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.

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.

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.