“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”

Several of us here at UDG, have at one point in our lives, worked for real estate market analysts (in fact, we have several current clients that are in this line of business).  This is where we learned the value of conducting a market analysis for planning and development purposes.  Our backgrounds in GIS and Urban Planning provide us with a unique perspective on the concept of the market analysis.  We have seen more than our fair share of good and bad examples of market analysis.  If you are a city conducting a land use or comprehensive plan, it is in your best interest to include market analysis as part of the planning process.  In addition, you should thoroughly vet the analyst to make sure they understand what the goals and objectives of the plan are.  Traditional, boilerplate market analysis is not going to suffice.  Cities, and the spaces within a city, are unique.   The market analyst must be willing to approach their task as part of the entire planning team, which means they must be engaged in the process from start to finish.

Traditional market analysis does not address the goals of a land use or comprehensive plan.  There are two basic questions planners need to answer with respect to the market analysis:  1.  Is there a market and 2. how “much” should we plan for? Further, planners (and the public in general) may ask questions regarding “what it takes” to achieve the critical mass required to achieve the desired results.  For example, “how many households do we need to add, at varying income levels, to achieve the critical mass required to support a medium-sized grocery store?”

Geographic Information Systems (GIS) are the perfect tool for conducting this type of analysis and far too few analysts invest the time and money to employ a robust GIS to help them answer these spatial questions.  A GIS makes it much easier to visualize the current conditions as well as visualize future conditions – which is at the heart of the concept of planning.

Urban Decision Group has been fine tuning this very type of analysis into a service we call “Planning Market Analytics“.  Planning Market Analytics is specifically designed for informing  comprehensive or land-use plans.  Like a traditional market analysis, field observations are required but the observations must be targeted and focused on the goals at hand.  Our service focuses on a data-driven GIS model to produce predictive analytics via established methods such as Huff Modeling.

The Planning Market Analytics service is usually 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, is focused on 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 city or regional 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 Market Analytics and you live in North America, contact Urban Decision Group at 614-383-8447 or email Rick Stein at rstein at urbandecisiongroup.com.

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.

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.

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.

Last week we calculated the average commuting time in minutes per trip.  From this we can calculate the the average number of hours a worker spends commuting per week regardless of the mode of transportation.  We then used two ACS datasets to compute the average hourly wages for a worker that lives within each grid cell (a 20 square mile area).  The  average hourly wage computation is based on a 40 hour work week and 48 weeks of work per year-this assumption factors in holidays and 2 weeks of vacation per year.  We then computed a new hourly wage by adding the number of hours spent commuting per week to 40.  In other words, if the average worker spends 3 hours per week commuting, then they are actually “working” 43 hours per week.  The base hourly wage is then subtracted from the “new” hourly wage (which factors in time spent commuting) to arrive at the decrease in hourly wages after factoring in the time spent commuting.

If you click on any cell in this week’s map, it will display the amount of dollars you can subtract from the average worker’s hourly wage to derive the actual hourly wage after considering the average worker’s commute time.  Why is this important?  It’s important because it considers things like negative externalities and opportunity costs.  The time spent commuting to a job is time that the worker could be spending with friends or family, working on another job, etc.  In other words, a worker may want to consider a lower paying job if the job is closer to hisher home.  This particular exercise is exclusive of gas prices, which is a whole other matter.

Keep in mind, the grid cells represent the location of the worker’s residence, not the location of employment.  The location of employment has already been factored in when we computed the time spent commuting.  When you click on a cell, the field named CHGINWAGES contains the dollar amount you should subtract from the worker’s hourly wage.  If you want to compute your actual hourly wage you first need to compute your base hourly wage by taking your salary, divide it by 40, and then divide that by 48.  Then locate the grid cell that represents your residence, click on it, and then subtract the amount displayed from your hourly wage.