three strike laws

Better Policy Decision Making: Methodical Data Analysis

Joseph Babadi & Illiana Tidd

A report submitted to the Public Policy Institute at Jacksonville University in partial fulfillment of the requirements for the degree Master of Public Policy.

April 2015Executive Summary

This report presents the results of the strategic use of data in implementing less-subjective methods to determine key areas for public policy development within a given city. In this case, our target city is Jacksonville, FL. This report seeks to answer the following research questions, (1) How, in the least subjective manner, can a policymaker determine what policy area(s) are least and most significant when implementing policy within a city? (2) How does the altering of a given variable affect the outcome of a city (Jacksonville) on a national scale? This study is perhaps the first of its kind- by first compiling a list of variables and comparison cities, gathering data on cities for each variable, and creating three different methods of scoring. We were able to generate a final sensitivity table that allows the policymaker to adjust variables, determine their significance, and analyze the cities final ranking in comparison to other cities. The final product of our capstone has allowed us to create a simulated policy environment where policy makers are able to adjust variables based on defined target areas.

Acknowledgements

We both had been considering policy topics that specifically affected Jacksonville. Yet with both of us having inquisitive minds, the prospect of a traditional policy analysis was uninspiring. We pondered how we could enhance the profile of Jacksonville, and decided the only way was to change how policy is analyzed. The first challenge we faced given the weight of what we were setting out to do were the limitations on time and objectivity. Changing how policy is analyzed means changing how people view whats important, and in matters concerning public policy that is not always easy. Knowing this, it was important to focus on how we were going to analyze policy objectively from the beginning. It was not always easy to stay objective, but we were able to surround ourselves with individuals who wanted us to succeed as much as we did.Our success is not without thanks. First we would like to thank Dr. J. Rody Borg. His continual guidance throughout the process has been immeasurable to the development of the capstone and to us as researchers. We would also like to give thanks to our professors. As a whole they provided us with the tools to succeed. Thanks to Susan Cohn with Jacksonville Community Council Inc. as well. She was kind enough to take time out of her schedule to meet with us and go over how the JCCI conducts its research for its yearly report card. Our classmates also have our thanks, pushing us to be our best. Richard Mullaney and the Board of the Public Policy Institute, for whom we would not be here without and their dedication to building the institute. Lastly we would like to thank our families. Their unwavering support has been vital to us throughout our academic journey. I. Background: The Policy Challenge

The phrase Jacksonville has so much potential will forever haunt policy makers who spend inordinate amounts of time attempting to debunk the riddle of moving the City of Jacksonville past simply exuding potential. As a policy maker this task is no simple feat. Outside of considering the limited amount of resources to allocate within the City, a policy maker must also consider political, financial, social and personal biases when making public policy. Policy makers should also consider the various unique demographics and characteristics that make up a given city and how their city relates to others. Thus, public policy is inherently subjective. Unfortunately, the issue of subjectivity will never cease to exist in light of the nature of government. However, there are ways in which governments can create processes to decrease subjectivity in decision making. In this situation, we seek to create a less-subjective method to expose target policy variables in Jacksonville that are the most and least significant in regards to potential implemented policies. In addition, we hoped to create a simulated policy environment where variables can be manipulated and conclusions can be drawn without real policy implementation. We are able to do this through a methodical process of data analysis. II. Study Introduction

Purpose. This capstone is very unique in its nature. It is not a typical capstone project in that it does not consider a specific policy question, implement a new policy or offer suggestions as to restructure an existing policy. With that in mind, it is worthwhile to the City of Jacksonville to consider what areas within its community are in most need of attention, what areas will generate the greatest return of investment in terms of public attention and resources, and creating a less-subjective method in determining how to do so. Arguably, this is a capstone imploring the methods that should be utilized at the onset of implementing or considering any public policy. How do we know what areas within a city, state or nation truly need the most attention, all biases aside? In a personal interview with Susan Cohn, Director of Research at JCCI, when asked how they determine what areas they decide to study within Jacksonville she responded, We get a panel of local citizens from different areas of Jacksonville to determine what areas within their community they would like to see improvement[footnoteRef:1]. This method works for JCCI whose primary focus is making Jacksonville better each year, in comparison to Jacksonville. The findings that JCCI publishes in their Quality of Life Index sometimes even paves the way for new public policy out of City Council. Basing public policy off of public participation and perception is not an unworthy method. However, one citizen may be extremely vehement about the quality of education in the city and another may be just as passionate about pedestrian safety. Both of these examples may be areas that Jacksonville as a city should focus on, but how do we quantify if they are truly serious problems facing the city? Does our education rate really vary that far from other cities? How bad really is the pedestrian death rate in Jacksonville? Do all of these issues add up to make Jacksonville not appear on any Best US Cities to Live rankings? By compiling the cities and variables, and applying an even scoring system to each city, we are able to less-subjectively measure what areas Jacksonville is faring well in, what areas it needs to improve upon, and how significant those variables are in comparison to other cities. It is also important to note that every city considered is demographically, geographically and mostly governmentally different than Jacksonville. However, by ranking all of the cities and keeping the data collected under each variable we can have a better idea of where a comparable city to Jacksonville might rank in comparison. It is also important to consider that Jacksonville competes with cities that are very different from it in all senses. There is no city that can match the demographics of Jacksonville, but by weeding out the various variables that go into making a city great, it is easier to consider policies while keeping those differences in mind. [1: Susan Cohn, Director of Research at JCCI ]

Research Questions. The initial discussion of this capstone project began with the question, Why does Jacksonville only seem to be considered as exhibiting potential? Some other questions followed, including: If Jacksonville only has potential, what cities are considered the best cities to live in? What makes those cities so different from Jacksonville? How, and in what areas, can Jacksonville improve upon to make it considered a best city? How do we know what variables are the most important to improve upon? In addition, it was determined that there needed to be a bottom line for comparison. For example, when considering what the best cities are to live in, we needed to also consider what the worst cities are as well. Considering the worst US cities to live in sparked the following questions, How does Jacksonville fare in comparison to the worst US cities? Did the best US cities consider the same variables used to determine the worst US cities? If not, should all cities be compared using the same variables? Will this allow for better insight to the truly best and worst cities in the US? After deciding to incorporate what are considered to be the best and worst cities in the United States, we were able to consider the following questions, How can we create the least subjective method in determining what the truly worst and best US cities to live in are? Should we consider more than one method in ranking the cities? Should each variable that the rankings used to determine the best and worst cities be measured with the same weight? Is median household income more important to consider than number of sports teams within a city? Are variables that are used within each ranking more important than variables that only occurred once? After completing the final data analysis, the following two questions emerged as the most significant: (1) How, in the least subjective manner, can a policymaker determine what policy area(s) are least and most significant when implementing policy within a city? and (2) How does the altering of a given variable affect the outcome of a city (Jacksonville) on a national scale?III. Research Design

Design. First, we deciphered what US cities are currently considered the best US cities to live in. Because perception is reality in policy, a simple google search provided the most accessible results of four different rankings from four resources listing the cities they considered to be the best US cities to live in: Kiplingers 10 great Places to Live, Forbes Magazines Americas Most Affordable Cities, Livabilitys 100 Best Cities to Live (of which we used the first 10 cities), and CNNs Best Places to Live. Please see Table 1 in the Appendix for the complete list of cities ranked by the aforementioned rankings. Jacksonville did not appear on any of these lists.After considering the best US cities to live, we decided to also research and include what cities are considered the worst US cities to live in. While utilizing the same method as described above, we found three accessible rankings: Forbes Magazines Top Ten Most Miserable Cities to Live in, Area Vibes 10 Worst Cities to Live in, and Neighborhood Scouts 100 Most Dangerous Cities (of which we used the first 10 cities). Please see Table 2 in the Appendix for the complete list of cities ranked by the aforementioned rankings. Jacksonville did not appear on any of these lists. Next, Jacksonville was added to the list of cities. Please see Table 3 for a complete list of cities used in this capstone. Some cities were deleted from the list if statistical data was not available as of 2010 by Cesus.gov. If a given city was repeated in a ranking, it was only counted once in this capstone. In order to create an even playing field for all of the cities to be measured against, and for the study to be significant, we compiled a complete list of all of the variables the articles used to determine their rankings. We then formatted this list to be comparable amongst the total list of cities by removing variables that had been repeated, were untraceable amongst all cities, or simply immeasurable given time and accessibility limitations. In addition, we matched vague descriptions of variables in the rankings with those most relatable. For example, Kiplinger considered good jobs, reasonably priced homes, decent schools, great health care, and manageable size as the basis for their rankings, which we translated to median and average household income, total number of educational services, total number of healthcare and social assistance services, and population per square mile[footnoteRef:2]. Forbes Magazine used a more complicated method of defining their best cities by creating their own scale centered on housing affordability[footnoteRef:3]. Livability considered the number of parks, museums, hospitals, and commuting options[footnoteRef:4]. CNNs rankings took into account the amount of green space, quality of schools, and sense of community[footnoteRef:5]. The variables considered for the worst US cities centered mostly around crime rates. AreaVibes looked at crime, economic instability, and general malaise[footnoteRef:6]. Forbes created a Misery Measure for their worst cities by considering unemployment rate, taxes (income and sales), commute times, violent crime rates, professional sports team performance, pollution, air quality, and conviction of public officials[footnoteRef:7]. Finally, Neighborhood Scout focused solely on violent crime rates per capita as reported to the FBI[footnoteRef:8]. Obviously, each of the rankings applied different methods when deciphering their best and worst cities. We again removed repetitive, and unmeasurable variables. In total we considered twenty-four variables. Please see Table 4 in the Appendix for a complete list of variables considered. [2: http://www.kiplinger.com/article/real-estate/T006-C000-S002-10-great-places-to-live.html?page ] [3: http://www.forbes.com/sites/erincarlyle/2014/03/11/americas-most-affordable-cities] [4: http://livability.com/top-100-best-places-to-live] [5: http://money.cnn.com/magazines/moneymag/best-places/)] [6: http://www.areavibes.com/library/top-10-worst-cities-to-live-2013] [7: http://www.forbes.com/sites/kurtbadenhausen/2012/02/02/americas-most-miserable-cities/ ] [8: http://www.neighborhoodscout.com/neighborhoods/crime-rates/top100dangerous2013]

Once the list of variables was compiled, data was collected for each variable in each city. In total, there are fifty-nine cities and twenty-four variables considered for each city. The following definition of each target area mentioned below is as defined by the United States Census Bureau[footnoteRef:9] whereas: [9: United States Census Bureau ]

Total number of arts, entertainment, and recreational businesses is defined:The Arts, Entertainment, and Recreation sector includes a wide range of establishments that operate facilities or provide services to meet varied cultural, entertainment, and recreational interests of their patrons. This sector comprises (1) establishments that are involved in producing, promoting, or participating in live performances, events, or exhibits intended for public viewing; (2) establishments that preserve and exhibit objects and sites of historical, cultural, or educational interest; and (3) establishments that operate facilities or provide services that enable patrons to participate in recreational activities or pursue amusement, hobby, and leisure-time interests. Some establishments that provide cultural, entertainment, or recreational facilities and services are classified in other sectors. Excluded from this sector are: (1) establishments that provide both accommodations and recreational facilities, such as hunting and fishing camps and resort and casino hotels are classified in Subsector 721, Accommodation; (2) restaurants and night clubs that provide live entertainment in addition to the sale of food and beverages are classified in Subsector 722, Food Services and Drinking Places; (3) motion picture theaters, libraries and archives, and publishers of newspapers, magazines, books, periodicals, and computer software are classified in Sector 51, Information; and (4) establishments using transportation equipment to provide recreational and entertainment services, such as those operating sightseeing buses, dinner cruises, or helicopter rides, are classified in Subsector 487, Scenic and Sightseeing TransportationTotal number of educational services is defined:The Educational Services sector comprises establishments that provide instruction and training in a wide variety of subjects. This instruction and training is provided by specialized establishments, such as schools, colleges, universities, and training centers. These establishments may be privately owned and operated for profit or not for profit, or they may be publicly owned and operated. They may also offer food and/or accommodation services to their students. Educational services are usually delivered by teachers or instructors that explain, tell, demonstrate, supervise, and direct learning. Instruction is imparted in diverse settings, such as educational institutions, the workplace, or the home, and through diverse means, such as correspondence, television, the Internet, or other electronic and distance-learning methods. The training provided by these establishments may include the use of simulators and simulation methods. It can be adapted to the particular needs of the students, for example sign language can replace verbal language for teaching students with hearing impairments. All industries in the sector share this commonality of process, namely, labor inputs of instructors with the requisite subject matter expertise and teaching ability.Total number of health care and social assistance services is defined:The Health Care and Social Assistance sector comprises establishments providing health care and social assistance for individuals. The sector includes both health care and social assistance because it is sometimes difficult to distinguish between the boundaries of these two activities. The industries in this sector are arranged on a continuum starting with those establishments providing medical care exclusively, continuing with those providing health care and social assistance, and finally finishing with those providing only social assistance. The services provided by establishments in this sector are delivered by trained professionals. All industries in the sector share this commonality of process, namely, labor inputs of health practitioners or social workers with the requisite expertise. Many of the industries in the sector are defined based on the educational degree held by the practitioners included in the industry. Excluded from this sector are aerobic classes in Subsector 713, Amusement, Gambling and Recreation Industries and nonmedical diet and weight reducing centers in Subsector 812, Personal and Laundry Services. Although these can be viewed as health services, these services are not typically delivered by health practitioners.The Most Common Means of Transportation included truck, car or van. The following classifications of crimes are included in the target area Number of violent crimes per year:1. Murders2. Rapes3. Robberies4. Assaults5. Burglaries6. Thefts7. Auto Thefts8. Arson

The target area Daily Average Temperature was calculated by adding the sum of the average high within the city to the average low within the city and then dividing by the total number of temperatures. The Percent of Population living under the poverty level is defined by the Census Bureau as a set of money income thresholds that vary by family size and composition to determine who is in poverty. If a family's total income is less than the family's threshold, then that family and every individual in it is considered in poverty. The official poverty thresholds do not vary geographically, but they are updated for inflation using Consumer Price Index (CPI-U). The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). The target area Number of bodies of water refers to any body of water that touches the land deemed within the city limits including lake, ocean and river. This variable is absolute, meaning a body of water occurs or it does not. The target area Number of professional sports teams refers to any type of sports team deemed at a professional level by their various governing bodies. These teams include, but are not limited to, football, soccer, rugby, bowling, hockey, baseball, etc.

After determining a final list of variables and applying the data to each city, we then grappled with deciphering the least subjective method in determining what variables are the most important when considering potential policy. In order to do so, we implored three different scoring methods to the variables in the cities. The following is a description of each score.

Difference from Average Score. This method measures how far any given variable within a city differs from the average of all cities considered and allocates a score based on that difference. In this score, we use the average as a basal point to measure each citys true performance. For example, the average cost of monthly median rent for all of the cities is $893.64. If Anchorage, Alaskas monthly median rent cost is $1,104, then the Difference to Average equals +210.36. This means that the monthly median rent is $210.36 above the average of all cities considered. Once we determined the Difference from Average for each variable within each city, we assigned a score between 1 and 59 (1 being the best and 59 being the worst) to each city within each variable category. These scores are also reflective of the positive or negative effect each variable has on the city. For example, Anchorage, Alaska received a score of 50 out of 59 cities within the monthly median rent category for being +$210.36 over the total city average. Whereas, Cleveland, Ohio received a score in the monthly median rent category of 5 out of 59 for being -$260.64 under the total city average. After calculating the Difference from Average score in each variable within each city, we averaged the score across all variables to determine one final Difference from Average Score for each city. See Table 5 for the final ranking of cities based on their Difference from Average score.

Policy Pyramid Weight Application Score. After applying the Difference from Average Score to the set of data, we determined that there needed to be a score that indicated that some variables are substantively more important to cities given basic human needs than others. Based on this idea, government justifies intervention within communities by recognizing that certain situations and variables make it permissible to do so. This same notion can be applied to the idea that there are certain inalienable necessities that humans require in order to achieve a higher quality of life. Abraham Maslow also recognized this phenomenon during his research as a psychologist. He stated, It is quite true that man lives by bread alone, when there is no bread. But what happens to mans desires when there is plenty of bread and when his belly is chronically filled? At once other (and higher) needs emerge and these, rather than physiological hungers, dominate the organism. And when these in turn are satisfied, again new (and still higher) needs emerge and so on. This is what we mean by saying that the basic human needs are organized into a hierarchy of relative prepotency[footnoteRef:10]. In other words, people are motivated by certain needs when they emerge, based on various degrees of given situations. For example, a person who is unable to provide food to his/her family is motivated much differently than an individual who enjoys spending free time in city parks. It is important for cities to consider these differences amongst their populations in order to determine the true needs of the city. In order to do this, we created the Policy Pyramid Weight Application Score, which assigns different weights to variables that fall within five different categories based on Maslows Hierarchy of Needs, see Figure 1 for Maslows Hierarchy of Needs Pyramid. [10: http://www.simplypsychology.org/maslow.html ]

Based on the pyramid, we created the Policy Pyramid Weight Application Score. The category color designators, category title, and weight applied to each are as follows:

Red: Basic Physiological Needs Weight: .4Orange: Safety Needs Weight: .3125Yellow: Relationships Weight: .225Blue: Accomplishments Weight: .1375Purple: Creativity/Recreation Weight: .05

Each variable was placed into the appropriate category. See Figure 2 for an example of the placement of variables. Please see data set for a complete visual of placement of variables into the various Policy Pyramid Weight categories. After assigning each variable to a category with a weight, we applied the weight to the Difference from Average Score in each variable within each city. For example, the monthly median rent was weighted under the Red: Basic Physiological Needs with a weight of .4. Once each weight was applied to each variable within the cities, an average of the final weighted scores was applied and a final ranking based on the Policy Pyramid Weight Application Score was found. Table 8 provides the final Policy Pyramid Weight Application Ranking.

Variable Frequency Score. Recognizing and applying weights to variables based on importance is significant to this study. However, it is also important to consider the fact that the rankings used to decipher the cities utilized in this capstone, decided on their own variables. If it is accepted that perception is reality (in this case people from any location can readily access the rankings used in the project through the same google search we used), then the variables used in these rankings implore some level of significance. Presumably, the authors of the rankings did not use complete randomness is selecting their best and worst cities and the variables by which they made their determinations. In order to further decrease subjectivity, we applied another score based on the number of times each variable was used throughout all of the initial rankings. For example, because average household income only appeared in 2 out of 7 of the rankings, it was applied a weight of 2/7 or .285714286. This score is referred to as the Variable Frequency Score. Figure 3 depicts an example of the weights and frequency of displayed variables.The weight determined by the Variable Frequency Score was applied to the Difference from Average Score under each variable within each city. No variables appeared in all of the seven rankings. Table 7 depicts the final city rankings based on the application of the Variable Frequency Score.

Final Ranking. The final ranking of all cities was found by averaging the rankings of all three scores; the Difference from Average Score, the Policy Pyramid Weight Application Score, and the Variable Frequency Score. The final city rankings are depicted in Table 8.

IV. Key Findings

Sensitivity. One of the most important outcomes of this capstone is the ability for an individual to be able to analyze how sensitive a given variable is in relation to a potential policy. For example, if a platform of policymakers in Jacksonville was to target the percent of the population with a high school diploma by enacting policy that focuses on increasing the percentage, then using the ranking models weve created one could forecast how that might affect Jacksonvilles rank among the cities. An example can be seen in Table 9. By removing the variable Percent of Population with a High School Diploma, we can see shifts in the rankings. Jacksonville for example moves from 12th to 11th. By simply removing a targeted variable within the raw data set a policymaker can analyze both how the adjusted variable fares in relation to other cities within the same variable category, and how the change affects the entire ranking of the city as a whole. In a similar fashion if policy makers wanted to know if a variable was significant, the model can be adjusted, and the resulting shifts in the rankings could be measured. In Table 10, we can see the shift in the rankings from our previous example of removing Percent of Population with a High School Diploma. Measuring the shift, can show how impactful a variable is within a given city. Having the ability to do this gives policy makers the opportunity to create simulated policy environments where policy initiatives could be tested for their potential effects on a city. Being able to forecast the effects of a policy on the publics perception of how good a city is would give policy makers a new tool to determine the validity and viability of a policy. It also illustrates just how important certain variables are when considering public policy. What this means for Jacksonville is access to a new way of viewing policy as the city continues to grow and the ability to act as a policy hotbed in the coming years.

V. Concluding Remarks

Contribution. This capstone contributes to the world of public policy by creating a fundamental method of data collection and analysis at the onset of policymaking. It is our hope that policymakers utilize models like this to consider outcomes of various policies in addition to other methods of consideration prior to enacting their policies. Considering outcomes, consequences, and effects before concretizing policy within a government is a responsible and more efficient way to deal with the true issues and needs of any given community.

Further research opportunities. From this capstone there are many more opportunities for further research. Using this as a base, research could expand the width of the model to more cities. Once you create a model, the next step is applying it to more subjects, and this would create a more substantive data set. Another option for further research would be to expand the depth of the data, and have a more intense collection method. Individual surveys would allow the model to access better information regarding public perception of policies and their importance.

Appendices

Table 1. Best US Cities to Live

Kiplinger:1. Little Rock, Arkansas2. Burlington, Vermont3. Bryan-College Station, Texas4. Santa Fe, New Mexico5. Columbia, South Carolina6. Billings, Montana7. Morgantown, West Virginia8. Ithaca, New York9. Anchorage, Alaska10. Dubuque, Iowa

Forbes:1. Buffalo, NY2. Memphis, Tennessee3. Cincinnati, Ohio4. Dayton, Ohio5. Knoxville, Tennessee6. Akron, Ohio7. Grand Rapids, Michigan8. Louisville, Kentucky9. Oklahoma City, Oklahoma10. Warren, Michigan

Livability:1. Palo Alto, California2. Boulder, Colorado3. Berkeley, California4. Durham, North Carolina5. Madison, Wisconsin6. Miami Beach, Florida7. Rochester, Minnesota8. Salt Lake City, Utah9. Eugene, Oregon10. Reno, Nevada

CNN:1. Sharon, Massachusetts2. Louisville, Colorado3. Vienna, Virginia4. Chanhassen, Minnesota5. Sherwood, Oregon6. Berkeley Heights, New Jersey7. Mason, Ohio8. Papillion, Nebraska9. Apex, North Carolina10. West Goshen Township, Pennsylvania

Table 2. Worst US Cities to Live.

Forbes:1. Cleveland, Ohio2. Stockton, California3. Memphis, Tennessee4. Detroit, Michigan5. Flint, Michigan6. Miami, Florida7. St. Louis, Missouri8. Buffalo, New York9. Canton, Ohio10. Chicago, Illinois

AreaVibes:1. Springfield, Massachusetts2. Hartford, Connecticut3. Detroit, Michigan4. Flint, Michigan5. East Los Angeles, California6. Philadelphia, Pennsylvania7. Cleveland, Ohio8. Paterson, New Jersey9. Anchorage, Alaska10. Buffalo, New York

Neighborhood Scout:1. East St. Louis, Illinois2. Camden, New Jersey3. Flint, Michigan4. West Memphis, Arkansas5. Saginaw, Michigan6. Detroit, Michigan7. Atlantic City, New Jersey8. St. Louis, Missouri9. Newburgh, New York10. Inkster, Michigan

Table 3. All Cities Considered

1.

1. Anchorage, Alaska2. Burlington, Vermont3. Akron, Ohio4. Apex, North Carolina5. Atlantic City, New Jersey6. Berkeley Heights, New Jersey7. Berkeley, California8. Billings, Montana9. Boulder, Colorado10. Buffalo, New York11. Camden, New Jersey12. Canton, Ohio13. Chanhassen, Minnesota14. Chicago, Illinois15. Cincinnati, Ohio16. Cleveland, Ohio17. College Station, Texas18. Columbia, South Carolina19. Dayton, Ohio20. Detroit, Michigan21. Dubuque, Iowa22. Durham, North Carolina23. East Los Angeles, California24. East St. Louis, Illinois25. Eugene, Oregon26. Flint, Michigan27. Grand Rapids, Michigan28. Hartford, Connecticut29. Inkster, Michigan30. Ithaca, New York31. Jacksonville, Florida32. Knoxville, Tennessee33. Little Rock, Arkansas34. Louisville, Colorado35. Madison, Wisconsin36. Mason, Ohio37. Memphis, Tennessee38. Miami Beach, Florida39. Morgantown, West Virginia40. Newburgh, New York41. Oklahoma City, Oklahoma42. Palo Alto, California43. Papillion, Nebraska44. Paterson, New Jersey45. Philadelphia, Pennsylvania46. Reno, Nevada47. Rochester, Minnesota48. Saginaw, Michigan49. Salt Lake City, Utah50. Santa Fe, New Mexico51. Sharon, Massachusetts52. Sherwood, Oregon53. Springfield, Massachusetts54. St. Louis, Missouri55. Stockton, California56. Vienna, Virginia57. Warren, Michigan58. West Goshen Township, Pennsylvania59. West Memphis, Arkansas

Table 4. All Variables Considered

1.Population2.Land area in square miles3.Population per square mile4.Monthly median rent cost5.Median house value6.Percent of high school graduates or higher7.Percent of population with bachelors degree8.Total number of firms 9.Total number of arts, entertainment, and recreational businesses10.Total number of educational services11.Total number of health care and social assistance services12.Full time law enforcement per capita13.Most common used means of transportation14.Average commute time to work15.Percentage of population who take public transportation to work16.Number of violent crimes per year17.Daily average temperature18.Percent of people living under the poverty level19.Average household income20.Median household income21.Unemployment level22.Air quality index23.Number of bodies of water24.Number of professional sports teams

Table 5. Difference from Average: Final City Rank Table

Difference from Average TableRank

Boulder, Colorado1

Berkeley, California2

Anchorage, Alaska3

Billings, Montana4

Palo Alto, California5

Eugene, Oregon6

Madison, Wisconsin7

Rochester, Minnesota8

Durham, North Carolina9

Oklahoma City, Oklahoma10

Sharon, Massachusetts11

Jacksonville, Florida12

Reno, Nevada13

Miami Beach, Florida14

Little Rock, Arkansas15

Louisville, Colorado16

Columbia, South Carolina17

Salt Lake City, Utah18

Santa Fe, New Mexico19

Chanhassen, Minnesota20

Apex, North Carolina21

Chicago, Illinois22

Burlington, Vermont23

Cincinnati, Ohio24

Knoxville, Tennessee25

Dubuque, Iowa26

College Station, Texas27

Memphis, Tennessee28

Berkeley Heights, New Jersey29

Grand Rapids, Michigan30

Ithaca, New York31

Philadelphia, Pennsylvania32

Morgantown, West Virginia33

West Goshen Township, Pennsylvania34

Mason, Ohio35

Akron, Ohio36

Vienna, Virginia37

Sherwood, Oregon38

St. Louis, Missouri39

Buffalo, New York40

Warren, Michigan41

Newburgh, New York42

Papillion, Nebraska43

Hartford, Connecticut44

Cleveland, Ohio45

Stockton, California46

Dayton, Ohio47

Springfield, Massachusetts48

Atlantic City, New Jersey49

Detroit, Michigan50

Saginaw, Michigan51

East Los Angeles, California52

Canton, Ohio53

West Memphis, Arkansas54

Paterson, New Jersey55

Flint, Michigan56

Camden, New Jersey57

East St. Louis, Illinois58

Inkster, Michigan59

Figure 1. Maslows Hierarchy of Needs Pyramid

Figure 2. Variable Placement into Policy Pyramid Weight Application Score

Table 6. Policy Pyramid Weight Application Final Ranking

Policy Pyramid Weight TableRank

Anchorage, Alaska1



Billings, Montana4


Boulder, Colorado6


Eugene, Oregon8



Madison, Wisconsin11

Reno, Nevada12







Dubuque, Iowa19





Sherwood, Oregon24


Vienna, Virginia26





Mason, Ohio31

Warren, Michigan32


Chicago, Illinois34


Cincinnati, Ohio36




Ithaca, New York40


Akron, Ohio42



Buffalo, New York45

Dayton, Ohio46

Saginaw, Michigan47


Cleveland, Ohio49

Detroit, Michigan50

Canton, Ohio51



Flint, Michigan54



Inkster, Michigan57



Figure 3. Variable Frequency Weight Application Example

Table 7. Variable Frequency Score Final Ranking

Variable Frequency Score TableRank

Billings, Montana1


Boulder, Colorado3

Anchorage, Alaska4


Eugene, Oregon6




Madison, Wisconsin10


Reno, Nevada12






Dubuque, Iowa18








Cincinnati, Ohio26



Chicago, Illinois29


Ithaca, New York31



Vienna, Virginia34

Mason, Ohio35

Akron, Ohio36

Sherwood, Oregon37



Warren, Michigan40

Buffalo, New York41




Dayton, Ohio45




Cleveland, Ohio49


Detroit, Michigan51

Canton, Ohio52

Saginaw, Michigan53



Flint, Michigan56



Inkster, Michigan59

Table 8. Final Ranking of Averaged Policy Pyramid Ranking and Variable Frequency Ranking

CitiesTotalAverageRank

Anchorage, Alaska82.671

Berkeley, California93.002

Billings, Montana93.002

Boulder, Colorado103.334

Palo Alto, California134.335

Rochester, Minnesota175.676

Eugene, Oregon206.677

Sharon, Massachusetts279.008

Madison, Wisconsin289.339

Oklahoma City, Oklahoma289.339

Durham, North Carolina3511.6711

Reno, Nevada3712.3312

Louisville, Colorado3812.6713

Jacksonville, Florida4715.6714

Miami Beach, Florida4816.0015

Little Rock, Arkansas5317.6716

Santa Fe, New Mexico5418.0017

Columbia, South Carolina5518.3318

Salt Lake City, Utah5518.3318

Apex, North Carolina5618.6720

Chanhassen, Minnesota5819.3321

Dubuque, Iowa6321.0022

Knoxville, Tennessee7525.0023

Burlington, Vermont7625.3324

Berkeley Heights, New Jersey7725.6725

College Station, Texas8227.3326

Chicago, Illinois8528.3327

Cincinnati, Ohio8628.6728

Grand Rapids, Michigan9030.0029

Memphis, Tennessee9130.3330

West Goshen Township, Pennsylvania9230.6731

Vienna, Virginia9732.3332

Sherwood, Oregon9933.0033

Mason, Ohio10133.6734

Ithaca, New York10234.0035

Morgantown, West Virginia10434.6736

Papillion, Nebraska11036.6737

Philadelphia, Pennsylvania11137.0038

Warren, Michigan11337.6739

Akron, Ohio11438.0040

Newburgh, New York12140.3341

St. Louis, Missouri12541.6742

Buffalo, New York12642.0043

Stockton, California12842.6744

Dayton, Ohio13846.0045

Hartford, Connecticut13846.0045

Springfield, Massachusetts13946.3347

Cleveland, Ohio14347.6748

Detroit, Michigan15150.3349

Saginaw, Michigan15150.3349

Atlantic City, New Jersey15250.6751

Canton, Ohio15551.6752

East Los Angeles, California15953.0053

Paterson, New Jersey16153.6754

West Memphis, Arkansas16254.0055

Flint, Michigan16655.3356

Camden, New Jersey17357.6757

East St. Louis, Illinois17458.0058

Inkster, Michigan17558.3359

Table 9. Adjusted Difference from Average: Final City Rank TableREMOVED VARIABLE: Percent of Population with High School Diploma

Adjusted Difference from Average TableRank

Boulder, Colorado1


Anchorage, Alaska3

Billings, Montana4


Eugene, Oregon6



Madison, Wisconsin9

Reno, Nevada10








Chicago, Illinois18



Cincinnati, Ohio21






Dubuque, Iowa27





Ithaca, New York32

Akron, Ohio33



Buffalo, New York36


Vienna, Virginia38

Mason, Ohio39


Sherwood, Oregon41

Warren, Michigan42

Cleveland, Ohio43




Dayton, Ohio47


Detroit, Michigan49



Saginaw, Michigan52

Canton, Ohio53




Flint, Michigan57


Inkster, Michigan59

Table 10. Adjusted Difference from Average: Shift TableREMOVED VARIABLE: Percent of Population with High School Diploma

Target CitiesShift

Akron, Ohio3

Anchorage, Alaska0





Billings, Montana0

Boulder, Colorado0

Buffalo, New York4


Camden, New Jersey1

Canton, Ohio1


Chicago, Illinois4

Cincinnati, Ohio3

Cleveland, Ohio2



Dayton, Ohio0

Detroit, Michigan1

Dubuque, Iowa1




Eugene, Oregon0

Flint, Michigan1



Inkster, Michigan0

Ithaca, New York1





Madison, Wisconsin2

Mason, Ohio4

Memphis, Tennessee2



Newburgh, New York4






Reno, Nevada3


Saginaw, Michigan-1




Sherwood, Oregon3




Vienna, Virginia1

Warren, Michigan1



Total102

Average1.728814

Bibliography

"10 Great Places to Live." Www.kiplinger.com. September 10, 2013. Accessed October 20, 2014. http://www.kiplinger.com/article/real-estate/T006-C000-S002-10-great-places-to-live.html?page."2014 Ranking of Top 100 Best Places to Live in America | Livability | Best Small to Mid-sized U.S. Cities to Live." 2014 Ranking of Top 100 Best Places to Live in America | Livability | Best Small to Mid-sized U.S. Cities to Live. March 12, 2014. Accessed October 20, 2014. http://livability.com/top-100-best-places-to-live. Badenhausen, Kurt. "America's Most Miserable Cities." Forbes. February 2, 2012. Accessed October 20, 2014. http://www.forbes.com/sites/kurtbadenhausen/2012/02/02/americas-most-miserable-cities/.Carlyle, Erin. "America's Most Affordable Cities." Forbes. March 11, 2014. Accessed October 20, 2014. http://www.forbes.com/sites/erincarlyle/2014/03/11/americas-most-affordable-cities. Cohn, Susan. "JCCI Quality of Life Indicators." Interview by authors. October 12, 2014.McLeod, Saul. "Maslow's Hierarchy of Needs." Simply Psychology. September 17, 2007. Accessed March 10, 2015. http://www.simplypsychology.org/maslow.html."MONEY's Best Places to Live in America." Money Magazie, Time. September 19, 2014. Accessed October 20, 2014. http://time.com/money/collection/best-places-to-live/."NeighborhoodScout's Most Dangerous Cities - 2013 Top 100 Most Dangerous Cities in the U.S." Top 100 Most Dangerous Places to Live in the USA 2013. September 5, 2013. Accessed October 20, 2014. http://www.neighborhoodscout.com/neighborhoods/crime-rates/top100dangerous2013"Top 10 Worst Cities - Worst Places to Live 2013." Top 10 Worst Cities - Worst Places to Live 2013. December 10, 2013. Accessed October 20, 2014. http://www.areavibes.com/library/top-10-worst-cities-to-live-2013. United States Bureau of the Census. (2014). American Fact Finder. Retrieved from: http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml. Accessed October 20, 2014.

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