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Vampire Analysis of Hillsborough County

Permanent Link: http://ufdc.ufl.edu/UFE0044322/00001

Material Information

Title: Vampire Analysis of Hillsborough County a Spatial Representation of Oil and Mortgage Vulnerability
Physical Description: 1 online resource (75 p.)
Language: english
Creator: Ice, Kevin T
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: final
Urban and Regional Planning -- Dissertations, Academic -- UF
Genre: Urban and Regional Planning thesis, M.A.U.R.P.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Oil Vulnerability refers to the degree in which an urban area, analyzed in this thesis at the level of the census tract, is vulnerable to negative economic impacts from rising oil and gas prices. An existing model has been applied to many different Australian cities, and is applied to Hillsborough County/Tampa Bay, FL in this thesis. This model is called the VAMPIRE, or vulnerability assessment for mortgage, petroleum, and inflation risks and expenditure. The results of the model show Tampa Bay to have an overall high level of vulnerability, without a clear "core" area of low vulnerability that can aid in a region-wide effort to mitigate the problem by expansion of services or spreading favorable development patterns outward. While this is the policy solution put forward for Australian cities, an analysis of Tampa Bay reveals the city center, and not the periphery, to have the strongest need for action. The discussion at the end of the thesis argues for improved transit in the jobs rich 275 corridor north of downtown Tampa as the most expedient way to address Hillsborough County's oil vulnerability.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kevin T Ice.
Thesis: Thesis (M.A.U.R.P.)--University of Florida, 2012.
Local: Adviser: Zwick, Paul D.
Local: Co-adviser: Steiner, Ruth L.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044322:00001

Permanent Link: http://ufdc.ufl.edu/UFE0044322/00001

Material Information

Title: Vampire Analysis of Hillsborough County a Spatial Representation of Oil and Mortgage Vulnerability
Physical Description: 1 online resource (75 p.)
Language: english
Creator: Ice, Kevin T
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: final
Urban and Regional Planning -- Dissertations, Academic -- UF
Genre: Urban and Regional Planning thesis, M.A.U.R.P.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Oil Vulnerability refers to the degree in which an urban area, analyzed in this thesis at the level of the census tract, is vulnerable to negative economic impacts from rising oil and gas prices. An existing model has been applied to many different Australian cities, and is applied to Hillsborough County/Tampa Bay, FL in this thesis. This model is called the VAMPIRE, or vulnerability assessment for mortgage, petroleum, and inflation risks and expenditure. The results of the model show Tampa Bay to have an overall high level of vulnerability, without a clear "core" area of low vulnerability that can aid in a region-wide effort to mitigate the problem by expansion of services or spreading favorable development patterns outward. While this is the policy solution put forward for Australian cities, an analysis of Tampa Bay reveals the city center, and not the periphery, to have the strongest need for action. The discussion at the end of the thesis argues for improved transit in the jobs rich 275 corridor north of downtown Tampa as the most expedient way to address Hillsborough County's oil vulnerability.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kevin T Ice.
Thesis: Thesis (M.A.U.R.P.)--University of Florida, 2012.
Local: Adviser: Zwick, Paul D.
Local: Co-adviser: Steiner, Ruth L.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044322:00001


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1 VAMPIRE ANALYSIS OF HILLSBOROUGH COUNTY: A SPATIAL REPRESENTATION OF OIL AND MORTGAGE VULNERABILITY By KEVIN ICE A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREME NTS FOR THE DEGREE OF MASTER S OF ARTS IN URBAN AND REGIONAL PLANNING UNIVERSITY OF FLORIDA 2012

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2 2012 Kevin Ice

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3 To Megan and the Cat, for keeping me sane

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4 ACKNOWLEDGMENTS T o my beautiful fianc e for leaving everything behind so I could pu rsue this degree T o my pa ept striving.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF FIGURE S ................................ ................................ ................................ .......... 7 LIST OF ABBREVIATIONS ................................ ................................ ............................. 8 ABSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 10 Problem Statement ................................ ................................ ................................ 10 Hypothesis ................................ ................................ ................................ .............. 12 2 REVIEW OF LITERATURE ................................ ................................ .................... 13 Transportation ................................ ................................ ................................ ......... 13 Economics of Rising Oil ................................ ................................ .......................... 20 Transportation and Housing Costs Combined ................................ ........................ 21 Transportation Costs Analyzed Alone ................................ ................................ ..... 22 Financial Benefits of Public Transportation ................................ ............................. 23 Public Transportation Demand Elasticity ................................ ................................ 24 Local Disadvantage and the Regressive City ................................ ......................... 25 United States Outlook for Oil Resilience ................................ ................................ 28 Oil Vulnerability Models ................................ ................................ .......................... 30 Conclusion of Literature Review ................................ ................................ ............. 33 3 METHODOLOGY ................................ ................................ ................................ ... 34 4 TAMPA BAY AND HILLSBOROUGH COUNTY CONTEXT AND BACKGROUND ................................ ................................ ................................ ...... 36 5 DISCUSSION OF RESULTS ................................ ................................ .................. 49 Results of Hillsborough VAMPIRE and Comparison to Melbourne VAMPIRE ........ 49 Discussion ................................ ................................ ................................ .............. 50 6 POLICY DISCUSSION ................................ ................................ ........................... 54 7 LIMITATIONS OF THE STUDY AND RECOMMENDATIONS FOR FURTHER RESEARCH ................................ ................................ ................................ ............ 57 8 CONCLUSION ................................ ................................ ................................ ........ 60

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6 APPENDIX : HILLSBOROUGH VAMPIRE DATA ................................ .......................... 63 LIST OF REFERENCES ................................ ................................ ............................... 73 B IOGRAPHICAL SKETCH ................................ ................................ ............................ 75

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7 LIST OF FIGURE S Figure page 4 1 A verage household expenditures on housing and transportation as a percentage of avera ge tract income, Tampa Bay ................................ ............... 42 4 2 Tampa, FL profile ................................ ................................ .............................. 43 4 3 Median household income for Hillsborough County by cen sus tract ................. 43 4 4 Percent of households with a mortgage for Hillsborough County by census tract. ................................ ................................ ................................ .................. 44 4 5 Percent of households with 2 vehicles for Hillsborough County by census tract. ................................ ................................ ................................ .................. 45 4 6 Percent of households with 3 veh icles for Hillsborough County by census tract. ................................ ................................ ................................ ................... 46 4 7 Percent of households with 4 or more vehicles for Hills borough County by census tract. ................................ ................................ ................................ ....... 47 4 8 Percent of commutes by car, truck, or van for Hillsborough County by census tract. ................................ ................................ ................................ .................. 48 5 1 Melbourne VAMPIR E ................................ ................................ ....................... 52 5 2 Hillsborough VAMPIRE ................................ ................................ ...................... 53

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8 LIST OF ABBREVIATION S CNT Center for Neighborhood Technology DEPTP D emand elasticities for public transport relative to fuel prices SEIFA Socioeconomic index for the area. USGAO US Government Accountability Office VAMPIRE Vulnerability assessment for mortgage, petroleum and interest rate expenditure VIPER Vulnerability index for petrol expen se rises VMT Vehicle miles travelled

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9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Masters of Arts in Urban and Regional Planning VAMPIRE ANALYSIS OF HILLSBOROUGH COUNTY : A S PATIAL R EPRESENTATION OF O IL AND M ORTGAGE V ULNERABILITY By K evin I ce May 2012 Chair: Paul Zwick Cochair: Ruth Steiner Major: Urban and Regional Planning Oil Vulnerability refers to the degree in which an urban area, analyzed in this thesis at the level of the census tract, is vulnerable to negative economic impacts from rising oil and gas prices. An existing model has been applied to many different Australian cities, and is applied to Hillsborough County/Tampa Bay, FL in this thesis. This model is called the VAMPIRE, or vulnerability assessment for mortgage, petroleum, and inflation risks and expenditure. The results of the model show Tampa Bay to have an overall high level of n aid in a region wide effort to mitigate the problem by expansion of services or spreading favorable development patterns outward. While this is the policy solution put forward for Australian cities, an analysis of Tampa Bay reveals the city center, and n ot the periphery, to have the strongest need for action. The discussion at the end of the thesis argues for improved transit in the jobs rich 275 corridor north of downtown Tampa as

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10 CHAPTER 1 INTRODUCTION Problem Statement Oil dependence is a ubiquitous problem throughout much of the i ndustrialized world. M any different predictions exist about when global oil production will peak, ranging from now to around 2050. What is clearly doc umented is that oil is getting (Chevron Oil Ltd advertisement, The Economist, 2005 as quoted in Dodson and Sipe, 2008) M any factors affect the global market of oil. F irst, global demand is rapidly increasing. China and India have a rapidly expanding middle class, and are adding global oil market in a big way China Furthermore, China in the history of energy," IEA chief economist Fatih Birol said in an interview with the Wall Street Journal The country's surging appetite has transformed global energy markets and propped up prices of oil and coal in recent years, and its continued growth stands to have long term implic ations for U .S. energy security 2010) What this all means is that oil demand is increasing against a flattening supply that is going to inevitably decrease. Indeed many major fields are already decreasing, decline since 1999 while Mexican oil production is also declining sharply While major fields are in decline, there is a large new discovery off the shore of Brazil as well as modest increases in the

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11 United States at their best, the Brazilian fields will not offset the production declines elsewhere in the world Furthermore, the oil. Oil prices are i ncreasing a nd show no signs of falling to levels seen before the price spike of 2008 price [between 2005 and 2008] brought out only a trivi (Deffeyes, 2010) Indeed th e era of $1 $2 gallons of gasoline appears to be over in the United States. Increasing oil prices are typically thought of in a macro economic manner in which they drive inflation and slow economic growth. The OPEC crisis of the late 70s/early 80s produced was caused by the spike in oil prices that peaked in 2008, topping $140 right before the collapse of Bear Stearns in March 2008 (Deffeyes, 2010) GDP in 1982 and again in 2008. Both of those price spikes caused extensive damage to the U.S. economy (Deffeyes, 2010) oil price spikes and their associated recessions. Oil inte nsity refers to the amount of oil needed to produce one unit of GDP. It has improved much since the 1980s, driven by technological advances, specifically by using oil more efficiently What has not improved, however, is our overall dependence on oil. We ar e more dependent on oil now than during the OPEC crisis. This study seeks to address this dependence at the

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12 be tween household income segments T his thesis aims to further the fledgling field of studying oil impacts at the household level. Hypothesis The hypothesi s of this thesis is that the overall pattern established by previous oil vulnerability studies in Australia will hold for Tampa Bay. This pattern sees higher oil vulnerability on the periphery of the urban area, with moderate vulnerability in the inner ring suburbs, and low vulnerability in the city center. Due to the large urbanized area in the Tampa Bay region, and relatively small center area, It is expect ed to see a disproportionately large high vulnerability area surrounding a small core of relatively low vulnerability. Another expectation is to see relatively low vulnerabilities follow ing the streetcar system in place, du e to it s mitigating influence on auto dependence Finally, due to the extremes of auto dependence in the Southern United States, of which Tampa Bay is no exception, it is expect ed that oil vulnerability is generally higher than what has been established in the study of Australian cities.

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13 CHAPTER 2 REVIEW OF LITERATURE Transportation When it comes to auto dependence, the United States outdoes other countries, even those commonly regarded as auto dependent such as Australia and Canada. l are out consuming oil in similar Western cities by a fac tor of between four and ten et al. 2009 ) There are many reasons for this, primarily public transpor (Dodson and Sipe 2008 ) Like many cities across the world, historic centers have relatively stron g public transportation options. H owever, suburbanization has largely disregarded any mode of travel other than the automobile. A second reason for our unmatched auto dependence is another product of suburbanization. As the suburbs have opened up inexpensive land and provided affordable housing, many of the working poor in the US have been locked into extremes of auto 2009 ) This fact is particularly troubling when addressing auto dependence from an oil vulner ability perspective. While the suburbs have brought affordable housing to the less affluent the method of providing that affordable housing has left those least able to afford oil price increases poised to bear the largest burden when prices do rise. The i ssue of high oil dependence in the context of stagnant global oil production and rapidly increasing oil consumption (due to increases in China and India), would seem to create a pressing need that demands grave respect and consideration. It is not

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14 widel and responding to the problems related to our automobile dependency challenges every aspect of life et al. 2009 ) Rubin puts it a different way, he states that either ou r spatial arrangements or our living or working where they are today when oil prices inevitably soar again? And if they are, will they still be driving cars? Either our li ving arrangements or our transportation (Rubin 2009 ) The issue with both of those methods of addressing auto dependence is that they are long term processes that cannot adapt overnight. This means that all the time no t spent preparing for the inevitable future of post use and transportation infrastructure decisions will have strong consequences long into the future. Our current land use patterns are increas ing, rather than decreasing, our auto dependence. Suburbs are ringing other suburbs in ever widening circles of development away from historical centers. Each new suburb on the outskirts of a city is more auto dependent than its predecessors, all things be journey lengths are almost double those of middle and inner suburban residents, and average daily distances travelled by those in outer regions are nearly triple those of the denizens of the middle and inner zones on and Sipe 2008 ) Therefore, those who are least able to afford oil price increases (those seeking the affordability of exurban locations) are those with the least ability to switch modes if needed due to lack of transport options. T hey are those who are driving the farthest distances. As oil prices

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15 have begun to rise, we can see the beginning of the ramifications of this spatial mismatch between socio economic needs and realities. Speaking about the oil price spike of 2008, Newman write n the United States, [it was] found that most people opted to stay at home and took fewer trips instead of shifting their mode of transport from the single occupancy vehicle to walking, mass transit, biking, or car pooling. In many places the infrastructure was not in place for the (Newman et al. 2009 ) This exurban reality is in contrast to a significant increase in growing: inner city residents are shu nning their cars and their trips are shrinking, while those in the outer suburbs wade further into the depths of car reliance and Sipe, 2008 ) Regardless of quality, public transportation access is a big issue that needs addressing. Rubin provides the f igure access to some form of public transit; only 50 percent of American households living in the suburbs have similar access. In rural areas, access to some form of public trans it plummets to abou t 25 percent 2009 ) The f igure s of the American poor and transit are worth exploring, if only because they are exceptional in the fact that they are the both the cause of our unmatched auto depende nce and the primary concern in studies of oil vul nerability. Rubin, in his book Why Your World is about to get a Whole Lot Smaller conducts an analysis of the driving income of less than $25,000 own at least one vehicle. There are more than 10 million such households that own and drive more than one car. (Rubin 2009 ) Rubin arrives at his 10 million f igure based on an analysis in which he

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16 uses European driving habits as a way to gauge how our driving will be affected once prices approach the high levels paid in Europe today because of taxes. His theory is that affluence (or just economics) determines driving habits; that people will drive if they can afford to. His main point to support thi s theory is that throughout Europe and areas with very high gas prices, the wealthy drive at the same rate as the wealthy in the car in American culture and life is not t he driving habits of the rich but rather the drivi ng 2009 ) It is clear that the working poor, subjected to the need to where their only housing options are the farthest flung suburbs or substandar d inner city, are going to need new transit options in a post peak oil world. However, as Dodson explains, when transit is expanded, it is most often to middle class areas that by virtue of their wealth are less likely to be impacted by rising oil prices. He has coined this term money get to have nicer things, or another explanation, inner suburbs are more likely to be denser and wealthier, making them more attractive for transit. In planning for a new future, socio economic realities should be taken into account when designing new dependent outer suburbs will face the greatest burden in this new world 2008 ) depopulate the suburbs. The farther they are from where people work, the emptier t hey will get 2009 ) The stress refers to negative economic impacts on an area. It is

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17 the logical aftermath of a situation where there is a need for a resource for the survival of an area, but that resource drains more money than can be financially sustained. Theoretically there is a price threshold for gas, based on how much driving an area does, that would tip the area into a negative financial situation, i.e. : the oil consumption that is driving economic activity is pricier than th e economic acti vity can justify; t his afford to drive the way we have been accustomed to 2009 ) In the oil vulnerability literature there is a divergence in proscribed w ays to address the issue. One view is that urban consolidation is the best way, promoting smarter, infill development in areas that already have transportation infrastructure to serve it. This argument is supported by the exponential cut to vehicle miles t ravelled (VMT) transit use and declining car use in the global cities database developed by Jeff Kenworthy. This helps explain why use of cars by inner city residents in Melbour ne is ten times lower than that of fringe residents, though transit use by inner city reside nts is only three times greater et al. 2009 ) It would suggest that either the areas that are good for transit are also good for promoting all modes of tra vel, the trips are shorter, or both. No matter the reason, a decrease in VMT is an increase in resiliency to oil prices. Another side claims that the market prevents urban infill development from helping those who are the most oil vulnerable, the people t hat have already been priced quoted as warning that much higher oil prices will mean a new residential abandonment

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18 in car her densities) w ill save the suburbs 2008 ) This premise is based on the fact that it is more expensive to develop infill area s so the developments have to be targeted at the high end of the spectrum to cover the increased costs. Affordable hous ing is built on the affordable land, and it is argued that there are not adequate ways to mitigate this to No matter where development efforts should take place, public transi t is the key way to build economically sustainable communities in the post the choice between propping up a collapsing way of life based on car dependent suburbs and designing and building systems better scaled to the future we face. Development always follows the transportation routes, just as water follows the path of get the ki nd of town suited to the future 2009 ) The key to being suit ed to the access to the cheap land that provided affordable housing. With this model now quivering in the face of higher fuel prices, we need to start planning for a public transport system that provides a level of mobility that can sustain our suburbs and their capacity to afford cheap hous ing for those on modest incomes 2008 ) Getting the kind of public transportation that is called for to address oil vulnerabi lity has historically required high densities, the scale of which is unattractive to people living auto five people and jobs per hectare (fourteen per acre) of urban land and for walking/cycling to be dominant requires densities over one hundred people and jobs per hectare (forty per

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19 (Newman, et al. 2009 ) The areas that most need increased public transit options (th infrastructure. However there is a trend that will help with the provision of transit to milli on more trips in 2008 than in 2007 2009 ) This suggests that the densities needed to sustain transit will be different based on the price of gas. In addition to adding transit riders, each dollar level of gas has produced lower Transportation statistics show that vehicle miles travelled peaked and leveled off at the onset of $3 gasoline in 2005. $4 gasoline in July 20 08 caused them to markedly drop 2009 ) This drop of VMT came at a time where the trajectory was to con we are driving them more. In 1970, the average American car was driven only 9,500 miles per year. By the time of the new millennium, it was driven over 12,000 miles (Rubin 200 9 ) The fact that gas prices have been a ble to dramatically reverse the trend of increasing travel as demonstrated by Ruppert, proves the power that they will have over our communities in a post peak oil world. Ruppert claims that simple fact of decreasin are not going to expand as oil runs out. They are already decreasing. There is no point in destroying arable land, paving it with petroleum products and maintaining it for traff ic 2009 ) While this viewpoint is extreme, the key point is that there should be an understanding of a future world with higher gas prices in any developments that are undertaken today.

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20 Oil prices are a complete game ch anger for the economics of everything they involve. They have the power to completely redefine what is sustainable economically in a spatial and functional manner. They also have the power to ruin communities. There ar e man y communities that are not at all prepared to cope with a post peak oil paradigm. This issue is especially relevant in the Southeast United States, which is one of the most car dependent regions in the most car dependent country in the world. It will be interesting to see how our cities a dapt, but it will not be pleasant to live in a city that has not adapted well. It is not hyperbole to suggest collapse on the scale seen in inner city Detroit for much of our communities, coupled with a strengthening of the competitiveness of many urban ar eas that have seen recent disinvestment. The end result may well be better cities that function better for society as a whole, but the two choices to get there seem to be planning, especially transportation planning, or chaos that will see many suffer. Eco nomics of Rising Oil The impact of rising oil prices on our communities is a key issue to this research. Some are set to be more resilient to the ill effects of rising prices than others. M any factors go into the economic impact of oil prices. There is a n eed for oil vulnerability analyses because our cities are set up to let oil price increases have a regressive impact. Transit provision has a clear financial benefit to residents that use it. Also, as demonstrated earlier, when oil prices rise, people swit ch modes to public transportation. This ability to cope with cost increase is not there for many of the most vulnerable communities. There is a small but growing body of literature that seeks to calculate what people are spending on their transportation, a nd what this means for their communities.

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21 Transportation and Housing Costs Combined Transportation is a major expense in our society, and it is one that is going to tr health care and food when combined, are less than transportation. It is an obligatory expense to get to and from work, home, school, and shopping, but is not categorized as a basic necessity, even though it is the second highest expenditure an d it continues to rise in price This means that there is no official policy to keep transportation affordable. This is problematic because not all areas have equal transport in many US cities, where those who seek homeownership and cannot afford adequate housing closer to the job centers are forced to search for housing farther and farther away. Increased transportation costs are incurred to access housing that is affordable. The drive to qualify sees people stretching their budgets to the maximum amount possible because only by undertaking extensive commutes is housing affordable, which implies that t hese are households of modest means that are undertaking the drive to qualify. the combined costs of housing and transportation. While the share of income devoted to housing or transportation varies from are a to area, the combined costs of the two expenses are surprisingly constant. However, in all the metropolitan areas there are neighborhoods where working families are saddled with both high housing and h igh transportation cost burdens Thes e working class families that are [working] families, their transportation c osts exceed their housing costs

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22 Our sprawling land use patterns is necessary b ecau se the drive to qualify has existed dominant for so long that land farther and farther away fro m the center has to be developed There are just too many people needing peripheral affordable housing to accommodate everyone in a reasonable space. Once th e commute becomes too long, 12 to 15 miles, the increase in transportation costs outweighs the savings on housing, and the share of household income required to meet th ese combined expenditures rises This upsets basic land rent balances that show housing cost to be a decreasing function of distance from the city center, and instead, overall affordability decreases at the s ame time housing costs decrease (Tanguay and Gingras, 2011) Therefore only looking at housing costs to measure affordability is inadequate and transportation must be accounted for. The primary method of providing working class h omeownership opportunities, peripheral development, will se e diminishing benefits the more it is exploited as a development strategy. Transportation Costs Analyzed Alone The cost of transportation is dramatic when analyzed on a regional scale. For example, the Baltimore area averages a household income expenditur e rate on transportation of 14% At the national average transportation rate, 19.1% Baltimore households would have spent an additional $2 Billion in 2003. Likewise, if Houston households would reduce their spending to national averages, it would save the region $1.2 Billion (CNT, 2005) These f igure s are extremely large despite the fact that transportation infrastructure is rarely analyzed in terms of costs to households. key contributor to a savings of $2 billion, money that would most likely leave the region as

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23 oil imports. This says nothing about the exposure to increasing costs, which will see enny 2005) T he US economy is extremely vulnerable to oil prices. Despite lowering the oil requirement per unit of GDP since the OPEC embargo Rubin y is almost twice as depend on imported the weekly feeding your stomach or filling your gas tank, your stomach is usually going to win (Rubin 2009). The fact that our economy and communities are so dependent on people continuing to fill up their gas tanks means that serious economic ramifications are in order when gas continues to rise in a post peak oil world. Even writing before $4 gas guideline for total transportation expenditures as a percent of income, it seems t hat the current spending levels [of] 1 4 1 There is a need to address the cost of transportation at the household lev el. The key way to do this is through provision of quality public transportation. Financial Benefits of Public Transportation Public transportation has a strong impact on the amount spent on transportation at the household level. In fact, transportation ex rich areas to nearly 25 percent in many other areas This household savings is capturing money

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24 that would be otherwise lost to the locality as o il imports and investing them into the transit system itself as well as other household needs. This is a critical feature in a world household from the increase i n gas p rices than those without This household level analysis done by the Center for Neighborhood T echnology has shown that among total transportation expenditures from income, heavy transi t users have a greater portion of their incomes left over, $41,567, than the non transit users, $38,322 2005) Transit use saves money, plain and simple. If transit use saves money that can then be recycled into a locality instead of lost to pay for oil imports, then it makes sense to address transit use from an economic perspective. In other words, our urban form has an effect on our economic health. Places that are isolated, in the sense that transit options do not exist, will suffer, especially in T he outer suburbs are likely to suffer most in the c 2008) Public Transportation Demand Elasticity Historically, at low oil prices, public transportation use has been sho wn to be per acre, and for walking and bicycling to be dominant requires densities over 100 people and jobs per acre. Most new suburbs are rarely more than six or seven p eople and jobs per acre However, the increase in oil prices that was seen in the 2000s suggests that suggested that the demand elasticities for public transport relative to fuel prices

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25 (DEPTP) was 0.07, suggesting that a 1.0 per cent fuel price increase will produce a 0.07 per cent increase in public transport use. De Jong et al. suggest the long run DEPTP is 0.26. [However] historic demand elasticity f igure s may not be valid bases for assessments in circumstances where a long term expectation of sustained fuel cost f igure s of 14 percent transport increases during over a period that saw a 20 percent increase in the price of gasoline. (July and August 2005 vs 2004). This is a faster rate of increase by an order of magnitude of 10. Indeed common sense would dictate that the DEPTP would not follow a linear growth pattern, as cost increas es that households are able to absorb will not affect change in a way that costs that cannot be afforded will. Put differently by important factor in travel choices [than d Local Disadvantage and the Regressive City In addressing the impacts of rising transportation costs on the poor, it may seem obvious but it bears pointing out that, as according to the Center for Neighborhood Technology report Driven to Spend wer income households are particularly burdened by higher transportation costs since these expenditures claim a higher percentage of While people are able to adapt their housing choices to their budgets, trans portation is a more obligatory cost. Accordin g to the Deputy Assistant Secretary for transportation policy at t he US Dept. of Transportation households in transit rich neighborhoods spend 9% of their budgets on transport costs, spend 25% of their household budgets on transportation. Their same f igure s cite all

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26 households in the three zones spending an average o f 32% of their budgets on housing costs. This suggests that people are willing to spend equal proportions of their incomes on housing, and those that are priced into exurban markets are bearing t he highest transportation costs (Osborne, 2011) This is born e out in the fact that low income households spent 4% of their total budgets on gasoline (at 2002 gas prices) while a median income family spent only 2.3% according to Bureau of Labor Statistics data as cited by the Center for Neighborhood Technology. The se f igure s are despite the fact that low income households spend less in aggregate than medium and high earning a household earning $40,000 and a household earning twice that much is only about Therefore, an increase in the cost of gas (with current prices near double 2002 levels) will adversely affect low income groups. While h ousing has a threshold of unaffordability (30% of household budget), transportation has no such me asure. What is documented is that when the proportion of portation becomes too high, mode shifts to save money will happen, if the infrastructure is there. A major issue with our current cities that households who are forced to make trade offs between affordability and access to infrastructure and services Sipe, 2008) This means that the people who undertake the drive to qualify in order to gain housing affordability are those that will most need to save money through mode shifts once oil prices rise. Locational disadvantage refers to the fact that working class people are moving outside of the

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27 service provision they need. Wealthier communities tend to be closer to the city center suburban areas to attain home ownership becom e more car dependent as a result of those with the least capacity to pay Because of this reality, any effort to address oil vulnerability m ust address service provision to isolated working class communities, through affecting land use decisions to bring more working class affordable housing closer to the center and bringing transport options to those that are isolated in car dependent exurbs. It is troubling, therefore, to note that the processes underlying the regressive city segregation of various socio economic groups [are] key di mensions of recent urban c hange Dodson, Gleeson and Sipe also note that spatial polarization and spatial exclusion in the world. Addressing our economic segregation would lend itself to increasing resil ience to oil vulnerability. Social exclusion can be defined as economic factors preventing households from accessing social services or adequate housing. It is our dis short term market interests it will l ead rapidly to the divided city et al. 2009) Another way to measure this polarization and its effect on oil vulnera bility is to look at the transportation costs of each income quintile. Dodson and Sipe (2008) have shown that the middle fifth quintile and the next income bracket down spend the largest

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28 g families who middle class stands to be most affected by an increase in oil prices. The rise in the price of oil will not be fairly distributed under our current urban form. United States Outlook for Oil Resilience In a study prepared by the US G overnment A ccountability O ffice (2007) designed oduction peak was found to be lacking. The main issue in adapting to an alternative technology as identified in the GAO report was the time necessary to get supporting infrastructure in place. No alternative technology has the infrastructure necessary to supply more than a trivial amount of the overall demand in the United States. While government investment in alternative infrastructure has not been nonexistent, it has been piecemeal. Ethanol, biodiesel, electric, hydrogen, syngas (biomass), coal gasific ation, and natural gas vehicles were all evaluated by the report, and despite sizeable investments in more than one of the technologies, none would be quickly enough to fill in necessary supply in a peak oil scenario in which production is expected to decrease at an increasing rate each year. the equivalent of 34 percent of annual U.S. co nsumption of petroleum products in the 2025 through 2030 time frame. However, DOE also considers these projections optimistic it assumes that sufficient time and effort are dedicated to the development

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29 of these technologies to overcome the challenges they timeframe. The report also assessed the preparation of the US government to respond to a crisis of peak oil. It found that efforts to add ress peak oil are dispersed throughout different agencies without proper coordination between efforts. The main recommendation of the report is to have the Secretary of Energy work with all other government agencies and prioritize goals with those agencie s. Of key concern are about Future Oil Supply Makes It Important to Develop a Strategy for Addressing a attitude in which oil is not needed to be replaced wholesale, but rather alternatives (in all their forms, including demand reduction) only need to keep pace with decreases of production. It is acknowledged that the transition period in which alternative s are expected to reviewed indicate that most of the U.S. recessions in the post World War II era were preceded by oil supply shocks and the associated sudden rise in oil pr 2011 ) However, the study stops short of calling for a major effort to prevent a harsh transition, which would entail reduction of oil dependence before the peak. Rather, the report puts emphasis on having the necessary preparations for alte rnatives to step in post peak. Regardless, the report acknowledges the consequences of inaction to be States, as the largest consumer of oil and one of the nations most he avily dependent on

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30 oil for transportation, may be especially vulnerable among the industrialized nations of Oil Vulnerability Models The work of Jago Dodson and Neil Sipe has been instrumental in establishing oil vulnerability a s an area of study. They have had success around their native Australia in Australian cities. Throughout their body of work they have created and revised oil vulnerability assessments for Brisbane, Sydney, Melbourne, Adelaide, and Perth. While this thesis will run a VAMPIRE model, Dodson and Sipe began their work with the VIPER model. VIPE R stands for vulnerability index for petrol expense rises. In socioeconomic outcomes arising from increased fuel costs, [and serve as] a basic locational measure of oil vulnera One point that Dodson and Sipe repeatedly stress in their papers is that they expect their rudimentary models will be expanded upon and refined. Th ere has not been any efforts to date to do so but there exists many opportunities to achieve a more precise measurement of oil vulnerability. How this can be done will be more apparent after detailing the individual components of the model. The VIPER model is composed of three variables that are readily available in the Australian census. They are mapped at the Census Collection District the Australian equivalent of the census block. The variables are socioeconomic index for the area (SEIFA) the percent of households with 2 or more cars, and car use for the journey to work. This model then gives an average for the entire census block.

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31 S EIFA particularly useful to the measurement of oil vulnerability, such as age. However, the households are more financially capable of absorbing increasing transport costs than lower socioeconomic status households emaining two variables take into account car dependence. The household motor vehicle ownership levels are assumed to be tied to the overall demand for car travel. It is assumed that increased exposure to motor vehicle need indicates greater exposure to oil usage. The journey to work mode is a good indicator of auto dependence, as the rate of auto use is characteristically less for commutes than overall mode choice. Therefore, people are more likely to take an alternate mode for their commute than on other trips. The auto usage for journey to work therefore reflects dependence better. The VIPER model then weights the variables into a composite index. Each variable is first assigned a percentile rank, between 5 and 0, based on which percentile (10,25,50,75,90 ) range the values fall in for each city/study area. The socioeconomic indicator (SEIFA), however, is given equal weight with the two automobile dependence variable s, and its 0 5 value is doubled (Dodson and SIpe, 2007) The results of the VIPER analysis city displays clear spatial patterns that indicate a highly uneven distribution of potential vulne rability to oil price pressures The VIPER model was the first model used to measure oil dependence. It has since been refined by the same authors, Dodson and Sipe, who now use the VAMPIRE

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32 (vulnerability assessment for mortgage, pertroleum, and inflation risks and expenditure) index, an updated version of the VIPER, and the index used in this thesis. The VAMPIRE index modifies the variables found in the VIPER, keeping the two measures of car dependence, but changing the composite socioeconomic indicator (SEIFA) to a simple median household income. The simple measure of household income is uel and general price increases Finally, the VAMPIRE adds in the proportion of households with a mortgage. By using mortgage prevalence, household exposure to interest rate rises is taken into increases have resulted in higher mortg Outer ring suburbs have, on average, higher rates of mortgage prevalence. This combines with moderate incomes and greater transportation/oil costs to heighten disproportionate impacts of oil vulnerability The VA It is important to note, however, that it is a relative, not absolute, oil vulnerability, meaning it can be used for c omparative assessments of localities within, but not between cities Sipe, 2005) This analysis will show what the most impacted areas within each city will be, but does not address the general state of resiliency for each urban area. For examp le, the least resilient American city, Atlanta, is likely to not show any areas of even moderate resilience if it were compared to a place like New York City, but will still

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33 show a wide range if its districts are compared solely within the region. This swi tch from relative to absolute measurements of oil vulnerability are doubtless what Jago Dodson and Neil Sipe had in mind when they expressed a desire to see their models elaborated the scope of a thesis and therefore their established method will be used. Conclusion of Literature Review The analysis of the oil vulnerability and resili ence literature has shown that housing and transportation policy need to be addressed together. The financial impact of poor transport options are too large, at household, regional, and even national scales, to ignore. To address transport cost as a percen tage of income, the housing market must play a role, along with the expansion of transportation options. Diversity must be achieved where there is economic segregation; diversity must be achieved where there are fewest transportation options. Rising oil pr ices will target these weaknesses in our society and magnify their impacts. Without bringing transportation into the equation of housing affordability, transportation policy will serve to strengthen these structural weaknesses.

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34 CHAPTER 3 METHODOLOGY The 4 statistics used in the VAMPIRE model are percent of households with 2 or more cars, percent of commutes to work by auto, median area income, and percent of households with a mortgage. These statistics were collected at the census tract level from the US C ensus Bureau at the website dataferrett.census.gov. The statistics were taken from the aggregate level American Community Survey 5 year summary file, from the period 2006 2010. Each of the 4 statistics were given a score on a scale from 0 to 5, with 0 bein g the best performance and 5 the worst. The score ranges used were: 0 9 % 0 10 24% 1 25 49% 2 50 74% 3 75 89% 4 90 100% 5 This range was used for all statistics except income, where the highest incomes were given the lowest scores. The hi ghest income in the county was set to 100%, and all incomes were calculated as a percent of that amount. The remaining variables are calculated as a simple percentage, the percentage amount of households with the desired characteristic in an area is simpl y converted straight to a VAMPIRE score. Each of the scores is then added to give an overall VAMPIRE score, with a possible range of 0 to 30. The variables are divided into 3 categories; each assigned an

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35 equal weight, 1/3 of the total. The 3 categories are income, mortgage tenure, and car dependence. Car dependence is composed of two variables, each contributing half of the weight of the category. Therefore mortgage and income each contribute a possible 10 points each to the total, and journey to work and c ar ownership each contribute a possible 5 points each to the total.

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36 CHAPTER 4 TAMPA BAY AND HILLSBOROUGH COUNTY CONTEXT AND BACKGROUND Figure 4 1 is a map of the Tampa Bay area, showing the different employment clusters. What stands out is the dispersion of the employment clusters. While the traditional downtowns of both Tampa and St. Pete rsburg are employment clusters themselves, the rest of the activity is dispersed throughout the region with clusters of employment in North Saint Pete rsburg /Clearwater a nd West Tampa. What Figure 4 1 shows, among other things, is a disconnect between employment clusters and transportation infrastructure. With the exception of the city centers of Tampa and St. Petersburg (themselves not strong clusters), the employment clu sters are not served by transit and are somewhat removed from the interstate system. This results in the high transportation costs shown throughout much of the central Tampa area. Hil lsborough County is: Population, 2010 1,229,226 Population, percent change, 2000 to 2010 23.1% Median household income, 2009 $47,129 Homeownership rate, 2005 2009 63.5% High school graduates, percent of persons age 25+, 2005 2009 85.4% Bachelor's degree or higher, pct of persons age 25+, 2005 2009 28.7% Mean travel time to work (minutes), workers age 16+, 2005 2009 25.7 The Tampa Bay region of Florida is an unusual metropolitan area. It has high tech ry educated population. Also, despite a non exceptional median income, housing and transportation costs combined are th e highest in the nation (Lipman 2006 ) Transportation infrastructure does not serve the population well:

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37 T his is one of the few metropo litan areas (Miami being the other) where increases in the local concentration of affordable housing are associated with increased transportation costs. This metropolitan area is also rather unique in that housing costs are negatively a ssociated with job d ensity (CNT 2005) The profile in Figure 4 2 done by the Center for Neighborhood Technology show s there to be a large burden of transportation costs, 31% of households are co mbination). In addition, it shows that very few of the population lives near where they work (14%), this is reflected in the relatively high commute times. Tampa Bay and Hillsborough County were selected for this analysis because the Tampa region is a uni que area with a broad mix of economic activity, and a chaotic transportation and land use pattern that has created extremes of unaffordability. In a table listing the most expensive regions by combined housing and transportation costs, the report Driven to Spend states that Tampa and Miami are the least affordable share of incomes, so that higher incomes will is still a good indicator of how the region works for its own economic situation. Transportation costs in the form of oil payments have no local multiplier effect. In an era where it is necessary to plan for transportation costs to increase due to tightening global oil markets, this lack of affordability needs to be addressed in an overarching plan that takes into account the location of jobs, p eople, and how they are linked.

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38 In areas where driving is the only way to get around, cutting back on driving can ot spending money on local entertainment or restaurants. In times [of increasing transportation costs], areas where people can walk or take transit to places of commerce may be better off. Higher density places with better transit options are losing less p er household than those with higher car ownership and lower transit use (CNT, 2005) Tampa Bay has a chaotic transportation and land use pattern that has resulted in it being the least affordable metropolitan area in the nation for its citizens to live a nd get around in. People do not live near where they work, and worse yet, do not have good access to their centers of employment. However, these employment centers are widely dispersed throughout the region. There is a small, expanding light rail system th at could be used in a program to address linkages between centers of residence and job clusters. The map in Figure 4 3 shows the first VAMPIRE statistic, median household income in Hillsborough County. What this map shows is higher incomes in the north of the county and south through the center of the county. There are pockets of high outward into the suburban Eastern part of the county. There are low income areas in the center of th The next VAMPIRE statistic to be used is the percent of households with a mortgage. The map in Figure 4 4 shows an overall high rate of mortgages throughout

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39 the area, with low rates of mortgages roughly corresponding with low income areas. This map differs from the Australian cities analyzed by Jago and Dodson in that the outer areas in the Eastern part of the coun ty have moderate levels of mortgages, while Australian cities all have higher rates in the periphery. The same general pattern of high mortgages follows the high income pattern, with high rates in the north, and extending south in the center of the county Parts of central Tampa Bay and the South of the county have low rates of mortgages. By and large, the combinations of higher incomes with increased rates of mortgages will have a moderating effect on oil vulnerability ratings. It will be the exceptions to this trend that will stick out either positively or negatively, but based on these two statistics, (2/3rds of the weight of the model) most census tracts will score in the mid ran ge of vulnerability. T his is due to the aforementioned atypical spatial di stribution of jobs and housing in which there is no dominant center, and furthermore there is no evidence of a strong historical center, in which concentric patterns of development would take place radiating out from the center. This concentric model is co Australia, but not in the United States, development The next statistics used by the VAMPIRE address car dependence. Percent of houses with 2 or more cars is shown in Figure 4 5 percent of houses with 3 cars in Figure 4 6 and percent of houses with 4 or more cars in Figure 4 7 These three

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40 In these maps, Tampa Bay is f inally conforming to what would be expected of it. The areas near the center of the city have lower rates of high auto ownership, while those areas least served by transit, in the Eastern part of the county, have the highest rates of auto ownership. This i s consistent with the expectations and results of Jago and Dodson in Australia. However, the results may be skewed by the widespread ownership of 2 cars per household. While 2 or more cars was sufficient to achieve a stark spatial segregation in Australia, the same general pattern does not emerge for Tampa Bay until 3 and 4 or more cars is taken into account. Therefore the VAMPIRE model may be distorted by the cultural difference of car ownership generally being higher in the United States. This statistic once again shows an overall moderate amount of vulnerability, by looking into the data further, we can see separation, but the VAMPIRE model only cares about 2 or more cars per household. A household with 2 cars is counted the same as one with 4 or more ca rs in the model. However, the amount of households with 2 or more cars may be adequate in that the amount of households with only 2 cars closer to the center of Tampa Bay may be constrained by other factors such as space and parking availability, and it is possible that their auto dependence is high despite showing less extreme rates of auto ownership. In this light, the 2 or more statistic would be adequate. Regardless to change, the model would require further research. It is quite possible, especially gi its size (even by US standards), that the widespread oil vulnerability shown in this statistic is accurate, and any desire for spatial differentiation is arbitrary.

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41 The final statistic used i n the VAMPIRE model is the percent of commutes done by auto. It is shown in Figure 4 8 The census statistic used includes car, truck, or van. Overall, Tampa Bay has a very high rate of auto dependent commuting, as would be expected given its poor public transportation system. What is not to be expected (but again in line with its spatial mish mash development shown in the other statistics) is that proximity to the center of Tampa Bay does not lower the rate of commuting by auto. I t is expected that this s tatistic would the existence of the small light rail system near the center of the city. While the area with light rail is on the lower side of auto commuting, it does not stand out in any pattern. Instea d it fits into an oddly distributed patch work of pockets of lower auto commuting throughout the region. What does stand out with this statistic is the pattern of high auto commute rates in the higher income areas. While the northern part of the county ha s mixed rates of high auto commuting, the high income area extending south through the center of the county has the highest rates. It would seem that income drives rates of commuting by auto, but there are many exceptions. The northern, high income area ha s exceptions of lower auto commuting, and the moderate income south eastern area has high rates of auto commuting. While the center of Tampa Bay does not look as one would expect, the south east does behave like the car dependent suburban area that would be expected.

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42 Figure 4 1 Tampa: average household expenditure s on housing and transportation as a percentage of average tract income, 2000. Center for Neighborhood Technology. (CNT) Surface Transportation Policy Project. ( 2005 )

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43 Figure 4 2 Tampa, FL profile Center for Neighborhood Technology. (CNT) to Surface Transportation Policy Project. ( 2005 ) Figure 4 3 Median household inco me for Hillsborough County by census tract Prepared with data from the US Census

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44 Figure 4 4 Percent of households with a mortgage for Hillsborough C ounty by census tract Prepared with data from the US Census

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45 Figure 4 5 Percent of households with 2 vehicles for Hillsborough County by census tract Prepared with data from the US Census

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46 Figure 4 6 Percent of households with 3 vehicles for Hillsborough County by census tract. Prepared with data from the US Census

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47 Figure 4 7 Percent of households with 4 or more vehicles for Hillsborough County by census tract Prepared with data from the US Census

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48 Figure 4 8 Percent of commutes by car, truck, or van for Hillsborough County by census tract. Prepared with data from the US Census

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49 CHAPTER 5 DISCUSSION OF RESULTS Results of Hill sborough VAMPIRE and Comparison to Melbourne VAMPIRE The Melbourne VAMPIRE (shown in Figure 5 1 ), mimics what the hypothesis expect ed of the Tampa Bay VAMPIRE (shown in Figure 5 2 ) There is low vulnerability close to the city center, as more renters, more transit options, and a mix of incomes keep vulnerability very low. The range of vulnerability for Melbourne is very wide, however, from one to 22. The high end of the range is again in the expected areas, the outer suburban areas. While there are pockets of low vulnerability exceptions, there are no areas of high vulnerability within the city center, and the majority of the first concentric circle surrounding the center is of a moderate vulnerability. This gives a very clear policy direction, as Jago and D odson (2008) have argued that extending services to the outer suburbs is of paramount importance. The results of mapping oil and mortgage vulnerability show the hypothesis to be wrong on two key points. First is the expectation for there to be a circular p attern to the data in which rings of vulnerability radiate from the city center. The opposite is in fact true; there are more moderate vulnerabilities in the outer periphery of the city center both to the north and to the south. Because of the lack of this organizational pattern, the second hypothesized point, core than those shown in the Australian cities, is also wrong. A final hypothesized point, that there wou ld be a greater overall oil vulnerability is by all standards of measurement correct. The range of vulnerability on the VAMPIRE score for Tampa Bay extends from 12 to 24, while the Melbourne VAMPIRE has a

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50 range from 1 to 22. In addition, there are less tra cts that score in the low range end of two vulnerability classes. Discussion The Hillsborough VAMPIRE analysis shows a largely unorganized dispersal of oil vulnerabilit y, especially when compared to the Melbourne or other Australian analyses. There are pockets of low vulnerability in the high income northern areas, and somewhat extending south into the high income swath through the center of the county. Perhaps most surp rising is the relatively low vulnerabilities in the southernmost part of the county, south of the Brandon area. What can be considered the center of Tampa Bay has pockets of low vulnerability, especially the area due west of downtown. Finally, the area bet ween Brandon and Tampa shows high vulnerability. The vulnerabilities shown in the index are a composite of the different variables used, but it is possible to analyze the data based on what different variables are contributing to the score. For example th e low income areas with high vulnerabilities are north and east of downtown Tampa. The more car dependent vulnerabilities are in the east of the county, and the more mortgage related vulnerabilities are in the higher income northern part of the county. A s tark contrast between the Tampa Bay and the Australian cities is the lack of a pattern in which vulnerability increases as the city extends farther from the center. The outline census tracts show a moderate level of vulnerability, and overall fit into the seemingly random manner in which vulnerability is dispersed throughout the county. Anoth er way in which the results run counter to expectations is the dispersal of very high vulnerability areas. Again, in Australian cities these are almost exclusively in

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51 t he periphery of the urban area, however in Tampa they are evenly distributed throughout the region. Perhaps most surprisingly of all is the existence of very high vulnerabilities near the center of Tampa Bay. Overall, the main takeaway from the mapping of general dispersal of moderate to high vulnerabilities, with the better performing clusters in the North West, the South West and areas to the West of the city center. Beyond these weak clusters, there is no clear pattern to the results. This general, random distribution makes more sense when viewed in a context of the bewildering distribution of transit affordability and jobs clusters. There is no real center to Tampa Bay in more than a symbolic sense. There is no transit system that exerts a significant control over the statistics used in the VAMPIRE model. Tampa Bay and Hillsborough County are part of a larger region that the data suggest has many

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52 Figure 5 1 Melbourne VAMPIRE. urbs:

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53 Figure 5 2 Hillsborough VAMPIRE Prepared with data from the US Census.

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54 CHAPTER 6 POLICY DISCUSSION In examining this data for policy implications, it is first helpful to frame how to look at the data. The measurement of oil vulner ability is not an exact process, and it has no correlation to expected quantities of damages. Instead, what it does is compare the different tracts within the region against the rest of the region. This is useful in determining what the most impacted areas are likely to be, as well as where negative effects are likely to first be seen. Because of these reasons, the middle range vulnerability data are less useful from a policy formation standpoint than the two extremes. The low vulnerability areas warrant a most in need of intervention. From a policy perspective, however, not all the vulnerabilities are of the most pressing need. The income based vulnerabilities to the nort h and east of downtown are of more concern than the mortgage=based vulnerabilities in the northern parts of the county. Tampa Bay has a general oil vulnerability problem. There is a distinct lack of transit options. Jobs are dispersed in a way that sees th e majority of commuters, even those living in the city, travelling a relatively large distance to get to work. Housing is not attractive close to the job centers. In effect the disparate job centers of Hillsborough County have all experienced a decentraliz ation process of their own that has resulted in substandard housing options. To address this, Tampa Bay needs to have an accurate accounting of jobs and transit, and make a concerted effort to link the two. An analysis of the Tampa Bay

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55 transportation system shows the existing rail system to be linke d to cultural amenities, and historic city attractions. The main job clusters, however, are along the 275 corridor extending north. The streetcar and transit infrastructure serving downtown starts South of 275 and continues South to the Canal District, wit h connections to the Ybor streetcar. Furthermore, Figure 4 1 shows the 275 corridor and North of the 275 corridor to suffer from both above average housing and transportation costs, and the VAMPIRE analysis has shown the area to fit into the greater Tampa costs. The area has a large amount of built in demand and it presents itself as the best possible way to address the Tampa Bay are viability of extending high quality transit north. ave been drawn to widen Interstate 275 in the Northeast Corridor (North of Downtown) to a cross section that is 10 12 lanes wide. Even with these added lanes, by 2035 this section of roadway would still be 28% over capacity; in order to make this roadway f it capacity it would have to be expanded to 16 This inadequate transportation infrastructure has resulted in high oil vulnerability and transportation costs precisely in the place where those qualities should be the be st in the region. To use this study for policy formation, it is important to understand why the model has given the results that it has. High rates of auto dependence in the poorer areas would present the most pressing areas of high oil vulnerability. The se areas are concentrated north and east of downtown Tampa. As it turns out, the areas that would

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56 most be served by the connection of jobs to housing are precisely the areas that are vulnerable due to higher car dependence coupled with low incomes. These a re the areas of highest priority to address oil dependence in.

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57 CHAPTER 7 LIMITATIONS OF THE STUDY AND RECOMMENDATIONS FOR FURTHER RESEARCH uncertainty over the future cost of fuel is matched by uncertainty about the nature of hou sehold response Another major limitation of the study is in the statistics used in the model. Dodson and Sipe (2007), l information about household socio economic status, vehicle and travel costs, and the The model could be refined by an attempt to measure more accurately the hous ehold level constraints. Such a study would likely take into account housing/travel well as transit accessibility and capacity. It could be assumed that given accessible transit service and sufficient capacity, individuals under severe transportation cost stress would make a modal change. In addition, measuring the VMT at a household level, perhaps though a length of commute analysis, would give more detail about not just the need for auto travel, but the need for how much auto travel. This could then be weighted against incomes to give a clearer assessment, than a basic median household income, of the amount of stress a household would experience if gas prices increased s disposable income is sacrificed to housing costs, underlines this concept, though it

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58 The other statist ics used have limitations worth noting. The mortgage variable has the most pressing limitation, given the adaptation of the model from Australia to the United States. The theory behind using the mortgage variable is based on the exposure of areas dependen t on homeownership to interest rate increases. As oil prices increase, inflation is fuelled. The typical response of a central bank is to combat inflation by increasing interest rates. This for example is what happened in the mid 2000s oil price spike in Australia. The United States, however, responded atypically. The Fed decided to actually lower interest rates, in an effort to boost Wall Street and stimulate consumer demand. When oil price spikes result in lowered interest rates, the mortgage statistic l oses its relevance to the model. The lowered interest rates result in increased inflation, which affects equally across society, magnifying the importance of the income variable in the model. Housing tenure in such a situation does not describe the necessa ry process. The mortgage statistic highlights the issues in adapting an Australian study to the United States. The nature of how their cities operate is different. They have not l city. This disinvestment associated with the suburbanization of our nation has given our cities a unique development pattern that would demand a tailor made approach to their study in order to get the best idea of the true nature of oil vulnerability. A good example of how the different realities found in the different countries can affect the study of oil vulnerability is found in the measurement of car dependence. The first of the two statistics used was the percent of households with 2 or more cars. I n Australian cities this generally results in higher rates in the periphery and lower rates

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59 closer to the central city. In Hillsborough county, however, this statistic produced overall high rates throughout the county. By breaking down the statistic into h ouseholds with 3 cars, and households with 4 or more cars, the patterns start to emulate the Australian results, with higher levels outside of the city center. Perhaps the relative increase in cars per household outside the city center does indeed reflect an increased need for auto based travel in those areas, but the threshold being set at 2 or more cars misses this nuance. 2 cars per household appears to be a cultural norm, and lower amounts appear to be found in the lower income areas, which does not app ear to reflect a travel, just an ability to pay for it. It is unlikely that central city areas with lower rates of households with 2 or more vehicles are that way because of a significant difference in transit provision, beca is largely unused throughout the region. In impoverished areas where transit ridership is higher and car ownership is lower, there is no basis to declare that transit provision is inherently better, or that car dependence is st ructurally different. cars per household. Household size and income may play just as large a role as A limitation of this thesis study is that of scope. By looking at only Hillsborough County, there is the potential that greater patterns could exist. However, expanded scope is unnecessary to determine that what is shown is valid. Both city cen ter and performance is concerned, speaks loudly for how the greater area is expected to behave.

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60 CHAPTER 8 CONCLUSION This thesis ran a VAMPIRE model in Hillsborough County, FL. The results were not entirely consistent with the assumptions made based on previous studies with the model. The fact that the results are contrary to the assumptions that can be taken from the results of the Australian VAMPIRE studies adds to the vali dity of the model, however, when the different contexts are taken into account. Given the scale of Tampa strong centers and weak peripheries, would be called into q uestion given the drastically different nature of Tampa Bay. What is seen in the Hillsborough County results is explained in the assessment made of Tampa Bay. The model has shown there to be a strong need to shore up transit service in the city of Tampa B ay, along the 275 corridor. This would begin to address the poor linkages of jobs to housing in the region, as well as target the most distressed high vulnerability areas. It is possible to differentiate between the different high vulnerability areas with due to low incomes are deemed to be of greater need than the areas of high mortgage prevalence. It is not a coincidence that these areas are also the jobs clusters for the region, because of the negative correlation to affordability and job density in the region. While there is room for improvement of the model, the results are clear enough to suggest that Tampa Bay begin to address its weakness, given that the core of the city is underperforming in energy resilience.

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61 There are issues in adapting the model to the United States, however, and the model could be refined to better take account of the United States context more appropriately. The following are the lessons gleaned from this analysis that could be used to tailor further study into the topic of oil vulnerability in the United States. A more accurate of public transportation accessibility, and a more accurate representation of VMTs at the household level would provide an improved understanding of car dependence in the American city. Due to differences in handling the interest rate response to inflationary pressure between the United States and Australia, the use of mortgage prevalence is not an effective measu re of oil vulnerability in the United States. Finally, while the use of median area income is a quality measure of capacity to absorb price increases at the household level, the topic could be clarified with the use of a housing and transportation combine d affordability assessment. This would more accurately reflect the ability of households to adjust their budgets due to rising oil costs. In conclusion, the adaptation of the Australian based VAMPIRE model has shown the potential for a more targeted Unite d States based study of the topic of oil vulnerability. It has given the basis to give future recommendations on what a model should attempt to do given the unique differences of the typical American city when compared to the rest of the industrialized wor ld. Given the fact that long term oil costs are going to rise, absent a major technological breakthrough that does not appear to be on the horizon, addressing the oil resilience of Tampa Bay is a realistic goal for policy formation. Global supplies of oil production are flat, and global demand is rising. It is the responsibility of the planner to

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62 foresee the consequences of our development decisions and work to alleviate their impacts. By not addressing our oil consumption patterns, and our ability to chang e

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63 APPENDIX A HILLSBOROUGH VAMPIRE DATA

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64 Census Tract Total Households 2 Vehicles 3 Vehicles 4 + Vehicles 2 + Vehicles JTW Auto Median Income Mortgage 1.01 1115 366 96 34 1450 1354 34693 209 1.02 2379 596 288 57 2572 2290 32721 589 2.01 1401 256 70 0 1618 1363 27745 308 2.02 1631 622 191 80 2408 1929 37361 649 3 2533 769 306 164 3186 3048 34557 1103 4.01 930 298 111 33 918 788 32100 329 4.02 1049 355 47 6 1003 912 31 733 467 5 1595 521 176 136 1865 1755 51127 1073 6.01 1770 622 257 25 2392 2200 37021 538 6.02 753 238 180 0 1104 947 48984 331 7 2109 512 243 32 1941 1734 25999 601 8 1149 439 141 48 1602 1486 41291 710 9.01 896 311 41 64 1184 1131 31907 211 9.02 20 15 546 42 0 1840 1721 27168 344 10.01 1220 266 42 0 1185 1071 30365 159 10.02 1880 434 152 56 1626 1364 26675 807 11 1010 394 147 14 1180 1138 55992 546 12 1009 316 22 0 1050 992 26565 344 13 2386 813 302 69 2849 2596 37474 1187 14 1809 529 179 16 19 24 1654 23812 708 15 1155 454 111 64 1687 1403 52128 655 16 975 326 110 14 1163 1079 48640 637 17 1726 647 228 26 1927 1773 35797 924 18 1295 434 119 35 1428 1273 27417 554 19 1080 402 90 50 1089 885 24960 520 20 837 166 113 41 944 833 32824 397 21 999 429 49 23 1156 991 34943 563 22 658 363 75 14 1061 1007 45821 391 23 1290 340 151 5 1522 1278 37840 522 24 1814 452 63 100 1885 1748 31844 505 25 2700 681 109 114 3034 2874 30000 467 26 562 159 39 0 717 557 27245 93 27 2554 656 268 27 2602 2319 3 5222 1022 28 1433 549 146 38 1703 1554 47159 946

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65 29 689 289 65 15 830 772 39563 265 30 981 211 23 13 739 620 16750 273 31 829 248 55 36 774 618 22621 238 32 877 271 109 0 1057 878 29018 227 33 626 160 6 34 636 447 25000 197 34 919 206 64 0 705 575 2 0506 292 35 843 85 104 0 742 466 22219 157 36 1350 331 140 86 1959 1568 31991 546 37 472 114 8 7 296 245 31932 109 38 437 133 56 0 437 353 26750 91 39 1093 225 0 0 923 714 31169 131 40 224 0 0 0 108 57 7924 0 41 726 150 19 32 393 320 13487 99 42 45 3 113 64 0 443 360 44107 228 43 1343 27 27 0 531 324 8544 14 44 768 184 22 5 567 503 22374 230 45 1477 429 119 25 1254 1095 31430 632 46 1308 327 29 35 1532 1303 34387 404 47 1274 413 91 41 1414 1326 42958 387 48 1718 562 185 39 1973 1788 51121 809 49 2122 606 173 0 2642 2122 55139 770 50 921 244 36 0 1439 970 19928 149 51.01 580 89 0 0 403 308 34750 139 51.02 1914 720 99 10 2225 1817 103659 854 53.01 568 246 31 38 808 753 95746 97 53.02 686 216 35 14 911 817 43767 214 54.01 2443 1051 271 93 29 67 2528 84608 1104 55 1095 309 50 29 1150 935 54974 467 57 2007 598 38 0 1655 1556 46188 735 58 2017 788 288 92 2461 2196 69239 1079 59 2162 1077 270 132 2511 2220 120615 1414 60 1957 1026 230 83 2630 2113 128036 1032 61.01 1523 526 166 32 1718 1407 76691 658 61.03 2236 677 127 0 2028 1920 51005 901 62 1528 809 159 19 2135 1943 101554 979 63 1465 717 166 30 1681 1494 77716 998 64 1451 717 273 78 1926 1689 117066 874 65.01 1676 656 144 6 1892 1783 40370 431

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66 65.02 1357 315 26 0 1265 1069 31042 245 66 1848 653 49 29 1810 1650 49008 818 67 2377 1019 347 93 3040 2529 93255 1307 68.01 2092 729 211 82 2241 1927 47571 765 68.02 1504 693 96 21 1512 1391 63456 770 69 2222 878 222 32 2546 2372 50192 1040 70.01 1492 513 133 26 1686 1415 47564 505 70.0 2 1136 481 38 0 1417 1247 32055 152 71.02 1211 341 22 0 1389 1312 49457 41 71.03 1473 574 125 99 1877 1719 63688 910 72 1337 468 124 19 1689 1404 44181 709 73 398 227 0 0 745 504 51346 24 101.03 1532 659 335 196 2316 2051 59597 960 101.05 1560 690 27 2 101 2257 2102 49944 842 101.06 1623 799 225 179 2145 1904 52321 992 101.07 1608 479 150 55 1284 1211 42500 499 101.08 770 377 97 60 902 778 54400 346 102.03 705 213 97 25 559 504 21125 218 102.04 1784 611 181 134 2389 2169 49026 744 102.05 2487 140 4 259 96 3469 3165 84639 1537 102.09 2939 1651 305 128 4141 3727 71797 1804 102.1 1213 533 89 39 1572 1444 53495 352 102.11 1947 939 256 84 2566 2152 68818 1143 102.12 1751 1237 221 95 2418 2026 119476 1319 102.13 791 345 129 49 994 971 44107 290 102 .14 107 30 39 0 137 137 46806 87 103.03 1117 321 102 26 910 830 32146 310 103.04 1237 561 246 130 1788 1663 58616 693 103.05 1320 609 109 33 1546 1382 42652 492 104.01 2136 662 311 54 2883 2762 43039 801 104.02 2049 673 160 45 2316 2183 31004 574 105 .01 2292 808 85 20 2321 2112 32194 928 105.02 880 368 62 16 1174 969 42500 427 106 1334 452 115 53 1459 1231 50086 492 107.01 2431 858 325 57 3171 2949 51426 1008 107.02 1111 533 80 26 1558 1386 44989 447 108.05 1597 310 64 9 1462 1155 22539 203 108. 08 1096 227 24 17 1116 940 21000 46

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67 108.09 1005 328 165 11 1227 1111 31427 182 108.1 1766 625 233 27 2384 2243 45667 134 108.11 2760 972 228 7 3361 3073 40780 661 108.12 2368 634 135 62 2745 2217 24367 200 108.13 2105 648 200 7 2543 2169 27461 0 108. 14 1198 146 0 68 772 666 21799 117 108.15 799 89 0 13 888 592 31599 13 108.16 1540 146 84 17 1044 851 12906 0 108.17 1357 238 47 20 1435 1265 21775 193 108.18 1985 388 54 0 2283 1792 19415 10 109 10 10 0 0 1485 580 null 0 110.03 2158 549 244 73 2185 1880 28598 514 110.05 1011 513 240 27 1227 1123 92083 739 110.06 1855 884 430 115 2800 2540 74174 1381 110.07 1293 627 237 68 1671 1534 74732 825 110.08 2628 1380 269 24 3261 2943 62692 1173 110.1 1921 692 107 91 2629 2490 43112 839 110.12 1696 1026 214 47 2409 2207 101557 1291 110.13 1232 751 125 12 1853 1701 62270 564 110.14 288 174 55 0 466 451 59583 0 110.15 2683 929 265 116 3598 2961 40860 567 110.16 1215 785 53 17 1641 1397 58920 454 111.03 1213 711 201 76 1844 1705 82225 807 111.06 961 30 2 48 1 1031 968 32620 285 111.07 2555 1017 509 160 3758 3306 73114 1266 111.08 806 286 36 22 671 546 37639 265 111.09 1028 623 163 39 1591 1507 88611 653 112.03 1334 679 74 34 1616 1397 57385 609 112.04 2681 1000 299 114 2818 2618 42601 1053 112.05 1 229 550 253 18 1518 1432 48229 609 112.06 1130 385 78 9 1176 1044 36675 438 113.01 1235 503 188 39 1501 1421 63464 582 113.03 1347 519 85 0 1386 1209 53153 483 113.04 2081 946 320 35 2509 2214 66104 1149 114.07 1098 577 33 36 1175 1022 79537 538 114. 08 1001 512 117 37 1552 1434 75531 723 114.09 1365 573 194 37 1463 1278 67266 708 11 4 1 2031 1249 348 79 2848 2346 70740 1607

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69 117.09 341 75 74 0 404 404 45089 68 117.1 3053 1086 197 0 3729 3305 51730 1138 117.12 2180 946 294 44 2638 2433 54254 947 118.02 2267 863 381 49 2828 2486 34436 874 118.03 2918 858 174 12 3289 3190 31590 794 118.04 1846 731 298 155 2618 2464 39335 936 119.01 2023 682 53 72 2601 2275 38652 306 11902 2699 1196 248 62 3199 2911 39675 1023 11904 949 249 29 0 1176 1095 33079 12 11905 493 227 28 0 642 585 38646 144 11906 1726 657 154 82 2092 1959 40762 625 12001 887 302 168 12 1078 1022 37578 381 12002 1264 467 157 47 1458 1278 38048 564 12103 2174 802 76 87 2860 2674 47426 582 12104 2491 858 223 66 3010 2809 37226 776 12106 1525 740 278 119 2225 2010 68514 1030 12107 1751 799 125 50 2271 2096 43442 773 12108 1125 380 1 28 45 1436 1362 50586 717 12206 2005 1030 288 79 2782 2629 59276 1508 12207 2497 1159 545 148 3591 3473 71513 1789 12208 2109 1355 195 50 2769 2607 62468 1541 12209 1519 772 203 117 2141 1852 74609 1030 12210 1658 684 124 0 1507 1361 43081 669 12211 100 67 0 0 152 152 46481 53 12212 1843 634 36 54 2048 1928 37559 475 12213 1681 866 217 69 2579 2358 57304 1097 12301 2209 737 465 51 2783 2604 55956 984 12303 1138 509 96 135 1777 1628 55735 467 12304 1675 730 154 79 2188 1921 54047 823 12401 1597 6 34 209 55 2061 1647 38955 462 12402 769 429 102 56 1252 1055 30958 204 12403 1418 793 177 102 1838 1688 44949 802 12501 1528 644 234 69 2005 1890 49100 655 12503 1820 827 125 64 2083 1845 55111 761 12504 1080 539 299 57 1649 1544 67012 750 12600 1739 630 207 102 2234 2071 47609 615 12701 2034 794 150 38 2709 2514 33500 749 12702 1232 514 81 38 1411 1273 41000 504

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70 12800 1304 549 190 81 1670 1576 48293 714 12900 944 227 92 12 1080 964 27979 328 13001 1218 611 163 14 1672 1550 55285 521 13002 1363 443 151 130 2158 1901 46685 719 13003 1049 432 171 41 1543 1162 40450 374 13004 1011 547 137 132 1525 1365 55284 578 13100 884 333 222 71 1498 1290 41859 373 13203 1153 453 187 68 1533 1399 87063 790 13204 1378 508 70 14 1119 945 44025 680 13205 2627 1346 415 248 3980 3684 79692 2030 13206 1960 1121 372 77 2782 2519 84480 1651 13207 1204 500 189 142 1746 1598 64828 630 13208 1645 1011 282 124 2577 2127 114274 1306 13305 1416 788 269 77 2114 1973 89143 967 13307 1566 526 70 0 1838 1713 41364 383 13310 1038 471 217 92 2123 1994 87667 638 13311 1205 620 47 26 1576 1432 54740 511 13312 1491 645 270 54 2183 2079 65236 981 13313 1750 762 314 176 2382 2183 73194 1088 13314 1025 530 147 79 1445 1375 65354 728 13315 965 327 70 4 1352 1216 52307 116 13316 1053 363 35 9 1300 1248 36857 0 13317 780 245 0 0 1026 957 51071 0 13318 1712 722 92 0 2206 1986 51709 71 13319 1758 521 129 33 2436 2135 56281 633 13320 1539 676 165 12 2027 1948 54936 558 13321 2678 1333 279 77 3278 3118 50131 1583 13322 2686 1244 197 43 3444 3143 43879 1244 13406 365 187 46 0 456 414 42446 206 13407 2213 1200 500 174 3409 2950 75313 1555 13409 1558 928 237 53 2049 1764 80873 1134 13410 1762 601 173 34 2384 2194 45437 819 13411 813 293 150 27 847 841 48615 339 13412 1282 628 237 71 1826 1812 67065 771 13413 1153 682 149 90 1772 1714 98831 841 13414 2131 1105 348 193 3335 2992 86975 1537 13415 1139 646 203 55 1870 1749 98724 932 13501 1170 343 107 43 1213 1068 31778 350

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71 13503 1229 406 160 61 1695 1560 35220 501 13504 1316 696 231 28 2173 2019 43333 846 13505 1060 383 65 75 1318 1188 32353 611 13602 673 362 52 49 820 711 41801 266 13604 392 146 69 9 426 426 34778 68 13702 2215 1000 186 66 2614 2570 44714 1061 13703 1707 740 49 36 1866 1700 33867 689 13704 2537 14 32 274 72 3644 3540 78894 1607 13801 744 180 104 40 1018 964 28929 230 13802 893 254 100 52 1180 1068 41348 235 13803 563 204 66 54 726 726 32073 238 13804 2137 1118 250 50 2763 2666 55151 1436 13806 494 292 78 13 691 691 65926 272 13807 1932 945 292 27 2672 2535 62430 1281 13903 1080 468 256 61 1535 1453 52500 576 13907 746 296 128 75 1016 934 46019 371 13908 2837 1259 328 14 3366 3197 53942 1384 13912 1068 448 169 172 1560 1374 48387 687 13913 1137 416 162 22 1342 1214 36659 433 13914 882 401 135 46 1145 1068 29670 461 13915 1907 763 298 179 2711 2399 64511 1410 13916 2050 1028 248 13 2851 2807 75152 1536 13917 1889 949 361 132 2466 2312 63653 1423 13918 607 216 104 82 820 784 69572 442 13919 2100 952 481 174 3211 2944 85338 1610 13920 12 18 503 412 77 1762 1698 86615 733 13921 598 284 166 0 1014 953 117025 392 13922 1562 924 230 15 2364 2115 89583 1205 13923 1253 945 140 57 1847 1522 105531 996 14002 1116 372 92 14 880 795 42727 329 14003 580 197 104 44 699 617 66990 362 14007 489 13 8 114 11 633 624 79063 300 14008 2057 344 16 0 363 340 44665 503 14009 686 269 0 31 423 357 46786 215 14010 1528 429 27 0 416 402 37875 473 14011 2306 278 12 0 343 297 38765 627 14012 1579 246 11 0 220 146 48717 519 14013 452 118 71 19 517 443 48125 121

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72 14014 1437 162 14 0 160 121 37219 169 14015 733 188 13 0 264 195 50673 325 14016 484 112 0 0 105 55 70192 166 14104 1055 230 0 9 444 410 32639 178 14106 1396 624 128 16 1544 1464 42297 665 14108 1820 876 129 79 1922 1757 39136 813 14109 1186 327 205 114 1606 1566 43651 675 14117 1244 475 331 0 1521 1438 80526 747 14118 1166 592 250 20 1378 1276 93750 771 14119 1407 836 245 11 2367 2146 75912 1031 14121 944 391 152 13 1063 967 87798 577 14122 825 244 147 34 1368 1267 47571 380 980100 0 0 0 0 0 0 null 0 980200 0 0 0 0 0 0 null 0 980300 43 0 0 0 43 43 null 0 980400 46 19 9 0 66 66 93333 27 980500 0 0 0 0 0 0 null 0 980600 0 0 0 0 0 0 null 0 980700 0 0 0 0 0 0 null 0 990000 0 0 0 0 0 0 null 0 990100 0 0 0 0 0 0 null 0

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73 LIST OF REFEREN CES Center for Neighborhood Technology. (CNT) Surface Transportation Policy Project. ( 2005 ) A Review of Literature and Methods. Urban Policy Program Research Monograph 5. ( 2004 ) Urban Re search Program Research Paper 6. (2005) Urban Studies Vol 44 No. 1. ( 2007 ) :37 62. urbs: Oil vulnerability in the Australian Press. ( 2008 ) H illsborough Metropolita n Planning Organization. 2035 Long R ange T ransportation P lan d of Oil, Climate Change, and Other Converging Catastrophes of the Twenty Grove Press. ( 2005 ) Center for Housing Policy (2006) Journal of Urban Technology 1 4 2 (2007) :15 30. Washington: Island Press. (2009) Conference Presentation ( 2011 ) Random House. (2009) s of Energy in a Post Peak Oil World

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74 ( 2009 ) ps U.S. in Energy Use: Asian Giant Emerges as Wall Street Journal July 18, 2010. Accessed online Feb. 9 th 2012: Serie Scientifique ( 2011 ) US Census Accessed Nov. 2011 US Government : Uncertainty about Future Oil Supply Makes It Important to Develop a Strategy for Addressing a Peak and Decline in Oil Production Report to Congressional Requesters (2007

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75 BIOGRAPHICAL SKETCH Kevin Ice holds a Master of Arts in u rban and r egional p lanning from the University of Florida, and a B achelor of S cience in c ommunity and r egional d evelopment fro m the University of California Davis He was born in Hawaii and grew up in Nevada. After high school, Kevin has preferred a nomadic lifestyle, never staying put for long. He has lived, worked, studied, and procrastinated in Boston, Reno, Honolulu, Sacramento, and San Francisco before moving to Gainesville. Kevin decided to p ursue a career in urban planning due to health concerns related to his previous career, asbestos consulting and a lifelong love of cities