Concentric Circles
DO NOT use simple concentric circles. Concentric circles (sometimes referred to as radials) ignore all the important information about any given area such as physical and psychological barriers, real travel time, and the area’s socioeconomic character. This method may be appropriate as a guide or a starting point, but it should never be used as the final market area for a project.

example of concentric circles

Drive Time Polygons
Drive Times are best expressed as polygons rather than concentric circles because polygons calculate the real travel time required to move from one point to another using actual road infrastructure. Drive Time polygons are a great starting point for a project’s market area delineation. It should be noted, however, that Drive Times ignore walking and users of public transportation, which could be problematic depending on the project. Drive times also do not reflect consumer preferences or psychological barriers.

example of drive time polygons

Census Tracts
Census Tracts, although critical to a proper analysis, are also not the best way to delineate a final market area. Census Tracts are a creation of the U.S. Census Bureau, and their sole purpose of is to make it easy for the U.S. Census Bureau to organize information: Tracts consist of several Census Block Groups which are an aggregation of individual Block Points. Block Points are nothing more than actual city and/or country blocks. Therefore, market areas created by simply aggregating entire Census Tracts are equally likely to include irrelevant areas as well as exclude relevant ones because they do not take any of the factors (barries, population density, etc.) that impact a market area into account. This ultimately results in an inaccurate market area that, much like a simple concentric circle, overstates or understates the true socioeconomic conditions that exist within the actual market area.

example of census tracts

Hand Drawn Market Areas
By far, hand drawn market areas are the best way to delineate a project’s market area. Hand trade areas rely on multiple sources of information to establish boundaries. These might include interviews with local stakeholders, thematic demographic maps that visually display socioeconomic character down to the Block Group level, or oversetting thematic data with drive time polygons. This is important because a useful market area isn’t bound by arbitrary political boundaries; it should be based on all of the information available.

example of a hand drawn market area

Once you’ve delineated the final market area, then you must spatially gather the data associated with the people, housing units and businesses in order to build a profile of exactly “what” the trade area contains. It is during this step that planners, developers and analysts sometimes make the mistake of choosing the wrong tools to do the job. The only proper tool for apportioning data to a market area is a Geographic Information System (GIS). Without a properly outfitted GIS, spatial data is going to be miscounted and miscalculated. Here’s why: This map shows a close-up of a market area boundary (red) and a Census Tract (black) that is bisected by the market area. The area to the right of the red boundary is INSIDE the market area. The area to the left of the red boundary is OUTSIDE of the market area.

Census Tracts bisected by a market area boundary

Let’s assume you have data from the U.S. Census Bureau for this particular Tract. How would you go about the process of determining the number of households within the market area? A lot of guesswork could be involved. For example, visually it seems that 70% of the Tract is within the market area and 30% is outside. It stands to reason, therefore, that 70% of the households must be within the market area and 30% must be outside. It sounds simple, but let’s look at the actual numbers. Using real Census data, we know the total number of households within this Tract is 2,300 Therefore, we are estimating that there are 1,610 (2,300 x 70%) households within this single Tract that reside within the market area. However, if we use a GIS to do this calculation, we find out the actual number of households within the market area is 1,343. That means we over counted this single Tract by 267 households. The typical market area cuts through 20 or more Census Tracts (depending on the type of project and the density of the population). That means there are at least 20 opportunities for estimation and calculation errors from manually assembling this market area data. The difference between the estimation and the actual number has real consequences: at this level, bad information could either potentially provide support for a project that should not be supported OR dissuade a project that is actually viable. In sum, there is a lot of money at stake when apportioning data to a market area. If you don’t use a GIS to apportion the data for you, then miscalculations will assuredly occur, effectively wasting many people’s time and money.

So why does a GIS do a better job of apportioning data to a market area? A GIS is capable of accurately apportioning population, household, housing unit and business data because it uses the location of Census Block Points to determine exactly how many people, households, housing units, or businesses, are within a market area’s boundaries. Let’s look at the zoomed-in map again.

Census Block Points in relation to the market area

The dots you see on the map are proportional symbols that represent the actual physical location of Census Block Points. Block Points contain four types of information for each city/country block that it represents – total population, total households, total housing units, and the total number of businesses. Virtually all Census data (and by extension, third-party demographic data) is associated with one of these “Universes” (that’s U.S. Census lingo). Let’s use the same example market area and Census Tract that we just looked at. A GIS will examine all of the Block Points that reside within the market area and will calculate the actual percent of population, households, housing units and businesses that reside within the market area. Those percentages can then be used to apportion all the data associated with that tract. In other words, we can now accurately determine the precise levels of all data elements within the market area. A GIS can also calculate things like median income for an entire trade area without you ever having to type in a bunch of numbers into a spreadsheet and apply a bunch of assumptions.

A properly outfitted GIS is without a doubt, the most effective tool available for market area delineation and more importantly, for data apportionment to the market area. There is a lot riding on your project(s). Shouldn’t you be using the right tools for the job?

Rick Stein is Principal & Owner of Urban Decision Group (UDG).  He is a trained urban planner, GIS expert, and software developer.

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.

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’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.