Seattle Sustainable Neighborhoods Assessment Project

This was the first session of the conference that really impressed me.  Peter Steinbrueck and Michaela Winter from Steinbrueck Urban Strategies, reviewed a project the City of Seattle had commissioned as part of the update of their comprehensive plan.  They identified 22 indicators (from four categories) and gathered data associated with the “levels” of each indicator.  The end game was to measure the achievements between the years 1994 (when the plan was written) and 2014 (when the plan was updated).  I found this interesting because we have worked on similar projects ourselves but we tend to approach it a little differently.  We heavily utilize GIS when we work on projects like this.  GIS provides:  a framework for organizing the data, tools for sustainability analysis, and a platform for communicating the results to the client and the public.  I spoke briefly with Mr. Steinbueck and Ms. Winter after the session about their approach.  We discussed the importance of projects such as this and the need for internal changes within planning departments that result in indicator data monitoring and collection – and ultimately analysis and feedback into the system in an effort to direct resources towards those issues that are not meeting sustainability (performance) standards.  Really good stuff.  You can check it out for yourself by searching for it on seattle.gov and you can click the link for it here.

Fostering Historic Preservation in Smaller Communities

This is a session I found myself in by accident, but I’m glad it happened.  The session featured:

1. Paul Ellis, AICP -Director of Community & Economic Development for the City of Columbia, Illinois (map)
2. Richard Seplar, AICP – Director of the Planning and Community Development Department of the City of Bellingham, Washington (map)
3. Catherine Powers, AICP -Planning and Sustainability Director for the City of Franklin, Tennessee (map)

What I liked about this session was the linkage the speakers established early on between the act of nurturing real, authentic places and sustained economic growth.  Towns that have historic structures in need of preservation are incredibly lucky.  Investments in the past tend to have longer, more sustained payouts than most new construction, whether it be housing or commercial.

Background

The Brice-Tussing area of Columbus was once a vibrant and viable retail district on the far east side of the city.  Over the years, retail activity shifted even further east and suddenly the area found itself out of favor with retailers of all sizes.  The preponderance of big box retail made the decline even more noticeable when preferences began to shift.

The Task

Tired of watching the Brice-Tussing area languish, the City engaged an enterprising team of consultants to study the area and identify opportunities for redevelopment.  During the study process, the team met with a variety of stakeholders including several local area commissions, residents, commercial realtors, local business leaders and potential investors.  Data was poured over and parcels were scrutinized for highest and best use as well as optimal land use and zoning.

Info

The plan is currently in the final stages of development.  In the interim, here is a page dedicated to the project and maintained by the City of Columbus.  Here you can read a midterm draft of the plan as well as view several display boards that were generated for the various open houses.

“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.

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.

Public and private planners not only have a responsibility to help develop disaster plans – they are some of the best equipped to do so.  Large-scale disaster planning requires professionals to think in terms of time and space – two skills planners are required to employ.  Disaster planning also requires knowledge of who you are planning for.

Here are some demographics for the aggregate area of the Contamination Zones (50 mile rings) to give you an idea of the scale of nuclear disaster planning that needs to take place.

Of particular concern are the young and the population that lives in group quarters.  These population bases are likely to require assistance in the event of a disaster.  They may also require special accommodations.  For example, if you had to evacuate a maximum security prison you are going to need a place to move them to AND a staff that is qualified to manage the prisoners.  Another likely scenario requires tending to the elderly that would be evacuated from nursing care facilities.  Hurricane Katrina taught us that it is not enough to have a plan in place – you need to have multiple plans for different scenarios.

FEMA has posted some nuclear disaster preparedness information that is worth reading.  It is important that each household is acquainted with the plan(s).  However, large-scale coordinated planning at the city, county, state, and national level is critical.  This  is where we’ve fallen short in the past (see Hurricane Katrina).  Effective planning (and execution) is largely a function of leadership.  Those in leadership positions should be capable of managing multiple large-scale plans.

If you would like to read more about disaster planning and disaster recovery, check out the American Planning Association’s disaster planning blog.

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.