As the plan develops, this blog will provide information related to the project.  You can also follow and participate in the planning process by visiting the following sites.

Important Links & Related Sites

www.westfranklinton.com – This site is serving as the primary information collection & dissemination portal for the duration of the project.  The software that is powering the site is provided by MindMixer.  The topics and information that is disseminated through this site, will evolve as the actual planning project evolves.

West Franklinton Facebook Page – West Franklinton has a Facebook page that will also provide a place to keep folks informed on the planning process.  Links to project documents and project photos can be found on this site.

@WFplan (Twitter) - this is the Twitter account for the West Franklinton Plan.  Follow it and you will never be out of the loop!

Project Timeline

October 2013 – The consultant team begins meeting with the Staff Working group (planners from the City of Columbus, recreation & parks, housing, mayor’s office, and others).  Later that month, the team conducts their first meeting with the Community Working Group, a group of leaders from Franklinton comprised of members of the Franklinton Development Association (FDA), Gladden Community House, Mount Carmel, to name a few.

November 2013 – The consultant team begins conducting stakeholder interviews and collecting “existing conditions” data.  Each parcel within the neighborhood is surveyed and vacancies are documented.  The commercial (retail) market analysis begins.

December 2013 – Stakeholder interviews continue as does the data collection and analysis for the existing conditions.  The housing market analysis begins.

January 2014 – The project’s website (www.westfranklinton.com) goes live.  The public information gathering begins in earnest.  Existing conditions data gathering concludes.  The market analyses (housing, commercial, office, and industrial) concludes.  The planning team meets with the Staff Working Group and the Community Working Group.  Both groups are briefed on the results of the existing conditions report and the market analyses.  Later that month, the first public workshop is held.

Public Workshop #1

On January 28, 2014, the first West Franklinton planning Public Workshop was held at the Gladden Community House.  Even though it was the coldest night in decades, almost 100 hearty souls turned out to provide their input and mingle with others that were interested in participating in the planning of Franklinton’s future.  NBC 4 in Columbus even came out to document the event with a nice video piece with an accompanying web article.

One of the exercises that the public was encouraged to partake in was a mapping exercise where four location-specific questions were posed to participants.  The results of that exercise were as follows:

1. Question:  What one place would make the neighborhood better if it was dramatically different from how it is today?  Please indicate on the map.
   Answer:  Click here to see the map
2. Question:  What do you consider to be the “heart” of West Franklinton?  Please indicate on the map.
   Answer:  Click here to see the map
3. Question:  What is the one place that a visitor to Franklinton should see?  Please indicate on the map.
   Answer:  Click here to see the map
4. Question:  If you could create a new neighborhood park in West Franklinton, where would you build it  Please indicate on the map.
   Answer:  Click here to see the map

February 2014 – The “plan development” phase begins.  This is when the rubber meets the road.  New topics are added to the westfranklinton.com site.  The new topics reflect the progression of the planning process.  The team begins working with the City and the Community Working Group to craft a comprehensive vacant housing & housing development strategy.  Late in the month, the team meets with the Community Working Group to initiate the plan development phase.  Discussion topics include improving community outreach for the next public workshop (scheduled for the end of April).

April 2014 – The 2nd community workshop is scheduled for Wednesday, April 30 from 5-7 PM.  The official press release can be found on westfranklinton.com (in the “about” section) OR you can link to it here.

October 2014 – The West Franklinton Plan has been completed.  You can download the plan here.  The plan is very progressive in its approach; emphasizing connectivity and public open space (parks) enhancements as property becomes available.  The flexibility built into the plan allows for the development of these features in a variety of places throughout West Franklinton.

November 2014 – The West Franklinton Plan has been adopted by Columbus City Council.  You can learn more about the Plan by checking out the City of Columbus’ website.

“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.

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.

Later that night as I was watching the election results, I heard something that caught my interest.  Some political pundit said, “this just in… Maine’s 4th electoral vote will be for President Obama.”  Wait, what did he say?  As it turns out, both Nebraska and Maine do not follow the “winner take all” electoral college system. Instead, they award an electoral vote to the winner of each congressional district and then an additional two electoral votes to the candidate that wins the popular vote. For example, Maine has 4 total electoral votes and a total of 2 congressional districts. This concept of not using a winner take all system got my mind to thinking – this system gives people in a state with strong political affiliation a chance to influence the election even if they don’t win the popular vote.

So I decided to perform a little exercise with the great state of Ohio. When looking at the voting results by county, the map appears predominately red even though the result of the popular vote favored Obama by 103,519 votes. The race is a lot closer than the 18 electoral votes would indicate.  Although the results by district are available nationwide for the 2004 & 2008 elections it has not been released for the 2012 election as of this writing. So, I took this one step further and attempted to apportion the votes to the boundaries of Ohio’s 16 congressional districts.*  According to this map,  Romney would have won nine of the sixteen electoral votes if the ME (Maine) model was used. Obama would have also won an additional two electoral votes since he won the popular vote in Ohio. This would have yielded a 9 – 9 split of Ohio’s electoral votes.

Data by congressional districts is available nationwide for the 2004 & 2008 elections. The below table summarizes what the results would have been assuming all states awarded electoral votes by congressional districts. Please note the numbers in the district column also include the two additional votes the candidate was awarded for winning the popular vote in that state.

**Won more districts than the candidate that won the popular vote in that state

Although the end results of the election did not change; the margins by which the victorious candidates won did shift. The 2004 election was not nearly as close as it seemed and in 2008, McCain put up a much better fight than the electoral college gave him credit.

While this “new” system seems to improve the importance associated with a single vote, this method of awarding votes is deeply flawed…especially in a state like Ohio.  The biggest problem is district gerrymandering – fabricating strange district boundaries to favor one party over the other. The best way to resolve this would be to create districts using established geographic boundaries in conjunction with Census data. Maine, for example, uses county and township borders.  A similar system would likely need adapted to make this system feasible. That topic will be address by us at another time. Coincidentally, Ohioans voted down a measure to change the State’s constitution to create a board to oversee district boundary creation.

What do you think?  Is there a better way to work within the constructs of the electoral college or is it simply an idea that has outlived its usefulness?

-Matt Hamaide, Senior Consultant, Urban Decision Group (UDG), LLC

Footnotes and References

**In an effort to reapportion the district data from the 2012 county data, I complied a list of all the counties either full or partially within their respective district boundaries. I then added up all the votes reported for each county to get district totals. The first method included all the votes in a partial county even though not all of them would fall within the district. The second time, I only included 50% of the votes of any county that was only partially in the district boundary. I then compared both. If both methods’ totals awarded the district to the same candidate, then that is who was given credit for that district. Only in Districts 5 & 6 did the totals disagree. For both, the district was awarded to the winner of the second method in which only 50% of the total votes of partial counties were included. For example, District 5, the City of Toledo (the bulk of the votes for Lucas County) was not included in the actual boundary of the district. The same was true for District 6 – Youngstown was not included within the boundary (the bulk of the votes for Mahoning County). When considering this, it appears the partial totals were more accurate.

Data and Boundary Sources:
Ohio Secretary of State
New York Times

I don’t think there is any powerful basketball inference you can make regarding the location of these schools.  However, nobody is more concerned about the location of these schools than CBS Sports because this could be a ratings black hole.  Baylor (Waco, TX) and Kansas (Lawrence, KS) are the westernmost schools in the Sweet Sixteen.  Syracuse is the closest school to the largest media market in the U.S. – New York City.

History has shown that the higher seeds bring in higher ratings.  Therefore, we can assume that CBS is rooting against the likes of Ohio University, NC State, Xavier and UC.  So while it may be exciting for us Ohioans to have four teams represented in this year’s Sweet Sixteen, CBS wants the madness to end no later than Friday evening.  One thing is for sure, there will be at least one less Ohio team after the next round – Ohio State plays Cincinnati in the East Region Semifinal in Boston on Thursday night.

I’m not advocating for a four-year degree for everyone, but the data is clear and the facts are unavoidable – you are in a substantially better position professionally (and economically) if you have at minimum a Bachelor’s degree.  Of course, this club has some obvious barriers to entry.  The two most obvious are cost and aptitude.  But another potential barrier is location – how far must one travel to attend an institute of higher learning?  Bill told me about an initiative that former Ohio governor James Rhodes had championed several decades ago.  Governor Rhodes wanted every Ohioan to live within 20 miles of a college or university.  That gave me an idea.  I wanted to see where this at-risk population lived in relation to the location of colleges and universities – hence this installment of Urban Decision Group’s Map of the Week series.

Colleges and universities were defined as anything having a NAICS code of 61131009.  The data was extracted from a business database provided by InfoGroup.  I don’t assume 100% accuracy with any third-party data sets, but the data we use from InfoGroup is actually pretty good stuff.  They provided point data geocoded to the address of the institution.  I then established 10-mile rings around each point.  Normally, if I were establishing a trade area, I would never use a simple ring around a point.  But we can get away with it in this case because of the shear volume of points create several areas of overlap.  The 10-mile radius around each college and university represent  areas that we are not concerned about.  The areas we are interested in are everything outside of these rings; they represent population centers that are more than ten miles away from an institution of higher learning.  So I laid out a 10 square mile grid across the U.S.only for those areas that were not within 10 miles of a college or university.  This area represents territory where location could prove to be a barrier to higher education.

The next step was to define what the undereducated at-risk population actually is.  The data was extracted from the American Community Survey (ACS) 2006-2010 data at the county level and ultimately aggregated into the 10 square mile grid cells.  I decided to focus on the age group of 35-64.  People in this age group are generally less mobile than young people.  This group consists of households with children, mortgages, and many other things that prohibit a semi-transient lifestyle.  Then I broke the data into three sets.  The first set consists of those people without a high school diploma.  The second set contains those with no college and just a high school diploma.  The final set was simply the percent of the population that only had a high school diploma.  The logic in choosing this data is that no single data set could define what the at-risk population was, but the combination of the  three would provide a pretty good definition.  Each of the data sets was normalized and a final normal score was calculated for each grid cell.  Normal score values are guaranteed to fall between 0 and 1.  A value trending towards 1 indicates more of the population is at-risk.

When viewed on a map, we can identify the location of the undereducated at-risk population.  If members of this population group were to become unemployed, they are the most at-risk for prolonged periods of unemployment.  You can make the argument that with the ubiquity of the Internet and the rise in online courses available through many colleges and universities, location no longer matters.  This may be true for a small subset of the population but the at-risk population that we identified is less likely to have high-speed internet or even awareness that such opportunities may exist.

Like Urban Decision Group’s previous Maps of the Week, our intent is not only to inform but to inspire.  Decision and policy makers can direct resources more efficiently if they have a clear picture illustrating where they should go.  This week’s map is no exception.  Again, I’m not advocating that everyone in this population group needs a four year degree.  But at minimum everyone should have reasonable access to technical job training and vocational schools.  Education not only benefits those that receive it, but improves the health of the entire economy.  The proof is in the gap between unemployment rates for the educated and the undereducated.

The map uses county data from the 2006-2010 American Community Survey (ACS) and is ultimately aggregated into 10 square mile grid cells.  There were two criteria used in calculating a popular location “score”.  First, we looked at the total number of workers that work from home (telecommuters) in each U.S. county.  Then the data was normalized.  Normalization is the process of ranking the data on a scale of 0 to 1 using the county with the most telecommuters as the base.  The top county gets a score of 1 and all other counties are scored in proportion to the top county.  For example, Los Angeles County, California had 200,450 people working from home; therefore, they received a score of 1 for this category.  Maricopa County, Arizona was second on the list with 88,689 people working from home.  Their normalized score is 0.44 which was calculated by dividing the number of commuters in Marcopa County (88,689) by the top value from Los Angeles County (200,450).  This step was repeated for each county to produce a normalized telecommuting score.

The top ten counties in terms of total number of people working from home are:

1. Los Angeles County, CA – 200,450
2. Maricopa County, AZ – 88,689
3. Cook County, IL – 88,287
4. San Diego County, CA – 86,297
5. Orange County, CA – 66,404
6. Harris County, TX – 57,861
7. King County, WA – 53,621
8. New York County, NY – 52,281
9. Riverside County, CA – 41,753
10. Miami-Dade County, FL – 41,560

The second category we looked at was the number of people working from home as a percentage of all workers in the county.  Analyzing the data in this fashion allows us to pay proper attention to those counties that are not as heavily populated, but yet have a high percentage of workers telecommuting.  The top county in this category is Wheeler County, Nebraska which had 40.45% of their workers working from home.  This data was also normalized.

The counties with the highest percentage of the workforce working from home are:

1. Wheeler County, NE – 40.45%
2. Chattahoochee County, GA – 39.24%
3. Slope County, ND – 38.19%
4. Arthur County, NE – 32.88%
5. Pulaski County, MO – 32.54%
6. Billings County, ND – 30.51%
7. Kidder County, ND – 29.20%
8. Carter County, MT – 28.83%
9. Harding County, SD – 28.11%
10. Loup County, NE – 27.76%

The final score used in our map is  simply the combination of these two scores for each county divided by two.   This allows us to give equal weight to both data categories.  The final top ten counties are thus:

1. Los Angeles County, CA  (normal score = 0.56)
2. Wheeler County, NE (normal score = 0.50)
3. Chatahoochee County, GA (normal score = 0.49)
4. Slope County, ND (normal score = 0.47)
5. Pulaski County, MO (normal score = 0.42)
6. Arthur County, NE (normal score = 0.41)
7. Billings County, ND (normal score = 0.38)
8. Kidder County, ND (normal score = 0.36)
9. Carter County, MT (normal score = 0.36)
10. Harding County, SD (normal score = 0.35)

The final step was to apportion the data into 10 square mile grid cells.  This final step accomplishes a couple of things.  First, it makes it quick and easy to display on a web map.  Second, it ignores political boundaries by considering  data from surrounding counties.  The result is a thematic map that displays the most popular locations for telecommuters.

Urban Decision Group (UDG) is responsible for the creation of this map.

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

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

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