RADIANCE BLOG

Category: Stuff We Like

Ugh, Millennials

Is anyone else tired of talking about millennials? Millennials have seemingly been on everyone’s mind, with many worrying over their spending habits, charitable giving, large debt, voting behaviors, and other things. Why do we care so much about this generation? Don’t they already have a problem with entitlement and being all about “me me me”; we probably shouldn’t feed into that, right?

Pictured: Gregory (myself) the Millennial
Fun fact: depending on where you draw the line, 70% of Corona staff are classified as millennials.

As annoying as it might be, there are some very good reasons to focus on the millennial generation. The baby boomer generation is now on the decline and currently there are 11 million more millennials. It is estimated that millennials will comprise over a third of adult Americans by 2020,  up to 75% of the American workforce by 2025, and currently account for over one trillion dollars in consumer spending in the U.S. Despite this, millennials have less money to spend and are encumbered with greater debt. Perhaps unsurprisingly, the conclusion is that millennials are important because they are the new money – they are very quickly becoming the largest group of consumers and are therefore greatly impacting all businesses and organizations.

The millennials, as a generation, share some commonly seen characteristics:

… and the facts don’t end there. If you haven’t already, I highly encourage you to pour over some of the linked materials to familiarize yourself with this impactful generation. If they haven’t yet, millennials will be disrupting your organization sometime in the near future, and it’s inescapable that we all need to adapt.


State of Our Cities and Towns – 2017

For many years, Corona has partnered with the Colorado Municipal League to conduct the research that is the foundation of their annual State of Our Cities and Towns report. CML produced the following short video, specifically for municipal officials, about the importance of investing in quality of life:

Learn more, view the full report, and watch additional videos on the State of Our Cities website.


Nonprofit Data Heads

Here at Corona, we gather, analyze, and interpret data for all types of nonprofits.  While some of our nonprofit clients are a little data shy, many are data-heads like us!  Indeed, several nonprofits (many of which we have worked for or partnered with) have developed amazing websites full of easy to access datasets.

Here are 4 of my favorite nonprofit data sources…check them out!!

The Data Initiative at the Piton Foundation

Not only do they sponsor Mile High Data Day, but the Piton Foundation produces a variety of user friendly data interfaces.  I really like the creative ways they allow website visitors to explore data–not just static pie and bar charts. Instead, their interface is dynamic and extremely customizable. While their community facts tool pulls most (but not all) of its data from the US Census, this tool is very easy and fun to use.  Further, they have already defined and labeled neighborhoods across the Denver Metro area, making it easy for users to compare geographies without trying to aggregate census tract or block group numbers. This is an invaluable feature for data users who don’t have access to GIS. I also appreciate the option to display margin of error on bar charts when its available.

Highlights:

  • Easy to use from novice to expert data user
  • Data available by labeled neighborhood
  • 7-County Denver Metro focus

Explore

OpenColorado

With over 1,500 datasets, OpenColorado is a treasure trove of raw data.  While this site doesn’t have a fancy user interface, it does provide access to data in many different file types, making it a great website for the intermediate to advanced data user with access to software such as GIS, AutoCAD, or Google Earth.  Most data on OpenColorado is from Front Range cities (e.g., Arvada, Boulder, Denver, Westminster) and counties (e.g., Boulder, Denver, Clear Creek), but unfortunately it is far from a comprehensive list, so you’d need to look elsewhere if your searching for information from Arapahoe County, for example.

There are over 200 datasets specific to the City and County of Denver.  I opened a few that caught my eye, including the City’s “Checkbook” dataset that shows every payment made from the City (by City department) to payees by year.  I give kudos to Denver and OpenColorado for facilitating this type of fiscal transparency.  I also downloaded a dataset (CSV) of all Denver Police pedestrian and vehicle stops for the past four years, which included the outcome of each stop along with the address, latitude and longitude.  For a GIS user, this is especially helpful if you want to search for patterns of police activity compared to other social and geographic factors.  Even without access to spatial software, this dataset is useful because it includes neighborhood labels.  I created a quick pivot table in Excel to see the top ten neighborhoods for cars being towed (so don’t park your car illegally in these neighborhoods).

Highlights:

  • Tons of raw data
  • Various file types, including shapefiles and geodatabases that are compatible with GIS, and KML files that are compatible with GoogleEarth
  • Search for data by geography, tags, or custom search words

Kids Count from the Colorado Children’s Campaign

Kids Count is a well-respected data resource for all things kids.  Each year, the Colorado Children’s Campaign (disclaimer, they are also our neighbor, working just two floors below us) produces the Kids Count in Colorado report, which communicates important child well-being indicators and indices statewide and by county when available.  The neat thing about Kids Count is that it’s also a national program, so you can compare how indicators in a specific county compare to the state and nation. In addition to the full report available as a PDF, you can also interact with a state map and point and click to access a summary of indicators by county.  Mostly, their data is not available in raw form, but their report does explain how they calculated their estimates and provides tons of contextual information that makes their key findings much more insightful.

Highlights:

  • Compare county data to state and national trends
  • Reports include easy to understand analysis and interpretation of data
  • Learn about trends overtime and across demographic groups

Outdoor Foundation

If you’re looking for information about outdoor recreation of any type in any state, there is probably an Outdoor Foundation report that has the data you’re seeking.  Based in Boulder, Colorado, the Outdoor Foundation’s most common reports communicate studies of participation rates by activity type, both at a top level and also by selected activity types such as camping, fishing, and paddle sports (haven’t yet heard of stand-up paddle boarding?  It’s one of the fastest growing in terms of participation).  The top-line reports show trends over the past ten years, while the more detailed Participation Reports break out participation, and other factors such as barriers to participation, by various demographics.  Multiple other special reports, focusing on topics such as youth and technology, round out what’s available from this site.

The participation and special reports are helpful, but I’m most impressed with the Recreation Economy reports, which are available nationwide and within each state.  These reports estimate the economic contribution of outdoor recreation, including jobs supported, tax revenue, and retail sales.  For example, the outdoor recreation economy supported about 107,000 jobs in Colorado in 2013.  Unfortunately, the raw data is not available for further analysis, but the summary results are still interesting and helpful.

Explore:


Art meets architecture in Denver this weekend

Looking for something fun to do this weekend in-between rides on the new A Line to DIA? Check out the arts and cultural activities during Doors Open Denver. Art meets architecture through pop-ups ranging from a nomadic art gallery to poetry, drama, and music performances among the 11 offerings. My favorite? Graffiti art. If you’ve been secretly wanting to learn the art of graffiti painting – and you’re 55 or older – then we’ve got the creative outlet for you. Bust through stereotypes as you create graffiti art inspired by two of Denver’s architectural gems.

  • April 23rd, 1-3 pm – Saturday’s pop-up will be hosted by Clyfford Still Museum on their front lawn. Clyfford Still Museum will give 4 – 20 minute architectural tours each day at 11:00, 11:30, 2:00 and 2:30.
  • April 24th, 1-3 pm – Sunday’s pop-up will be hosted by the new Rodolfo “Corky” Gonzales Library and include 3 tours led by architect Joseph Moltabano of Studiotrope, a Denver-based architecture and design agency. DPL staff will share how the library’s design informs their work. Since Sunday is Día del Niño the artist will be prepared to host a multi-generational event at the library.VSA Colorado/Access Gallery

Thanks to our collaborative partners: VSA Colorado/Access Gallery, studiotrope design, Denver Public Library, Studiotrope Designand Clyfford Still Museum. I’d like to give a special shout out to Damon McLeese of Access Gallery; Joseph Montalbano  of DPLstudiotrope; Ed Kiang, Viviana Casillas and Diane Lapierre of DPL; and Sonia Rae of Clyfford Still Museum.

Please join me in thanking the Bonfils-Clyfford Still Museum Stanton Foundation for funding this engaging spotlight on art and architecture.

For more information visit this Doors Open Denver link. 


Your Baby Is Increasingly Special and Unique, Apparently

It seems like when I’m in the mall and hear parents talking to their kids, I hear unusual names more and more often.  I’ve been developing a theory that parents are enjoying creativity more and valuing tradition less when that birth certificate rolls around, so in keeping with Corona Insights tradition, I thought I’d explore it a little more with some data analysis.  Off I went to the Social Security Administration website to put together a database of names.

I took a look at the most popular baby names in 2014, and compared them with those of 2004, 1994, 1984, and so on, all the way back to 1884.  Are unusual names more common in 2014?  It was straightforward to analyze, even if it meant sifting through a lot of data.

First, I looked at the 30 most popular names in each decade, and compared them to the total number of babies born.  If there’s a trend toward giving babies more unusual names, then we would expect a smaller concentration of babies with the most common names.

And wow, is that true, particularly for girls.  Let’s examine female names first.

If we look at the 30 most common female baby names, they constituted 41 percent of baby girl names in 1884.  There was some variation over the next 70 years but not much, ranging from 36 to 43 percent.  In 1954, the figure still stood at 40 percent for the girls destined to duck under their desks in the Cold War.  (As an important methodological note, recognize that these aren’t the same 30 names that were most common in 1884 – I adjusted the top 30 in each decade to reflect the most popular names of each particular decade.  This holds true throughout the analysis – I’m not tracking the popularity of a specific set of names, but rather I’m examining the likelihood of parents following popular trends in naming.)

But then something happened.  By 1964, the figure had declined to 32 percent.  It stayed roughly at that level until 1994, when it dropped further to 24 percent.  And since then, it had declined dramatically to 18 percent in 2004 and 16 percent in 2014.  The most common female names in 2014 are not very widespread.

If the most common names are less widely used, the next question is what other names are being used?  Are parents merely spreading their wings a little to other relatively well-recognized names, or are they pushing the boundaries of names?  To test this, I broadened my analysis and looked at the 100 most common female names.  In 1884, the most common 100 names covered 70 percent of girls born that year.  Moving forward in time, we see a very similar pattern that we saw for the top 30.  The figure declined slightly through 1954 (65%), and then those hippies from the 60s started becoming parents.  The figure dropped 58% by 1964, 51% by 1974, and continues to decline.  In 2014, the top 100 female names covered only 31 percent of births.

So how much dispersion do we actually have here?  Let’s look at the top 500 female names in each decade.  Most of us probably couldn’t even come up with 500 different names, so surely they’re covering almost the entire female population, right?

Well, that certainly used to be the case.  In 1884, the top 500 names covered 90 percent of the female baby population, and sure enough, it follows the same pattern as my earlier analyses.  The figure floated between 87 and 89 percent up until 1954, with remarkable consistency.  After all, who can’t find a favorite name among the top 500?

A lot of modern people, apparently.  The figure dropped to 85 percent in 1964, 75 percent in 1974, and currently stands at 58 percent.  Think about that for a moment.  42 percent of girls today have a name that does not fall into the top 500 most common names of her decade.

How does such a phenomenon happen?  One might speculate that this is due to a trend for adopting spelling variants.  Evelyn, for example, has branched into both Evelyn and Evelynn.  While I suspect that this is a significant factor, though, it appears to not be the main factor.  Instead, what we see among our top 500 names for 2014 is that many names appear to be newly created, or at least exceedingly rare in past decades because they’ve never appeared on a top-500 list until now.  Names like Brynlee and Cataleya and Myla and Phoenix have replaced more standard names.

Another theory that I can’t confirm at this point is that perhaps the United States has more diverse immigration these days, which could be producing a greater diversity of baby names.

Now let’s take a look at male names.

The first thing we see is that male names have historically been compressed relative to female names.  Looking across all of the decades since 1884, there are 1,286 male names that have placed in the top 500 in popularity, while there are 1,601 female names.  So are male names still more concentrated among fewer options?  We’ll repeat the analysis we just did for female names.

If we look at the 30 most common male baby names, they constituted 56 percent of baby boy names in 1884.  Per our earlier observation, this is much more concentrated than the 41 percent that we saw for females.  Similar to female trends, though, the proportion was relatively stable for decades afterwards, still standing at 54 percent in 1954.

The proportion began dropping in the 1960s, but was more stable than female names.  By 1964, the figure had declined gracefully to 51 percent, then 46 and 45 percent in the 1970s and 1980s.  The major decentralization for boys began in earnest in 1994, when the figure dropped to 35 percent, then 25 percent in 2004 and 20 percent in 2014, which isn’t notably higher than the female figure at this point.

An interesting difference by gender occurs when we examine the top 100 male names.  Whereas the distribution of female names was only minor through the 1950s, the distribution of male names actually decreased during that era.  In other words, the 100 most common names became slightly more concentrated for boys from 1884 to 1954.  Names became more dispersed through the 1920s, but the trend then reversed.  The proportion of boys with top-100 names dropped from 74 percent to 69 percent between 1884 and 1924, then rose back to 76 percent by 1954.  Perhaps during hard times of depression and war, parents get more conservative when naming boys.  Or maybe mothers working on World War II assembly lines became enamored with mass production.

However, from 1954 on, male names paralleled the diffusion of female names, dropping steadily to only 42 percent today.  This is still more concentrated than the 31 percent figure for females, but is far lower today than at any time in the past 130 years.

Finally, we look at the top 500 male names.  Have males had the same dispersal as females?

Contrary to other findings, male names were actually slightly more dispersed among the top 500 than female names in 1884.  The 500 most popular male baby names constituted 89 percent of births, compared to 90 percent for females.  But this discrepancy didn’t last long.  While the top 500 female names dispersed slightly from 1884 through 1954, male names actually converged, reaching a high point of 94 percent in 1954.  So while parents were practicing more creativity in female names over this period, they were becoming less daring with male names, choosing more often to follow popular trends.

However, creativity took hold soon thereafter.  Male convergence dropped slightly to 93 percent by 1964, then dropped steadily to a figure of 71 percent in 2014.  So again, parents are increasingly choosing uncommon names for their babies in modern times, though to a much greater extend with boys than with girls.  As with the girls, these boys’ names appear to be a combination of new spellings and also new names that have never before shown up in the top 500, names such as Daxton and Finnegan and Kasen.

This is all well and interesting, but what does it mean?

I’m first interested in the differences for women versus men?  Why do parents feel greater freedom to give a female child an uncommon name?  Do they feel a greater need to make a female child stand out from the crowd, and if so, why?  Are males better situated to succeed with a more traditional name, or do more men simply get named after their fathers or other family members?  Is the difference sexism in a very indirect form, or is there some logical reason?  I’m at a loss to come up with a logical reason that doesn’t reflect different attitudes toward girl babies than boy babies, but I’d love to hear your theories.

While the level of standardization differs between males and females, though, the patterns are moving in the same direction, and doing so strongly.  Why are babies – both boys and girls – increasingly likely to be given uncommon names?  One can surmise that it describes a society where individualism is being sought out more and more.  It may also point toward a lesser desire or obligation to pass down family names and a lesser emphasis on tradition.  So are we increasingly a nation of creative individualists or are we increasingly lost and rootless?  Or both?


DIY Tools: Network Graphing

Analyzing Corona’s internal data for our annual retreat is one of my great joys in life.  (It’s true – I know, I’m a strange one.)  For the last few years I’ve included an analysis of teamwork at Corona.  Our project teams form organically around interests, strengths, and capacity, so over the course of a year most of us have worked with everyone else at the firm on a project or two, and because of positions and other specializations some pairs work together more than others.  Visualizing this teamwork network is useful for thinking about efficiencies that may have developed around certain partnerships, and thinking about cross-training needs, and so on.  The reason I’m describing this is that I’ve tried out a few software tools in the course of this analysis that others might find useful for their data analysis (teamwork or otherwise).

For demonstration purposes, I’ve put together a simple example dataset with counts of shared projects.  In reality, I prefer to use other metrics like hours worked on shared projects because our projects are not all of equal size, and I might have worked with someone on one big project where we spent 500 hours each on it, and meanwhile I worked on 5 different small projects with another person where we logged 200 hours total.

But to keep it simple here, I start with a fairly straightforward dataset.  I have three columns: the first two are the names of pairs of team members (e.g., Beth – Kate, though I’m using letters here to protect our identities), and the third column has the number of projects that pair has worked on together in the last year.  To illustrate:

My dataset contains all possible staff pairs.  We have 10 people on staff, so there are 45 pairs.  I want to draw a network graph where each person is a vertex (or node), and the edge (or line) between them is thicker or thinner as a function of either the count of shared projects or the hours on shared projects.

This year I used Google Fusion Tables to create the network graph.  This is a free web application from Google.  I start by creating a fusion table and importing my data from a google spreadsheet.  (You can also import an Excel file from your computer or start with a blank fusion table and enter your data there.)  The new file opens with two tabs at the top – one called Rows that looks just like the spreadsheet I imported and the other called Cards that looks like a bunch of notecards each containing the info in one row of data.  To create the chart, I click the plus button to the right of those tabs and select “Add chart”.   In the new tab I select the network graph icon in the lower left, and then ask to show the link between “Name 1” and “Name 2” and weight by “Count of Shared Projects”.  It looks like this:

There are a few things I don’t love about this tool.  First, it doesn’t seem to be able to show recursive links (from me back to me, for example).  We have a number of projects that are staffed by a single person, and being able to add a weighted line indicating how many projects I worked on by myself would be helpful.  As it is, those projects aren’t included in the graph (I tried including rows in the dataset where Name 1 and Name 2 are the same, but to no avail).  As a result, the bubble sizes (indicating total project counts) for senior staff tend to be smaller on average, because more senior people have more projects where they work alone, and those projects aren’t represented.  Also, the tool doesn’t have options for 2D visualizations, so if you need a static image you are stuck with something like the above which is quite messy.

However, the interactive version is quite fun as you can click and drag the nodes to spin the 3D network around and highlight the connections to a particular person.

Another tool option that I’ve used in the past (and that is able to show recursive links and 2D networks) is an Excel template called NodeXL.  You can download the template from their website – you’ll need to install it (which requires a restart of your computer) – and then to use it just open your Windows start menu and type NodeXL. Instructions here.  I had some difficulties using it with Office 2016, but in Office 2013 it worked quite well.

If you try these out, share your examples with us!

 


Dieting with Data

I don’t know about you, but one of my favorite things about the holidays is all of the great food that we get to eat. In just a month-and-a-half, we get to enjoy a beautiful Thanksgiving Turkey, succulent Holiday ham, and our favorite hors d’oeuvres on New Year’s Eve. It’s during these wonderful days I find myself overindulging in these rich, delicious foods.

With the new year comes new resolutions, and mine this year once again include losing weight (but this time I mean it.) It’s also a great time to make resolutions for your career or business, such as aiming to be more decisive or to better understand your customers. When the first step towards achieving those resolutions involves gathering data, just like when dieting it’s important that you don’t overindulge.

Recent trends have continued towards gathering as much data as you can and using it to make data-driven decisions. As I’m sure you know, here at Corona we love data; everything we do is driven by data. However, having too much data can often-times be crippling. You might have more than you know what to do with, or you could simply not know how to properly analyze it. It’s also possible that, even if all of that data is analyzed, you could be faced with a paralyzing number of options and decisions to be made.

So how can you ensure you collect a healthy amount of the right data? Similar to dieting, it’s important to always start with a clearly defined goal. Once your goals are well-defined, they inform the rest of the process: What questions should be answered? How is the data gathered – is there previously collected data that can be used, or should new data be gathered from survey(s), focus groups, or something else? What analyses need to be done to accurately answer the questions? Finally, the goals will let you know if you’re in over your head and need help.

Hopefully we can all stick to our resolutions, because when you look back a year from now you’ll only be thankful you did.


Harness the Power of Cumulative Effects

This week we’re all getting a fresh start with the new year.  The start of a new year is one of several “fresh start” events (along with birthdays, 1st of the months, Mondays, and so on) throughout the year that provide motivation to embark on changes.  New research shows we use these events to mentally distance ourselves from our past, less perfect, selves and decide we can be different moving forward.

At Corona we use the start of the year as an opportunity to take stock and make resolutions both personally and for the firm.  But we’re in a unique position in that it’s part of our day-to-day jobs throughout the year to think about behavior change from a variety of angles:

  • We design evaluations to measure behavior change outcomes that result from our clients’ interventions and campaigns to encourage and support behavior change (e.g., tobacco cessation, seatbelt wearing, teen pregnancy prevention, DUI prevention, increasing healthy behaviors, and many more).
  • We conduct research to gather data about motivators and barriers to change to help clients develop behavior change interventions and campaigns.
  • We provide strategic planning services to help clients take stock and set goals to move their organizations forward.


This year, as you think about the changes you’d like for yourself, your organization, or the groups you serve, think about cumulative effects.  Many of the behaviors we seek to change are habits – behaviors that we make repeatedly and automatically.  And our status at the end of the year is the result of adding up all of those actions throughout the year.  Every time you choose fruit instead of candy, every time you suck it up and go to the gym instead of watching TV, every time you don’t light up that cigarette in the car, you’re adding a data point to your cumulative health status.  Similarly, the choices you make for operations-as-usual within your organization will add up to your impact, your profitability, your employee engagement, etc.

Changing habits requires you to automate a new behavior to replace the undesirable one.  Practice, practice, practice is the road to automation.  At first it will take a lot of conscious effort to remember and apply the new behavior, but the more you do it, the more automated it will become.  And before you know it, it will be the new normal and you/your org/your clients will be the better/happier/healthier/wealthier version you imagined.

Good luck with your resolutions for the new year!  And if you need any help setting goals, gathering data, or measuring behavior change, we’re here to help!



The Challenges of Measuring Home

Home has many different definitions, both physical and conceptual.  It is where the heart is, according to Pliny the Elder, or it’s where you keep your stuff according to George Carlin.  It’s a structure, a family, a memory, or just anywhere you feel comfortable.  Or maybe it’s all of these things.

I spend a fair amount of time on the road, traveling for work or for pleasure.  While my true ‘home’ is certainly Denver and my house and my wife, I fall into the camp that believes that ‘home’ can exist in other places as well.  If my stuff is there and I’m sleeping there, it’s home, even if it’s just for a night.

I started thinking about where these temporary homes have been, and decided to do an analysis of it, because, you know, that’s who we are here at Corona Insights.

Like most data analyses, the question becomes more complicated once you start measuring.  The first issue is scale.  What am I measuring?  Is it as specific as a hotel room?  The hotel itself?  The city?  The state?  If I measure actual hotel rooms, the data is so detailed that it’s not particularly useful.  And if I measure it on a state level, then I omit a lot of overnight trips in Colorado where my ‘home’ for the evening was not in Denver.  I needed to define my research goal to answer this question.

The second issue is how I classify data.  It seems like measuring where one spends the night is easy, but is it?  I’ve spent some nights on long-distance flights, so is ‘home’ the destination, the origin, or the plane itself?  The same question arises on overnight train trips.  Was my ‘home’ in the state where I went to bed or the state where I woke up or the state where I spent the most sleeping hours?

And ships make things even more complicated.  I’ve slept on ships in international waters, ferry boats plying coastal waterways, and cruise ships docked in port.  What are the rules?

The third issue is developing and verifying a data source.  In this case, I frankly relied on a very healthy memory for trivial facts and a couple of calls to my parents.  Sure, I could dig out 50+ years of receipts, but in this case my research goal was to develop a good overview.  I could be quite accurate just based off memory and a very structured system for testing that memory, even if perhaps my precision isn’t perfect.  Do I remember if I spent 8 nights in Madagascar or 10?  No, not really.  But that’s not necessary to achieve my big-picture goals.

Like any data analysis, one has to define rules for classification, stick with them, and document them.  So I pondered my goals, and decided on the following rules:

  • I decided that my research goal was to measure where my nightly ‘home’ was, rather than the nights spent away from my primary home. This is in keeping with my philosophy that home is where I’m sleeping that night.
  • I decided to measure based on state for domestic travel and nation for international travel. This was a bow toward creating easy to digest data and keeping the data presentation manageable.
  • I decided that sleeping on a moving vehicle that crossed multiple states or nations would mean that ‘home’ was the vehicle. Sleeping in a stopped vehicle would count toward the land that was underneath the vehicle, and sleeping in a moving vehicle that did not cross multiple states or nations would count toward the land beneath the vehicle.
  • In the case of ships, the above rule would generally apply, but with some specific additional interpretations. “International Waters” would be equivalent to a state or nation, and if the ship did not enter another jurisdiction that night then my ‘home’ was International Waters.  However, if the ship crossed international waters between an origin and destination in the same state or nation, then my home was that state or nation.  For example, an overnight ferry in 1997 that crossed the Tasman Sea from Tasmania to Melbourne counts as Australia in the data, while bouncing from island to island in the Aleutians aboard the Alaska Marine Highway System counts as Alaska even if the ship occasionally ventured into international waters en route.
  • In the case of air travel across multiple time zones, I would merely count a 24-hour cycle from the time of takeoff. Otherwise the data get skewed by long east-west trips given time changes.

So in summary, it seems really straightforward to measure where you spent the night, but you still have to define rules to deal with ambiguity.  If spending that night requires a half-dozen rules, one realizes the extent to which we have to define rules in our more complex demographic analyses.

After defining the rules, the data collection began.  It primarily involved developing a list of states and nations and then asking myself, “when have I ever been there”?  I also asked my parents a few odd questions, such as, “Remember that time we drove cross-country to Disneyland in 1966?  Did we spend the night in Utah or Nevada along the way”?

The process went relatively smoothly overall, and I present to you below a compendium of every state, nation, and moving vehicle where I’ve ever spent the night (including leap years).

Kevin Travel Table second half

 

And to make it easier on the eyes, here’s a map, courtesy of Matt Bruce.

 

 

 

 

 

 

 

So what did I learn from this?  Aside from recognizing that I like to analyze things a little too much, I learned several things, with the following highlights.

  • Missouri had more than a 50 percent market share until approximately November 26th of 2013. It now stands at 48.0% and no state has a 50% market share.
  • I’ve lived in Colorado for over 22 years, but am still trailing Missouri. At my current rate of travel, Colorado should move into the #1 ranking on approximately September 30th of the year 2020.
  • I’ve spend 1.5% of my life sleeping in foreign countries. That’s a lot more than I would have expected.
  • Six of my top 20 temporary homes are foreign countries and two are transit homes (ships and planes), but that’s understandable. Vacations overseas tend to be longer, so I spent more time there once I arrived.
  • California will very likely move into the #6 spot in 2017, if not 2016.
  • I really need to get to New England.

And that conclusion about liking to analyze things too much?

Nah.