self consistent and non-contradictory advice, or to live within basic constraints of reality, such as the Agency’s actual budget.

It is easy to forget that the nation’s space program is not monolithic, nor is there clear consensus about how best to use the nation’s investment in space.  Individual communities – such as science disciplines – have made great strides in setting their priorities through “decadal surveys”, and, even, beginning to confront the affordability of their desires.   However, few if any external groups provide advice and guidance on the nation’s civil space investment portfolio as a whole, spanning technology, science, human space flight, exploration, communications, launch services, and aeronautics.   (Even fewer consider integrated civil and security space investments).  As the nation confronts a decade or more of intense pressure on both civil and defense discretionary spending, space stakeholder communities – or the Federal government itself – must evolve stronger mechanisms to “self-police” competing interests, and arrive at consensus around integrated portfolios, to make the best use of increasingly scarce investment dollars.

I’m now going to have to find a way to re-insert bunco [1907] into my working vocabulary.  The proliferation of flavors of white collar crime in recent years is particularly interesting.

Here’s a link to the files in printable PDF format: Memeogram_Capital, Memeogram_Perfidy

Next week: a wrapup of all the Words of Work.

Last week’s theme was blue collar, this week, the focus is white collar [black font] and management [brown font].   My primary take-home here is: work has gotten a heck of a lot more complicated over time.

There are some great little nostalgic nuggets: Christmas bonus peaking in 1946 (how retro…), Madison Avenue [1961] (pour me a Martini, Don Draper), Japanese industry [1987] (lifetime corporate loyalty, now that’s quaint), total quality management [1995] (when we used to make products for the physical, rather than virtual, world).   Typist [1983] and secretarial pool [1983] also harken back to a white collar world that no longer exists.   Inspired by these I was tempted to add aerobics and Jane Fonda, but opted not to.

The plethora of management “tools” from 1990 to present helped inform my previous post on Strategic Management Fads.

Here’s a link to the file in printable PDF format: Memeogram_Sale

Next week: the Captains of Industry set sail.

The color scheme used throughout renders the “worst” scores red, and the “best” scores blue.   Yes, there are political overtones to that (it was, after all, 2004), but at the end of the day, the data are the data.

In almost all cases the valuation of ‘better or worse’ is obvious from the scores.  In a couple of cases I rely on valuations that shouldn’t be too controversial (regressive tax schemes are those which favor sales taxes, and I value as ‘worse’; higher than average statewide Federal subsidy is valued as bad; etc).

The rankings are broken out into 8 major categories, each of which is a composite of a number of specific indicators.  The diagram at left lays out the relationships.   Loosely, the major categories attempt to capture:

• Mental well-being (“Sound mind”)
• Financial independence (“Self reliance”)
• Physical well-being (“Body / temple”)
• Domestic well-being (“Family values”)
• Sexual well-being (“Chastity & abstinence”)
• Financial well-being (“Blessed / meek”)
• Social well-being, or crime (“Do unto others”)
• Educational well-being (“Right reason”)

The choice of cute-sy taglines which align along virtues is, of course, a not so subtle poke at the fact that states commonly equated with championing these virtues have (c.f. the data) some of the lowest track records at cultivating them within society.   In a less mean spirited way, this could be restated as a call in those states to double down and ensure that charitable social safety nets are firmly in place, since the choice of public policies alone seems to be insufficient.

Blessed be the meek

Starting with basic financial metrics helps set the baseline for the other indicators that follow.   As I’ve defined it, this metric includes not just income but the relative portions of state populations in poverty and child poverty, as well as the gaps between lower, middle, and upper income brackets.   States have an opportunity to “earn some credit back” with higher rates of charitable giving.   Tax regressiveness I have defined as (sales tax revenue / property + corporate + income tax revenue), and higher is worse.

There should be no surprises with worse scores in the south and in rural areas … again, this is a baseline.

Self-reliance

As defined, this metric is largely dominated by the relative amount of Federal cash infusion into states (compared with what they pay in taxes).   This issue is (c.f. tea party) as pointed today as it was in 2004.   Unemployment is included as an indicator since it will correlate directly with unemployment insurance (not included in the Federal subsidy numbers).   Consumer credit scores is included as a throw-in indicator to allow some capture of relative “thrift” between state cultures.

This map is interesting when placed alongside “Blessed / Meek”.   Some states are clearly better than others (Alaska) at capturing Federal subsidy, and possibly well beyond their social need.

Do Unto Others

Apparently, latitude has a chilling effect upon crime (could not resist that pun).   This map is also interesting in that it shows that crime and poverty do not map one to one.   The previous maps showed rural areas with significant economic stress, and/or federal subsidy, which nonetheless have comparatively low crime rates.  In the Dakotas, culture seems to prevail; the Appalachians are also lower than might be expected based on the other indicators.

These data were from the 2003 time frame, which was comparatively early on in the assault of methamphetamine on middle America.   It would be interesting to revisit the statistics today.

Family Values

Keeping within the social arena, this metric includes indicators on marriage, divorce and child abuse.   I have also chosen to include census-registered domestic partnerships as a “good thing”; regardless of individual views on same sex relationships, I’m following the interpretation that two are better than one.

The spread in this metric is less pronounced from state to state, and the dominant trend seems to be a ‘challenged’ situation in the Southeast.   Western states fare comparatively well, overall.   Alaska, not so much.

Chastity & Abstinence

Moving to the related topic of sexual well being, this metric captures pregnancy, abortions, and sexually transmitted disease rates.   Here the South is joined by states with major population centers (New York, Maryland, California) as standing out.

It would appear that proximity to Canadian mores has a beneficial effect on the worst impacts of promiscuity.   Or, it’s just harder to get lucky up there.

Body / Temple

This metric includes a variety of health-related indicators.  Again the coupled issues of poverty, health and culture in the south are implied.

One of the more interesting features of this map is that states that stand out on the “good side” really stand out, and often have very sharp geographic contrasts with their neighbors.   To me, this suggest that health is a social concern which is strongly amenable to influence by public policy (i.e., cultural or economic influences would tend to have broader and less sharply defined boundaries; policy impacts would have sharp state-by-state contrasts).

Sound Mind

Turning to mental health, this metric includes both overall mental health rankings, as well as specific indicators for mental illness, suicide and drug use.

There are trends in this map for which I have no explanation.   The band of “comparative sanity” from the Dakotas through Texas, save  Oklahoma, is perplexing.  Is it a stoic, farmer-culture holdover?   Similarly put for the apparently “Wild West”.   Do the wide open spaces encourage and attract mentally marginal behaviors?

Joking aside, this looks like fodder for a good sociology Masters’ thesis topic.

Right Reason

If education is the first step in breaking the cycle which reinforces these social ills, then this metric is sobering.  The states which – from the previous metrics – need the most intervention also lag in the area of education.   This is not surprising, but particularly stark when contrasted so directly with the statistics on poverty, health and crime.

Summary

While the data for these maps dates from 2004, I strongly doubt there have been radical geographic shifts since then, unless the impacts of the 2008 financial meltdown have caused entirely new fractures to appear.

Most striking in these maps is the sheer persistence of “repeat offender” states across multiple metrics.   It is a sobering and painful reminder of the pervasive, insidious and interconnected impacts of poverty across the spectrum of societal needs.  In an era when it has become less politically fashionable to talk about the American underclass, the data nonetheless speak loud and clear.

The red-shaded terms are intended to capture “blue collar” themes from a worker perspective; grey to capture production through an industry perspective.   Orange captures the disappearance of artisans/craftsmen.

Some of my favorites include vicious circle, peaking in 1969, and  rat race [1973] (“tune out, man!”), and coffee break [1977] … as union demands go, a long way from 40 hour week [1945]. As someone who grew up in the 1980′s, the whole memeogram is interesting to me in showing how our focus on industrial production has shifted since then (the Japanese really had us spooked … but that’s fodder for next week’s memeogram).

Here’s a link to the graphic in PDF format: Memeogram_Labor.