Acknowledgments Smart Growth America is the only national organization dedicated to researching, advocating for and leading coalitions to bring smart growth practices to more communities nationwide. From providing more sidewalks to ensuring more homes are built near public transportation or that productive farms remain a part of our communities, smart growth helps make sure people across the nation can live in great neighborhoods. Learn more at www.smartgrowthamerica.org. This report is based on original research published by the Metropolitan Research Center at the University of Utah, prepared for the National Cancer Institute at the National Institutes of Health, as well as the Ford Foundation. The Metropolitan Research Center conducts basic and applied research on the built environment at the metropolitan scale, focusing on key forces shaping metropolitan form such as demographics, environment, technology, design, transportation, arts and culture and governance. It seeks to expand knowledge in city and metropolitan affairs to improve policy and practice and educate the general public on important issues facing communities. Learn more at www.arch.utah.edu/cgi-bin/wordpress-metroresearch/. This report was made possible with support from the National Institutes of Health and the Ford Foundation. Researchers Reid Ewing, Professor of City and Metropolitan Planning, University of Utah Shima Hamidi, Graduate Research Assistant, University of Utah Project team Sarah Absetz, Policy Associate, Smart Growth America Geoff Anderson, President and CEO, Smart Growth America David Berrigan, Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute Craig Chester, Press Manager, Smart Growth America Alex Dodds, Deputy Director of Communications, Smart Growth America Ilana Preuss, Vice President and Chief of Staff, Smart Growth America Zaria Tatalovich, Health Statistician and Geospatial Scientist, National Cancer Institute Special thanks to David Goldberg, Transportation for America; Chris Zimmerman, Smart Growth America; Gail Meakins, Martin Buchert, and Allison Spain, Metropolitan Research Center; Professor William Greene, New York University; and James B. Grace, U.S. Geological Survey. ii Table of Contents Executive Summary ................................................................................................................... iv Introduction ................................................................................................................................. 1 About the research..................................................................................................................... 1 Measuring “sprawl” .................................................................................................................... 2 The four factors .......................................................................................................................... 2 Scoring ...................................................................................................................................... 2 The 2014 Sprawl Index rankings ............................................................................................... 4 Most compact, connected metro areas...................................................................................... 4 Most sprawling metro areas ....................................................................................................... 6 What sprawl means for everyday life........................................................................................ 9 Seeking better quality of life .................................................................................................... 12 Santa Barbara, CA ................................................................................................................... 12 Madison, WI ............................................................................................................................. 13 Trenton, NJ .............................................................................................................................. 13 Los Angeles, CA ...................................................................................................................... 14 Conclusion................................................................................................................................. 15 Appendix A: Full 2014 metro area Sprawl Index rankings .................................................... 16 Appendix B: County-level information .................................................................................... 20 County-level findings ................................................................................................................ 20 Appendix C: Quality of life analysis......................................................................................... 43 Endnotes.................................................................................................................................... 45 iii Executive Summary Some places in the United States are sprawling out and some places are building in compact, connected ways. The difference between these two strategies affects the lives of millions of Americans. In 2002, Smart Growth America released Measuring Sprawl and Its Impact, a landmark study that has been widely used by researchers to examine the costs and benefits of sprawling development. In peer-reviewed research, sprawl has been linked to physical inactivity, obesity, traffic fatalities, poor air quality, residential energy use, emergency response times, teenage driving, lack of social capital and private-vehicle commute distances and times. Measuring Sprawl 2014 updates that research and analyzes development patterns in 221 metropolitan areas and 994 counties in the United States as of 2010, looking to see which communities are more compact and connected and which are more sprawling. Researchers used four primary factors—residential and employment density; neighborhood mix of homes, jobs and services; strength of activity centers and downtowns; and accessibility of the street network—to evaluate development in these areas and assign a Sprawl Index score to each. This report includes a list of the most compact and most sprawling metro areas in the country. This report also examines how Sprawl Index scores relate to life in that community. The researchers found that several quality of life factors improve as index scores rise. Individuals in compact, connected metro areas have greater economic mobility. Individuals in these areas spend less on the combined cost of housing and transportation, and have greater options for the type of transportation to take. In addition, individuals in compact, connected metro areas tend to live longer, safer, healthier lives than their peers in metro areas with sprawl. Obesity is less prevalent in compact counties, and fatal car crashes are less common. Finally, this report includes specific examples of how communities are building to be more connected and walkable, and how policymakers at all levels of government can support their efforts. iv Introduction As regions grow and develop, residents and their elected leaders have many decisions to make. What kind of street network should they build, and how extensive should it be? Should neighborhoods have a mix of homes, shops and offices, or should different types of buildings be kept separate? Will people be able to walk, ride a bicycle or take public transportation through the community, or will driving be the only realistic way for people to get around? Everyone experiences the outcomes associated with these development decisions. How much families pay for housing and transportation, how long workers spend commuting home, the economic opportunities in communities and even personal health are all connected to how neighborhoods and surrounding areas are built. Measuring Sprawl 2014 analyzes development in 221 metropolitan areas across the United States, as well as the relationship between development and quality of life indicators in those areas. This report includes a list of the most compact and most sprawling metro areas in the country. About the research In 2002, Smart Growth America released Measuring Sprawl and Its Impact, a landmark study that has been widely used by researchers to examine the costs and benefits of sprawling development. That report was made available to researchers and has been used in peer-reviewed research in the years since. From that original analysis, sprawl has been linked to physical inactivity, obesity, traffic fatalities, poor air quality, residential energy use, emergency response times, teenage driving, lack of social capital, and commute distances and times. Measuring Sprawl 2014 is an update and refinement of that research. This report is based on research originally published in the Metropolitan Research Center at the University of Utah in April 2014. The University of Utah’s report, titled Measuring Urban Sprawl and Validating Sprawl Measures, represents the most comprehensive effort yet undertaken to define, measure and evaluate metropolitan sprawl and its impacts. The first peer-reviewed article based on this research was published in October 2013 in the journal Health & Place. The data from 2010 used in this analysis are the most recent available. The complete analysis, methodology and databases included in the University of Utah’s research are available at http://gis.cancer.gov/tools/urban-sprawl/. 1 Measuring “sprawl” This study analyzed development in 193 census-defined Metropolitan Statistical Areas (MSAs)—or metro areas—as well as 28 census-defined Metropolitan Divisions, which comprise MSAs, in the largest 11 MSAs. All of the analyzed areas had at least 200,000 people in 2010. MSAs with populations less than 200,000 people were not included in the study.1 This study also analyzed development in 994 metropolitan counties. The four factors Development in both MSAs and metropolitan counties was evaluated using four main factors: 1) development density; 2) land use mix; 3) activity centering; and 4) street accessibility. These factors are briefly explained below.2 Development density Development density is measured by combining six major factors: 1) total density of the urban and suburban census tracts; 2) percent of the population living in low-density suburban areas; 3) percent of the population living in medium- to high-density areas; 4) urban density within total built-upon land; 5) the relative concentration of density around the center of the MSA; and 6) employment density. Land use mix Land use mix is also measured through a combination of factors: the balance of jobs to total population and mix of job types within one mile of census block groups, plus the WalkScore of the center of each census tract. Activity centering The proportion of people and businesses located near each other is also a key variable to define an area. Activity centering is measured by looking at the range of population and employment size in different block groups. MSAs with greater variation (i.e., a wider difference between blocks with a high population and a low one) have greater centering. This factor also includes a measure of how quickly population density declines from the center of the MSA, and the proportion of jobs and people within the MSA’s central business district and other employment centers. Street accessibility Street accessibility is measured by combining a number of factors regarding the MSA’s street network. The factors are average length of street block; average block size; percent of blocks that are urban in size; density of street intersections; and percent of four-way or more intersections, which serves as a measure of street connectivity. Scoring Researchers used these factors to evaluate development in all 221 MSAs and 994 counties. These four factors are combined in equal weight and controlled for population to calculate each area’s Sprawl Index score. The average index is 100, meaning areas with scores higher than 100 tend to be more compact and connected and areas with scores lower than 100 are more sprawling. 2 MSA versus county scales Census-defined MSAs and the Metropolitan Divisions within them include a wide variety of places within a given region. An MSA’s boundaries may include one county (like the Detroit, MI Metropolitan Division, which includes only Wayne County) or many counties (like the Washington, DC MSA, which contains 16 counties).3 This difference has a significant impact on how a given region scores on the index, and it is important to note that these census-defined divisions create some counterintuitive outcomes. For example, the greater Washington, DC area ranks 91st on the index based on its MSA. Evaluated at the county level, however, Washington, DC ranks 6th. Many other communities face similar distinctions between scores at the MSA level versus the county level. Our findings are presented at the MSA scale because much of the data, such as economic mobility, is only available at this level. Health data is available at the county level, so in those cases we provide analysis at that scale. Future versions of this analysis would benefit from economic mobility, transportation and housing costs and health databases available at more refined scales. For more information about index scores and findings at the county scale, see Appendix B. For information about the data sources available at different geographic scales, see Appendix C. 3 The 2014 Sprawl Index rankings Based on the index standards described in the previous section, we evaluated development in 221 metro areas in the United States. The most compact, connected metro area in the United States is, perhaps not surprisingly, New York, NY, with an index score of 203.4. The country’s most sprawling metro area is Hickory, NC, with an index score of 24.9. To provide a more comprehensive look at how communities compare, we also present here the most compact and most sprawling MSAs by size. Among large metro areas (defined as having a population more than one million people), New York, the national leader, is the most compact and connected. Atlanta, GA, is the most sprawling, with a score of 41.0. Of medium metro areas (defined as having a population between 500,000 and 1 million), Madison, WI, is the most compact and connected with a score of 136.7 and Baton Rouge, LA, is the most sprawling, with a score of 55.6. Of small metro areas (defined as having a population less than 500,000), Atlantic City, NJ, is the most compact and connected, with a score of 150.4, whereas Hickory, NC, is the most sprawling.4 Most compact, connected metro areas Tables 1–4 rank metro areas that are more compact and connected, with homes and jobs closer together. TABLE 1 Most compact, connected metro areas, nationally Rank Metro area Index score 1 New York/White Plains/Wayne, NY-NJ 203.4 2 San Francisco/San Mateo/Redwood City, CA 194.3 3 Atlantic City/Hammonton, NJ 150.4 4 Santa Barbara/Santa Maria/Goleta, CA 146.6 5 Champaign/Urbana, IL 145.2 6 Santa Cruz/Watsonville, CA 145.0 7 Trenton/Ewing, NJ 144.7 8 Miami/Miami Beach/Kendall, FL 144.1 9 Springfield, IL 142.2 10 Santa Ana/Anaheim/Irvine, CA 139.9 4 TABLE 2 Most compact, connected large metro areas Large metro areas are defined as having a population more than one million. Rank Metro area Index score 1 New York/White Plains/Wayne, NY-NJ 203.4 2 San Francisco/San Mateo-Redwood City, CA 194.3 8 Miami/Miami Beach/Kendall, FL 144.1 10 Santa Ana/Anaheim/Irvine, CA 139.9 12 Detroit/Livonia/Dearborn, MI 137.2 15 Milwaukee/Waukesha/West Allis, WI 134.2 21 Los Angeles/Long Beach/Glendale, CA 130.3 24 San Jose/Sunnyvale/Santa Clara, CA 128.8 25 Oakland/Fremont/Hayward, CA 127.2 26 Chicago/Joliet/Naperville, IL 125.9 TABLE 3 Most compact, connected medium metro areas Medium metro areas are defined as having a population between 500,000 and 1 million. Rank Metro area Index score 13 Madison, WI 136.7 28 Allentown/Bethlehem/Easton, PA-NJ 124.4 37 Bridgeport/Stamford/Norwalk, CT 121.7 41 Stockton, CA 120.3 52 New Haven/Milford, CT 116.3 54 Scranton/Wilkes-Barre, PA 115.8 64 Oxnard/Thousand Oaks/Ventura, CA 113.8 66 Modesto, CA 113.3 67 Wilmington, DE-MD-NJ 112.9 68 Lancaster, PA 112.6 5 TABLE 4 Most compact, connected small metro areas Small metro areas are defined as having a population less than 500,000. Rank Metro area Index score 3 Atlantic City/Hammonton, NJ 150.4 4 Santa Barbara/Santa Maria/Goleta, CA 146.6 5 Champaign/Urbana, IL 145.2 6 Santa Cruz/Watsonville, CA 145.0 7 Trenton/Ewing, NJ 144.7 9 Springfield, IL 142.2 11 Reading, PA 137.9 14 Burlington/South Burlington, VT 135.1 16 Boulder, CO 133.7 17 Appleton, WI 132.7 Most sprawling metro areas Tables 5–8 rank communities that are the least dense, least connected and most likely to separate land uses. TABLE 5 Most sprawling metro areas, nationally Rank Metro area Index score 212 Kingsport/Bristol/Bristol, TN-VA 60.0 213 Augusta/Richmond County, GA-SC 59.2 214 Greenville/Mauldin-Easley, SC 59.0 215 Riverside-San Bernardino/Ontario, CA 56.2 216 Baton Rouge, LA 55.6 217 Nashville-Davidson/Murfreesboro/Franklin, TN 51.7 218 Prescott, AZ 49.0 219 Clarksville, TN-KY 41.5 220 Atlanta/Sandy Springs/Marietta, GA 41.0 221 Hickory/Lenoir/Morganton, NC 24.9 6 TABLE 6 Most sprawling large metro areas Large metro areas are defined as having a population more than one million. Rank Metro area Index score 182 Houston/Sugar Land/Baytown, TX 76.7 184 Richmond, VA 76.4 189 Rochester, NY 74.5 192 Birmingham-Hoover, AL 73.6 196 Memphis, TN-MS-AR 70.8 197 Charlotte/Gastonia-Rock Hill, NC-SC 70.5 201 Warren/Troy/Farmington Hills, MI 67.0 215 Riverside-San Bernardino/Ontario, CA 56.3 217 Nashville/Davidson/Murfreesboro/Franklin, TN 51.7 220 Atlanta-Sandy Springs/Marietta, GA 41.0 TABLE 7 Most sprawling medium metro areas Medium metro areas are defined as having a population between 500,000 and 1 million. Rank Metro area Index score 185 Little Rock/North Little Rock/Conway, AR 76.1 191 Durham/Chapel Hill, NC 73.8 195 Jackson, MS 72.3 199 Knoxville, TN 68.2 200 Columbia, SC 67.5 207 Chattanooga, TN-GA 63.6 208 Greensboro/High Point, NC 63.5 213 Augusta/Richmond County, GA-SC 59.1 214 Greenville/Mauldin-Easley, SC 59.0 216 Baton Rouge, LA 55.6 7 TABLE 8 Most sprawling small metro areas Small metro areas are defined as having a population less than 500,000. Rank Metro area Index score 204 Green Bay, WI 65.4 205 Fort Smith, AR-OK 64.8 206 Lynchburg, VA 64.0 209 Winston-Salem, NC 63.4 210 Florence, SC 61.1 211 Lake Havasu City-Kingman, AZ 60.1 212 Kingsport/Bristol/Bristol, TN-VA 60.0 218 Prescott, AZ 49.0 219 Clarksville, TN-KY 41.5 221 Hickory/Lenoir/Morganton, NC 24.9 8 What sprawl means for everyday life The researchers found that as Sprawl Index scores improved—that is, as areas became less sprawling—several quality of life factors improved along with them.5 • • • • People have greater economic opportunity in compact and connected metro areas. People spend less of their household income on the combined cost of housing and transportation in these areas. People have a greater number of transportation options available to them. And people in compact, connected metro areas tend to be safer, healthier and live longer than their peers in more sprawling metro areas. The researchers controlled for socioeconomic factors. Below is more information about each of these quality of life indicators. People in more compact, connected metro areas have greater economic mobility. Could metro areas with homes and jobs far apart and limited connections between those areas directly affect the ability of low-income children to get ahead as adults? The researchers compared the 2014 Sprawl Index scores to models of upward economic mobility from Harvard and the University of California at Berkeley.6 They examined the probability of a child born to a family in the bottom quintile of the national income distribution reaching the top quintile of the national income distribution by age 30, and whether communities’ index score was correlated with that probability. The researchers found that compactness has a strong direct relationship to upward economic mobility. In fact, for every 10 percent increase in an index score, there is a 4.1 percent increase in the probability that a child born to a family in the bottom quintile of the national income distribution reaches the top quintile of the national income distribution by age 30. Compactness has a strong direct relationship to upward economic mobility. For example, the probability of an individual in the Baton Rouge, LA area (index score: 55.6) moving from the bottom income quintile to top quintile is 7.2 percent. In the Madison, WI area (index score: 136.7) that probability is 10.2 percent. People in more compact, connected metro areas spend less on the combined expenses of housing and transportation. The cost of housing is often higher in compact areas compared with sprawling ones. However, families’ transportation costs are often significantly lower in these places. Shorter distances to travel and a wider range of low-cost travel options means individuals and families in these places spend a smaller portion of their household budget on transportation. How do the two expense categories relate in compact areas versus sprawling ones? The researchers found that the average percentage of income spent on housing is indeed greater in compact communities than in sprawling areas. Each 10 percent increase in an index score was associated with a 1.1 percent increase in housing costs relative to income.7 9 The researchers also found that the average percentage of income spent on transportation is smaller in compact areas than sprawling ones. Each 10 percent increase in an index score was associated with a 3.5 percent decrease in transportation costs relative to income.8 For instance, households in the San Francisco, CA area (index score: 194.3) spend an average of 12.4 percent of their income on transportation. Households in the Tampa, FL metro area (index score: 98.5) spend an average of 21.5 percent of their income on transportation.9 Perhaps the most notable finding was that the combined cost of housing and transportation declines as an index score increases. As metropolitan compactness increases, transportation costs decline faster than housing costs rise, creating a net decline in household costs.10 An average household in the San Francisco, CA metro area (index score: 194.3) spends 46.7 percent of its budget on housing and transportation, while an average household in the Tampa, FL metro area (index score: 98.5) spends 56.1 percent of its budget on the same items.11 The combined cost of housing and transportation declines as an index score increases. People in more compact, connected metro areas have more transportation options. Part of the reason transportation costs are lower in more compact areas is that these areas have a wider range of options for how to get around—nearly all of which cost less than driving or are even free. The researchers found that people in metro areas with higher index scores walk more: For every 10 percent increase in an index score, the walk mode share (i.e., the portion of travelers who choose to walk) increases by 3.9 percent. The researchers found that people in high-scoring metro areas take transit more: For every 10 percent increase in an index score, transit mode share (i.e., the portion of travelers who choose to use transit) increases by 11.5 percent. This means, for example, that a person in the Lincoln, NE metro area (index score: 132.0) is two and a half times more likely to choose transit for his or her transportation needs than a similar person in the Greenville, SC area (index score: 59.0). The researchers also found that people in high-scoring metro areas own fewer cars and spend less time driving. For every 10 percent increase in an index score, vehicle ownership rates decline by 0.6 percent and drive time declines by 0.5 percent.12 Data about transportation options are even more compelling at the county level. See Appendix B for that information. People in more compact, connected areas have longer, healthier and safer lives. Health data are available at the county level; for this reason, health outcomes are assessed at this scale rather than the MSA level. At the county level, an area’s compactness is also related to individuals’ health.13 10 First and foremost, people in compact, connected counties tend to live longer. For every doubling in an index score, life expectancy increases by about four percent.14 For the average American with a life expectancy of 78 years, this translates into a three-year difference in life expectancy between people in a less compact versus a more compact county. Driving rates (and their associated risk of a fatal collision), body mass index (BMI), air quality and violent crime all contribute to this difference, albeit in different ways. Counties with less sprawl have more car crashes, but fewer of those crashes are fatal. For every 10 percent increase in an index score, fatal crashes decrease by almost 15 percent. That means a person in Walker County, GA, for example, has nearly three times the chance of being in a fatal crash as compared with a similar person in Denver County, CO. The researchers found that BMI is strongly and negatively related to index scores. As a county’s index score decrease (that is, as a metro area sprawls more), the BMI of its population increases, after accounting for sociodemographic differences. For example, a 5’10” man living in Arlington County, VA is likely to weigh four pounds less than the same man living in Charles County, MD.15 Similarly, the likelihood of obesity increases. People in less sprawling counties also have significantly lower blood pressure and rates of diabetes. 11 Seeking better quality of life As this research shows, metro areas with more compact, connected neighborhoods are associated with better overall economic, health and safety outcomes—on average a better quality of life for everyone in that community. As residents and their elected leaders recognize the health, safety and economic benefits of better development strategies, many decisionmakers are reexamining their traditional zoning, economic development incentives, transportation decisions and other policies that have helped to create sprawling development patterns. Instead, they are choosing to create more connections, transportation choices and walkable neighborhoods in their communities. The following are examples of cities in metro areas that performed well on each of the four index factors, as well as the local public policies that contributed to their success. LAND USE MIX Santa Barbara, CA Santa Barbara, CA—the fourth most compact, connected metro area nationally—had the best score among small metro areas for its land use mix. Several public policies have contributed to Santa Barbara’s high land use mix score. Forward-thinking zoning codes The City of Santa Barbara’s zoning codes allow residential uses in most commercial zones.16 This is as a result of a public planning process in the 1990s that sought to create more affordable housing. The process resulted in amendments to the General Plan and Zoning Ordinance that encouraged mixed use developments in certain areas.17 Now, mixed use is characteristic of Santa Barbara’s urban form. Encouraging mixed use in the general plan The City of Santa Barbara also made this strategy a development priority by including it in the city’s 2011 General Plan Update. The update outlined three principles of development, one of which is to “encourage a mix of land uses to include strong retail and workplace centers, residential living in commercial centers with easy access to grocery stores and recreation, connectivity and civic engagement and public space for pedestrians.”18 County-level support Santa Barbara County, which encompasses the City of Santa Barbara, maintains community plans for unincorporated areas of the county. The county has established mixed use zones and encourages mixed use in many of the community plans in order to encourage a variety of uses throughout the county.19 12 ACTIVITY CENTERING Madison, WI The City of Madison, WI—the most compact, connected medium-sized metro area in the country—also had the highest score nationally for activity centering, meaning people and businesses are concentrated downtown and in subcenters. Several public policies have contributed to Madison’s high activity centering score. Homebuyer assistance programs Madison has several programs that help residents purchase homes, many of which encourage residency downtown and reinvestment in existing housing stock.20 One example is the Mansion Hill—James Madison Park Neighborhood Small Cap TIF Loan Program.21 This program provides zero percent interest, forgivable second mortgage loans to finance a portion of the purchase price and the rehabilitation costs of a residential property located in the Mansion Hill—James Madison Park neighborhood of downtown Madison. A comprehensive focus on downtown development In 1994, Madison adopted a series of strategic management system goals, which outlined ways for Madison to “share in the growth that is occurring in Dane County…in such a way to balance economic, social and environmental health.”22 Directing new growth toward existing urban areas, increasing owner-occupied housing in the city and creating economic development areas were all among the strategies recommended to achieve these goals. The goals later influenced the city’s 2006 comprehensive plan.23 Downtown Plan In 2012, the City of Madison adopted a new Downtown Plan, which aims to strengthen Madison’s downtown neighborhood. The plan includes nine strategies to guide the future growth of this core neighborhood while “sustaining the traditions, history and vitality that make Madison a model city.” STREET ACCESSIBILITY Trenton, NJ The street connectivity factor examines average block sizes; percent of urban blocks that are small; density of intersections; and percent of intersections that are four-way or more. Trenton, NJ—the seventh most compact, connected metro area nationally—had the highest score for street connectivity among all small- and medium-sized metro areas. A number of public policies helped Trenton achieve its high street connectivity score. A city designed for people Trenton is the historic center city of the larger metro area, and a number of small town centers surround it. This interconnected network of city and town centers encouraged reinvestment within the existing city grid. 13 Transportation Master Plan Trenton’s Transportation Master Plan focuses on maintaining the existing transportation network, using investments to support downtown and supporting multimodal options for all the neighborhoods.24 A walkable city, by definition, has small blocks and frequent intersections. The plan also places a high priority on key objectives to reach these goals, such as improve and maintain the city’s transit infrastructure, encourage transit-supportive land uses and avoid increases in street capacity unless addressing a critical transportation problem. Investing in transportation Greater Trenton has a long history of investing in transportation. In 1904, the state legislature appropriated $2 million to improve roads when other states with similar programs spent less than one-third that amount. Today, the metro area predominantly uses county bonds to maintain its road network and make improvements to its rail and bus service. DEVELOPMENT DENSITY Los Angeles, CA Los Angeles, CA, had the second-highest density score in the country, topped only by the New York metro area, an outlier nationally. Several public policies have contributed to Los Angeles’s high development density score. A plan for development around transit stations In 2012, Los Angeles’ Department of City Planning began an initiative to create detailed plans for development surrounding 10 light rail stations. The Los Angeles Transit Neighborhood Plans project “aims to support vibrant neighborhoods around transit stations, where people can live, work and shop or eat out, all within a safe and pleasant walk to transit stations.”25 Allowing higher density in exchange for affordable housing Los Angeles’ Affordable Housing Incentives Ordinance gives developers the option to build up to 25 percent above the otherwise allowable residential density level if they include affordable housing in their project.26 It also reduces parking requirements and expedites the development approval process. A zoning code for Los Angeles today and tomorrow In 2013, Los Angeles began a multi-year process to update its zoning code, which was first drafted in 1946. While this process is nascent, the city plans to have a new code in place by 2017. The new code will be web-based, easier to use and create a unified development code for projects downtown. These public policies have helped Santa Barbara, Madison, Trenton and Los Angeles achieve high index scores. These are by no means the only policies, however, that can improve how a community is built and the quality of life for the people who live there. For more ideas about local policy that can help your town grow in better ways visit www.smartgrowthamerica.org. 14 Conclusion How we choose to build and develop affects everyone’s day-to-day lives. How much we pay for housing and transportation, how long we spend commuting to and from work, economic opportunities in our communities and even personal health are all connected to how our neighborhoods and surrounding areas are built. This study shows that life expectancy, economic mobility, transportation choices and personal health and safety all improve in less sprawling areas. As individuals and their elected leaders recognize these benefits, many decisionmakers choose to encourage this type of growth through changes to public regulations and incentives. This report represents a rigorous statistical analysis of how communities have developed in the United States. It is not, however, a complete picture of every community across the country. The analysis included in this research is an important part of understanding how communities have developed in the United States. We recognize that qualitative information—such as the design of the streets and buildings, the quality of park space and the types of businesses nearby, among many other factors—also has a significant impact on the quality of life within a neighborhood and a region. Local elected officials, state leaders and federal lawmakers can all help communities as they seek to grow in ways that support these improved outcomes. Smart Growth America helps communities understand the long-term impact of their development decisions. We work with public and private sectors so local communities can achieve multiple outcomes such as increased economic mobility and improved personal health. By providing this type of research, alongside best practices used in many of these communities, we hope more places will closely consider development decisions as a key to long-term success. This report is an opportunity to reflect on many communities’ successes, and to highlight the places where we, as a country, can do better. Visit www.smartgrowthamerica.org to learn more about our work and how your community can grow in more compact, connected ways. 15 Appendix A: Full 2014 metro area Sprawl Index rankings Table 1A below contains the Sprawl Index scores for all 221 metro areas included in the 2014 analysis, as well as the score for each metro area in the four sprawl factors, based on 2010 data. All regions are census-defined Metropolitan Statistical Areas unless marked with an asterisk (*). Those places with an asterisk are Metropolitan Divisions, which comprise MSAs. Composite scores are controlled for population. TABLE A1 Metropolitan Statistical Areas Sprawl Index Scores, 2014 Rank Metro area 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 New York-White Plains-Wayne, NY-NJ* San Francisco-San Mateo-Redwood City, CA* Atlantic City-Hammonton, NJ Santa Barbara-Santa Maria-Goleta, CA Champaign-Urbana, IL Santa Cruz-Watsonville, CA Trenton-Ewing, NJ Miami-Miami Beach-Kendall, FL* Springfield, IL Santa Ana-Anaheim-Irvine, CA* Reading, PA Detroit-Livonia-Dearborn, MI* Madison, WI Burlington-South Burlington, VT Milwaukee-Waukesha-West Allis, WI Boulder, CO Appleton, WI Lincoln, NE Laredo, TX Erie, PA Los Angeles-Long Beach-Glendale, CA Spokane, WA Medford, OR San Jose-Sunnyvale-Santa Clara, CA Oakland-Fremont-Hayward, CA* Chicago-Joliet-Naperville, IL* Eugene-Springfield, OR Allentown-Bethlehem-Easton, PA-NJ Vallejo-Fairfield, CA Salem, OR Yakima, WA Ann Arbor, MI Philadelphia, PA* Tuscaloosa, AL Fargo, ND-MN South Bend-Mishawaka, IN-MI Bridgeport-Stamford-Norwalk, CT Fort Lauderdale-Pompano Beach-Deerfield Beach, FL* Las Vegas-Paradise, NV 39 Density score Land use mix score Activity centering score Street connectivity score Composite (total) score 384.29 185.97 96.33 112.28 100.00 98.88 115.88 160.18 90.39 161.91 102.22 125.20 101.00 88.32 113.31 106.89 90.65 111.55 104.20 97.73 187.39 98.98 89.67 149.50 136.28 145.50 95.35 98.76 105.38 93.11 90.95 103.27 141.01 85.85 99.18 90.94 110.63 140.93 159.34 167.17 100.10 148.85 123.27 146.15 128.00 136.41 100.51 155.02 121.83 124.65 115.83 102.21 126.73 115.32 99.81 132.99 117.12 130.61 160.18 115.82 115.31 148.76 145.75 140.09 125.70 128.59 132.03 123.48 117.91 105.04 142.25 68.60 118.65 94.08 132.86 136.53 213.49 230.92 154.52 109.48 153.64 107.90 97.36 117.91 160.03 79.64 129.72 107.48 168.11 168.79 153.40 100.09 156.72 96.74 99.89 113.69 115.66 108.57 128.06 86.80 88.11 143.24 116.84 101.10 79.32 113.50 133.08 123.11 115.95 154.72 106.96 111.91 118.02 61.79 193.80 162.83 130.71 122.05 82.81 112.18 139.06 166.90 96.74 181.81 113.76 183.98 94.85 70.68 130.35 118.95 79.92 96.78 106.87 88.92 154.40 128.26 80.42 131.45 159.44 160.21 91.29 135.97 115.90 98.10 65.81 89.95 140.06 92.03 73.56 118.68 100.81 153.66 203.36 194.28 150.36 146.59 145.16 145.02 144.71 144.12 142.24 139.86 137.90 137.17 136.69 135.06 134.18 133.68 132.69 131.95 131.25 130.39 130.33 129.40 128.86 128.76 127.24 125.90 125.63 124.40 124.16 123.35 123.19 122.76 122.42 122.18 121.82 121.71 121.64 121.41 142.12 105.02 136.42 114.29 121.20 16 Rank Metro area 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 Reno-Sparks, NV Stockton, CA New Orleans-Metairie-Kenner, LA Charlottesville, VA San Luis Obispo-Paso Robles, CA Huntington-Ashland, WV-KY-OH Bellingham, WA Corpus Christi, TX Waco, TX Nassau-Suffolk, NY* Lexington-Fayette, KY Saginaw-Saginaw Township North, MI New Haven-Milford, CT Seattle-Bellevue-Everett, WA* Scranton--Wilkes-Barre, PA Savannah, GA Charleston, WV Baltimore-Towson,* Salinas, CA Fort Collins-Loveland, CO Rockford, IL Bethesda-Rockville-Frederick, MD* Olympia, WA Santa Rosa-Petaluma, CA Oxnard-Thousand Oaks-Ventura, CA Lubbock, TX Modesto, CA Wilmington, DE-MD-NJ* Lancaster, PA Manchester-Nashua, NH Cedar Rapids, IA College Station-Bryan, TX Lansing-East Lansing, MI Beaumont-Port Arthur, TX Lafayette, LA Harrisburg-Carlisle, PA Gainesville, FL Tyler, TX Peoria, IL Chico, CA Portland-Vancouver-Hillsboro, OR-WA Newark-Union, NJ-PA* Las Cruces, NM Bremerton-Silverdale, WA Norwich-New London, CT Provo-Orem, UT Omaha-Council Bluffs, NE-IA Columbus, GA-AL Portland-South Portland-Biddeford, ME Amarillo, TX Tacoma, WA* Washington-Arlington-Alexandria, DC-VA-MDWV* Density score Land use mix score Activity centering score Street connectivity score Composite (total) score 100.78 106.54 104.84 91.16 89.90 84.25 85.29 98.68 87.96 123.33 99.56 86.77 106.86 121.27 91.28 90.08 83.81 115.97 101.65 94.53 94.78 115.08 89.23 93.70 107.91 97.23 109.91 102.42 95.61 95.10 92.94 102.49 101.03 85.37 90.03 93.54 94.58 85.76 88.93 91.18 111.14 126.86 89.33 90.48 87.22 104.53 102.64 94.45 86.06 96.16 103.62 122.35 93.69 135.75 117.83 86.08 119.8 67.73 92.75 118.31 96.10 144.75 110.42 93.77 127.52 123.99 116.46 84.94 67.01 123.21 116.00 106.30 110.04 123.84 80.87 132.31 133.35 116.70 140.69 109.29 110.05 104.38 105.64 94.65 92.21 88.45 87.35 102.14 87.63 72.48 100.39 114.46 136.12 139.67 84.27 87.55 84.71 123.55 120.53 84.78 79.09 109.27 105.56 117.61 137.29 82.11 96.09 141.81 103.87 142.77 113.43 90.15 100.62 81.01 115.34 110.97 113.51 121.68 95.07 115.36 136.8 123.12 102.94 96.44 91.83 98.97 121.00 91.91 78.01 87.56 62.32 96.53 124.31 114.15 104.67 91.03 141.56 112.62 115.90 99.29 102.79 122.62 109.76 88.79 100.81 90.43 108.16 112.87 137.44 77.37 99.67 125.19 157.47 76.98 92.25 133.16 94.06 121.04 149.94 71.77 88.53 108.91 96.89 110.41 107.83 155.85 95.11 93.62 97.82 131.86 123.01 115.03 112.05 136.35 90.70 100.59 107.05 118.94 98.73 96.82 118.31 90.44 102.89 120.29 84.74 89.28 81.25 91.47 72.80 113.76 92.72 119.17 99.45 93.19 97.72 79.93 124.98 113.76 89.06 86.20 71.04 100.08 103.54 77.79 80.24 91.56 119.05 125.91 120.85 120.28 119.74 119.08 118.90 118.43 118.01 117.29 117.11 117.04 116.76 116.62 116.29 116.11 115.84 115.81 115.68 115.62 115.19 115.15 114.98 114.66 114.63 113.92 113.87 113.41 113.28 112.94 112.64 112.19 111.81 111.72 111.61 111.54 111.44 111.4 111.36 110.66 110.49 109.94 109.85 109.62 109.17 108.86 108.85 108.45 108.42 108.38 107.72 107.49 107.48 107.21 17 Rank Metro area 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 Denver-Aurora-Broomfield, CO Canton-Massillon, OH Salt Lake City, UT Lafayette, IN Flint, MI Buffalo-Niagara Falls, NY Colorado Springs, CO Merced, CA El Paso, TX Davenport-Moline-Rock Island, IA-IL North Port-Bradenton-Sarasota, FL San Diego-Carlsbad-San Marcos, CA York-Hanover, PA Kennewick-Pasco-Richland, WA Des Moines-West Des Moines, IA Virginia Beach-Norfolk-Newport News, VA-NC Providence-New Bedford-Fall River, RI-MA Greeley, CO Camden, NJ* Akron, OH Duluth, MN-WI Lake County-Kenosha County, IL-WI* Austin-Round Rock-San Marcos, TX Sioux Falls, SD Dayton, OH Toledo, OH Houma-Bayou Cane-Thibodaux, LA Ogden-Clearfield, UT Sacramento-Arden-Arcade-Roseville, CA Cape Coral-Fort Myers, FL Tallahassee, FL Charleston-North Charleston-Summerville, SC Tampa-St. Petersburg-Clearwater, FL West Palm Beach-Boca Raton-Boynton Beach, FL* Albuquerque, NM Mobile, AL Edison-New Brunswick, NJ* Gary, IN* Syracuse, NY Binghamton, NY Pittsburgh, PA Albany-Schenectady-Troy, NY Topeka, KS Hagerstown-Martinsburg,*-WV Roanoke, VA Hartford-West Hartford-East Hartford, CT Columbus, OH Fresno, CA Wichita, KS Evansville, IN-KY Visalia-Porterville, CA Montgomery, AL 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 Density score Land use mix score Activity centering score Street connectivity score Composite (total) score 118.31 90.54 117.77 95.46 89.57 107.94 102.94 93.90 114.90 91.78 97.45 125.08 90.92 92.84 97.68 106.41 105.40 87.33 105.39 94.55 85.24 101.65 100.42 97.68 93.65 95.30 83.73 100.96 111.65 91.87 91.64 95.29 105.18 110.73 119.44 106.64 125.49 90.63 90.58 127.67 108.37 114.76 99.42 121.21 101.45 130.37 95.83 108.63 120.63 105.24 83.28 99.05 125.72 113.13 89.56 112.39 99.66 104.85 114.40 120.34 75.47 120.39 119.11 81.41 68.25 89.19 105.35 121.02 109.11 76.45 93.32 94.82 114.82 102.46 75.94 96.48 73.41 70.03 84.95 100.90 113.20 81.96 99.46 102.38 112.77 94.05 78.53 90.69 117.03 67.78 138.78 95.96 95.13 85.46 106.77 62.22 104.19 91.52 130.77 108.94 93.00 69.66 125.16 117.92 97.63 83.10 97.49 95.10 121.76 66.25 128.66 102.95 126.69 119.95 90.32 85.86 82.83 131.60 141.95 85.82 120.07 106.81 77.22 132.08 102.88 60.16 105.55 95.85 86.11 103.52 108.92 126.34 79.80 99.03 150.09 118.46 107.10 106.99 106.96 106.55 106.48 106.36 106.33 105.86 105.64 105.59 105.49 105.18 105.12 105.03 104.90 104.45 104.34 103.61 103.22 103.15 103.14 103.10 102.44 101.75 101.48 100.90 100.13 99.58 99.27 99.22 98.95 98.53 98.49 98.18 103.60 92.43 109.41 94.53 94.75 89.70 96.16 95.40 88.98 84.10 90.65 100.12 101.58 101.75 95.63 91.57 91.94 90.01 102.57 88.23 125.05 107.73 100.93 88.92 115.14 105.96 83.12 74.10 85.88 113.10 112.24 126.18 107.27 92.59 106.37 85.97 99.36 78.79 69.02 82.31 122.57 102.07 107.78 108.19 102.18 112.54 83.67 119.54 95.56 81.45 88.57 86.07 79.64 98.71 97.51 112.30 137.91 106.33 69.91 69.84 119.33 86.04 71.38 78.51 93.21 72.59 112.19 82.42 83.65 84.34 83.98 80.50 98.07 97.48 96.77 96.70 96.65 95.97 95.45 95.12 94.82 94.13 93.77 93.50 93.00 92.24 91.74 91.67 91.55 91.20 18 Rank Metro area Density score Land use mix score Activity centering score Street connectivity score Composite (total) score 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 Boise City-Nampa, ID Deltona-Daytona Beach-Ormond Beach, FL Myrtle Beach-North Myrtle Beach-Conway, SC Minneapolis-St. Paul-Bloomington, MN-WI Lakeland-Winter Haven, FL Gulfport-Biloxi, MS Fort Wayne, IN Tulsa, OK Dallas-Plano-Irving, TX* Cleveland-Elyria-Mentor, OH Utica-Rome, NY Raleigh-Cary, NC Orlando-Kissimmee-Sanford, FL Springfield, MO Indianapolis-Carmel, IN McAllen-Edinburg-Mission, TX Killeen-Temple-Fort Hood, TX Louisville/Jefferson County, KY-IN Oklahoma City, OK St. Louis, MO-IL Bakersfield-Delano, CA Jacksonville, FL Cincinnati-Middletown, OH-KY-IN Port St. Lucie, FL Macon, GA Poughkeepsie-Newburgh-Middletown, NY Grand Rapids-Wyoming, MI Tucson, AZ Fort Worth-Arlington, TX* Phoenix-Mesa-Glendale, AZ Holland-Grand Haven, MI Youngstown-Warren-Boardman, OH-PA Huntsville, AL Palm Bay-Melbourne-Titusville, FL Kansas City, MO-KS San Antonio-New Braunfels, TX Wilmington, NC Pensacola-Ferry Pass-Brent, FL Houston-Sugar Land-Baytown, TX Asheville, NC Richmond, VA Little Rock-North Little Rock-Conway, AR Naples-Marco Island, FL Brownsville-Harlingen, TX Ocala, FL Rochester, NY Spartanburg, SC Durham-Chapel Hill, NC Birmingham-Hoover, AL Longview, TX Shreveport-Bossier City, LA Jackson, MS Memphis, TN-MS-AR 95.80 91.35 83.43 105.92 87.51 86.03 92.42 90.54 111.46 105.11 90.87 96.99 102.40 89.10 98.11 94.43 89.16 98.44 94.64 97.68 101.29 96.81 98.75 92.74 84.72 89.38 91.39 100.79 103.71 111.60 86.45 87.36 86.18 96.94 96.84 100.67 85.89 88.54 108.3 80.71 96.36 88.00 91.57 90.92 80.80 96.12 81.26 91.59 86.67 81.66 87.79 87.35 96.6 110.45 88.02 54.95 110.34 54.24 69.80 93.70 92.40 105.90 123.72 83.53 87.30 85.79 89.25 99.65 76.78 79.86 89.48 96.26 108.29 114.13 82.50 107.80 77.05 71.90 95.38 91.78 90.96 100.89 102.36 81.52 100.76 58.29 79.64 109.49 93.56 73.12 81.12 102.66 64.12 78.08 75.36 81.95 77.74 41.30 103.86 68.26 74.84 67.88 71.62 76.94 64.41 77.76 75.15 66.48 104.88 111.41 95.32 80.53 89.90 93.54 94.21 95.54 98.35 109.43 89.29 75.99 98.42 90.99 78.17 93.12 89.86 93.86 76.82 90.17 98.95 62.73 86.32 97.49 99.15 78.71 72.55 96.37 78.64 74.10 89.43 60.02 80.45 95.15 83.92 75.12 92.56 97.61 101.95 93.55 55.19 51.43 105.49 96.77 91.26 80.27 99.52 81.06 72.39 105.46 94.23 91.88 116.35 95.40 108.60 128.15 97.52 73.85 103.35 129.74 84.96 61.91 88.16 129.14 91.87 102.31 104.60 94.80 102.87 100.38 113.80 73.14 111.76 93.67 106.43 74.47 70.30 74.75 94.72 117.21 111.33 71.71 81.52 99.31 105.42 103.52 102.43 84.13 88.65 129.43 88.53 92.83 90.35 90.69 105.96 91.78 62.00 72.48 84.98 105.21 68.46 84.53 73.8 90.62 91.06 89.68 88.70 88.69 87.64 87.61 86.67 86.65 86.15 85.62 84.71 84.25 83.97 83.96 83.89 83.89 83.12 82.92 82.07 82.06 81.78 80.85 80.75 80.75 79.92 79.51 79.18 78.92 78.56 78.32 78.17 78.08 78.02 77.91 77.60 77.37 77.27 76.84 76.74 76.52 76.41 76.08 75.23 74.69 74.67 74.50 74.00 73.84 73.55 73.06 72.63 72.30 70.77 19 Rank Metro area Density score Land use mix score Activity centering score Street connectivity score Composite (total) score 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 Charlotte-Gastonia-Rock Hill, NC-SC Kalamazoo-Portage, MI Knoxville, TN Columbia, SC Warren-Troy-Farmington Hills, MI* Fayetteville-Springdale-Rogers, AR-MO Fayetteville, NC Green Bay, WI Fort Smith, AR-OK Lynchburg, VA Chattanooga, TN-GA Greensboro-High Point, NC Winston-Salem, NC Florence, SC Lake Havasu City-Kingman, AZ Kingsport-Bristol-Bristol, TN-VA Augusta-Richmond County, GA-SC Greenville-Mauldin-Easley, SC Riverside-San Bernardino-Ontario, CA Baton Rouge, LA Nashville-Davidson-Murfreesboro-Franklin, TN Prescott, AZ Clarksville, TN-KY Atlanta-Sandy Springs-Marietta, GA Hickory-Lenoir-Morganton, NC 94.55 85.55 88.10 89.63 97.88 84.55 91.13 89.90 80.74 81.51 86.14 88.22 86.43 81.22 85.24 78.73 85.25 86.69 103.72 91.27 91.54 82.33 84.48 97.80 78.64 84.71 75.00 60.62 69.14 110.33 67.95 71.69 90.49 56.78 57.07 61.15 80.57 68.62 51.13 55.15 40.53 60.69 72.89 111.18 72.03 63.92 53.19 39.67 85.47 40.46 103.05 85.58 100.77 108.38 70.54 80.67 72.57 66.77 75.30 76.38 94.27 84.94 87.42 87.85 73.04 89.67 88.47 81.15 77.03 69.74 96.17 58.15 74.47 89.89 67.00 86.93 64.97 82.53 66.63 96.17 81.81 71.77 53.34 86.02 77.42 72.90 70.70 68.47 61.44 65.97 82.87 73.85 71.40 80.33 80.40 77.00 69.96 60.83 75.92 56.95 70.45 70.32 68.22 67.45 67.03 66.26 66.02 65.35 64.84 63.97 63.63 63.50 63.44 61.06 60.13 60.00 59.18 58.98 56.25 55.60 51.74 48.96 41.49 40.99 24.86 Appendix B: County-level information County-level findings Table B1 below shows Sprawl Index scores for all metropolitan counties. As discussed on page 10 of this report, this research shows that people in high-scoring metro areas have more transportation options than people in lower-scoring metro areas. In addition to conducting this analysis at the metro-area level, the researchers also examined this question at the county level, where the findings and their implications for everyday life are even more compelling. High-scoring counties have lower rates of car ownership. For every 10 percent increase in an index score, car ownership decreases by 3.8 percent. High-scoring counties have higher rates of walking. For every 10 percent increase in an index score, the proportion of people who choose to walk as a mode of transportation increases by 6.6 percent. More people in high-scoring counties ride public transit. For every 10 percent increase in an index score, the proportion of transit users in the county increases by 24 percent. People in high-scoring counties spend less time driving. For every 10 percent increase in an index score at the county level, people spend on average 3.5 percent less time driving. Data were not available for a limited number of counties. Factors are provided where available. 20 TABLE B1 County-level Sprawl Index Scores, 2014 County Blount County Calhoun County Chilton County Colbert County Elmore County Etowah County Houston County Jefferson County Lauderdale County Lawrence County Lee County Limestone County Madison County Mobile County Montgomery County Morgan County Russell County St. Clair County Shelby County Tuscaloosa County Walker County Coconino County Maricopa County Mohave County Pima County Pinal County Yavapai County Yuma County Benton County Craighead County Crawford County Crittenden County Faulkner County Garland County Grant County Jefferson County Lincoln County Lonoke County Madison County Miller County Poinsett County Pulaski County State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AZ AZ AZ AZ AZ AZ AZ AR AR AR AR AR AR AR AR AR AR AR AR AR AR 90.36 91.58 89.98 95.11 91.59 93.78 94.83 99.01 94.46 89.38 96.48 91.62 97.61 99.06 102.14 96.47 94.83 91.04 94.43 96.71 90.60 95.58 110.50 96.20 102.91 96.42 96.00 99.68 95.22 95.83 92.25 96.93 95.11 92.69 89.11 94.66 88.97 91.76 88.44 97.29 89.31 100.95 37.85 86.70 52.55 104.27 60.63 91.28 102.37 110.72 84.43 51.74 87.90 58.45 98.59 108.17 120.67 95.35 90.91 55.96 91.33 101.44 65.74 105.89 118.07 90.76 109.55 74.63 89.71 105.56 95.05 97.46 90.19 115.43 92.10 89.51 79.34 97.82 51.59 79.64 61.16 106.83 105.78 111.48 74.28 117.70 81.61 76.99 86.59 116.86 98.64 122.44 105.63 86.98 104.17 89.78 103.31 93.94 118.34 116.51 78.65 81.95 88.20 136.82 86.66 159.70 118.48 97.35 129.25 93.08 88.28 142.91 104.81 113.68 82.88 79.24 83.67 116.53 77.98 96.55 72.47 91.84 73.67 82.03 77.99 116.72 60.14 104.38 62.37 124.68 85.71 93.10 88.97 126.81 88.50 66.67 84.55 82.64 114.82 113.78 105.98 101.04 93.54 84.47 92.91 110.56 92.50 80.11 118.04 95.37 101.54 100.74 86.40 107.38 89.33 76.68 80.03 89.18 74.78 103.18 60.72 113.66 62.71 75.65 72.44 115.58 71.03 127.01 56.60 100.11 64.14 100.33 76.15 98.43 95.20 118.64 91.48 66.75 91.50 75.51 104.53 104.72 114.89 102.96 86.71 72.65 89.53 114.39 79.62 113.04 120.56 93.58 113.66 88.90 87.49 117.54 95.07 94.83 82.74 93.93 82.83 100.60 70.67 100.85 60.74 80.69 67.05 100.54 82.34 117.74 21 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Saline County Sebastian County Washington County Alameda County Butte County Contra Costa County El Dorado County Fresno County Imperial County Kern County Kings County Los Angeles County Madera County Marin County Merced County Monterey County Napa County Orange County Placer County Riverside County Sacramento County San Benito County San Bernardino County San Diego County San Francisco County San Joaquin County San Luis Obispo County San Mateo County Santa Barbara County Santa Clara County Santa Cruz County Shasta County AR AR AR CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA 92.78 97.44 98.58 137.65 99.20 112.02 96.18 103.35 99.38 102.91 100.77 152.55 96.68 109.25 100.54 109.05 102.69 134.15 101.97 105.36 115.28 103.10 106.82 118.35 250.84 106.50 97.52 130.72 116.62 131.02 104.20 96.00 80.99 103.71 104.46 143.40 121.87 128.70 88.17 127.85 132.78 121.33 115.21 145.20 110.34 141.52 122.04 122.36 135.45 142.55 116.93 117.55 128.54 115.79 122.13 129.64 153.79 132.92 124.79 144.53 139.70 139.68 138.71 110.79 106.43 93.42 109.89 115.28 106.28 100.81 84.58 104.03 99.61 99.62 108.98 121.62 104.67 96.85 112.80 110.26 131.01 95.13 90.93 108.49 135.70 78.56 95.87 121.82 258.47 104.79 111.43 93.82 112.02 107.58 114.16 114.25 75.80 108.24 91.83 151.09 91.90 121.28 77.80 94.25 82.71 92.21 90.98 141.02 69.69 111.15 85.94 101.72 110.28 144.21 98.05 98.38 129.68 105.10 92.42 116.14 215.72 118.62 102.74 131.35 116.13 132.85 107.34 88.66 86.10 100.89 101.50 146.57 106.08 119.84 83.17 109.31 104.58 105.08 105.04 150.67 94.12 118.57 106.74 113.71 125.09 136.66 102.49 109.41 134.50 100.81 105.45 127.15 251.27 119.85 111.53 131.72 126.69 135.11 120.35 103.07 Solano County Sonoma County Stanislaus County Sutter County Tulare County Ventura County Yolo County Yuba County Adams County Arapahoe County Boulder County Broomfield County Clear Creek County CA CA CA CA CA CA CA CA CO CO CO CO CO 106.86 100.37 107.86 98.92 100.44 110.13 107.3 97.57 106.63 114.44 107.71 105.87 90.58 130.60 131.12 135.71 119.22 117.82 131.48 126.92 95.43 122.25 124.30 122.00 113.80 67.38 103.94 101.87 94.54 126.45 102.53 99.80 98.50 82.17 82.26 102.43 111.33 83.11 – 114.95 97.67 107.84 82.89 93.41 114.98 110.10 89.37 122.37 134.20 115.52 129.14 117.81 117.80 109.81 114.52 108.68 104.49 117.82 113.53 88.80 110.59 123.81 117.87 110.09 – County 22 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Denver County Douglas County Elbert County El Paso County Jefferson County Larimer County Mesa County Pueblo County Teller County Weld County Fairfield County Hartford County Middlesex County New Haven County New London County Tolland County Kent County New Castle County District of Columbia Alachua County Baker County Bay County Brevard County Broward County Charlotte County Clay County Collier County Duval County Escambia County Flagler County Gadsden County Hernando County CO CO CO CO CO CO CO CO CO CO CT CT CT CT CT CT DE DE DC FL FL FL FL FL FL FL FL FL FL FL FL FL 129.34 102.77 88.27 104.62 106.94 100.68 101.69 100.43 94.68 97.29 110.88 107.85 95.74 107.16 96.76 96.05 94.72 108.44 193.52 100.66 89.21 99.21 102.39 120.61 94.98 97.16 99.42 106.31 99.94 96.82 90.27 96.20 137.67 97.61 44.14 119.18 125.25 117.76 113.73 112.15 82.25 114.35 131.47 126.56 116.02 128.91 106.51 89.61 97.37 126.15 138.05 110.17 63.21 105.55 103.2 133.24 97.96 92.55 104.70 113.10 109.08 82.32 57.12 80.29 174.54 92.17 72.69 95.89 90.89 111.95 124.35 112.96 81.88 111.18 125.41 138.02 98.90 137.15 131.52 97.77 102.26 111.75 219.97 115.43 89.68 93.70 86.39 95.43 103.74 98.14 83.67 118.71 100.14 79.96 83.72 108.25 181.54 97.77 50.26 123.96 112.99 103.05 107.33 121.67 108.04 95.06 101.99 92.46 81.98 102.88 85.24 63.29 89.82 121.39 185.15 107.74 61.02 115.16 110.4 148.86 114.83 95.40 105.06 125.06 116.67 99.05 95.13 102.08 170.48 96.94 54.30 113.79 111.40 110.57 114.88 114.91 89.53 105.65 122.04 120.50 97.68 124.04 106.33 83.17 95.00 121.40 206.37 110.74 69.39 104.31 100.75 131.01 103.64 94.71 97.74 119.96 108.16 86.78 76.69 95.84 Hillsborough County Indian River County Lake County Lee County Leon County Manatee County Marion County Martin County Miami-Dade County Nassau County Okaloosa County Orange County Osceola County FL FL FL FL FL FL FL FL FL FL FL FL FL 106.16 97.10 95.53 98.87 102.05 102.17 93.51 98.62 137.38 93.25 100.20 108.01 98.45 115.63 101.81 87.32 104.60 106.83 114.33 83.3 110.16 132.85 78.04 113.18 110.76 86.64 127.60 112.72 121.33 119.36 149.96 112.33 140.38 106.69 131.33 98.01 109.67 118.48 87.23 128.18 132.01 116.84 121.83 99.11 129.01 98.85 113.84 156.48 97.21 105.87 124.47 114.77 124.51 113.79 106.64 114.11 118.31 118.27 105.07 109.26 149.93 89.42 109.14 119.5 95.92 County 23 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Palm Beach County Pasco County Pinellas County Polk County St. Johns County St. Lucie County Santa Rosa County Sarasota County Seminole County Volusia County Wakulla County Barrow County Bartow County Bibb County Bryan County Butts County Carroll County Catoosa County Chatham County Chattahoochee County Cherokee County Clarke County Clayton County Cobb County Columbia County Coweta County Dade County Dawson County DeKalb County Dougherty County Douglas County Effingham County FL FL FL FL FL FL FL FL FL FL FL GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA 107.77 99.18 114.66 96.76 97.43 100.74 92.28 101.61 105.12 99.33 89.66 92.36 90.76 98.07 89.84 91.10 92.24 93.34 99.64 97.14 97.06 100.91 106.35 106.99 96.83 92.69 89.57 89.94 111.99 97.65 95.83 91.03 125.08 100.48 132.11 90.29 86.85 97.46 93.99 116.04 116.39 107.91 45.54 70.78 77.69 113.15 61.04 82.26 80.47 79.45 117.03 100.48 94.58 115.76 106.15 116.91 95.43 85.33 56.36 63.53 120.73 109.27 89.53 60.74 107.06 84.02 93.74 115.86 85.06 102.45 81.78 113.62 81.81 100.70 78.68 85.30 86.60 103.59 81.95 87.09 108.64 88.25 126.17 70.87 80.91 98.31 84.62 91.39 80.24 81.74 80.64 86.08 96.18 95.60 103.33 84.13 118.32 117.84 163.76 120.94 106.86 120.07 80.59 124.42 121.13 115.72 79.41 72.18 80.47 112.70 71.54 67.51 59.41 78.55 126.88 98.62 83.44 92.89 98.10 107.76 72.04 72.61 69.91 69.43 100.65 107.90 70.96 75.90 118.40 100.48 132.94 107.53 92.48 106.54 83.78 117.59 107.72 107.47 66.29 74.92 79.63 108.69 69.79 77.24 81.28 80.91 122.03 89.61 86.10 102.49 98.49 107.28 82.48 78.64 67.30 71.24 109.34 103.30 87.25 72.13 Fayette County Floyd County Forsyth County Fulton County Glynn County Gwinnett County Hall County Haralson County Harris County Henry County Houston County Jones County Lamar County GA GA GA GA GA GA GA GA GA GA GA GA GA 93.23 92.92 96.31 107.63 92.87 106.36 94.45 90.08 89.51 95.26 99.67 90.26 90.01 94.36 90.67 91.93 122.60 102.00 111.94 89.10 73.41 34.28 81.75 97.7 80.32 68.75 100.88 103.37 97.11 146.48 95.73 88.70 139.3 78.3 71.89 86.07 89.66 81.59 79.24 78.34 89.35 68.48 108.57 111.38 89.68 87.59 82.15 62.25 74.28 91.56 59.82 69.42 89.51 92.52 85.41 126.94 100.62 98.95 103.3 75.97 55.12 80.21 93.23 72.19 70.75 County 24 Activity centering score Street connectivity score 67.38 88.85 91.72 72.18 61.79 65.55 66.44 68.86 108.41 77.77 69.72 45.28 74.96 61.08 104.91 86.78 85.73 74.53 75.62 54.96 88.51 68.22 108.68 124.04 109.57 106.10 113.29 88.98 101.44 113.12 121.33 109.49 69.06 91.21 98.88 71.40 61.49 64.31 63.06 68.85 120.64 86.46 74.87 45.49 74.76 68.89 112.49 87.13 88.83 74.90 79.24 70.32 95.63 66.48 112.28 124.18 108.39 102.41 89.06 78.26 111.14 107.00 – 87.79 80.13 100.72 106.87 78.49 73.41 79.40 77.43 84.75 133.98 123.65 74.86 70.81 83.49 81.67 124.13 82.64 102.12 78.22 88.88 87.33 115.72 84.69 102.02 128.18 99.62 90.60 76.44 83.29 122.32 92.82 70.12 129.58 95.37 127.58 87.01 141.34 111.36 135.96 136.48 101.16 116.08 78.12 120.57 119.77 90.54 81.63 141.54 82.04 155.66 84.27 88.41 78.31 86.63 84.59 85.72 90.86 105.98 82.01 85.74 107.66 94.50 170.12 93.39 126.48 83.16 110.27 81.22 85.66 109.06 97.47 95.42 87.08 127.19 85.06 169.04 96.51 119.67 96.19 97.17 91.31 80.72 109.11 105.96 88.08 State Density score Land use mix score Lee County Liberty County Lowndes County McDuffie County Madison County Meriwether County Monroe County Murray County Muscogee County Newton County Oconee County Oglethorpe County Paulding County Pickens County Richmond County Rockdale County Spalding County Terrell County Walker County Walton County Whitfield County Worth County Ada County Bannock County Bonneville County Canyon County Gem County Jefferson County Kootenai County Nez Perce County Alexander County Bond County GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA ID ID ID ID ID ID ID ID IL IL 90.74 96.95 95.78 89.94 89.81 89.17 89.72 90.63 103.92 94.48 90.84 88.61 93.49 90.19 99.09 95.92 93.04 88.84 91.84 91.96 94.64 88.76 103.58 101.28 98.84 98.64 92.23 89.10 97.55 99.34 89.05 91.76 63.81 85.66 102.08 68.85 53.09 52.92 49.47 57.18 119.01 61.24 85.05 22.76 68.19 68.61 111.4 93.91 83.74 78.95 77.95 71.8 87.29 52.25 124.60 123.06 118.52 112.28 83.41 69.82 113.96 116.89 – Boone County Champaign County Clinton County Cook County DeKalb County DuPage County Ford County Grundy County Henry County Jersey County Kane County Kankakee County Kendall County IL IL IL IL IL IL IL IL IL IL IL IL IL 96.36 109.28 89.17 151.40 99.94 111.41 90.00 92.99 90.62 89.46 108.34 95.65 94.30 County 25 Composite (total) score 105.89 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Lake County McHenry County McLean County Macon County Macoupin County Madison County Marshall County Menard County Mercer County Monroe County Peoria County Piatt County Rock Island County St. Clair County Sangamon County Tazewell County Vermilion County Will County Winnebago County Woodford County Allen County Bartholomew County Boone County Brown County Carroll County Clark County Clay County Dearborn County Delaware County Elkhart County Floyd County Franklin County IL IL IL IL IL IL IL IL IL IL IL IL IL IL IL IL IL IL IL IL IN IN IN IN IN IN IN IN IN IN IN IN 103.98 98.53 104.94 95.56 92.20 96.83 89.56 88.81 88.81 89.84 100.95 88.83 101.09 96.60 97.54 96.01 91.84 101.35 100.8 89.23 100.69 96.38 94.39 92.73 89.42 97.57 91.51 91.96 103.15 94.95 101.1 90.85 121.02 105.24 120.63 114.15 111.71 119.34 95.57 90.20 97.30 90.63 120.84 107.89 128.28 114.62 115.25 107.55 99.84 114.01 123.79 111.21 113.30 101.42 103.90 36.11 86.26 113.96 101.15 82.67 118.8 104.81 121.02 54.82 97.08 83.23 110.85 112.75 78.10 103.17 68.03 83.80 71.15 77.62 143.87 81.61 104.97 90.19 157.52 85.37 112.75 92.55 117.91 85.84 110.06 108.25 79.83 76.30 86.24 86.06 76.58 89.51 91.63 89.66 86.15 78.33 118.15 95.57 102.41 97.28 115.16 114.28 113.51 84.09 95.19 91.70 112.87 83.39 116.10 113.08 108.44 110.59 117.88 100.58 120.01 94.01 100.51 114.65 90.61 63.42 85.98 107.2 109.38 96.29 109.13 114.82 99.15 95.48 112.71 94.49 112.27 106.24 99.10 110.62 89.47 83.22 84.98 84.14 124.81 87.90 115.93 104.58 124.88 99.85 107.05 102.68 119.75 93.77 107.76 106.54 90.12 58.47 83.54 101.51 93.25 87.50 107.18 101.34 102.35 74.56 Gibson County Greene County Hamilton County Hancock County Harrison County Hendricks County Howard County Jasper County Johnson County Lake County LaPorte County Madison County Marion County IN IN IN IN IN IN IN IN IN IN IN IN IN 92.92 90.44 99.85 93.31 91.11 95.72 98.37 89.52 98.31 102.28 95.04 96.40 108.62 109.39 93.15 104.30 95.10 56.70 91.32 114.28 90.18 116.23 124.13 104.81 113.83 123.19 77.46 82.02 81.69 82.93 85.50 79.42 95.94 73.22 81.08 124.40 108.11 107.92 125.02 124.54 88.86 94.95 84.80 61.31 89.16 109.61 51.82 102.48 126.26 96.11 112.32 127.04 101.36 85.62 93.93 86.14 66.71 85.98 105.75 69.90 99.40 124.35 101.29 109.63 126.50 County 26 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Monroe County Morgan County Ohio County Owen County Porter County Posey County Putnam County St. Joseph County Shelby County Sullivan County Tippecanoe County Tipton County Vanderburgh County Vermillion County Vigo County Warrick County Washington County Wells County Whitley County Benton County Black Hawk County Bremer County Dallas County Dubuque County Harrison County Johnson County Jones County Linn County Madison County Mills County Polk County Pottawattamie County IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN IA IA IA IA IA IA IA IA IA IA IA IA IA 104.36 94.61 91.06 91.06 96.95 92.19 91.01 100.67 98.24 89.97 104.58 89.55 101.79 103.23 96.90 99.66 94.15 89.98 90.31 88.87 99.10 89.00 95.45 100.57 89.16 103.02 89.77 100.19 90.62 89.93 102.96 97.53 112.59 85.99 97.13 35.65 108.40 75.20 96.03 117.65 116.00 94.33 112.14 85.73 119.70 90.48 111.19 102.11 67.81 90.10 89.14 108.97 129.91 112.79 106.94 130.56 113.13 124.12 115.53 118.29 124.56 84.78 129.31 120.78 163.85 85.60 78.90 78.62 88.88 81.37 82.78 124.80 82.26 85.42 101.52 80.10 120.43 79.32 114.75 81.65 80.30 83.04 84.12 90.60 94.20 82.24 79.89 115.08 76.21 157.95 71.55 121.29 70.25 77.08 116.94 95.92 98.52 99.46 99.39 99.32 87.95 81.92 73.04 131.20 97.84 79.03 96.00 62.84 116.35 155.06 128.65 82.32 87.16 70.18 56.30 97.81 118.50 77.70 91.67 106.99 76.79 85.78 95.83 103.21 103.16 92.04 112.82 99.22 125.06 89.15 89.41 69.87 94.37 78.10 81.95 123.48 98.21 83.81 104.5 74.17 118.41 108.87 116.27 89.18 77.70 78.93 74.69 95.65 113.18 87.91 91.77 116.81 85.87 122.39 91.37 113.58 96.40 82.25 119.60 104.25 Scott County Story County Warren County Washington County Woodbury County Butler County Douglas County Franklin County Geary County Harvey County Jackson County Johnson County Leavenworth County IA IA IA IA IA KS KS KS KS KS KS KS KS 100.21 96.60 93.98 90.00 97.33 95.93 100.21 89.92 96.96 90.56 88.64 104.45 95.13 128.03 115.01 105.61 104.89 125.17 116.69 127.37 101.1 – 85.19 125.73 82.31 78.56 117.13 81.59 99.68 85.19 84.76 75.64 79.63 86.47 87.24 130.22 97.63 83.56 86.53 122.41 76.86 98.22 101.84 128.69 73.36 44.65 101.88 93.72 113.79 111.05 89.09 87.36 119.60 90.86 108.05 93.07 – County 115.17 77.77 125.43 99.39 27 85.7 65.47 105.76 92.25 Activity centering score Street connectivity score 118.91 111.59 98.41 113.88 101.93 93.99 98.55 83.26 124.27 94.37 102.00 109.86 128.66 52.57 87.52 90.76 105.95 76.60 119.34 102.50 117.51 63.30 46.63 66.86 74.42 97.32 91.76 31.97 79.03 68.66 81.55 93.38 117.57 125.79 84.72 103.10 95.37 80.83 126.68 81.17 85.29 87.11 79.27 121.56 134.26 80.01 78.55 131.65 76.39 77.64 118.64 84.93 88.49 84.72 84.90 78.24 80.90 80.79 112.29 75.02 102.93 75.02 95.3 105.56 112.30 108.80 92.96 127.92 84.83 92.96 104.55 86.62 109.72 104.06 98.84 106.12 116.37 76.95 112.22 93.87 103.24 85.73 123.85 91.02 119.32 65.93 78.41 89.54 81.70 97.28 86.78 76.42 116.34 114.14 88.76 114.79 94.26 88.94 107.65 83.25 106.95 94.59 91.64 111.6 128.22 68.44 91.41 103.72 95.15 77.68 122.42 91.41 109.28 69.47 69.46 76.81 78.36 90.72 95.79 60.36 102.72 105.61 90.20 94.84 108.22 105.58 61.88 113.92 34.23 93.69 132.12 114.45 99.35 124.59 79.51 93.22 83.39 98.44 123.81 140.34 97.85 66.17 84.62 84.47 110.96 143.72 100.77 90.95 86.92 90.35 110.2 94.14 77.66 114.04 64.67 92.02 148.19 106.53 98.05 109.46 90.36 88.20 88.54 104.82 106.07 90.19 109.39 53.79 88.54 124.62 110.08 111.43 State Density score Land use mix score Miami County Osage County Pottawatomie County Riley County Sedgwick County Shawnee County Sumner County Wyandotte County Boone County Bourbon County Boyd County Bullitt County Campbell County Christian County Clark County Daviess County Fayette County Grant County Greenup County Hardin County Henderson County Henry County Jefferson County Jessamine County Kenton County Larue County Meade County Nelson County Oldham County Scott County Shelby County Spencer County KS KS KS KS KS KS KS KS KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY KY 89.17 89.37 89.00 98.61 102.93 98.59 88.32 101.91 99.70 97.22 94.45 95.94 102.73 97.34 93.45 99.18 110.05 90.59 94.52 95.48 99.09 89.37 109.11 94.35 104.06 89.43 93.39 91.95 94.48 95.24 95.85 91.13 87.98 97.03 – – Warren County Woodford County Ascension Parish Bossier Parish Caddo Parish Calcasieu Parish De Soto Parish East Baton Rouge Parish Grant Parish Iberville Parish Jefferson Parish Lafayette Parish Lafourche Parish KY KY LA LA LA LA LA LA LA LA LA LA LA 101.86 93.43 92.32 95.13 98.39 95.68 89.07 103.91 88.67 93.41 113.17 99.95 95.04 County 28 Composite (total) score 87.08 77.91 – – Activity centering score Street connectivity score 62.05 137.94 94.61 91.73 71.09 98.11 121.48 97.97 101.63 70.42 94.37 103.72 71.18 93.51 103.78 114.38 98.83 75.85 89.80 117.81 115.29 130.43 73.94 95.07 88.61 88.84 108.73 109.82 128.35 129.94 124.13 67.98 84.88 153.63 111.60 81.72 – 73.3 172.01 103.15 90.00 – 100.74 80.94 81.23 88.78 94.32 97.06 99.01 70.25 81.41 136.26 138.89 131.29 95.72 93.72 106.32 100.72 100.71 82.27 100.64 89.42 83.65 104.01 96.6 97.95 123.29 90.27 77.17 75.38 214.43 108.52 104.87 98.29 101.17 130.72 108.41 109.44 86.13 109.33 107.65 78.43 106.35 91.39 90.26 77.32 87.89 78.52 116.79 118.53 118.19 107.81 94.25 100.50 107.96 100.82 99.78 107.27 116.70 125.16 76.61 73.80 110.91 106.22 143.97 – – 82.53 127.59 124.92 183.84 – – – – – – – – – 110.34 95.52 114.15 196.44 119.45 95.18 120.97 122.20 83.51 112.97 85.50 122.51 117.59 86.69 109.90 113.05 190.94 – – – – – – – – – State Density score Land use mix score Livingston Parish Orleans Parish Ouachita Parish Plaquemines Parish Pointe Coupee Parish Rapides Parish St. Bernard Parish St. Charles Parish St. John the Baptist Parish St. Martin Parish St. Tammany Parish Terrebonne Parish Union Parish West Baton Rouge Parish Androscoggin County Cumberland County Penobscot County Sagadahoc County York County Allegany County Anne Arundel County Baltimore County Calvert County Carroll County Cecil County Charles County Frederick County Harford County Howard County Montgomery County Prince George's County Queen Anne's County LA LA LA LA LA LA LA LA LA LA LA LA LA LA ME ME ME ME ME MD MD MD MD MD MD MD MD MD MD MD MD MD 93.18 121.91 95.23 90.01 91.55 93.23 100.03 93.42 97.39 90.60 95.66 96.62 89.87 92.80 94.76 98.75 92.40 91.37 92.68 94.56 105.04 109.47 95.09 95.33 93.63 97.94 97.32 100.16 104.93 117.80 112.70 91.01 Somerset County Washington County Wicomico County Baltimore city Barnstable County Berkshire County Bristol County Essex County Franklin County Hampden County Hampshire County Middlesex County Norfolk County MD MD MD MD MA MA MA MA MA MA MA MA MA 91.18 97.32 96.00 163.61 – – – – – – – – – County 33.82 36.98 – 32.99 – 38.77 34.74 29 Composite (total) score 97.87 110.48 94.01 99.13 81.51 98.87 102.21 71.48 91.81 108.27 113.36 99.95 84.47 85.70 111.21 112.50 118.58 87.08 95.35 91.20 93.17 103.44 102.01 112.17 127.72 116.51 72.44 County State Density score – – – Plymouth County Suffolk County Worcester County Barry County Bay County Berrien County Calhoun County Cass County Clinton County Eaton County Genesee County Ingham County Ionia County Jackson County Kalamazoo County Kent County Lapeer County Livingston County Macomb County Monroe County Muskegon County Newaygo County Oakland County Ottawa County Saginaw County St. Clair County Van Buren County Washtenaw County Wayne County Anoka County Benton County Blue Earth County MA MA MA MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MN MN MN 90.18 96.11 94.04 95.50 89.45 91.92 94.44 97.37 109.11 92.27 94.83 97.50 99.67 92.22 92.30 107.83 92.58 96.94 89.64 103.79 96.62 96.26 95.48 90.64 105.17 112.50 101.07 99.34 97.06 Carlton County Carver County Chisago County Clay County Dakota County Dodge County Hennepin County Houston County Isanti County Nicollet County Olmsted County Polk County Ramsey County MN MN MN MN MN MN MN MN MN MN MN MN MN 89.72 94.80 91.23 101.35 104.83 90.15 114.74 89.84 91.07 97.81 98.99 89.65 117.31 Land use mix score – 53.29 30.90 57.23 112.33 108.26 103.98 65.94 77.85 101.46 109.34 118.48 71.44 98.29 106.35 119.56 70.09 81.87 131.48 95.56 110.29 63.71 122.43 104.73 111.36 93.49 78.99 117.06 126.50 111.72 111.80 – 89.44 100.10 72.57 118.95 115.9 114.35 127.82 94.39 89.01 – 108.08 106.65 135.35 30 Activity centering score Street connectivity score – – – 87.88 108.40 90.63 103.91 94.70 131.40 85.64 123.51 141.89 96.34 137.01 113.21 128.07 131.99 104.20 92.09 109.24 96.74 82.85 99.39 106.96 121.05 115.33 85.30 155.39 136.09 98.03 83.26 81.38 104.20 201.99 98.17 75.47 104.10 99.01 94.09 73.69 63.62 72.87 103.52 104.33 76.97 86.66 90.33 96.76 63.03 80.88 106.26 75.47 107.62 79.68 107.48 84.83 101.28 87.56 71.88 87.03 148.34 105.23 89.21 83.73 86.19 82.70 80.16 84.41 86.85 78.13 151.96 70.75 80.16 77.60 166.15 85.6 105.13 89.97 100.41 79.33 81.24 107.32 95.81 129.69 100.51 86.90 107.27 100.70 58.59 148.75 Composite (total) score – – – 71.80 106.61 97.45 99.21 75.91 88.88 85.60 110.66 123.32 80.10 105.30 102.33 113.92 86.52 87.13 111.9 91.42 103.66 73.43 110.46 97.83 109.46 97.42 76.88 120.43 139.00 105.07 94.82 – 85.88 93.05 75.77 95.56 104.71 93.19 139.24 85.94 83.30 – 123.35 81.20 133.66 Street connectivity score 103.63 85.26 79.35 96.54 119.28 109.35 74.14 81.93 78.18 100.75 92.68 112.70 113.32 102.57 104.57 69.99 87.79 80.95 77.70 94.49 94.96 95.88 70.41 76.11 106.69 103.07 141.17 84.65 102.52 83.45 90.63 98.64 116.70 89.75 80.37 104.25 97.11 100.38 80.87 79.29 87.83 99.35 78.23 88.44 107.51 116.18 103.05 77.50 89.40 66.29 79.89 80.34 82.31 75.76 68.56 75.86 96.26 111.60 118.55 85.87 – 94.94 89.25 113.96 140.27 81.51 85.40 109.13 80.16 82.51 85.17 72.41 99.48 96.31 77.91 80.99 107.35 141.59 120.77 82.62 91.18 77.07 81.61 81.01 70.63 71.62 81.24 72.60 80.53 126.76 95.28 97.28 114.42 79.62 81.10 88.28 103.72 101.06 78.89 122.96 114.83 85.07 96.15 101.22 71.96 82.43 88.95 136.74 88.44 85.42 74.98 85.39 68.41 102.74 79.77 88.13 93.59 115.29 127.96 114.86 99.04 94.53 93.02 89.59 93.49 94.12 Density score Land use mix score St. Louis County Scott County Sherburne County Stearns County Wabasha County Washington County Wright County Copiah County DeSoto County Forrest County George County Hancock County Harrison County Hinds County Jackson County Lamar County Madison County Marshall County Rankin County Simpson County Stone County Tate County Tunica County Andrew County Bates County Boone County Buchanan County Callaway County Cape Girardeau County Cass County Christian County Clay County MN MN MN MN MN MN MN MS MS MS MS MS MS MS MS MS MS MS MS MS MS MS MS MO MO MO MO MO MO MO MO MO 95.96 96.04 92.57 95.49 89.66 100.91 92.03 90.59 95.25 95.34 90.76 92.04 97.88 100.02 95.32 90.94 96.21 89.58 94.27 89.83 90.38 92.63 88.41 88.73 89.22 98.98 101.70 90.40 95.78 94.15 91.93 97.62 113.02 104.74 80.55 112.29 101.77 108.44 88.12 89.53 88.58 105.53 69.74 77.68 105.23 107.02 88.99 85.24 91.29 45.70 82.77 72.44 88.05 63.13 60.42 86.17 111.73 107.90 120.56 82.96 – Clinton County Cole County MO MO MO 90.37 94.77 Crawford County Franklin County Greene County Jackson County Jasper County Jefferson County Lafayette County Lincoln County Moniteau County Newton County Platte County Activity centering score State County *(pt.) MO MO MO MO MO MO MO MO MO MO 89.11 91.10 100.74 105.14 94.90 96.02 89.16 90.59 90.40 92.11 98.15 – 94.49 119.9 126.53 113.72 87.54 87.92 52.94 117.93 83.25 104.96 31 Composite (total) score 84.89 85.12 99.52 – 87.87 107.86 130.44 103.76 89.90 83.13 75.34 89.37 91.02 92.73 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Ray County St. Charles County St. Louis County Warren County Washington County Webster County St. Louis city Carbon County Cascade County Missoula County Yellowstone County Cass County Dakota County Douglas County Lancaster County Sarpy County Saunders County Seward County Washington County Clark County Washoe County Carson City Hillsborough County Rockingham County Strafford County Atlantic County Bergen County Burlington County Camden County Cape May County Cumberland County Essex County MO MO MO MO MO MO MO MT MT MT MT NE NE NE NE NE NE NE NE NV NV NV NH NH NH NJ NJ NJ NJ NJ NJ NJ 89.65 104.37 107.75 90.25 89.88 89.70 126.98 88.78 97.85 98.92 103.87 89.10 98.92 110.08 109.75 101.37 88.71 89.14 89.99 119.01 103.05 104.88 101.22 94.00 95.77 103.00 128.56 100.52 115.67 97.81 99.51 161.02 108.59 118.40 126.19 65.09 65.15 58.65 137.55 68.92 123.74 119.30 120.17 86.96 114.43 132.45 133.02 112.49 95.50 99.79 86.51 116.44 110.72 133.53 116.91 101.41 105.80 114.8 150.29 120.12 137.68 117.44 113.21 146.99 73.35 86.54 95.35 88.50 71.89 78.35 194.29 85.23 127.17 111.04 119.97 86.25 75.16 125.37 115.33 87.29 88.74 77.47 117.82 140.45 131.45 80.10 121.07 97.51 88.23 142.81 86.86 99.61 105.55 101.22 119.51 128.46 65.04 121.39 120.59 88.94 94.61 95.58 185.95 93.01 118.61 110.74 115.07 95.22 122.40 138.38 121.45 118.08 85.06 81.06 94.88 122.06 103.68 118.62 97.04 82.02 82.45 120.73 143.25 99.94 141.06 145.73 98.78 148.71 79.98 109.70 115.76 78.76 75.21 75.45 177.33 79.76 121.28 112.64 118.66 86.59 103.44 133.58 125.13 106.08 86.74 83.40 96.59 130.94 115.45 111.73 111.45 92.08 91.23 125.70 134.43 106.38 131.58 119.65 109.80 158.50 Gloucester County Hudson County Hunterdon County Mercer County Middlesex County Monmouth County Morris County Ocean County Passaic County Salem County Somerset County Sussex County Union County NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ 100.59 223.23 93.84 114.81 118.29 105.74 103.00 105.44 143.82 94.41 101.83 95.74 140.17 121.22 156.67 90.14 128.87 135.37 133.26 125.29 110.28 148.45 98.00 120.78 89.17 153.96 87.46 92.82 95.20 109.53 114.47 84.28 87.76 91.35 101.63 80.11 86.24 86.54 89.87 104.71 176.49 74.00 119.34 132.03 121.16 100.05 129.32 135.66 92.91 103.35 87.85 148.90 104.41 178.73 85.21 122.92 131.64 114.04 105.09 111.5 140.93 89.08 103.86 87.14 141.99 County 32 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Warren County Bernalillo County Dona Ana County Sandoval County San Juan County Santa Fe County Valencia County Albany County Bronx County Broome County Chemung County Dutchess County Erie County Herkimer County Kings County Livingston County Madison County Monroe County Nassau County New York County Niagara County Oneida County Onondaga County Ontario County Orange County Orleans County Oswego County Putnam County Queens County Rensselaer County Richmond County Rockland County NJ NM NM NM NM NM NM NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY 95.86 110.26 99.20 97.97 93.52 99.91 94.94 107.10 336.70 99.92 98.96 97.07 109.71 96.91 355.50 93.13 94.67 106.45 128.98 654.01 100.04 101.65 104.46 94.36 101.31 94.19 96.64 94.19 266.34 99.20 175.08 117.77 119.17 122.46 106.04 91.24 88.26 106.29 85.92 128.39 143.95 115.80 117.49 110.29 131.45 100.82 142.16 102.59 96.7 123.67 149.38 144.57 115.62 107.32 122.19 101.34 113.59 97.46 90.83 95.77 147.42 109.08 131.67 134.18 85.21 113.45 114.72 110.10 135.96 116.83 108.47 135.96 100.25 121.53 130.79 128.55 111.78 82.72 199.99 78.75 85.84 121.06 111.6 400.25 92.59 112.12 142.75 91.19 90.33 78.22 108.43 83.82 91.93 97.62 78.94 81.37 97.52 131.01 103.66 85.16 78.81 88.05 76.38 104.63 211.61 93.89 99.06 81.19 93.59 80.37 225.25 53.09 57.89 93.28 160.85 230.33 94.32 84.48 96.45 62.58 87.33 53.47 70.57 88.92 224.01 92.25 179.98 105.52 99.29 124.38 107.46 95.09 98.91 103.50 89.17 124.04 224.01 109.84 114.63 105.40 114.70 87.62 265.20 77.11 79.49 114.04 147.65 425.15 100.81 101.76 120.80 84.03 97.65 75.78 89.4 88.21 204.16 99.41 152.34 112.27 Saratoga County Schenectady County Schoharie County Suffolk County Tioga County Tompkins County Ulster County Warren County Washington County Wayne County Westchester County Alamance County Alexander County NY NY NY NY NY NY NY NY NY NY NY NC NC 95.36 107.32 90.59 105.86 94.68 102.44 95.12 94.99 92.47 92.68 129.24 95.78 91.03 98.37 130.66 78.79 126.74 75.76 95.84 96.80 105.93 80.23 85.72 146.99 102.85 78.52 102.26 104.18 84.01 94.53 82.48 144.53 124.18 183.56 80.51 85.91 93.74 94.52 79.96 80.90 110.94 56.05 115.53 64.79 72.43 81.42 89.94 59.21 55.37 123.66 96.28 55.54 92.70 116.78 71.39 113.48 74.00 104.82 99.22 123.51 72.33 74.62 129.58 96.66 70.00 County 33 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Anson County Brunswick County Buncombe County Burke County Cabarrus County Caldwell County Catawba County Chatham County Cumberland County Currituck County Davie County Durham County Edgecombe County Forsyth County Franklin County Gaston County Greene County Guilford County Haywood County Henderson County Hoke County Johnston County Madison County Mecklenburg County Nash County New Hanover County Onslow County Orange County Pender County Person County Pitt County Randolph County NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC NC 89.44 90.81 95.14 90.80 96.20 92.41 93.56 91.14 100.01 90.42 91.08 102.68 91.45 98.47 91.13 95.33 90.47 100.36 91.09 92.12 91.51 93.03 89.40 105.91 91.58 102.34 94.97 99.40 91.15 91.24 98.36 92.22 65.32 69.18 101.18 78.73 97.46 74.22 91.54 56.42 104.64 69.81 61.13 108.43 83.77 107.56 52.43 103.37 47.46 113.56 79.15 98.21 57.98 70.60 44.18 115.35 88.78 118.86 82.72 106.99 64.41 74.11 104.23 84.74 80.36 88.65 126.22 87.53 88.76 123.75 85.36 79.76 91.45 77.63 81.22 103.83 99.40 110.15 78.63 110.64 83.61 102.77 80.84 84.83 83.07 103.97 77.93 135.51 88.52 107.70 104.59 120.04 81.67 81.98 117.55 100.63 52.48 85.96 94.85 75.57 88.00 80.60 88.36 62.63 90.81 76.98 60.37 103.70 93.79 95.01 63.74 94.20 40.96 95.45 102.68 93.59 70.19 64.44 90.45 101.84 79.45 121.50 82.75 75.56 60.61 61.12 87.14 57.18 64.49 79.34 105.50 78.72 90.65 90.83 86.99 65.23 95.86 73.10 66.45 105.89 90.02 103.53 63.96 101.12 56.56 103.84 85.39 90.13 69.27 78.53 69.03 118.52 83.68 115.92 88.95 100.63 67.72 71.08 102.30 79.39 Rockingham County Stokes County Union County Wake County Wayne County Yadkin County Burleigh County Cass County Grand Forks County Morton County Allen County Belmont County Brown County NC NC NC NC NC NC ND ND ND ND OH OH OH 90.85 90.59 94.98 103.07 93.55 90.06 96.52 99.52 104.24 91.13 95.85 92.89 90.42 72.36 52.98 81.73 115.17 78.79 70.68 118.46 125.90 124.99 108.21 114.27 98.58 54.19 83.70 81.84 100.88 134.61 130.76 79.45 128.76 113.31 97.01 82.17 117.83 83.73 85.62 76.47 64.72 84.45 96.60 84.88 49.29 90.68 97.15 96.71 85.86 118.07 112.11 78.68 75.79 65.29 88.01 115.62 96.20 65.08 110.87 111.34 107.25 89.69 114.54 95.99 71.22 County 34 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Butler County Carroll County Clark County Clermont County Cuyahoga County Delaware County Erie County Fairfield County Franklin County Fulton County Geauga County Greene County Hamilton County Jefferson County Lake County Lawrence County Licking County Lorain County Lucas County Madison County Mahoning County Medina County Miami County Montgomery County Morrow County Ottawa County Pickaway County Portage County Preble County Richland County Stark County Summit County OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH OH 101.42 89.77 96.98 98.23 112.92 97.21 96.77 95.20 111.37 90.59 90.84 97.09 110.12 95.10 100.55 93.75 95.01 98.61 105.01 92.38 98.98 96.03 92.97 102.99 89.85 93.01 95.16 94.89 90.05 94.98 98.73 101.67 116.84 69.05 111.55 97.66 133.64 109.37 121.77 100.29 131.41 113.35 82.83 114.93 134.12 103.84 123.58 81.53 99.59 117.13 131.81 85.12 121.53 105.54 103.49 130.21 49.60 98.23 82.72 103.80 70.46 105.89 120.66 125.68 94.22 94.41 97.15 83.05 119.54 84.07 104.84 89.76 124.87 82.43 86.85 85.08 141.56 109.52 82.99 83.82 98.19 93.18 114.29 84.52 107.96 93.20 85.25 114.82 83.41 86.34 83.74 90.32 86.69 118.65 98.80 109.41 101.13 68.25 102.52 84.14 109.64 87.68 102.29 89.15 127.88 93.65 50.20 94.01 113.68 107.80 88.29 104.35 106.48 95.05 116.4 84.97 102.09 57.23 95.62 117.40 46.82 94.39 78.20 100.22 100.99 103.59 120.61 114.42 104.30 75.19 102.60 88.34 123.93 93.15 108.11 91.91 130.18 93.69 71.79 97.19 131.43 105.14 98.55 88.45 99.77 101.26 121.33 83.25 109.66 84.83 92.84 120.67 58.82 91.15 80.99 96.60 83.63 107.30 112.26 116.17 Trumbull County Union County Warren County Washington County Wood County Canadian County Cleveland County Comanche County Creek County Grady County Le Flore County Logan County McClain County OH OH OH OH OH OK OK OK OK OK OK OK OK 95.85 94.04 97.43 93.06 94.89 97.03 101.04 99.03 90.09 91.37 89.15 89.70 89.63 111.81 77.41 106.62 88.20 111.78 97.68 107.98 118.45 85.48 75.37 67.37 68.27 80.94 91.49 81.94 84.37 86.67 91.96 82.74 106.44 98.20 84.46 86.82 83.45 90.56 81.73 95.52 86.51 88.63 83.86 82.11 92.01 102.24 116.33 104.69 102.85 99.19 98.34 88.92 98.31 81.01 92.75 84.77 93.91 90.35 105.59 110.11 88.85 86.23 80.78 83.21 81.43 County 35 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Oklahoma County Okmulgee County Osage County Pawnee County Rogers County Sequoyah County Tulsa County Wagoner County Benton County Clackamas County Columbia County Deschutes County Jackson County Lane County Marion County Multnomah County Polk County Washington County Yamhill County Allegheny County Armstrong County Beaver County Berks County Blair County Bucks County Butler County Cambria County Carbon County Centre County Chester County Cumberland County Dauphin County OK OK OK OK OK OK OK OK OR OR OR OR OR OR OR OR OR OR OR PA PA PA PA PA PA PA PA PA PA PA PA PA 103.44 89.76 93.63 88.73 92.33 89.78 102.60 93.20 100.72 101.80 93.28 95.73 97.76 101.73 101.62 120.53 94.97 110.39 99.08 109.54 92.89 95.17 108.58 97.22 102.39 93.68 95.43 93.36 110.10 98.81 98.59 104.58 120.48 90.51 66.07 75.14 79.74 72.88 121.46 77.70 123.18 126.17 102.74 115.65 122.20 127.48 130.36 142.82 105.79 132.91 122.85 133.89 85.75 110.16 126.11 121.95 126.03 105.26 107.43 98.43 115.70 117.12 111.24 124.71 122.50 83.84 86.07 77.53 87.59 91.90 117.13 83.08 126.52 90.03 80.42 115.30 122.65 138.05 123.77 150.58 80.13 85.02 81.32 145.40 101.54 84.42 116.00 124.31 79.87 120.02 120.16 90.96 149.49 91.20 85.52 129.24 117.89 122.81 96.84 99.62 95.45 101.22 113.15 102.13 95.34 96.25 84.73 80.19 91.71 98.88 101.10 166.68 83.85 113.10 93.49 135.70 84.86 111.13 110.71 123.01 99.58 79.27 119.48 97.65 91.83 89.11 112.72 125.68 120.32 95.86 81.87 81.37 85.82 86.03 117.17 86.14 114.46 104.50 87.73 102.17 110.84 120.90 117.96 157.06 88.86 113.09 98.97 139.34 88.95 100.28 119.40 121.01 102.49 99.44 113.43 93.81 121.21 98.81 102.55 126.61 Delaware County Erie County Fayette County Lackawanna County Lancaster County Lebanon County Lehigh County Luzerne County Lycoming County Mercer County Montgomery County Northampton County Perry County PA PA PA PA PA PA PA PA PA PA PA PA PA 119.69 102.74 93.03 101.86 102.63 96.31 111.48 99.44 97.09 95.34 107.67 103.88 89.79 141.69 130.88 102.25 133.13 119.90 122.77 134.36 121.47 120.85 106.25 136.32 133.01 63.67 83.25 122.48 96.86 134.53 128.60 84.72 115.73 93.27 113.98 83.44 85.84 101.8 91.33 137.90 102.40 108.42 123.50 94.47 116.98 137.75 114.55 117.91 87.04 109.26 124.28 79.02 126.07 118.48 100.17 129.39 114.41 106.56 131.38 109.08 115.74 91.17 112.35 119.89 75.93 County 36 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Philadelphia County Pike County Washington County Westmoreland County Wyoming County York County Bristol County Kent County Newport County Providence County Washington County Aiken County Anderson County Berkeley County Charleston County Darlington County Dorchester County Edgefield County Fairfield County Florence County Greenville County Horry County Kershaw County Laurens County Lexington County Pickens County Richland County Spartanburg County Sumter County York County Lincoln County Meade County PA PA PA PA PA PA RI RI RI RI RI SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SD SD 206.38 91.08 95.07 95.84 90.40 99.69 109.79 103.82 99.45 121.10 94.03 93.29 92.29 98.30 103.20 91.78 103.61 89.95 89.55 96.07 98.68 94.78 90.43 89.91 94.92 92.45 101.53 93.37 93.59 95.01 92.75 89.23 144.48 56.19 106.69 111.77 51.38 112.24 144.16 122.09 121.07 142.01 102.13 79.37 82.54 88.34 119.32 86.08 98.38 55.96 49.53 90.47 106.59 90.85 61.70 59.53 94.04 92.02 109.51 97.98 86.69 95.83 107.03 75.07 178.43 144.75 93.55 104.88 86.24 115.21 83.56 81.70 99.03 141.75 88.56 103.25 110.42 80.72 138.48 84.55 81.02 76.27 76.12 109.63 100.39 112.78 129.24 87.21 88.00 97.27 144.33 112.28 119.72 94.28 82.73 81.40 209.98 90.61 102.25 108.50 74.76 96.33 135.16 122.57 118.74 134.74 97.10 96.65 81.70 78.85 116.56 73.08 84.79 60.96 74.02 83.71 91.07 101.88 61.49 79.89 80.44 82.26 110.91 90.54 90.32 80.22 77.53 103.16 207.19 94.51 99.23 106.63 69.28 107.42 122.96 109.54 112.10 144.11 94.26 91.33 89.56 83.00 124.50 79.62 89.83 63.08 65.00 93.64 98.97 100.09 81.95 73.63 86.54 88.63 120.94 98.16 96.94 89.05 87.38 83.84 Minnehaha County Pennington County Anderson County Blount County Bradley County Carter County Cheatham County Chester County Davidson County Dickson County Fayette County Grainger County Hamblen County SD SD TN TN TN TN TN TN TN TN TN TN TN 102.86 96.18 92.32 94.52 94.75 93.30 93.65 91.73 104.68 91.19 89.34 89.49 95.73 120.06 101.49 81.10 79.63 85.38 77.41 56.61 79.08 111.86 65.43 50.43 45.66 85.00 105.90 117.26 121.37 87.08 114.48 129.08 86.41 69.11 121.78 90.57 89.51 74.08 142.29 107.25 95.04 89.51 89.16 87.22 96.48 61.81 55.42 111.57 73.70 51.46 70.51 95.50 111.40 103.15 95.04 84.33 94.26 98.82 67.92 66.93 115.76 75.01 62.32 62.01 105.85 County 37 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Hamilton County Hawkins County Jefferson County Knox County Loudon County Macon County Madison County Marion County Montgomery County Robertson County Rutherford County Sequatchie County Shelby County Smith County Sullivan County Sumner County Tipton County Trousdale County Unicoi County Union County Washington County Williamson County Wilson County Aransas County Atascosa County Austin County Bandera County Bastrop County Bell County Bexar County Bowie County Brazoria County TN TN TN TN TN TN TN TN TN TN TN TN TN TN TN TN TN TN TN TN TN TN TN TX TX TX TX TX TX TX TX TX 98.48 90.78 91.49 99.46 90.60 90.08 95.08 89.77 97.02 91.68 97.98 90.25 105.33 90.53 93.76 97.36 92.75 90.52 94.94 89.52 94.93 97.00 93.71 91.90 89.05 88.89 89.19 89.76 99.90 107.69 93.73 96.54 101.33 69.01 63.63 102.38 74.46 45.11 104.99 69.94 80.87 72.06 90.60 76.45 109.94 70.87 86.37 86.46 59.76 71.81 90.30 50.58 91.12 85.43 71.92 104.27 79.50 64.78 38.15 76.25 110.30 116.02 106.36 96.26 119.36 90.10 91.38 136.24 83.62 73.25 108.51 73.16 113.11 85.62 108.29 78.98 122.61 66.08 119.66 115.60 87.84 67.37 80.78 82.78 94.03 133.03 85.24 84.03 85.77 86.07 69.25 87.26 106.90 115.57 80.75 92.15 103.40 81.51 79.72 96.83 96.59 47.03 91.26 87.72 75.99 63.10 83.25 57.33 114.90 83.13 101.34 76.15 64.39 64.82 113.03 73.69 93.77 87.19 70.33 122.27 94.63 82.34 101.83 96.10 110.75 118.94 99.24 97.38 107.13 78.33 76.69 111.03 82.71 54.34 99.95 74.91 89.57 72.35 93.72 69.36 116.68 71.76 100.36 92.28 69.90 66.68 93.38 67.32 91.74 100.84 75.10 100.78 83.87 75.38 67.91 84.01 108.80 118.40 93.71 94.42 Brazos County Burleson County Caldwell County Calhoun County Cameron County Chambers County Clay County Collin County Comal County Coryell County Dallas County Delta County Denton County TX TX TX TX TX TX TX TX TX TX TX TX TX 105.72 89.32 89.63 97.89 100.34 88.91 88.03 106.24 93.66 97.23 116.03 88.85 104.96 112.86 100.91 89.32 104.62 102.76 43.66 67.28 114.06 86.53 77.14 123.21 80.30 107.37 101.13 77.93 84.60 74.17 87.93 75.63 76.56 85.45 108.62 87.93 125.52 68.73 91.25 110.13 109.68 100.93 145.39 110.32 77.45 111.02 118.59 88.26 86.13 139.21 127.14 114.16 109.43 93.00 88.78 106.98 100.42 63.87 81.95 107.69 92.76 83.70 132.85 88.95 105.61 County 38 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Ector County Ellis County El Paso County Fort Bend County Galveston County Grayson County Gregg County Guadalupe County Hardin County Harris County Hays County Hidalgo County Hunt County Jefferson County Johnson County Kaufman County Kendall County Lampasas County Liberty County Lubbock County McLennan County Medina County Midland County Montgomery County Nueces County Orange County Parker County Potter County Randall County Rockwall County Rusk County San Patricio County TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX TX 101.41 92.65 109.16 104.19 100.94 93.05 96.10 96.53 89.38 112.9 95.58 100.21 91.85 99.99 94.62 91.56 94.46 89.18 89.41 101.82 96.64 88.53 103.45 95.68 104.85 90.28 90.72 101.40 101.51 97.13 89.28 93.48 123.37 86.97 113.33 96.20 113.67 103.96 114.14 93.38 75.62 122.96 87.83 101.69 76.80 118.66 85.00 77.63 97.53 74.92 54.79 123.12 112.13 55.51 123.85 87.52 127.12 87.97 77.89 118.20 122.09 97.42 80.54 114.78 112.23 84.65 102.45 101.96 106.27 92.14 103.02 84.13 84.66 115.12 131.77 104.76 100.17 127.39 88.74 83.06 79.63 86.25 90.70 97.75 100.28 85.30 110.90 111.61 106.59 84.52 87.88 99.33 78.97 79.27 82.05 84.07 111.89 100.12 125.22 111.59 130.51 102.59 99.15 94.73 83.17 138.63 84.13 109.10 94.77 137.42 91.72 108.05 72.72 95.76 83.18 110.77 109.99 81.66 119.62 84.05 121.30 104.13 79.00 132.71 110.72 94.18 67.69 111.29 115.45 88.75 115.85 104.41 116.24 97.39 103.92 90.14 78.78 128.31 99.78 104.98 88.50 126.37 87.39 87.46 82.42 82.98 74.12 110.57 106.02 71.88 118.27 93.32 118.91 89.54 79.62 116.32 104.20 89.89 74.59 101.14 Smith County Tarrant County Tom Green County Travis County Upshur County Victoria County Waller County Webb County Wichita County Williamson County Wilson County Wise County Cache County TX TX TX TX TX TX TX TX TX TX TX TX UT 95.50 108.94 97.73 108.45 90.15 103.10 95.59 101.78 98.04 101.28 89.22 89.07 100.03 100.31 119.35 119.81 120.81 67.18 120.55 60.29 122.77 121.94 106.24 46.70 68.46 120.88 119.02 100.17 103.96 148.98 79.57 119.38 82.16 102.69 121.17 98.74 88.44 80.23 128.98 100.60 128.90 106.90 110.66 86.71 119.70 92.14 121.89 110.29 101.69 72.24 80.04 82.21 104.88 118.12 108.97 128.09 75.86 119.82 77.94 115.53 116.25 102.51 67.33 74.03 110.14 County 39 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Davis County Juab County Salt Lake County Summit County Tooele County Utah County Washington County Weber County Chittenden County Franklin County Grand Isle County Albemarle County Amherst County Appomattox County Arlington County Bedford County Botetourt County Campbell County Caroline County Chesterfield County Clarke County Dinwiddie County Fairfax County Fauquier County Fluvanna County Franklin County Frederick County Gloucester County Goochland County Greene County Hanover County Henrico County UT UT UT UT UT UT UT UT VT VT VT VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA 103.45 88.62 112.04 90.70 97.75 108.21 95.06 105.74 101.56 92.87 89.13 95.30 89.69 89.68 174.41 89.97 89.85 91.88 89.04 100.63 89.87 90.02 117.83 90.61 92.01 91.30 93.79 92.66 90.23 90.55 94.37 105.97 125.21 93.30 129.10 90.55 102.75 127.19 98.96 124.44 121.65 95.99 86.07 102.67 70.62 39.87 153.20 55.41 72.00 77.31 40.80 98.15 79.72 49.10 123.70 73.98 71.24 47.21 81.33 69.24 55.11 59.72 84.41 114.27 80.47 78.14 106.26 91.28 79.12 89.82 84.85 97.16 152.59 82.45 69.37 87.34 84.60 90.05 95.54 91.02 83.63 83.38 74.87 114.36 79.01 78.23 113.17 90.24 75.82 88.85 87.14 89.69 75.26 70.10 82.56 86.41 105.19 83.59 116.30 75.60 75.88 106.36 91.60 108.01 89.97 75.67 90.87 78.58 75.08 58.37 177.13 73.51 88.06 109.02 77.09 102.77 86.65 71.08 114.82 80.50 69.22 77.48 85.85 99.14 78.66 78.44 88.35 123.03 104.52 82.20 120.12 83.61 85.94 109.98 90.67 111.17 120.78 83.25 79.60 88.59 74.72 61.45 163.28 71.54 79.00 87.87 62.65 105.03 79.55 64.75 121.96 79.57 71.02 69.94 83.61 84.43 68.17 68.03 84.10 109.38 Isle of Wight County James City County King William County Loudoun County Mathews County Montgomery County New Kent County Pittsylvania County Powhatan County Prince George County Prince William County Pulaski County Roanoke County VA VA VA VA VA VA VA VA VA VA VA VA VA 90.76 93.70 90.95 102.68 92.20 95.29 89.75 89.61 94.07 90.96 106.28 91.55 96.03 75.64 97.02 56.69 116.85 52.08 95.57 43.95 42.72 44.51 66.68 106.57 84.58 110.04 77.65 79.60 79.27 81.49 72.32 85.40 80.36 80.80 74.52 75.53 94.52 83.02 80.69 79.82 106.28 102.10 113.55 78.22 102.18 72.40 66.85 65.38 81.97 115.14 103.9 98.89 75.95 92.61 77.57 104.60 66.77 93.19 64.13 62.08 61.61 73.19 107.11 88.33 95.46 County 40 State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score Rockingham County Scott County Spotsylvania County Stafford County Sussex County Warren County Washington County York County Alexandria city Bedford city Bristol city Charlottesville city Chesapeake city Colonial Heights city Danville city Fairfax city Falls Church city Fredericksburg city Hampton city Harrisonburg city Hopewell city Lynchburg city Manassas city Manassas Park city Newport News city Norfolk city Petersburg city Poquoson city Portsmouth city Radford city Richmond city Roanoke city VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA 90.09 89.25 97.94 98.78 102.08 93.50 90.49 97.29 176.94 94.78 105.00 128.80 103.40 108.95 99.84 116.97 127.12 120.16 110.55 122.83 112.29 104.80 115.54 129.66 112.21 129.98 101.48 97.09 111.16 105.79 120.46 109.84 73.51 50.38 84.86 84.11 63.80 92.21 77.47 99.00 154.32 123.63 130.60 148.33 108.24 135.66 126.20 152.84 177.53 145.13 123.19 143.99 124.58 130.42 140.36 128.88 121.94 131.46 127.00 105.92 129.35 135.40 133.06 129.71 86.01 78.01 88.47 81.07 – 88.78 81.92 86.14 115.16 72.04 82.35 210.83 88.28 77.65 121.82 73.00 72.72 97.72 114.92 144.42 79.39 104.85 76.57 82.19 86.53 210.96 104.35 77.55 88.86 81.24 160.69 120.97 88.97 92.28 92.55 88.85 – 94.07 90.19 108.50 173.76 113.62 145.26 152.37 109.52 153.60 120.33 131.05 164.07 154.28 150.96 131.80 185.81 132.31 150.36 133.50 137.18 179.44 144.23 104.32 163.76 156.21 172.23 155.62 80.60 71.54 88.57 85.09 – 90.07 81.06 97.13 169.56 101.29 119.97 175.93 102.98 123.97 121.54 123.34 144.69 137.06 131.48 145.19 132.25 122.87 126.17 123.45 118.28 179.57 124.34 95.22 129.42 124.84 158.90 136.69 Salem city Suffolk city Virginia Beach city Williamsburg city Winchester city Asotin County Benton County Chelan County Clark County Cowlitz County Douglas County Franklin County King County VA VA VA VA VA WA WA WA WA WA WA WA WA 107.30 95.77 111.75 108.92 114.03 106.62 98.56 97.97 102.63 96.07 103.94 101.59 114.85 128.88 99.14 123.10 118.37 135.13 134.33 118.73 126.31 123.40 103.40 116.98 119.22 128.93 76.93 103.14 86.61 158.90 133.91 77.00 109.61 120.30 89.55 128.01 82.17 82.23 159.34 140.41 98.02 137.93 136.03 150.19 134.97 97.28 99.04 105.28 99.00 91.30 111.14 131.70 116.91 98.76 118.77 138.61 142.10 116.72 107.64 113.78 106.59 108.37 98.23 104.48 142.60 County 41 Activity centering score Street connectivity score Composite (total) score 107.82 117.02 112.71 116.86 122.39 103.71 110.62 124.46 90.23 61.03 93.32 112.81 110.72 75.67 108.14 89.16 75.55 117.16 67.70 115.77 44.98 87.87 81.82 116.84 115.40 80.84 85.15 92.46 126.20 99.68 115.50 109.78 115.62 126.32 101.76 122.73 122.32 132.90 115.26 128.18 97.70 – 87.28 183.48 86.79 87.64 147.64 137.78 159.67 120.10 90.00 150.91 90.63 78.21 84.99 107.75 101.30 87.75 89.40 87.63 153.67 81.91 116.85 153.06 96.04 119.43 99.87 100.03 127.12 95.16 99.00 89.38 94.03 123.52 116.81 119.12 118.07 98.81 125.60 120.37 111.67 115.01 74.66 129.79 80.67 99.34 106.16 121.08 91.01 80.59 88.50 90.90 106.96 108.53 96.62 94.09 105.81 120.78 103.48 113.62 123.13 109.35 106.54 112.84 92.67 – 96.34 135.99 103.07 85.44 124.48 112.53 111.91 116.02 75.31 129.14 70.06 86.98 89.48 113.37 102.26 82.35 85.86 87.68 129.63 95.30 108.70 116.57 78.00 119.03 103.67 119.38 102.58 139.35 49.35 120.79 116.53 92.07 122.63 113.90 87.72 83.48 123.52 77.23 88.95 121.29 178.96 77.77 164.21 106.77 143.31 111.62 108.04 93.45 83.09 118.90 79.49 117.4 83.21 155.69 66.91 97.96 87.76 81.67 107.68 98.59 67.27 79.07 119.67 85.01 107.65 100.38 164.06 62.99 125.91 101.95 103.61 113.40 105.70 81.19 State Density score Land use mix score Kitsap County Pierce County Skagit County Snohomish County Spokane County Thurston County Whatcom County Yakima County Berkeley County Boone County Brooke County Cabell County Hancock County Jefferson County Kanawha County Marshall County Mineral County Monongalia County Morgan County Ohio County Preston County Putnam County Wayne County Wood County Brown County Calumet County Chippewa County Columbia County Dane County Douglas County Eau Claire County Fond du Lac County WA WA WA WA WA WA WA WA WV WV WV WV WV WV WV WV WV WV WV WV WV WV WV WV WI WI WI WI WI WI WI WI 98.92 103.02 96.68 103.47 101.37 97.83 95.83 98.64 94.85 90.83 91.02 98.52 94.13 91.79 96.10 92.36 90.81 98.42 89.50 95.76 88.93 93.37 93.73 96.66 99.46 94.95 92.19 90.01 106.96 95.01 98.55 95.54 Iowa County Kenosha County Kewaunee County La Crosse County Marathon County Milwaukee County Oconto County Outagamie County Ozaukee County Pierce County Racine County Rock County St. Croix County WI WI WI WI WI WI WI WI WI WI WI WI WI 89.19 100.80 92.15 98.49 94.14 128.75 88.82 99.06 95.11 94.38 100.48 97.51 92.02 County 42 County Sheboygan County Washington County Waukesha County Winnebago County Laramie County Natrona County State Density score Land use mix score Activity centering score Street connectivity score Composite (total) score WI WI WI WI WY WY 97.60 94.74 96.89 100.65 100.71 100.14 115.59 96.05 112.13 118.29 112.98 116.47 94.01 128.67 147.79 97.48 132.64 136.24 98.77 75.35 101.06 113.49 114.68 117.49 101.88 98.36 118.28 109.45 119.28 122.22 Appendix C: Quality of life analysis In addition to analyzing development at the Metropolitan Statistical Area (MSA) and county levels, the researchers also generated index scores for the census-defined urbanized areas (UZAs) within MSAs. For more information about the methodology of the research and for UZA scores, see the full report at http://gis.cancer.gov/tools/urban-sprawl/. To provide a better understanding of what data sources informed analyses at the MSA, county and UZA levels, an overview is below in Table C1. TABLE C1 Data sources used to evaluate quality of life outcomes, by geographic scale Housing affordability Location Affordability Index27 MSA Relationship to sprawl positive and significant Transportation affordability Location Affordability Index MSA negative and significant Combined housing and transportation affordability Upward mobility Location Affordability Index MSA negative and significant Equality of Opportunity databases28 American Community Survey29 MSA negative and significant MSA, county, UZA MSA, county, UZA MSA, county, UZA MSA, county, UZA County positive and significant Outcome Average household vehicle ownership Percentage of commuters walking to work Percentage of commuters using public transportation (excluding taxi) Average journey-to-work drive time in minutes Traffic crash rate per 100,000 population Injury crash rate per 100,000 population Fatal crash rate per 100,000 population Body mass index Obesity Any physical activity Data Source American Community Survey American Community Survey American Community Survey States30 Geography negative and significant negative and significant positive and significant negative and significant States County negative and significant States County positive and significant Behavioral Risk Factor Surveillance System (BRFSS)31 BRFSS BRFSS County positive and significant County County positive and significant not significant 43 Diagnosed high blood pressure BRFSS County Relationship to sprawl positive and significant Diagnosed heart disease Diagnosed diabetes Average life expectancy BRFSS BRFSS Institute for Health Metrics and Evaluation32 County County County not significant positive and significant negative and significant Outcome Data Source 44 Geography Endnotes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 This study excludes Metropolitan Statistical Areas (MSAs) with populations less than 200,000 people due to data availability and because impacts are more difficult to measure at smaller scales. For a more detailed explanation of how Sprawl Index scores are calculated, see Ewing, R. and Hamidi, S. (2014). Measuring Urban Sprawl and Validating Sprawl Measures. Metropolitan Research Center, University of Utah. Available at http://gis.cancer.gov/tools/urban-sprawl/. The Washington-Arlington-Alexandria, DC-VA-MD-WV Metropolitan Statistical Area includes District of Columbia, DC; Calvert County, MD; Charles County, MD; Prince George's County, MD; Arlington County, VA; Clarke County, VA; Culpeper County, VA; Fairfax County, VA; Fauquier County, VA; Loudoun County, VA; Prince William County, VA; Rappahannock County, VA; Spotsylvania County, VA; Stafford County, VA; Warren County, VA; Alexandria City, VA; Fairfax City, VA; Falls Church City, VA; Fredericksburg City, VA; Manassas City, VA; Manassas Park City, VA; Jefferson County, WV. From: http://www.whitehouse.gov/sites/default/files/omb/bulletins/2013/b-13-01.pdf. Metropolitan areas with populations less than 200,000 were not included in this analysis. See the full analytical report for more information on these assessments: Ewing, R. and Hamidi, S. (2014). Measuring Urban Sprawl and Validating Sprawl Measures. Metropolitan Research Center, University of Utah. Available at http://gis.cancer.gov/tools/urban-sprawl/. The Equality of Opportunity Project. Retrieved March 27, 2014, from www.equality-of-opportunity.org/. Ewing, R. and Hamidi, S. (2014). Measuring Urban Sprawl and Validating Sprawl Measures. (Page 89) Metropolitan Research Center, University of Utah. Available at http://gis.cancer.gov/tools/urban-sprawl/. Ewing, R. and Hamidi, S. (2014). Measuring Urban Sprawl and Validating Sprawl Measures. (Page 90). Metropolitan Research Center, University of Utah. Available at http://gis.cancer.gov/tools/urban-sprawl/. U.S. Department of Housing and Urban Development (HUD). Location Affordability Index. Available at http://portal.hud.gov/hudportal/HUD?src=/program_offices/sustainable_housing_communities/location_affordability. See note 10. These calculations represent a weighted average of census block group values based on transportation and housing cost data from the HUD’s Location Affordability Index. Available at http://portal.hud.gov/hudportal/HUD?src=/program_offices/sustainable_housing_communities/location_affordability. Ewing, R. and Hamidi, S. (2014). Measuring Urban Sprawl and Validating Sprawl Measures. (Pages 73–74). Metropolitan Research Center, University of Utah. Available at http://gis.cancer.gov/tools/urban-sprawl/. Data for health outcomes is not available at the metropolitan level. The researchers use information available at the county level to inform these conclusions. Ewing, R. and Hamidi, S. (2014). Measuring Urban Sprawl and Validating Sprawl Measures. (Page 83). Metropolitan Research Center, University of Utah. Available at http://gis.cancer.gov/tools/urban-sprawl/. This calculation is based on the researchers’ models. According to the Center for Disease Control’s Behavioral Risk Factor Surveillance System (BRFSS), the actual difference in weight is greater due to income and racial differences. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. Available at www.cdc.gov/brfss/. City of Santa Barbara. Uses permitted in various zones. Available at www.santabarbaraca.gov/civicax/filebank/blobdload.aspx?BlobID=17638. City of Santa Barbara. (2011). General Plan Update. (Page 105). Available at www.santabarbaraca.gov/civicax/filebank/blobdload.aspx?BlobID=16916. City of Santa Barbara. (2011). Land Use Element. (p. 2). Available at https://www.santabarbaraca.gov/civicax/filebank/blobdload.aspx?BlobID=16898. Learn more about the County of Santa Barbara’s Long Range Planning Division at http://longrange.sbcountyplanning.org/landuse_element.php. Learn about Madison, WI’s homebuyer assistance programs at www.cityofmadison.com/dpced/economicdevelopment/home-loans/228/. Learn more about the Mansion Hill—James Madison Park Neighborhood Small Cap TIF Loan Program from the City of Madison's Economic Development Department at http://www.cityofmadison.com/dpced/economicdevelopment/mansion-hill-james-madison-park-neighborhoodsmall-cap-tif-loan-program/229/. City of Madison, WI. (2006, January). Appendix 4: City of Madison Strategic Management System Goals and Strategies re: Growth Management. City of Madison Comprehensive Plan, Volume I. Available at http://www.cityofmadison.com/planning/ComprehensivePlan/dplan/v1/chapter5/v1c5.pdf. For more information about Madison, WI’s comprehensive plan see www.cityofmadison.com/planning/ComprehensivePlan/. 45 24 City of Trenton, NJ. (2004, January). Trenton Transportation Master Plan: Phase One Summary Report. Available at: http://www.trentonnj.org/documents/housingeconomic/city_master_plan/phase%20one%20summary%20report.pdf. 25 Learn more about the Los Angeles Transit Neighborhood Plans project at www.latnp.org/. 26 City of Los Angeles. (2008, February). Ordinance No. 179681. Available at cityplanning.lacity.org/Code_Studies/Housing/DensityBonus.pdf. 27 U.S. Department of Housing and Urban Development. Location Affordability Index. Available at http://portal.hud.gov/hudportal/HUD?src=/program_offices/sustainable_housing_communities/location_affordability. 28 The Equality of Opportunity Project. Mobility in All Commuting Zones. Available at www.equality-of-opportunity.org/index.php/city-rankings/city-rankings-all. 29 U.S. Census Bureau. American Community Survey. Available at www.census.gov/acs/www/. 30 Crash data were obtained from all states via online databases or email/phone request. Survey years ranged from 2008 to 2011, with the majority between 2010 and 2011. The individual state crash data were compiled into a national database that includes nearly 6.1 million crashes, 1.8 million injury crashes and 30,000 fatal crashes. 31 See note 15. 32 Institute for Health Metrics and Evaluation. Available at www.healthmetricsandevaluation.org/. 46 Smart Growth America is the only national organization dedicated to researching, advocating for and leading coalitions to bring better development to more communities nationwide. From providing more sidewalks to ensuring more homes are built near public transportation or that productive farms remain a part of our communities, smart growth helps make sure people across the nation can live in great neighborhoods. For more information visit www.smartgrowthamerica.org.