The Kids Aren’t Alright – Uninsured Children in America

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.

		Total Pop. < 18	< 18 Without Health Insurance	Percent Without Health Insurance
Alabama Total		1,071,630	70,772	6.6%
Alaska Total		144,851	16,657	11.5%
Arizona Total		1,624,338	216,476	13.3%
Arkansas Total		593,495	42,116	7.1%
California Total		9,274,650	882,629	9.5%
Colorado Total		1,147,198	126,766	11.1%
Connecticut Total		820,097	31,432	3.8%
Delaware Total		205,913	12,830	6.2%
District of Columbia Total		101,791	3,099	3.0%
Florida Total		3,958,142	592,951	15.0%
Georgia Total		2,295,163	242,291	10.6%
Hawaii Total		301,761	9,466	3.1%
Idaho Total		368,636	39,933	10.8%
Illinois Total		3,005,936	144,781	4.8%
Indiana Total		1,543,436	139,610	9.0%
Iowa Total		548,377	23,805	4.3%
Kansas Total		601,649	46,330	7.7%
Kentucky Total		835,244	52,255	6.3%
Louisiana Total		1,071,213	64,478	6.0%
Maine Total		273,464	14,527	5.3%
Maryland Total		1,353,004	67,091	5.0%
Massachusetts Total		1,415,769	21,783	1.5%
Michigan Total		2,329,562	102,834	4.4%
Minnesota Total		1,187,773	73,817	6.2%
Mississippi Total		655,268	65,091	9.9%
Missouri Total		1,262,025	82,132	6.5%
Montana Total		152,439	16,694	11.0%
Nebraska Total		355,303	21,512	6.1%
Nevada Total		650,492	118,891	18.3%
New Hampshire Total		290,932	14,269	4.9%
New Jersey Total		2,065,677	132,937	6.4%
New Mexico Total		489,603	59,350	12.1%
New York Total		4,331,689	215,694	5.0%
North Carolina Total		2,225,931	188,509	8.5%
North Dakota Total		98,789	4,860	4.9%
Ohio Total		2,720,367	172,157	6.3%
Oklahoma Total		837,486	95,185	11.4%
Oregon Total		851,325	89,707	10.5%
Pennsylvania Total		2,785,173	152,367	5.5%
Rhode Island Total		226,106	12,877	5.7%
South Carolina Total		1,055,142	110,882	10.5%
South Dakota Total		116,218	6,066	5.2%
Tennessee Total		1,399,547	83,749	6.0%
Texas Total		6,499,839	1,039,324	16.0%
Utah Total		818,116	92,042	11.3%
Vermont Total		127,624	3,578	2.8%
Virginia Total		1,706,859	117,666	6.9%
Washington Total		1,550,751	109,517	7.1%
West Virginia Total		331,148	17,270	5.2%
Wisconsin Total		1,286,524	61,897	4.8%
Wyoming Total		103,963	8,676	8.3%
Grand Total		70,620,816	6,105,505	8.6%

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?

Tornado Hot Spots in the U.S.

This week is the anniversary of one of the worst tornado outbreaks in U.S. history.  On April 3-4, 1974, at least 148 tornadoes roared across the United States.  Since then, this has been eclipsed by only the May 21-26, 2011 tornado outbreak.  A tornado is generally the result of cold air diving south into warm moist air while a strong jet stream streaks across the convergence.  This “setup” is unique to the U.S. and, therefore, we are the tornado capital of the world.

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:

Primary County Area	State	2011 Population
NW Kay County, OK/SE Sumner County, KS	OK	466
NE Cullman County, AL	AL	13,407
WC Bossier Parish LA/EC Caddo Parish, LA/E Harrison County, TX	LA	138,159
SC Pulaski County, AR/WC Lonoke County, AR	AR	111,338
EC Simpson County, MS	MS	13,837
EC Hinds County, MS	MS	72,116
SE Thayer County, NE	NE	231
SW Oklahoma County, OK	OK	275,475
EC Cass County, TX	TX	11,230
NE Marlboro County, SC	TX	16,166

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 city\urban 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.

U.S. Nuclear Facilities and Disaster Planning

It’s been over a year since the Fukushima nuclear disaster in Japan.  It was a dark reminder that man-made disasters are sometimes harder to manage because there is often little warning.  It is therefore critical that the population within the Evacuation Zone (10 miles) and the Contamination Zone (50 miles) have plans in place to follow in the event of a disaster.  But the actual areas that would be affected in the event of a meltdown would be determined by the strength and direction of the wind (The National Resource Defense Council did some modeling of this for a U.S.-based Fukushima type disaster.  The results show that in several cases, the fallout plumes extend way beyond the 50 mile Contamination Zone).  Therefore, it is a good idea for most of the U.S. population to have plans in place.  But would you know where to go and what to do if you found yourself in the path of radioactive fallout?

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.

2011 Total Population	120,344,948
2011 Total Households	45,609,967
2010 Pop Age 0-4	7,560,657
2010 Pop Age 5-9	7,687,670
2010 Pop Age 10-14	7,903,607
2010 Pop Age 15-19	8,499,429
2010 Group Quarters (GQ) Pop	3,046,237
GQ – Institutionalized	1,366,304
GQ – Prison	664,487
GQ – Juvenile Detention	56,363
GQ – Nursing Facilities	613,558
GQ – Other Institution	31,896
GQ – Noninstitutionalized	1,679,933
GQ – College Dorms	1,088,388
GQ – Military Quarters	132,555
GQ – Other Noninstitutionalized	458,990
Square Miles	414,654

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.

The Location of the Sweet Sixteen 2012

I am a resident of Ohio.  Every four years Ohioans find themselves the center of the political universe.  It starts out as flattering and ends up just being annoying.  This year we find ourselves at the heart of the 2012 NCAA Basketball Tournament more commonly known as March Madness.  Ohio has placed four schools in the Sweet Sixteen:  Ohio State University, Xavier University, University of Cincinnati, and Ohio University.  The Ohio River Valley has a total of seven teams – the four teams from Ohio plus the University of Kentucky, University of Louisville, and Indiana University.  Other small clusters of power include Tobacco Road (North Carolina and North Carolina State) and Southern Wisconsin (University of Wisconsin and Marquette University).  See Map Here!

I don’t think there is any powerful basketball inference you can make regarding the location of these schools.  However, nobody is more concerned about the location of these schools than CBS Sports because this could be a ratings black hole.  Baylor (Waco, TX) and Kansas (Lawrence, KS) are the westernmost schools in the Sweet Sixteen.  Syracuse is the closest school to the largest media market in the U.S. – New York City.

History has shown that the higher seeds bring in higher ratings.  Therefore, we can assume that CBS is rooting against the likes of Ohio University, NC State, Xavier and UC.  So while it may be exciting for us Ohioans to have four teams represented in this year’s Sweet Sixteen, CBS wants the madness to end no later than Friday evening.  One thing is for sure, there will be at least one less Ohio team after the next round – Ohio State plays Cincinnati in the East Region Semifinal in Boston on Thursday night.

Advice from a Retail Expert (Mistakes Planners Make when Creating Retail Districts)

* The following excerpt appeared in the March 2012 issue of Planning magazine; published by the American Planning Association.

“Creating successful urban retail districts is a goal of planners and community leaders alike. But as Robert J. Gibbs points out in Principles of Urban Retail Planning and Development(2012; Wiley; 272 pp.; $80), planners may be hampered in that task by an overly romantic view of an ideal shopping area. Even in the best planned new urbanist developments, he points out, retail components often fail to live up to expectations.

….(Gibbs) explodes various myths about what makes a successful retail district and lists some of the common mistakes made by planners, business owners, and community leaders — failing to begin a project with a professional market analysis, for instance. He shies away from easy answers. While clearly in favor of the walkable retail districts that planners typically espouse, for instance, he concedes that they don’t always succeed financially.

Gibbs includes plenty of useful information on specifics such as parking. His book will be most useful to private-sector planners and those who work with public-private partnerships. But the material it contains will also be helpful to public planners dealing with zoning issues. — Ryan Smith”

At one point in my life I worked for a real estate market analysis firm where I learned the value of conducting a market analysis for planning and development purposes.  Today my background in GIS and Urban Planning provides me with an unique perspective on the concept of the market analysis.  I believe that a traditional market analysis is unnecessary to planners creating new spaces or rehabbing existing ones.  The traditional market analysis is way more detailed than what a planning professional requires.  Planners need to know only two things:  1.  Is there a market and 2. how “much” should we plan for? GIS is the perfect tool for this analysis.

Urban Decision Group has been fine tuning this very analysis into a service we call “Planning Analytics“.  Planning Analytics is specifically designed for informing  comprehensive or land-use plans.  Like a traditional market analysis, there is some field observation involved but not nearly to the extent that the traditional market analysis employs.  Rather, our service focuses on a data-driven GIS model to produce predictive analytics via established methods such as Huff Modeling.  It is also much less expensive than a traditional market analysis.

The Planning Analytics service is less expensive because of its intended audience.  The audience for a traditional market analysis generally consists of developers and  financiers.  That group is looking for very specific price points, rents, and lease rates for defined product types like town homes or 2 bedroom apartments.  The planning audience, on the other hand, mainly requires the larger picture.  They need to  know if a project has a  chance at being successful (is there a market?), how much space should be allocated, what infrastructure improvements will be necessary, etc.  Two different audiences require two difference approaches.

So if you’re a city, county, region or state that is engaging in some sort of “district” planning, I agree with the letter writer above.  Do you your homework first.  It’s a nominal portion of the project cost that can literally save you millions on the back end.

If you would like more information on Planning Analytics and you live in North America, contact Urban Decision Group at 614-383-8447 or email Rick Stein at rstein@urbandecisiongroup.com.  If you live in the Columbus, Ohio area, he might even take you to lunch.

Location of the Undereducated At-risk Population

Several days ago I was discussing the link between education and unemployment with my economist friend Bill Lafayette, PhD.  The seemingly endless Republican Primary had recently thrust higher education into the national spotlight.  At issue was whether or not we should always encourage people to seek higher education.  This is political season in the U.S.and issues like this become cloudy and distorted to the point they are unrecognizable.  But the timing of the discussion was interesting.  The Bureau of Labor Statistics (BLS)  just released the most recent unemployment statistics  showing that the national unemployment rate for those without a high school degree was 12.9% while the national unemployment rate for those with at least a Bachelor’s degree was 4.2%.  The difference between these unemployment rates during the current U.S. recession has been consistently between 8 and 10%.

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.

Most Popular Locations for Telecommuters

This week’s Map of the Week is the third in Urban Decision Group’s series of maps that examine commuting in the U.S.  Our first map dealt with Average Commuting Times in the U.S.  Last week’s map showed the Impact on Wages When Factoring in Commuting.  This week we decided to take a look at the Most Popular Locations for Working From Home.

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.