“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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

UDG has been thrilled to work with Oakstone Academy to provide a unique internship opportunity that challenges the creative limits of their students. Two Oakstone high school students have been working at our main office with Urban Decision Group’s principal, Rick Stein since September. Each week, Kyle and Robby –along with their supervisor, Jill McQuaid–are given a new task dealing directly with urban planning and/or geographic information systems (GIS). To date, the students have been exposed to the wonderful world of U.S. Census data, Google PublicTransit Data, and regional bicycle transportation networks.

Oakstone interns and their supervisor diligently download data.

We’re writing this blog post to highlight our latest and greatest interns, Kyle and Robby. They’ve done a fantastic job and Urban Decision Group is lucky to have them. Moreover, this is something we believe in. Kyle and Robby, as you’ll see below, are smart, talented kids, and they deserve the opportunity to challenge themselves in a work environment. There are special schools similar to Oakstone all over the country – please consider reaching out to one in your area to set up a similar internship program.

And, without further ado, our interns and their supervisor:

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Kyle’s perspective

When I was first introduced to the idea of working at Urban Decision Group, I was skeptical. My first response was “no I’m not interested,” but when the opportunity was further explained I started to consider it. I may have a stubborn personality, but nonetheless I attempted what I originally thought wouldn’t interest me. Eventually I had given the job a chance and, before I knew it, I had a change of heart.

The process which my boss Rick Stein explained the procedures was very involved. He explained his goals and methods very thoroughly. This is very important for anyone who manages employees and sets the tone for the vital exchange of communication with employee and employer. With that stated, I never felt uncomfortable asking questions or for help. He’s a really good man with a good sense of what should be done. For example, he created his own map of bus routes from the COTA (Central Ohio Transit Authority) system which COTA didn’t have and it only took him around 30 minutes to lay it out. He really is helping people and the community.

“How does this job compare to the previous jobs you’ve held?”

During my hours at a national pet store, my previous job, I first thought it was within my interest area to deal with the animals and such. That soon changed after adjusting to the usual shift at maintenance. It was mainly the cleaning I didn’t enjoy. But here at Urban Decision Group, I work with what I’m used to as a hobby: with technology, files, computers, and the web.

Also, this job is more relaxed with very little noise or commotion. Unlike the pet store, I only need to interact with a few people in person and, if needed, I could work within the comfort of my home. Also, I’ve got more to offer than just cleaning up after animals. This gets my creative juices flowing and keeps my brain stimulated instead of mindless labor.

“What aspects of GIS and/or Urban Planning do you find interesting?”

GIS and Urban Planning are not really within my areas of interest. However, I do think it’s vital to my learning experience through their use of technology in a job environment. I feel confident at this job and it seems to open doors to similar areas within my interest. It’s mainly the cause that I’m working for – maybe if I was working with the same environment, or just different data on a project I that can directly relate to, then that would be nice.

Examples of data I can relate to include creating banners for websites, posters, digital design and, if it’s working with files, I would prefer it be pertaining to a server, or game, or gaming servers like I’m doing now on my spare time.

The Google Earth application really caught my eye because of its vast complexity and astounding features along with satellite photos.

“Please describe your dream job.”

My dream job would probably not be too different from the environment which I’m in today.
The work environment would consist of a relaxed environment with the option to work within my home and still have an office I can go to – similar to a “homework style” work setting. I would always have a supervisor who I may ask for help or ask questions. I would be working from 9:00 AM to 5:00 PM Monday-Friday. Preferably within 30 minutes from my house by car.

My comfort level strongly needs a supervisor, or structured environment, but at the same time I’d like the options above. The purpose of my job (or company goal) would be supporting a game company/content and/or a graphic arts design requests as a profitable hobby.

Robby’s perspective

I had a lack of confidence when first starting this internship. I beat myself up over the judgment of other people and over-thought things a lot, which lead me to think that I’m inferior to others’ standards. The day that my internship started I knew I would try my best and learn what I could. Another thing I fear is the unknown. I rely on logic and things I know that I can prove to be true since I like things to make sense. After the first day of interning, I realized that I had lots of potential to do this job. Sometimes I doubt my abilities and degrade myself into thinking that I can’t do things the correct way. I learned some things that I never knew and have a passion for this internship. The things I learned along the way were how to download transit data, Census tracts, use Google Earth to plot out buildings for a retirement home in New York, and how to collect data on bicycle paths in networks. I feel like I’m putting a lot of effort into helping a greater cause. I realized that you must have faith in your abilities or it will weigh you down in the end and prevent you from being successful in life. I feel like you have to give things a chance to see if it’s right for you. When I was interviewed, I came with the mindset that I was going to hate this internship and it wasn’t for me. The reason I felt that way was because I had a lot of information fed to me at once. I have limits on the amount of information given to me; I tend to filter out jargon if the information is too much.

“How does this job compare to the previous jobs you’ve held?”

This job is ranked second in jobs I liked, the first being at an aquarium store since I felt comfortable at that internship and I seemed to open up. I like order to things. If things are abstract, then I tend to panic (mostly on the inside so no one knows that I feel like that) so I’m not considered rude.

“What aspects of GIS and/or Urban Planning do you find interesting?”

I have mixed feelings. I liked some of it and hate some of it. I find some of it boring and repetitive -downloading Census data for all 50 states and searching for pictures pertaining to bicycles can get old fast. I did manage to learn different file types such as png, jpeg, kmz, kml, and other file types that have odd suffixes. I feel that this job is within my field of interest if there is anything art involved or includes coding in languages like SQL and XML.

“Please describe your dream job.”

My dream job would be working with big gaming companies like Valve, Steam, Blizzard, Capcom, or a few others. I’m a huge gamer and take great passion in such. If you were to sit down with me in a room and talk about games you would be there for two days. I have a vast collection of video games and consoles dating back to the NES. I’ve always wanted to make a video game. I want the player feel what the characters feel and make like you live his or her life and struggles that he or she has to overcome. I love games that tell a story and make you feel like that world is real, and I also love a good plot within a game -something that could be compared to a novel. I feel that this internship is one of the many steps to becoming a game developer.

Jill McQuaid’s take

In my years at Oakstone Academy, I have assisted dozens of high school students in community-based internships aimed at helping them advance their cognitive, social and behavioral skills. While students at Oakstone Academy, these young people with disabilities have been immersed into an inclusive education setting for years as they have worked alongside their typically developing peers. It has been my passion to secure them with internship experiences during their transitional years that are equally inclusive in nature.

The students have spent many hours in a classroom learning the skills necessary for successful employment. When given a chance to apply these skills to a real-life work experience, the students have become empowered to understand the impact of their abilities to a real world situation. The work these two young boys are doing with Rick at Urban Decision Group has given them a place to come and realize what they are capable of offering to the work force.

Robby and Kyle were both very nervous about starting at Urban Decision Group. This work site has helped them step outside their comfort zone and work through the anxiety of new challenging expectations. The nature of this internship gives these students a chance to see a big project as a whole, and then learn from Rick as he breaks their assignments down into smaller segments to make them more manageable. They have been taught this strategy for years in school and now they can see it applied to real life. It is my hope that they will be able to relate to their experiences here to more effectively handle future situations with confidence.

Overall, this internship has taught the boys the meaning of self-advocacy, honesty and respect. They have learned to confidently ask questions when they don’t understand or when they feel overwhelmed with a situation. Rick’s responses are understandable and concise, complete and to the point, which is how our students learn best. This internship has offered them an experience that does not pass judgment on what challenges them socially, intellectually and emotionally. We are thankful that Urban Decision Group has opened their doors to our students here at Oakstone Academy!

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And there you have it. Thanks again, Jill for bringing us these great interns. Thanks to Kyle and Robby for their brutal honesty and hard work (is downloading Census data really that boring?). We’re really looking forward to continuing this relationship, and would also like to encourage other firms, big and small, to look into opening their offices for similar internship programs.

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.