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1 TESTING A METHODOLOG Y FOR CALCULATING THE IMPLICATIONS OF LAND DEVELOMPENT PATTERNS ON TRIP LENGTHS, AND GHG EMISSIONS IN ALACHUA COUNTY FLORIDA By KELLY RHINESMITH A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSIT Y OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN URBAN AND REGIONAL PLANNING UNIVERSITY OF FLORIDA 2010
2 2010 Kelly N. Rhinesmith
3 To my f amily
4 ACKNOWLEDGME NTS First and foremost I would like to thank my parents, sister and fianc for their constant encouragement and support through my academic career. Without their unconditionally love, encouraging words patience and understanding the completion of this th esis would not have been possible. M y deepest gratitude is extended to my committee members. I would like to especially thank my Chair, Dr. Ruth Steiner for her time, energy, and intellectual thoughts throughout my thesis process. I would also like to than k Andres Blanco (Special Committee Member) for going out of his way to provide me guidance knowledge, and wisdom during this process Additionally I would like to thank Kathryn Frank (Special Committee Member) for stepping in at last minute to make the c ompletion of my thesis possible. Finally I would like to thank Russell Provost. I appreciate all of the time and effort you spent in helping me prepare and analyze the data for this project.
5 TABLE OF CONTENTS page ACKNOWLE DGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF ABBREVIATIONS ................................ ................................ ............................. 9 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTERS 1 INTRODUCTION ................................ ................................ ................................ .... 12 2 LITERATURE REVIEW ................................ ................................ .......................... 18 Urban Sprawl ................................ ................................ ................................ .......... 18 Built Environment VMT Connection ................................ ................................ ..... 20 Aggregate Studies ................................ ................................ ............................ 21 Disaggregate Studies ................................ ................................ ....................... 25 ................................ ................................ .............................. 25 Self s election ................................ ................................ .............................. 28 Comprehensive Look ................................ ................................ ....................... 31 Forecasting VMT Reduction due to More Compact Development .......................... 35 Su mmary ................................ ................................ ................................ ................ 36 3 METHODOLOGY ................................ ................................ ................................ ... 38 Study Area ................................ ................................ ................................ .............. 39 Land Use Patterns Trip Length Analysis ................................ ................................ 41 Data ................................ ................................ ................................ .................. 41 Models ................................ ................................ ................................ .............. 42 Analysis ................................ ................................ ................................ ............ 43 Policy Analysis ................................ ................................ ................................ ........ 44 4 MODEL RESULTS ................................ ................................ ................................ 48 Assumptions Made By Explanatory Variables ................................ ........................ 48 Model Results ................................ ................................ ................................ ......... 51 Neighborhood Comparisons ................................ ................................ ................... 52 Rural Neighborhood ................................ ................................ ......................... 52 Suburban Neighborhood ................................ ................................ .................. 53 Urban Neighborhood ................................ ................................ ........................ 53 Conclusion ................................ ................................ ................................ .............. 54
6 5 POLICY ANALYSIS RESULTS ................................ ................................ ............... 62 Comprehensive Planning in Florida ................................ ................................ ........ 62 sive Plan ................................ .............................. 66 ................................ ................................ .. 72 Conclusion ................................ ................................ ................................ .............. 78 6 DI SCUSSION ................................ ................................ ................................ ......... 79 Discussion ................................ ................................ ................................ .............. 79 ......... 80 Recommendations on Where Future Development Should Occur ................... 82 Limitations and Opportunities for Future Research ................................ ................. 84 7 CONCLUSION ................................ ................................ ................................ ........ 93 LIST OF REFERENCES ................................ ................................ ............................... 96 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 101
7 LIST OF TABLES Table page 4 1 Models for trip lengths with land use descriptors at the production end ............. 55 4 2 Neighborhood comparison ................................ ................................ ................. 61 5 1 use element of their comprehensive plan ................................ ........................... 71 5 2 Policies that encourage com comprehensive plan ................................ ................................ ........................... 76
8 LIST OF FIGURES Figure page 3 1 Activity centers ................................ ................................ ................................ ... 46 3 2 Residential centers ................................ ................................ ............................. 47 4 1 Home based other trip length predictions ................................ ........................... 56 4 3 Neighborhood locations ................................ ................................ ...................... 58 4 4 Home based other predicted trip lengths with neighborhood locations ............... 59 4 5 Home based work predicted trip lengths with neighborhood locations ............... 60 6 2 Home based work predicted trip lengths including city boundaries .................... 89 6 3 Areas where HBW and HBO predicted trip len gths are 1 mile or less ................ 90 6 4 Areas where HBW and HBO predicted trip lengths are 3 miles or less .............. 91 6 5 Transit routes with bicycle service area ................................ .............................. 92
9 LIST OF ABBREVIATION S CO 2 Carbon Dioxide CO 2 e Carbon Dioxide Equivalent DCA Department of Community Affairs DOE Department of Energy DU LA Dense Urban Land Areas ECSC Alachua County Energy Conservatio n Strategies Commission EECBG Energy Efficiency and Conservation Block Grant EISA Energy Independence and Security Act EPA Environmental Protection Agency F.S. Florida Statute FDOR Florida Department of Revenue GHG Greenhouse Gas GMA Growth Management Act HBO Home Based Other Production HBW Home Based Work Production ICLEI Local Governments for Sustainability LEED Leadership in Energy and Environmental Design NHB Non Home Based Production RTS SGI Smart Growth IN DEX VMT Vehicle Miles Traveled VT Vehicle Trips
10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Urban and Regional Planning T ESTING A METHODOLOGY FOR CALCULATING THE IMPLICATIONS OF LAND DEVELOMPENT PATTERNS ON TRIP LENGTHS, AND GHG EMISSIONS IN ALACHUA COUNTY FLORIDA By Kelly N. Rhinesmith May 2010 Chair: Ruth Steiner Major: Urban and Regional Planning Over the past decade there has become an unprecedented awareness of the impact of global climate change, greenhouse gas (GHG) emissions and the need to increase energy efficiency in our daily lives As a result Alachua County and the City of Gainesville are receiving pressure from the state and various government agencies to reduce GHG emissions. With transportation accounting for approximately one third of GHG emissions in the United States, it is imperative that these local governments look for ways to reduce emissions from the transportation section, specifically through the reduction of vehicle miles traveled (VMT). Research has shown that land development patterns have the ability to influence VMT. However this research has predominately been conducted in areas on the west coast of the United States and cannot be applied to Alachua County due to differences in land development patterns. In order to provide a more accurate representation on the affect land use has on trip length (one aspect of VMT), this research uses model s adopted from a Southeast Florida study as an analytical tool to diagnose how land use patterns specific to Alachua County may affect trip length. These models are validated as an appropriate
11 measurement of this relationship by studying three neighborhood s, representing different land development types, within the County. Because future land use patterns are greatly influenced by policies included in local comprehensive plans, the results of these models are used as a tool to determine what the City and Co comprehensive plans need to change in order to promote land use patterns that decrease trip length. This research has found that the models utilized in this research are a useful tool for local planners to determine areas that contain land use cha racteristics that have the potential to decrease trip lengths. The results of the models found that areas located closer to the City of characteristics that have the potential to decrease trip lengths. These models also help ed indicate the areas where local comprehensive plans should include policies that encourage compact development to meet the increasing demands to reduce GHG emissions from the transportation sector.
12 CHAPTER 1 INTRODUCTION Over th e past decade there has become an unprecedented awareness of global climate change, greenhouse gas (GHG) emissions and energy efficiency. So much so day vocabulary A s a result there has been a movement towards more sustainable building practices and lifestyles; however, our current land development patterns do not reflect this new area of concern. Since World War II the United States has predominately grown outwards in a relatively low dense, discontinuous, suburban development pattern. This spread out pattern of development, also known as sprawl, has only been made possible by the automobile (Ewing, 1997) Through rising incomes, increasing percentages of automobil e ownership and public policies, such as public investme nt in extensive road networks, sprawled development continues to be the dominate development type in America. Existing literature argues that sprawled development uses more energy than traditional ne ighborhoods (Newman and Kenworthy 1989; Holtzclaw, 1990 as cited in Handy, 1996; Frank and Pivo, 1994; and Kenworthy 1999; Ewing and Cervero, 2001; Bento et al., 2005; Brownstone and Golob, 2008; Transportation Research Board, 2009). In conjunction, it is now widely accepted that climate change is occurring at an unprecedented rate due to human activity. The Intergovernmental Panel on Climate % chance) that most of the warming we have experienced since the 1950s is due to the increase in greenhouse gas emiss C, 2007). Greenhouse gases
13 consisting mainly of carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), hydrofluorocarbons (HFCs), perfluorocarbon s (PFCs) contain heat trapping properties that are attributed to the cause of climate change (EPA, 2009). Climate change potentially brings with it a long list of impacts to ecological systems, agriculture, public health, infrastructure, and commerce (U. S. Department of Transportation, n.d.). On December 7, 2009, the Administrator of the EPA found that these gases in the atmosphere were an endagerment to human health that threaten the public health and welfare of current and future generations (EPA, 2009) The human activities that are attributed to the rise of GHG emissions are mainly from the residential, commercial, institutional and transportation sectors. According to the U.S. Department of Transportation (n.d.) heat homes and businesses, and power factories is responsible for about 80 % of the U.S. society's carbon dioxide emissions, about 25 % of U.S. methane emissions, and about 20 % The rate at which these gases are being rel eased into the atmosphere is steadily increasing. Total emissions from the residential, commercial and transportation sectors each increased by more than 25 % during the past 25 years (US Department of Energy, 2008). In the future, residential, commercial b uildings, and road transportation are expected to continue to dominate energy demand and carbon growth in the United States. As a result, there has been increasing efforts to find ways to curtail the rate of GHG emissions and improve energy efficiencies th rough public policy and voluntary energy rating systems, such as LEED and Energy Star. However, many of these policies and programs fail to recognize land
14 influence on the transportation sector as a way to mitigate energy consumption and GHG emissions. The transportation sector is one of the largest contributors to GHG emissions. Transportation on U.S. roads and highways totaled 3 trillion VMT in 2007, and consumed about 176,100 million of gallons of gasoline (FWHA, 2009). Between 19 70 and 2005, the average annual VMT per U.S. household increased by almost 50 % to 24,300, as vehicle ownership per household increased even as household size fell (BTS, 2007; Brown, Southworth and Sarzynski, 2009). There has been increasing awareness of the linkage between fuel consumption and the creation of Carbon Dioxide (CO 2 ). According to the U.S. Environmental % of U.S. carbon dioxide emissions come from the use of coal and petroleum fuels (US Department of Energy, 2007 a ). EPA inventory found that the transportation sector was the second largest emitter of carbon dioxide equivalent (CO 2 e) producing 2,036.4 million metric tons of CO 2 e. While it is not the largest source of CO 2 e emissions the transportation sect or releases the greatest amount of C O 2 emissions (1,902.5 million metric tons of CO 2 ) than any other sector (US Department of Energy, 2007 b ). About 80 % of GHG emissions from transportation are highway (auto, truck, and bust) transport, with air water an d rail based transport responsible for most of the remainder (Brown, Southworth and Sarzynski, 2009). Thus transportation is a major factor that needs to be inclu ded in discussions related to slowing the rate of climate change and reducing GHG emissions The responses to climate change are ultimately local and regional. As a Result, t he City of Gainesville and Alachua County are receiving increasing pressures from the
15 state and federal government to reduce GHG emissions. The implementation of recently pass ed legislature will make it mandatory for local governments to monitor GHG emissions and create action plans to reduce their emissions. As of July 1, 2008, the Florida Legislature made changes to Chapter 163, Part II, of the Florida Statutes, included in House Bill 697 and codified in the Laws of Florida Chapter 2008 191, concerning the inclusion of energy reduction strategies in the local comprehensive plans. This bill is avant garde in that it recognizes the occurrence of climate change due to human acti vities and the role local governments can play in slowing down the rate of this occurrence. Section 163.3177 (6) (a), F.S. states that the future land use section shall be efficient land use patterns a ccounting for existing and future electric power generation and transmission systems; 191, p. 4, 2008). Section 163.3177 (6) (b) F.S. states that local comprehensive plans will be required to in corporate transportation strategies to address reduction in GHG emission from the transportation section in the Traffic Circulation Element. Section 163.3177 (6) (d) F.S. requires the future land use map to identify and depict areas of energy conservation. Section 163.3177 (6) (h) requires the housing element to contain principles that pertain to the energy efficiency in the design and construction of new homes. The reduction of energy and GHG emissions is also endorsed at the federal level. On Decembe r 19, 2007 the Energy Independence and Security Act (EISA; Pub. L. no 110 140) created the Energy Efficiency and Conservation Block Grant (EECBG) Program (ICLEI, 2009). The program was established to provide federal grants to assist
16 local government in imp lementing strategies that reduce fossil fuel emissions, total energy used by eligible entities and to improve energy efficiencies. As part of the American Recovery and Reinvestment Act, EECBG funds are being awarded to US states, territories, local govern ments and Indian tribes to lower energy use and reduce carbon pollution. To date the Department of Energy has awarded more than 1,800 Energy Efficiency and Conservation Block Grants totaling over two billion dollars (DOE, 2010). The newly established req uirements put in place by the HB 697 and the incentive funding from the EECBG funds provide urban planners an opportunity to implement strategies to reduce energy and GHG emissions in their local municipalities especially through reductions in VMT Resear ch has shown that land development patterns have the ability to influence VMT. However this research has predominantly been conducted in areas located on the west coast of the United States and cannot be applied to Alachua County due to differences in land use patterns. As a result this study uses models derived from Southeast Florida to attempt to capture the characteristics of land use patterns unique to Florida. These models are used to map the locations where the land use patterns are more favorable tow ards the reduction of trip length (one aspect of VMT), in Alachua County. They are validated as an appropriate measurement of this relationship by studying three neighborhoods, representing different land development types, within the County. Because futur e land use patterns are greatly influenced by policies included in local comprehensive plans, the results of these models are used as order to promote land use patterns th at decrease trip length.
17 The focus of this research is to show a connection between land use patterns and trip lengths in Alachua County. In this research, trip lengths are used as a proxy for measuring GHG emissions. If individuals drive more vehicle mi case have longer trips) they will consequently emit more GHG emissions into the have an inverse relationship with trip lengths. In other words, ar eas possessing land use characteristics such as compact and diverse development will experience shorter trip lengths than areas that are less compact and less diverse. If this is the case, this research will support the idea that local governments can hel p reduce GHG emissions released into the environment by encouraging policies that promote compact, diverse development in areas that are more conducive to reduce trip lengths as indicated by the models The overall intent of this study is to: Provide fur ther insight of the possible impacts that land use patterns in Alachua County poses on trip lengths Raise awareness of the relationship between land use patterns and the GHG emissions from the transportation sector. Evaluate Local Comprehensive Plans to d etermine if they incorporate effective policies that encourage compact development as a means to reduce GHG emissions from the transportation sector. Following this introduction, the document is organized to include chapters summarizing the existing resea rch, research methodology, results, and conclusions. Chapter 3 sets forth the methodology, Cha p ter 4 and 5 6 offers discussions on the results, limitations and challenges, and the overall significance of the study.
18 CHAPTER 2 LITERATURE REVIEW This chapter provides a review of the literature pertaining to the relationship between the built environment, VMT and energy consumption. Overall the research has found that the built environment has multiple dimensions that affect VM T. These dimensions include but are not limited to density, the mix of land uses, employment density, residential density, spatial arrangement of land uses and design. The review of literature begins by providing a brief overview of the current dominate la nd development pattern urban sprawl in contrast to compact development Then the remaining portion of the literature review summarizes the existing literature that examines the relationship between the built environment and VMT. The two dominate study de sign types in the literature are aggregate and disaggregate studies. The aggregate studies use census track and traffic analysis zones to explore the relationships while disaggregate studies use individual travel behaviors as the unit of analysis. E xistin g research is analyzed on both methodology used and the findings of statistically significance results. The studies are also analyzed on whether the context of the study is applicable to Alachua County. Finally the literature review concludes with a n analy zes of a recent study that forecast estimates on the potential impact that changing our development patterns could have on household VMT and energy consumption. Urban Sprawl Urban sprawl is a multidimensional issue, which has been at the forefront of many planning debates. The term was first used in its modern sense in 1937 by Earle Draper Walsh, 2004, p. 1; Black, 1996). By the end of WWII, the major debate on the po sitive
19 and negative externalities of sprawl had fully emerged. Sprawled development has flourished and spread just as fast as the controversy that surrounds it. Since the 1950 census, data has become readily available documenting the rise of sprawled devel % of the urbanized population lived in central cities in 1950, with the remaining 35 % residing in suburbs. By 1990, these percentages had flipped, with central city populations down to 35 of growth of development in the suburbs continues to raise many questions with regards to whether s prawled development is a desirable and sustainable urban form. Throughout the years sprawl has been defined by what it is not, rather than what it is (Ewing 1994). In many cases sprawl has been defined in contrast to compact development (Ewing, 1994). Spra wl development lacks the high density, centralized development, open space and a spatial mixture of functions that the ideal compact city possesses. Today, a commonly accepted definition of sprawl is expressed by Ewing ttered development; (2) commercial strip development; and (3) large expanses of low density or single use developments as Many proponents of sprawl argue that this form o f development is not as bad as planners and policy makers make it out to be (Gordon and Richardson, 1997). They argue that sprawl is the dominate form of development because it provides people with s, urban sprawl provides more square feet of housing for less cost, more privacy, improved schools and a
20 perceived sense of safety (Linberger, 2008). Therefore authors such as Gordon and Richardson (1993, 1997) argue that urban sprawl is the product of the market, consumer preference and technological advancements and thus an attempt to reverse the current trends to more compact development is not feasible or desirable. On the other hand, opponents of sprawl link this type of urban form with automobile depe ndence, social segregation, ecosystem degradation, increases in greenhouse gas emissions, health implications such as obesity, increase oil dependency and increased cost of public amenities incurred by the public and local governments (Ewing, 1997; Speir a nd Stephenson, 2002; Carruthers and Ulfarsson, 2003; Leinberger, 2008 ). As its consequences suggest both good and bad, urban sprawl is a multidimensional issue possessing many forms and degrees. Some of these dimensions include but are not limited to den sity, the mix of land uses, employment density, residential density, spatial arrangement of land uses and design (Ewing and Cervero, 2001). Built Environment VMT Connection There is an extensive literature that examines the relationship between the buil t environment and VMT. Study designs are generally of two types: aggregate studies and disaggregate studies. Aggregate studies consider relationships at a level of aggregation such as census track or traffic analysis zone, while disaggregate studies use in dividual travelers as the unit quality. There is overwhelming consensus that density is an essential dimension of land development patterns. It is also the most commonly used measure of the built environment because it is readily measured and easily replic ated. Therefore, density is the dominate dimension of the built environment used in all of the studies examined in this paper. However, research suggests that density by itself does
21 imensions such as mix use and accessibility should be included (Ewing and Cervero, 2001; and Brownstone, 2008) VMT itself is a composite measure a product of trip length, trip frequency and mode choice (Ewing and Cervero, 2001; Transportation Research B oard, 2009). A review of the literature has shown that different combinations of the characteristics of the built environment and VMT have been used in various studies and models to attempt to depict an accurate relationship between the built environment a nd VMT. The following sections provide a summary of what is known about the relationship between the built environment and VMT from the current literature. For the purpose of this study, the following research is analyzed on the context in which the study takes place and the findings of statistically significance results. For organizational purposes the exiting literature is categorized by the type of study. Aggregate Studies When comparing average travel characteristics in neighborhoods of different des ign or cities of different densities, researchers mainly use aggregate studies. Generally, this type of research helps to quantify the potential impact of the built environment on overall travel by using simple correlations and comparisons as well as regre ssion procedures (Handy, 1996). The majority of these studies that look at the built environment and VMT have found a highly significant inverse relationship. For example, households that are located in more compact developments will have fewer household V MT. However as, this paper will point out, aggregate studies tend to mask the differences within metropolitan areas, and fail to account for self selection and socioeconomic characteristics, such as income, household size and employment. By
22 not accounting for self selection and socioeconomic characteristics, researchers are not able to determine the causal relationships, if there are any, between the built environment and VMT. A widely cited and debated st udy (Newman and Kenworthy, 1989; Newman and Kenworth y, 1999) compared 32 international cities to evaluate the physical planning policies for conserving transportation energy in urban areas. They found that per capita gasoline consumption a proxy for automobile use is higher in U.S. cities than abroad. R esidents of American cities consumed nearly twice as much gasoline per capita as Australians, nearly four times as much as more compact European cities and ten times that of three compact westernized Asian cities, Hong Kong, Singapore and Tokyo (Newman and Kenworthy, 1989) The authors attributed this to the lower metropolitan densities found in the United States. In 1999, they conducted a follow up study of 37 international cities and again found a direct link between low density cities, particularly in th e U.S. and Australia to higher gasoline consumption per capita. While the authors find a highly significant inverse relationship between density and VMT their results should be used with caution due to methodological flaws such as its inability to be gene ralized across different areas of the United States and accounting for residential self selection (Gordon and Richardson, 1989; Handy, 1996; and Brownstone, 2008). First there is a fundamental issue in comparing international cities due to differences in culture, governance, income levels and fuel prices. The authors simplify the measure of density and fail to account for some of its complexities. Since the authors are looking at metropolitan areas as a whole, their results mask the differences in the dens ities within these metropolitan areas. In addition, by not encompassing the
23 neighborhood differences in each of the metropolitan area, they are not taking into account socioeconomic characteristics that are shown to influence individual travel behaviors, including the use of public transit. A study with sim ilar findings (Holtzclaw, 1990 as cited in Handy, 1996) analyzed five neighborhoods in the San Francisco Bay Area, using data from biannual odometer l. The neighborhoods ranged from density suburban neighborhoods with three households per residential acre, and were characterized in terms of net household density, gross population density and local job serving density. Density was used as a determinate of transit service and accessibility to shopping, jobs, entertainment and recreation. The annual VMT was calculated across each neighborhood, and then used to calculate a non linear relationship between density and VMT. suggest that residents of higher density neighborhoods drove less by up to 30 % per household. One of the major flaws in this study was that new cars in the San Francisco Bay Area do not have to participate in the odometer readings unti l they are two years old, thus excluding a sizable portion of the research population. In addition, as with the previous study, socioeconomic factors were not factored into the analysis. The results are also limited by the fact that the study did not facto r in the importance of accessibility to different activities and transit. In 1994, Holtzclaw et al. updated their previous work by expanding the number and location of neighborhoods and by including other determinates of the built environment using a tran sit accessibility index, a neighborhood shopping index and a
24 pedestrian accessibility index. Thus the authors began to overcome their previous associated with differ ence in travel behaviors. Never theless, since this study only looked at each neighborhood as one entity, the authors were still not able to account for socioeconomic characteristics and self selection within each neighborhood. In addition this study only looked at how the built environment and VMT are related in the San Francisco area, which has a relatively high density. This prevents this study from being generalized to areas that have a more dispersed pattern of development. As a result of these flaws in agg regate studies, researchers started to incorporate components of disaggregate studies into their aggregate studies to account for self selection and socioeconomic characteristics. For instance, an empirical analysis by pacts of land use mix, population density, and employment density on the use of the single occupant vehicle, transit and walking for household travel behavior and demographics from the Puget Sound Transportation Panel, and data on travel behavior, land use density and land use mix from the census tract. Because the authors incorporated trip distances and travel time for both work trips and shopping trips at the individual level, they were able to account for multiple characteristics of travel. The study first correlated the built environment variables and percentage of trips from the three travel modes. Each correlation was calculated for both work trips and shop trips. Then, m ultivariate regressions equations were estimated at the census travel level with the dependent variables being the trips by mode and the independent
25 variables being the built environment and socioeconomic variables. The results of uggest transit share of work trips is greater at higher employment densities; transit share of shopping trips is greater at higher population and employment densities; walk share of work trips is greater at higher population densities, at higher employment densities and in areas where there is a greater mixture of uses. Because this study accounted for socioeconomic variables, it provides a better understanding of the causal link between the built environment and VMT. Disaggregate Studies To account for th e flaws in aggregate studies, the majority of recent studies have turned to disaggregate studies. In contrast to aggregate studies, these studies look at household and individual level travel data instead of neighborhood or zonal level (Handy, 1996). There fore they are able to account for socioeconomic and travel characteristics as well as begin to control for residential self selection. Disaggregate studies help contribute to the how and why urban form is linked to travel (Handy, 1996). It is important to note that some aggregate studies that take into account disaggregate variables are included in this section to help emphasize other important causal relationships. F Furthermore, disaggregate studies tend to account for other dimensions of th e built environment in addition to density. These dimensions are diversity, design, destination accessibility and distance to transit and are commonly referred to in an d Kockelman (1997) in their study of 50 residential neighborhoods in the San Francisco Bay Area. Using 1990 travel diary data and land use records obtained from
26 the US Census, regional inventories and field surveys, they modeled the impact of density, dive rsity and design on VMT as well as their collective impact as a whole on VMT. They also accounted for socio demographic data obtained from the 1990 Bay Area Travel Survey. This study was the first of its kind of research and found that use diversity and pedestrian oriented designs generally reduce trip rates and encourage non automobile travel in statistically significant ways, though their When looking at each of the three main variables th ey found that compact development exerted the strongest influence on personal business trips and land use diversity strongly influenced mode choice especially for work trips. Their results were density may actually be co existent with accessibility. Limitations in their study included it s inability to be generalized, beyond the San Francisco Bay Area, and the lack of data availability for the Bay Area population at large (the majority of the sam ple was wealthier than the average Bay Area resident). Overall they found modest but moderate results between the collective impact of density, diversity and design on household VMT. des tination accessibility, also referred to as regional destinations, and distance to from point of origin, often measured at the zonal level in terms of distance from t he p. 32). Recent literature has shown destination accessibility as an important variable when looking at household VMT (Ewing and Cervero, 2001).
27 A comprehensive liter ature review by Ewing and Cervero (2001), looked at more than 50 recent empirical studies to analyze the relationship between the built environment and travel behavior using the four Ds Density, diversity, design, and destination accessibility (at the ti me of the study distance to transit was not included in VT (vehicle trips) with respect to land elasticities were then entere completeness, route directness, and street network density is used to represent t when regional accessibility, density, diversity and design are accounted for together they have a significant inverse relationship with VMT and VT. The determinant, distance to transit has also gained increasing attention in recent research. As the name home or work (e.g., bus or rail stop within Research Board, 2009, p. 32). A recent study by Chen, Gong and Paaswell (2007) incorporated transi t accessibility into their model and found that it had a significant influence in determining the use of transit. Overall their study assessed the role density plays in mode choice decisions in home based work tours while controlling for confounding factor s. Using data collected from the New York Metropolitan region from
28 1997 1998, the authors conducted a two equation simultaneous system taking into account car ownership, the propensity to use the automobile, and multiple exogenous variables. They represen ted the built environment through measuring population and employment densities, job accessibilities, and distance to transit stops. After controlling indicator in de right combination of a set of variables that includes cost variables, job accessibility, density, and transit accessibility will likely encourage people to use transit for home bas density development patterns. Self s election An important strength of disaggregate studie s is that it accounts for self selection and socioeconomic attributes. Self selection accounts for individuals who choose their residential location based on their lifestyle preferences including preferred mode choice. An example of this would be a residen t who chooses to live in a higher density, mixed use neighborhood because they dislike driving and would rather bike or walk to places. In this case self selection would provide a causal link for why decrease was seen in VMT in the higher density neighbo rhood. It is argued in the Transportation Board Research (2009) report that if self selection is not accounted for the predicted impacts from aggressive land use policies could be over estimated. This wrong prediction could consequently lead to a high opp ortunity cost. Never the less, even after socioeconomic variables and self selection are accounted for, the majority of recent studies still find a strong correlation between
29 density and travel. However there have been a few of studies that have found this n ot to hold true. A study by Cao, Mokhtarian and Handy (2007) found accessibility to be the most important factor in reducing driving. The authors employed a structural equations model to investigate the relationship among changes in the built environment, auto ownership and travel behavior. Their study population was 547 movers (people who had moved ). By using a quasi longitudinal design and by controlling for residential preferences and selection. The authors concluded that self selection has a significant impact on the choice of residence, auto ownership and driving and walking behavior. In addition they found that on travel behavior, even after controlling for socio demographics and other exogenous variables. idea that attitudinal and socioeconomic variables influence travel behavior s more than residential neighborhood type. Their study looked at five neighborhoods in the San Francisco Bay Area in 1993. The neighborhoods were characterized as either ou t surveys and travel diaries from the neighborhood residents. Participants responded to 39 statements that dealt their attitudes to different variables attributable to travel
30 behavior. These responses were then analyzed using a structural equations modeli ng small sample size that only looked at one region. It is also biased towards long t erm residents, in that on average respondents had lived in the Bay Area for 25 years. Therefore it would be hard to generalize these findings to other areas. Another discrepancy in this study is that it looked at individual travel behavior rather than hous ehold travel. Therefore it does not account for the dynamics of household interaction on travel behaviors, which could drastically change the results. udy showed a significant relationship between the built environment and VMT even after accounting for self selection and socioeconomic characteristics. The authors took into account the variables: urban design, transportation infrastructure, auto ownershi p and distance driven. The traffic zones within three metropolitan areas Chicago, Los Angeles and San Francisco were used as the geographic unit of analysis, controlling for household size and income effects. nd transportation infrastructure have a highly significant influence on auto ow ] even after the Overall a comprehensive literature review by Cao, Mokhtarian, and Han dy (2008) assessed 38 studies that controlled for self selection and found that the majority of the studies found that the built environment has a significant influence on travel behavior. They analyzed the literature to see the extent to which the observ able patterns of travel
31 behavior can be attributed to the residential built environment or be attributed to residents self selecting a built environment that is consistent with their travel modes and land use preferences. The vast majority of the studies t hey examined found a statistically significant relationship between the built environment and VMT after self selection was accounted for. However, a greater majority of the studies found that accounting for self selection tended to decrease the strength of the relationship. Comprehensive Look socioeconomic attributes was conducted by Bento et al. (2005). The authors used a disaggregate study to examine a variety of built environm ent variables and socioeconomic measures to determine the effects on the annual VMT of over 20,000 U S households in 114 U.S. Metropolitan Statistical Areas. The authors used the effects of city shape, density of the road network, and the job housing bal ance, as determinates of the built environment. The automobile ownership and travel patterns of the households were drawn from the 1990 National Personal Travel Survey. Bento et al. used the data in two sets of disaggregate models. The first set of models looked at commute mode choice distinguishing between driving, walking/bicycling, commuting by bus and commuting by rail. The second set of models looked at the relationship between the number of vehicles owned and the miles driven per vehicle for househol ds. (p.477). The findings suggest that the density of the road network, job housing balance, city shape, population centrality, and rail supply all have a significant effect on annual % change in either the
32 urban form or the transit supply variables is associated with at most a 0.7 % change in average miles driven with the exception of population centrality which is associated with a somewhat larger, 1.5 % environment has a limiting impact on VMT and mode choice. However what makes this study noteworthy is that Bento et al. took their estimated modes and applied it to the metropolitan area of Atlanta, which is one of the most sprawled out urban areas, and to the Boston Metropolitan Areas, considered to be one of the most dense and diverse urban areas. When travel data and travel behavior environment they found that household VMT could be lowered by as much as 25 % Similarly, Brownstone an d Golob (2008) found a significant inverse relationship between residential density and vehicle usage. They analyzed the impact of residential density on vehicle usage and energy consumption in California using data from the 2001 National Household Travel Survey. Their structural equations model included three endogenous variables annual VMT, annual fuel usage and housing units per square mile along with multiple socioeconomic variables including self selection. The two households that are similar in all respects except residential density, a lower density of 1000 (roughly 40 % of the mean value) housing units per square mile implies a positive difference of almost 1200 miles per year (4.8 % ) and about 65 more gallons o f fuel per household (5.5 % mileage lead to a difference of 45 gallons, but there is an additional direct effect of density through lower fleet fuel economy of 20 gallons per year, a result of vehicle type at the most important socioeconomic factors included
33 the number of household drivers and the number of workers, with education and income also being significant. While the authors found a significant relationship, they do not recommend that policies be pu t in place to encourage more compact development. They concluded that % 97). They based their argument off of historic development patterns that showed that the average U S city saw a decrease in population density by 36 % between the years 1950 and 1990. Thus Brownstone and Golob are assuming, the current trend of predominately suburban development will continue. However with the rising oil and energy prices and a growing environmentally conscious population it is hard to believe that the current development trend will proliferate. A study by Kahn (2000) likewise suggests that instead of creating policies that encourages more compact development, we should focus on improv ements to fuel the environment using the variables vehicle miles, residential energy consumption, housing lot size, household income and size and county farm acreage in a cr oss sectional regression. Using the 1995 Nationwide Personal Transportation Survey (NPTS) to study the driving patterns and land consumption of 22,000 city and suburban % more than their urban counter emissions generated from this increase in travel is through the improvement in rebound effect of more fuel efficient vehicles.
34 Past studies have showed that a rebound effect for motor vehicles, by which improved fuel efficiency causes additional travel, does exist to some extent. A study by Small and Van Dender (2006) looked at pooled cross section al time series data from the U.S State level for the time period of 1966 2001. Their model accounted for income, urbanization and the fuel cost of driving and distinguishes between effects for the U.S. as a whole are 4.5 % in the short term and 22.2 % research differed from past research on the rebound effect that they found evidence that the rebound effect diminishes with income with the possibility of increasing with fuel cost of driving. A flaw in this research is that it did not account for changes in transit Nonetheless, it provides evidence that we cannot solely rely on improvements in automobile fuel efficiencies to lower the GHG emissions from the transportation sector. approach to deciphering the built environments relationship with energy con sumption and GHG emissions. Their study used density as a proxy to energy usage for both the transportation and residential sectors. The authors compared energy usage of a complex with high residential density and a neighborhood with low residential densit y using a Life Cycle analysis approach. Their research was able to quantify the energy used to produce the building materials and to construct the infrastructure, the energy used for building operations and the energy used for transportation over the whole life of the building. The research involved two case studies in the area of Toronto, Canada: a high density residential development and a low density residential development. The
35 low uburban fringes and the high contributes far more significantly to overall energy use and GHG emiss ion in low density provide a complete picture on how density affects the energy used in both the utility and the transportation sides. However this research only looks at tw o case studies in the Toronto area, thus it cannot be generalized to the general public, especially to Florida due to the differences in climate and overall development patterns. Forecasting VMT Reduction due to More Compact Development The Transportation Research Board (2009) used previous reviews of literature to from more compact, mixed three scenarios, two low end e stimates of density, and a high end estimate density, to predict the reductions of VMT in 2030 and 2050 due to more compact and mixed use development. The first scenario assumed that 25 % of all new growth would be more compact and that there would be a 12 % reduction in household VMT. The second scenario assumed that 75 % of all new growth will be more compact and that there would be a 25 % reduction in VMT. The third scenario assumed that 25 % of all new growth would be more compact. However due to technology advancements in the improvement in gasoline internal combustion in automobiles, there will only be a 5% reduction in emissions due to VMT. All of the scenarios assumed that reductions seen in CO2 emissions were proportional to reductions in VMT.
36 The forec asted results showed an overall decrease in VMT in cases where new development is more compact and contains a mixture of uses. The first scenario that assumed a 25 % increase in density and a decrease in VMT by 12 % saw a reduction in 2 emissions of nearly 1 to 1.2 % reduction of nearly 1.3 to 1.7 % by 2050. The second scenario, that assumed a 75 % increase in density and a decrease in VMT by 25 % saw a reduction in VMT, energy use and CO2 emissions of nearly 8 % by 2 030 and nearly 8 to 11 % by 2050. The third scenario, that assumed a 25 % increase in density but only a 5 % reduction in VMT found that even by 2050 the reductions in VMT, energy use and CO2 emissions would be less than 1 % energy use and CO2 emissions resulting from compact, mixed use development are estimated to be in the range of less than 1 % to 11 % Summary An extensive amount of literature has been developed on household VMT and development patterns. Overall existing research has found that more compact development patterns are likely to reduce VMT, however to different extents in different contexts. The most recent and reliable studies have estimated that developing more compactly can reduce VMT as mu ch as 25 % Unfortunately due to the fact that the majority of existing research has focused on specific neighborhoods that are generally located on the west coast of the United States and are characterized by higher densities their results cannot be gener alized across the rest of the nation and especially cannot be generalized to Alachua County, Florida. Therefore, while research shows that it is extremely probably that more compact development will reduce VMT,
37 there is still a need for more research to be done in order to det ermine the extent to which land development patterns impact trip lengths in Alachua County, Florida. The next chapter presents the research methodology used in this study to exami ne the relationship between land use patterns and trip length in Alachua County, Florida.
38 CHAPTER 3 METHODOLOGY The focus of this research is to show a connection between land development patterns and trip length In this research, trip length is used as a proxy for measuring GHG emissions. The idea being, if individuals drive further distances they will consequently emit more GHG emissions into the atmosphere. Th e s land development patterns have an inverse relationship with trip lengths In other words, areas possessing land use characteristics such as compact and diverse de velopment will experience shorter trip lengths than areas that are less compact and less diverse. If this is the case, this research will support the idea that local governments can help re duce GHG emissions released into their environment by encouraging policies that promote compact, diverse development in areas that are more conducive to reduce trip lengths as indicated by the models. Overall this research hopes to achieve the two objecti ves outlined below. 1) To show a connection between the land development patterns and trip lengths and thus GHG emissions in Alachua County, Florida a) Provide insight on the possible impacts that land development patterns pose of trip lengths b) Raise awareness o f the relationship between land development patterns and GHG emission from the transportation sector 2) comprehensive plans to see if they incorporat e effective policies that encourage compact development as a means to reduce G HG emissions from the transportation sector The study will achieve these two objectives by first applying models, adopted from research conducted by Dr. Ruth Steiner, to map the locations where the land development patterns are more favorable towards the reduction of trip length. The research conducted by Steiner et al. ( 2010 ) included three main endogenous variables
39 parcel characteristics, neighborhood characteristics and origins proximity to major activity centers to represent the land development p atterns (also referred to as land characteristics). These variables along with other land use characteristics are incorporated into models to predict trip lengths. Analyzing the results from this study will provide a detailed account of land development pa tterns in Alachua County that are favorable towards shorter trip lengths. To achieve the second objective, the study will perform a policy analysis on two local comprehensive plans ensive Plan to determine the extent to which the local governments promote compact development in appropriate areas as a means to reduce trip lengths and thus GHG emissions Study Area Alachua County, Florida was selected as the study area for this rese arch for several reasons. First and foremost, Alachua County has been at the forefront of the energy conservation movement. In 1991, the C ounty started its initiative to curtail energy use by implementing the County Energy Management Program. Since then A lachua County has been striving to reduce energy consumption and its GHG emissions. In 2001, Alachua County conducted its first GHG inventory for the calendar year 1998 This inventory created a baseline that enabled the C ounty to tr ack and measure the suc cess of C ounty programs to reduce emissions. In response to the results of the 1998 inventory, the C ounty drafted a GHG reduction Plan in 2002 whi ch listed several ways for the C ounty to reduce emission levels by as much as 20 % In conjunction with this p lan, the Board of County Commissioners amended the Conservation Element of their Comprehensive Plan adding Policy 4.1.3. 7 which state s
40 that by the year 2010 the County would reduce 1990 GHG emission levels by 20 % In order to accomplish this reduction the C ounty has established an Energy Conservation Strategy Commission (ECSC), as well as started to retrofit buildings, buy hybrids vehicles and started using blende d biodiesel fuel in county vehicles. Alachua County is currently updating their 1998 inventor y to assess their progress in accomplishing their goal to reduce emissions by 20 % Alachua County has seen a continued steady growth rate and unlike the rest of the state has con tinued to experience a steady growth in population into 2010. A recent study commissio ned by the ECSC, estimated the C ounty to grow by 3.135% per year totaling 3, 631,793 people by 2094 ( Hoot 2008). It is important to note that this is a high estimate of p opulation growth, but it is still relevant in that it shows what could growth in population provides the County the opportunity to adopt land use policies that will direct development to occur i n certain areas of the County and in such a manner that reduces the average VMT. Finally Alachua County was chosen because of its unique demographics. Alachua County is located in North Central Florida with a population of about 247,561 people (US Census n.d. a ). The C ounty contains eight local municipalities, the largest being the City of Gainesville with a population of approximately 114,916 people (US Census n.d. b ). University o f Florida and Shands Hospital. Both the County and the City of Gainesville are predominately comprised of single use, low density development characterized by a
41 western expansion of single family residential units (City of Gainesville, 2001). This separate s it from past studies that examined the connection between land development patterns and trip lengths due its population composition and predominately low density development patterns Land Use Patterns Trip Length Analysis The relationship between land development patterns and trip lengths in Alachua County, FL was determined by analyzing the results of research conducted by Steiner et al. ( 2010 ). This research used linear regression models to map the locations where the land use patterns are more favora ble towards the reduction of trip length in the County The following sub section describes the data collected, and briefly how the data was aggregated in the VMT study. The next sub section provides a brief overview of the empirical models used in the stu dy. Finally the last subsection describes the analysis of the data used in this study. Data 2010 ) study used the 1999 Southeast Florida Regional Travel Characteristics Study as the primary source of data for trip lengths. The study used one day travel information, which recorded each respondents trip timing (start and end times), mode (including occupancy for auto mode), purpose, and trip end lo cations (addresses) (Steiner et al 2010 ). The roadway network and characteristics linear road miles, number of intersections and number of cul de sacs within each neighborhood variables was collected from the Florida Department of Revenue (FDOR). The data included the follo wing attributes for each parcel: 1) parcel identifie r s, 2) parcel area, 3) land use type, 4) number of residential unit s for residential parcels, and 5 ) building
42 square footage for non residential parcels. In addition the following six land use categories were used: 1) residential (single family, multi family, mobile homes), 2) commercial (large retail, regular retail, convenience store, drive through), 3) office (professional and non professional service building), 4) Industrial (light, heavy, warehousing) 5) Institutional, 6) other. In order to capture the land use at the neighborhood level, Steiner et al. ( 2010 ) square miles across the entire County. In addition the authors iden tified four activity centers and four residential centers in the County to capture the land use at the regional level. The footage (includes, retail, office, and entertainme p. 9, 2010 ). The % of its land use t al., p. 9, 2010 ). The location of both the activity centers and the residential centers are sho wn in Figure 3 1 and Figure 3 2 respectively. p. 9 2010 ). For more information on the dat a refer to Steiner et al. ( 2010 ). Models The research by Steiner et al. ( 2010 ) developed two sets of models, containing use at the production end of trips. T he second set look ed at the impact land use at the attraction 2010 ). Each set was comprised of three models: one for each of the three trip purposes -home based work (HBW), home based other (HBO), and non home based (NHB). For the purposes of this research, the study only
43 examines the protection end of trips. For more information on the models refer to Steiner et al. ( 2010 ). Analysis To determine which land development patterns are more favorable towards the reduction in trip length in A lachua County Florida, the researcher applied the models 2010 The researcher identified three neighborhoods, the first in an urban location, the second in a suburban locatio n and the third in a rural neighborhood to validate that the adopted models as appropriate measurements of the relationship between land development patterns and trip length These neighborhoods were chosen based off of the prior knowledge of the area. The Duckpond neighborhood was chosen to represent the urban neighborhood. This neighborhood is well established and is located the vicinity of downtown Gainesville. Its built environment is characterized as compact mixed use, with high accessibi lity. The Town of Tioga was chosen as the suburban neighborhood. This neighborhood is located west of I 75 in an area that has been experiencing significant growth. The houses in the neighborhood meet some energy star standards as well as the neighborhood itself is seen as a new urbani st development. Forest Grove was chosen as the rural neighborhood. This neighborhood is located in the northwest part of the county and consists of mostly agricultural land. The results of the predicted trip lengths produced by these locations are compared and contrasted to determine the land use with the shortest length.
44 Policy Analysis A policy analysis was used to analyze the policies and goals located in two local comprehensive plans to determine whether they incorporated effective policies that encourage compact development as a means to reduce GHG emissions produced by were chosen to be evaluated. Alachu Comprehensive Plan was chosen since it incorporated the entire area being studied. Gainesville containing approximately 50 % and also incl uding the majority of the major activity and neighborhood centers in the County These two Comprehensive Plans were evaluated based on common themes and variables that were developed in the literature and reinforced by the results of the models from the fi rst part of this research that these common themes and variables reduce trip lengths These themes and variables included policies and goals relating to: Density: encouraging infill, redevelopment or higher densities Accessibility: how accessible differe nt destinations are including accessibility to transit and regional destinations (activity centers) Diversity: encouragement of mixed land uses Design: route directness and street network density, and connectivity The Comprehensive Plan the thr ee tiers mandated by the State (cite Rule 9J 5). The first tier identifies the overall goal and broadly defines the purpose of the element. The next tier consists of the objectives that establish how the features of the goal will be achieved. Then the final tier consists of the policies which outlines the specific actions to achieve that objective.
45 To effectively evaluate the Comprehensive Plans, the research first identified each goal that incorporated Density, Accessibility, Diversity, and Design in a way that had the potential to promote a reduction in trip length as indicated by the existing research. The they were oriented towards density, accessibility, diversi ty, and design. When it was determined which one it pertained to it was labeled as such. For instance, Future Land Use Element policy 1.1.2 states that To the extent possible, neighborhoods should be sized s o that housing job, daily needs and other activities are within easy walking distances to each other accessibility. I t is important to note that some policies fell under more than one characteristic. I f that was the case, the policy was li sted under each characteristic that it pertained to. In addition to these four themes, the researcher evaluated the Comprehensive Plans on whether they included any policies relating to overall energy reduction strategies. The researcher then determined w hether the policy influenced development in a way to reduce trip length or whether they prevented any type of reduction in trip length and even deterred from reduction.
46 Figure 3 1. Activity c enters
47 Figure 3 2. Residential c enters
48 CHAPTER 4 MODEL RESULTS The focus of this research is to show a connection between the land development patterns and trip lengths in Alachua County, Florida. To accomplish this, the study used models from research conducted by Steiner et al. ( 2010 ) to locate where land use patterns are more favorable towards the reduction of trip length within the County. The models contain four explanatory variables that are classified by: 1) p arcel characteristics, 2) neighborhood land use characteristics, 3) neighborhood roadway characteristics, and 4) location of neighborhoods within the region The first part of this chapter list the assumptions made under each of these variables. Then this chapter presents the predicted trip lengths produced in Alachua County by the models. Th e chapter concludes with a comparison of the characteristics of three neighborhood types classifie d as urban, suburban and rural as a way to validate the applicability of the results of the models to Alachua County. Assumptions Made By Explanatory Variab les Steiner et al. ( 2010 on an extensive literature review and a case study of three counties in South East Florida: Miami Dade, Broward and Palm Beach. For the purpose of this research, the assumption is being made that thes e models will also predict the trip lengths in Alachua County as a function of its specific land use characteristics. In order to predict trip lengths the models used the four explanatory variables listed above. In this section, the rationale behind and th e influence each variable plays in predicting trip lengths is outlined. This information is based off of t hat collected in Steiner et al. 2010 ) research. Table 4 1 research provides a list of the coefficients for each var iable used in the model.
49 The first category of explanatory variables is the parcel characteristic. Each parcel is characterized by a land use type which can be residential, commercial, office, institutional, industrial or other (Steiner et al 2010 ). The model for Non Home Based ( NHB ) trips indicates that such trips produced in commercial parcels are shorter in length compared to those produced in any other type of parcel. This could be explained by the fact that many shopping trips are chained together, with shopping destinations being close to one another. The model on NHB trips also indicates that larger size establishments produce longer NHB trips. The second category of explanatory variables is neighborhood land use characteristics. There are five va riables of interest under this category. The first variable is the fraction of developed area by each land use type. Steiner et al. ( 2010 ) calculated use categories (residential, commercia l, office, institution, industrial, and other) to the total area of the Home Based Work ( HBW ) trips indicates that the trip length increases with the increase fraction of the developed area under residential type. This can be explained by the notion that in large areas of residential land use there are not many opportunities for employment in that vicinity, therefore one would have to travel further to an employment center. Home Based Other ( HBO ) trips produced i n neighb orhoods with a larger fraction of commercial areas are shorter and residential, non other land use (i .e. greater fraction of commercial, office, institutional or industrial) would lead 2010 ).
50 which is only applicable for NHB trips. This variable is calculated as the proportion of developed areas in all land use types except the land use type of the production end parcel (Steiner et al 2010 opportunities or a NHB trips pr p. 16, 2010 ). Neighborhood density is also a significant variable. This variable is defined as the number of residential units in the neighborhood divided by the area of the neighborhood that is residential (Steiner et al ., 2010 ). This variable is negatively correlated with lengths of home based trips. It implies that residential units in high density neighborhoods produce shorter trips (Steiner et al 2010 ). The next variable is the size of buildings, measured by squ are feet. For HBW trips land uses lead to shorter trips. Finally the numbe r of parcels that are classified as convenient commercial is the last variable under the classification of neighborhood land use characteristics. This variable represents that the greater the number of convenient commercial land uses the shorter the HBO an d NHB trips are. It is important to note that this variable is not significant in the case of HBW trips. The next significant explanatory variable is neighborhood roadway characteristics. Roadway characteristics include d intersection density, number of cu l de sacs per mile and length of roadway. In the case of HBW more intersections per mile of roadway decreases the trip length and more cul de sacs per mile of roadway increase the trip
51 length. In the case of non work trips the length of roadway and inter section density are significant predictors of trip length. Finally the last explanatory variable is the location of neighborhoods within the region. This variable indicates that with increasing distance of the production end of the t rip to the regional ac tivity centers, the lengths of home based trips increase. Home based trips produce d closer to regional activity centers are of shorter distances. The length of HBW trips is determined by the distance of the home from regional residential centers. Model Re sults The results of running the models with land use data from Alachua County produced the expected results, showing an inverse relationship between land development patterns and trip lengths Overall the trips predicted were the shortest in length in the City of Gainesville. In the case of the Home Based Other Production model, the predicted trips increased in length the far ther one moved from Gainesville, as shown in figure 4 1. There were also pockets of relatively short trip lengths around the surroun ding municipalities. The trend of development to occur west of Gainesville is evident in the outcome of this model. For instance the trip lengths are shorter in length in the area that stretches between Gainesville and Newberry and in the area that stretc hes between Gainesville and Archer. These results seem reasonable because development, including shopping centers have been predominately occurring west of Interstate 75. In addition the Oaks Mall is located in that vicinity and is a major producer of trip s. As shown in Figure 4 2 the Home B results. The trips were predicted to be of a shorter distance the closer to Gainesville the
52 home was and around each of the other municipalities located in the County. These results seem reasonable due to the fact that the University of Florida and Shands Hospital are the largest employers in the County and are located in the City of Gainesville. The pockets of shorter length trips at each of the municipalities represent the businesses that are located in each of these cities. However the trips lengths predicted at these locations are probably on the low end. It is unlikely that these municipalities can contain enough jobs to support their population. Therefore it is likely t hat many of these c residents commute into Gainesville for employment purposes. Neighborhood Comparisons Three neighborhoods were compared in Alachua County; one representing a rural area, another one representing a suburban area and the last one r epresenting an urban area to validate the models as appropriate measurements of the relationship in the County The neighborhood site locations are presented on F igure 4 3 As expected, the trips predicted from the urban neighborhood were the shortest in length, while trips produced from the rural neighborhood were the longest in length. Figure 4 4 and Figure 4 5 illustrate the predicted trip lengths for Home Based Other produced trips and Home Based Work Produced Trips, respectively, for each of the neigh borhoods. Table 4 2 presents the results of the neighborhood comparisons. Rural Neighborhood The rural neighborhood contained the least amount of land uses. Residential was the dominate land use (418 acres) followed by industrial (234.7 acres) and then fi nally Agriculture (73.35 acres) The greatest proportion of the land was undeveloped land (1,825 acres) This neighborhood had the least amount of roadways and was the
53 furthest from both the nearest regional activity center (13.5 miles) and the nearest reg ional residential center (15 miles). As predicted the rural neighborhood produced on average the longest trips in length The average predicted HBW trip was 9 miles while the average HBO predicted trip was 8.2 miles. Suburban Neighborhood The Suburban Nei ghborhood had a greater mix of uses compared to the rural neighborhood. The greatest land use was residential (724.4 acres) while all the other land uses each consumed 55 acres or less. Similar to the rural neighborhood, undeveloped land consumed the grea test proportion of the neighborhood. The average number of intersections per mile of roadway was 3.4. This supports the notion that suburban areas lack connectivity. The suburban neighborhood was located closer to the regional activity center (6.3 miles) a nd Regional residential center (7.9 miles) than the rural neighborhood. The predicted trip lengths for HBW trips were 5.2 miles in length and the predicted trips lengths for HBO trips were 2.8 miles. It seems reasonable to believe that the HBO trips are pr edicted to be shorter than the HBW trips, because the HBO trips include not only shopping trips but social trips to see friends. As expected these trips are shorter in length than the rural neighborhood. Urban Neighborhood The Urban neighborhood ha d the greatest mix of land use, containing all of the land use categori es except Agriculture. The two dominate land uses were residential (1,009 acres) and institutional land uses (245.4 acres) Not surprisingly it contained the least amount of undeveloped land (724 acres). Interestingly the urban neighborhood also contained the largest number or residential units (7,383 units) It is also important to highlight that this neighborhood had the greatest amount of roadways. As the
54 literature and the models suggest the urban neighborhood produced predicted trips of the shortest length. In fact, the average predicted trip length for both HBW and HBO productions were less than one mile. Conclusion The models produced the expected results. Both the HBO and the HBW pro duction models predicted an increase in trip lengths the farther one moved away from the City of Gainesville. From the use of these models the researcher was able to compare and contrast three neighborhoods characterized by different built environments. T he first neighborhood was rural, and expectantly produced that longest predicted trip lengths. The suburban neighborhood, characterize d as dispersed development, with low connectivity, was predicted to produce shorter trips lengths than the rural neighborh ood but longer trip lengths than the urban neighborhood. Finally the urban development characterized by mix use, compact development that has high connectivity, was predicted to produce the shortest trip lengths. These results support thesis that areas characterized by compact development and are located closer to the city center are more favorable toward the reduction of trip lengths.
55 Table 4 1 Models for t rip l engths with l and use d escriptors at the p roduction e nd S ource: Steine r et al ( 2010 )
56 Figure 4 1. Home b ased o ther t rip l ength p redictions
57 Figure 4 2. Home b ased w ork p redicted t rip l engths
58 Figure 4 3 Neighborhood l ocations
59 Figure 4 4 Home b ased o ther p redicted t rip l engths w ith n eighborhood l ocations
60 Figure 4 5 Home b ased w ork p redicted t rip l engths w ith n eighborhood l ocations
61 Table 4 2 Neighborhood c omparison Rural Suburban Urban Neighborhood Land Characteristics Residential area (acres) 418.0 724.4 1,009 Commercial area (acres) 0 26.5 245.4 Office Area (acres) 0 35.7 187 Institutional area (acres) 0 55.1 313 Industrial areas (acres) 234.7 10.2 31 Other area (acres) 0 40.8 42.2 Agriculture area (acres) 73.35 128.6 0 Undeveloped area (acres) 1,825 1,538 724 Num ber of residential units 95 1,031 7,383 Building area commercial (1000 sq feet) 0 117,857 1,480,590 Building area Office (1000 sq feet) 0 141,926 1,362,564 Building area Institutional (1000 sq feet) 0 6,992 655,767 Building area Industr ial (1000 sq feet) 0 17,924 274,416 Building area other (1000 sq feet) 8,875 15,316 412,792 Number of "convenient commercial" parcels 0 1 26 Neighborhood Roadway Characteristics Length of roadway (miles) 2.2 20.3 85.8 Intersections per mile of roadway 23.6 3.5 11.3 Location of Neighborhood within Region Distance to nearest regional activity center (miles) 13.5 6.28 0 Distance to farthest regional activity center (miles) 17.9 11.2 5.2 Distance to nearest regional residential center (miles) 15 7.9 1 Predicted Trip Lengths Home Based Work Production (miles) 9 5.2 0 Home Based Other Production (miles) 8.2 2.8 0
62 CHAPTER 5 POLICY ANALYSIS RESULTS The literature and models used to predict trip lengths in Alachua Coun ty suggest compact development, characterized by density, design, destination accessibility, and diversity, reduces the length of trips This research assumes that a reduction in trip length results in a reduction in GHG emissions from the transportation sector. This knowledge is worth very little if it is not put into practice. As the Growth Management Act of 1985 recognizes, land development practices are best implemented at the local level (DCA, n.d.) This alone is reason enough to examine the prevalen ce of the promotion of compact development in two local comprehensive plans: the City of This section provides the results of the policy analysis on the two comprehensive plans. Fir st, a background on the Growth Management Act of 1985 is given, to provide the context in which the local comprehensive plans were created. Following the background, a determination is made on whether the City of Gainesville and Alachua ive plans are successful in incorporating policies that encourage compact development in a way that will lead to reduction s in GHG emissions. Comprehensive Planning in Florida communi ties in providing a pleasant, livable and well ordered urban environment s (Mandelker, p. 900, 1976). Florida first declared a state interest in state and local comprehensive planning with the passing of The Comprehensive Planning Act of 1972 [Florida Stat ute 186.001]. However, this act gained little momentum and the Division of
63 was rejected by the legislature (Carriker, 2009). The importance of Comprehensive plan ning reemerged with The Local Government Comprehensive Planning Act of 1975. This act recognized the role that local governments play in land use controls and required local governments to adopt comprehensive plans (Carriker, 2009). It also provided a thre e pronged approach for the state government to control growth at the local level: mandating that local governments adhered to State guidelines, protect critical environmental areas and mandated that development of regional impacts be conducted for developm ent s that are expected to have significant regional impact (Nicholas, 2001). However this act created a backlog of paper work that neither the state nor the local governments could keep up with, causing project delays and increased development cost (Nic h ol as, 2001). There was also little guidance and enforcement of this act from the state level. The result was the creation of local comprehensive plans that were inconsistent and ineffective in managing growth ( Rhodes, 2010 ). Therefore this act was later subs tantially amended by the Local Government Comprehensive Planning and Land Development Act of 1985 [Florida Statute 163.3161]. Part II, Florida Statutes, The Local Government Co mprehensive Planning and Land to adopt Local Government Comprehensive Plans that guide future growth and development The passing of this act provided the foundation f or community participation and managed growth in Florida c ommunities. Unlike the previous
64 Comprehensive Acts this act includes the creation of Dense Urban Land Area (DULA) designations, as a means to curb urban sprawl (DCA, n.d.) Overall this act requires local comprehensive plans to: (as cited in Carriker, 2009) Guide and control future development Address existing problems (such as urban sprawl) as well as problems that may arise in the future as a result of the development and use of land Preserve, pro mote, protect and improve public health, safety, comfort and good order Protect human, environmental, social and economic resources The explicit minimum criteria and requirements as well as the legal precedence for local comprehensive plans can be found in Rule 9J 5 of the Florida Administrative Code, adopted by the Department of Community Affairs. Rule 9J 5 outlines the specific elements, goals and policies that must be mentioned and contains a detailed format that all comprehensive plans must follow. Incl uded in these rules is the requirement of local land development regulations will be initiated, modified or continued to implement the comprehensive plan in a consistent 5.005 (6)). As stated in Rule 9J 5, local comprehensive plans are intended to include 12 elements. These 12 elements are: capital improvements, future land use, transportation, sanitary sewer, solid waste, drainage, potable water and natu ral groundwater recharge, conservation of natural resources, recreation and open space, housing, coastal management, and inter governmental coordination. In addition local governments have the option to include the elements: historic preservation, arts and culture, economic development, public education and community design. The statute does not mandate
65 that these elements have to be independent but represented throughout the entire comprehensive plan in an interwoven, consistent manner (Rule 9J 5.005). Wi th the addition of the DUDA to the Growth Management Act, one of the main components of Comprehensive Plans is to discourage the proliferation of urban sprawl. The Future Land Use section of Rule 9J 5 provides a general methodology for examining whether or not a plan or plan amendment discourages urban sprawl a s well as what controls local governments can use to mitigate the presence of sprawl (Rule 9J 5.006 section 5) Due to urban sprawl being multidimensional, Rule 9J 5 includes over 22 determinates tha t will be evaluated in local comprehensive plans to determine the extent it discourages sprawl. One of these determinants is including the establishment of minimum development density and intensity, affecting the pattern and character of development (Rule 9J 5 section j.3). Local governments are held accountable to produce comprehensive plans consistent with the state and regional plans. Once a local government completes or am ends their comprehensive plan it Comm unity Affairs (DCA) for approval and certification. DCA evaluates each plan based on their consistency and if they abide by the rules out lined in Rule 9J 5. If the DCA rules that a plan is not in compliance, the state can withhold grants and funding from them, as well as pursue legal action through the state court system (Dawson, 1996). standards that must be met before development plans can be certified and approved. These standa rds outlined in Rule 9J 5 help provide local governments with guidelines and standards that they must upho ld and be accountable for.
66 2001. At the time of adoption, the City was experiencing a declining share of the overall population of Alachua County; so much so that the urban population growth in unincorporated Alachua County had been increasing at nearly double the rate of growth than wit hin city limits (City of Gainesville, p. 5, 2001 a ). Before 2001, development in the City of Gainesville tended to be density, single use land use patterns, characterized mostly by a western expansion of single family residential development, intersper sed with conventional, car oriented shopping centers at major street a ). To be in compliance with the Growth Management Act of 1985, the main focus of Future Land Use El ement strong central core, redevelopment and revitalization of older areas, and a continued p. 1, 2001 a ) This new focus for the Future Land Use Element of the comprehensive plan was Section (City of Gainesville, 2001 a ). The Data and Analysis section identified the main issues that were to be addressed in the 2000 2010 Comprehensive Plan. These areas of focus included: 1) the declining share of the overall Alachua County population, 2) infill and redevelopment, 3) density, 4) urb an design, 5 6 ) the incorporation of more mixed use categories, and 7 ) provide more transportation choices. In their Data and Analysis the City of Gainesville recognized that low density,
67 sing le use land use character results in high levels of car dependence and decreases the viability of transportation choices (City of Gainesville, p. 6, 2001 a ). Therefore they designated the key objectives of the Future Land Use Element of the Comprehensive Pl Gainesville, p. 6, 2001 a ). It is importan t to note that at the time the Comprehensive P la n was adopted the City was already 9 not to prevent sprawl from happening within its borders but rather to keep development within This is reinforced by the fact that Ga boundaries as stated in policy 1.5.7 of the Future Land Use Element (City of Gainesville, p. A 5 2001 b e plan is about Nevertheless, many characteristics that the existing literature suggests as and policies in the Future Land Use Element, Concurrency Management Element and the Urban Design Element. These characteristics, discussed more in depth in previous chapters are density, urban design, destination accessibility, and mixed uses. The use of these characteristics is transcribed from the data and analysis section to the comprehensive plan. Analysis Overall the lan incorporates many of the strategies, listed in Rule 9J 5 to help combat the proliferation of sprawl as a
68 means to encourage other modes of transportation than the automobile as well as increase densities. The comprehensive plan encourages compact development by encouraging mixed uses, infill and redevelopment developing traditional neighborhoods increasing transit options, incorporating village centers and encouraging College (City of Gainesville, 2001). Table 5 1 provides a summary of how often the four charac teristics of compact development density, urban design, destination accessibility and mixed uses were mentioned in the comprehensive plan. Overall three out of the five goals under the Future Land Use Section and one goal in the Concurrency Managemen t section incorporate at least one of the characteristics of compact development Because the overarching goal of th is content analysis is to evaluate whether the Comprehensive Plan incorporates effective policies that encourage compact development, the re maining analysis will focus on the content of the specific policies listed under each of the three goals identified. In the Future L and Use Section all of the variables were mentioned approximately the same amount of times in the policies that encouraged compact development Urban Design and Density standards were incorporated in the most policies ( 7 policies each ), followed by diversity ( 6 policies each), and lastly Accessibility was incorporated in the least amount ( 5 policies ). A summary of these polic ies is contained in Table 5 1 As shown in Table 5 1 these characteristics are used throughout the and the Urban Design Element, however they are rarely explicitly stated as a means to create more compact development. As mentioned previously lan
69 gravitates towards creating a livable built environment. While the plan has many rarely explicitly encourages compact de velopment to achieve this goal. In the Future Land Use Element of the 2000 2010 Comprehensive Plan, the explicit use of the word compact is only used twice. The encouragement of compact development is first mentioned in Objective 2.1 of the Future Land Us e Element. This vibrant urbanism, improve the condition of blighted areas, discourage urban sprawl, and ity of Gainesville, p. A 5, 2005). In this objective compact development is used in conjunction with transportation choice T herefore it indicates the City understands the relationship between development patterns and transportation choice. The policies un der this objective in totality include each of the elements of compact development. The only other time compact development is utilized again is in Goal 4 of the uniq ue character of the city by directing growth and redevelopment in a manner that uses neighborhood centers to provide goods and services to city residents; protects neighborhoods; distributes growth and economic activity throughout the city in keeping with the direction of this element; preserves quality open space and preserves the tree canopy of the city. The land use element shall promote statewide goals for compact development 10, 2000 ; italics added ). In this goal the use of compact development is tagged on at the end. This goal can be interpreted in one of two ways. First as contradictory, the goal states that it will
70 disperse the growth and economic activity throughout the city and then tags on at the end that it will promote compact development. Distributing and compact can be seen as contradicting goals. On the other hand, the goal can also be interpreted in that the growth and economic activity will be in compact clusters across the city an d that quality open space will be preserved Unfortunately the verbiage of this goal and the coinciding objectives a nd policies make it unclear. It is almost as if the city included the last sentence to meet the requirements mandated by DCA. The results of the analysis found a deficit in the amount of goals, objectives and policies that supported the reduction in energy consumption and GHG emissions. The was used in p olicies that support the d ecrease of emissions and energy use from the utility sector. does not mention anything about decreasing emissions from the transportation sector.
71 Table 5 1. Policies that e ncourage c ompact d evelopment in G f uture l and use e lement of t heir c omprehensive p lan Diversity/Mixed Uses Density Urban Design Destination Accessibility Policy 1.1.1 : Planning shall be in integrated communities Policy 1.3.4 : Densities should cascade from h igher densities at the core to lower densities at the edges Policy 1.2.2 : Incorporate design standards that create livable densities Policy 1.1.2: Housing, job and daily needs be within easy walking distance Policy 1.1.3 : Neighborhoods should contain a diversity of housing Policy 1.5.7 : Establish redevelopment areas Policy 1.2.7 : Form interconnected network of neighborhood streets and sidewalks Policy 1.1.6: Encourage community serving facilities to be centrally located Policy 1.1.4 : The city and its neighborhoods should have a center focus that combines uses Policy 2.1.1 : Encourage student housing to develop within 1/2 mile of UF Policy 1.2.8 : Restriction of gated residential developments in order to promote connectivity Policy 1.2.5: Creation of short cuts for pedestrians and bicyclists with additional connections Policy 1.2.3 : Encourage mixed use development Policy 2.1.4 : Designate an urban infill and development area Policy 1.3.1 : Neighborhood centers should include gridded interconnecte d street network Policy 1.2.7: Form interconnected network of neighborhood streets and sidewalks Policy 1.3.3 : Centers should contain mixed uses Policy 4.1.5 : Discourage strip commercial uses and encourage residential uses along 13th St Policy 1.4.3 : Mixed use developments should emphasize transit design and compatible scale Policy 4.2.2 : Shall adopt land development regulations that encourage better access between residential neighborhoods and neighborhood centers Policy 1.4.1 : Office complexes a s least 10 acres shall include retail and service and residences Policy 1.1.2 : Housing, job and daily needs be within easy walking distance Policy 1.4.4: In mixed use zoning districts the city will prohibit land uses that discourages pedestrian activity Policy 1.5.9 : Encourage the establishment of residential retail, office and civic uses within 1/4 mile of the center of neighborhood centers Policy 2.1.1: Encourage neighborhood enhancement and stabilization for areas designated as redevelopment are as
72 2020 Comprehensive Plan, was most recently approved in t cies that deter urban sprawl as Plan incorporates the use of compact development more explicitly as a means to prevent the continuation of urban sprawl, A population in the unincorporated part of the C ounty was growing at twice the rate of that of the City of Gainesville when the comprehensive plan was adopted in 2001 Alachua County also incorporates a larger percent of vacant land. For instance 48% of the total urban residential land uses consist of vacant land (Alachua County, 2005 ). Therefore Alachua County has a greater opportunity to protect undeveloped land and direct where fut ure growth should occur. The County does so by incorporating policies that encourage compact development, as well as energy conservation in their comprehensive plan policies ou tlined in Rule 9J 5. However the encouragement of compact development including its benefits to the community at large is more explicit. Unlike the City of section devoted to the conservation e lement that incorporates policies that encourage the improvement of air quality and the reduction of GHG emissions. The Future Land Use Element in the comprehensive plan serves as a guide to the sdiction. In the S cope and
73 P urpose S location of future land uses through the relationship between land use and the transportation system County, p.1, 2002). Analysis Two out of the four overall principles in the Fut ure Land Use Element, use language that endorses compact development. In addition, t wo of the three general strategies specifically mention increasing densities and creating compact development as a way to promote cohesive communities. These strategies in clude providing incentives for higher average densities for residential development and mixed uses in the urban cluster, transfer of development ri ghts, urban service area, creating neighborhoods that are compact and connected to adjacent development, avoi d ing areas of single use and similar densities, and providi ng infill where appropriate (Alachua County, p.1, 2002). The County Comprehensive Elements Future Land Use, Transportation Mobility and Conservation and Open Space all included policies that enco uraged compact development. These policies accomplished this through the encouragement of clustering, implementing higher urban densities, encouragement of activity/village centers, traditional neighborhoods, increase transit options, increasing interconn ected corridors, the encouragement of higher densities around the University of Florida and Santa Fe Community College Corridors (Alachua County, 2002). Table 5 2 provides a
74 summary of the policies that correspond with the different characteristics of comp act development. Overall, density was mentioned the most frequent in policies that encouraged development patterns that had the potential to decrease VMT (1 2 policies). Diversity/mixed uses and Destination Accessibility/Connecti vity followed (8 policies each) Finally Design was incorporated in the least amount of policies (5 policies). It is interesting to note that Destination Accessibility/Connectivity was the element that was mentioned the most in the Transportation Mobility Element but the least in the Future Land Use Element. Plan. It is most commonly used (4 out of 5 times ) in an objective or policy referring to village centers or activity centers. Village centers are defi compact, mixed use areas, integrated into residential areas within the Urban Cluster s designated on the Future Land Use p.138, 2005). The word compact is also utilized in General Strategy 3(a) of the Future Land Use that are compact, connected to adjacent development, have limited mixed uses at centers, and have interconnected, County, p. 2, 2005). Again the word compact in this incidenc e is used in a descriptive manner.
75 incorporates multiple policies that relate to energy conservation, GHG emissions and decrease in air pollutants. In the Conservation Ope n Space Element policies on air emissions and energy can be found under Goal 4 and Goal 5. Goal 4 promotes the improvement of air quality, pollution prevention and reductions in GHG emissions (Alachua County, 2005) Policy 4.1.6 of the Conservation Open Sp ace Element specifically correlates land use and transportation as a means to decrease air pattern conducive to support of public transportation, including containment of urban (Alachua County, p.17, 2005). In addition the management of energy is utilized in Goal 5. The Objective of Goal 5 ities, land uses, and development (Alachua County, p.45, 2005). This objective is to be implemented using policies that ally feasible and environmentally safe, innovative energy sources and management techniques for housing, [and] More specific to this research, the ing GHG emissions by 20%.
76 Table 5 2 Policies that encourage c ompact omprehensive p lan Diversity/ mixed uses Density Urban Design Destination Accessibility Policy 1.2.1 of the Future Land Use Element: M ixed uses all owed in traditional neighborhood developments Policy 1.1.1 of the Future Land Use Element: E ncouragement of clustering Policy 220.127.116.11 of the Future Land Use Element: D esigned to integrate into the surrounding community Policy 18.104.22.168 of the Future Land U se Element: I nterconnected system of internal circulation Policy 22.214.171.124 of the Future Land Use Element: M ixes of housing types in planned developments and village centers Policy 1.1.4 of the Future Land Use Element: E ncouragement of higher urban densit ies Policy 1.4.2e of the Future Land Use Element: G rid system interconnecting streets Policy 1.4.2e of the Future Land Use Element: G rid system interconnecting streets Policy 1.6.1 of the Future Land Use Element: M ixed uses in village centers Policy 1.3 .3 of the Future Land Use Element: H igher densities urban activity centers lower outlying areas Policy 1.6.6 of the Future Land Use Element: Site and building design and sale integrate with surrounding community Policy 2.1.11 of the Future Land Use Elemen t: P rovide connections to adjacent commercial development and to adjacent residential development Policy 1.6.5 of the Future Land Use Element: M ixed uses to reduce overall trip lengths Policy 1.4.2c of the Future Land Use Element: E ncouragement of clust ering Policy 2.1.8 of the Future Land Use Element: B uilding design and scale integrated within community Policy 1.2.3 of the Transportation and Mobility Element: C onnectivity index standards Policy 2.1.5 of the Future Land Use Element: A ctivity centers s hall be compact multi purpose Policy 1.6.3 of the Future Land Use Element: V illage center urban cluster Policy 2.5.3 of the Future Land Use Element: Oaks Multi modal access Policy 1.2.4 of the Transportation and Mobility Element: P rovide pedestrian acces sibility Policy 2.1.6 of the Future Land Use Element: M ixed uses to reduce overall trip lengths in activity centers policy 1.6.4 of the Future Land Use Element: Village centers shall be compact, multiple purpose, mixed use centers Policy 1.2.12 of the Transportation and Mobility Element: D evelopment eligible for TCE if located 1/4 mile from transit line, mixed uses, range of densities Policy 2.5.3 of the Future Land Use Element: Oaks Mall Activity Center, high density and mixed use surrounding it Po licy 2.1.5 of the Future Land Use Element: A ctivity centers shall be compact multi purpose Policy 1.3.2 of the Transportation and Mobility Element: A dopt connectivity index standards
77 Table 5 2. Continued. Diversity/Mixed Uses Density Urban Design Destin ation Accessibility Policy 1.2.12 of the Transportation and Mobility Element: D evelopment eligible for TCE if located 1/4 mile from transit line, mixed uses, range of densities Policy 2.5.3 of the Future Land Use Element: Oaks Mall Activity Center, hig h density and mixed use surrounding it Policy 1.3.3 of the Transportation and Mobility Element: N ew development shall be connected to roadways, bikeways and pedestrian systems Policy 3.1.2 of the Future Land Use Element: New commercial facilities enco urage to locate on vacant parcels of land within designated activity centers Policy 3.4.1 of the Future Land Use Element: O nly infill of commercial strips are allowed Policy 1.2.12 of the Transportation and Mobility Element: D evelopment eligible for TCE if located 1/4 mile from transit line, mixed uses, range of densities Policy 3.4.4 of the Transportation and Mobility Element: C reate future densities and intensities suitable for mass transit
78 Conclusion The City of Gainesville and Alachua County both incorporate the characteristics that support compact development in their goals, objectives and polici es in their comprehensive plans, h owever both governmental entities lack substantial content on policies to support the reduction of energy consumption and GHG emissions. The future land use patterns outlined in the comprehensive plans promote compact development, but the plans fail to overall relate compact development to the reduction in trip length This is a considerable shortcoming of the comprehensive plans. As the purpose of comprehensive plans are to serve as a guide for future development and land use de cisi ons, it is imperative that the p lans include policies that promote land use designations that reduce trip length as a mean s to reduce GHG emissions.
79 CHAPTER 6 DISCUSSION This chapter will provide a discussion on the implications of the results of the models and policy analysis. The results of the models will be utilized in determining how the City of Gainesville and Alachu incorporate polices that have the potential to reduce trip lengths. The discussion will focus on the type of development that is needed to reduce trip lengths and evaluate the effectiveness of current polic ies in local comprehensive plans in promoting this type of development The discussion then uses two simulations derived from the model results to provide recommendations of where the most suitable locations for new development to occur are The chapter co ncludes with the limitations of this study as well as recommendations for future research. Discussion The results of this study provide the locations where the land use patterns in Alachua County are more favorable towards the reduction in trip lengths. Compact neighborhoods characterized by density, destination accessibility, urban design, and diversity, were shown to be more favorable toward a decrease in trip lengths when compared neighborhoods characterized by urban sprawl. A decrease in trip lengths can also be translated as a reduction in fuel consumption. Accordingly, a reduction in fuel consumption can be translated into a reduction in GHG emissions released into the atmosphere. With one third of GHG emissions produced by the transportation sector, it is imperative for planners to look for innovative ways to reduce these emissions. The responses to climate change are ultimately local and regional. Finding new ways to meet energy needs, lower GHG emissions, and face the impacts of climate
8 0 change wi ll be critical to the future success of Alachua County. Climate change potentially brings with it a long list of impacts to ecological systems, agriculture, public health, infrastructure and commerce. As this research suggests development patterns can play an important role in reducing GHG emissions. Employing strategies to promote compact development in areas that are favorable to shorter trip distances could reduce GHG emissions and improve environmental quality. Comprehensive plans are seen as a guide f or the future development and use of land within local government jurisdictions. Since the purpose of these plans is to guide future development and land use, it is vital that they include elements that promote the reduction of GHG emissions. The analysi s of two local comprehensive plans reveal that goals related to energy conservation and the reduction of GHG emissions are not directly associated with land use policies and designations. This is a major shortcoming in our comprehensive plan system. The mo dels utilized in this research can be used as a tool to help improve the comprehensive plans by suggesting the type of development and location of where new development should occur within the County. elopment influenced by the type of growth they were experiencing at the time their comprehensive plan was being drafted. The City of Gainesville was experiencing a declining shar e in the overall population of the county. Therefore the goals, objectives and policies were tailored towards implementing strategies to encourage the movement of people back i ncorporates policies with the intent to make Downtown Gainesville a more livable environment, by means such as enhancing the schools within city limits (City of
81 Gainesville, p. A 4, 2000). The importance of creating a desirable built environment in downtow n Gainesville is again evident in that the City created an element called Urban Design. This element contains goals, objectives and policies all oriented toward creating a friendlier and more welcoming downtown area (City of Gainesville, 2000). On the ot her hand, unincorporated Alachua County was experiencing a greater share of the population and was experiencing a population growth rate twice as large as planning tools th at instead of encouraging growth to occur in a certain area, directed where growth should occur. For instance the County incorporated an urban growth boundary in their Comprehensive Plan. This urban growth boundary contains growth in a specific section of the unincorporated part of the County. The County also utilizes an urban service area to designate where growth should occur. While both comprehensive plans incorporate polices that promote compact development and to influence the location of new growth t hey have not been implemented in a way that supports compact development as a means to reduce trip length. For instance Both the City and the County used the encouragement of mixed use development as a way to curtail sprawl and promote compact development. In County sites the Town of Tioga as an example of mixed use development (Alachua County, 2001). In the neighborhood comparison the Town of Tioga represented the suburban neigh borhood. As the results of that section showed the HBW predicted trip lengths were on average 5.2 miles in length, compared to the urban neighborhood where the predicted trip lengths were less than one mile. This indicates that while mixed
82 use is an elemen t that makes up compact development, it has to be implemented in an area that contains the other three identified characteristics of compact development: density, urban design and destination accessibility, to contribute to the reduction in trip lengths. T his example helps illustrate the need for all four characteristics identified by the literature as compact development, to coincide with one another in order to see reductions in trip lengths. The models also suggest that mixed use is more likely to reduc e the trip lengths for HBO trips while access to major employment centers is more likely to reduce HBW trip lengths. The Town of Tioga would is also a good example of this. The Town of Tioga is located in an area of mixed use and therefore the results indi cates that it the development is predicted to have relatively short HBO trip lengths (2.8 miles). However orates all of the characteristics of compact development it will see the largest decrease in both H BW and HBO trip lengths. This was illustrated with the Duckpond neighborhood. To determine areas that possess all of the characteristics of compact developme nt in the County, the following section combines the results of the HBO and HBW models in two simulations that illustrate the ideal locations for new development to occur. Recommendations on Where Future Development Should Occur The ultimate way to reduc e GHG emissions from the transportation sector is to get people out of their cars. The only way to do so is by providing development patterns that allow for alternative forms of transportation such as by foot, bicycle and transit. The areas wher e the model s represented shortest trip lengths, also encourages higher level of transportation choice than areas that represented longer trip lengths. Therefore the
83 most appropriate places to encourage further development is in areas that are depicted as having the s hortest trip lengths for both HBO trips and HBW trips. Two simulations were run using the results of the HBW and HBO models to determine the most suitable locations for development to occur in order to reduce trip lengths. The first simulation looks at wh ere both the HBO and HBW trip lengths are predicted to be one mile or less. The results of this simulation, shown in Figure 6 3 represent the ideal location for development to occur in order to create a walkable community. It is predicted that if developm ent occurs in these areas individuals will not be reliant on an automobile because they will be in close enough proximity to both shopping destinations and employment centers that they will be able to walk to these destination. The encouragement of develo pment in these locations will further minimize walking distances and create a more pedestrian friendly area. Therefore the results of this simulation denote where both the City and the County should prioritize future development to occur. The second simul ation looks at where both HBO and HBW trip lengths are predicted to be three miles or less. The results of this simulation, shown in Figure 6 4 represents where shopping destinations and employment centers are easily reachable by the use of a bicycle. The encouragement of development in this area should be the second area of prioritization for future development. The encouragement of development to occur in both of these areas will also increase the accessibility to transit. A study conducted by Gainesvil le Regional Transit ridership is drawn from a one quarter miles walking distance from bus routes, but
84 expands to approximately 84 square miles if ridership is drawn from a one mile bicycling their bicycle service area is represented in Figure 6 5 This map shows that the majority of the areas within the City limits are accessible to transit with the use of a bicycle. However transit use on some of these routes is greatly inhibited by their low trip frequency. Routes that have a frequency of equal to or greater than once ever hour are highlighted on the map. Therefore while these routes are ea sily accessible by bicycle their low trip frequency decreases the convenience of these routes and thus will see a lower ridership than bike routes that have a greater trip frequency. Thus the City should encourage development in areas in close proximity to bus routes that have a greater trip frequency or increase the trip frequency of the highlighted routes in order to decrease trip lengths produced by the automobile more effectively. Limitations and Opportunities for Future Research It is important to rec ognize some of the shortcomings of the models and policy analysis used in this research. First the models do not control for socio economic characteristics of the traveler. As mentioned in the literature review, this is an area of concern. However these mo dels are intended to predict the trip lengths associated with land development. In this case, it is almost impossible to know what the characteristics of the traveler are going to be. As shown in Table 4 by the regression model are relati vely small. Therefore while land development patterns have a significant inverse relationship with trip length that there are other additional predictors of trip length. This could be a result of not incorpor ating socioeconomic characteristics.
85 In addition this some of the trip lengths produced could be biased low. As mentioned previously in Chapter 4, the HBW production model predicted trips lengths of less than a mile and a half in the small municipalities located in Alachua County. Due to limited employment opportunities in these cities, it is very unlikely that all of the residents work within their borders. Therefore this model does not take into account the residents in these municipalities who commute t o the City of Gainesville for employment. Additionally, VMT is a composite measurement of trip length and trip frequency. The model used only accounts for trip length and fails to account for trip frequencies as related to the land development patterns P ast research has shown that greater accessibility has a tendency to encourage trip making and thus increase the overall VMT (Polzin, 2006). This is an inverse effect of accessibility and should be considered in future research looking at the relationship b etween land development patterns and VMT Congestion created by more compact developments was also not considered in this research. Increased congestion as a result of more compact development could cause an increase in GHG emissions from idling cars. It is recommended that further research be conducted to account for the potential influence of congestion on the relationship between the built environment and GHG emissions. The policy analysis conducted in this research only considered the goals, objective guide for future development and land use decisions, it is not the only planning tool that
86 influences develop ment patterns and planning practices. By not examining other City and County operations, the research is unable to provide a full account of the initiatives currently being undergone by the City and County to promote energy conservation and GHG emissions. Thus while the City and County lack content in their comprehensive plans related to energy conservation and GHG emissions, it does not mean that both governments are not actively addressing these issues through specific city and county department operati ng procedures. Both the City of Gainesville and Alachua County are members of the ICLEI Local Governments for Sustainability (ICLEI). This membership is associated with local governments committed to advancing climate protection and sustainable developme nt (ICLEI, n.d.). In addition Alachua County has established the Energy Conservation Strategies Commission (ECSC) whose purpose to look for way to reduce energy consumption in the County. The County is continuing to monitor their GHG emissions through the Pollution Protection section of the Environmental Protection Department, while the City is monitoring their GHG emissions through their Gainesville Regional Utility Department. The purpose of the thesis was to determine areas within the County that were c ontained land use patterns that were the most conducive to reductions in trip length. By doing so this research was able to effectively evaluate areas where the City of order to encourage development patterns that will help mitigate GHG emissions from the transportation sector. Further research is needed to look deeper into the relationship between land development patterns and trip lengths and to explore to a greater extent
87 ho w local governments can incorporate policies supporting compact development into their comprehensive plans and their land development codes. As policies are implemented at both the federal and state level to promote energy conservation and GHG reductions, it would be valuable to conduct a follow up study to examine the extent to which Alachua County and the City of Gainesville amend their comprehensive plans to incorporate the state and federal goals.
88 Figure 6 1 Home b ased o ther p redicted t rip l en gths i ncluding c ity b oundaries
89 Figure 6 2 Home b as ed w ork p redicted t rip l engths i ncluding c ity b oundaries
90 Figure 6 3 Areas w here HBW and HBO p redicted t rip l engths a re 1 m ile or l ess
91 Figure 6 4 Areas w here HBW and HBO p redicted t rip le ngths a re 3 m iles or l ess
92 Figure 6 5 Transit r outes with b icycle s ervice a rea
93 CHAPTER 7 CONCLUSION With the transportation sector accounting for approximately one third of GHG emissions in the United States, it is imperative that local government find new ways to curb the rate that these emissions are released into the atmosphere. Through the government, b oth the City of Gainesville and Alachua County are receiving pre ssure to come up with innovative ways to decrease GHG emissions, especially from the transportation sector. The literature has shown that land development patterns have the potential to decrease trip lengths. A decrease in trip lengths can also be transla ted as a reduction in fuel consumption. Accordingly, a reduction in fuel consumption can be translated into a reduction in GHG emissions released into the atmosphere. The HBO and HBW trip generation prediction models utilized in this research map the loc ations where land use patterns are more favorable to the reduction in trip lengths in Alachua County. These models can be used as a tool to help local governments determine what policies the City and County need to include in their comprehensive plans to p romote land development patterns that decrease trip length. The results of this study provide the locations where the land use patterns in Alachua County are more favorable towards the reduction in trip lengths. Compact neighborhoods characterized by dens ity, destination accessibility, urban design, and diversity, were shown to be more favorable toward a decrease in trip lengths when compared to neighborhoods characterized by urban sprawl. The models showed that on average trip lengths were expected to inc rease the farther the neighborhood was from The models also suggest that mixed use is more likely to
94 reduce the trip lengths for HBO trips while access to major employment centers is more likely to reduce HBW trip lengths. There fore, i n order to effectively reduce trip lengths, local governments need to encourage development to occur in areas where both HBO and HBW trips are predicted to be the shortest in length. As a result the researcher recommends that areas on the map that r epresent where the predicted trip lengths are one mile or less for both HBW and HBO trips are the most suitable locations for new development to occur. These areas should be identified in the local comprehensive plans as the first priority of where to guid e f uture development Areas where HBW and HBO trips that are predicted to be three miles or less should be the second area of prioritization for future development. Comprehensive plans are seen as a guide for the future development and use of land within local government jurisdictions. Since the purpose of these plans is to guide future development and land use, it is vital that they include elements that promote the reduction of GHG emissions. The analysis of two local comprehensive plans reveal that g oals related to energy conservation and the reduction of GHG emissions are not directly associated with land use policies and designations. This is a major shortcoming in our comprehensive plan system. The models utilized in this research can be used as a tool to help improve the comprehensive plans by suggesting the type of development and location of where new development should occur within the County. The responses to climate change are ultimately local and regional. Finding new ways to meet energy nee ds, lower GHG emissions, and face the impacts of climate change will be critical to the future success of Alachua County. Climate change potentially brings with it a long list of impacts to ecological systems, agriculture, public
95 health, infrastructure and commerce. As this research suggests development patterns can play an important role in reducing GHG emissions. Employing strategies to promote compact development in areas that are favorable to shorter trip distances could reduce GHG emissions and improve environmental quality.
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101 BIOGRAPHICAL SKETC H Kelly Rhinesmith was born in 1987 in Bamberg, Germany. She grew up in Oviedo, Flo rida and graduated from Oviedo High School in 2005. Upon graduating from high school, Kelly attended the University of Florida and enrolled in the 4+1 program with the Department of Urban and Regional Planning graduating with her Bachelor of Arts degree i n sociology in December 2009 and Master of Arts in urban and regional planning degree with a certificate in historic preservation in May 2010. While attending Departme nt. This experience fueled her interests environmental planning specifically on the topics energy and climate change In addition to environmental planning, her interests include transportation planning, historic preservation and community redevelopment. O utside of school, Kelly enjoys spending time with her family and friends, cooking, and traveling. Upon graduating Kelly looks forward to pursuing her planning career in Little Rock, Arkansas.