Cincinnati, OH:

Cleveland, OH:

Columbus, OH:

Detroit, MI:

Indianapolis, IN:

Kansas City, MO:

Louisville, KY:

Milwaukee, WI:

Pittsburgh, PA:

St. Louis, MO:

After deciding on the cities I wanted to research, my next step was to figure out which variables to use for my ranking system. I was somewhat limited by data availability and time constraints, but I was able to narrow it down to ten variables that give a pretty idea about the quality of life in each downtown:

• Overall Pop. Change 2000-2012 (ESRI Estimated)
• Age 20-34 Pop. Change 2000-2012 (ESRI Estimated)
• Age 65+ Pop. Change 2000-2012 (ESRI Estimated)
• Housing Occupancy Rate 2012 (ESRI Estimated)
• Median Income 2012 (ESRI Estimated)
• Median Rent 2010 (2006-2010 ACS)
• Disposable Income 2012 (ESRI Estimated)
• Population Density 2012 (ESRI Estimated)
• Percentage of Workers Using Public Transit (2006-2010 American Community Survey)
• Walk Score

While there are many more variables we could look at to judge quality of life, I chose these ten subjectively because they provide a good snapshot of a downtown’s health and the data was somewhat convenient to gather. I chose the two age categories because they are good indicators of the attractiveness of living downtown. The 20-34 year old cohort typically have more disposable income than any other age group and are thus more likely to put more of their money back into the downtown economy, similar to the 65+ age cohort. I also wanted to look at Walk Score and public transit use to get an idea of the commuting patterns of each downtown and median income and median rent to measure affordability.

After collecting all of the data, I ranked each city 1-10 based on each category, with 1 being the best score and 10 being the worst. I then totaled up all ten categories to get the final rankings. They are as follows:

1. Pittsburgh- 43
2. Milwaukee- 46
3. Kansas City- 49
4. Cincinnati- 51
5. Cleveland- 51
6. St. Louis- 51
7. Louisville- 59
8. Columbus- 62
9. Indianapolis- 66
10. Detroit- 72

And the winner is…Pittsburgh! Given how much the effort the city has put in to transform its downtown as an attractive location for the creative class, I am not really shocked. While our estimates show downtown Pittsburgh losing population from 2000-2012, the latest numbers from the 2007-2011 American Community Survey show an addition of another 1,000 people, bringing it closer to the 5,000 figure from the 2000 census. What puts Pittsburgh ahead of Milwaukee in the total score is its top ranking in median income, disposable income, and population 65+. It also has the highest median rent at $935 in 2010.

On the other end of the spectrum Detroit had very high population decline, losing 20.9% of its downtown population from 2000-2012. It also has the second lowest median income at $16,736, but the fourth highest median rent, meaning that many people are likely burdened with a high rent/income ratio and have little disposable income to put back in the city. They also have the highest percentage of people using public transit to get to work at 23%, which is both an indicator of a good transit system and a lack people that cannot afford their own means of transportation.

So where does Columbus fall in all of this? Unfortunately we are towards the bottom. We rank third lowest in both median and disposable income and last in population density. Columbus is also very low in the percentage of people using public transit to get to work at just 5%, but we have the fourth highest WalkScore. One bright spot for Columbus is that we rank second highest in population 65+ at 10.9%, only behind Pittsburgh at 12.1%. It seems that more older people like this nice couple are finding it feasible to sell their house in the suburbs and move to the city where almost everything is within walking distance. I believe that this trend says a lot about the attractiveness and sense of security of downtown Columbus and why it appeals to people of all ages.

What does these rankings say about the overall health of the downtowns in the Midwest? A little or a lot depending on how much weight you put into each variable. For the purposes of this analysis each category was weighted equally, but for instance some may have feel that a city’s WalkScore is not as important as median income. There are also many economic factors that go into creating a vibrant downtown that I did not get into in this analysis and It is important to remember that there are many neighborhoods in each city that are more attractive to residential living than the central business district. It isn’t necessarily every city’s goal to create a full service live/work/play environment downtown, but it should would be nice to see. Also, in case you are interested, here are the complete Downtown Rankings.

So Franklin and Delaware Counties are bright spots in a state where most counties lose population overall, but what other information can we infer from these growth rates? A friend asked if these growth trends might indicate that individuals in Central Ohio are starting to prefer urban environments over the suburban, with Franklin County representing urbanity and Delaware County the suburbs. It’s a good question, but the short answer is… not really.

Beyond the fact that one year is not enough information to establish any sort of statistically relevant trend, that more people are moving into Franklin County matters a little less than where in Franklin County they are moving (as some have already discussed). After all, there’s a big difference between greenfield development outside of Columbus City limits and moving into the city center. It’s also important to separate natural population changes from migration. Natural population growth includes births and deaths, and the remainder are individuals moving in and out of an area.  This type of voluntary population change would obviously be the more interesting demographic for exploring a preference for urban or suburban environments.

But getting back to my friend’s question, there are some basic ways we can look at this data to get a rough idea of where things stand. Again the really interesting question isn’t whether people are moving into Franklin County, it’s where they’re going. We can get an idea of exactly where people are going in the county by disaggregating the Census data from the entire county to census block groups. Since there’s no question on the US Census about a preference for urban or suburban communities, the next step is picking some kind of proxy. I decided to use population density. Population density is usually expressed by the number of people per square mile or square kilometer. Basically, it is the ratio of people to space: a small area with a large number of individuals has a higher population density than a large area with few individuals. Higher population densities, therefore, correlate with more urban environments and lower population densities with suburban or rural areas. My thought was that if people in central Ohio were starting to prefer urban communities, then the population density of Columbus proper should increase over time. Sounds like a perfect excuse to make a few maps.

Before I get to the maps, I have to emphasize that this is a quick survey exercise. I’m an academic at heart and I’d feel horrible if I didn’t point out that I didn’t triple check my numbers, I didn’t account for natural vs. migratory population changes, and I didn’t account for the growth rate (percentage increase) of each census block, or any number or time consuming things that would better validate these results. This is a sketch of population patterns in Franklin County, Ohio, and it’s a game anyone can play. If you have some kind of access to a mapping program, I encourage you to download this free data from American FactFinder and explore on your own. It’s a fun nerdy time.

First we have Franklin County’s population density in the year 2000.  I calculated population density by dividing the population of each census block group by its area.  If I were making this map again I would probably leave the numbers in the population density scale, but at the time I thought the numbers were too confusing because the census block groups can be such small areas.  I’d also retain labels for the highways and major roads to make the map easier to understand.  Regardless, I think this map largely reflects what you’d expect if you’re familiar with Columbus or Franklin County.  The areas with the highest population density are generally within I-270 (the circular outerbelt highway) and are further concentrated immediately south and north of downtown (downtown is smack in the middle of the map within the rectangle of highways).

Next is Franklin County’s population density in 2012 using the census estimates.  You can’t see it on the map, but I decided to keep the numeric range behind the density scale (lowest, low, middle, high, highest) the exact same as in the 2000 map because I wanted to see exactly how the population densities did or did not change.  My theory was that, given the same scale, an influx of people into the city (a preference for urban living) would result in more dark blue areas around the core.

It might be a bit difficult to see at first, but instead of a steady gain in population density in the heart of the city, it seems that the population densities spread out a bit throughout the county, meaning there was actually some density loss in Columbus, particularly in the German Village/South Side area.  (It would be interesting to go through foreclosure data to see if this area was particularly affected by the housing crash – perhaps that can be a future map series.)  North of downtown in the Short North/University area held pretty steady and density gains are apparent in the communities that surround Columbus proper.

After making these maps to demonstrate why an increase in Franklin County’s population doesn’t necessarily mean an increased desire for urban living, I started to wonder if I was over complicating the issue.  Maybe it would be better to simply see how areas lost or gained population over the twelve years.  I decided to make another map that just looked at whether each census block group lost population, gained population, or held steady from 2000 – 2012.  Again, I did not differentiate between natural and migratory population change.

The green areas in this map represent a net population gain since the year 2000 while the grey displays a net population loss. This map largely confirms the 2012 population density map in that the areas that lost density experienced a net loss of population over the same time period.  It also suggests in simple binary terms that areas outside of Columbus seem to have experienced population growth in equal or greater terms as the city proper.  To double check I made one more map highlighting Columbus’s city limits.

The opaque areas of the map are Columbus proper.  This makes it a bit easier to see that although the city itself has experienced a healthy amount of growth over its entire geographic area, there were losses in eastern portion of the city and much of the growth is in the periphery.

At the end of the day, these maps confirmed my suspicions: Sorry, friend.  Franklin County’s growth rate doesn’t really mean that more people are choosing an urban life style.  However, the last map brought a bit of unexpected optimism.  Columbus may not be the densest urban environment, it may have even lost some population density since 2000, but it has experienced positive growth downtown and downtown’s surrounding neighborhoods.  I can’t help but feel that this is a good sign; if you’ve seen the development boom in central Columbus lately, you might agree.  From the mixed-use development underway at High and Hubbard in the Short North, to the construction of apartments around Columbus Commons, to the the grand opening of the downtown Hills Market, it certainly feels like there’s a renewed momentum for the central area of the city.    Perhaps in another ten or so years we’ll look at the census data and see, thanks to present day efforts, that individuals are in fact expressing a preference for urban living in Central Ohio.

However, many of these transit authorities have not yet published their own data for public consumption via their websites and\or mobile applications. In my opinion, this is a wasted opportunity to improve a product that needs improving – public transportation. One such example is from our hometown, Columbus Ohio. The Central Ohio Transit Authority does have a website, but the route maps are all in PDF format and they aren’t easy to read. They do provide access to a Google widget that can help route your trip by displaying the results on a Google map, but there is no comprehensive system map on the site to see ALL of the stops and routes.

For fun, we here at Urban Decision Group decided to build such a map by using the GTFS data as input. This is relatively easy to do if you have the right tools, which we do. After downloading the data and using ESRI’s ArcGIS to convert the data into GIS shapefiles, we were able to create this map application.  It’s nothing fancy…pretty much a straight conversion of the data with a little housekeeping to make the info windows readable.  It probably took a total of 30 minutes from download to completion.  The point is,…if it’s this easy to build a very basic application, then why aren’t more transit authorities doing it?  I must add, I am aware that COTA is planning on giving their site a makeover and quite possibly adding this type of functionality is part of the face-lift (attention COTA, could we also get updates via Twitter please?).  I’m anxious to see what they come up with.

In the interim, I’m begging the rest of the transit authorities that are stuck in 1998 – please give us a better product to get us on the bus (or train)!  This should not be an afterthought – it is indeed a subset of your product offering.  If you ran a business that provided transportation services, wouldn’t you put a fair amount of energy into marketing and advertising to ensure you were getting the information out to not just your customers, but your potential customer….and anybody that lives within a metropolitan region IS a potential customer.

I’m not picking on COTA, they just happen to be my transit authority.  There are plenty of examples of website fails – too many for me to list.  So that’s why I’m asking for your help.  If you are aware of a transit authority that is falling short in the web and mobile department, then let us know via the “comments” section of this post.  Let’s start a discussion and maybe we can use a little peer pressure to spur some change.

This comparative map from I Love Charts via Julia Topaz display’s Nate Silver’s final election prediction vs the final outcome.  It’s an amazing example of how statistics and probability can be used to understand complex human systems in understandable ways.  We also like to think that it shows something else – we believe that, no matter your political affiliation, the most important thing is making sure our voices are heard and dialogue is had.  It’s the only way to get anything done.

-Jenna Silcott
Information Architect
Urban Decision Group

Later that night as I was watching the election results, I heard something that caught my interest.  Some political pundit said, “this just in… Maine’s 4th electoral vote will be for President Obama.”  Wait, what did he say?  As it turns out, both Nebraska and Maine do not follow the “winner take all” electoral college system. Instead, they award an electoral vote to the winner of each congressional district and then an additional two electoral votes to the candidate that wins the popular vote. For example, Maine has 4 total electoral votes and a total of 2 congressional districts. This concept of not using a winner take all system got my mind to thinking – this system gives people in a state with strong political affiliation a chance to influence the election even if they don’t win the popular vote.

So I decided to perform a little exercise with the great state of Ohio. When looking at the voting results by county, the map appears predominately red even though the result of the popular vote favored Obama by 103,519 votes. The race is a lot closer than the 18 electoral votes would indicate.  Although the results by district are available nationwide for the 2004 & 2008 elections it has not been released for the 2012 election as of this writing. So, I took this one step further and attempted to apportion the votes to the boundaries of Ohio’s 16 congressional districts.*  According to this map,  Romney would have won nine of the sixteen electoral votes if the ME (Maine) model was used. Obama would have also won an additional two electoral votes since he won the popular vote in Ohio. This would have yielded a 9 – 9 split of Ohio’s electoral votes.

Data by congressional districts is available nationwide for the 2004 & 2008 elections. The below table summarizes what the results would have been assuming all states awarded electoral votes by congressional districts. Please note the numbers in the district column also include the two additional votes the candidate was awarded for winning the popular vote in that state.

**Won more districts than the candidate that won the popular vote in that state

Although the end results of the election did not change; the margins by which the victorious candidates won did shift. The 2004 election was not nearly as close as it seemed and in 2008, McCain put up a much better fight than the electoral college gave him credit.

While this “new” system seems to improve the importance associated with a single vote, this method of awarding votes is deeply flawed…especially in a state like Ohio.  The biggest problem is district gerrymandering – fabricating strange district boundaries to favor one party over the other. The best way to resolve this would be to create districts using established geographic boundaries in conjunction with Census data. Maine, for example, uses county and township borders.  A similar system would likely need adapted to make this system feasible. That topic will be address by us at another time. Coincidentally, Ohioans voted down a measure to change the State’s constitution to create a board to oversee district boundary creation.

What do you think?  Is there a better way to work within the constructs of the electoral college or is it simply an idea that has outlived its usefulness?

-Matt Hamaide, Senior Consultant, Urban Decision Group (UDG), LLC

Footnotes and References

**In an effort to reapportion the district data from the 2012 county data, I complied a list of all the counties either full or partially within their respective district boundaries. I then added up all the votes reported for each county to get district totals. The first method included all the votes in a partial county even though not all of them would fall within the district. The second time, I only included 50% of the votes of any county that was only partially in the district boundary. I then compared both. If both methods’ totals awarded the district to the same candidate, then that is who was given credit for that district. Only in Districts 5 & 6 did the totals disagree. For both, the district was awarded to the winner of the second method in which only 50% of the total votes of partial counties were included. For example, District 5, the City of Toledo (the bulk of the votes for Lucas County) was not included in the actual boundary of the district. The same was true for District 6 – Youngstown was not included within the boundary (the bulk of the votes for Mahoning County). When considering this, it appears the partial totals were more accurate.

Data and Boundary Sources:
Ohio Secretary of State
New York Times

In 2008, the American Community Survey (ACS) began surveying the U.S. population on the subject of health insurance coverage.  To date, the most complete data set available is the 2008-2010 3 year ACS which excludes counties and cities with less than 20,000 people.  Therefore, it’s not a complete count like the decennial census.

The ACS collects data for this category by age.  Although, health insurance is an important topic for all ages, we wanted to focus on the most vulnerable sector of the population – those under age 18.  The following table contains the state by state tabulations  for the uninsured population under age 18.

The percent of the U.S. population under age 18 that is uninsured is approximately 8.6% (excluding towns and counties with a population under 20,000).  Check out this map which illustrates where the uninsured young people live.  There are some areas that stand out.  Florida, Texas, Nevada, and Arizona have rather significant shares of the uninsured under 18 population.  There are also notable pockets of uninsured children in Ohio, Indiana, and Pennsylvania.  This reflects the concentrations of Amish communities.

It’s estimated that the insured population directly pays an additional $1,017 in health insurance premiums to pay for the health care costs incurred by the uninsured.  But what about the long term ramifications of having so many uninsured children.  Are these children more likely to be unhealthy adults and if so, what is the cost to society?

National health care is a hot topic in America.  Is it a right or a privilege?  What about the long term economic impact of having so many uninsured children.  Are they more likely to become uninsured adults?  Are they more likely to develop health problems at a younger age?  Who pays for all the negative externalities?

I’ve always been fascinated by tornadoes.  They take on many different shapes and sizes and can be quite beautiful.  But tornadoes are serious business.  Researchers and chasers study them relentlessly.  They have their own reality television shows.  The art and science of predicting where and when a tornado will strike has improved greatly since 1974, but there is still much we don’t know about tornadoes.

I’m sure at one time you’ve seen a traditional “Tornado Alley” map or maybe you’ve seen a map of the U.S. counties most likely to get hit with a tornado.   I wanted to create a map that was more detailed than something at the county level.  I wanted to zero in on precise locations where tornadoes have historically occurred because the past is likely to predict the future.

To start, I located some data provided by the National Weather Service (NWS).  They had a GIS file of tornado tracks from 1950-2006.   Information on the intensity (EF scale), the length and width of the track, property and crop loss estimates, as well as fatalities and injuries were included in the file’s attributes.  In order to quantify the impact of a tornado without including biased data,  I chose two variables  – the number of tornadoes and the intensity of each tornado.  Next, I simply laid out an imaginary 10 square mile grid across the U.S. as a geography for aggregating my data.  I chose a 10 square mile grid because it is usually much smaller than a county (on average you can fit 4-5 grid cells within an average sized county).  I counted each tornado that crossed into a grid cell and summed up the EF scale intensity of each tornado (actually, I added a value of 1 to each storm’s EF number to account for storms with an intensity of EF 0 ).  Each of the data values were normalized before computing a final value for each between 0 and 1.

The results of the exercise can be found here in this interactive map.  Based on our methodology, the part of the country most likely to experience a tornado is located on the Oklahoma and Kansas border – specifically, the the northwest corner of Kay County, OK and the southeast corner of Sumner County, KS:

Luckily, this is not a densely populated area.  In fact, less than 500 people live in this particular cell.  However, the Top Ten Tornado Hot Spots include several areas where the population is high:

As you can see, there are several heavily populated corridors that are historically most likely to experience a tornado.  Oklahoma City (OK), Shreveport (LA), Little Rock (AR), and Jackson (MS) are the most heavily populated cities within our computed danger zone.

If we assume that small changes in the climate over time will not result in dramatic shifts of tornadic activity, then we can safely predict that the areas of high tornadic activity in the past will continue to experience intense, long-track tornadoes into the future.  This knowledge should affect things like building design and cityurban design, disaster preparedness, and insurance rates.

We’ll be posting various maps related to this exercise on our Pinterest site over the next couple of weeks.  Check back from time to time to see what we’ve come up with.

Public and private planners not only have a responsibility to help develop disaster plans – they are some of the best equipped to do so.  Large-scale disaster planning requires professionals to think in terms of time and space – two skills planners are required to employ.  Disaster planning also requires knowledge of who you are planning for.

Here are some demographics for the aggregate area of the Contamination Zones (50 mile rings) to give you an idea of the scale of nuclear disaster planning that needs to take place.

Of particular concern are the young and the population that lives in group quarters.  These population bases are likely to require assistance in the event of a disaster.  They may also require special accommodations.  For example, if you had to evacuate a maximum security prison you are going to need a place to move them to AND a staff that is qualified to manage the prisoners.  Another likely scenario requires tending to the elderly that would be evacuated from nursing care facilities.  Hurricane Katrina taught us that it is not enough to have a plan in place – you need to have multiple plans for different scenarios.

FEMA has posted some nuclear disaster preparedness information that is worth reading.  It is important that each household is acquainted with the plan(s).  However, large-scale coordinated planning at the city, county, state, and national level is critical.  This  is where we’ve fallen short in the past (see Hurricane Katrina).  Effective planning (and execution) is largely a function of leadership.  Those in leadership positions should be capable of managing multiple large-scale plans.

If you would like to read more about disaster planning and disaster recovery, check out the American Planning Association’s disaster planning blog.

I’m not advocating for a four-year degree for everyone, but the data is clear and the facts are unavoidable – you are in a substantially better position professionally (and economically) if you have at minimum a Bachelor’s degree.  Of course, this club has some obvious barriers to entry.  The two most obvious are cost and aptitude.  But another potential barrier is location – how far must one travel to attend an institute of higher learning?  Bill told me about an initiative that former Ohio governor James Rhodes had championed several decades ago.  Governor Rhodes wanted every Ohioan to live within 20 miles of a college or university.  That gave me an idea.  I wanted to see where this at-risk population lived in relation to the location of colleges and universities – hence this installment of Urban Decision Group’s Map of the Week series.

Colleges and universities were defined as anything having a NAICS code of 61131009.  The data was extracted from a business database provided by InfoGroup.  I don’t assume 100% accuracy with any third-party data sets, but the data we use from InfoGroup is actually pretty good stuff.  They provided point data geocoded to the address of the institution.  I then established 10-mile rings around each point.  Normally, if I were establishing a trade area, I would never use a simple ring around a point.  But we can get away with it in this case because of the shear volume of points create several areas of overlap.  The 10-mile radius around each college and university represent  areas that we are not concerned about.  The areas we are interested in are everything outside of these rings; they represent population centers that are more than ten miles away from an institution of higher learning.  So I laid out a 10 square mile grid across the U.S.only for those areas that were not within 10 miles of a college or university.  This area represents territory where location could prove to be a barrier to higher education.

The next step was to define what the undereducated at-risk population actually is.  The data was extracted from the American Community Survey (ACS) 2006-2010 data at the county level and ultimately aggregated into the 10 square mile grid cells.  I decided to focus on the age group of 35-64.  People in this age group are generally less mobile than young people.  This group consists of households with children, mortgages, and many other things that prohibit a semi-transient lifestyle.  Then I broke the data into three sets.  The first set consists of those people without a high school diploma.  The second set contains those with no college and just a high school diploma.  The final set was simply the percent of the population that only had a high school diploma.  The logic in choosing this data is that no single data set could define what the at-risk population was, but the combination of the  three would provide a pretty good definition.  Each of the data sets was normalized and a final normal score was calculated for each grid cell.  Normal score values are guaranteed to fall between 0 and 1.  A value trending towards 1 indicates more of the population is at-risk.

When viewed on a map, we can identify the location of the undereducated at-risk population.  If members of this population group were to become unemployed, they are the most at-risk for prolonged periods of unemployment.  You can make the argument that with the ubiquity of the Internet and the rise in online courses available through many colleges and universities, location no longer matters.  This may be true for a small subset of the population but the at-risk population that we identified is less likely to have high-speed internet or even awareness that such opportunities may exist.

Like Urban Decision Group’s previous Maps of the Week, our intent is not only to inform but to inspire.  Decision and policy makers can direct resources more efficiently if they have a clear picture illustrating where they should go.  This week’s map is no exception.  Again, I’m not advocating that everyone in this population group needs a four year degree.  But at minimum everyone should have reasonable access to technical job training and vocational schools.  Education not only benefits those that receive it, but improves the health of the entire economy.  The proof is in the gap between unemployment rates for the educated and the undereducated.

The map uses county data from the 2006-2010 American Community Survey (ACS) and is ultimately aggregated into 10 square mile grid cells.  There were two criteria used in calculating a popular location “score”.  First, we looked at the total number of workers that work from home (telecommuters) in each U.S. county.  Then the data was normalized.  Normalization is the process of ranking the data on a scale of 0 to 1 using the county with the most telecommuters as the base.  The top county gets a score of 1 and all other counties are scored in proportion to the top county.  For example, Los Angeles County, California had 200,450 people working from home; therefore, they received a score of 1 for this category.  Maricopa County, Arizona was second on the list with 88,689 people working from home.  Their normalized score is 0.44 which was calculated by dividing the number of commuters in Marcopa County (88,689) by the top value from Los Angeles County (200,450).  This step was repeated for each county to produce a normalized telecommuting score.

The top ten counties in terms of total number of people working from home are:

1. Los Angeles County, CA – 200,450
2. Maricopa County, AZ – 88,689
3. Cook County, IL – 88,287
4. San Diego County, CA – 86,297
5. Orange County, CA – 66,404
6. Harris County, TX – 57,861
7. King County, WA – 53,621
8. New York County, NY – 52,281
9. Riverside County, CA – 41,753
10. Miami-Dade County, FL – 41,560

The second category we looked at was the number of people working from home as a percentage of all workers in the county.  Analyzing the data in this fashion allows us to pay proper attention to those counties that are not as heavily populated, but yet have a high percentage of workers telecommuting.  The top county in this category is Wheeler County, Nebraska which had 40.45% of their workers working from home.  This data was also normalized.

The counties with the highest percentage of the workforce working from home are:

1. Wheeler County, NE – 40.45%
2. Chattahoochee County, GA – 39.24%
3. Slope County, ND – 38.19%
4. Arthur County, NE – 32.88%
5. Pulaski County, MO – 32.54%
6. Billings County, ND – 30.51%
7. Kidder County, ND – 29.20%
8. Carter County, MT – 28.83%
9. Harding County, SD – 28.11%
10. Loup County, NE – 27.76%

The final score used in our map is  simply the combination of these two scores for each county divided by two.   This allows us to give equal weight to both data categories.  The final top ten counties are thus:

1. Los Angeles County, CA  (normal score = 0.56)
2. Wheeler County, NE (normal score = 0.50)
3. Chatahoochee County, GA (normal score = 0.49)
4. Slope County, ND (normal score = 0.47)
5. Pulaski County, MO (normal score = 0.42)
6. Arthur County, NE (normal score = 0.41)
7. Billings County, ND (normal score = 0.38)
8. Kidder County, ND (normal score = 0.36)
9. Carter County, MT (normal score = 0.36)
10. Harding County, SD (normal score = 0.35)

The final step was to apportion the data into 10 square mile grid cells.  This final step accomplishes a couple of things.  First, it makes it quick and easy to display on a web map.  Second, it ignores political boundaries by considering  data from surrounding counties.  The result is a thematic map that displays the most popular locations for telecommuters.

Urban Decision Group (UDG) is responsible for the creation of this map.