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Date Created: 09/04/15
MEASURING SPRAWL AND ITS IMPACT Reid Ewing Rutgers University Rolf Pendall Cornell University Don Chen Smart Growth America 5 Growth merlca Better Chuices for Our Camrvwnilies Acknowledgements Measmmg Sprawl and Its Impact is the product of three years of research The authors wish to acknowledge numerous individuals who generated or processed data upon which our sprawl and outcome measures are based Key contributors were John Ottensmann of Indiana University who processed Census data and helped design the sprawl measures and David Miller of the Claritas Corporation who provided proprietary data on centers and subcenters Alex Zakrewsky of Global Geometrics worked with the TIGER files and William Dolphin ofRutgers University also processed census data The following Rutgers students worked with such datasets as the American Housing Survey and Census Transportation Planning Package Robert Diogo Danny Knee Rachael Kennedy Kurt Paulsen Jee Shin and Yue Wu The authors also gratefully acknowledge the study s four academic peer reviewers Robert Cervero of UC Berkeley Randall Crane ofUCL A Susan Handy ofUC Davis and Jonathan Levine ofthe University of Michigan Others who helped us review this report include Deron Lovaas Elizabeth Humphrey Lee Epstein Scott Bernstein Kaid Benfield and Niki Mitchell Barbara McCann and David Goldberg helped write the summary version of the report and developed and edited all the supporting materials John Bailey and Barbara McCann worked with SGA s partners to ar range the national and local releases Linda Bailey and Michelle Ernst helped analyze the findings and prepared charts and tables for the final reports and Kate Bicknell helped edit Marquis Clayton prepared the online version of the report and the metro fact sheets The authors thank them for their important contributions This research was made possible by generous support from the Surdna Foundation the William and Flora Hewlett Foundation the George M Gund Foundation the David and Lucile Packard Foundation the H M Jackson Foundation the Turner Foundation and the US Environmental Protection Agency Smart Growth America is a nationwide coalition promoting a better way to grow one that protects farmland and open space revitalizes neighborhoods keeps housing affordable and provides more trans portation choices This report as well as metro area fact sheets and the full technical report are available online at wwwsmartgrowthamericaorg 2 Smart GrowthAmerica Executive Summary Measuring Sprawl S1 Its Impact Much as Justice Potter Stewart said of pornography most people would be hard pressed to define urban sprawl but they know it when they see it Increasingly however that is not good enough As more and more metropolitan areas debate the costs and conse This study is the rst to create a quences ofpoorly managed expansion there is an increas multidimensional picture of the ing need to be clear about the terms ofthe discussion Poli sprawl phenomenon and analyze ticians and planners aiming to contain sprawl also must have an agreed upon way to define and measure it in order to Idath ImpaCts track their progress Beyond that it is important for policy makers to be able to demonstrate how and to what degree sprawl has real implications for real people The study underlying this report the product of three years of research by Reid Ewing of Rutgers University Rolf Pendall of Cornell University and Don Chen of Smart Growth America represents the most compre hensive effort yet undertaken to define measure and evaluate metropolitan sprawl and its impact This report is the first in a series of ndings to be issued based on the ongoing analysis ofthat work Sprawl Defined Beginning with an exhaustive review of the existing academic and popular literature the researchers identi fied sprawl as the process in which the spread of development across the landscape far outpaces population growth The landscape sprawl creates has four dimensions a population that is widely dispersed in low density development rigidly separated homes shops and workplaces a network of roads marked by huge blocks and poor access and a lack of well defined thriving activity centers such as downtowns and town centers Most of the other features usually associated with sprawl the lack of transportation choices relative uniformity of housing options or the difficulty ofwalking are a result ofthese conditions The Four Factor Sprawl Index Based on this understanding the researchers set about creating a sprawl index based on four factors that can be measured and analyzed Residential density o Neighborhood mix ofhomes jobs and services Strength of activity centers and downtowns Accessibility of the street network Each of these factors is in turn composed of several measurable components a total of 22 in all Residential density for example includes the proportion of residents living in very spread out suburban areas the portion of residents living very close together in town centers as well as simple overall density and other measures Before being included each variable was tested through technical analysis to ensure that it added something unique to the overall portrait of sprawl The information assembled for each of 83 metropolitan areas representing nearly half of the nation s 3 Smart GrowthAmei39ica population produced a richly textured database that offers the most comprehensive assessment of metro politan development patterns available to date This study is the first to create such a multidimensional picture of the sprawl phenomenon and analyze related impacts Comparing and Evaluating Metropolitan Regions Based on its performance each metro area earned a score in each of the four factors indicating where it falls on the spectrum relative to other regions Much of the value of this study is in this ability to look at the particular ways in which individual regions sprawl Some metro areas were found to sprawl badly in all dimensions These include Atlanta Raleigh and Greens boro NC A few metros did better than other regions in all four factors among them are San Francisco Boston and Portland Oregon Other metro areas are more ofa mixed bag in those cases the individual factor scores can tell us more about the characteristics of individual metro areas For example while the Columbia SC or Tulsa OK metro areas contain large swaths oflow density development the presence ofa number of strong centers bring them up in the overall ranking And while San Jose California has slightly higher density than most metro areas its lack of centers of activity pulls it down in the overall ranking The scores for the four factors were combined to calculate the overall Four Factor Sprawl lndex ranking the most and least sprawling metropolitan areas On the Index the average is 100 with lower scores indicating poorer performance and more sprawl while higher scores show less sprawl Using this Index the most sprawling metro area of the 83 surveyed is Riverside California with an Index value of 1422 It received especially low marks because o it has few areas that serve as town centers or focal points for the community for example more than 66 percent of the population lives over ten miles from a central business district o it has little neighborhood mixing ofhomes with other uses one measure shows that just 28 percent of residents in Riverside live within one halfblock of any business or institution o its residential density is below average less than one percent ofRiverside s population lives in commu nities with enough density to be effectively served by transit o its street network is poorly connected over 70 percent of its blocks are larger than traditional urban size In the overall national ranking Riverside is followed by Greensboro NC Raleigh NC Atlanta GA Greenville SC West Palm Beach FL Bridgeport CT Knoxville TN Oxnard Ventura CA and Ft Worth TX Most Sprawling Metropolitan Regions Metropolitan Region Overall Sprawl Index Score Rank Riverside San Bernardino CA PMSA 142 1 Greensboro WinstonSalem High Point NC MSA 468 2 Raleigh Durham NC MSA 542 3 Atlanta GA MSA 577 4 GreenviIIe Spartanburg SC MSA 586 5 West Palm Beach Boca Raton Delray Beach FL MSA 677 6 Bridgeport Stamford Norwalk Danbury CT NECMA 684 7 Knoxville TN MSA 687 8 Oxnard Ventura CA PMSA 751 9 Fort Worth Arlington TX PMSA 772 10 4 Smart GrowthAmerica People living in more sprawling regions tend to drive greater distances own more cars breathe more polluted air face a greater risk of traffic At the other end of the scale the metro area with the highest overall score is not surprisingly New York City closely followed by Jersey City just across the Hudson River New York and Jersey City are such extreme outliers that they fatalities and walk and use tranSJt less were excluded from most of the comparative analysis discussed later in the report Provi dence San Francisco and Honolulu round out the top five most compact metros followed by Omaha NE Boston Portland OR Miami and New Orleans Sprawl s Impact on Quality of Life This initial report examines several transportation related measures and impacts and finds that people liv ing in more sprawling regions tend to drive greater distances own more cars breathe more polluted air face a greater risk of traffic fatalities and walk and use transit less Although this study was not designed to prove that land use patterns cause those conditions sprawl and its component factors were found to be a greater predictor than numerous demographic control variables that were also tested Among the impacts of sprawl found Higher rates of driving and vehicle ownership The research indicates that in relatively sprawling regions cars are driven longer distances per person than in places with lower than average sprawl Over an entire region that adds up to millions ofextra miles and tons ofadditional vehicle emissions Also the study found that in the ten most sprawling metropolitan areas there are on average 180 cars to every 100 households in the least sprawling metro areas excluding New York City and Jersey City which are outliers there are 162 cars to every 100 households The research indicates that this is not simply a matter of greater or lesser af uence even controlling for income households are more likely to bear the expense of additional vehicles in more sprawling areas Increased levels of ozone pollution The study found that the degree of sprawl is more strongly related to the severity of maximum ozone days than per capita income or employment levels The difference in ozone peaks appears significant enough to potentially mean the difference between reaching or failing to meet federal health based standards Failing to reach the standard not only imperils the health of children and other vulnerable populations but also subjects regions to a raft of rigorous compliance measures Greater risk of fatal crashes Residents of more sprawling areas are at greater risk ofdying in a car crash the research indicates In the nation s most sprawling region Riverside CA 18 of every 100000 resi dents die each year in traffic crashes The eight least sprawling metro areas all have traffic fatality rates of fewer than 8 deaths per 100000 The higher death rates in more sprawling areas may be related to higher amounts of driving or to more driving on high speed arterials and highways as opposed to driving on smaller city streets where speeds are lower Speed is a major factor in the deadliness of automobile crashes Depressed rates of walking and alternative transport use In more sprawling places people on their way to work are far less likely to take the bus or train or to walk Twice the proportion of residents take public transit to work in relatively non sprawling metro areas versus those with below average scores Likewise thousands more residents walk to work in regions that sprawl less 5 Smart GrowthAmerica No significant differences in congestion delays The research found that sprawling metros exhibited the same levels of congestion delay as other regions This finding challenges claims that regions can sprawl their way out of congestion Policy Recommendations This study shows that sprawl is a real measurable phenomenon with real implications for peoples everyday lives Regions wishing to improve their quality of life should consider taking steps to reduce sprawl and promote smarter growth Based on this research Smart Growth America offers six policy recommenda tions 1 Reinvest in Neglected Communities and Provide More Housing Opportunities 2 Rehabilitate Abandoned Properties 3 393 New D l or n l l t in Already Built Up Areas 4 Create and Nurture Thriving Mixed Use Centers of Activity 5 Support Growth Management Strategies 6 Craft Transportation Policies that Complement Smarter Growth Smart GrowthAmerica Introduction Measuring Sprawl Across the nation growing numbers of communities are discovering links between urban sprawl and a wide range of problems from traffic and air pollution to central city poverty and the degredation of scenic areas As more civic leaders take steps to ameliorate these costs they are in increasing need of meaningful informa tion about the characteristics extent and consequences of sprawl To help meet these needs Smart Growth America SGA has sponsored this groundbreaking research by Rutgers University Professor Reid Ewing and Cornell University Professor Rolf Pendall It represents a rigorous effort to measure the characteristics of sprawl and their impacts on quality of life We define sprawl as low density development with residential shopping and office areas that are rigidly segregated a lack of thriving activity centers and limited choices in travel routes These features constitute fom factors that can then be measured and analyzed 1 Residential density 2 Neighborhood mix ofhomes jobs and services 3 Strength of centers such as business districts and 4 accessibility via the street network All of these are well established descriptors of urban sprawl in the relevant academic literature but this study represents the first effort to attempt to measure sprawl in all of these dimensions The heart of this project is an extensive database that allows for both the careful measurement of urban sprawl as well as the assessment of its relationship to a wide variety of quality of life indicators The database contains two sets of variables The first set includes 22 variables grouped into the four factors that character ize sprawl The second set of data includes dozens of indicators of community quality of life including everything from how much people drive every day to the consumption of farmland and forests This report is the first of several that will assess the impact of sprawl on these important outcomes This research is significant for two main reasons First it is by far the most comprehensive attempt to define and quantify urban sprawl in the US Some studies have defined sprawl simply in terms ofthe amount of land used as the population grows but ignoring the form in which that growth occurs This study shows that sprawl is not just growth but is a specific and dysfunctional style of growth By evaluating metropolitan growth patterns based on four factors we present a highly detailed portrait of sprawl that will enable deci sion makers to target their growth management strategies more effectively Second and perhaps more importantly the research analyzes how growth patterns and affect everyday things that people value In other words the researchers have demonstrated that sprawl is a real measurable phenomenon and it has real measurable consequences for people This first volume presents sprawl measures for 83 of the largest metropolitan areas in the United States and examines the relationships between urban sprawl and transportation related measures including vehicle miles traveled traffic fatalities the extent of walking and public transit use roadway congestion and air quality Future volumes will address how sprawl may be influencing other outcome measures such as the decline of central cities the loss of open space and impacts on public health Also some data will be exam ined at the county level to explore the variation of development patterns within different metropolitan areas Previous Attempts to Define and Measure Urban Sprawl In recent years a number of academics advocates and journalists have sought to define and measure sprawl Previous attempts to measure or operationalize urban sprawl have mostly used only one or two variables The best known effort may be USA Today s sprawl index published in 2001 which measured the proportion of the metropolitan population living outside the Census defined urbanized area and the change 7 Sm art GrowthAmerica in that proportion over time Unfortunately the inherent complexity of sprawl cannot be captured by one or two variables The result has been not only highly simplistic characterizations of urban sprawl but also wildly different estimates of which regions sprawl the worst In one study for example Portland Oregon is ranked as the most compact region while Los Angeles appears to be very sprawling In another their rankings are essentially reversed A third study characterizes certain Northeastern metros as very sprawling while a fourth finds them to be relatively compact There are only a few consistent performers such as Atlanta which always appears to be among the most sprawling Previous studies also fall short by equating sprawl with density Leading scholars and practitioners emphati cally reject the notion that the degree of density is equivalent to the degree of sprawl and contend that other characteristics such as the strength of city and town centers the neighborhood mix of uses and the degree of street accessibility also play a significant role Finally past studies of metropolitan area sprawl have also paid little attention to the impacts of sprawl on daily life With the exception of a few studies focusing on a single outcome each the literature is nearly devoid of such analysis Most comparisons of metropolitan regions simply presume that sprawl has negative consequences Smart Growth America as well as Professors Ewing and Pendall believe that such impacts need to be proven and that ultimately sprawl can only be judged according to its outcomes A brief summary ofprevious sprawl indices can be found in Appendix I 8 Smart GrowthAmerica Characterizing Sprawl The Four Factor Sprawl Index The presence of sprawl may seem obvious when driving past a suburban strip mall but actually measuring development patterns for empirical analysis is a highly challenging and complex undertaking because of the multifaceted nature of sprawl To be investigated empirically sprawl must be operationalized that is it must be represented by variables that can be objectively measured In this study the researchers operationalized or measured sprawl using 22 variables that represent differ ent aspects of development patterns Among these variables are several measures of residential density from the US Census land use data from the National Resource lnventory from the Department of Agricul ture and data on the proximity among homes offices and retail stores from the American Housing Sur vey Variables and their sources are listed in Appendix ll The 22 variables were grouped into four factors of sprawl Residential density Neighborhood mix ofhomes jobs and services Strength of activity centers and downtowns Accessibility of the street network The use of four factors to de ne sprawl means we get a more detailed picture of how sprawling development looks in different metro areas How to Read the Index For the sake of comparability and ease of understanding the scores for the four factors have been standardized so that the average of each factor is represented by a score of 100 This means that the metro areas that are more compact than average have scores above 100 while those that are less compact have scores below 100 Two thirds of the metro areas fall between 75 points and 125 points on the scale in other words 25 points below and 25 points above 100 In statistical terms this 25 unit increment is known as a standard deviation To construct the overall Four Factor Sprawl lndex these factors were combined and standardized for the population size of the surrounding metropolitan region It is important to note that this ranking is relative not absolute US cities tend to be much more sprawling than metro areas in Europe for example and in an international ranking most US metro areas would fall to the bottom of the scale Residential Density Residential density is the most widely recognized indicator of sprawl Spread out suburban subdivisions are a hallmark of sprawl and can make it dif cult to provide residents with adequate nearby shopping or services civic centers or transportation options Yet higher density does not necessarily mean high rises Densities that support smart growth can be as low as six or seven houses per acre typical of many older urban single family neighborhoods Such densities allow neighborhoods that can support convenience stores small neighborhood schools and more frequent transit service In this study this factor is an attempt to measure the efficiency of land use in a metro area It quantifies the amount of land used per person and measures the degree to which housing is spread out or compact The measure ofresidential density used in this study is a composite ofvariables from the US Census the American Housing Survey the Natural Resources Inventory and the Claritas Corporation1 A list of all the variables is available in Appendix 2 9 Smart GrowthAmerica mug mp eswlazu m MuslSpuwhngResxdenndDensxw m M g mm H n x 3 mm 5 WWW m u 3 mm 6mm m m x NGCBMMQ k g mm m a Bumme ham AL m m 91m Pammdx um g smlnnyand mm mmnmsmm m mmaom a m 51m mu m and muemmmmom m a m E m a gym Lyme a a m a m m m mummyquot 7th m my 5 ma Vlad h smllzl Je eY Cay mm a second m m m mm a worm mam g M m 1m mm c m m mam mu uranium mm in mm m 7 m M San 1m ka sgmampwaamwmmmwm Luau Nethhnrlnnd Mix n Haunt ShnF and O icu y amok n mama 5 mQO mm W mmquot a m mm a was new mum 7th mm mm Bud Lawn M m w 91m 9139ka Mm MM 97 y smegma Menunn die a arm g mum in mm Mammary Mms mum Mum e e s P Meuupumamegmn my show m n m Mm clearly capturing something distinct from density Rankings The place with the poorest mix of homes jobs and other land uses is Raleigh NC followed by Riverside CA Greensboro NC Greenville SC and West Palm Beach FL all of which appear in the top ten in the ranking ofmost sprawling metros The mixed use ranking is consistent with low scores in residential density The metros with the greatest degree of land use mixing are medium sized and mostly concentrated in the Northeast In descending order the top five are jersey City N New Haven CT Providence RI Oxnard CA and Bridgeport CT Strength of Metropolitan Centers Metropolitan centers be they down Most Sprawling Strength of Centers towns small towns or so called edge cities are concentrations of activity Centeredness Score Rank that help businesses thrive and sup Vallejo Fame39d Napa CA PMSA 40399 1 1 d Riverside San Bernardino CA PMSA 414 2 port a tematwe transportauon mo es Tampa St Petersburg Clearwater FL MSA 51 3 and multipurpose trip making They West Palm BeachBoca RatonDelray Beach FL MSA 539 4 Oxnard Venture CA PMSA 555 5 foster a sense of place in the urban OaklandYCAPMSA 576 6 landscape Centeredness can be rep Gary Hammond IN PMSA 612 7 resented b concentrations of either Datroit MIPMSA 63390 8 r y Greensboro WinstonSalem High Point NC MSA 691 9 population or employment It can also Anaheim Santa Ana CA PMSA 721 10 re ect a single dominant center or multiple subcenters The academic literature associates compactness with centers of all types and sprawl with the absence of centers of any type The centers factor was determined using variables from the Census and the Claritas Corporation as well as from a Brookings Institution study that used the US Department of Commerce s Zip Code Business Pat terns The centers factor measures two distinct conditions the focus of development on the downtown and central city and the presence of important subcenters within the metropolitan area The former dominates the latter in the resulting rankings Centering appears to operate quite independently ofresidential density metro areas can have strong centers with or without high density In fact this is the only factor that bears no relationship to density and therefore makes a unique contribution to the characterization of sprawl Rankings The metro areas ranking lowest in the strength of their metropolitan centers are Vallejo CA Riverside CA Oak land CA and Gary IN Most of the metros with a low score in this factor are close to larger metropolitan regions where strong centers may exist not too far beyond their borders Two of the bottom ten are metro areas that stand on their own but have exceptionally weak downtowns I 3 Tampa FL and Detroit Los Angeles whose downtown is i W 7 W also weak just misses the bottom ten in this ranking Sprawling regions often lack strong centers such as downtowns or main streets With the exception of New York the metros scoring highest on this factor are medium sized and are focused on one major center downtown In descending order they are Honolulu Columbia SC Spring eld MA 11 Smart GrowthAmerica am Pmaeme omen m due mp hen meme Colorado spnny Omaha NE am wash KS Odqerdqan New York due only large mm nmni metro near the gay n sm mem Accessibility of Lhe Sueet Nawork St gamma m be Ame mm nmmnema m Amnnecma Blackmmaiaut ma Aw A manmlg ge Bum 51415 edby n as almeeu As a pawl Cream huge mpermmk mmmmm mamas 9 name anm afew mum ma hamper gammy mm m m1ng 4 mm meme 5 mamas a nmmnema mm mm AME of mm hammpaamm ma mm Lam h W mdmw wquot u A 1 H H m wlmch mega are mnemannecneni These xaumex are the 2mm ami cemu TIGER le king The mm N am gut the mm mm fur meet magnum we Wham swims Adam EMMY A A J H A A A A H A was ad m connect Place mm the xmallext block and mm accelnble meet network rank ma an the xtxeeu mm am molt are older mampaman area NewYmlc Jens cxw San mem and New or u A A A A A A A A A A A A mam dqexrurbanxzed area Ft LauAEYAale Anaheme and anmx What Lhe Four Factors Can Tell Us A A A A A A A A A A A A A A A A A A A A y A A A A A A A mm W L H A m m A A A Ralexgh NC and Gremxbom NC A few metro are compact m A1 Amemam my a NewYark San F R w A r A A 0mgan A A A A L A A A u A A A A A A MA A A scmdmm V A A A A A A A A AA A A M Jute Calemn k ANN H A A H For Ample 12 SmmethAmenca Case Study Tucson Arizona and Ft Lauderdale Florida A closer comparison of two metropolitan areas with a similar overall ranking shows just how different sprawl can be in these areas Tucson Arizona and Ft Lauderdale Florida have very similar overall sprawl scores Tucson at 109 and Ft Lauderdale at 108 meaning that both are a bit more compact than average for their size Yet they arrive at this score in very different ways Tucson scores above average in the mix of neighborhood uses and focus on activity centers while Ft Lauderdale does much better than average in terms of street accessibility and residential density Tucson has large blocks and very low density housing Tucson s score for street accessibility is 88 ranking it 291 most sprawling in terms of its street layout One indication ofpoor street accessibility is the size of its blocks in Tucson only 45 percent ofblocks are less than a hundredth ofa square mile or about 500 feet on a side Tucson s housing is also extremely spread out the metro area scored 90 on the residential density factor in part because its average urban density is only 1767 persons per square mile one ofthe lowest ofall metros in our sample Tucson s growth has remained focused on its own centers rather than relating to centers in neighboring counties as in Ft Lauderdale and the presence of mountains ringing the Tucson valley has kept nearly all employment within 10 miles of downtown 1n degree of centering Tucson gets an above average score of 106 Tucson also does well in its mix ofhomes offices stores and other uses scoring 121 on this scale Ft Lauderdale s blocks are smaller than Tucson s and its housing is denser Ft Lauderdale scores 137 on the street index 68 percent of its blocks are less than one hundredth of a square mile one of the highest percentages in our sample It also has higher than average residential density with an average urban density of 4837 persons per square mile way above average for our sample But offsetting these factors Ft Lauderdale s degree of centering is below average the metro area scored just 75 on this measure making it the 14th most sprawling place in this regard It has a weaker than average downtown for its size few signifi cant subcenters and more than a third of its population relating to centers outside the metropolitan area Only 15 percent of its employment falls within a three mile ring of the central business district It also keeps homes and workplaces farther apart than average scoring 94 on the mixed use factor Metro Areas with Similar Overall Scores Sprawl in Different Ways 200 Density Factor Mixed Use 150 l Street Access bility Score Lower is more sprawling 6 O Fort Lauderdale Hollyw oodPompano Tucson AZ MSA Beach FL PMSA 13 Smart GrowthAmerica Overall Sprawl Rankings The four factors were combined to produce an overall Sprawl Index The Index ranking shows which metro areas are most sprawling overall and which factors make them that way The most sprawling metro area of the 83 surveyed is Riverside California with an Index value of 1422 It received especially low marks because o it has few areas that serve as town centers or focal points for the community for example more than 66 percent of the population lives over ten miles from a central business district it has little neighborhood mixing of homes with other uses one measure shows that just 28 percent of residents in Riverside live within one halfblock of any business or institution its residential density is below average less than one percent of Riverside s population lives in commu nities with enough density to be effectively served by transit o its street network is poorly connected over 70 percent of its blocks are larger than traditional urban size In the overall national ranking Riverside is followed by Greensboro NC Raleigh NC Atlanta GA Greenville SC West Palm Beach FL Bridgeport CT Knoxville TN Oxnard CA and Ft Worth TX At the other end of the scale the metro area with the highest overall score is not surprisingly New York City closely followed by Jersey City just across the Hudson River Providence San Francisco and Honolulu round out the top five most compact metros followed by Omaha NE Boston Portland OR Miami and New Orleans The table on pages 15 and 16 presents all ofthe Sprawl Index values for metro areas in 2000 and is ranked in order from most to least sprawling The overall Index score appears in the first column and the individual dimensions of sprawl are displayed in columns three through six It is important to point out that metropolitan areas that look less sprawling should not assume that sprawl is not a problem According to our analysis of impacts which is presented below sprawl is strongly associated with a wide range ofproblems Therefore even policy makers in the least sprawling metros should not be complacent and should ensure that their decisions avoid the spread of sprawl Ten Most Sprawling Metropolitan Regions Overall Sprawl Index Score Rank Riverside San Bernardino CA PMSA 142 1 Greensboro WinstonSalem High Point NC MSA 468 2 Raleigh Durham NC MSA 542 3 Atlanta GA MSA 577 4 GreenviIIe Snartanburg SC MSA 586 5 West Palm Beach Boca Raton Delray Beach FL MSA 677 6 Bridgeport Stamford Norwalk Danbury CT NECMA 684 7 Knoxville TN MSA 687 8 Oxnard Ventura CA PMSA 751 9 Fort Worth Arlington TX PMSA 772 10 13 Smart GrowthAmerica Sprawl Scores for 83 Metropolitan Regions The average score for each factor is 100 The table is ranked in order from most sprawling to least sprawling on the overall Four Factor Sprawl lndex Overall Sprawl Street Connectivity Centeredness Mixed Use Density Metropolitan Region Score Score Score Score Score RiversideuSan Bernardino CA PMSA 142 805 414 415 935 GreensboroWinstonSalemHigh Point NC MSA 468 663 691 467 742 RaleighDurham NC MSA 542 808 772 395 762 Atlanta GA MSA 577 57 823 737 845 GreenvilIeSpartanburg SC MSA 586 621 985 504 719 West Palm BeachBoca Raton Delray Beach FL MSA 677 1047 539 547 94 BridgeportStamfordNorwaIk Danbury CT NECMA 684 807 948 1375 925 Knoxville TN MSA 687 755 978 629 712 OxnardVentura CA PMSA 751 1065 555 1394 1039 Fort WorthArlington TX PMSA 772 975 739 891 903 GaryHammond IN PMSA 774 1005 612 1237 864 Rochester NY MSA 779 372 1207 823 914 Dallas TX PMSA 783 902 811 826 995 VaejoFairfiedNapa CA PMSA 784 1097 409 1163 974 Detroit MI PMSA 795 93 63 1025 973 Syracuse NY MSA 803 526 1249 72 858 Newark NJ PMSA 813 1154 822 1204 1189 Little RockNorth Little Rock AR MSA 823 882 1059 683 775 AbanySchenectady Troy NY MSA 833 732 985 893 829 HartfordNew BritainMiddletown Bristol CT NEC 852 596 846 1194 863 Oklahoma City OK MSA 856 691 956 1013 845 TampaSt PetersburgCearwater FL MSA 863 1336 519 80 936 Birmingham AL MSA 88 104 1125 622 771 Baton Rouge LA MSA 901 762 1062 959 808 Worcester FitchburgLeonminster MA NECMA 905 745 1227 823 812 Washington DCMDVA MSA 908 98 978 787 1069 Columbus OH MSA 911 972 1015 765 915 Jacksonville FL MSA 916 1046 1021 729 856 Kansas City MOKS MSA 916 888 89 100 909 Cleveland OH PMSA 918 668 1009 1074 997 Memphis TNARMS MSA 922 765 1042 97 889 Houston TX PMSA 933 956 87 1101 953 Indianapolis IN MSA 937 845 1024 962 893 Columbia SC MSA 942 795 1473 671 746 St Louis MOL MSA 945 106 762 1074 903 Grand Rapids MI MSA 952 637 1103 1157 827 15 Smart GrowthAmerica Overall Sprawl StreetConnectivity Centeredness Mixed Use Density Metropolitan Region Score Score Score Score Score NorfolkVirginia BeachNewport News VA MSA 956 1131 82 872 95 MinneapolisSt Paul MNW MSA 959 877 1078 947 947 Cincinnati OHKYN PMSA 96 854 1102 958 888 Orlando FL MSA 964 1206 1035 608 938 AnaheimSanta Ana CA PMSA 971 1364 721 1215 1288 Oakland CA PMSA 988 1334 576 1063 1166 Tulsa OK MSA 991 962 115 88 827 Seattle WA PMSA 1009 1171 98 794 1036 Los AngelesLong Beach CA PMSA 1018 1233 724 1231 1515 San Diego CA MSA 1019 106 744 1054 1134 Sacramento CA MSA 1026 984 874 1109 991 Las Vegas NV MSA 1047 1088 998 801 110 Akron OH PMSA 1059 842 1195 1187 868 Tacoma WA PMSA 1059 1112 1227 856 908 Pittsburgh PA PMSA 1059 1242 1045 868 904 New HavenWaterburyMeriden CT NECMA 107 865 789 1443 916 Toledo OH MSA 1072 776 1122 1196 913 San Antonio TX MSA 1078 103 1084 1006 95 Fort LauderdaIeHoywood Pompano Beach FL PMSA 1084 1372 75 947 1139 Tucson AZ MSA 1091 88 1064 1218 904 San Jose CA PMSA 1097 1252 939 966 1248 Wichita KS MSA 1101 786 1314 1131 844 Austin TX MSA 1103 944 1158 1119 89 Fresno CA MSA 1103 73 1126 1301 935 Salt Lake CityOgden UT MSA 1109 117 938 1032 995 Phoenix AZ MSA 1109 1072 926 116 1068 Philadelphia PA NJ PMSA 1126 113 959 1195 1147 Baltimore MD MSA 1159 1052 1156 1068 1043 El Paso TX MSA 1172 1023 1195 1034 1001 Milwaukee WI PMSA 1173 939 1177 1179 1014 Buffalo NY PMSA 1191 706 1352 1247 1021 Chicago IL PMSA 1212 1349 858 1151 1429 Springfield MA NECMA 1225 873 1486 1157 863 AentownBethehemEaston PA NJ MSA 124 131 917 1334 862 Colorado Springs CO MSA 1244 967 1352 119 912 Albuquerque NM MSA 1245 1178 124 1037 97 Denver CO PMSA 1252 1257 1089 1157 1037 New Orleans LA MSA 1254 1386 1237 804 1059 MiamiHiaeah FL PMSA 1257 1364 927 1047 1291 Portland OR PMSA 1261 128 1218 1023 1013 BostonLawrenceSaemLowe Brockton MA NECM 1269 1191 1094 1244 1136 Omaha NEA MSA 1284 1046 1323 1193 964 Honolulu HI MSA 1402 1143 1673 843 1165 San Francisco CA PMSA 1468 1398 1286 1073 1552 ProvidencePawtucket Woonsocket RI NECMA 1537 1359 1403 1405 991 Jersey City NJ PMSA 1623 1668 987 1729 1957 New York NY PMSA 1778 1549 1446 1298 2425 16 Smart GrowthAmerica Measuring Sprawl s Impact Ultimately sprawl must be judged by its consequences No development pattern is inherently good or bad Citizens and policy makers will decide whether one development pattern is preferable to another based on the conditions they create for people and the environment It is in evaluating these outcomes that this study is likely to prove most useful As noted above future work will include measuring sprawl against a wide variety of measures including public health infrastructure expenditures loss of resource lands and racial segregation among others Correlational studies ofwhich this is one cannot be used to establish cause effect relationships But they can establish statistically significant associations a necessary condition for causality This study has also controlled for potentially confounding influences such as population size average size of households per capita income and the proportion of the population ofworking age For this initial report researchers compared the overall Sprawl Index and the four sprawl factors to out come measures related to transportation because the effect of sprawl on transportation has been relatively well researched Finding relationships between sprawl and transportation that agree with the existing litera ture helps to validate this measurement of sprawl The outcome measures came from a variety of The Impact of Sprawl on sources including the US Census Bureau the Quality of Life Outcomes Texas Transportation Institute the Federal I Iigh way Administration s Highway Performance Moni For this report the four factors and the overall toring System and the National Highway and Traf Sprawl Index were compared to the following travel fic Safety Administration s Fatal Accident Report and transportation outcomes ing System Outcome measures are listed in Appen Clix H o Distance Driven per Person per Day DailyVe hicle Miles Traveled Per Capita Average Vehicle Ownership per Household Percent of Commuters Taking Transit to Work Percent of Commuters Walking to Work Overview Sprawl Affects Daily Life This analysis found that for nearly all of these travel and transportation outcomes sprawling regions per form less well than compact ones The degree to 39 Average Commute Times which a region sprawls as represented by the In 39 Average Annual Traf c Delay dex bears strong relationships to six of the travel 39 Traf c Fatalities per 100000 People Ozone Pollution Levels related outcome variables As sprawl increases so do the number of miles The study controlled for several demographic and driven each day daily vehicleam es traVeled or socioeconomic variables that might have an inde DVMT the number ofvehicles owned per house pendent influence on the outcome measures This hold the annual traf c fatality rate and concen helps ensure that the relationships between sprawl trations of ground level ozone a component of and the above outcomes are genuine associations smog At the same time the number of commut and nOt driVen by Other factors ers walking biking or taking transit to work de creases to a significant extent Interestingly the 39 Metropontan Area Population Index is not significantly related to two indicators 39 Average HousehOId Size of traffic congestion either average commute time 39 Percent Of the POPUIation ofWorking Age 20 or annual traffic delay per capita That is impor 6439 tant to note because defenders of sprawl often arr 39 Per Capita Income 17 Smart GrowthAmerica gue that spreading out reduces conges 39on and travel times This report s findings undermine that claim People in Sprawlng Metros Drive More and Own More Cars Compared with all of the control variables the degree of sprawl was the sn39ongest in uence on vehicle4niles n39ava Average Distance nriverr pe rCapita eled per person This was somewhat surprising because some scholars contend that metropolitan popula 39on and per capita income have the greatest influence on the amount of vehicle travel within a metro area The statis 39cal relationships we found show thatvehicle use rises quite non39ceably as sprawl increases For every 25aunit decline one standard deviation in the Sprawl Index there is an almost twoqnile increase 1 96 in daily vehidemiles Ave Dally Vehlcle Mlles Traveled percaplta Ten Least Sprawllng Metros Top Ten Most Sprawllng Metros traveled DVMT per person While the numbers may appear modest n39ansportan39on planners will recog nize the enormity of the implica 39ons since even a small rise in per capita miles of travel represents a sizeable increase in n39affic emissions and fuel expenditures when viewed across an entire men39o region With a range on the Sprawl Index of 55 standard deviations excluding the two extreme outliers New York City and Jersey City this represents a difference of approximately 10 8 DVMT per capita between the most sprawling and most compact regions Some metro area comparisons illusn39ate this difference In highly sprawling men39opolitan Atlanta Index score of 58 for example vehicles rack up 34 miles each day for every person living in the region On the other end of the scale in Portland Oregon Index score of 126 vehicles are driven fewer than 24 miles per person per day 391 Ul dW lr quot quotdrrity 1 39 theamount An analysis of of driving per person A 25aunit increase in this factor is associated with a decrease of 54 miles driven per day per person With the wide range on the residential density factor 34 standard devia 39ons excluding the two extreme outliers New York City and Jersey City residential density is associated with a difference of roughly 18 daily vehicle miles of travel per capita between People in more Sprawlmg memos Eve lowest density and highest density areas further each day Average household vehicle ownership is an indicator of the degree to which a region s populan39on is dependent on auto Allxnl GA 34 S 7 mobiles for basic n39ansporta 39on The assumpu39on is that in mlle sprawling areas where driving is the only way to get around more households feel compelled to have a vehicle for each licensed driver This appears to be the case even after conn39ola ling for income Sprawl is associated with higher levels of aua tomobile ownership The overall Sprawl Index is associated with a difference of 26 vehicles per 100 households between the exn39eme cases 55 standard devian39ons Some of the cone trol variables also had an impact on the number of cars per household There were more cars per household in places where the average household is larger and in larger metroa Dally Vahlcla Mlles Traveled VMT per Caplla 9 B 7quot 15 politan areas But the number of cars per household was more Sprawl quotmax sn39ongly related to the degree of sprawl than to the propora 18 New York City and Jersey City Excluded Smart GrowthAmerica tion of the o ulation of workin a e or er ca ita in p p g g p p Average Number ofVehioles per 100 Households come This nding suggests that in sprawling regions automobile ownership may be more a matter of survival 200 than a matter of personal choice 180 160 3 0 Among the individual factors residential density has a far stronger association with average household vehicle ownership than does overall sprawl A 25 unit increase Vehicles per 100 Households in the compactness factor score is associated with a 013 Most Ten Least Sprawling Sprawling Metros Metrosquot drop in average number of vehicles per household That is controlling for other factors each standard deviation increase in housing compactness has every 100 household shedding an average of 13 cars Residential density alone is associated with a difference ofover 44 cars per 100 households between the most sprawling and least sprawling metro areas the range on this factor is 34 standard deviations Again viewed in the aggregate across a metro area such increases in vehicle ownership represent significantly more cars that must be supplied with parking fuel insurance and road capacity to say nothing of the associated air emissions and roadway runoff In Sprawling Areas Fewer Get to Work by Taking Public Transit and Walking This study also found that in more sprawling places people on their way to work are far less likely to take the bus or train or to walk The metro areas that are more sprawling than average have only 23 percent of workers taking public transportation to work while the Average Share of Commute Trips by Transit places that are less sprawling than average have 51 per 10 cent of workers taking public transportation comparing 8 metros 25 points above the average Sprawl Index to those 6 25 points below the average Sprawl Index 4 r The residential density factor was found to have a highly significant association with the share of public transit trips Percent of all Commute Trips Top Ten Most Ten Least Sprawling to work A 25 unit increase in this factor is associated Sprawling Metros Metrosquot with a nearly 3 percentage point increase in public trans portation mode share on the journey to work That is controlling for other factors every 25 point rise in the density factor score increases public transportation mode share by almost 3 percentage points With a range on this factor of about 85 points 34 standard deviations density alone is associated with a 10 percentage point increase in public transportation use between more and less sprawling metros In examining whether people walk to work the degree of sprawl is by far the most powerful predictor associations with all of the control variables were insignificant Roughly 2 percent of commuters walk to work in more sprawling places those with scores 25 points below average and 31 percent walk to work in less sprawling places 25 points above the average sprawl index Between extreme cases there is a difference of 3 percentage points in walk share to work The residential density factor shows a comparable association with regard to walk share to work Regions with the lowest residential density can be expected to have 27 percent fewer people walking to work than metros with the highest residential density The relationship New York City and Jersey City Excluded 19 Smart GrowthAmerica with the centers factor was also significant but less so with a 25 percent difference between extreme cases No Effect on Travel Delays Surprisingly the analysis did not find statistically significant relationships between sprawl and either the amount of travel delay that drivers experience or the average travel time for commuters Both outcomes were found to be primarily a function of metropolitan area population and secondarily ofother demographic variables In other words big metro areas tend to generate long trips to work and high levels of traf c congestion After controlling for population size and other demographic variables sprawl does not appear to have a marginal effect on either outcome Average Commute Time Minutes 3 Top Ten Most Sprawling Ten Least Sprawling Metros Metros Why not7 Some contend that in sprawling regions a greater proportion ofjobs and housing are dispersed into suburban areas that do not suffer from traffic gridlock The ndings with regard to the individual factors may shed some light The centers factor showed an inverse relationship to annual delay per capita so regions with stronger centers tend to have fewer traffic delays The mix factor was similar showing a signifi cant inverse relationship with travel time to work so regions with a better mix of uses shows lower levels of traffic delay However the streets factor was found to have direct relationships with both average commute times and annual delay per capita When combined these three dimensions of sprawl appear to cancel each other Therefore one of the strongest purported benefits of sprawling development lower traffic congestion is not borne out by this study Those who believe that metropolitan regions can sprawl their way out of congestion appear to be wrong Those who believe that metropolitan regions can sprawl their way out of congestion appear to be wrong More Sprawl More Traffic Fatalities Sprawling places are likely to have more traffic fa talities per capita than more compact regions due to higher rates of vehicle use and perhaps more aggres sive driving For example in Riverside CA the most sprawling region according to the Index 18 of every 100000 residents die each year in traffic crashes The eight least sprawling metro areas all have traffic fa tality rates of fewer than 8 per 100000 This differ ence of 10 fatalities per 100000 is approximately what can be expected between extremely sprawling and extremely compact regions This relationship is statistically significant outweighing the effect of all the control variables including per capita income Fatal Accidents per 100000 Average Annual Traffic Death Rate Ten Most Top Spraw ling Metros Ten Least Sprawling 20 New York City and Jersey City Excluded Smart GrowthAmerica The residential density factor was also found to be statistically significant with regard to traffic fatalities Areas of the highest residential densities can be expected to have up to 18 fewer fatalities per 100000 than their low density counterparts The mix factor and the centers factor shows a similar relationship though less strong for example regions with the strongest centers exhibit nearly 5 fewer fatalities per 100000 than regions with the weakest centers Air Quality Poorer in Sprawling Areas This analysis found a strong relationship between main Maxim um 8Hour Ozone Level ppb 150 mum ozone levels based on the Environmental Protec tion Agency s standard of 80 parts per billion averaged over an 8 hour period and the overall Sprawl Index Of all the variables tested the degree to which a region sprawls was the best indicator of a metro area s ozone levels Every shift of 25 points upwards on the Sprawl Maximum 8Hour Ozone parts per bllllon 0 Top Ten Most Sprawling Ten Least Sprawling maximum ozone levels Looking at the range on the Metros Metrosquot Index is related to a 75 parts per billion decrease in Sprawl Index 55 standard deviations Ozone levels be tween the most sprawling and least sprawling areas can differ by 41 parts per billion Residential density appears to have the strongest impact on maximum ozone levels with areas ofthe highest density expected to have 51 ppb lower ozone levels than the lowest density metros quite signi cant relative to the ozone standard of 80 ppb Elevated levels of ozone have been shown to be dangerous for children the elderly asthma sufferers and other vulnerable populations New York City and Jersey City Excluded 21 Smart GrowthAmerica Surprisingly the mix factor appears to have an aggravating effect on maximum ozone level though the relationship is just barely statistically significant and may be spurious If it is valid it may be because a fine grained mix of land uses encourage more short vehicle trips and therefore more cold starts and hot soaks that contribute to air pollution Conclusion The relationships found between urban sprawl and the quality of life outcomes show that traffic and trans portation related problems appear to increase in more sprawling areas Even when controlling for income household size and other variables people drive more have to own more cars breathe more polluted air face greater risk of traffic fatalities and walk and use transit less in places with more sprawling development patterns While these findings may seem obvious this is the first study to explicitly measure sprawl and explicitly relate sprawl so measured to an important set of transportation outcomes This study suggests that if Houston for example were only somewhat more compact thousands more people would walk to work residents would drive less and children would breathe cleaner air Generalizing to other transportation related outcomes these findings suggest that even after controlling for numerous demographic factors urban sprawl has a major influence on energy gasoline consumption and other outcomes that are tied to vehicle miles traveled Future reports will further quantify the costs in health safety time and money associated with this phenomenon Policy Recommendations Even for metropolitan regions that appear relatively compact urban sprawl is a serious problem because of its strong association with numerous societal problems For the nation s most sprawling regions it is even more urgent to devise strategies that can reduce sprawl Advocates and practitioners associated with the Smart Growth movement have devised a wide array of techniques and policies to manage growth and help regions avoid haphazard sprawl The following recommendations however are focused on the specific issues examined in this report namely the four factors and the transportation outcomes measures For more information see Getting to Smart Growt published by the Smart Growth Network wwwsmartgrowthorg This study found strong evidence that at the regional scale increased residential density has the potential to diminish the need to own and drive automobiles which in turn can help protect air quality and reduce traffic fatalities while increasing the share of commuters who use transit or walk That is not a prescription for high rises in every neighborhood far from it The research indicates that even modest increases in average density from one or two houses per acre to as few as six or seven can offset the negatives examined in this report There are many strategies that can result in attractive communities with higher densities Some of these strategies tend to fall under the general heading of community economic development At the same time the development of compact walkable neighborhoods is gaining momentum in the real estate market with growing numbers of retiring baby boomers expressing a preference for in town living greater conveniences and a stronger sense of community 1 Reinvest in Neglected Communities and Provide More Housing Opportunities For decades thousands of community based organizations have sought to use policy and financing tools to improve the quality of life in distressed communities These tools include state and local low income housing tax credit the Community Reinvestment Act Community Development Block Grants state affordable Smart GrowthAmerica housing trust funds and a whole range of state and local programs Such strategies infuse badly needed resources into long neglected neighborhoods and may reverse the abandonment of such neighborhoods To reduce the impacts of sprawl these reinvestment and housing programs should at least be maintained at current funding levels and preferably increased In particular a federal proposal to create a national afford able housing trust fund should be enacted into law 2 Rehabilitate Abandoned Properties A related strategy is the rehabilitation of individual abandoned properties be they old vacant buildings taxi delinquent homes empty historic buildings or other potentially useful properties New Jersey for example passed a new rehabilitation code to facilitate the restoration of older buildings Such measures have led to a large increase in rehab investment in New Jersey cities and have been adopted by Maryland Rhode Island and other states Other states have reformed tax foreclosure laws and initiated improved inventory and tracking systems to more quickly identify negligent owners of abandoned properties and transfer them to new investors 3 393 New D I or n I I in Already Built Up Areas Smart growth is not about stopping growth or even slowing growth rather it is about focusing growth in places where it can properly be accommodated Chief among those would be areas that already are within the urban footprint Most metro regions contain ample redevelopment opportunities which may include old industrial sites brownfields empty shopping malls greyfields and vacant lots Such properties tend to have existing infrastructure roads water sewer and other utilities are large enough to accommodate en tire new neighborhoods with a mix ofhomes shops offices civic buildings and parks linked together by a grid of streets and sidewalks 4 Create and Nurture Thriving Mixed Use Centers of Activity This study found that strong urban and suburban downtowns and other centers of activity are associated with fewer traffic fatalities lower vehicle mileage and more transit use and walking to work As such the fostering of such centers is an essential smart growth strategy One of the most promising approaches to accomplishing this is to concentrate mixed income housing shops and offices around train stations and bus stops which is commonly referred to as transit oriented development TOD Another important strategy involves rezoning to permit multifamily housing in and around the jobs rich edge cities This can make it possible for more people to live near work while also introducing the resi dents needed to support neighborhood retail 5 Support Growth Management Strategies The low scores for the overall Sprawl Index indicating more sprawl were associated with more driving vehicle ownership traffic fatalities peak ozone levels and lower levels of transit use and walking to work Key strategies for curbing sprawl include planning and zoning tools that help regions better manage growth Portland Oregon has developed one oft cited model wherein a regional growth framework is established and managed by an elected regional council in concert with local governments Another method is the strategic preservation of prime farmland sensitive environmental lands forests and other green spaces in conjunction with careful planning for development in designated areas 6 Craft Transportation Policies that Complement Smarter Growth In the coming year Congress will consider the reauthorization of the nation s transportation law the Trans portation Equity Act for the 21St Century TEA ZI This reauthorization is not only the means by which states receive federal gas tax dollars for much needed transportation projects but it is also the main federal Z5 Smart GrowthAmerica opportunity to improve the interaction between local and regional development plans and transportation planning and programming In keeping with the previous ve recommendations this reauthorization should Support fix it first state and federal transportation infrastructure policies which favor the mainte nance of existing streets and highways over the construction of new ones Prioritize and increase funding that serves community development goals in lower income neighbor hoods Create incentives for transit oriented development particularly mixed use development and mixed in come housing and Maintain important funding programs for historic preservation walking and cycling facilities and Main Street and streetscape improvement projects In addition the new law should include resources that enable communities to better coordinate transporta tion and land use including 1 Census tracts with very low densities less than 100 persons per square mile were excluded from the calculation ofthesevariables to eliminate rural areas desert tracts and other undeveloped tracts that happen to be located within metro area boundaries Z Reid Ewing Is Los AngelesaSter Sprawl Desirablen Journal ofthe American Planning Association Winter 1997 pp 1074 26 24 Sm art GrowthAmerica Funds to support more sophisticated scenario planning for both corridors and regions Better predictive models that cover not only transportation outcomes but also community impacts and Tools for improved community involvement in the planning process Appendix One Previous Attempts to Measure Sprawl This is a brief overview of previous studies measuring and analyzing sprawl For a more complete discus sion see the full research paper Studies Simply Measuring Sprawl1 USA Today The sprawl index to receive the most attention despite its limitations was developed by USA Today 2 The USA Today index assigned a score to each of 271 metropolitan areas based on two density related measures Percentage of a metro area s population living in urbanized areas For the years in question the Census Bureau defined urbanized as those parts ofa metro with 1000 or more residents per square mile o Change in the percentage of metropolitan population living in urbanized areas between 1990 and 1999 Metropolitan areas were ranked 1 through 271 on each measurement with lower numbers representing less sprawl The two rankings were summed to produce each metro area s sprawl score The highest possible score was 542 the lowest 2 The advantage of the USA Today index is its simplicity which makes it easy to explain The big disadvantage is its total reliance on density as an indicator of sprawl and density measured in a way that fails to distinguish between development at low suburban densities as low as 1000 persons per square mile something less than one dwelling unit per acre and development at high urban densities Based on this index USA Today declared Los Angeles whose legendary traffic congestion and spread out development have epitomized suburban sprawl for decades isn t so sprawling after all In fact Portland OR the metropolitan area that enacted the nation s toughest anti growth laws sprawls more Indeed according to USA Today s index Los Angeles is less sprawling than even the New York metropolitan area Sierra Club In a report titled The Dark Side of the American Dream The Costs and Consequences of Suburban Sprawl the Sierra Club ranked US metropolitan areas on the degree to which they sprawl3 Sprawl was defined as low density development beyond the edge of service and employment which separates where people live from where they shop work recreate and educate thus requiring cars to move between zones Metros were subjectively rated as more or less sprawling based on population shifts from city to suburb growth of land area vs growth of population time wasted in traffic and loss of open space Sprawl was thus defined not only by its characteristics but its effects Among the largest metros 1 million or more 25 Smart GrowthAmerica people Atlanta St Louis and Washington DC were rated most sprawling Among medium size metros 500000 1000000 population Orlando Austin and Las Vegas shared that distinction Galster et al Galster et al developed the most complex and multifaceted sprawl index to date4 Sprawl was character ized in eight dimensions density continuity concentration clustering centrality nuclearity mixed use and proximity The condition sprawl was defined as pattern of land use that has low levels in one or more of these dimensions Variables representing causes and consequences of sprawl such as fragmented governance and auto dependence were explicitly excluded from the de nition Each dimension was operationally defined and six of the eight were quantified for 13 urbanized areas New York and Philadel phia ranked as the least sprawling of the 13 and Atlanta and Miami as the most sprawling The main drawback of Galster et al s index is its availability for only 13 areas Studies Measuring Sprawl and Relating It Outcomes Kahn Kahn explored one potential benefit of sprawl increased housing affordability and greater equality of 5 Using 1997 American Housing Survey data Kahn measured housing opportunity across racial lines housing consumption for blacks and whites in metropolitan areas characterized as more or less sprawling Housing consumption was represented by number of rooms unit square footage homeownership rates and year ofconstruction For his measure of sprawl Kahn drew upon his research with Glaeser see below Sprawl was represented by the degree of employment decentralization in a metro area specifi cally by the proportion of metropolitan employment located more than 10 miles from the central busi ness district If all employment were located inside a 10 mile ring around the CBD Kahn s sprawl level would be zero If all were located outside the 10 mile ring the sprawl level would be 1 As it is values of this index varied from 0196 for Portland to 0786 for Detroit Downs In Chapter 13 of The Cost of Sprawl Revisited Anthony Downs reviewed his earlier research on sprawl and its effects on urban decline6 His conclusion No meaningful and significant statistical relationship exists between specific traits of sprawl and measures of urban decline He tested for statistically significant relationships between suburban sprawl and urban decline and found none Sprawl was defined in terms ofan assortment of land use patterns root causes of these patterns and specific consequences ofthese patterns Thus Downs conception of sprawl failed to distinguish causes and consequences from charac teristics of sprawl In addition to mixing characteristics causes and effects of sprawl Downs index suffers from reliance on political and hence economically arbitrary boundaries of central cities to define centeredness reliance on the urbanized area definition of 1000 residents per square mile to define the worst of all sprawl In this last respect Downs index is subject to the same criticism as USA Today s see above Studies Measuring Sprawl and Exploring Causes Glaeser et al Edward Glaeser et al related sprawl to the degree of decentralization of employment using data from the US Department of Commerce s Zip Code Business Patterns for 19967 For the 100 largest US metro politan areas the share of overall metropolitan employment within a three mile ring of the Central Business District was computed as were the shares inside and outside a 10 mile ring The share within 26 Sm art GrowthAmerica three miles reflects the presence or absence of a well defined employment core while the share beyond 10 miles captures the extent ofjob sprawl Metros were then divided into four categories based on values of these indices Dense employment metros like New York have at least one quarter of their employment within three miles of the city center Centralized employment metros like Minneapolis St Paul have between 10 and 25 percent of employment within three miles of the city center and more than 60 percent within 10 miles Decentralized employment metros like Washington DC have 10 to 25 percent of employment within the three mile ring and less than 60 percent within 10 miles Finally extremely decentralized employment metros like Los Angeles have less than 10 percent of their employ ment within the three mile ring Pendall Pendall sought to explain the incidence of sprawl for large metropolitan areas in terms of land values metropolitan political organization local government spending traffic congestion and various local land use policies8 Among land use policies adequate public facilities requirements which force new develop ment to pay its own way were found to discourage sprawl while low density zoning and building caps were associated with more sprawl Among control variables high valued farmland and expensive housing reduced sprawl while jurisdictional fragmentation increased it Fulton et 31 Building on Pendall s earlier work Fulton et al studied urban land consumption relative to population change for every US metropolitan area9 If land is consumed at a faster rate than population is growing sprawl is said to be increasing As with Pendall s earlier work this concept of sprawl is strictly density related By this criterion the West is home to some of the least sprawling metropolitan areas in the nation By contrast the Northeast and Midwest are in some ways the nation s biggest sprawl problems since they add few new residents yet consume large amounts of land In this study Honolulu and Los Angeles were rated most compact in 1997 and Las Vegas and Phoenix often characterized as sprawling WW compactness Las Vegas and Phoenix were rst and third in density gain over the 15 years studied 1982 to 1997 1 Sprawl has been measured in other ways for individual metropolitan areas This literature survey is limited to studies which like this one use a comparative index to rank metros in terms of sprawl For examples of individual area studies see Cameron Speir and Kurt Stephenson Does Sprawl Cost Us All Isolating the Effects ofHousing Patterns on Public Water and Sewer Costsn Journal of 39Le American Planning Association Vol 68 No 1 Winter 2002 pp 5670 and Lance Freeman The Effects of Sprawl on Neighborhood Social Ties An ExploratoryAnalysisn Journal ofthe American Planning Association Vol 67 No 1 Winter 2001 pp 6977 Z USA Today February 22 2001 3 Sierra Club The Dark Side of die American Dream The Com and Consequences of Suburban Sprawl Challenge to Sprawl Campaign College Park MD undated 4Cieorge Galster Royce Hanson Michael Ratcliffe Harold Wolman Stephan Coleman and Jason Freihage Wrestling Sprawl to the Ground Defining and Measuring an Elusive Conceptn Housing Policy Debate Vol 12 no 4 2001 p 685 5 Matthew Kahn Does Sprawl Reduce the BlackWhite Housing Consumption Gapn Housing Policy Debate Vol 12 No 1 2001 pp 7786 6 Robert Burchell et al Com ofSprawl Revisited Transit Cooperative Research Program Transportation Research Board Washing ton DC 2001 Chapter 13 Anthony Downs Some Realities About Sprawl and Urban Declinen Housing Policy Debate Vol 4 No 4 1999 pp 955974 7 Edward Glaeser Matthew Kahn and Chenghuan Chu lob Sprawl Employment Location in US Metropolitan Areas Center for Urban amp Metropolitan Policy The Brookings Institution Washington DC July 2001 27 Smart GrowthAmerica B Rolf Pendall Do LandaUse Controls Cause Sprawln Environment and Planning B Vol 26 No 1999 pp 9 William Fulton Rolf Pendall Mai Nguyen and Alicia Harrison Who Sprawl Most7 How Growth Patterns Di 39er Across the US Center for Urban ampMetropolitan Policy The Brookings Institution Washington DC July 2001 Appendix Two Brief Methodology This report is intended as a layperson s introduction to a complex academic study The first technical research paper based on this research is available as a companion to this document and is recommended reading for those with a strong interest in methodology In fact for researchers the painstaking methodol ogy may be of primary interest The paper Mcasming Urban Sprawl and It Impacts has undergone an aca demic review process and versions of it are being submitted to academic journals Metropolitan Area Data and Definitions The study began with 139 metro areas but many metro areas had to be dropped because of a lack of complete data A listing ofall 139 metro areas as well as the missing data components that prevented the inclusion of some areas in the final analysis can be found in the research paper Our final sample of US metropolitan areas consists of 83 metropolitan areas This includes every metro over 500000 population for which we could obtain a complete dataset Our basic unit ofanalysis is a piece of geography created by the Census Bureau and known as metropolitan statistical area or a primary metro politan statistical area or PMSA PMSAs are generally larger than political jurisdictions such as cities but smaller than the entire metropolitan region some regions may include several PMSAs which are then combined to form a Combined Metropolitan Statistical Area CMSA For a listing of PMSA and CMSA boundaries visit wwwcensusgov Variables Used to Define Sprawl Factor Variable Source Residential Density Gross Population Density in persons US Census per square mile Percentage of population living at densities US Census less than 1500 persons per square mile low suburban density Percentage of population living at densities US Census greater than 12500 persons per square mile urban density Estimated density at the center of the US Census metro area Gross population density of urban lands USDA Natural Resources Inventory Weighted average lot size for single family American Housing Survey dwellings in square feet Weighted density of all population centers Clarims Corporation within a metro area 28 Smart GrowthAmerica Factor Neighborhood Mix of Homes Shops and Offices Strength of Metropolitan Centers Accessibility of the Street Network Variable Percentage of residents with businesses or institutions within 12 block of their homes Percentage of residents with satisfactory neighborhood shopping within 1 mile Percentage of residents with a public elemenmry school within 1 mile Balance of jobs to residents Balance of population serving jobs to residents Population serving jobs include retail personal services enterminment health education and professional services Mix of population serving jobs Variation of population density by census tract Rate of decline in density from center density gradient Percentage of population living within 3 miles of the central business district Percent of the population living more than 10 miles from the CBD Percentage of the population relating to centers within the same metropolitan smtistical area Ratio of population density to the highest density center in the metro area Average block length in urbanized portion of the metro area Average block size in square miles Percentage of small blocks 29 Smart GrowthAmerica Source American Housing Survey American Housing Survey American Housing Survey Census Transportation Planning Package Census Transportation Planning Package Census Transportation Planning US Census US Census Edward Glaeser Brookings Institution Edward Glaeser Brookings Institution Clarims Clarims Census TIGER files Census TIGER files Census TIGER files The table below and on the next page lists the variables included in each of the four sprawl factors and their source For a more detailed discussion ofthe variables please refer to the research paper Combination of the 22 variables to create four sprawl factors Twenty two variables were combined into four sprawl factors using a technique known as principal com ponent analysis Seven variables contributed to the residential density factor six to the land use mix factor six to degree of centering factor and three to the street accessibility factor The principal compo nent selected to represent each set of variables was the component explaining the greatest variation in the original dataset We reasoned that the single factor that captures the greatest variance among mul tiple variables is likely to be a valid and reliable measure of density mix centers or streets The Question of Size These four factors could simply be summed to obtain an overall Sprawl Index for the 83 metropolitan areas but there is a problem with this approach As metro areas grow so do their labor and real estate markets and their land prices Their density gradients accordingly shift upward and other measures of compactness street density for example follow suit Thus the largest metro areas perceived as the most sprawling by the public actually appear less sprawling than smaller metros when sprawl is measured strictly in terms of the four factors with no consideration given to size Some of the technical literature on sprawl includes size in the definition5 Certainly sheer geographic size is central to popular notions of sprawl Despite their relatively high densities metro areas such as Los Angeles and Phoenix and even Chicago and Philadelphia are perceived as sprawling because they go on forever A Sprawl lndex that disregarded this aspect of urban form would never achieve face validity Accordingly as a last step prior to creating the overall Sprawl lndex we used regression analysis to transform the sum of the four sprawl factors into a Sprawl Index that is neutral with respect to popula tion size As a result this index is uncorrelated with population The degree of sprawling development measured is consistent whether looking at Los Angeles or Wichita Kansas Outcome Variables This report only provides correlations for a few of the dozens of outcome measures that have been collected for the sprawl database The table below lists the outcome variables and their source for a more thorough discussion please see the full research paper 30 Smart GrowthAmerica Explznzlinnanes Relau39ons to Outcomes A A A I L LL A A AAA A A A A A V LA AAN A A N A A AA hon that one vanable saute or canmbma m another A A A L L L A L A I L A A N L A A A A A A A A A A A N A A AA AA A A A A A AA AA A A relmmmp Lem Apml ma the mums 391quot A AA A A A A A A A A AA A L L A A A A g 4 AN A A A A A A A A AA A AA A AA A AA A The we A magma ma Amman m the Emma aquaan z SmthwimAmuxca Complete Sprawl Index Scores and Rankings Sprawl Index ank from Sprawl most to least Density Centeredness lMsApMSA Name Index sprawling Density Rank Mix Mix Rank Centeredness Rank Street Factor Street Rank Akron OH PMSA 10588 49 8682 22 11869 63 11950 65 8417 23 AbanySchenectadyTroy NY MSA 8328 19 8293 12 8930 29 9845 38 7321 12 Albuquerque NM MSA 12445 72 9696 49 10369 44 12397 72 11780 64 AllentownBethlehemEaston PA NJ MSA 12403 70 8625 18 13339 78 9170 27 13102 72 AnaheimSanta Ana CA PMSA 9714 41 12884 77 12151 70 7215 10 13643 78 Atlanta GA MSA 5766 4 8450 15 7370 13 8231 21 5700 3 Austin TX MSA 11026 59 8901 25 11187 54 11576 63 9436 35 Baltimore MD MSA 11586 64 10428 64 10684 48 11564 62 10522 50 Baton Rouge LA MSA 9013 24 8084 8 9589 33 10616 50 7616 15 Birmingham AL MSA 8797 23 7712 6 6225 7 11248 59 10400 46 BostonLawrenceSalemLoweII Brockton MA NECM 12693 77 11359 70 12445 74 10943 55 11907 65 BridgeportStamfordNorwalkDanbury CT NECMA 6839 7 9246 38 13746 79 9476 32 8065 21 Buffalo NY PMSA 11909 67 10214 60 12467 75 13520 78 7057 10 Chicago IL PMSA 12120 68 14290 79 11506 56 8581 23 13487 75 Cincinnati OHKYN PMSA 9604 39 8878 23 9583 32 11015 56 8542 25 Cleveland OH PMSA 9175 30 9966 56 10742 50 10091 42 6677 8 Colorado Springs CO MSA 12440 71 9122 33 11895 64 13518 77 9672 38 Columbia SC MSA 9417 34 7457 4 6712 9 14734 81 7951 19 Columbus OH MSA 9113 27 9148 36 7652 14 10147 43 9716 39 Dallas TX PMSA 7826 13 9950 55 8260 22 8106 18 9023 32 Denver CO PMSA 12522 73 10370 62 11567 57 10887 54 12572 70 Detroit MI PMSA 7947 15 9731 50 10254 41 6297 8 9295 33 El Paso TX MSA 11718 65 10005 57 10345 43 11953 66 10231 44 Fort LauderdaleHoywoodPompano Beach FL PMSA 10844 55 11393 71 9471 31 7496 14 13723 79 Fort WorthArington TX PMSA 7723 10 9033 28 8915 28 7392 12 9748 40 Fresno CA MSA 11028 60 9349 39 13012 77 11255 60 7300 11 GaryHammond IN PMSA 7737 11 8641 21 12372 73 6125 7 10051 43 Grand Rapids MI MSA 9518 36 8269 10 11573 58 11032 57 6371 6 GreensboroWinstonSalemHigh Point NC MSA 4678 2 7416 3 4670 3 6908 9 6626 7 Appendix 3 page 1 lMSAPMSA Name Complete Sprawl Index Scores and Rankings GreenviIIeSpartanburg SC MSA HartfordNew BritainMiddletownBristol CT NEC Honolulu HI MSA Houston TX PMSA Indianapolis IN MSA Jacksonville FL MSA Jersey City NJ PMSA Kansas City MOKS MSA Knoxville TN MSA Las Vegas NV MSA Little RockNorth Little Rock AR MSA Los AngelesLong Beach CA PMSA Memphis TNARMS MSA MiamiHiaeah FL PMSA Milwaukee WI PMSA MinneapolisSt Paul MNW MSA New HavenWaterbury Meriden CT NECMA New Orleans LA MSA New York NY PMSA Newark NJ PMSA Norfo kArginia BeachNewport News VA MSA Oakland CA PMSA Oklahoma City OK MSA Omaha NE A MSA Orlando FL MSA OxnardVentura CA PMSA Philadelphia PANJ PMSA Phoenix AZ MSA Pittsburgh PA PMSA Portland OR PMSA Sprawl Index ank from Sprawl most to Iea Density Centeredness Index sprawling Density Rank Mix Mix Rank Centeredness Rank Street Factor Street Rank 5856 5 7192 2 5039 4 9851 39 6209 5 8517 20 8633 20 11936 66 8457 22 5957 4 14021 79 11652 73 8434 23 16729 83 11433 60 9330 32 9526 47 11013 52 8696 24 9564 36 9373 33 8929 26 9622 34 10237 45 8452 24 9158 28 8561 16 7288 12 10214 44 10458 47 16227 82 19565 82 17287 83 9868 40 16679 83 9164 29 9088 32 10005 37 8904 26 8883 31 6868 8 7122 1 6291 8 9775 35 7552 14 10474 48 11003 68 8010 18 9975 41 10882 55 8227 18 7750 7 6827 10 10586 49 8817 30 10179 45 15151 80 12308 72 7237 11 12330 67 9215 31 8887 24 9701 36 10424 47 7652 16 12568 75 12909 78 10469 45 9268 29 13637 77 11729 66 10142 59 11791 62 11774 64 9386 34 9586 38 9474 44 9469 30 10781 52 8766 28 10697 52 9156 37 14427 82 7889 17 8652 26 12539 74 10590 65 8038 19 12370 71 13856 80 17778 83 24249 83 12981 76 14459 80 15487 82 8132 17 11888 75 12039 69 8217 20 11538 61 9563 37 9501 45 8717 26 8198 19 11306 59 9881 42 11661 74 10634 47 5760 6 13344 73 8558 21 8449 14 10129 39 9561 33 6912 9 12835 78 9638 48 11929 65 13231 76 10464 48 9639 40 9377 42 6081 6 10348 46 12060 66 7512 9 10394 63 13939 80 5552 5 10645 53 11261 63 11473 72 11952 67 9586 34 11300 58 11093 62 10684 66 11598 60 9264 28 10723 54 10594 51 9044 30 8680 25 10447 48 12416 68 12612 76 10134 58 10228 40 12181 68 12797 71 Appendix 3 page 2 Complete Sprawl Index Scores and Rankings Sprawl Index ank from Sprawl most to least Density Centeredness lMsApMSA Name Index sprawling Density Rank Mix Mix Rank Centeredness Rank Street Factor Street Rank ProvidencePawtucketWoonsocket RI 15371 81 9910 52 14046 81 14034 79 13591 76 RaleighDurham NC MSA 5417 3 7619 5 3948 1 7723 16 8076 22 RiversideSan Bernardino CA PMSA 1422 1 9353 40 4150 2 4142 2 8052 20 Rochester NY MSA 7793 12 9137 35 8231 21 12070 67 3723 1 Sacramento CA MSA 10264 47 9912 53 11090 53 8737 25 9841 42 Salt Lake CityOgden UT MSA 11092 61 9950 54 10316 42 9384 30 11704 62 San Antonio TX MSA 10776 54 9504 46 10062 38 10839 53 10297 45 San Diego CA MSA 10186 46 11341 69 10545 46 7441 13 10597 51 San Francisco CA PMSA 14683 80 15519 81 10734 49 12862 74 13982 81 San Jose CA PMSA 10970 57 12480 76 9663 35 9387 31 12522 69 Seattle WA PMSA 10091 44 10362 61 7942 16 9801 37 11707 63 Spring eld MA NECMA 12249 69 8629 19 11574 59 14860 82 8729 27 St Louis MOL MSA 9451 35 9029 27 10744 51 7616 15 10599 52 Syracuse NY MSA 8027 16 8583 17 7197 11 12492 73 5258 2 Tacoma WA PMSA 10588 50 9076 31 8562 24 12267 69 11120 57 TampaSt Petersburg Cearwater FL MSA 8626 22 9361 41 7997 17 5185 3 13361 74 Toledo OH MSA 10719 53 9132 34 11963 68 11217 58 7757 17 Tucson AZ MSA 10913 56 9038 29 12177 71 10642 51 8804 29 Tulsa OK MSA 9906 43 8271 11 8800 27 11497 61 9620 37 VallejoFair elanapa CA PMSA 7838 14 9744 51 11626 61 4086 1 10969 56 Washington DCMDVA MSA 9083 26 10688 67 7872 15 9785 36 9802 41 West Palm BeachBoca RatonDeray Beach FL MSA 6775 6 9396 43 5472 5 5393 4 10470 49 Wichita KS MSA 11009 58 8437 13 11306 55 13137 75 7856 18 WorcesterFitchburgLeonminster MA NECMA 9048 25 8116 9 8228 20 12272 70 7454 13 Appendix 3 page 3 lMSAPMSA Name Akron OH PMSA AIbany SchenectadyTroy NY MSA Albuquerque NM MSA AIIentownBethlehemEaston PA NJ MSA AnaheimSanta Ana CA PMSA Atlanta GA MSA Austin TX MSA Baltimore MD MSA Baton Rouge LA MSA Birmingham AL MSA BostonLawrenceSalemLowell Brockton MA NECM BridgeportStamfordNorwalkDanbury CT NECMA Buffalo NY PMSA Chicago IL PMSA Cincinnati OHKYN PMSA Cleveland OH PMSA Colorado Springs CO MSA Columbia SC MSA Columbus OH MSA Dallas TX PMSA Denver CO PMSA Detroit MI PMSA El Paso TX MSA Fort LauderdaIeHoywoodPompano Beach FL PMSA Fort WorthArington TX PMSA Fresno CA MSA GaryHammond IN PMSA Grand Rapids MI MSA GreensboroWinstonSalemHigh Point NC MSA ro rea utcome araesquot onro 5 e0 Annual Delay Distance Vehicles per Transit to Work Walk to Work Commute Time per Capita Driven VMT Fatal Accidents 8hr Ozone 100 HH min hours per Capita per 100 000 ppb 179 143 203 23 239 878 97 160 332 389 23 58 269 941 85 174 154 265 21 206 284 1078 71 175 133 363 25 211 1283 102 188 292 207 27 569 69 181 392 131 31 327 338 1386 120 172 283 220 25 283 311 1543 87 160 640 303 30 205 214 967 106 168 107 202 25 222 2073 93 180 079 121 27 143 348 1643 90 152 1288 497 29 281 215 567 83 178 850 239 28 228 816 93 147 417 273 21 50 193 726 89 144 1539 362 32 274 205 790 83 171 360 231 24 198 277 804 89 160 489 221 25 85 212 671 89 186 099 386 22 127 182 1373 54 175 135 379 23 181 2031 89 175 232 243 23 171 257 955 94 172 259 157 28 371 311 1199 102 179 480 219 27 346 221 1100 69 171 183 144 27 252 241 1007 89 168 227 223 23 97 186 1015 62 151 236 134 27 285 233 1355 71 178 056 141 27 1216 102 165 179 246 22 111 209 1551 103 172 277 207 27 1299 98 182 099 231 20 224 1304 92 185 097 162 22 338 1678 96 Appendix 3 page 4 lMSAPMSA Name GreenvilleSpartanburg SC MSA HartfordNew BritainMiddletownBristol CT NEC Honolulu HI MSA Houston TX PMSA Indianapolis IN MSA Jacksonville FL MSA Jersey City NJ PMSA Kansas City MOKS MSA Knoxville TN MSA Las Vegas NV MSA Little RockNorth Little Rock AR MSA Los Angeles Long Beach CA PMSA Memphis TNARMS MSA MiamiHialeah FL PMSA Milwaukee WI PMSA MinneapolisSt Paul MNWI MSA New HavenWaterburyMeriden CT NECMA New Orleans LA MSA New York NY PMSA Newark NJ PMSA Norfo kVirginia BeachNewport News VA MSA Oakland CA PMSA Oklahoma City OK MSA Omaha NEA MSA Orlando FL MSA OxnardVentura CA PMSA Philadelphia PANJ PMSA Phoenix AZ MSA Pittsburgh PA PMSA Portland OR PMSA ro rea utcome araesquot onro 5 e0 Annual Delay Distance Vehicles per Transit to Work Walk to Work Commute Time per Capita Driven VMT Fatal Accidents 8hr Ozone 100 HH min hours per Capita per 100 000 ppb 179 046 208 22 148 2002 97 170 296 256 23 106 250 1036 97 161 856 575 27 111 180 730 48 166 368 164 29 358 369 1284 102 176 143 168 24 202 321 1065 94 167 156 171 27 146 283 1617 79 93 3422 871 33 591 106 175 134 141 23 87 290 1258 81 184 053 199 23 356 2244 100 159 453 238 24 180 192 1345 73 169 087 132 23 284 1833 83 162 682 303 29 625 227 775 77 162 178 134 24 159 247 1473 97 150 538 221 30 330 192 1327 74 160 443 294 22 149 208 700 91 176 478 250 23 255 245 753 73 161 328 327 23 211 765 98 144 590 281 27 103 150 1225 85 74 4849 961 39 234 154 483 101 157 1144 316 31 803 100 172 197 281 24 116 230 721 94 177 1037 265 32 731 73 173 062 173 22 58 240 1071 81 176 122 192 19 113 188 794 72 170 189 132 27 312 278 1708 81 198 113 217 25 244 1168 149 1015 420 29 154 189 929 101 166 217 213 26 279 273 1396 81 151 733 380 25 70 227 879 92 173 762 346 24 229 236 772 57 Appendix 3 page 5 Metro Area Outcome Variablesquot Control Variables below lMSAPMSA Name ProvidencePawtucketWoonsocket RI RaleighDurham NC MSA RiversideSan Bernardino CA PMSA Rochester NY MSA Sacramento CA MSA Salt Lake CityOgden UT MSA San Antonio TX MSA San Diego CA MSA San Francisco CA PMSA San Jose CA PMSA Seattle WA PMSA Spring eld MA NECMA St Louis MOL MSA Syracuse NY MSA Tacoma WA PMSA TampaSt PetersburgCearwater FL MSA Toledo OH MSA Tucson AZ MSA Tulsa OK MSA VallejoFair elanapa CA PMSA Washington DCMDVA MSA West Palm BeachBoca RatonDeray Beach FL MSA Wichita KS MSA WorcesterFitchburgLeonminster MA NECMA Annual Delay Distance Vehicles per Transit to Work Walk to Work Commute Time per Capita Driven VMT Fatal Accidents 8hr Ozone 100 HH min hours per Capita per 100 000 ppb 162 264 376 23 187 225 769 88 179 197 258 24 310 1239 108 181 172 225 31 297 222 1803 101 165 216 363 21 35 235 896 89 176 283 226 26 195 209 1035 91 194 309 191 22 93 248 900 80 166 303 245 24 204 293 1231 83 175 352 355 25 241 237 931 71 150 1976 592 29 415 224 624 52 196 363 184 26 334 236 612 72 178 853 335 27 338 258 700 60 154 255 516 22 216 707 87 170 254 168 25 204 284 1276 89 158 215 425 21 244 892 84 186 281 301 28 140 227 1027 65 154 145 177 26 212 228 1765 84 169 145 244 20 237 1407 83 161 262 268 24 113 216 1576 69 174 070 170 21 87 224 1357 88 191 244 231 30 228 1156 78 167 1238 319 33 346 228 860 96 153 146 142 26 204 243 1512 61 182 061 162 19 210 1027 78 165 171 309 26 247 866 93 See page 30 for the source of the outcome variable data Appendix 3 page 6 ControlOther Variables Percent of Population Household Working Age Per Capita IMSNPMSA Name Metro Population Size Income Akron OH PMSA 694960 247 589 22314 AlbanySchenectadyTroy NY MSA 892196 240 588 22281 Albuquerque NM MSA 556678 247 601 20790 AllentownBethlehemEaston PANJ MSA 740395 251 579 21864 AnaheimSanta Ana CA PMSA 2846289 300 604 25826 Atlanta GA MSA 3945450 268 633 25303 Austin TX MSA 1159836 255 643 25094 Baltimore MD MSA 2552994 255 600 24398 Baton Rouge LA MSA 602894 263 593 18866 Birmingham AL MSA 991819 249 596 20992 BostonLawrenceSaemLowe Brockton MA NECM 4001752 252 613 28322 BridgeportStamfordNorwalkDanbury CT NECMA 882567 267 590 38350 Buffalo NY PMSA 950265 241 572 20357 Chicago IL PMSA 6540979 269 598 24474 Cincinnati OHKYN PMSA 1553843 249 589 23487 Cleveland OH PMSA 1863479 245 576 22818 Colorado Springs CO MSA 516929 261 607 22005 Columbia SC MSA 536691 249 615 20902 Columbus OH MSA 1581066 246 614 22957 Dallas TX PMSA 3369303 271 617 24639 Denver CO PMSA 2109282 252 626 26206 Detroit MI PMSA 4598502 259 591 24481 El Paso TX MSA 679622 318 550 13421 Fort LauderdaleHoywoodPompano Beach FL PMSA 1623018 245 582 23170 Fort WorthArington TX PMSA 1661525 268 604 22112 Fresno CA MSA 799407 309 546 15495 GaryHammond IN PMSA 631362 263 579 20643 Grand Rapids MI MSA 812649 269 580 21643 GreensboroWinstonSalemHigh Point NC MSA 1120709 244 609 21626 Appendix 3 page 7 lMSAPMSA Name ControlOther Variables GreenviIIeSpartanburg SC MSA HartfordNew BritainMiddletownBristol CT NEC Honolulu HI MSA Houston TX PMSA Indianapolis IN MSA Jacksonville FL MSA Jersey City NJ PMSA Kansas City MOKS MSA Knoxville TN MSA Las Vegas NV MSA Little RockNorth Little Rock AR MSA Los Angeles Long Beach CA PMSA Memphis TNARMS MSA MiamiHialeah FL PMSA Milwaukee WI PMSA MinneapolisSt Paul MNWI MSA New HavenWaterbury Meriden CT NECMA New Orleans LA MSA New York NY PMSA Newark NJ PMSA Norfo kVirginia BeachNewport News VA MSA Oakland CA PMSA Oklahoma City OK MSA Omaha NEA MSA Orlando FL MSA OxnardVentura CA PMSA Philadelphia PANJ PMSA Phoenix AZ MSA Pittsburgh PA PMSA Portland OR PMSA Percent of Population Household Working Age Per Capita Metro Population Size Income 744164 249 605 20249 1148618 248 592 26277 876156 295 601 21998 4151815 282 805 21818 1474128 250 601 23480 1100491 254 601 21763 608975 260 635 21154 1757083 251 595 23373 713116 239 611 20105 1375785 285 813 21785 583845 247 603 20263 9519338 298 593 20883 1106808 262 589 20388 2253362 284 592 1500741 250 583 23158 2887585 255 810 28408 824008 250 584 24439 1289753 258 590 18987 9314235 261 610 24076 1930552 274 600 28578 1512416 260 602 20315 2392557 271 615 28241 1083346 247 597 19366 692664 254 593 22215 1434033 263 613 21383 753197 304 586 24600 5036646 258 584 23912 3072149 267 585 22251 2003200 235 575 21176 1529211 251 621 23836 Appendix 3 page 8 lMSAPMSA Name Percent of Population ProvidencePawtucketWoonsocket RI RaleighDurham NC MSA RiversideSan Bernardino CA PMSA Rochester NY MSA Sacramento CA MSA Salt Lake CityOgden UT MSA San Antonio TX MSA San Diego CA MSA San Francisco CA PMSA San Jose CA PMSA Seattle WA PMSA Spring eld MA NECMA St Louis MOL MSA Syracuse NY MSA Tacoma WA PMSA TampaSt Petersburg Clearwater FL MSA Toledo OH MSA Tucson AZ MSA Tulsa OK MSA VallejoFair eldNapa CA PMSA Washington DCMDVA MSA West Palm BeachBoca RatonDelray Beach FL MSA Wichita KS MSA WorcesterFitchburgLeonminster MA NECMA Household Working Age Per Capita Metro Population ize lncome 962886 248 583 21236 1016647 247 643 25472 3254821 307 550 17726 1037831 251 585 21809 1796857 265 586 22302 1333914 304 568 19781 1559975 277 579 18544 2813833 273 600 22926 1731183 247 661 36651 1682585 292 631 32795 2343058 245 636 27942 608479 249 577 20077 2540138 252 581 22812 650154 249 576 20254 700820 260 596 20948 2395997 233 567 21784 618203 247 580 20565 843746 247 579 19785 803235 250 586 20092 518821 283 590 22848 4544944 261 634 31059 1131184 234 535 28801 545220 254 573 20692 750963 256 586 22983 Appendix 3 page 9 WASC Standards for Accreditation Standard 1 Defining Institutional Purposes and Ensuring Educational Objectives The institution de nes its purposes and establishes educational objectivesaligned With its purposesand character lthasza clear and conscious sense of its essential values and relatibnshiptosbcietyat large Through its purpb39ses and edUcational L the institution dedicates itself to higherleaming the search for truth andlthe dissemination of knowledge The institution functions with integrityandautonomy llnstitutional Purposes Criteria for Review 11 The institutions formally approved statements of purpose and operational practices are appropriate for an institution of higher education and clearly define its essential values and character 12 Educational objectives are clearly recognized throughout the institution and are consistent with stated purposes The institution has developed indicators and evidence to ascertain the level of L39 of its purposes an 39 39 39 bjectives 13 The institutions leadership creates and sustains a leadership system at all levels that is marked by high performance appropriate responsibility and accountability llntegrity Criteria for Review 14 The institution publicly states its commitmentto academic freedom forfaculty staff and students and acts accordingly This commitment affirms that those in the academy are free to share their convictions and responsible conclusions with their colleagues and students in theirteaching and in their writing 15 Consistent with its purposes and character the institution demonstrates an appropriate response to the increasing diversity in society through its policies its educational and cocurricular programs and its administrative and organizational practices 16 Even when supported by or affiliated with political corporate or religious organizations the institution has education as its primary purpose and operates as an academic institution W39th 39 autono 17 The institution truthfully represents its academic goals programs and services to students and to the larger public demonstrates tha its academic programs can be completed in a timely fashion and treats students fairly and equitably through established policies and 39 4quot 39 student conduct nrip ances human subjects in research and refunds 18 The institution exhibits integrity in its operations as demonstrated by the implementation of appropriate policies sound business practices timely and fair responses to complaints and grievances and regular evaluation of its performance in these areas 19 The institution is committed to honest and open communication with the Accrediting F 39 39 to 39 39 39 the quot 39 review process with seriousness and candor and to abiding by Commission policies and procedures including all substantive change policies WASC Standards for Accreditation Standard 2 Achieving Educational Objectives Through Core Functions The institutiOnyachieve39s its institutionalpurposes a39nda ttains its quot 39thiuuyh the c39ore nf teaching and Ieaming scholarship and creative activity and support for student learning It demonstrates that these core functionsareperfoirhed effectiver and that they stIppOIT39 one another in theinstitution39s efforts to attain educational effectiveness Teaching and Learning Criteria for Review 21 The institution s educational programs are appropriate in content standards and nomenclature forthe degree level awarded regardless of mode of delivery and are staffed by sufficient numbers of faculty qualified for the type and level of curriculum offered 22 All degrees undergraduate and graduate awarded by the institution are clearly defined in terms ofentrylevel requirements and in terms of levels of student achievement necessary for graduation that represent more than simply an accumulation of courses or credits 5 Baccalaureate programs engage students in an integrated course of study of sufficient breadth and depth to prepare them for work citizenship and a fulfilling life These programs also ensure the development of core learning abilities and competencies including but not limited to collegelevel written and oral communication collegelevel quantitative skills information literacy and the habit of critical analysis of data and argument In addition baccalaureate programs actively foster an understanding ofdiversity civic responsibility the ability to work with others and the capability to engage in lifelong learning Baccalaureate programs also ensure breadth for all students in the areas of cultural and aesthetic social and political as well as scientific and technical knowledge expected of educated persons in this society Finally students are required to engage in an indepth focused and sustained program of study as part of their 39 programs 5 Graduate programs are consistent with the purpose and character oftheir institutions are in keeping with the expectations of their respective disciplines and professions and are described through nomenclature that is appropriate to the several levels of graduate and professional degrees offered Graduate curricula are visibly structured to include active involvement with the literature of the field and ongoing student engagement in research andor appropriate highlevel professional practice and training experiences Additionally admission criteria to graduate programs normally include a baccalaureate degree in an appropriate undergraduate program 23 The institution s expectations for learning and student attainment are clearly reflected in its academic programs and policies These include the organization and content of the institution s curricula admissions and graduation policies the organization and delivery of advisement the use of its library and information resources and Where applicable experience in the Wider learning environment provided by the campus andor cocurriculum 24 The institution s expectations for learning and student attainment are developed and Widely shared among its members including faculty students staff and Where appropriate external stakeholders The institutions faculty takes collective responsibility for 39 quot 39 39 reviewing fostering and 39 39 the attainment of these 25 The institution s academic programs actively involve students in learning challenge them to achieve high expectations and provide them with N r 39 and ongoing feedback about their and how it can be improved 26 The institution demonstrates that its graduates consistently achieve its stated levels of attainment and ensures that its expectations for student learning are embedded in the standards faculty use to evaluate student work 27 In orderto improve program currency and effectiveness all programs offered by the institution are subject to review including analyses of the achievement of the program s learning objectives and outcomes Where appropriate evidence from external 39 39 such as employers and 39 39societies is included in such reviews Scholarship and Creative Activity Criteria for Review 28 The institution actively values and promotes scholarship curricular and instructional innovation and creative activity as well as their quot 39 39 at levels and of the kinds N r 39 to the institution s purposes and character 29 The institution recognizes and promotes 39 linkages among 39 39 39 39 teaching student learning and service WASC Standards for Accreditation Standard 2 Achieving Educational Objectives Through Core Functions continued The institution rachieves its institutionalpurposes a39nda ttain s itsquot 39 quot 39t hiuuyh the c39oref nf teaching and Ieaming scholarshipaand creative activity and suppOrt for student learning It demonstrates that these core functions are perforrned effectiver and that they support one another in theinstitution39s efforts to attain educational effectiveness Support for Student Learning Criteria for Review 210 Regardless of mode of program delivery the institution regularly identifies the characteristics of its students and assesses their needs experiences and levels of satisfaction This information is used to help shape a learningcentered environment and to actively promote student success 211 Consistent with its purposes the institution develops and implements cocurricular programs that are integrated with its academic goals and programs and supports student 39 39 nd personal development 212 The institution ensures that all students understand the requirements of their academic programs and receive timely useful and regular information and advising about relevant academic 39 213 Student support services including financial aid registration advising career counseling computer labs and library and information services are designed to meet the needs of the specific types of students the institution serves and the curricula it offers 214 Institutions that serve transfer students assume an obligation to provide clear and accurate information about transfer requirements ensure equitable treatment for such students with respect to academic policies and ensure that such students are not unduly 439 A A by transfer 39 WASC Standards for Accreditation Standard 3 Developing and Applying Resources and Organizational Structures to Ensure Sustainability The institutionsustains its operations and supports the achievement of its educational objectives through its investment in human physical scal and information resourcesand through an appropriate and effective set of organizational and decisionmaking structures These key resources and organizational stmctures promote the achievementof institutional purposes and educational objectives and create a high quality environment for leaming Faculty and Staff Criteria for Review 31 The institution employs personnel sufficient in number and professional qualifications to maintain its operations and to support its academic programs consistentwith its 39 39 39 39 and 39 39 39 objectives 32 The institution demonstrates that it employs a faculty with substantial and continuing commitmentto the institution sufficient in number professional qualifications and diversity to achieve its educational objectives to establish and oversee academic policies and to ensure the integrity and continuity of its academic programs Wherever and however delivered 33 Faculty and staff recruitment workload incentive and evaluation practices are aligned with institutional purposes and educational objectives Evaluation processes are systematic include appropriate peer review and for instructional faculty and other teaching staff 39 volve 39 39 39 of evidence ofteaching effectiveness including student evaluations of instruction 34 The institution maintains appropriate and sufficiently supported faculty development activities designed to improve teaching and reaming consistentwith its 39 39 39 objectives and 39 39 39 39 purpose Fiscal Physical and Information Resources Criteria for Review 35 Fiscal and physical resources are effectively aligned with institutional purposes and educational objectives and are sufficiently developed to support and maintain the level and kind of educational programs offered both now and forthe foreseeable future 36 The institution holds or provides access to information resources sufficient in scope quality currency and kind to support its academic offerings and the scholarship of its members For oncampus students and students enrolled at a distance physical and information resources services and information technology facilities are sufficient in scope and kind to support and maintain the level anc kind of education offered These resources services and facilities are consistentwith the institution s purposes and are appropriate sufficient and sustainable 37 The institution s information technology resources are sufficiently coordinated and supported to fulfill its 39 39 39 purposes and to provide key academic and 39 39 39 functions Organizational Structures and DecisionMaking Processes Criteria for Review 38 The institution s organizational structures and decision making processes are clear consistent with its purposes and sufficientto support effective decision making 39 The institution has an independent governing board or similar authority that consistentwith its legal and fiduciary authority exercises appropriate oversight over institutional integrity policies and ongoing operations including hiring and evaluating the chief executive officer 310 The institution has a chief executive Whose fulltime responsibility is to the institution together with a cadre of administrators qualified and able to provide effective 39 39 39 leadership and at all levels 311 The institution s faculty exercises effective academic leadership and acts consistently to ensure both academic quality and the 39 39 of the institution s 39 39 39 purposes and character rrr Urban Colleges Learn to Be Good Neighbors woshinglonpostcom NEWS OPINIONS SPORTS ARTS amp LIVING Discussions Photos amp Video City Guide CLASSIFIEDS JOBS CARS REAL ESTATE Urban Colleges Learn to Be Good Neighbors M Universities Also Reap Bene ts From Investing in Their Communities By Lois Romano Washington Post Staff Writer Monday January 9 2006 A01 WeightWatchers Of ine I FOR MEN PPHLADELPPHA Ten years ago the University of Pennsylvania was under siege its ivy towers wreathed by an abandoned industrial wasteland lth and soaring crime Parents feared for their children after two student homicides The 7 neighborhood McDonald s was nicknamed McDeath Students were virtual A CUStOrIrIIZEd DnI 1e prisoners on campus Just for men Administrators began to worry that enrollment was threatened as one of the nation s oldest and most prestigious schools was fast developing a reputation as unsafe quotThey had one of two choices after the murders They could build up more barricades surround them with a moat and ll the moat with dragonsquot said Barry Grossbach a community activist in the West Philadelphia neighborhood quotOr they could reach out and save the community It was selfpreservationquot Penn chose the latter The university and private developers have invested about a billion dollars over the past decade in security retail schools the local housing market and what Penn refers to as quoteconomic inclusionquot making sure the community and minority companies get a piece of the success Today Penn is the among the hottest schools in the country sitting smack in the middle of a clean and vital retail neighborhood where crime has been reduced by 49 percent in the past decade and where students swarm the streets shopping at upscale stores Penn has jumped in the US News amp World Report college rankings to No 4 and attracts signi cantly more applicants successes that school administrators attribute in large part to Penn s quotWest Philadelphia Initiativequot Penn is at the forefront of a national trend of urban colleges that are aggressively trying to bridge quottowngownquot tensions by investing heavily in adjacent troubled neighborhoods and by making a connection with local civic life Since Penn launched its efforts in 1996 officials from more than 100 schools have made pilgrimages to study how it transformed a decaying neighborhood with a thriving drug traffic into a vibrant college community The sea change on city campuses comes when urban school applications are at an alltime high up 14 percent since 2002 as the children of baby boomers drift away from bucolic academic settings toward the action quotThe return to urban schools re ects a broad shift in popular culture cities are cool againquot said Bruce Katz urban expert at the Brookings Institution Consequently quotthere is a greater appreciation that a university s fortunes re ect the place in which they are situated there is no separating the interestsquot he added quotThey know they have to step up to the platequot Many schools have Yale University in the notoriously shabby downtown of New Haven Conn has developed retail and office space nearby offered nancial incentives to employees to buy homes in the neighborhood and joined with local schools to offer tutoring internships and college advisers Trinity College and local partners spent more than 100 million to turn a rundown area in Hartford Conn beset by driveby shootings and condemned buildings into a 16acre Learning Corridor with four local schools Temple University in a marginal neighborhood in North Philadelphia is involved in running local schools and is working with developers to bring in restaurants and retail Clark University in Worcester Mass took similar steps improving the historically poor and rundown area around the college by http 39 r 39 154Jarhtm11 of35142007 33251 PM Urban Colleges Learn to Be Good Neighbors opening a school that starts in seventh grade renovating housing and providing funding to refurbish storefronts In Columbia University s historic struggle with Harlem in 1968 the school proposed building a gym near the campus touching off neighborhood opposition and the student takeover of ve buildings Facing new suspicions over eXpansion plans the school established a 40member community advisory council in 2003 to assure residents that the plans will come with job training jobs and opportunity for small businesses In the District schools have struggled to smooth community tensions brought on by campus eXpansion and rowdy students At Howard University administrators started investing in the community about a decade ago agreeing to rehabilitate 28 rundown boardedup houses that the school had owned for 30 years and had once intended for use in an eXpansion Howard took a loss to offer the homes at reasonable prices to university staff members Community relations improved overnight Howard established its Center for Urban Progress to tie academic programs to work in the community and last August opened a magnet middle school on campus The college is working to develop a new residentialretail center on Georgia Avenue that it hopes will bring life back to community streets quotWe sees ourselves as an eXtension of the community quot said Maybelle Taylor Bennett director of the Howard University Community Association quotIt s enlightened selfinterestquot The issues are different for Georgetown University and George Washington University which are in upscale residential and business areas that do not need the intervention and nancial support required by Hartford or West Philadelphia Still seeking to maintain strong relations the two schools established a 24hour hotline so neighbors can report loud parties or other inappropriate student behavior As a case study Penn s urban renewal effort is probably the most comprehensive targeting every service and institution that makes a community vibrant The university restored shuttered houses and offered faculty incentives to move into the neighborhood invested 7 million to build a public school brought in a muchneeded 35000squarefoot grocery store and movie theater and offered the community resources such as hundreds of used Penn computers quotWe said we teach our students about civic engagement You can t do that and not be role models for civic engagementquot said former Penn president Judith Rodin who was a catalyst in the renewal efforts But Penn was a long time coming to that philosophy and when it began its overtures the community was skeptical In the 1950s and 60s the university with the help of federal and local of cials displaced residents to eXpand Homes were abandoned businesses ed crime took over and Penn simply forti ed its walls quotWe destroyed a neighborhood that had eXisted for 50 years And we replaced it with a neighborhood that had no life no vibrancy on the streets quot said Omar Blaik Penn s senior vice president for facilities and real estate services quotThe animusquot Rodin said quotwas legitimatequot Rodin arrived in 1994 at a low point for the university During her rst month a 26yearold graduate student was robbed and killed outside his West Philadelphia apartment By mid1996 30 armed robberies had occurred near the university an undergraduate was shot and wounded and Vladimir Sled a Russian doctoral student was stabbed to death trying to thwart a robbery quotWe hit the wall quot recalled Maureen Rush Penn s vice president for public safetyquot It was clearly becoming an issue for admissionsquot Administrators quickly agreed that there had to be a fullscale assault on the problem The rst steps were to form a partnership with community leaders and neighborhood associations and to light the neighborhood clean it and make it green Lights were enhanced at 1200 properties and 400 trees were planted as well as 10000 ower bulbs Gradually university buildings were refaced to open out toward the streets and all new buildings had ample windows facing the street making the school appear welcoming and providing additional lighting on the streets for safety The school spends more than 20 million annually on security among the highest amount in the country It employs 350 security of cers and 100 sworn police of cers who operate out of a station on campus http 39 r 39 1543mm 2 of35142007 33251 PM
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