Borrowed Ground: Evaluating the Potential Role of Usufruct in Neighborhood-Scale Foodsheds

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Graduate School of The Ohio State University

By

Benjamin Carl Kerrick, B.A.

Environmental Science Graduate Program

The Ohio State University

2013

Thesis Committee:

Casey Hoy, Adviser

Maria Manta Conroy

Jill Clark

Steven Gordon Copyright by

Benjamin Carl Kerrick

2013

Abstract Urban growth in the 20th and 21st centuries has had, and will continue to have, two direct effects on urban food systems: greater urban populations require more food, and expanding urban footprints diminish agricultural production areas around cities. Increasing globalization, consolidation, and industrialization of the food supply have been concurrent with this urban growth, leading to a profound physical and psychological distance between urban dwellers and the sources of their food. As concerns about climate change, sustainability, and food insecurity have come to the forefront of conversations about our food system, scholars, practitioners, and policymakers have explored the extent to which cities might feed themselves. Urban agriculture can expand food access in cities, engage urban dwellers in food production, and provide a beneficial use for unused urban lots. Idle or vacant land, both publicly and privately owned, represents an important resource for urban gardeners and farmers, and potentially for their rural counterparts as well. Applying the concept of usufruct, or productively using another’s unused land, could increase agricultural use of both privately and publicly owned land resources. However, little is understood about how these land resources vary according to degree of urbanization, or the degree to which they might provide land access and food relative to the needs of the local population. This research aims to gain a better understanding of how vacant land resources vary according to ownership and urbanization, and to evaluate usufruct’s potential provision of vegetables and land with respect to the local population. Over one hundred “walking distance”-defined study sites (300-meter radius) were randomly selected in seven central Ohio counties comprising and surrounding the city of Columbus; these study sites

ii represented four urban categories referred to as Rural, Suburban, Urban Employment and Urban Residential. Within each study site, GIS-based classification was used to identify publicly and privately owned vacant land that is suitable for vegetable production, based on soil quality, slope, water access, and solar exposure. These vacant land resources were compared among urban categories and between public and private ownership. Production scenarios were modeled, and the estimated vegetable yields were measured against the dietary needs of the study site populations. Available land area was also related to the number of study site households. These results were compared among urban categories. No appreciable differences were found between publicly and privately owned land in terms of parcel size, perimeter-area ratio, or quality; however, privately owned land was more prevalent, and when present, was likely to occur in greater quantities. Idle vacant land is rarest in the least urban settings, where it is likely to already be in production, and most prevalent in low- or mid-density urban contexts, whereas parcel size was found to decrease as urbanization increased. The proportion of vegetable serving needs that could be met decreased from less urban to more urban contexts, as did the land-to-household ratio. However, when compared with other studies documenting the ratio of production area to household, maximizing production in less urban areas may be limited by lack of demand from the local population. The findings of this research demonstrate that privately owned land is a significantly more prevalent land resource for urban food production than public land, suggesting that policy instruments which facilitate or incentivize usufruct agreements between private owners and urban farmers would be effective in increasing urban agriculture. The land suitability index used in this research shows that soil quality is the most significant obstacle to enabling food production in more urban areas. Finally, this research demonstrates that land-to-household ratios vary significantly according to urbanization, and that this ratio should be considered when assessing the production potential of urban land. iii Acknowledgments

I would like to thank my adviser Casey Hoy for his unwavering support, guidance and patience over the course of my graduate program and thesis research. Thanks also to my committee members Jill Clark, Maria Conroy and Steve Gordon for their valuable feedback and suggestions.

Thank you to my cohort and comrade Liz Kolbe, whose ongoing feedback and perspective have greatly influenced my research, and whose friendship and good humor have made the research and writing process so much more enjoyable.

Thanks to Nathan Hilbert for his development and application of the LIDAR-based portion of this research.

Thank you to Brookes Hammock, whose incisive copy edits and writing suggestions have greatly refined this thesis, and whose encouragement has been invaluable during the final months of my research.

Thanks to John Taylor for running additional analysis on his unpublished data and sharing those results with me.

Thanks to the program managers, farmers, gardeners, and others I interviewed: Kevin Bayuk, Rich Buquet, Aaron Carmack, Tim Carter, Bill Dawson, Kyle Ezell, Vicki Garrett, Sandy Pernitz, Amanda Stanfield, Nick Stanich, Jim Thompson, and Beth Urban.

This work would not have been possible without the funding support of the Environmental Science Graduate Program, the National Science Foundation GK-12 fellowship program, and the Ohio Agricultural Research and Development Center Director’s fellowship.

Finally, thank you to my friends and family for their support, and most of all to my parents for their constant love and encouragement.

iv Vita

July 24, 1980 ...... Born in Caldwell, Idaho

1998 ...... Graduated from Lewiston High School, Lewiston, Idaho

2002 ...... B.A. Theatre, Whitman College, Walla Walla, Washington

2005-2010 ...... Employed at Lower Manhattan Cultural Council, New York, New York

2010-2011 ...... Ohio Agricultural Research and Development Center Director’s Fellow, The Ohio State University

2011-2012 ...... National Science Foundation GK-12 Fellow, The Ohio State University

2012-2013 ...... Graduate Teaching Associate, The Ohio State University

Fields of Study

Major Field: Environmental Science (Specialization: Agroecosystem Science)

Major Field: City & Regional Planning

v Table of Contents

Abstract ...... ii Acknowledgments ...... iv Vita ...... v List of Tables ...... viii List of Figures ...... ix

Chapter 1: Introduction ...... 1 1.1 Urban agriculture ...... 2 1.2 Multifunctional agriculture ...... 4 1.3 The urban-rural “divide” ...... 5 1.4 Usufruct ...... 9 1.5 Local food systems ...... 14 1.6 Foodshed analysis ...... 16 1.7 Land inventories and land suitability ...... 24 1.8 Ownership ...... 28 1.9 This research ...... 28

Chapter 2: Analysis and comparison of vacant land resources ...... 34 2.1 Introduction ...... 34 2.2 Methods ...... 35 2 .2 .1 Study area: Central Ohio ...... 37 2 .2 .2 Creation of urbanization categories ...... 39 2 .2 .3 Study site selection ...... 40 2 .2 .4 Identification of vacant parcels ...... 42 2 .2 .5 Land suitability index ...... 44 2 .2 .6 Calculation of study site characteristics ...... 50 2 .2 .7 Statistical analysis ...... 51 2.3 Results ...... 53 2 .3 .1 Differences between publicly and privately owned land ...... 53 2 .3 .2 Differences in vacant land among urban categories ...... 56 2.4 Discussion ...... 66 2 .4 .1 Urbanization categories and sampling ...... 66

vi 2 .4 .2 Use of publicly available data ...... 67 2 .4 .3 Land suitability index ...... 68 2 .4 .4 Implications and conclusions ...... 70

Chapter 3: Foodshed analysis of study sites ...... 73 3.1 Introduction ...... 73 3.2 Methods ...... 75 3 .2 .1 Sample study sites and vacant parcels ...... 75 3 .2 .2 Study site demographic characteristics ...... 75 3 .2 .3 Diet model ...... 76 3 .2 .4 Yield model ...... 76 3 .2 .5 Production scenarios ...... 80 3 .2 .6 Calculation of foodshed analysis ...... 82 3 .2 .7 Statistical analysis ...... 82 3.3 Results ...... 83 3 .3 .1 Percentage of vegetable requirements met ...... 83 3 .3 .2 Land per occupied household ...... 88 3.4 Discussion ...... 91 3 .4 .1 Normative scenario parameters ...... 91 3 .4 .2 Vegetable servings and land access ...... 93 3 .4 .3 Implications and conclusions ...... 97

Chapter 4: Summary and conclusions ...... 102 4.1 Overview of Research ...... 102 4.2 Key findings and policy implications ...... 107 4.3 Implications for future research ...... 110 4.4 Conclusion ...... 113

References ...... 115. .

Appendix A: Chapter 2 Statistical Results ...... 126 Appendix B: Crop Yield Data ...... 131 Appendix C: Whole Parcel Data ...... 141 Appendix D: Study Site Data ...... 184

vii List of Tables

TABLE 2.1. Study area population by county ...... 37 TABLE 2.2. Urbanization categories ...... 40 TABLE 2.3. Soil quality indicator ratings ...... 47 TABLE 2.4. Land suitability tiers ...... 50 TABLE 2.5. Data transformations ...... 53 TABLE 2.6A. Summary of Results: Abundance and spatial characteristics of publicly owned vs. privately owned land ...... 54 TABLE 2.6B. Summary of Results: Suitability of publicly owned vs. privately owned land ...... 57 TABLE 2.6C. Summary of Results: Abundance and spatial characteristics of vacant land by urbanization category ...... 60 TABLE 2.6D. Summary of Results: Land suitability by urbanization category . . .63 TABLE 3.1. USDA vegetable subgroups and selected crops ...... 77 TABLE 3.2. Data transformations ...... 83 TABLE 3.3. Summary of Chapter 3 results ...... 84

viii List of Figures

FIGURE 2.1. Schematic of methods ...... 36 FIGURE 2.2A. Study area and urbanization categories ...... 38 FIGURE 2.2B. Study area and study sites ...... 38 FIGURE 2.3. Study site with whole and clipped parcels ...... 44 FIGURE 2.4. Examples of land suitability ratings by urban category ...... 51 FIGURE 2.5. Example of suitability rating process ...... 52 FIGURE 2.6. Parcel size: Category B (Suburban) parcels by ownership . . . . . 55 FIGURE 2.7. Presence of vacant parcels: By ownership ...... 55 FIGURE 2.8. Vacant area per site, when present: Urban Employment category by ownership ...... 55 FIGURE 2.9. Vacant area per site, when present: Urban Residential category by ownership ...... 56 FIGURE 2.10. Tier 2, 3, 4, and 5 land area per site, when present: Urban Employment category by ownership ...... 58 FIGURE 2.11. Tier 2 and Tier 3 percent of total vacant area, when present: By ownership ...... 58 FIGURE 2.12. Parcel size: By urbanization ...... 61 FIGURE 2.13. Presence of vacant parcels: By urbanization ...... 61 FIGURE 2.14. Presence of Tier 2 and Tier 3 land, when vacant land is present: By urbanization ...... 64 FIGURE 2.15. Tier 3 area per site, when present: By urbanization ...... 64 FIGURE 2.16. Presence of Tier 5 land, when vacant land is present: By urbanization ...... 64 FIGURE 2.17. Overall land composition: By urbanization ...... 65 FIGURE 3.1. Portion of servings for each crop in the model diet ...... 79 FIGURE 3.2. Crop area proportions for each yield scenario ...... 81 FIGURE 3.3A. Proportion of vegetable requirements met under Scenario 1 . . . 85 FIGURE 3.3B. Proportion of vegetable requirements met under Scenario 1 (detail) ...... 85 FIGURE 3.4A. Proportion of vegetable requirements met under Scenario 2 . . . 86 FIGURE 3.4B. Proportion of vegetable requirements met under Scenario 2 (detail) ...... 86

ix FIGURE 3.5A. Proportion of vegetable requirements met under Scenario 3 . . . 87 FIGURE 3.5B. Proportion of vegetable requirements met under Scenario 3 (detail) ...... 87 FIGURE 3.6A. Area of land per household by production scenario ...... 89 FIGURE 3.6B. Area of land per household by production scenario (detail) . . . 89 FIGURE 3.7. Illustration of land area per household by production scenario . . .90

x Chapter 1: Introduction

Urban growth in the 20th and 21st centuries has had, and will continue to have, two direct effects on urban food systems: greater urban populations require more food, and expanding urban footprints diminish agricultural production areas around cities. These trends are almost certain to continue. When the world’s seven billionth person was born in 2011, that person was likely born in one of the world’s cities (United Nations, 2011). The earth’s population is now more than half urban, with even higher proportions in the developed world; the United States is now 82% urban (Central Intelligence Agency, 2011). How to feed the world’s population has become more pressing because of this increasing urbanization and the attendant conversion of productive agricultural land into other uses (Sonnino, 2009). As a result, cities must import large amounts of food for their sustenance, and that food requires tremendous land area to be produced (Deelstra and Girardet, 2000). Contemporary urban dwellers are more separated—physically and psychologically—from their sources of food because of increasing urbanization as well as the consolidation and mechanization of the food system. Urban planners, for their part, have largely ignored food system issues until recently, perhaps because they have viewed these issues as essentially rural (Morgan, 2009). City dwellers and urban planners have begun to realize, however, that cities can and must play major roles in the development of adaptable, resilient food systems that will be responsive to climate change, population growth, and other challenges. Indeed, Morgan & Sonnino (2010) argue that an increasingly urban-dominated landscape means that cities must be the primary engines

1 of sustainability. Clancy (2011) posed the question, “What food system roles are cities most suited for?” to argue that distribution—or getting food to people—is the key role that cities play in the food system. Others, by contrast, have focused on the small but important role that cities can play in food production. The topic of urban agriculture has thus become a rich area for research, dialogue, and innovation in the contemporary food system. The practice of usufruct, or productively using another’s unused land, is a common strategy for urban food production, and if expanded could increase agricultural use of both privately and publicly owned land resources. However, little is understood about how these land resources vary according to degree of urbanization, or the degree to which they might provide land access and food relative to the needs of the local population. This research aims to gain a better understanding of vacant land resources and their potential provision of food and land access. This research also refines current urban foodshed analysis methods by including considerations of land suitability, labor, and urban context. Following an overview of key ideas relating to urban and multifunctional agriculture, this introduction discusses the key methods and frameworks used in this research: urban gradient analysis, foodshed analysis, and land inventories, concluding with a consideration of the relevant differences between publicly and privately owned land resources.

1 .1 Urban agriculture “Urban agriculture” has been defined as “the growing, processing, and distribution of food and other products through intensive plant cultivation and animal husbandry in and around cities” (Brown et al., 2002), or more simply, “growing food within cities” (Urban Design Lab at the Earth Institute, 2011). It can take a range of forms, including home and community gardening, urban farming and market gardening, and school or

2 institutional gardening; its products can include vegetables, fruit, honey, eggs, meat, and any other food or fiber product. It is, in short, agriculture, but its urban context entails certain implications and limitations. Ellis & Sumberg (1998) describe a simple spatial economic model to suggest that high-value, high-transport-cost goods, such as vegetables, eggs, poultry, and milk, are the agricultural goods most likely to be produced in or near cities. Current agricultural distributions appear to confirm this. Development pressures and high demand from urban markets dictate more intensive production and higher value products closer to cities (Tacoli, 2003). Oberholtzer, Clancy, & Esseks (2010) cite a 2003 report from the American Farmland Trust, noting that in 1997, 86 percent of US fruits, nuts, and vegetable production took place in “the most urban-influenced counties” – counties which either overlap with or are adjacent to metropolitan areas (Ghelfi and Parker, 1997). In addition to these economic forces, local policies can also shape urban agriculture. Policies relating to resource use, technical support, public health, land use, funding, and zoning/permitting can all either help or hinder urban agriculture efforts (Bourque, 2000; Mukherji and Morales, 2010). Critics both within and outside the planning field have urged planners to play a larger role in the food system; their suggestions include the facilitation and encouragement of urban gardening and farming (Deelstra and Girardet, 2000; Morgan, 2009). For example, in his “Ten Municipal Policies to Support the New York City Foodshed,” Cohen (2010) urged policymakers to “Support Urban Food Production,” specifically by increasing funding to organizations that support urban agricultural activities, and providing technical assistance and equipment. Planners’ and policymakers’ rationale for providing such support is frequently couched in terms of the myriad benefits that urban agriculture is said to provide. Duchemin, Wegmuller, & Legault (2009) grouped these benefits into eight categories: education, social interaction, environment, food security, health, leisure activities,

3 economic development, and urban planning. Many studies have focused on identifying and quantifying specific benefits. Alaimo, Packnett, Miles, & Kruger (2008), for example, found that participation in community gardens was associated with higher intake of fresh fruits and vegetables. Smit (2000) discussed the ways that urban agriculture supports biodiversity, an important ecosystem service. Pollinator research has demonstrated that community gardens play a role in supporting bee richness and abundance in urban contexts (Matteson and Langellotto, 2009; Matteson et al., 2008). Because of its intensiveness and integration into areas of dense population, urban agriculture presents a unique opportunity to generate many of these benefits within relatively small spaces and to deliver them to nearby residents.

1 .2 Multifunctional agriculture The framework of multifunctional agriculture is particularly apt for urban food production. Multifunctional agriculture and multifunctional landscapes describe land use that contributes to several objectives at once, highlighting the multiple roles that agriculture can play to meet production, ecological, societal, economic and cultural goals (Abler, 2004; Bergstrom, 2009; Boody, 2008; Gliessman, 2010; Jordan and Warner, 2010). Although the multifunctional agriculture framework comes from a rural European context, Lovell (2010) argues that its application to urban contexts in the U.S. could more fully account for the multiple benefits of urban food production. A single urban agriculture site, for example, can provide education, open space, and fresh food to local residents while also mitigating stormwater runoff and providing habitat for insects and other fauna. Lovell (2010) loosely groups urban agriculture’s functions into production, ecological, and cultural categories and points out that the urban context provides great potential for the transfer of these benefits to local populations. By emphasizing the wide- ranging benefits of urban agriculture, multifunctional frameworks have the potential to

4 garner support from policymakers and residents alike (Deelstra et al., 2001). Some researchers have highlighted urban agriculture’s ability to minimize waste and increase sustainability by “closing the resource loop”. Urban waste streams can be diverted and intercepted to foster soil fertility, while idle lands can be put to productive use (Smit and Nasr, 1992). Deelstra & Girardet (2000) discuss this in terms of a city’s “metabolism.” If a city is thought of as an organism, then its metabolism refers to the flows of resources into, out of, and within its boundaries. A more “circular,” efficient urban metabolism would increase sustainability by minimizing reliance on inputs. McClintock (2010) deepens this metabolic metaphor by applying the Marxist notion of “metabolic rift” to urban agriculture. According to this framework, modern capitalism has disrupted natural metabolic processes by alienating humans from nature; the resulting metabolic rifts have ecological, social, and individual and/or personal dimensions. Urban agriculture is a response to and a solution for these rifts: it regenerates ecological relationships, infuses the act of eating with social and cultural value, and places consumers into a more direct and engaged relationship with the food system. The idea of metabolic rift suggests that urban agriculture acts in opposition to the increasingly globalized food system by reforging ecological and social relationships. McClintock (2010) also claims that metabolic rift deepens the chasm between the concepts of urban and rural: “this rift reifies a false dichotomy between city and country, urban and rural, humans and nature, obscuring and effacing the linkages between them.” Indeed, this binary conceptualization of the urban-rural “divide” is a problematic and inaccurate metaphor, “one that oversimplifies and even distorts the realities” (Tacoli, 2003). The contemporary landscape is more accurately characterized by complex relationships between urban, rural, and much of the landscape that has features of both.

1 .3 The urban-rural “divide”

5 The food system provides a useful entry point to consider the urban-rural “divide,” since the flow of food from agricultural production areas to urban consumption areas is such a central element of the relationship—with rifts and linkages—between urban and rural locales. Rifts are manifest in the distance (both physical and psychological) that separates urban dwellers from the sources of their food. This distance fosters indifference and ignorance about food systems, agriculture, and the ecological functions of rural landscapes (Francis et al., 2005). Emerging alternative food networks, including CSAs, farmers’ markets, and pick-your-own farms, have the potential to minimize and mitigate that distance by bringing urban consumers and rural producers into contact with each other. Jarosz (2008) agrees that these types of encounters and activities can stimulate deeper understanding across the urban-rural divide:

Increased face-to-face interaction between growers and eaters… engenders trust and cooperation within a community and is also an important way to educate consumers about where their food comes from, including the environmental and social conditions of its production.

This form of education, according to Francis et al. (2005), is the “best hope” for urban dwellers to gain a deeper understanding of where and how their food is produced. These “best hopes,” however, keep urban dwellers situated as passive consumers. As a result, their “education” is limited to observation and consumption; a more expansive role is foreclosed. Consumption, DeLind (2002) notes, is not equivalent to citizenship. Urban agriculture, on the other hand, affords urban dwellers the opportunity for a more experiential, immersive, and participatory education about the food system and provides even greater potential for fostering understanding across the urban-rural “divide.” In response to the simplistic binary framework of the urban-rural divide, recent research, particularly in the field of urban ecology, has conceived of an urban gradient to evaluate how various factors, such as population density, spatial pattern, land use, or impervious surface cover, change progressively and predictably from less urban to more 6 urban environments (Alberti, 2008, 2005; Breuste et al., 2008; Hahs and McDonnell, 2006; McDonnell and Hahs, 2008). Though frequently evaluated using a simple transect approach, with distance from the urban core providing the sole basis for the gradient, more recent studies (Alberti, 2008; Hahs and McDonnell, 2006) have combined multiple factors to create a gradient that more accurately reflects the complex, multi-core structures of modern cities (Alberti, 2005; Dow, 2000; McDonnell and Hahs, 2008; McIntyre et al., 2000). Although the vast majority of urban gradient studies have been applied to ecological questions such as species diversity and distribution, the urban gradient approach could also be applied to broader research questions. In particular, the food system and its varied functions and forms across the urban gradient provide a rich area for potential investigation. The planning field has developed a prescriptive model analogous to the urban gradient, “a basis for normative planning” (Talen, 2002). The New Urbanists’ Transect approach, developed primarily by Andrés Duany, divides the urban-rural gradient into zones. Each zone calls for particular urban forms and can thus be used as “an instrument of design” (Duany, 2002). The Transect approach pursues two overarching goals that both relate to the rural-urban divide: (1) to integrate urban development with natural ecosystems, and (2) to create and embed spaces that honor the “integrity of place”. The result can be a “proper integration of town and country” that unites rather than separates the two poles (Talen, 2002). Urban agriculture provides a unique opportunity to integrate the urban and the rural by inserting pockets of food production – a traditionally rural activity – within the urban landscape. A desire to preserve a conventionally urban aesthetic may limit the scale of urban agriculture (Colasanti et al., 2012), but Hagan (2009) argues for a different conception of city and non-city that is based on productivity and inclusive of ecosystemic complexity; in this way, “land empty of built development is no longer

7 viewed as empty, simply full of something else.” Within this frame, “urban” is defined not by the built environment but by intensity of use. One example of the value of unbuilt sites is the community gardens in New York City’s Loisaida neighborhood. Though situated “in the midst of dirty, crime-ridden streets,” the gardeners valued these sites because they provided a sense of community and a more integrative experience of urban and rural (Schmelzkopf, 1995). When threatened with potential loss of their gardens, Loisaida’s gardeners challenged the conventional urban aesthetic and power structures, aiming to “plant in the marginal spaces of the city a quite different vision of how urban development should proceed.” This is a vision that incorporates more opportunities for “rural” experiences within the urban core. When such integration occurs along the urban gradient, the resulting landscape could be one “tangibly linking the urban core… to its surrounding suburbs and distant rural areas” (Erickson et al., 2011). So what, then, stands in the way of more widespread adoption of urban agriculture, with opportunities for urban and suburban dwellers to directly experience and participate in the food system not just as consumers but also as producers of food? Probably the most frequently cited constraint is a lack of access to land, particularly in the urban context, where competing land uses and insecure tenure create barriers to agricultural use (Deelstra and Girardet, 2000; Lovell, 2010). In a survey of community gardeners in upstate New York, Armstrong (2000) found that “a lack of access to land, which people were permitted to cultivate,” was a common motive for gardeners’ participation in their community garden; interestingly, this lack of access motivated gardeners in both urban and rural settings. And while limited land access challenges those who want to produce food in cities, many gardeners and farmers overcome that challenge by “borrowing” land that is under public or private ownership. Their use of the land is an interim activity, and although their tenure is often uncertain, they utilize the otherwise idle land to produce food. This arrangement is known as usufruct.

8 1 .4 Usufruct Usufruct, which comes from the Latin usus (“a use”) and fructus (“fruit”), is defined by the Oxford English Dictionary (Oxford University Press, 2013a) as:

1a. Law. The right of temporary possession, use, or enjoyment of the advantages of property belonging to another, so far as may be had without causing damage or prejudice to this.

2a. gen. Use, enjoyment, or profitable possession (of something).

2b. esp. Beneficial use or enjoyment of land.

Oxford Dictionaries Online (Oxford University Press, 2013b) defines usufruct more simply as:

the right to enjoy the use and advantages of another’s property short of

the destruction or waste of its substance.

The concept has ancient roots, with versions appearing in the Code of Hammurabi, Roman Law, Aztec society, and the Napoleonic code, as well as the writings of Thomas Jefferson; of course, such a concept stands in opposition to most contemporary American concepts of private property rights (Kovel, 2007; Laing, 1976; Sementelli, 2007). Usufruct is in one sense a legal concept that implies a right to use another’s property, but in the context of this research, it is used simply to designate the temporary use of property owned by another (public or private) entity. Although the term “usufruct” is rarely used, its practice is common in urban contexts for the same reasons that access to land or property is a challenge in cities: development pressures and high property costs put scarce urban space at a premium. At the same time, vacancies occur in all cities, with particularly high rates in Rust Belt cities

9 like Detroit and Cleveland (Cleveland Urban Design Collaborative, 2008; Colasanti et al., 2012). Interim use of these vacant lots or built spaces for purposes including art projects, business start-ups, or community spaces, can prove mutually beneficial to both the users and the owners of the space. Owners have their property activated and maintained by engaged users, thus minimizing the deleterious effects of vacancy, while the users of the space are able to develop or expand their activities (LocusLab, 2009). Indeed, widespread vacancies in a city like Cleveland can be understood not as a deficit, but as an asset and opportunity for invigorating the urban sphere through diverse and spontaneous activities. “In this context, vacant land is a great luxury, one that expands the practice of urban design beyond form-making to include the ever-changing dimensions of space, time, and lived experience” (Schwarz et al., 2009). This type of fluid, flexible land use gives rise to unregulated “loose-fit” places that often “serve people’s needs (and a wide range of needs) in ways that designed spaces do not” (Thompson, 2002). Of course, usufruct for food production is already practiced in and around our cities, with many community gardens and urban farms utilizing publicly and privately owned land, through formal or informal agreements with property owners, or even through unsanctioned “squatting.” These spaces likely make a significant contribution to urban food production (Ellis and Sumberg, 1998; Taylor and Lovell, 2012), though there is a dearth of research that has specifically evaluated their yields. Despite its apparent benefits, such use is usually precluded or limited by development and competing uses. In Nigeria, for example, farming the idle land on the edge of the city of Ibadan has become much less common as that land has become less available (Tacoli, 2003). Particularly in disadvantaged neighborhoods, housing projects have competed with urban agriculture for vacant land (Duchemin et al., 2009). Yet many scholars, policymakers, and practitioners agree that access to land for urban agriculture should be facilitated. Two opposite rationales for encouraging usufruct follow from its mutual provision of benefits. Some

10 see urban agriculture as a solution or “temporary expedient” for widespread vacancies. They point out that unmanaged vacant land costs municipalities great sums of money (Brown et al., 2002; Keating, 2010). From this perspective, usufruct is justified by the benefits it confers to those who would otherwise bear the burden of vacant land, such as neighboring residents or businesses, or the cities and landowners that are responsible for its management. Others see vacancy as an asset and resource that can be leveraged for food production and greater sustainability (Smit and Nasr, 1992). By this rationale, the direct benefits of food and open space that usufruct delivers to gardeners and others are a sufficient justification. Colasanti et al. (2012) argue that the latter perspective is becoming more prevalent in contemporary discourse around urban agriculture. In other words, food production in the urban sphere is increasingly seen as a benefit in its own right, rather than as a strategy for outsourcing management of wide-scale vacancy. Because usufruct and urban agriculture are seen as opportunities for neighborhood revitalization, community building, food security, and many other benefits, some policy options have been suggested to facilitate their implementation. Incentivizing the lending of land through tax breaks might encourage private landowners to more freely offer use of their land (Bourque, 2000), while legal assistance, formal agreements, and predictable and certain tenure periods may assuage the reticence of would-be gardeners and farmers (Brown et al., 2002; Smit and Nasr, 1992). Finally, land banks can serve as repositories of abandoned and vacant properties that are available for urban agriculture (LaCroix, 2010). But even without these incentives, private owners may be willing to lend their land for agricultural use. In a study of suburban Chittenden County, Vermont, Erickson et al. (2011) found that more than 60% of responding residential landowners would “definitely” or “most likely” participate in a program allowing vegetable production on their land, and only 7% expected any sort of monetary compensation. In a sense, usufruct mitigates a market failure by connecting potential users of

11 land to owners of land that would otherwise remain idle. The fact that such land would go unused can be considered through Marx’s (1867) lens of use value vs. exchange value. Exchange value, as an abstracted value assigned to a good or asset, allows such a good to be exchanged with other goods according to a common metric, a value quantity. In the case of a piece of land in contemporary capitalist society, this is equivalent to its property value. Use values, by contrast, capture the quality of a good or asset, its value to a potential user in terms of its utility and potential to meet the user’s needs. Idle, cultivable land has a market-defined exchange value. At the same time, while its use value to the owner may be nominal, its use value to community members who seek access to land for social interaction, gardening, and green space may be significant. Current Agricultural Use Value (CAUV) programs acknowledge the inequity between property value and agricultural use value by reducing the tax burden on rural farmers. But these programs effectively exclude urban agriculture. In Ohio, for example, eligible land must be greater than 10 acres and exclusively devoted to commercial production (Gearhardt, 2010; Jeffers and Libby, n.d.). Yet the discrepancy between use and exchange values provides an explanation and rationalization for why urban landowners might be willing to allow interim use of their land. The dominance of exchange value in capitalist systems drives contemporary conceptions of private property rights, wherein individual interests trump the interests of the common good (Francis et al., 2005; Kovel, 2007; Purcell, 2002; Sementelli, 2007). Purcell (2002) explores the ways that Henri Lefebvre’s “right to the city” framework re-envisions the relationship between urban inhabitants and urban space in terms of such rights. Lefebvre’s “right to the city” entails both a right of participation and, more relevant to a discussion of usufruct, a right of appropriation. Appropriation allows for a “right of inhabitants to physically access, occupy, and use urban space” (emphasis added), indicating that use value is the driving consideration behind such appropriation. The

12 right of appropriation firmly prioritizes use value for urban dwellers over the exchange value interests of property owners; this reversal “reworks control over urban space, resisting the current hegemony of property rights and stressing the primacy of the use- rights of inhabitants” (Purcell, 2002). Kovel (2007) likewise advocates for an “ecosocialist” paradigm, wherein use value overcomes exchange value, and “those things essential for social production are to be shared by all and not owned by the few” (Kovel 2007, p. 240). Although these concepts of use vs. exchange value and a theoretical “right to the city” may seem abstract and disconnected from actual practice of urban agriculture, the late 1990s conflict over New York City’s Loisaida community gardens illustrates how these frameworks can be applied to contemporary usufruct practices. From the 1970s to the late 1990s, community gardens on public land in Loisaida were subject to the vagaries of market value dynamics. During the fiscal crisis of the 1970s, the city willingly allowed gardening on its vacant public land; but as the economy regained strength in the 1990s, Mayor Giuliani planned to reclaim the sites and auction them off. Ultimately, Bette Midler’s New York Restoration Project and the Trust for Public Land intervened and purchased most of the gardens to be held in trust for the gardeners (Schmelzkopf, 2002). Schmelzkopf (2002) points out that incommensurability, or the inability to measure and compare values according to a common metric, is an inherent characteristic of the comparison between exchange value against use value. That is, the social capital, open space, and other use values embodied in Loisaida’s community gardens could not be compared to the land’s property or exchange value.

When social goods have use value but no exchange value, intrinsic value but no instrumental value, they cannot be measured by ‘price alone.’ The fact that goods are incommensurable with the commodified valuation of the market becomes a problem in a society where social relations are embedded in the economic system (Schmelzkopf, 2002).

Schmelzkopf (2002) also highlights the role that scale and perspective have on

13 how these spaces are perceived. From the gardeners’ perspective, their gardens had inexchangeable value embedded in their location and infused with meaning; they were, in other words, real places (also Smith & Kurtz, 2003). From Mayor Giuliani’s perspective on the other hand, the gardens represented abstract, interchangeable units of land that were only measurable in terms of market/exchange value. These appropriated spaces became central in a conflict between different conceptions of rights (property vs. appropriated/ community rights) and value (exchange vs. use values) (Schmelzkopf, 2002; Staeheli et al., 2002).

1 .5 Local food systems Local food systems engage with notions of “place” that underlie some of the gardeners’ experiences of their gardens in Loisaida. Alternative local and regional food economies connect consumers more directly to the sources and producers of their food than does the national or global food system and its market channels. Although this dominant globalized system is highly efficient in a narrow sense, it has also been detrimental to ecosystems, social structures, and smaller-scale production systems (Feenstra, 1997). Some have gone so far as to call it a “farmer/environment destruction machine” (Gussow, 1999). Local food systems, by contrast, are rooted in their own ecosystems and societies, and provide opportunities to directly experience the specificity of their place through seasonality, direct producer-consumer relationships, engaged and complex social networks, and greater care of the environment (Feenstra, 1997). The actual experience of these opportunities has not been extensively researched, but a survey of southwestern Virginia residents found that participation in the local food economy led to increased knowledge and greater enjoyment of local foods, as well as positive feelings about direct interactions with food growers (Bykera et al., 2010). Sustainability is often discussed in terms of its environmental, social, and

14 economic dimensions: all three must be addressed and balanced for a system to be considered truly sustainable. Local food systems support each dimension of sustainability. Economic returns stay within the system rather than being exported, relationships between and among producers and consumers are more engaged and direct, and smaller- scale production practices and supply chains are likely to be more ecologically sound (Clancy and Ruhf, 2010; Duram and Oberholtzer, 2010; Feenstra, 1997; Lyson, 2000). Lyson (2000) conceives of these relationships in the framework of a “civic agriculture,” a local food system that is integrated with its community’s “social and economic development.” Within this framework, communities have greater control over their own sustainability. DeLind (2002) emphasizes that true civic agriculture does not simply re- organize traditional market relations, but must also foster environmental citizenship and physical engagement with place. Feenstra (1997) argues that when a community’s food system is healthy, the community itself is more sustainable. Considerations of food system health, like those discussed above, are typically approached from a human or social perspective while including environmental dimensions. An agroecosystem health framework, on the other hand, approaches the problem from the opposite direction, grounded in a more rigorous evaluation of the ecological function of a food production system while also incorporating social and economic considerations. In agroecosystems, the intersecting dynamics of economic, social, and ecological processes give rise to structural and functional complexity (Conway, 1987). Echoing the three-pronged framework of sustainability, Conway (1987) identified four key properties of agroecosystem health: productivity, stability, sustainability, and equitability. Vadrevu et al. (2008) integrated and quantified the ecological, economic, and social dimensions of an agroecological landscape in northeastern Ohio as a means of estimating a current state of the agroecosystem with respect to these four properties. This agroecosystem health index (AHI) incorporated

15 biodiversity, soil quality, topography, social organization, and farm and land economics into a single measure of agroecosystem health. Agroecosystem and food system health are defined by tightly linked relationships between a system’s environmental, social, and economic dimensions. These integrated health and sustainability frameworks suggest that a localization of food systems might maximize food system health, though there is no clear definition of what constitutes “local” (Duram and Oberholtzer, 2010). DuPuis and Goodman (2005) caution against a one-size-fits-all, “unreflexive” localism that ignores complexities of race, class, and gender in pursuit of a “right” way to eat. A “reflexive” localism, they argue, would instead engage with the “politics of the local” for a more socially just and equitable concept of a local food system. Clancy and Ruhf (2010) also add complexity to the consideration of “local,” suggesting that regional food systems could be more comprehensive and resilient. A regional food system would comprise many “locals,” with production, processing, distribution, and purchasing occurring at multiple scales, yet would still be rooted in an idea of “place.” Localization of the food system at the regional scale might more effectively maximize sustainability, resilience, and food security (Clancy and Ruhf, 2010).

1 .6 Foodshed analysis The concept of the “foodshed” has been applied as a conceptual framework for considering the current and potential structures of local food systems. Peters et al. (2009b) define a foodshed as “the area of land from which a population center derives its food supply.” Hedden (1929) introduced the term in his book How Great Cities Are Fed, which discussed the sourcing and distribution of food in New York City. Comparing the “flow of foodstuffs” to the circulation of water, Hedden used a watershed analogy to discuss the economic (rather than physical) barriers that define the shape and reach of the foodshed.

16 Hedden noted that technological innovations like refrigeration and rapid transport had dismantled the traditional geographic contiguity of the foodshed by enabling the production of food far from its ultimate point of consumption. Although the term did not become widespread in the decades after Hedden used it, Getz (1991) reintroduced it in an article suggesting a re-shaping of the urban foodshed—from one resembling “an octopus with long tentacles” reaching all over the globe, to one more based in its own locality. Kloppenburg et al. (1996) broadened the foodshed concept to one less focused on a literal interpretation of actual food flows, calling it a “unit of analysis” and “a frame for action as well as thought” and emphasizing its value in envisioning alternative food systems. Since Kloppenberg et al.’s article, the term has gained popularity, and is now frequently used in food system parlance by scholars, policymakers, planners, and practitioners. The foodshed-as-watershed metaphor is useful in two ways. First, like watersheds, foodsheds provide a “graphic imagery” for visualizing the flows, courses, and confluences of our food supply (Kloppenburg et al., 1996). The commodity flows of industrialized agriculture might be likened to major rivers, while the more modest yields of small-scale farmers or home or community gardeners are analogous to minor tributaries. Also like watersheds, foodsheds can be considered at a range of scales, with smaller foodsheds nested within larger ones (Kurita et al., 2009). Unlike watersheds, however, which only obey topography, foodsheds are constrained by “hybrid social and natural constructs” (Feagan, 2007)1. Furthermore, whereas water is destined ultimately to flow out of the watershed, the foodshed concept is often used to envision maximizing the flows of food to populations within the foodshed (e.g. Giombolini et al., 2010) – similar to the hydrological phenomena of evapotranspiration and infiltration. The second way in which the watershed metaphor is useful is its ability to engage

1 Swaney et al. (2011) actually use “watershed” in this constructed “foodshed” sense, describing the expansion of New York City’s “watershed” to meet the drinking water needs of its growing population by adding reservoirs further upstate.

17 with the idea of place. The title of Kloppenburg et al.’s (1996) article, “Coming in to the Foodshed,” is a reference to an essay entitled “Coming in to the Watershed” (Snyder, 1993). In that essay, Snyder discussed the value of the watershed in terms of its capacity for connecting people to, and embedding them within, their locales. Quoting Missoula, Montana, mayor Daniel Kemmis, Snyder argues that such connection can engender “a profound citizenship in both the natural and the social worlds” (Snyder, 1993). If the watershed can play such a role, then certainly the foodshed, founded on the physical act of eating, can also serve to connect people to—and embed them within—their locales. For Kloppenberg et al. (1996), eating is the consummation of “humanity’s most… essential connections to the earth.” Cast in this light, the foodshed embeds us within the social and ecological relationships of our environment, allowing us to “become native” to our place. If the act of eating directly connects us to the land, then how we eat fundamentally defines, and is defined by, how we use that land. The current food supply fails to meet basic standards for both human nutrition (Buzby et al., 2006; Desjardins et al., 2009; Kantor, 1998; Young and Kantor, 1999) and environmental sustainability (Gussow and Clancy, 1986; Peters et al., 2003, 2009a). The goals of better nutrition and better land use need not lead down divergent paths; indeed, a plant-based diet including a variety of foods and reduced intake of sweeteners and saturated fat is consistent with human health and environmental sustainability (Gussow and Clancy, 1986; Gussow, 1999; Peters et al., 2007). Gussow and Clancy (1986) argue that prescriptive dietary guidelines provide an opportunity for encouraging a “sustainable diet” that aligns these two goals. In this way, dietary guidelines can be used to visualize the land use implications of a shift in diet (Buzby et al., 2006; Young and Kantor, 1999). The foodshed concept, directly linking people to the land via what they eat, can thus function as a “framework for envisioning alternative food systems” (Peters et al., 2009a). Kloppenburg et al. (1996) argue that the foodshed should be applied as a

18 normative concept that can provide “a bridge from thinking to doing”—a tool for visualizing potential scenarios and charting the paths to their realization. “Normative,” in this sense, refers to the attempt to pursue or establish a new norm, model, or standard. Normative scenarios visualize “futures that should be”; they are optimistic “prospective scenarios,” rather than “projective scenarios” that portray the future implications of continued business as usual (Nassauer and Corry, 2004). Normative scenario research uses data to model potential desirable futures with the hope that such information might better inform and guide decision-making. The foodshed as a normative concept refers, then, to potential alternative structures for the food system, with distribution, diet, and production as the key parameters in scenario visualization. Research on normative foodshed scenarios forms the growing field of “foodshed analysis,” a term coined by Getz (1991) and defined by Peters et al. (2009a) as:

[the] study of the actual or potential sources of food for a population, particularly those factors influencing the movement of food from its origin as agricultural commodities on a farm to its destination as food wherever it is consumed.

Swaney et al. (2011) note, however, that the foodshed framework is much more frequently employed to evaluate potential rather than actual (existing) food systems; hence the concept is primarily used for normative, rather than descriptive or projective purposes. By providing research-based understanding of the potential of the food system, foodshed analysis helps to ground the local foods movement in plausibility (Kurita et al., 2009). Simultaneous evaluation of “the geography of food production and food consumption” (Peters et al., 2009a) enables a holistic consideration of potential food system structures. Foodshed analyses typically employ a combination of estimated (current/ actual) and normative (potential) parameters. Research objectives and available data dictate whether estimated or normative figures or circumstances are considered for the 19 parameters discussed below. Often, foodshed analyses will include both estimated and normative parameters in order to make comparisons among scenario outcomes.

• Distribution Nearly all foodshed analyses assume a normative food distribution scenario in which either all food produced within the foodshed is directed to the population of the foodshed, or the dietary needs of the foodshed are prioritized and met before assuming any export. Giombolini et al. (2010) are explicit about this normative assumption, noting that their study is predicated on “all commodities [being] designated for sale in the local retail food market throughout the entire year.” Although assumptions about distribution underlie any foodshed analysis, alternative distribution scenarios are rarely the focus, with the most robust analysis applied to the following two parameters.

• Production Production parameters combine land use and yield figures to answer the question, “How much food is, or could be, produced within the foodshed?” Estimated production in toto elides land use and yield by simply relying on production data (from USDA, for example) for a given area, as in Giombolini et al. (2010). Estimated land use scenarios apply current land use data to identify current agricultural land, while normative land use scenarios assume re-allocation of existing cropland to different crops (e.g. Desjardins et al., 2009; Kurita et al., 2009; Peters et al., 2012) and/or conversion of currently non-productive areas to food production (e.g. Colasanti and Hamm, 2010; Desjardins et al., 2009; Grewal and Grewal, 2012; MacRae et al., 2010). Estimated yield scenarios use actual yield figures from the foodshed; it should be noted, however, that pairing estimated

20 yield figures with a normative land use scenario is fundamentally normative, since it assumes crop yield from land which is not currently producing that crop. Other normative yield scenarios assume shifts in production practices, such as to organic (e.g. Risku-Norja et al., 2008), increased diversification, or season extension, and could even include assumptions about food storage and processing/preserving when incorporating the temporal considerations of seasonality into the scenario (e.g. Colasanti and Hamm, 2010).

• Diet Similar to production, diet scenarios have a wide range of possibilities. Estimated diet scenarios attempt to model current food consumption. In the United States, this generally means using per capita food consumption data from the USDA’s Economic Research Service (ERS) (e.g. Colasanti and Hamm, 2010; Grewal and Grewal, 2012; Kremer and Schreuder, 2012; Timmons et al., 2008), although Urban Design Lab at the Earth Institute (2011) used New York City retail data. International examples from Canada (MacRae et al., 2010), Japan (Kurita et al., 2009), and Finland (Risku-Norja et al., 2008) also use current per capita consumption rates. Normative diets are most often defined by governmental dietary guidelines or nutrition recommendations. Because these guidelines are usually organized by food group, they allow flexibility in terms of specific foods within each group. Some foodshed analyses use current per capita food consumption data to calculate weighted relative preferences for foods within each group, and use these preferences to construct a model diet (e.g. Colasanti and Hamm, 2010; Desjardins et al., 2009); others construct normative diets without regard to current preferences (e.g. Risku-Norja et al., 2008). Peters et al. (2007, 2009b) apply a particularly robust range of diet scenarios, constructing 42 distinct

21 diets that meet the USDA guidelines with differing amounts of meat/eggs and fat.

Foodshed analysis applies these production and diet scenarios to land and population within the foodshed. Outcomes are then compared either in terms of land (area available vs. area needed to produce required food) or food (amount of potential food produced vs. food needed by the population). This binary relationship between agriculture and diet suggests a reciprocal causal relationship: the diet and the food supply are defined by each other, and in order for one to change, both must change. The alternatives envisioned by foodshed analysis can shape the future of the food system in pursuit of increased environmental sustainability and human health. Foodshed analysis is an emerging framework with flexible methodologies and can be employed in different ways in response to specific research objectives. Peters et al. (2012) incorporate considerations of land value to prioritize foods for local production according to their economic return. Grewal and Grewal (2012), who actually never use the term “foodshed,” conduct their analysis in terms of dollars, asking whether the city of Cleveland could be economically “self-reliant” in its food supply. Desjardins et al. (2009) assess food needs in the year 2026 based on current population growth, making their future scenario temporally specific. Risku-Norja et al. (2008) conduct environmental impact assessments of their scenarios. As these examples demonstrate, foodshed analysis is a versatile tool without rigid rules and is not limited to answering the question of how many people could be fed. Just as foodsheds can be framed at different scales, foodshed analysis can be applied to geographic areas of any size; most, however, have been conducted at the scale of city, sub-state region, or state. But drilling down to smaller frames reveals complexities masked by more macro-level analyses. Kurita et al. (2009) conducted their analyses at two “micro levels:” the municipality (within a larger metropolitan region) and the 1 km

22 grid cell. The variations revealed by conducting analysis at these levels demonstrate that “foodsheds are not singular, but plural and multi-scalar.” As the field of foodshed analysis develops, similarly small-scale analyses might better inform changes to the food system while also more accurately capturing the small-scale “tributaries” of foodsheds. Urban foodshed analysis applies the methodology to an urban land base not traditionally considered in terms of significant levels of food production. Urban agriculture, as previously discussed, performs many functions in addition to food production, and although quantifying the production potential of cities is a worthwhile pursuit, Urban Design Lab at the Earth Institute (2011) and McClintock et al. (2010) are careful to note that urban agriculture should not be assessed exclusively on the basis of its food production potential. Urban foodshed analyses are also distinct from larger-scale analyses in that the production and consumption zones are highly integrated. Whereas Peters et al. (2012) model production zones and consumption zones separately (echoing notions of an urban-rural divide), Colasanti and Hamm (2010) identify a single zone—the city of Detroit—and align the potential production and consumption of that zone. Kurita et al. (2009) urge urban foodshed analysts to avoid rigidly defining these zones so that small-scale integrated production-consumption relationships might be better captured. Unlike the regional-scale work of Peters et al. (2012, 2007, 2009b) and others, which typically assumes a re-allocation of existing production land, the urban foodshed analyses conducted by Colasanti and Hamm (2010), Grewal and Grewal (2012), and MacRae et al. (2010) are based on normative production scenarios that assume a substantial increase in production area. These scenarios rely on conversion of currently non-productive land (and, in Grewal and Grewal (2012), rooftops) to intensive food production, raising important questions about labor. While it may be plausible to imagine a farmer shifting to a different mix of crops on his or her land, large-scale cultivation of vacant lots may be somewhat less plausible without significant intervention. Certainly

23 some cultivation can be expected. Urban residents have many motivations for wanting to farm or garden (Kortright and Wakefield, 2010), and many also lack access to their own land (Armstrong, 2000). Finally, urban land offers the benefit of being close to consumers, which may make it desirable for some prospective farmers.

1 .7 Land inventories and land suitability While urban foodshed analysis typically involves identifying vacant land, urban land inventories focus on the land identification process with greater rigor and detail. Land inventories have been undertaken in Portland (Balmer et al., 2006), Seattle (Horst, 2008), Vancouver (Kaethler et al., 2010), Madison (Eanes, 2012), Oakland (McClintock et al., 2010), and Toronto (MacRae et al., 2010); Urban Design Lab at the Earth Institute (2011), Cleveland Urban Design Collaborative (2008), and Kremer and DeLiberty (2011) conducted land inventories for New York City, Cleveland and Philadelphia respectively, but did not apply suitability criteria as rigorously as the other studies. Graduate students conducted the inventories in Portland, Vancouver, and Seattle for city planning departments. These inventories were meant to pursue a practical rather than scholarly goal and in the cases of Portland and Vancouver succeeded in integrating considerations for urban agriculture into their respective city planning processes (Mendes et al., 2008). Although land inventory methods vary, they all attempt to identify available land suitable for food production. This process begins with an initial inventory of vacant parcels or other parcels of interest. Multiple suitability criteria are then applied to these parcels to assess and prioritize them. The outcome is a refined list and maps of suitable parcels, often with specific suitability and other characteristics itemized for each parcel. The suitability criteria are the key components of the land inventory methodology, and each inventory selects and assesses these criteria differently. Key criteria are discussed below.

24 • Solar Exposure Most vegetable crops need ample direct sunlight to provide good yields; eight hours per day during the growing season has been identified as a reasonable goal (Cleveland Urban Design Collaborative, 2008; Eanes, 2012). Access to sufficient sun can be a concern for growers, especially in the urban context (Kortright and Wakefield, 2010). Land inventories thus far (Balmer et al., 2006; Horst, 2008; Kaethler et al., 2010; McClintock et al., 2010) have relied on manual aerial imagery analysis and site visits to assess solar exposure for parcels. Light Detection and Ranging (LIDAR) data provide a potential avenue for modeling solar exposure and thus streamlining assessment of this criterion for land suitability. LIDAR measures surface elevation at a high sampling density and captures the morphology of tree canopy, buildings, and other structures. Bailang Yu et al. (2009) used LIDAR data to model solar radiation in downtown Houston at 10-minute intervals. Their model created highly detailed imagery of solar exposure in the study area. Nipen (2009) used a simpler LIDAR-based solar model to assess suitability of land on the Halifax peninsula for urban agriculture. Although LIDAR solar modeling has not yet been integrated into more comprehensive land inventory methodologies, it could potentially simplify assessment of adequate sun for candidate sites.

• Soil Quality Consideration of soil quality in urban settings has two components: assessment of soil contamination risk, and assessment of soil suitability for food production in terms of its biological, chemical, and physical properties. Most land inventories, when including soil considerations, focus on the first component. Kaethler et al. (2010) list soil contamination as a criterion without being clear about how this was

25 assessed or incorporated. Eanes (2012) demonstrated a site history analysis as a precursor to testing for contaminants but did not perform this analysis and testing for all sites. Carter and Anderson (2012) used spatial data for lead contamination to distinguish parcels with lead ppm above the safe threshold. Urban Design Lab at the Earth Institute (2011) mapped environmental remediation sites and Superfund sites but did not relate these to specific vacant parcels.

None of the urban land inventories found in the literature assessed the physical, chemical and biological characteristics of in situ soils. Many addressed the issue by noting that reliable data was not available and/or by encouraging soil testing for both contamination and quality prior to utilizing any vacant lot. As Carter and Anderson (2012) argue, most if not all urban food growers amend their soil to improve its quality, so the assumption of soil amendment practices precludes the necessity of rigorous soil quality assessment. Furthermore, urban growers could have access to ample organic matter simply by virtue of their proximity to organic waste streams from urban populations. Beniston (2013) demonstrated that by amending degraded soils in an urban site with substantial amounts of compost, vegetable yields were greatly improved.

Yet quality of in situ soils may still be an important consideration for urban land inventories. Urban soils tend to be heavily disturbed and highly variable, with compaction, lack of organic matter, and poor drainage being the key obstacles to vegetable production (Beniston and Lal, 2012). Usufruct agreements often imply a limited or uncertain tenure, and the time and financial investment of amending soils may be a disincentive to improving a parcel if use will be short-lived. Other studies that include rural land assessments, like foodshed analyses by Peters et

26 al. (2012, 2007, 2009b) and Vadrevu et al.’s (2008) Agroecosystem Health Index, incorporate considerations of soil quality. Urban land inventories that do the same may provide greater insight to the potential of urban land, but accurate assessment of urban soil is difficult without performing on-site tests of parcels. Federal soil mapping efforts have typically focused only on the agricultural capability of rural soils and the development potential of urban land; the Soil Survey Geographic (SSURGO) database thus lacks high resolution in urban contexts (Shuster et al., 2011). SSURGO is, however, the most detailed soil database available and may provide at least a preliminary assessment of urban soil quality.

• Slope Farms and gardens require a relatively level surface for normal production practices. Balmer et al. (2006) and McClintock et al. (2010) used DEM data to model slopes in ArcGIS. “Level” parcels were those under 4% or 5% slopes, while the 5-10% slope range was considered feasible but not optimal. Carter and Anderson (2012), describing a land inventory in-process in Indianapolis, assessed slope visually without consulting DEM data.

• Water Access Access to water is a key consideration for gardeners and farmers in urban settings, since most common vegetable and fruit crops require irrigation. Balmer et al. (2006), Carter and Anderson (2012), Eanes (2012), and McClintock et al. (2010) all include water access in their land inventories. Access could take the form of on-site faucets; proximity to water meters, mains, or hydrants; or access to downspouts from adjacent structures.

27 • Other considerations Land inventories include a range of additional criteria. Parcel size, parcel perimeter-area ratio, impervious surface, ground cover and visual impression were all included in at least one of the previously discussed land inventories. Horst (2008), Kaethler et al. (2010) and McClintock et al. (2010) also included some consideration of the surroundings, such as public transportation, demographics, or proximity to schools.

1 .8 Ownership Urban foodshed analyses and land inventories have typically either focused exclusively on publicly owned land (Balmer et al., 2006; Colasanti and Hamm, 2010; Horst, 2008; McClintock et al., 2010) or have failed to differentiate between public and private ownership (Grewal and Grewal, 2012; MacRae et al., 2010). Yet privately owned land is likely to be much more plentiful than publicly owned land (Colasanti and Hamm, 2010; Urban Design Lab at the Earth Institute, 2011) and carries with it certain implications about usufruct agreements for food production. Whereas public entities usually have formalized processes for allowing use of their land, agreements between private landowners and “borrowers” range from non-existent to formal, with many informal arrangements in between (Bayuk, 2011; Dawson, 2011). Policy instruments that encourage use of privately owned land would thus be distinct from initiatives that enable access to public land. Foodshed analyses and land inventories that incorporate more detailed considerations of public vs. private ownership may help guide policy to develop and expand urban food production.

1 .9 This research This research employs a novel integration of urban gradient analysis, foodshed

28 analysis, and land inventory methodologies to gain a better understanding of the potential role that usufruct can play in the provision of vegetable servings and land access to urban populations, and how that role changes according to degree of urbanization and land ownership. Foodshed analysis quantifies normative scenarios for locally- or regionally- based food production and consumption by visualizing how shifts in diet, land use, and/or production might impact the food system. This research evaluates a normative scenario based on usufruct access to publicly and privately owned vacant land in Central Ohio, centered on the metropolitan area of Columbus. Colasanti and Hamm (2010) and Kremer and DeLiberty (2011) have identified a need for greater understanding of the potential contributions of urban land; this research directly responds to that need. A small number of foodshed analyses have been conducted at the city scale, but with the exception of work by MacRae et al. (2010), none have employed rigorous land inventory methods to assess the quality of land on which their projected food yields would be produced. Land inventory methods, which apply suitability criteria to vacant urban land, have up to this point been used primarily for practical or applied objectives, particularly for integrating urban agriculture into municipal planning. The basic land inventory methodology, however, provides a crucial opportunity to hone the accuracy of urban foodshed analysis, which unlike most regional/rural foodshed analysis, assumes substantial conversion of non-productive land into food production. This normative scenario also assumes a designed vegetable component of a diet that meets the USDA Dietary Guidelines for four of the five vegetable sub-groups with a diverse selection of 17 crops. As Buzby et al. (2006), Kantor (1998), and Young and Kantor (1999) have noted, Americans do not currently meet USDA guidelines for vegetable intake. Furthermore, vegetables are high-value crops that are particularly likely to be cultivated in or near cities due to land economics (Ellis and Sumberg, 1998; Peters et al., 2012). This research accounts for these considerations of nutrition deficiencies and

29 land and food value realities by focusing on vegetable production. Although it may be optimistic to imagine Americans meeting national dietary guidelines, Alaimo et al. (2008), Colasanti et al. (2010), and Kortright and Wakefield (2010) have found that the experience of growing food can lead to changes in eating habits. In this way, the normative production scenario imagined by this research could help realize the normative diet scenario: by broadening access to the experience of growing food, more urban residents might have that experience and alter their diets in the process. Foodsheds and food systems are plural, nested, overlapping, irregular, and multiscalar (Clancy and Ruhf, 2010; Kurita et al., 2009). Relationships between potential food supply and food consumption can thus be assumed to vary over space and time. This research conducts many small-scale foodshed analyses in replicate over the scale of a region in tandem with a quantified and categorized urban gradient in order to gain a deeper understanding of the variations in potential foodshed structures. The result is a deeper understanding of food system variation that avoids the oversimplification of a binary urban-rural framework. This research also sidesteps questions of unrealistic distribution scenarios by conducting foodshed analyses on the scale of a walking-distance neighborhood. As previously discussed, most foodshed analyses implicitly assume a distribution scenario wherein all food produced within the system stays within the system and is also distributed appropriately to the population within the system. Though this assumption is problematic at a regional scale, it may be somewhat safer to make such distributional assumptions at the level of a neighborhood. Finally, this research incorporates and quantifies a new variable not included in foodshed analyses or land inventories to date: access to land. Urban agriculture confers many benefits to its communities, and it should not be assessed and valued on the basis of its yields alone (McClintock et al., 2010; Urban Design Lab at the Earth Institute, 2011). By quantifying the potential access to land enabled by usufruct, this research captures

30 another of the myriad benefits generated by urban agriculture. Furthermore, while urban foodshed analyses assume a normative scenario wherein massive amounts of idle land are converted to production, they do not answer the question of who will cultivate that land. By quantifying and relating potential yields with land access for residents who may be motivated to cultivate that land, this research proposes a conceptual framework wherein potential food production is a function of both available land and capacity for cultivation by the local population. Admittedly, however, much further research is needed to understand the dynamics of demand for land access by urban populations. This research is expected to support my thesis that privately owned vacant land represents a substantially more abundant land resource for urban food production than publicly owned vacant land, and that vacant land suitable for vegetable production will decrease in abundance as urbanization increases . Therefore, I expect that the ability to meet dietary vegetable requirements for the local (study site) population will vary according to degree of urbanization . Finally, I conceive of potential vegetable production as a function of (1) vacant suitable land and (2) capacity for cultivation by the local population . The amount of suitable vacant land per household increases from more urban to less urban settings . Therefore, households in more urban sites may have less available land than they could productively use based on current trends in urban gardening . However, if there is an upper limit for the average amount of land per household that can be cultivated, then the aggregate capacity of households to use the available land in less urban areas could be a limiting factor to maximum production . To support this thesis, I will address the following research objectives: 1. Determine how vacant land resources suitable for vegetable production vary according to urbanization, in terms of quantity, quality, and spatial pattern. 2. Determine how vacant land resources suitable for vegetable production differ between public and private ownership, in terms of quantity, quality, and spatial

31 pattern. 3. Determine the potential contributions of these land resources to the local (study site) populations, in terms of both vegetable servings and land access. 4. Determine whether the provision of vegetable servings is limited by lack of land resources or realistic expectations for cultivation by the study site population and how these limiting factors shift across the urban continuum.

Chapter 2 evaluates and compares the characteristics of vacant land resources according to ownership and urbanization. Neighborhood-scale (300-meter radius) study sites were selected in seven central Ohio counties representing four categories of quantified urbanization, and vacant parcels were identified within each study site. A composite land suitability index was created based on solar exposure, soil quality, slope, and water access, and this suitability index was applied to the vacant land within each study site. Spatial characteristics of parcels, total land area per study site, and the suitability of vacant parcels were compared between public and private ownership and between the four urbanization categories. Chapter 3 employs foodshed analysis methods to quantify the extent to which the vacant land resources assessed in Chapter 2 could provide vegetable servings for the study site population while also characterizing the relationship between land area and household. Three of the five tiers of Chapter 2’s suitability index were configured as production scenarios. USDA Dietary Guidelines were used to select a mix of 17 vegetable crops from four of the five USDA vegetable sub-groups. Three vegetable yield datasets were used to estimate potential vegetable production of these crops on the land under each production scenario. The projected vegetable output was then measured against the USDA-recommended number of vegetable servings for the study site population. The amount of land under production in each scenario was also averaged by number

32 of study site households. This ratio illustrates the amount of land access created under each scenario; conversely, it also quantifies the level of per-household demand for land that would be required to realize maximum production potential. Study site results for proportion of required vegetable servings met (under each production scenario and each yield scenario) and land per household (under each production scenario) were compared between urbanization categories.

33 Chapter 2: Analysis and comparison of vacant land resources

2 .1 Introduction As concerns about climate change, sustainability, and food insecurity have come to the forefront of conversations about our food system, scholars, practitioners, and policymakers have explored the extent to which cities might feed themselves. Urban agriculture can expand food access in cities, engage urban dwellers in food production, and provide a beneficial use for unused urban lots. Idle or vacant land, both publicly and privately owned, represents an important resource for urban gardeners and farmers, and potentially for their rural counterparts as well. Applying the concept of usufruct, or productively using another’s unused land, could increase agricultural use of both privately and publicly owned land resources. But little is understood about how these land resources vary according to degree of urbanization or ownership. Urban land inventories identify and assess vacant land resources with potential for urban food production. This approach typically begins with identification of vacant parcels and then applies a series of criteria to these parcels to assess their suitability for urban agriculture. Land inventories have been conducted in Portland (Balmer et al., 2006), Seattle (Horst, 2008), Vancouver (Kaethler et al., 2010), Madison (Eanes, 2012), Oakland (McClintock et al., 2010), and Toronto (MacRae et al., 2010). Most of these inventories were meant to pursue a practical rather than scholarly goal, and in the cases of Portland and Vancouver succeeded in integrating considerations for urban agriculture into their respective city planning processes (Mendes et al., 2008). Land inventories have typically either focused exclusively on publicly owned land

34 (Balmer et al., 2006; Horst, 2008; McClintock et al., 2010) or have failed to differentiate between public and private ownership (MacRae et al., 2010). Privately owned land is likely to be much more plentiful than publicly owned land (Colasanti and Hamm, 2010; Urban Design Lab at the Earth Institute, 2011), but it carries with it certain implications about usufruct agreements for food production. Whereas public entities usually have formalized processes for allowing use of their land, agreements between private landowners and “borrowers” of their land range from non-existent to formal, with many informal arrangements in between (Bayuk, 2011; Dawson, 2011). Policy instruments that encourage use of privately owned land are thus distinct from initiatives that enable access to public land. Foodshed analyses and land inventories that incorporate more detailed considerations of public vs. private ownership may help guide policy to develop and expand urban food production. This chapter evaluates and compares the characteristics of vacant land resources according to ownership and urbanization. Neighborhood-scale (300-meter radius) study sites were selected in seven central Ohio counties representing 4 categories of quantified urbanization, and vacant parcels were identified within each study site. A composite land suitability index was created based on solar exposure, soil quality, slope, and water access, and this suitability index was applied to the vacant land within each study site. Spatial characteristics of parcels, total land area per study site, and the suitability of vacant parcels were compared between public and private ownership and among the four urbanization categories.

2 .2 Methods Figure 2.1 provides an overview of the methods described below.

35 CHAPTER 2 FIG 2.1. Schematic of methods U.S. Census population and housing data MORPC employment data FIGURE 2.1. Schematic of Creation of urban categories methods Categorized Census block groups Random selection of study sites

300-meter radius study sites

County parcel data CHAPTER 3 ID of vacant parcels by land use class Study sites Con rmation by Aerial imagery aerial imagery U.S. Census population and housing data

Selected vacant parcels Study site population and housing estimates Soil quality Application of Solar exposure suitability index Water access to vacant parcels USDA dietary guidelines Slope

Vacant parcels with Study site land area rated dietary requirements for suitability for vegetables

USDA dietary guidelines vegetable subgroups Selection of 17 vegetable crops

Biointensive yields Small market farm yields Commercial yields

Three vegetable yield scenarios

Three production scenarios for suitable land Study site Study site dietary requirements housing estimates for vegetables

Suitable land Proportion of vegetable per household requirements met

36 2 .2 .1 Study area: Central Ohio Seven counties in central Ohio were selected for this study: Delaware, Fairfield, Franklin, Licking, Madison, Pickaway, and Union. These counties, along with Morrow (which was not included in this study) make up the Columbus Metropolitan Statistical Area (MSA). Columbus is Ohio’s capital, the largest city, and home to The Ohio State University. Unlike Ohio’s two other major cities, Cleveland and Cincinnati, Columbus has been steadily gaining population for the past half century. For this reason, Columbus offers a contrast to vacant land research that has focused on shrinking cities with vast vacancies, such as Detroit and Buffalo. The Columbus MSA population grew 6.2% from 2004 to 2009—higher than the average rate of 5.4% for the 100 largest MSAs in the U.S. (Community Research Partners, 2011). Of the cities where land inventories have been conducted, Indianapolis and Portland have the most in common with Columbus in terms of population growth and density; however, studies in these cities did not compare public or private ownership or assess urbanization. Central Ohio thus provides an opportunity to better understand the dynamics of vacant land resources in growing U.S. metropolitan areas. Figure 2.2A shows the location of the study area, and Table 2.1 provides basic population statistics for the seven counties.

TABLE 2.1. Study Area Population By County County 2010 Population Population Per Sq. Mile 2000-2010 Growth Delaware 174,214 393.2 +58.4% Fairfield 146,156 289.8 +19.1% Franklin 1,163,414 2,186.1 +8.8% Licking 166,492 243.9 +14.4% Madison 43,435 93.2 +8.8% Pickaway 55,698 111.1 +5.6% Union 52,300 121.1 +27.8%

37 Rural (A) Suburban (B) Urban Employment (C) Urban Residential (D) Study Site

FIGURE 2.2A. Study area and urbanization categories

Union Delaware

Licking

Madison Franklin Fairfield

Pickaway

FIGURE 2.2B. Study area and study sites

38 2 .2 .2 Creation of urbanization categories Urban gradient analysis evaluates how various factors, such as population density, spatial pattern, land use, or impervious surface cover, change progressively and predictably from less urban to more urban environments (Alberti, 2008, 2005; Breuste et al., 2008; Hahs and McDonnell, 2006; McDonnell and Hahs, 2008). Selected factors can be reduced to a single gradient using principal components analysis (PCA), and the resulting gradient can be used as an independent variable in studying additional factors (Alberti, 2008; Hahs and McDonnell, 2006). For this research, population density, housing density, and employment density were used to create a 2-factor PCA, which was the basis for a k-means cluster analysis. This cluster analysis sorted U.S. Census block groups into 4 discrete categories of urbanization. Figure 2.2A illustrates these categories. U.S. Census Bureau 2010 Decennial Census block group-level population and housing data were acquired from the Mid-Ohio Regional Planning Commission (MORPC), as were proprietary 2010 employment point data aggregated by MORPC to a ¼-mile grid (Mid-Ohio Regional Planning Commission, 2010a; U.S. Census Bureau, 2010). U.S. Census employment data was not yet available for 2010. Job totals were redistributed from the MORPC ¼-mile grid to census block group geographies in ArcMap 10.1 (ESRI, 2012) using a basic areal weighting method (Yale University, 2007). ArcMap’s “Calculate geometry” function was used to calculate the area in hectares of each block group, and the population, housing, and employment totals were divided by this area to calculate densities for each block group in terms of persons, housing units, or jobs per hectare. The data table with these densities was imported to SYSTAT 13.1 (Systat Software, Inc., 2012). PCA with two factors was used to analyze the three density variables for population, housing, and employment. The resulting two factors explained slightly more than 96% of the total variance. Based on the component loadings, Component 1 generally

39 corresponds (positively) to population and housing, while Component 2 generally corresponds (negatively) to employment. K-means cluster analysis was applied to the standardized PCA factor scores to group census blocks into seven clusters. The spatially fine gradations in resulting clusters would have made sampling with 300-meter study sites problematic, so some of the seven clusters were combined based on interpretation of their PCA scores. Cluster 1 block groups were designated as urban category A; Cluster 2 block groups were designated as urban category B; Clusters 3 and 4 were combined to form urban category C; and Clusters 6 and 7 were combined to form urban category D. Cluster 5 was omitted because it only contained two block groups. Table 2.2 shows descriptive statistics for each designated category in terms of the original three variables of population, housing and employment density, as well as a descriptive name based on these variables. For ease of interpretation, henceforth these categories will be referred to as Rural (A), Suburban (B), Urban Employment (C), and Urban Residential (D).

TABLE 2.2. Urbanization Categories Population per Housing units Jobs per hectare hectare per hectare Description Category A 2.9 +/- 4.4 5.4 +/- 4.5 2.3 +/- 2.0 Rural Category B 4.3 +/- 4.4 21.5 +/- 5.4 9.9 +/- 2.8 Suburban Category C 61.5 +/- 50.0 17.4 +/- 14.4 7.8 +/- 6.1 Urban Employment Category D 6.4 +/- 7.0 51.1 +/- 29.8 23.9 +/- 9.6 Urban Residential

2 .2 .3 Study site selection Randomly sampled study sites of 300-meter radius were selected within each of the four urban categories. A distance of ¼-mile is often used in urban planning parlance as a proxy for “walking distance”; however, study sites with ¼-mile radii were in some cases too large to represent census block groups, so the slightly smaller size of 300-meter

40 radius was selected. To minimize inclusion of bordering urban categories in sample sites, each category zone was reduced in size by a 200-meter interior buffer created along each category’s boundary. The “Create random points” tool was used to generate 30 random points separated by at least 600 meters (to prevent overlapping 300-meter buffers) in the Rural and Suburban categories. The limited extent of the Urban Employment and Urban Residential categories did not allow the same approach, however, because the “Create random points” tool was unable to generate points at the necessary density. Instead, the tool was used to place one point randomly within each Urban Residential block group not eliminated by the boundary buffer. All points that were more than 600 meters from the nearest point were kept. The remaining points were manually edited by a process of selecting a point at random, deleting all points within 600 meters, selecting another point just beyond 600 meters of the first, and repeating the process. A dense coverage of random points all separated by at least 600 meters resulted. This process was repeated for the Urban Employment category, but because this category’s block groups were larger, three random points were initially created within each block group. After all points for all categories were finalized, these points were given a 300-meter buffer to establish study sites. Categorization of Ohio State University’s campus was suspect because all university employment appeared to be assigned to a single location. For this reason, two Urban Employment sites located on the OSU campus were omitted. In spite of the 200-meter interior buffer created for each zone, some study sites occurring near the boundary of their zone included portions of neighboring categories. Percentage composition of each site was calculated to confirm that sites were composed of at least 70% of the category they were representing. This process resulted in many more Urban Residential sites than the other categories. A random number generation code (Iowa State University, 2012) was used to assign a random value to each Urban Residential site. Sites were then sorted by this value and the sites with the 17 lowest values were omitted, with the exception of two

41 sites located outside of the central footprint of Columbus. These were preserved because all other Urban Residential sites were located within the footprint of Columbus. An additional Suburban site was ultimately deleted because no parcel data was available for that area. This process resulted in the following number of study sites per urban category: Rural (A): 30 Suburban (B): 29 Urban Employment (C): 25 Urban Residential (D): 32 Total: 116 study sites Figure 2.2B illustrates the location of study sites within the study area.

2 .2 .4 Identification of vacant parcels County parcel data was acquired from each of the seven counties (Delaware County Auditor, 2012; Fairfield County, 2012; Franklin County, 2012; Licking County, 2012; Madison County, 2012; Pickaway County, 2012; Union County, 2012). All parcels that overlapped study sites were extracted using ArcMap’s “Spatial Join” tool, resulting in a separate shapefile for each relevant combination of county and urban category. A field denoting vacancy status was created in each attribute table, and each parcel was classified as vacant or non-vacant based on its land use class. Some vacant land use classes were omitted (classified as non-vacant): agricultural vacant land was omitted because it was assumed already to be in production, and industrial vacant land was omitted because of soil contamination concerns. Parcels classified as rights-of-way or lacking any land use class were also omitted. Some land uses did not denote vacancy status, such as “Owned by County,” “Zero valued parcels,” and any “Exempt” land use. These were all designated as “TBD” for later assessment using aerial imagery. Franklin County’s parcel data has two attributes relevant to vacancy status: land use class and property type. When these

42 attributes agreed, the parcel in question was designated as either vacant or non-vacant. When land use class indicated one status and property type indicated another, that parcel was also designated as “TBD”. Aerial imagery for each county was acquired from the Ohio Statewide Imagery Program (Ohio Statewide Imagery Program, 2006a, 2006b, 2006c, 2006d, 2006e, 2006f, 2006g). All parcels previously designated with “TBD” vacancy status were visually assessed on the basis of aerial imagery. Google Maps and Google Street View were also occasionally consulted (Google, 2013). Parcels that appeared to be entirely free of structures and not currently in use (as parking lots, for example) were classified as vacant, and all others were classified as non-vacant. Some individual parcels were composed of non-contiguous separate fragments, some of which were located entirely outside of study site boundaries. ArcMap’s “Multipart to single part” operation was used to disaggregate these parcels, and outlying fragments were deleted. All parcels that had thus far been classified as vacant were given a final assessment using aerial imagery. This led to a 10.3% reduction in the number of parcels classified as vacant, ultimately resulting in 696 parcels being included in the subsequent analysis. No study sites in Madison, Pickaway, or Union counties contained any vacant parcels, so the subsequent analysis was conducted on vacant land in Delaware, Fairfield, Franklin, and Licking counties. A field denoting public or private ownership was created and populated with “Public” or “Private” based on the owner name of each parcel. Parcels owned by public entities (city, county, or state) were classified as publicly owned and all others were classified as privately owned. Spatial characteristics of whole vacant parcels (including contiguous portions lying outside of study site boundaries) were calculated in the ArcMap attribute tables. Fields

43 were added for parcel area and perimeter and calculated using the “Calculate Geometry” tool. A third field for perimeter-area ratio (PAR) was calculated using the following equation: PAR = Perimeter / √(Parcel area). Parcels were then clipped to study site boundaries. Subsequent land suitability assessment was only performed for the portions of parcels lying within study site boundaries. Figure 2.3 illustrates whole vs. clipped parcels in an example study site.

Publicly owned parcels

Privately owned parcels FIGURE 2.3. Study site with whole and clipped parcels

2 .2 .5 Land suitability index Four factors were selected to assess vacant parcels for vegetable production suitability. Selection was based on existing land inventory methods, conversations with practitioners, and available data. These factors were soil quality, slope, solar exposure, and access to water.

• Soil quality Although quality of in situ soils has not been included in urban land inventories

44 to date, it is a key consideration for gardeners and farmers, particularly those cultivating borrowed land. Usufruct agreements often imply a limited or uncertain tenure, and the time and financial investment of improving soils may be a disincentive if use of a parcel will be short-lived. Urban soils tend to be heavily disturbed and highly variable, with compaction, lack of organic matter, and poor drainage being the key obstacles to vegetable production (Beniston and Lal, 2012). Accurate assessment of these and other characteristics is difficult without performing on-site soil tests of parcels. The Soil Survey Geographic (SSURGO) database, though extensive and detailed, lacks high resolution in urban contexts (Shuster et al., 2011). It is, however, the most detailed soil database available, and is used in this research as a broad indicator of potential in situ soil quality. SSURGO data for each county was downloaded from the USDA Soil Data Mart (Natural Resources Conservation Service, United States Department of Agriculture, 2012a, 2012b, 2012c, 2012d, 2010a, 2010b, 2010c). SSURGO data has two components: the spatial soil map, which is composed of polygon soil map units, and the soil survey attribute database. The Soil Data Viewer (Natural Resources Conservation Service, United States Department of Agriculture, 2011) is a free, publicly available add-in extension for ArcMap that allows integration of the attribute database with the spatial soil map units in the ArcMap environment. This tool was used to query and process soil attributes for this analysis. A composite soil quality rating was developed based primarily on the Cornell Soil Health Assessment Training Manual (SHATM), which rates soils based on 12 indicators grouped into three equally weighted categories: physical, biological, and chemical (Gugino et al., 2009). Because only some of these indicators were available in the SSURGO dataset, the rating system used was simplified from the Cornell SHATM, with one indicator from each category, as well as an additional attribute for drainage class, as follows: • Physical: Available water capacity (AWC)

45 • Biological: Percent organic matter (OM) • Chemical: pH • Drainage class Some of the soil map units in the SSURGO database lacked values for these attributes. This incomplete data was remedied in one of two ways: (1) attributes were transferred from a map unit of the same soil type found in another county (e.g. Franklin County map unit CfB was given the attributes of Fairfield County map unit CfB); or (2) attributes were transferred from a corresponding soil type elsewhere within the same county (e.g. Cardington Urban soils – CbB – were given the attributes of other Cardington soils – CaB – found elsewhere in the same county). In this way, all soil map units were assigned attributes for the four soil quality indicators, with the exception of Udorthents, or imported fill soils, and gravel quarry/pits. For AWC, OM, and pH, scoring functions provided by the Cornell SHATM were used to rate each indicator value into three tiers, with 3 being the highest (or best) and 1 being the lowest. The tier thresholds for AWC and OM were determined by texture class. Drainage class ratings were also grouped into three categories. Indicator values are specified in Table 2.3. After each of the four indicators was given a 1-3 rating, these ratings were combined using a weighting system based on Vadrevu et al.’s (2008) Agroecosystem Health Index (AHI). In the AHI, soil is rated according to seven attributes: soil organic matter (%), available water capacity, pH, erosion factor, land capability class, farmer’s reliance on fertilizer, and farmer’s reliance on herbicides. Each indicator was assigned a weight based on the input of experts in an analytical hierarchy process. The relative weights for AWC, OM, and pH from Vadrevu et al. (2008) were used for this 4-indicator system, and drainage class was assigned the same weight as AWC. Indicators were thus weighted as follows: AWC (21.22%), drainage class (21.22%), OM (40.95%), and pH

46 TABLE 2.3. Soil quality indicator ratings Indicator Rating 1 Rating 2 Rating 3 Coarse soils Available Water Capacity (m/m) < 0.096 0.096-0.164 > 0.164 Percent Organic Matter (%) < 2.34 2.34 – 3.85 > 3.85

Medium soils Available Water Capacity (m/m) < 0.134 0.134 - 0.186 > 0 .186 Percent Organic Matter (%) < 2.85 2.85 – 4.15 > 4.15

Fine soils Available Water Capacity (m/m) < 0.142 0.142 – 0.217 > 0.217 Percent Organic Matter (%) < 3.54 3.54 – 4.75 > 4.75

All soils pH < 5.7 ; > 7.6 5.7 – 6.1 ; 7.5 – 7.6 6.2 – 7.4 Drainage Class Poorly drained; Somewhat poorly Moderately well very poorly drained drained drained; well drained

(16.61%). A composite soil quality rating was calculated for each map unit using these weights. Udorthents soils and gravel/quarry pits, which did not have values for the four indicators, were assigned the lowest possible score of “1.”

• Slope Farms and gardens require a relatively level surface for normal production practices. In land inventories for Portland and Oakland, Balmer et al. (2006) and McClintock et al. (2010) used Digital Elevation Model (DEM) data to model slopes in ArcMap. “Level” parcels were those under 4% or 5% slope, while the 5-10% slope range was considered feasible but less optimal. Slope assessment methods and ratings used in this research were based on these studies. ESRI GRID Digital Elevation Model (DEM) Mosaic data was downloaded for each county (Ohio Statewide Imagery Program, 2006h, 2006i, 2006j, 2006k, 2006l, 2006m, 2006n). ArcMap’s “Slope” tool was used to generate slope TIFF files for each study site using the same cell size as the DEM mosaics (2.5-foot). Percent slope values were then

47 reclassified to a 3-tier rating with 0-5% slopes rated “3”, 5-10% slopes rated “2”, and slopes above 10% rated “1”.

• Solar exposure Most vegetable crops need ample direct sunlight to provide good yields; eight hours per day during the growing season has been identified as a reasonable goal (Cleveland Urban Design Collaborative, 2008; Eanes, 2012). This can be a challenge for urban vegetable growers because of the shading effects of the built environment and the tree canopy. Light Detection and Ranging (LIDAR) data provides a potential avenue for modeling solar exposure to assess a parcel’s suitability for vegetable production. LIDAR measures surface elevation at a high sampling density, capturing the morphology of tree canopy, buildings, and other structures. Yu et al. (2009) used LIDAR data to model solar radiation in downtown Houston at 10-minute intervals, and Nipen (2009) used a much simpler LIDAR-based model to assess suitability of land on the Halifax peninsula for urban agriculture. This research used a solar modeling approach somewhat simplified from the approach of Yu et al.’s, but more extensive than Nipen’s. Individual LIDAR data tiles corresponding to the study sites were downloaded from the Ohio Statewide Imagery Program (Ohio Statewide Imagery Program, 2006o). Tiles were converted to TIFF format, and ArcMap’s “Hillshade” tool was used to model sun and shade accounting for the azimuth and altitude of the sun. Shade was modeled for every hour of every day for 21 full weeks starting with the estimated last spring frost date to the end of the week of the estimated first autumn frost. Assuming 2012 dates, this means that the model was run from Wednesday, May 9 to Tuesday, October 2. The resulting daily rasters were averaged to generate a final single raster of average hours of sun per day during the growing season. Raster cells were given a binary rating designating whether they received 8 or more hours of sun per day on average.

48 • Access to water Access to water is a key consideration for gardeners and farmers in urban settings, since most common vegetable and fruit crops require irrigation. Land inventories in Portland, Indianapolis, Madison, and Oakland all included water access as a criterion for parcel suitability (Balmer et al., 2006; Carter and Anderson, 2012; Eanes, 2012; McClintock et al., 2010). In this research, a binary water access rating was based on parcel data when available. Franklin County parcel data included an attribute for “public water,” denoting when a public water line was available on-site (though without specifying whether a spigot or other infrastructure was installed). Delaware, Fairfield, and Licking county parcels lacked such an attribute. Parcels in these counties were assumed to have water access if they fell within the boundaries of a municipality, based on a shapefile of municipal boundaries acquired from MORPC (2010b). Parcels that did not fall within municipal boundaries were assumed to not have access to water.

• Five-tier land suitability index The ratings for soil quality, slope, solar exposure, and access to water were processed to create five tiers of land that would be suitable for vegetable production under different assumptions of improvement. Only land receiving eight or more hours of sun per day on average during the growing season was included. Tier 1 land was assumed to require no improvement at all: in addition to receiving necessary sun, it had soils rated 2.25 or higher, slopes under 5%, and access to water. Soil amendment is a common improvement activity undertaken by urban gardeners and farmers (Carter and Anderson, 2012), so the next tiers assumed increasing levels of soil improvement. Tier 2 included soils rated 1.75 or higher, and Tier 3 included all soils. Rainwater catchment structures are a solution for land without access to water; this level of investment was assumed for Tier 4, which included land meeting the previous criteria whether or not it had access to water.

49 Tier 5 land assumed landscaping to level moderate slopes, and included land meeting the previous criteria with up to 10% slopes. These five tiers exclude any land not receiving eight hours of sun or with slopes over 10%. These tiers were processed in ArcMap as a 2-meter-cell raster that combined slope and solar exposure ratings and were clipped by the parcel and soil polygons depending on the tier criteria. Table 2.4 summarizes the characteristics of these tiers. Figure 2.4 illustrates the suitability tiers in example study sites from each of the four urban categories, and Figure 2.5 demonstrates the suitability rating process.

TABLE 2.4. Land Suitability Tiers Soil Quality Water Access Slope Average sun Tier 1 2.25+ Yes <5% 8+ hours/day Tier 2 1.75+ Yes <5% 8+ hours/day Tier 3 1.0+ (all) Yes <5% 8+ hours/day Tier 4 1.0+ (all) No <5% 8+ hours/day Tier 5 1.0+ (all) No <10% 8+ hours/day

2 .2 .6 Calculation of study site characteristics The following characteristics were calculated for each of the 116 study sites: • Mean parcel size (in terms of area): publicly owned, privately owned, and combined. • Mean perimeter-area ratio: publicly owned, privately owned, and combined. • Total vacant area: publicly owned, privately owned, and combined. • Total area for each tier of land suitability: publicly owned, privately owned, and combined. • Percent of total vacant land qualifying for each tier of land suitability: publicly owned, privately owned, and combined.

50 Rural (A) Suburban (B)

Tier 1 Tier 2 Tier 3 Tier 4 Tier 5

Publicly owned parcels

Privately owned parcels

Urban Employment (C) Urban Residential (D)

FIGURE 2.4. Examples of land suitability ratings by urban category

2 .2 .7 Statistical analysis Statistical analysis was performed to test the following null hypotheses:

• H0: Urbanization has no effect on vacant parcel size, abundance, or quality.

• H0: Ownership has no effect on vacant parcel size, abundance, or quality. Many study sites had no vacant land, and many sites with vacant land had no land qualifying for a given suitability tier. These characteristics resulted in non-normal data distribution, which was caused by a large number of zero values. When zero values were excluded, however, and data was transformed, data values were normally distributed. As a result, a mixed model was used: the first part of the model differentiated between zero

51 FIG 2.X. Example of suitability index process

Privately owned parcel

Publicly owned parcel

Study site boundary

LIDAR data was used to model average daily hours of sun during the growing season. The images at left and right show the solar exposure layer with partial transparency.

These images show the solar exposure layer with no transparency. The lighter cells represent higher averages of daily sun.

The solar exposure layer was reclassi ed to a binary rating. White cells represent areas receiving at least eight hours of sun per day on average, and black cells represent areas receiving less than eight hours.

Slopes were calculated using DEM data and reclassi ed to a three-tiered rating. 0-5% slope

5-10% slope

10+% slope

The slope and solar ratings were combined with rated soil polygons and parcels rated for water access to create a ve-tiered rating system. Suitability decreases from 1 to 5. Only ratings 2, 4 and 5 are shown here.

1 2 3 4 5

FIGURE 2.5. Example of suitability rating process

52 and non-zero values, and the second part of the model determined the distribution of non-zero values (Hyndman, 2010). Chi-square tests were used to analyze both likelihood of presence of vacant land within each study site and likelihood of presence of land qualifying for each tier when vacant land is present, according to urbanization category and public or private ownership. ANOVA tests were run on transformed values (Table 2.5 shows types of transformation used) to measure the effect of urbanization category and public or private ownership on each of the calculated study site characteristics. Because only one Rural category study site had a publicly owned parcel, the effect of ownership within the Rural category could not be analyzed, and analysis of the effect of urbanization on publicly owned parcels was limited to the Suburban, Urban Employment and Urban Residential categories. Statistical analysis was performed in SYSTAT 13 (Systat Software, Inc., 2012).

TABLE 2.5. Data Transformations Variable Transformation Mean Parcel Size Log10(x) Mean Perimeter-Area Ratio 1/X2 Total Vacant Area Log10(x) Scenario (1-5) Area Log10(x) Percent Scenario (1-5) Area arcsine √x

2 .3 Results A complete summary of statistical results can be found in Appendix A.

2 .3 .1 Differences between publicly and privately owned land Results indicate that there are some significant differences in abundance, spatial

53 characteristics and suitability between publicly and privately owned vacant land, and that in some cases these differences depend on the urban context. Publicly owned parcels were

found to be larger in the Suburban category (F(1,31)=14.073, p=0.001; Fig. 2.6), but this difference did not carry through to more urban contexts. Results support the hypothesis that privately owned land is more abundant than publicly owned land. Privately owned land is more likely to be present across all categories (χ2=53.593, df=1, N=232,

p=<0.001; Fig. 2.7). In the Urban Employment (F(1,24)=6.555, p=0.017; Fig. 2.8) and Urban

Residential categories (F(1,28)=10.467, p=0.003; Fig. 2.9), it is also likely to occur in greater quantity. This difference was not found in the Suburban category, however (F(1,31)=0.316, p=0.578). This result may correspond to the larger parcel size of publicly owned land in suburban areas. Ownership was not found to have an effect on parcel perimeter-area

ratio (F(1,95)=0.191, p=0.663). Statistically significant differences in abundance and spatial characteristics between publicly and privately owned land are summarized in Table 2.6A. Analysis of the land suitability index also revealed some differences in quality between publicly and privately owned vacant land. Tier 1 land, which had the most stringent criteria, was too sparse to analyze in detail by ownership, but it was no more

TABLE 2.6A. Summary of Results: Abundance and spatial characteristics of publicly owned vs. privately owned land Chi- ANOVA Data Restriction Effect square N F-Ratio df p-value Mean Parcel Size Urbanization x None Ownership 97 4.291 3,89 0.007 Category B Ownership 33 14.073 1,31 0.001

Presence of Vacant Land None Ownership 53.593 232 1 <0.001

Total Vacant Area (when present) Urbanization x None Ownership 97 4.363 3,89 0.006 Category C Ownership 26 6.555 1,24 0.017 Category D Ownership 30 10.467 1,28 0.003

54 200,000

100,000

65,477 Area in square meters in square Area

3,606

Privately owned parcels Publicly owned parcels

FIGURE 2.6. Parcel size: Category B (Suburban) parcels by ownership

Privately Owned Parcels Publicly Owned Parcels

18% Sites with vacant parcels present 66% 34% Sites with no vacant parcels present 82%

FIGURE 2.7. Presence of vacant parcels: By ownership

50,000

25,000

16,845 Area in square meters in square Area

2,580

Privately owned Publicly owned

FIGURE 2.8. Vacant area per site, when present: Urban Employment category by ownership

55 15,000

7,500 4,995 Area in square meters in square Area

1,174

Privately owned Publicly owned

FIGURE 2.9. Vacant area per site, when present: Urban Residential category by ownership

likely to be present in publicly owned vacant land than in privately owned (χ2=0.523, df=1, N=97, p=0.470). In the Urban Employment category, privately owned land qualifying

for Tier 2 (F(1,24)=8.010, p=0.009), Tier 3 (F(1,24)=6.586, p=0.017), Tier 4 (F(1,24)=6.586,

p=0.017), and Tier 5 (F(1,24)=6.456, p=0.018) was found to be more plentiful than publicly owned land qualifying for those tiers (Fig. 2.10). These results are consistent with the difference in abundance of vacant land and do not suggest any actual differences in land composition. In fact, publicly owned land was found to have a higher proportion of land qualifying for Tier 3 (F(1,86)=4.695, p=0.033; Fig. 2.11), suggesting that although it is more rare, publicly owned land is of higher quality. Statistically significant differences in suitability between publicly and privately owned land are summarized in Table 2.6B. These results support rejection of the null hypothesis stating that “ownership has no effect on vacant parcel size, abundance, or quality.”

2 .3 .2 Differences in vacant land among urban categories Significant differences were also found in vacant land resources among

56 TABLE 2.6B. Summary of Results: Suitability of publicly owned vs. privately owned land Chi- ANOVA Data Restriction Effect square N F-Ratio df p-value Tier 1 Area (when present) None Ownership 14 4.387 1,12 0.058

Tier 1 Percent of Total Vacant Land (when Tier 1 land is present) None Ownership 14 5.074 1,12 0.044

Tier 2 Area (when present) Urbanization x None Ownership 88 3.412 3,80 0.021 Category C Ownership 26 8.010 1,24 0.009

Tier 2 Percent of Total Vacant Land (when Tier 2 land is present) None Ownership 88 4.361 1,86 0.076

Tier 3 Area (when present) Urbanization x None Ownership 88 2.994 3,80 0.036 Category C Ownership 26 6.586 1,24 0.017

Tier 3 Percent of Total Vacant Land (when Tier 3 land is present) None Ownership 88 5.658 1,86 0.033 Categories B-D Ownership 89 0.583 1,87 0.068

Tier 4 Area (when present) Urbanization x None Ownership 93 3.527 3,85 0.018

Category C Ownership 26 6.586 1,24 0.017

Tier 4 Percent of Total Vacant Land (when Tier 4 land is present) Category D Ownership 29 2.939 1,27 0.098

Tier 5 Area (when present) Urbanization x None Ownership 95 3.580 3,87 0.017 Category C Ownership 26 6.456 1,24 0.018

57 30,000

Tier Tier 2 2 Tier Tier 3 3 15,000 Tier Tier 4 4 Tier Tier 5 5 Area in square meters in square Area

7,051 7,094 7,094 11,175 490 839 839 1,487 Privately owned Publicly owned

FIGURE 2.10. Tier 2, 3, 4, and 5 land area per site, when present: Urban Employment category by ownership

100

Tier Tier 2 2 46% 48% Tier Tier 3 3 36% 37% Percent of vacant land area of vacant Percent

Privately owned Publicly owned

FIGURE 2.11. Tier 2 and Tier 3 percent of total vacant area, when present: By ownership

58 urbanization categories. Parcels in the Rural category were substantially larger than in the other categories, with parcel size getting progressively smaller in the Suburban,

Urban Employment, and Urban Residential categories (F(3,75)=6.563, p=0.001; Fig. 2.12). Among privately owned parcels, those in the Rural category were again the largest, but those in the Urban Employment category were larger than those in the Suburban or

Urban Residential categories (F(3,72)=6.855, p=<0.001; Fig. 2.12). Suburban category publicly owned parcels were larger than publicly owned parcels in the Urban Employment or Urban Residential categories (F(2,17)=6.838, p=0.007; Fig. 2.12). Together these results indicate that, excluding the Rural category, publicly owned parcels are largest in the Suburban category, while privately owned parcels may be largest in the Urban Employment category. Private landowners in Urban Employment areas are perhaps more likely to be employers or companies with larger tracts of land, while Suburban landowners are likely to be individual owners of residential parcels. The larger publicly owned parcels in the Suburban category suggest that publicly owned land occurs in larger swaths outside of denser urban contexts. Urbanization was not found to have an effect on parcel perimeter-area ratio (F(3,75)=0.646, p=0.588). The Rural category is significantly less likely than the other categories to have vacant land included in this analysis (χ2=34.679, df=3, N=116, p=<0.001; Fig. 2.13). In an effort to identify “idle” vacant land not currently in production, this analysis excluded parcels with an “agricultural vacant” land use class. It is apparent (and not surprising) that when vacant land occurs in this traditionally agricultural setting, it is likely already to be in production, rather than “idle.” Statistically significant differences in abundance and spatial characteristics among urban categories are summarized in Table 2.6C. Urbanization did not have a statistically significant effect on the presence

2 (χ =3.762, df=3, N=79, p=0.288) or amount of land (F(3,9)=1.426, p=0.298) qualifying for Tier 1. Tier 2 and Tier 3 land, which has a less stringent soil quality criterion, is less likely

59 TABLE 2.6C. Summary of Results: Abundance and spatial characteristics of vacant land by urbanization category Chi- ANOVA Data Restriction Effect square N F-Ratio df p-value Mean Parcel Size Urbanization x None Ownership 97 4.291 3,89 0.007 Publicly-owned B-D only Urbanization 20 6.838 2,17 0.007 Privately-owned Urbanization 76 6.855 3,72 0.000 Public/Private combined Urbanization 79 6.563 3,75 0.001

Presence of Vacant Land None Urbanization 28.233 232 3 <0.001 Public/Private combined Urbanization 34.679 116 3 <0.001

Total Vacant Area (when present) Urbanization x None Ownership 97 4.363 3,89 0.006 Publicly-owned B-D only Urbanization 20 3.199 2,17 0.066

60 250,000

200,000

150,000

All parcels ALL PrivatelyPrivate owned Public Publicly owned

100,000 Area in square meters in square Area

50,000

Rural Suburban Urban Employment Urban Residential

FIGURE 2.12. Parcel size: By urbanization

Urban Urban Rural Suburban Employment Residential Sites with vacant parcels 10% 12% present

27% 28% Sites with no vacant parcels present 73% 90% 88% 72%

FIGURE 2.13. Presence of vacant parcels: By urbanization

61 to occur in Rural vacant land (χ2=32.921, df=3, N=79, p=<0.001; Fig. 2.14). When Tier 3 land is present, it occupies larger areas in the Rural category than in the other categories

(F(3,68)=2.742, p=0.050; Fig. 2.15), with the Urban Residential category having the least area. The occurrence of Tier 4 land was not found to vary according to urbanization (χ2=4.514, df=3, N=79, p=0.211). Tier 4 broadens the criteria to include parcels without access to public water. Public water access typically corresponds to higher-density, more urban settings, so it is not surprising that broadening the criteria in this way would nullify differences found between the Rural category and other categories under more stringent criteria. Tier 5 land, which broadens previous criteria by including slopes of 5-10%, is less likely to occur in the Rural category (χ2=8.989, df=3, N=79, p=0.029; Fig. 2.16), but

does not vary between categories in terms of area (F(3,74)=1.225, p=0.307) or percentage

(F(3,74)=1.932, p=0.132). Figure 2.17 illustrates some overall differences in the composition of urban categories by separating the proportions of land that qualify for each tier. The Rural category has a lower percentage of land qualifying for Tier 2 (but not Tier 1) than the other categories (F(3,75)=7.925, p=<0.001), but overcomes this discrepancy with Tier 4 land (F(3,75)=11.852, p=<0.001), which composes a higher proportion of land here than in the other three categories. These results indicate that a substantial portion of land in the rural category is limited by access to water, as modeled in this analysis. The Suburban, Urban Employment and Urban Residential categories appear to have roughly similar composition, but the Urban Employment category has a higher proportion of land only qualifying for Tier 3 (F(3,75)=3.075, p=0.033) – otherwise suitable land which is limited by very poor soils as modeled by this index. Statistically significant differences in land suitability among urban categories are summarized in Table 2.6D. When analyzing results at alpha = 0.10, some additional effects emerge. Publicly owned land was found to have a higher proportion qualifying for Tier 2 (F(1,86)=3.215,

62 TABLE 2.6D. Summary of Results: Land suitability by urbanization category Chi- ANOVA Data Restriction Effect square N F-Ratio df p-value Presence of Tier 2 Land None Urbanization 30.841 97 3 <0.001 Public/Private Combined Urbanization 32.921 79 3 <0.001

Tier 2 Area (when present) Urbanization x None Ownership 88 3.412 3,80 0.021 Public/Private Combined Urbanization 72 2.557 3,68 0.062

Presence of Tier 3 Land None Urbanization 30.841 97 3 <0.001 Public/Private Combined Urbanization 32.921 79 3 <0.001

Tier 3 Area (when present) Urbanization x None Ownership 88 2.994 3,80 0.036 Privately-owned Urbanization 69 2.359 3,65 0.080 Public/Private Combined Urbanization 72 2.742 3,68 0.050

Tier 4 Area (when present) Urbanization x None Ownership 93 3.527 3,85 0.018

Tier 4 Percent of Total Vacant Land (when Tier 4 land is present) Privately-owned Urbanization 73 2.446 3,69 0.071

Public/Private Combined Urbanization 76 2.377 3,72 0.077

Presence of Tier 5 Land Public/Private Combined Urbanization 8.989 79 3 0.029

Tier 5 Area (when present) Urbanization x None Ownership 95 3.580 3,87 0.017

Overall land composition Urbanization x None Suitability 474 6.441 15,450 <0.001 Suitability Tier 2 only Urbanization 79 7.925 3,75 <0.001 Suitability Tier 3 only Urbanization 79 3.075 3,75 0.033 Suitability Tier 4 only Urbanization 79 11.852 3,75 <0.001 Suitability Tier 5 only Urbanization 79 2.658 3,75 0.054

63 Urban Urban Rural Suburban Employment Residential Sites with Tier 2 and 8% Tier 3 land present

Sites with no Tier 2 38% or Tier 3 land 62% 100% 100% 92% present

FIGURE 2.14. Presence of Tier 2 and Tier 3 land, when vacant land is present: By urbanization

100,000

50,000 39,844 Area in square meters in square Area

6,962 4,740 2,090

Rural Suburban Urban Urban Employment Residential FIGURE 2.15. Tier 3 area per site, when present: By urbanization

Urban Urban Rural Suburban Employment Residential Sites with Tier 5 12% land present

Sites with no Tier 5 100% 100% 100% land present 88%

FIGURE 2.16. Presence of Tier 5 land, when vacant land is present: By urbanization

64 Tiers shown in color had significant differences among urban categories at alpha=0.10.

Tier 1 Tier 2 Tier 3 Tier 4 Tier 5 Non-qualifying land

Urban Residential

Urban 1 Employment 2 3

4

5 Suburban 6

Rural

20% 40% 60% 80% 100%

FIGURE 2.17. Overall land composition: By urbanization

p=0.076; Fig. 2.11). In the Urban Residential category, publicly owned land had a higher

proportion of land qualifying for Tier 4 (F(1,27)=2.939, p=0.098), indicating that publicly owned land there is less likely to be limited by slopes above 5% or insufficient sun. Publicly owned land occurs in higher amounts in the Suburban category than in the more urban categories (F(2,17)=3.199, p=0.066). This result corresponds to the larger parcel size of publicly owned land in the Suburban category. When Tier 2 land is present, it occupies larger areas in the Rural category than in the other categories, with the Urban Residential category possibly having the least area of Tier 2 land (F(3,68)=2.557, p=0.062). Tier 4 land

composes a higher percentage of privately owned land (F(3,69)=2.446, p=0.071) and all

vacant land (F(3,72)=2.377, p=0.077) in the Rural category than in the other categories. Land that qualifies for Tier 5 but not Tier 4 composes a smaller proportion of land in the

Rural category than the other categories (F(3,75)=2.658, p=0.054), suggesting that land in

65 the Suburban, Urban Employment, and Urban Residential categories is more limited by 5-10% slopes. These results support rejection of the null hypothesis stating that “urbanization has no effect on vacant parcel size, abundance, or quality.”

2 .4 Discussion 2 .4 .1 Urbanization categories and sampling This research compared results according to urbanization categories, which were grouped according to a two-factor PCA based on employment, population, and housing densities. Urban gradient analysis, which has typically been employed in urban ecology studies, creates a gradient from one or more factors and uses that gradient as the basis for analyzing other dependent variables. As the varied approaches to urban gradient analysis demonstrate (e.g. Alberti, 2008; Hahs and McDonnell, 2006), even an empirically based concept of “urban” is neither monolithic nor definitive, and variables used to characterize an urban gradient should be selected on the basis of their relevance to the research question. In this case, population, housing, and employment densities were selected as general indicators of intensity of urban land use, as well as for their availability and simplicity of analysis. Because the resulting categories were based on two PCA factors, it cannot simply be said that these categories increase in urbanization from Rural (A) to Urban Residential (D). As the descriptive statistics in Table 2.1 illustrate, “urbanization” in this case increases from Rural to Suburban to Urban Employment and Urban Residential, with the latter two categories being characterized by highest intensity in employment and population respectively. Selecting representative sample sites from these categories proved challenging in some ways. Census block groups are the geographical unit of the urban categorization scheme, but 300-meter radius study sites were the ultimate unit of analysis. This boundary

66 discrepancy meant that an individual study site could contain portions of more than one urban category if it fell near a block group border. An arbitrary threshold of 70% composition (of its “home” category) was applied for study site inclusion, but this choice resulted in bias against small block groups of one category surrounded by other categories. The results of this research, however, suggest that the application of an urban gradient to questions of land resources may reveal key differences in vacant land from more to less urban contexts.

2 .4 .2 Use of publicly available data This research relied exclusively on publicly available geospatial data for its identification of vacant parcels and for its assessment of land suitability. This data included county parcels, municipal boundaries, SSURGO, LIDAR, DEM, and aerial imagery. Publicly available data offers significant benefits: it provides extensive spatial coverage, is generally easily accessible, and is often of high quality and detail. But these data sources also carry some associated drawbacks—foremost among these is the potential temporal inconsistency within and between datasets. Parcel datasets, for example, are typically updated periodically throughout the year (with varying frequency between counties), whereas the LIDAR and aerial imagery used were from 2006-2010. In some cases, parcel data would classify a parcel as vacant, while older aerial imagery would show it to be built. Such discrepancies are expected when using datasets of different vintage to analyze something as dynamic as the built environment. Another drawback of publicly available data is its varied spatial scales, and the potential inaccuracies that can result from aggregation of diverse datasets. For example, SSURGO data is not intended for interpretation at the parcel level (Shuster et al., 2011), but it is the finest resolution soil dataset available. The results described include generalizations based on the overlay of multiple data sources which may not be consistent

67 with each other or accurate on the scale at which analysis was conducted. Parcel data has been used in other studies to identify vacant parcels for potential food production (e.g. Balmer et al., 2006; Colasanti and Hamm, 2010; Eanes, 2012). Colasanti and Hamm (2010) cross-referenced a subset of parcels identified as “vacant” against aerial imagery and found a 3.4% error rate. The final cross-check between vacant parcels and aerial imagery in this research turned up a higher error rate of 10.3%, which could be the result of actual inaccuracy in the parcel data and/or a vestige of the span of time between datasets (up to six years between aerial imagery and county parcel data). Aerial imagery was only used to confirm vacancy of parcels categorized as such in the parcel datasets and was not consulted to identify additional vacant parcels not already categorized as vacant. Thus, the vacant land included in this analysis represents a conservative estimate of the actual prevalence of vacant land.

2 .4 .3 Land suitability index Previous land inventories have applied various sets of suitability criteria to assess and prioritize land suitable for urban food production. The four criteria included in this research—soil quality, slope, solar exposure, and water access—were selected on the basis of these previous studies as well as conversations with practitioners and the availability of data. This study is the first use of SSURGO data to assess soil quality of land for urban agriculture, and while the SSURGO dataset is extensive and detailed, its accuracy within the urban context should be considered with caution. Federal soil mapping efforts have typically focused only on the agricultural capability of rural soils and the development potential of urban land; furthermore, urban soils can exhibit high variability even on the scale of a single parcel (Shuster et al., 2011). This research also explores the use of LIDAR to assess solar exposure, which to the author’s knowledge has not been applied to assessment of land for urban food production, with the exception of Nipen (2009).

68 Although this use of LIDAR requires access to mapping software and some expertise, it may provide a more accurate and feasible method for wide-scale assessment of solar exposure than individual site visits or assessment of aerial images, as have been the norm in previous land inventories. The water access component was a binary rating based solely on access to public water, which in many cases was assumed based on municipal boundaries. Aside from the potential inaccuracy of this assumption, vegetable growers may have access to other sources of water on-site, such as wells or ponds. These other water sources were not accounted for in this analysis. The four-factor suitability index developed and applied in this research provides a potentially replicable index for other locales but should be checked for accuracy before broader application. In particular, the soil quality and solar exposure components, given their novel inclusion, should be assessed for how well they capture actual conditions. This index should also be approached as an initial assessment and prioritization tool rather than a final selector of parcels. Other characteristics beyond the scope of this research, such as soil contamination, impervious surfaces, and surrounding neighborhood demographics, may all come into play in further assessments of appropriate parcels. Finally, it should be noted that this assessment of vacant land and its suitability for food production represents a snapshot of dynamic characteristics. Even if perfectly accurate real-time datasets were available for all of the included variables, these datasets would still not capture the ways in which these characteristics change over time. Vacant land becomes built; built land becomes vacant. In the process of these changes, solar dynamics shift, soils become degraded or replaced, and slopes are created or leveled. Further research on the temporal dynamics of vacant land and its characteristics may be highly relevant to practitioners of urban farming and gardening, particularly those utilizing borrowed land on a temporary basis.

69 2 .4 .4 Implications and conclusions The results of this research suggest some key differences in vacant land between urban contexts and according to ownership. The potential implications of these for practitioners and policymakers are noted below.

• Privately owned land is more abundant than publicly owned land . Some studies have focused on the availability of publicly owned land for food production, but this research is consistent with expectations that privately owned land is much more abundant. Policies that support, facilitate, and incentivize usufruct agreements between landowners and farmers and gardeners may be more successful in maximizing urban food production than simply encouraging use of publicly owned land. Two programs in Ohio (OSU Extension Urban Agriculture program in Cleveland, and Franklin Park Conservatory Growing to Green program in Columbus) provide sample use agreements for gardeners and landowners (Dawson, 2011; Thompson, 2011), but incentives and support at the municipal level could further encourage these agreements. The city of Escondido, California, for example, manages an “Adopt-a-Lot” program that facilitates agreements between private landowners and potential users of vacant land. The city provides liability coverage and can waive zoning restrictions that might restrict gardening activities (Buquet, 2011; City of Escondido, 2013). Although programs like these encourage private landowners to allow interim use of their land, it would be optimistic to expect all or even most landowners to participate. Absentee landlords in particular are unlikely to engage with such programs (Bayuk, 2011). Efforts to maximize urban food production should be comprehensive in targeting both publicly and privately owned land.

70 • Soil quality is the most crucial obstacle to food production in urban settings . The Urban Employment and Urban Residential categories show a substantial increase in suitable land area in the transition from Tier 1 to Tiers 2 and 3. Even this finding is based on an optimistic model using SSURGO data, which may overestimate the quality of urban soils. One of the many benefits of urban agriculture is its ability to “close the resource loop” by rerouting urban waste streams to develop soil fertility (Smit and Nasr, 1992). As Beniston (2013) demonstrated, amendment of urban soils with ample compost can greatly increase vegetable productivity. Policies and programs that encourage or enable conversion of organic waste to compost—and make that compost available to urban farmers or gardeners—could greatly increase the area of land suitable for food production, while also minimizing landfill waste. Imported compost can also mitigate contamination concerns by enabling food production without disturbing contaminated in situ soils. • Vacant land in high-density residential contexts is rare . In the Urban Residential category, only 72% of study sites had any vacant land, and those that did had an average of only 2,090 square meters of land qualifying for Tier 3—fewer than any of the other categories. Vacant land in these areas was also more likely to be privately owned. The lack of vacant land in these contexts is particularly notable because it is also these areas—with high populations living in close proximity—where residents are least likely to have access to their own land. If expanding access to the experience of food production is a goal for policymakers, then policies that facilitate usufruct agreements with private landowners and convert waste streams to compost would be particularly crucial and effective in these settings. • Urban employment areas offer opportunities for food production near worker

71 populations . Study sites in the Urban Employment category had greater areas of privately owned vacant land than the Suburban or Urban Residential categories. Targeting employers and corporations for usufruct incentives could provide land access and food production opportunities for workers at their place of employment.

Utilization of vacant urban land for food production could increase access to local foods for urban residents while also providing access to land and opportunities for deeper engagement with the food system. By combining urban gradient analysis and land inventory methodologies, this research demonstrates that vacant land resources vary according to urbanization and ownership in terms of abundance and the measures of suitability considered in this study. Policymakers and practitioners who want to expand urban food production could do so more effectively by customizing their approaches to the urban context and by targeting privately owned land as a potential resource.

72 Chapter 3: Foodshed analysis of study sites

3 .1 Introduction Urban and peri-urban food production can perform multiple functions in local food systems. Although its yields may be smaller than those of large-scale commercial production, urban agriculture provides opportunities for urban residents to experience the food system directly through the cultivation and consumption of fresh seasonal food. Encouraging usufruct access to land could increase yields and broaden participation in food production within the urban sphere. The small-scale food production sites made possible by usufruct might be thought of as “tributaries” within the foodshed framework, which visualizes the sources and flows of food for a population. Though these tributaries are small, their proximity to urban residents creates specific opportunities for more resilient foodsheds and local food systems. Kloppenburg et al. (1996) argue that the foodshed should be applied as a normative concept that can provide “a bridge from thinking to doing”—a tool for visualizing potential scenarios and charting the paths to their realization. Normative scenarios visualize “futures that should be”; they are optimistic “prospective scenarios,” rather than “projective scenarios” that portray the future implications of continued business as usual (Nassauer and Corry, 2004). Normative scenario research uses data to model potential desirable futures with the hope that such information might better inform and guide decision-making. The foodshed as a normative concept refers, then, to potential alternative structures for the food system, with distribution, diet, and production as the key parameters in scenario visualization.

73 Research on normative foodshed scenarios forms the growing field of “foodshed analysis,” a term coined by Getz (1991) and defined by Peters et al. (2009a) as:

[the] study of the actual or potential sources of food for a population, particularly those factors influencing the movement of food from its origin as agricultural commodities on a farm to its destination as food wherever it is consumed.

Swaney et al. (2011) note that the foodshed framework is much more frequently employed to evaluate potential rather than actual (existing) food systems; hence the concept is primarily used for normative rather than descriptive or projective purposes. Foodshed analysis grounds the local foods movement in plausibility by providing research-based visualization of alternative food system scenarios (Kurita et al., 2009). Simultaneous evaluation of “the geography of food production and food consumption” (Peters et al., 2009a) enables a holistic consideration of potential food system structures. Foodshed analyses that focus on the urban sphere have typically limited their analysis to the amount of food that could be produced on urban land without quantifying the many ancillary benefits of urban agriculture (e.g. Colasanti and Hamm, 2010; Grewal and Grewal, 2012; MacRae et al., 2010). Land access potentially provided by usufruct is a particularly relevant benefit to include in assessment of urban foodsheds, because the relationship between land and population may define the potential for local food production. Especially in densely populated residential neighborhoods, where many people lack access to their own land, the capacity for cultivation of vacant land by the local population may be great. This chapter employs foodshed analysis methods to quantify the extent to which the vacant land resources assessed in Chapter 2 could provide vegetable servings for study site populations and characterizes the relationship between land area and household. Three of the five tiers of Chapter 2’s suitability index were configured as production scenarios. USDA Dietary Guidelines were used to select a mix of 17 vegetable crops from 74 four of the five USDA vegetable sub-groups. Three vegetable yield datasets were used to estimate potential vegetable production of these crops on the land under each production scenario. The projected vegetable output was then measured against the USDA- recommended number of vegetable servings for the study site population. The amount of land under production in each scenario was also averaged by the number of study site households. This ratio illustrates the amount of land access created under each scenario; conversely, it also quantifies the level of per-household cultivation that would be required to realize maximum production potential. Study site results for proportion of required vegetable servings met (under each production scenario and each yield scenario) and land per household (under each production scenario) were compared among urbanization categories.

3 .2 Methods Figure 2.1 provides an overview of the methods described below.

3 .2 .1 Sample study sites and vacant parcels Study sites representing four categories of urbanization were included in this analysis. Chapter 2 discusses the methodology of urban categorization and study site selection. Within each study site, vacant parcels were identified and their land was assessed for suitability based on soil quality, solar exposure, access to water, and slope.

3 .2 .2 Study site demographic characteristics Population by age/gender group and occupied housing units of each study site were estimated using basic areal weighting (Yale University, 2007) of block-group level data from the 2010 U.S. Decennial Census (U.S. Census Bureau, 2010). Three study sites in the Rural category were estimated to have no households and no population and,

75 therefore, were excluded from this analysis. Because U.S. Census age groups do not align with USDA Dietary Guidelines age groupings (United States Department of Agriculture, 2013a), even distribution was assumed within each Census cohort and divided accordingly to estimate populations within each USDA age group (Giombolini et al., 2010). Since USDA guidelines for vegetables do not change for ages 14-50, all age groups within that range were aggregated. In this way, populations were estimated for each age group (gender-specific above age 8) within each study site.

3 .2 .3 Diet model This research assumed a normative scenario for the vegetable component of a diet adhering to the USDA “MyPlate” guidelines. These guidelines group vegetables into five subgroups: dark green, red and orange, beans and peas, starchy, and other. The guidelines provide recommended weekly amounts for each of these categories in terms of servings for gender-specific age groups. Because the “beans and peas” category refers to dry, canned, or frozen mature legumes, and this research is focused on production of vegetables for fresh consumption, that category was omitted. The annual number of servings by vegetable subgroup required for each study site was calculated by multiplying the weekly USDA recommended amount for that subgroup by the appropriate population for each age group; these were then summed for each vegetable subgroup. The resulting figures were the total annual requirements in terms of servings for each vegetable subgroup to meet the USDA guidelines for the population within each study site. Vegetable subgroup totals were then summed to find the total number of all vegetable servings required for each study site.

3 .2 .4 Yield model Seventeen vegetable crops were selected to provide a diverse mix of vegetables that

76 serve as a representative proxy for all vegetables listed in the USDA guidelines (a similar approach was taken by MacRae et al., 2010). With the exception of potatoes, all selected crops were found in the Top 25 Crops City-Wide (Per Pound or Per Plant) in the 2010 Farming Concrete Harvest Report, a survey of community gardens in New York City, and can thus be assumed to be popular crops for urban food production (Farming Concrete, 2011). Crops were also selected for their suitability to the Ohio climate and to provide a range of plant types from each USDA vegetable subgroup. Table 3.1 shows the USDA categorization of vegetables, with selected crops in bold.

Table 3.1. USDA Vegetable Subgroups and Selected Crops Dark Green Vegetables Starchy vegetables Red & orange vegetables Other vegetables bok choy cassava acorn squash artichokes broccoli corn butternut squash asparagus collard greens carrots avocado fresh cowpeas, field peas, or dark green leafy lettuce hubbard squash bean sprouts black-eyed peas (not dry) kale pumpkin beets mesclun green bananas red peppers Brussels sprouts mustard greens green peas sweet potatoes cabbage romaine lettuce green lima beans tomatoes cauliflower spinach plantains tomato juice celery turnip greens potatoes cucumbers watercress taro eggplant water chestnuts green beans green peppers iceberg (head) lettuce mushrooms okra onions turnips wax beans zucchini

Three sets of yield figures for these 17 crops were used to determine a range of potential vegetable production on land that is determined to be suitable:

• Yield figures from How to Grow More Vegetables: Than You Ever Thought

77 Possible on Less Land Than You Can Imagine (Jeavons, 2006). Jeavons’ book provides a range of yields (low, medium, and high) considered possible using the “biointensive” methods described in the book. These figures have been used in published hypothetical urban agriculture yield scenarios for Detroit and New York City (Colasanti and Hamm, 2010; Urban Design Lab at the Earth Institute, 2011). Because the original site of Jeavons’ research is located in California, which has a longer growing season and more temperate climate, the “low” yield figures were used in this study. This yield scenario will be referred to as “biointensive production.” • USDA National Agricultural Statistics Service (NASS) commercial yields (United States Department of Agriculture, 2013b). When possible, USDA yield data from Ohio was used. Otherwise, average yield data from the U.S. or data from “Other states” was used. USDA yield data for zucchini was unavailable, so zucchini yield in this scenario was based on the New England Vegetable Management Guide (University of Massachusetts Amherst, 2013). This yield scenario will be referred to as “commercial production.” • Yield data gathered by Liz Kolbe for her Masters research (Kolbe, 2013). Kolbe worked with four diversified farmers in Northeast Ohio to gather yield data for the 2009-2012 growing seasons. Yields were averaged for each crop. This yield scenario will be referred to as “small market farm production.”

Yield data for each crop was converted to servings per square meter after adjusting for estimated refuse loss using figures in Jeavons’ (2006) book. The proportional crop mix was designed to meet the need for each vegetable subgroup approximately equally, and for the vegetables within each subgroup to contribute equally to the servings for that subgroup. The recommended weekly servings

78 for each vegetable subgroup for the male and female 14-50 age groups were summed to find the percentage of servings that each category should contribute. This percentage was then equally divided among the crops within each category, as follows. Figure 3.1 visualizes these crops as portions of the diet.

FIG 3.1. Portion of servings for each crop in the diet

Dark Green: broccoli, collard Other: zucchini, cabbage, greens, lettuce, spinach cucumber, eggplant onion, beets, green peppers Starchy: green peas, potatoes, corn

Red and Orange: squash, carrots, tomatoes

FIGURE 3.1. Portion of servings for each crop in the model diet

• Dark Green: 10% of servings, with 4 crops each providing 2.5% of servings. • Starchy: 31% of servings, with 3 crops each providing 10.3% of servings. • Red and Orange: 33% of servings, with 3 crops each providing 11% of servings. • Other: 26% of servings, with 7 crops each providing 3.7% of servings. By setting the target number of servings to 100, each percentage becomes the actual number of servings that each crop should contribute to a 100-serving yield. Dividing this number by the “servings per square meter” yield provided the area necessary to generate this number of servings. This area for each crop was then divided by the

79 total area necessary for this 100-serving scenario to generate an area multiplier for each crop. The area multiplier, when multiplied by any amount of available, suitable land, determines how much area should be devoted to a given crop. The resulting area will then be multiplied by the “servings per square meter” figure to determine the yield in terms of servings for each vegetable crop. Because different yield figures generate different area multipliers, there is a distinct set of area multipliers for each of the three yield sets. These area multipliers and yield figures were used to calculate the total vegetable servings produced from 1.0 square meter under each of the three yield scenarios. This total was applied to land deemed suitable under the following production scenarios. Figure 3.2 visualizes the area proportions for each yield scenario.

3 .2 .5 Production scenarios In Chapter 2, vacant land was assessed and sorted into five qualifying tiers on the basis of soil quality, slope, access to water, and solar exposure. Areas qualifying for suitability tiers 1, 3, and 5 are the bases for production scenarios 1, 2, and 3, respectively. These production scenarios cover a range of potential improvements that increase the suitability of vacant land.

• Scenario 1: Production only on land that is suitable with minimal or no improvement . This was applied to land with top-tier soil quality, slopes under 5%, access to public water, and at least 8 hours of sun per day. • Scenario 2: Production only on land that could be made suitable with soil improvement . This was applied to land with any soil quality, slopes under 5%, access to public water, and at least 8 hours of sun per day.

80 FIG. 3.2. Crop area proportions for each yield scenario.

Biointensive production yield scenario: Small market farm production yield scenario: 10.053 servings per square meter 6.781 servings per square meter

1 2 5 8 10 12 13 1 3 5 8 10 12 13

3 6 6 9 14 2 14 4 9 7 11 11 4 7 15 16 15 16

17

17

1 Broccoli Commercial production yield scenario: 2 Collard Greens 6.126 servings per square meter 3 Lettuce (dark green leafy) 4 Spinach

1 2 5 8 10 12 13 5 Zucchini 3 6 14 6 Cabbage 7 11 4 9 7 Cucumber 8 Eggplant 15 16 9 Onion 10 Beets 11 Green peppers

12 Squash (butternut) 13 Carrots 14 Tomatoes

15 Green peas 17 16 Potatoes 17 Corn

FIGURE 3.2. Crop area proportions for each yield scenario

• Scenario 3: Production only on land that could be made suitable with soil improvement, rain catchment infrastructure or other added water access, and grading of 5-10% slopes . This was applied to land with any soil quality, slopes under 10%, and at least 8 hours of sun per day, with or without access to public water.

81 This progression of scenarios is intended to represent a typical progression of additive improvement, starting with land that is good “as-is” and advancing through progressive stages of needed improvement. Since soil management is a basic part of any agricultural endeavor, it can be assumed that urban farmers and gardeners will amend their soil (Carter and Anderson, 2012). Thus, Scenario 2, which only includes soil improvement, is a logical precursor to Scenario 3, which also includes rainwater catchment and leveling of slopes under 10%. In order to allow space for walkways, border zones, and structures, a maximum proportion of 80% of each vacant parcel was considered available for production (Grewal and Grewal, 2012). After this limitation was applied, the total production area under each scenario was calculated for each study site. The total vegetable servings per square meter for each yield scenario were multiplied by the total production area for each production scenario. The resulting number of vegetable servings is the total amount of vegetable servings possible if all production area were assumed to produce the modeled yields under each production and yield scenario.

3 .2 .6 Calculation of foodshed analysis For each of the nine production-yield scenarios (3 production scenarios x 3 yield scenarios), the vegetable servings produced in each study site were divided by the vegetable serving requirements of the study site population. This calculation provided the proportion of local vegetable serving requirements that could be met under each scenario. The ratio of production area to occupied households was also calculated for each study site under each production scenario.

3 .2 .7 Statistical analysis Statistical analysis was performed to test the following null hypothesis:

82 • H0: Urbanization has no effect on the vegetable serving and land access contributions of vacant land to local populations. ANOVA tests were run on transformed values (Table 3.2 shows types of transformation used) to measure the effect of urbanization on the foodshed analysis results. Only study sites with qualifying land present were included in each scenario analysis. Chapter 2 discusses results comparing the presence of such land. Statistical analysis was performed in SYSTAT 13 (Systat Software, Inc., 2012).

3 .3 Results

Table 3.2. Data Transformations Variable Transformation Land per occupied household Log10(x) Proportion of vegetable requirements met Log10(x)

The following results are summarized in Table 3.3. 3 .3 .1 Percentage of vegetable requirements met Urbanization was found to have an effect on the percentage of study site vegetable requirements that could be met under each production scenario (Scenario 1: F(3,8)=4.574,

p=0.038; Scenario 2: F(3,66)=16.199, p=<0.001; Scenario 3: F(3,71)=18.289, p=<0.001; Figs. 3.3-3.5). Because all yield scenarios were simple multipliers of the area in each production scenario, ANOVA results were the same for all yields within each production scenario. When the Rural category was omitted from the analysis, urbanization still had

a statistically significant effect in Scenario 2 (F(2,64)=10.185, p=<0.001; Fig. 3.4B) and

Scenario 3 (F(2,65)=7.781, p=0.001; Fig. 3.5B). Study sites in the sparsely populated Rural category were projected to produce an average of 22 (using commercial production methods) to 36 (using biointensive

83 Table 3.3. Summary of results Variable Data Restriction N ANOVA F-­‐Ratio df p -­‐value Scenario 1 land per occupied household None 12 4.594 3,8 0.038 B-D 10 1.583 2,7 0.271

Scenario 2 land per occupied household None 70 16.547 3,66 0.000 B-D 67 10.191 2,64 0.000

Scenario 3 land per occupied household None 76 17.644 3,72 0.000 B-D 69 6.517 2,66 0.003

Scenario 1 Percentage of Vegetable Requirements Met None 12 4.574 3,8 0.038

Scenario 2 Percentage of Vegetable Requirements Met None 70 16.199 3,66 0.000 B-D 67 10.185 2,64 0.000

Scenario 3 Percentage of Vegetable Requirements Met None 75 18.289 3,71 0.000 B-D 68 7.781 2,65 0.001

production methods) times the vegetable requirements of the local population in Scenario 1 for sites with some land qualifying for production under that scenario. These averages decreased slightly in Scenario 2 (21 to 35 times study site requirements) because additional sites with less qualifying area were included in the average. In Scenario 3, sites in the Rural category were projected to meet a range of 32 to 53 times their vegetable requirements on average. For all scenarios, these figures were much higher than those for the other three urban categories. Urban Employment sites were projected to meet more of their vegetable serving requirements than sites in the other two urban categories: 12-20% in Scenario 1, 18-30% in Scenario 2, and 33-54% in Scenario 3. Sites in the Suburban category were projected to meet 2-4% of their vegetable requirements in Scenario 1, 8-12% in Scenario 2, and 13-21% in Scenario 3. Sites in the Urban Residential category were least able to meet their vegetable requirements. These sites were projected

84 90 Biointensive production

Commercial production

Small market farm production 60

requirements met requirements 30 Proportion of vegetable Proportion of vegetable See Figure 3.3B

Rural Suburban Urban Employment Urban Residential

FIGURE 3.3A. Proportion of vegetable requirements met under Scenario 1

0.6

Biointensive production

Commercial production

Small market farm production

0.3 requirements met requirements Proportion of vegetable Proportion of vegetable

3.7% 2.2% 2.5% 20.3% 12.4% 13.7% 0.3% 0.2% 0.2% Suburban Urban Employment Urban Residential

FIGURE 3.3B. Proportion of vegetable requirements met under Scenario 1 (detail)

85 90 Biointensive production

Commercial production

Small market farm production

60 requirements met requirements

Proportion of vegetable Proportion of vegetable 30

See Figure 3.4B

Rural Suburban Urban Employment Urban Residential

FIGURE 3.4A. Proportion of vegetable requirements met under Scenario 2

0.9 Biointensive production

Commercial production

Small market farm production

0.6 requirements met requirements Proportion of vegetable Proportion of vegetable

0.3

12.4% 7.6% 8.4% 30.3% 18.5% 20.5% 2.7% 1.7% 1.8% Suburban Urban Employment Urban Residential

FIGURE 3.4B. Proportion of vegetable requirements met under Scenario 2 (detail)

86 150 Biointensive production

Commercial production

Small market farm production 100 requirements met requirements

Proportion of vegetable Proportion of vegetable 50

See Figure 3.5B

Rural Suburban Urban Employment Urban Residential

FIGURE 3.5A. Proportion of vegetable requirements met under Scenario 3

1.8 Biointensive production

Commercial production

Small market farm production

1.2 requirements met requirements Proportion of vegetable Proportion of vegetable

0.6

21.2% 12.9% 14.3% 54.2% 33.0% 36.6% 4.5% 2.7% 3.0% Suburban Urban Employment Urban Residential

FIGURE 3.5B. Proportion of vegetable requirements met under Scenario 3 (detail)

87 to produce 0.2-0.3% of their required servings in Scenario 1, 1.7-2.7% in Scenario 2, and 2.7-4.5% in Scenario 3. These results support rejection of the null hypothesis stating that “urbanization has no effect on the vegetable serving contributions of vacant land to local populations.”

3 .3 .2 Land per occupied household The proportion of vegetable servings met is a function of the relationship between population and land area. Thus, comparisons of the ratio of land area to households closely correspond to those results. Urbanization had an effect on the area of land

per occupied-household under Scenario 1 (F(3,8)=4.594, df=3, p=0.038), Scenario 2

(F(3,66)=16.547, df=3, p=<0.001), and Scenario 3 (F(3,72)=17.644, df=3, p=<0.001; Fig. 3.6). When the Rural category was excluded, the remaining three categories still differed

significantly in the ratio of land area to households under Scenario 2 (F(2,64)=10.191, df=2,

p=<0.001) and Scenario 3 (F(2,66)=6.517, df=2, p=0.003; Fig. 3.6B). Sites in the Rural category had the highest land-to-household ratios for all production scenarios: 8516 square meters per household in Scenario 1, 8130 square meters per household in Scenario 2, and 10,607 square meters (or 1.06 hectares) per household in Scenario 3. The Urban Employment category had the next highest average ratios, with 43.0 square meters per household in Scenario 1, 58.5 square meters per household in Scenario 2, and 107.0 square meters per household in Scenario 3. Sites in the Suburban category provided averages of 7.7 square meters per household in Scenario 1, 22.9 square meters per household in Scenario 2, and 39.7 square meters per household in Scenario 3. Sites in the Urban Residential category provided the least amount of land per household, with averages of 0.6 square meters per household in Scenario 1, 5.1 square meters per household in Scenario 2, and 8.2 square meters per household in Scenario 3. Figure 3.7 illustrates these differences in land area per household.

88 30,000

Scenario 1

Scenario 2

Scenario 3

15,000 Area in square meters in square Area See Figure 3.6B

8516 8130 10,607 Suburban Urban Employment Urban Residential Rural

FIGURE 3.6A. Area of land per household by production scenario

400

300

200

Area in square meters in square Area 100

7.7 22.9 39.7 43.0 58.5 107.0 0.6 5.1 8.2 Suburban Urban Employment Urban Residential

FIGURE 3.6B. Area of land per household by production scenario (detail)

89 FIG 3.X. Land area per household

Each square represents mean area of qualifying land per 2.8 households, for study sites where qualifying land is present

Scenario 1 Scenario 2 Scenario 3

Rural

2.8 households per study site

8516.4 square meters 8129.5 square meters 10,607.3 square meters per household per household per household

Suburban

234.7 households per study site 7.7 square meters 22.9 square meters 39.7 square meters per household per household per household

Urban Employment

170.6 households per study site

43.0 square meters 58.5 square meters 107.0 square meters per household per household per household

Urban Residential

525.0 households per study site 0.6 square meters 5.1 square meters 8.2 square meters per household per household per household

FIGURE 3.7. Illustration of land area per household by production scenario

90 These results support the rejection of the null hypothesis stating that “urbanization has no effect on the land access contributions of vacant land to local populations.”

3 .4 Discussion 3 .4 .1 Normative scenario parameters The vegetable production projections applied in this analysis represent ambitious alternative scenarios for local food production. These scenarios are predicated on a number of normative assumptions that represent a departure from the status quo. First, and perhaps most ambitious, is the assumption of usufruct access to all idle vacant land. Usufruct has been justified within ecosocialist, Marxist, and Lefebvrian paradigms, but it conflicts with most contemporary American concepts of private property (Kovel, 2007; Laing, 1976; Marx, 1867; Purcell, 2002; Schmelzkopf, 2002; Sementelli, 2007). Nonetheless, usufruct already occurs on both publicly and privately owned land. Policy incentives and municipal programs may further encourage its adoption, while increasing food costs and declining food security resulting from climate change may reinforce its value. These scenarios also assume that the population adheres to the USDA Dietary Guidelines for four of five vegetable subgroups. This diet would represent a major shift from current eating patterns. To meet USDA guidelines, vegetable consumption would need to increase 31%, with especially substantial increases for dark green vegetables (175%) and red-orange vegetables (183%) (Buzby et al., 2006). Although it may be optimistic to imagine Americans meeting these guidelines, Alaimo et al. (2008), Colasanti et al. (2010), and Kortright and Wakefield (2010) have found that the experience of growing food can lead to changes in eating habits. In this way, the normative production scenario imagined by this research might be assumed to help realize the normative diet scenario: by broadening access to the experience of growing food, more urban residents

91 might have that experience and alter their diets in the process. The three production scenarios assume varying degrees of improvement to vacant land. Scenario 1 is restricted by estimated soil quality, but because the accuracy of SSURGO data is suspect in urban environments (Shuster et al., 2011), and urban growers can be assumed to amend or import their soil (Carter and Anderson, 2012), Scenario 2 might be the most appropriate baseline scenario for this foodshed analysis. Scenario 3 assumes leveling of 5-10% slopes and addition of rainwater catchment to provide water access to parcels without public water. While feasible, these improvements would be less likely when land tenure is uncertain, as is often the case with usufruct agreements. This analysis does not address the complex spatial configurations of land suitability that arise when suitability is assessed at the scale of 2-meter grid cells. Many parcels had only a small percentage of their area qualifying for production under a given scenario; but if only this small area were appropriate for production, the “return on investment” of securing usufruct access may not be enough to justify it. Contiguity of suitable land within parcels was also not considered. If suitable areas within a parcel were small and highly dispersed, however, these conditions may also deter cultivation. The yield datasets used in this research are intended to represent a range of feasible vegetable yields. Although the “low” biointensive production figures published by Jeavons (2006) were used, the differences in climate and grower knowledge may mean that even these are optimistic for Ohio yields from community-based food production. The commercial production and small market farm production yields were closely aligned (6.126 vs. 6.781 servings per square meter) and probably provide more accurately feasible yields. In some sense, then, these scenarios are highly optimistic assessments of the potential for local food production. They rely on broad access to idle property, improvements to the land, and ambitious vegetable yields. Yet this analysis only included

92 parcels designated by county auditors as vacant and confirmed through aerial imagery to be entirely free of structures. In addition to other vacant parcels potentially overlooked in this process, this analysis does not include unbuilt portions of non-vacant parcels—a category of land that includes vast areas of residential yards, schoolyards, and institutional land, much of which might be suitable for vegetable production. Furthermore, this analysis was limited to land that receives at least eight hours of sun per day, but cool- season crops can fare well with just four hours of sun per day (Jeavons, 2006). For these reasons, this analysis provides conservative estimates of the amount of food that could be produced within these contexts. Finally, the proportions of vegetable servings provided are based on an increase in vegetable consumption. If vegetable consumption were to remain at its current rates, these proportions would be higher.

3 .4 .2 Vegetable servings and land access The proportion of vegetable servings met in each scenario varied predictably by urbanization: study sites in urban categories with higher population densities, the Suburban and Urban Residential categories, were least able to meet their own needs, potentially achieving only 21% and 4.5% respectively under the most optimistic production-yield scenario. Study sites in the Rural category, on the other hand, had the potential to produce far more than the modest needs of their sparse populations and could be net exporters if such yields were achieved. These results align with a prevailing dichotomy: urban “consumption zones” rely on imported food, and rural “production zones” produce more food than their local populations require (Peters et al., 2012). The variation found between categories, however, belies the accuracy of a simple rural-urban framework. The Urban Employment category, by some measures, would be considered more “urban” than the Suburban category. But with more vacant land and smaller residential populations, sites in that category were able to meet a higher proportion of

93 their populations’ vegetable requirements. Of course, people don’t only eat at their homes. The proximity of vacant land to large worker populations suggests opportunities for production and provisioning of food to workers near their places of employment. This research did not account for seasonality. In Ohio, it would be impossible to meet all of the population’s vegetable requirements with fresh, seasonal produce throughout the year. Colasanti and Hamm (2010) considered seasonality for their foodshed analysis of Detroit and estimated that a maximum of 31% of vegetable servings could be met with fresh produce. Adding crop storage increased this to 65%, and a combination of crop storage and season extension (e.g. hoophouse cultivation) increased it to 76%. Colasanti and Hamm (2010) did not consider the potential of processing for long-term storage, but this activity could potentially achieve 100% self-sufficiency for vegetable servings. Of course, each of these scenarios requires additional levels of investment and labor. Using the fresh-only 31% figure as a point of reference and considering commercial yields under production scenario 2, none of the three urban categories would, on average, need to resort to storage or season extension for their populations to take full advantage of vegetable yields. Even in the most optimistic scenario (biointensive yields and Scenario 3), only the Urban Employment category, estimated to meet 54% of its vegetable requirements, would need crop storage to fully realize that potential. Although urban foodshed analyses have quantified the potential agricultural yields of urban land, they have not simultaneously measured any of the additional benefits of urban agriculture, such as stormwater mitigation, increased biodiversity, and improvements to diet (Alaimo et al., 2008; Colasanti and Hamm, 2010; Grewal and Grewal, 2012; Smit and Nasr, 1992; Smit, 2000). Of these benefits, the provision of land access has not been extensively researched—certainly not in the context of urban foodshed analysis. This research calculated the ratio of production area to occupied

94 household under each of the three different production scenarios. The result was a measure of potentially increased land access in terms of square meters per household. Analysis of how this access varied by urbanization aligned closely with the results for vegetable requirements met. Unlike vegetable servings, for which there are established consumption standards, there are no broadly accepted standards for an ideal amount of land per household. One modest goal might be to align urban land access with the national average for vegetable gardening: 23% of households participating with a 96 square foot (8.9 m2) median garden size or 600 square foot (56 m2) mean garden size (National Gardening Association, 2009). Applying the same 80% adjustment used in this research to allow for pathways and borders, these figures translate to net ratios of 1.64 square meters and 10.3 square meters per household (including non-participating households). While the Rural, Suburban, and Urban Employment categories all easily meet both thresholds under scenarios 2 and 3, the Urban Residential category surpasses the lower (median garden size) threshold under both scenarios but only achieves about 80% of the higher threshold under Scenario 3. Another potential standard for assessing sufficient land access might be found in planned communities that set aside land for gardening by their residents. A proposed model “sustainable community” on the outskirts of Vancouver, BC, designates 3 hectares for allotment gardens for its 2,185 households (Paterson and Connery, 1997). Again applying the 80% limit, this allotment translates to 11.0 square meters per household—a figure close to the upper threshold based on U.S. gardening averages. Finally, unpublished data from a study that measured food production area in Chicago provides figures roughly similar to these, with high-end ratios of 3.25 square meters per household for residential gardening, 5.37 square meters per household for single-plot vacant lot gardening, and 12.57 square meters per household for multi-plot vacant lot gardening (Taylor, 2013; Taylor and Lovell, 2012). Interpreted cautiously and in combination, these sources

95 indicate that land access on the order of one to 10 square meters per household could be an appropriate goal for urban land access. This level of access is only achievable under scenarios 2 and 3 in the Urban Residential category. Land access and vegetable yields are not simply parallel, independent benefits that flow from urban agriculture to local populations. The land-to-household ratio is a reciprocal relationship wherein households actively engage with the land by cultivating it. As such, the land-to-household reference figures discussed above might also serve as measures of the potential capacity for cultivation within a given population. Urban foodshed analyses typically assume conversion of large amounts of idle land to production without answering the question of who will cultivate that land. In these models, the measure of potential food production is a simple function of the amount of vacant land. Colasanti and Hamm (2010), for example, rely on the assumed cultivation of thousands of acres of vacant land to achieve the yields they discuss. Many of these acres are likely to occur in areas of high vacancy rates with few or no nearby residents that might contribute to cultivation. Potential food production would be more accurately assessed as a function of both vacant land area and the potential for that land to be cultivated. Measures of the land-to-household ratio can contextualize urban foodshed analyses by identifying which approach is more likely to maximize production: cultivation by the local population, or cultivation by farmers or gardeners from outside the immediate area. Assuming a production capacity of five square meters per household (arbitrary but within the previously discussed range), the Scenario 2 results from this research suggest that Urban Residential populations would, on average, be nearly able to maximize production on available land. The Urban Employment, Suburban, and Rural categories, however, have far more vacant land than their local populations would likely be able to cultivate. These imbalances should be considered when modeling potential food production. In the case of this research, these ratios suggest that potential food

96 production is likely to be much lower than that modeled on the basis of yield figures alone, unless large amounts of land are expected to be cultivated by farmers traveling from outside the immediate area.

3 .4 .3 Implications and conclusions The results of this research suggest that policies for local food production should be customized and targeted to their specific urban context. Potential policy approaches are suggested below. • Food production in dense urban residential areas could be maximized by enabling and encouraging cultivation by the local population . Comparisons with other land-to-household ratios suggest that urban residential food production is more likely to be limited by land area than capacity for cultivation by nearby residents. Community and allotment gardens would provide land access to urban residents who are less likely to have their own yards. • Food production in less urban contexts could be maximized by encouraging gardening or farming by entrepreneurial growers . The amount of vacant land in urban employment, suburban, and rural areas is likely to outstrip local populations’ ability to cultivate it. Thus, maximizing food production in these areas would require cultivation by gardeners or farmers from outside the immediate area or by gardeners or farmers interested in cultivating much larger areas than the “average” urban resident. These growers are likely to be entrepreneurial farmers growing food for sale to others. Usufruct dynamics may be different when food is grown for market. But a survey of suburban landowners in Vermont found that many were willing to lend their land for a range of purposes including production agriculture, and only 7% expected monetary compensation (Erickson et al., 2011). Policies and programs

97 that support entrepreneurial growers and facilitate or incentivize usufruct agreements would likely encourage increased food production in these contexts. • Large areas of vacant land occur in areas of high job density and could be leveraged to increase food access and land access for workers at their place of employment . This research calculated a study site’s dietary needs based solely on its residential population, but the employee population is also likely to have a major impact on the amount of food consumed within the area. The presence of large areas of vacant land in the Urban Employment category suggests opportunities for production and provisioning of food to workers near their places of employment. More efforts like the “Workforce Food Centers” business plan in northeast Ohio, which hopes to produce food near a General Motors plant for the benefit of the plant’s employees, could leverage these vacant sites to increase food access for workers (Fortenberry, 2013).

This research also has implications for the continued practice of foodshed analysis, particularly in urban settings. The following suggestions for future research would add nuance and accuracy to current understandings of potential urban food systems. • Include considerations of labor . Urban foodshed analyses conducted by Colasanti and Hamm (2010), Grewal and Grewal (2012), and MacRae et al. (2010) are based on normative production scenarios that assume a substantial increase in production area. These scenarios rely on conversion of currently non-productive land (and, in Grewal and Grewal (2012), rooftops) to intensive food production, but they do not address the question of who might cultivate this land. Local populations are one viable source for cultivation, but their capacity is limited. For urban foodshed analyses to present optimistic but

98 feasible scenarios, labor capacity should be explored and addressed. • Gain a deeper understanding of demand for land access by urban populations . There is a dearth of research evaluating the dynamics of land access and demand in urban contexts. Urban residents are less likely to have access to their own land, but the amount of access that would meet their needs is unknown. Research-based evidence of these dynamics could help frame the labor dimension of urban foodshed analyses and provide more concrete goals for policymakers. • Strive for more accurate yield models . The use of California yields published by Jeavons (2006) to predict yields in Detroit (Colasanti and Hamm, 2010) and New York City (Urban Design Lab at the Earth Institute, 2011) is likely to lack accuracy for those climate conditions. In this research, Jeavons’ biointensive production yields were considerably higher than locally obtained Ohio small market farm yield figures. And even the Ohio figures used here may be overly optimistic in predicting community-based food production due to different levels of grower knowledge. When modeling and predicting potential yields, foodshed analysts should use locally obtained yield figures whenever possible and also consider the ramifications of differences in management skill and knowledge. • Quantify additional benefits of food production in urban foodshed analyses . Urban agriculture should not be valued on the basis of its yields alone (McClintock et al., 2010; Urban Design Lab at the Earth Institute, 2011). Although urban food production is likely to make only a small contribution to overall food needs, it simultaneously provides a number of other benefits. Urban foodshed analyses that only measure potential food yields run the risk of underestimating the value of urban agriculture’s multiple functions.

99 • Conduct foodshed analysis at a range of scales and in varied contexts . Similar to research by Kurita et al. (2009), this research conducted foodshed analysis at a scale much smaller than most analyses, and found significant variation over the scale of the region. More macro-level analyses mask these complexities. Although large-scale foodshed analyses are valuable in assessing overall production capacity, small-scale analyses capture the variations that occur in different urban contexts. Applying the analytical lens at the neighborhood- scale is particularly useful in assessing potential urban foodsheds. • Explore the social and economic dimensions of usufruct . As previously mentioned, urban foodshed analyses visualize normative scenarios wherein substantial areas are converted to food production. Such shifts in land use would necessitate wide-scale adoption of usufruct agreements for both publicly and privately owned land. Yet usufruct, with its uncertain tenure, has implications for gardeners and farmers who may not be comfortable with such indefinite agreements. Research that provides a deeper understanding of the motivations and reservations of landowners and land “borrowers” would be crucial to policy agendas that seek to broaden urban food production.

Urban foodshed analysis provides an opportunity to assess the food production capacity of cities, but issues of labor and urban context should be included in any consideration of normative food production scenarios. By relating potential vegetable yields to land access for residents, this research proposed a conceptual framework wherein potential food production is a function of both available land and capacity for cultivation by the local population. Within this framework, the variations between urban contexts are key in understanding urban foodshed dynamics. This research explored the inclusion of urbanization and land access measures within the foodshed analysis methodology and

100 demonstrated that such considerations provide a more nuanced picture of potential urban food systems.

101 Chapter 4: Summary and conclusions

4 .1 Overview of Research Parallel trends of population growth and increasing urbanization will profoundly shape and constrain the future of our food system. While ever-expanding city footprints convert agricultural land to urban development, increasing city populations require greater amounts of food. Yet patches of undeveloped land remain within the boundaries of these cities and offer the potential to restructure food production systems for urban populations. The conventional urban-rural paradigm conceives of separate rural production zones and urban consumption zones, but food production may be nested in pockets of land from the dense urban core to the suburban fringe. The proximity of such sites to human populations enables more direct, efficient transfer of food to urban residents. It also provides less tangible ancillary benefits, such as access to land and open space, habitat for biodiversity, and recycling of waste streams (Lovell, 2010; Smit and Nasr, 1992; Smit, 2000). This study assessed the presence and suitability of vacant land for food production across an urban-rural continuum in central Ohio and quantified its potential provision of land access and vegetable servings to local populations. By envisioning a normative scenario wherein vacant land is made accessible by usufruct, the results of this study provide a more concrete path to realization of an alternative future food system. Foodshed analysis relates the potential or actual productive capacity of land to the consumption of a population, usually within a circumscribed geographical boundary. Most foodshed analyses have been conducted at the sub-state region scale or larger (e.g.

102 Desjardins et al., 2009; Giombolini et al., 2010; Kremer and Schreuder, 2012; Peters et al., 2007, 2009b); such studies “value” the rural agricultural land in terms of the quantity of food that it produces or could produce. Urban foodshed analyses have applied this lens at the city scale (e.g. Colasanti and Hamm, 2010; Grewal and Grewal, 2012; MacRae et al., 2010), and in so doing apply a fundamentally rural framework for land valuation to an urban sphere characterized by different dynamics. Agriculture—whether urban or rural—performs multiple ecological, social, and economic functions. By virtue of its integration with urban development, urban food production simultaneously performs multiple roles for its neighboring populations. It is rich in function. Urban foodshed analyses that exclusively assess the food yields of urban land risk greatly underestimating its holistic value. This research sought to broaden the scope of foodshed analysis by including the quantification of one additional benefit: access to land. Although urban dwellers may be particularly unlikely to have access to their own land, even suburban and rural residents sometimes lack such access (Armstrong, 2000). This relationship between land and population may also have special bearing on the actual food production potential of vacant urban land. Urban foodshed analyses typically explore a scenario wherein vast amounts of land are converted to production. The question of who will cultivate this land remains unanswered. Urban agriculture often takes the form of community gardens or vacant lot cultivation by nearby residents, so it can be assumed that some latent capacity for cultivation exists within any urban population. Where populations are dense and vacant land is sparse, this latent capacity for cultivation is likely to be greater than the available land stock: the amount of land would limit potential food production. By contrast, where populations are sparse and vacant land is plentiful, the capacity for cultivation is likely to be smaller than the available land area, and so potential production would be limited not by land but by the potential for cultivation. Thus, the rationale for

103 inclusion of land access in this analysis is twofold: (1) as a benefit in its own right, and (2) as a potential parameter in modeling food yields. Of course, this “latent capacity for cultivation” within a population would be a profoundly complex and difficult variable to quantify and model accurately. It can be thought of, in part, as a function of any of the objectives that motivate a person to farm or garden. A survey of backyard gardeners in Toronto found that these objectives are diverse and often emphasize the social and educational benefits of gardening over subsistence (Kortright and Wakefield, 2010). Conversations with community garden program managers in Columbus, Cleveland, and Seattle confirmed that the level of demand for a community garden is unpredictable and sometimes seems driven by a motivated neighborhood group or a large, visible, successful site (Dawson, 2011; Pernitz, 2011; Thompson, 2011). Because this capacity for cultivation (or demand for land access) has not been rigorously researched, it was not directly included as a parameter in this study’s modeling of yields. Instead, some plausible figures based on national gardening averages and planned developments were applied post-hoc to contextualize the vegetable yield and land access figures modeled in this study. This research, broadly speaking, has supported the thesis that privately owned vacant land represents a substantially more plentiful land resource for urban food production than publicly owned vacant land, and that vacant land suitable for vegetable production will decrease in abundance as urbanization increases . The ability to meet dietary vegetable requirements for the local (study site) population varies according to degree of urbanization . Finally, potential vegetable production is a function of (1) vacant suitable land and (2) capacity for cultivation by the local population . The amount of suitable vacant land per household increases from more urban to less urban settings . Therefore, households in more urban sites may have access to less available land than they could productively use based on current trends in urban gardening . However, if there is an upper limit for the

104 average amount of land per household that can be cultivated, then the aggregate capacity of households to use the available land in less urban areas could be a limiting factor to maximum production . In support of the above thesis, this study pursued the following objectives and tested the following hypotheses: 1. Determine how vacant land resources suitable for vegetable production vary according to urbanization, in terms of quantity, quality, and spatial pattern.

• H0: Urbanization has no effect on vacant parcel size, abundance, or quality. This hypothesis was tested by comparing parcel size, abundance, and suitability among urban categories. Statistical tests found significant differences between some categories, and therefore this null hypothesis was rejected.

2. Determine how vacant land resources suitable for vegetable production differ between public and private ownership, in terms of quantity, quality, and spatial pattern.

• H0: Ownership has no effect on vacant parcel size, abundance, or quality. This hypothesis was tested by comparing parcel size, abundance, and suitability between publicly and privately owned parcels. Statistical tests found significant differences between publicly and privately owned parcels for some attributes and in some specific urban contexts; therefore, this null hypothesis was rejected.

3. Determine the potential contributions of these land resources to the local (study site) populations, in terms of both vegetable servings and land access, and whether these contributions vary according to urbanization.

• H0: Urbanization has no effect on the potential vegetable serving and land access contributions of vacant land.

105 This hypothesis was tested by comparing the potential vegetable serving and land access contributions among urban categories. Statistical tests found significant differences between some categories; therefore, this null hypothesis was rejected.

4. Determine whether the potential provision of vegetable servings is limited by lack of land resources or realistic expectations for cultivation by the study site population, and how these limiting factors shift across the urban continuum.

• H0: Limiting factors for vegetable yields do not change according to urbanization. This hypothesis was tested by comparing modeled land-to-household ratios with published figures for these ratios in other contexts. These comparisons suggest that in dense urban contexts land will be a limiting factor, whereas in other contexts the capacity for cultivation will be limiting. Therefore, this null hypothesis was rejected. Privately owned land was significantly more likely to be present, and in the Urban Employment and Urban Residential categories, it occupied larger areas. Areas of suitable land at the Tier 3 level, which includes suitable land regardless of soil quality, were smallest in the Urban Residential category. Urbanization had a significant effect on the percentage of a study site’s vegetable requirements that could be met by production on that site’s vacant land. The area of suitable land per household was lowest in the Urban Residential category, followed in order by the Suburban, Urban Employment, and Rural categories. Applying an estimated figure of 5 square meters per household for cultivation capacity, all categories except Urban Residential were limited not by land but by this capacity for cultivation. The Urban Residential category would be limited by land under Scenarios 1 and 2. Some other results of this research were unexpected. The Rural category was

106 found to have a lower incidence of vacant land than the other categories. As noted in Chapter 2, this analysis excluded parcels with an “agricultural vacant” land use class. It is apparent (and not surprising) that when vacant land occurs in this traditionally agricultural setting, it is likely to be in production, rather than “idle.” The Urban Employment category also defied some expectations. By a few measures it ranked between the Suburban and Rural categories, even though it was described as more “urban” in this research. The Urban Employment category is characterized by a high density of jobs, which likely has different land use implications than high residential density. Privately owned parcels tend to be larger in the Urban Employment category than in the Suburban category—possibly because landowners are likely to be employers or companies with large tracts of land. When land and food yields were averaged for households and population, this category was again found to have values falling between the Suburban and Rural categories. Although the Urban Employment category may be more “urban” by some land use measures, its lower residential density sets it apart from the more population-dense Suburban and Urban Residential categories.

4 .2 Key findings and policy implications The results of this research suggest that vacant land resources vary according to ownership and urbanization, and that the potential contribution of those resources to local populations also vary by urban context. Policies for local food production should be customized and targeted to their specific urban context. The key findings and potential implications for practitioners and policymakers are noted below.

• Privately owned land is more abundant than publicly owned land . Some studies have focused on the availability of publicly owned land for food production, but this research demonstrates that privately owned land is much

107 more abundant. Policies that support, facilitate, and incentivize usufruct agreements between landowners and farmers and gardeners may be more successful in maximizing urban food production than simply encouraging use of publicly owned land. Two programs in Ohio (OSU Extension Urban Agriculture program in Cleveland and Franklin Park Conservatory Growing to Green program in Columbus) provide sample use agreements for gardeners and landowners (Dawson, 2011; Thompson, 2011), but additional incentives and support at the municipal level would likely encourage these agreements. The city of Escondido, California, for example, manages an “Adopt-a-Lot” program that facilitates agreements between private landowners and potential users of vacant land. The city provides liability coverage and can waive zoning restrictions that might restrict gardening activities (Buquet, 2011; City of Escondido, 2013). Although programs like these encourage private landowners to allow interim use of their land, it would be optimistic to expect all or even most landowners to participate. Absentee landlords in particular are unlikely to engage with such programs (Bayuk, 2011). Efforts to maximize urban food production should be comprehensive in targeting both publicly and privately owned land. • Soil quality is the most crucial obstacle to food production in urban settings . The Urban Employment and Urban Residential categories show a substantial increase in suitable land area in the transition from Tier 1 to Tiers 2 and 3. Even this result is based on an optimistic model using SSURGO data, which may overestimate the quality of urban soils. One of the many benefits of urban agriculture is its ability to “close the resource loop” by rerouting urban waste streams to develop soil fertility (Smit and Nasr, 1992). As Beniston (2013) demonstrated, amendment of urban soils with ample compost can

108 greatly increase vegetable productivity. Policies and programs that encourage or enable conversion of organic waste to compost—and make that compost available to urban farmers or gardeners—could greatly increase the area of potential land suitable for food production, while also minimizing landfill waste. Imported compost can also mitigate contamination concerns by enabling food production without disturbing contaminated in situ soils. • Vacant land in high-density residential contexts is rare . In the Urban Residential category, only 72% of study sites had any vacant land, and those that did had an average of only 2,090 square meters of land qualifying for Tier 3—fewer than any of the other categories. Vacant land in these areas was also more likely to be privately owned. The lack of vacant land in these contexts is particularly notable because it is also these areas—with high populations living in close proximity—where residents are least likely to have access to their own land. If expanding access to the experience of food production is a goal for policymakers, then policies that facilitate usufruct agreements with private landowners and convert waste streams to compost would be particularly crucial and effective in these settings. • Food production in dense urban residential areas could be maximized by enabling and encouraging cultivation by the local population . Comparisons with other land-to-household ratios suggest that urban residential food production is more likely to be limited by land area than capacity for cultivation by nearby residents. Community and allotment gardens would provide land access to urban residents who are less likely to have their own yards. • Food production in less urban contexts could be maximized by encouraging gardening or farming by entrepreneurial growers . The amount of vacant land in urban employment, suburban, and rural areas is likely to be far greater than

109 could be cultivated by local populations. Thus, maximizing food production in these areas would require cultivation by gardeners or farmers from outside the immediate area, or by gardeners or farmers interested in cultivating much larger areas than the “average” urban resident. These growers are likely to be entrepreneurial farmers growing food for sale to others. Usufruct dynamics may be different when food is grown for market. But a survey of suburban landowners in Vermont found that many were willing to lend their land for a range of purposes including production agriculture, and only 7% expected monetary compensation (Erickson et al., 2011). Policies and programs that support entrepreneurial growers and facilitate or incentivize usufruct agreements would likely encourage increased food production in these contexts. • Large areas of vacant land occur in some areas of high job density and could be leveraged to increase food access for workers at their place of employment . This research calculated a study site’s dietary needs based solely on its residential population, but the employee population is also likely to have a major impact on the amount of food consumed within the area. The presence of large areas of vacant land in the Urban Employment category suggests opportunities for production and provisioning of food to workers near their places of employment. More efforts like the “Workforce Food Centers” business plan in northeast Ohio, which hopes to produce food near a General Motors plant for the benefit of the plant’s employees, could leverage these vacant sites to increase food access and land access for workers (Fortenberry, 2013).

4 .3 Implications for future research This research also has implications for the continued practice of foodshed analysis,

110 particularly in urban settings. The following suggestions for future research directions would add nuance and accuracy to current understandings of potential urban food systems.

• Include considerations of labor . Urban foodshed analyses conducted by Colasanti and Hamm (2010), Grewal and Grewal (2012), and MacRae et al. (2010) are based on normative production scenarios that assume a substantial increase in production area. These scenarios rely on conversion of currently non-productive land (and, in Grewal and Grewal (2012), rooftops) to intensive food production, but they do not address the question of who might cultivate this land. Although local populations are one viable source for cultivation, their capacity is limited. For urban foodshed analyses to present optimistic but feasible scenarios, labor potential should be explored and addressed. • Gain a deeper understanding of demand for land access by urban populations . There is a dearth of research evaluating the dynamics of land access and demand in urban contexts. Urban residents are less likely to have access to their own land, but the amount of access that would meet their needs is unknown. Research-based evidence of these dynamics could help frame the labor dimension of urban foodshed analyses while also providing more concrete goals for policymakers. • Strive for more accurate yield models . The use of California yields published by Jeavons (2006) to predict yields in Detroit (Colasanti and Hamm, 2010) and New York City (Urban Design Lab at the Earth Institute, 2011) is likely to lack

111 accuracy for those climate conditions. In this research, Jeavons’ biointensive production yields were considerably higher than locally obtained Ohio small market farm yield figures. And even the Ohio figures used here may be overly optimistic in predicting community-based food production due to different levels of grower knowledge and skill. When modeling and predicting potential yields, foodshed analysts should use locally obtained yield figures whenever possible, and also consider the ramifications of differences in management skill and knowledge. • Quantify additional benefits of food production in urban foodshed analyses . Urban agriculture should not be valued on the basis of its yields alone (McClintock et al., 2010; Urban Design Lab at the Earth Institute, 2011). Although urban food production is likely to make only a small contribution to overall food needs, it simultaneously provides a number of other benefits. Urban foodshed analyses that only measure potential food yields run the risk of underestimating the value of urban agriculture’s multiple functions. • Conduct foodshed analysis at a range of scales and in varied contexts . Similar to research by Kurita et al. (2009), this research conducted foodshed analysis at a scale much smaller than most analyses and found significant variation over the scale of the region. More macro-level analyses mask these complexities. Although large-scale foodshed analyses are valuable in assessing overall production capacity, small-scale analyses capture the variations that occur in different urban contexts. Applying the analytical lens at the neighborhood- scale is particularly useful in assessing potential urban foodsheds. • Explore the social and economic dimensions of usufruct . As previously mentioned, urban foodshed analyses visualize normative scenarios wherein substantial areas are converted to food production. Such shifts in land use

112 could include wide-scale adoption of usufruct agreements for both publicly and privately owned land. Yet usufruct, with its uncertain tenure, has implications for gardeners and farmers who may not be comfortable with such indefinite agreements. Research that provides a deeper understanding of the motivations and reservations of landowners and land “borrowers” would be crucial to policy agendas that seek to encourage broader urban food production.

4 .4 Conclusion Thompson et al. (2001) argue for the development of an “ecological topology”—an integrated, multiscalar analytical framework—to deepen our understanding of ecosystem processes and how we affect them. Although humans profoundly shape their ecosystems, they are still in and of them: an organism interacting with other organisms and physical and chemical processes. The food system is the juncture for one of our most intimate relationships with our ecosystem. It is also the medium by which we have profoundly impacted and altered ecological processes. Thompson et al. (2001) describe the alteration of watersheds in California as “the most massive rearrangement of nature ever attempted.” But this “rearrangement” is dwarfed by the massive flows of our global foodsheds. A multiscalar framework is crucial if we are to develop an understanding of the impacts of our food system. This study applied an analytical lens at a smaller scale than previous foodshed analyses and found significant variation over the scale of the region. Although this research quantified two specific benefits of food production—yields and land access— these are simply two different expressions of the same ratio: the land-human relationship. We rely on land for many of our most basic needs. To fully capture the value of that land, we must conduct a full accounting of the benefits it provides. The foodshed concept was

113 borne of a desire for a more intimate, place-based relationship with the land that supports us (Kloppenburg et al., 1996); however, foodshed analysis has thus far only narrowly measured the potential of that relationship. When functions like food production and land access are stacked in a single space, that space is enriched and intensified, its potential is maximized, and net land use is made more efficient. A more holistic assessment of urban foodsheds which accounts for the value of these stacked functions will guide the development of more efficient, sustainable, and resilient food systems.

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125 Appendix A: Chapter 2 Statistical Results

126 TABLE 2.6. Summary of Results

Chi- ANOVA Data Restriction Effect square N F-Ratio df p-value Mean Parcel Size Urbanization x None Ownership 97 4.291 3,89 0.007 Publicly-owned B-D only Urbanization 20 6.838 2,17 0.007 Privately-owned Urbanization 76 6.855 3,72 0.000 Category B Ownership 33 14.073 1,31 0.001 Category C Ownership 26 0.061 1,24 0.807 Category D Ownership 30 0.031 1,28 0.862 Public/Private combined Urbanization 79 6.563 3,75 0.001

Mean Perimeter-Area Ratio Urbanization x None Ownership 97 0.715 3,89 0.546 None Urbanization 97 0.106 3,93 0.956 None Ownership 97 0.191 1,95 0.663 Public/Private combined Urbanization 79 0.646 3,75 0.588

Presence of Vacant Land None Urbanization 28.233 232 3 <0.001 None Ownership 53.593 232 1 <0.001 Public/Private combined Urbanization 34.679 116 3 <0.001 Public/Private combined, B-D only Urbanization 4.065 86 2 0.131

Total Vacant Area (when present) Urbanization x None Ownership 97 4.363 3,89 0.006 Publicly-owned B-D only Urbanization 20 3.199 2,17 0.066 Privately-owned Urbanization 76 1.010 3,72 0.393 Category B Ownership 33 0.316 1,31 0.578 Category C Ownership 26 6.555 1,24 0.017 Category D Ownership 30 10.467 1,28 0.003 Public/Private combined Urbanization 79 0.492 3,75 0.689

Presence of Tier 1 Land None Urbanization 3.904 97 3 0.272 None Ownership 0.523 97 1 0.470 Public/Private combined Urbanization 3.762 79 3 0.288

Tier 1 Area (when present) None Ownership 14 4.387 1,12 0.058 127 TABLE 2.6. Summary of Results

Chi- ANOVA NoneData Restriction OwnershipEffect square 14N 4.387F-Ratio 1,12df 0.058p-value MeanPrivately Parcel-owned Size Urbanization 12 0.730 3,8 0.563 Public/Private Combined Urbanization x 13 1.426 3,9 0.298 None Ownership 97 4.291 3,89 0.007

Publicly-owned B-D only Urbanization 20 6.838 2,17 0.007 Tier 1 Percent of Total Vacant Land (when Tier 1 land is present) Privately-owned Urbanization 76 6.855 3,72 0.000 None Ownership 14 5.074 1,12 0.044 Category B Ownership 33 14.073 1,31 0.001 Privately-owned Urbanization 12 0.730 3,8 0.563 Category C Ownership 26 0.061 1,24 0.807 Public/Private Combined Urbanization 13 0.953 3,9 0.455 Category D Ownership 30 0.031 1,28 0.862

Public/Private combined Urbanization 79 6.563 3,75 0.001 Presence of Tier 2 Land None Ur banization 30.841 97 3 <0.001 Mean Perimeter-Area Ratio Categories B-D Urbanization x 2.942 89 2 0.230 None Ownership 0.002 9797 0.715 13,89 0.9650.546

Public/PrivateNone Combined Urbanization 32.921 7997 0.106 33,93 <0.0010.956 Public/PrivateNone Combined, B-D Ownership 97 0.191 1,95 0.663 only Urbanization 3.562 71 2 0.168 Public/Private combined Urbanization 79 0.646 3,75 0.588

Tier 2 Area (when present) Presence of Vacant Land Urbanization x None OwnershipUrbanization 28.233 88232 3.412 3,803 0.021<0.001

PubliclyNone -owned, B-D UrbanizationOwnership 53.593 18232 2.556 2,151 0.111<0.001

PrivatelyPublic/Private-owned combined Urbanization 34.679 69116 2.133 3,653 0.105<0.001 CategoryPublic/Private B combined, B-D Ownership 30 0.000 1,28 0.993 only Urbanization 4.065 86 2 0.131 Category C Ownership 26 8.010 1,24 0.009

Category D Ownership 29 0.809 1,27 0.376 Total Vacant Area (when present) Public/Private Combined Urbanization x 72 2.557 3,68 0.062 None Ownership 97 4.363 3,89 0.006

Tier Publicly 2 Percent-owned of Total B-D only Vacant LandUrbanization (when Tier 2 land is present)20 3.199 2,17 0.066 Privately-owned Urbanization x 76 1.010 3,72 0.393 None Ownership 88 1.173 3,80 0.325 Category B Ownership 33 0.316 1,31 0.578 None Urbanization 88 0.064 3,84 0.835 Category C Ownership 26 6.555 1,24 0.017 None Ownership 88 4.361 1,86 0.076 Category D Ownership 30 10.467 1,28 0.003 Categories B-D Ownership 85 2.172 1,83 0.144 Public/Private combined Urbanization 79 0.492 3,75 0.689 Public/Private Combined Urbanization 72 0.382 3,68 0.767

Presence of Tier 1 Land Presence of Tier 3 Land None Urbanization 3.904 97 3 0.272 None Urbanization 30.841 97 3 <0.001 None Ownership 0.523 97 1 0.470 Categories B-D Urbanization 2.942 89 2 0.230 Public/Private combined Urbanization 3.762 79 3 0.288 None Ownership 0.002 97 1 0.965

Public/Private Combined Urbanization 32.921 79 3 <0.001 Tier Public/Private 1 Area (when Combined, present) B -D onlyNone UrbanizationOwnership 3.562 7114 4.387 21,12 0.1680.058

128 TABLE 2.6. Summary of Results

Chi- ANOVA Data Restriction Effect square N F-Ratio df p-value MeanTier 3 Parcel Area (when Size present) UrbanizationUrbanization x x NoneNone OwnershipOwnership 9788 4.2912.994 3,893,80 0.0070.036

PubliclyPublicly-owned-owned, B- BD- Donly UrbanizationUrbanization 2018 6.8381.845 2,172,15 0.0070.192

PrivatelyPrivately-owned-owned UrbanizationUrbanization 7669 6.8552.359 3,723,65 0.0000.080

CategoryCategory B B OwnershipOwnership 3330 14.0730.000 1,311,28 0.0010.995

CategoryCategory C C OwnershipOwnership 2626 0.0616.586 1,241,24 0.8070.017

CategoryCategory D D OwnershipOwnership 3029 0.0310.809 1,281,27 0.8620.376

Public/PrivatePublic/Private combined Combined UrbanizationUrbanization 7972 6.5632.742 3,753,68 0.0010.050

Mean Tier 3 Perimeter Percent of-Area Total Ratio Vacant Land (when Tier 3 land is present) UrbanizationUrbanization x x NoneNone OwnershipOwnership 9788 0.7150.794 3,893,8 0 0.5460.501 NoneNone UrbanizationUrbanization 9788 0.1060.202 3,933,84 0.9560.894 NoneNone OwnershipOwnership 9788 0.1915.658 1,951,86 0.6630.033 Public/PrivateCategories B- combinedD UrbanizationOwnership 7989 0.6460.583 3,751,87 0.5880.068 Public/Private Combined Urbanization 72 0.707 3,68 0.551

Presence of Vacant Land

Presence None of Tier 4 Land Urbanization 28.233 232 3 <0.001 NoneNone OwnershipUrbanization 53.5932.899 23297 1 3 <0.0010.408 Public/PrivateNone combined UrbanizationOwnership 34.6790.028 11697 3 1 <0.0010.868 Public/PrivatePublic/Private combined, Combined B -D Urbanization 4.514 79 3 0.211 only Urbanization 4.065 86 2 0.131

Tier 4 Area (when present) Total Vacant Area (when present)Urbanization x Urbanization x None Ownership 93 3.527 3,85 0.018 None Ownership 97 4.363 3,89 0.006 Publicly-owned, B-D Urbanization 19 2.660 2,16 0.101 Publicly-owned B-D only Urbanization 20 3.199 2,17 0.066 Privately-owned Urbanization 73 1.969 3,69 0.127 Privately-owned Urbanization 76 1.010 3,72 0.393 Category B Ownership 31 0.121 1,29 0.730 Category B Ownership 33 0.316 1,31 0.578 Category C Ownership 26 6.586 1,24 0.017 Category C Ownership 26 6.555 1,24 0.017 Category D Ownership 29 0.938 1,27 0.341 Category D Ownership 30 10.467 1,28 0.003 Public/Private Combined Urbanization 76 1.909 3,72 0.136 Public/Private combined Urbanization 79 0.492 3,75 0.689

Tier 4 Percent of Total Vacant Land (when Tier 4 land is present)

Presence of Tier 1 Land Urbanization x NoneNone UrbanizationOwnership 3.904 9793 0.193 3 3,85 0.2720.901

NonePublicly -owned, B-D OwnershipUrbanization 0.523 9719 0.015 1 2,16 0.4700.985

Public/PrivatePrivately-owned combined UrbUrbanizationanization 3.762 7973 2.446 3 3,69 0.2880.071

Category B Ownership 31 1.357 1,29 0.254

Tier 1 Area (when present) None Ownership 14 4.387 1,12 0.058 129 TABLE 2.6. Summary of Results

Chi- ANOVA Data Restriction Effect square N F-Ratio df p-value MeanCategory Parcel CSize Ownership 26 0.218 1,24 0.645 Category D UrbanizationOwnership x 29 2.939 1,27 0.098 None Ownership 97 4.291 3,89 0.007 Public/Private Combined Urbanization 76 2.377 3,72 0.077

Publicly-owned B-D only Urbanization 20 6.838 2,17 0.007

Privately -owned Urbanization 76 6.855 3,72 0.000 Presence of Tier 5 Land Category B Ownership 33 14.073 1,31 0.001 None Urbanization 5.799 97 3 0.122 Category C Ownership 26 0.061 1,24 0.807 None Ownership 0.968 97 1 0.325 Category D Ownership 30 0.031 1,28 0.862 Public/Private Combined Urbanization 8.989 79 3 0.029 Public/Private combined Urbanization 79 6.563 3,75 0.001

Tier 5 Area (when present) Mean Perimeter-Area Ratio Urbanization x None UrbanizationOwnership x 95 3.580 3,87 0.017 None Ownership 97 0.715 3,89 0.546 Publicly-owned, B-D Urbanization 19 2.316 2,16 0.131 None Urbanization 97 0.106 3,93 0.956 Privately-owned Urbanization 75 1.510 3,71 0.219 None Ownership 97 0.191 1,95 0.663 Category B Ownership 33 0.417 1,31 0.523 Public/Private combined Urbanization 79 0.646 3,75 0.588 Category C Ownership 26 6.456 1,24 0.018

Category D Ownership 29 2.208 1,27 0.149 Presence of Vacant Land Public/Private Combined Urbanization 78 1.225 3,74 0.307 None Urbanization 28.233 232 3 <0.001

None Ownership 53.593 232 1 <0.001 Tier 5 Percent of Total Vacant Land (when Tier 5 land is present) Public/Private combined UrbanizationUrbanization x 34.679 116 3 <0.001 Public/PrivateNone combined, B-D Ownership 95 0.274 3,87 0.844 only Urbanization 4.065 86 2 0.131 None Urbanization 95 0.909 3,91 0.440

None Ownership 95 1.254 1,93 0.266 Total Vacant Area (when present) Public/Private Combined Urbanization 78 1.932 3,74 0.132 Urbanization x

None Ownership 97 4.363 3,89 0.006

Overall Publicly land-owned composition B-D only Urbanization 20 3.199 2,17 0.066 Privately-owned UrbanizationUrbanization x 76 1.010 3,72 0.393 None Tier 474 6.441 15,450 <0.001 Category B Ownership 33 0.316 1,31 0.578 Tier 1 Urbanization 79 1.964 3,75 0.127 Category C Ownership 26 6.555 1,24 0.017 Tier 2 Urbanization 79 7.925 3,75 <0.001 Category D Ownership 30 10.467 1,28 0.003 Tier 3 Urbanization 79 3.075 3,75 0.033 Public/Private combined Urbanization 79 0.492 3,75 0.689 Tier 4 Urbanization 79 11.852 3,75 <0.001

Tier 5 Urbanization 79 2.658 3,75 0.054 Presence of Tier 1 Land Tier 6 Urbanization 79 0.477 3,75 0.699 None Urbanization 3.904 97 3 0.272 None Ownership 0.523 97 1 0.470 Public/Private combined Urbanization 3.762 79 3 0.288

Tier 1 Area (when present) None Ownership 14 4.387 1,12 0.058 130 Appendix B: Crop Yield Data

131 82 89 36 30 36 91 149 145 160 145 150 140 104 124 128 136 180 g per cup 463.84 456.52 990.18 2722.52 2135.63 4443.13 3954.88 1708.90 7328.73 4218.53 7421.49 1757.72 4218.53 3468.57 4003.70 1799.22 4638.43 net g/sq m 6.00 1.02 4.71 9.80 1.01 8.72 3.77 9.30 3.88 9.30 7.65 8.83 3.97 2.18 16.16 16.36 10.23 net lb/sq m 91 81 35 36 82 95 9.5 152 9.35 86.4 86.4 55.76 43.74 150.1 71.04 36.85 20.28 net lb/100 sq ft 9 5 5 5 18 62 19 30 19 28 45 18 10 36 26 33 22 % Refuse 68 25 50 54 50 17 96 96 55 26 100 100 160 100 158 135 100 intensive Low intensive lb/100 sq ft Bio- Biointensive Yield Figures (Jeavons, 2006) (Jeavons, Figures Yield Biointensive CROP Red & Orange (33% of servings)Red & Orange Starchy (31% of servings) Starchy Other (26% of servings) Dark Green (10% of servings) Green Dark Onions (regular) Potatoes (irish) Potatoes Green peppers Green Zucchini Green Peas (bush) Peas Green Butternut squash (winter squash) squash (winter Butternut Carrots Cucumbers Eggplant Spinach Corn Dark Green Leafy Lettuce (leaf lettuce) Lettuce Leafy Dark Green Tomatoes (assumed 5% refuse) Tomatoes Cabbage (regular) Cabbage Collard Greens (kale %) Greens refuse Collard Beets roots) (regular; Broccoli (heads) Broccoli 132 1.00 0.006 0.013 0.020 0.014 0.005 0.008 0.028 0.043 0.035 0.091 0.039 0.325 0.330 0.009 0.005 0.004 0.023 Multiplier Area Area 0.00062 0.09947 0.00134 0.00203 0.00143 0.00053 0.00078 0.00281 0.00427 0.00352 0.00901 0.00392 0.03230 0.03282 0.00085 0.00052 0.00043 0.00230 serving sq m for 1 sq m for 1.00 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.110 0.110 0.110 0.103 0.103 0.103 0.025 0.025 0.025 0.025 % of total % of total servings 3.20 3.15 59.85 27.77 18.27 26.04 70.47 47.40 13.23 25.77 31.28 12.21 26.37 29.30 48.17 58.59 10.88 511.94 servings / sq m 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 cups / serving 3.20 3.15 59.85 27.77 18.27 26.04 70.47 47.40 13.23 25.77 31.28 12.21 26.37 58.59 96.35 10.88 117.18 cups / sq m Biointensive Yield Figures (Jeavons, 2006) (Jeavons, Figures Yield Biointensive CROP Dark Green (10% of servings) Green Dark (31% of servings) Starchy (33% of servings)Red & Orange Other (26% of servings) Broccoli (heads) Broccoli (kale %) Greens refuse Collard (leaf lettuce) Lettuce Leafy Dark Green Spinach Corn (bush) Peas Green (irish) Potatoes squash) squash (winter Butternut Carrots (assumed 5% refuse) Tomatoes Beets roots) (regular; (regular) Cabbage Cucumbers Eggplant peppers Green Onions (regular) Zucchini

133 10.053 0.251328149 0.251328149 0.251328149 0.251328149 1.038823015 1.038823015 1.038823015 1.105843855 1.105843855 1.105843855 0.373401821 0.373401821 0.373401821 0.373401821 0.373401821 0.373401821 0.373401821 10.05312595 1 sq m yield Biointensive Yield Figures (Jeavons, 2006) (Jeavons, Figures Yield Biointensive CROP Dark Green (10% of servings) Green Dark (31% of servings) Starchy (33% of servings)Red & Orange Other (26% of servings) Broccoli (heads) Broccoli (kale %) Greens refuse Collard (leaf lettuce) Lettuce Leafy Dark Green Spinach Corn (bush) Peas Green (irish) Potatoes squash) squash (winter Butternut Carrots (assumed 5% refuse) Tomatoes Beets roots) (regular; (regular) Cabbage Cucumbers Eggplant peppers Green Onions (regular) Zucchini

134 1.80 3.10 2.18 2.93 1.56 0.35 5.40 3.46 6.20 5.53 7.90 5.82 6.59 5.52 4.16 4.65 11.29 net lb/sq m 3.23 16.69 28.83 20.22 27.18 14.52 50.21 32.14 57.60 51.37 73.35 54.11 61.18 51.25 38.60 43.18 104.87 net lb/100 sq ft 5 5 9 5 28 22 26 36 45 62 19 30 18 33 10 19 18 % Refuse 8 23 37 27 42 26 62 46 70 54 77 81 41 67 75 45 115 lb/100 sq ft 37 101 161 119 185 115 270 200 306 235 334 355 177 291 325 502 198 cwt/acre Source CA, TX)NJ, US 2011 US 2001 OH 1998 Oth 2011 (not AZ, OH 2011 US 2011 OH 2011 OH 2011 US 2011 OH 2011 US 2001 OH 2011 US 2011 US 2001 OH 2011 US 2011 NE VMG Commercial Yield Figures (United States States (United Figures Yield Commercial CROP Department of Agriculture, 2013b) Department of Agriculture, Starchy (31% of servings) Starchy Dark Green (10% of servings) Green Dark (33% of servings)Red & Orange Other (26% of servings) Corn Broccoli (heads) Broccoli squash) squash (winter Butternut Beets roots) (regular; Collard Greens (kale %) Greens refuse Collard (leaf lettuce) Lettuce Leafy Dark Green Spinach (bush) Peas Green (irish) Potatoes Carrots (assumed 5% refuse) Tomatoes (regular) Cabbage Cucumbers Eggplant peppers (Bell) Green Onions (regular) Zucchini

135 0.025 0.025 0.025 0.025 0.103 0.103 0.103 0.110 0.110 0.110 0.037 0.037 0.037 0.037 0.037 0.037 0.037 % of total % of total servings 4.89 1.09 15.47 13.71 18.43 13.59 16.34 11.21 21.97 13.90 18.44 40.24 18.12 32.22 20.05 32.00 17.00 sq m servings / 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 cups / serving 4.89 1.09 27.42 36.86 27.17 16.34 11.21 21.97 13.90 18.44 40.24 18.12 32.22 20.05 32.00 17.00 15.47 cups / sq m 36 36 30 89 82 91 145 145 150 140 128 180 136 104 149 160 124 g per cup 987.05 815.11 708.96 157.60 1327.12 2451.37 1569.23 2812.52 2502.37 2508.31 3581.22 1884.76 2642.03 2987.15 5120.41 2108.38 1407.60 net g/sq m Commercial Yield Figures (United States States (United Figures Yield Commercial CROP Department of Agriculture, 2013b) Department of Agriculture, Dark Green (10% of servings) Green Dark (31% of servings) Starchy (33% of servings)Red & Orange Other (26% of servings) Broccoli (heads) Broccoli (kale %) Greens refuse Collard (leaf lettuce) Lettuce Leafy Dark Green Spinach Corn (bush) Peas Green (irish) Potatoes squash) squash (winter Butternut Carrots (assumed 5% refuse) Tomatoes Beets roots) (regular; (regular) Cabbage Cucumbers Eggplant peppers (Bell) Green Onions (regular) Zucchini

136 6.126 0.153151094 0.153151094 0.153151094 0.153151094 0.633024522 0.633024522 0.633024522 0.673864814 0.673864814 0.673864814 0.227538768 0.227538768 0.227538768 0.227538768 0.227538768 0.227538768 0.227538768 6.126043766 1 sq m yield 0.010 0.011 0.008 0.011 0.129 0.582 0.039 0.060 0.031 0.048 0.012 0.006 0.013 0.007 0.011 0.007 0.013 1.000 Multiplier Area Area 0.00182 0.00136 0.00184 0.02113 0.09507 0.00632 0.00981 0.00501 0.00791 0.00201 0.00092 0.00205 0.00115 0.00185 0.00116 0.00218 0.16324 0.00162 serving sq m for 1 sq m for Commercial Yield Figures (United States States (United Figures Yield Commercial CROP Department of Agriculture, 2013b) Department of Agriculture, Dark Green (10% of servings) Green Dark (31% of servings) Starchy (33% of servings)Red & Orange Other (26% of servings) Broccoli (heads) Broccoli (kale %) Greens refuse Collard (leaf lettuce) Lettuce Leafy Dark Green Spinach Corn (bush) Peas Green (irish) Potatoes squash) squash (winter Butternut Carrots (assumed 5% refuse) Tomatoes Beets roots) (regular; (regular) Cabbage Cucumbers Eggplant peppers (Bell) Green Onions (regular) Zucchini

137 296.86 553.68 493.19 624.97 1743.07 2802.59 4406.51 1602.95 1142.52 2256.99 1300.71 7865.81 2319.22 1463.30 3283.03 2310.43 2458.37 net g/sq m 0.65 1.22 3.84 1.09 6.18 9.71 3.53 2.52 4.98 1.38 2.87 5.11 3.23 7.24 5.09 5.42 17.34 net lb/sq m 6.08 11.34 35.70 10.10 57.40 90.25 32.83 23.40 46.23 12.80 26.64 47.50 29.97 67.24 47.32 50.35 161.10 net lb/100 sq ft 5 5 9 5 19 30 45 62 18 33 10 22 26 36 28 19 18 % Refuse 14 51 18 16 70 95 49 30 62 20 37 50 37 82 52 53 179 lb/100 sq ft 80 cwt/acre (Kolbe, 2013) (Kolbe, Small Market Farm Yield Figures Figures Yield Small Market Farm CROP Red & Orange (33% of servings)Red & Orange Other (26% of servings) Dark Green (10% of servings) Green Dark Starchy (31% of servings) Starchy Potatoes (irish) Potatoes squash) squash (winter Butternut Green Peas (bush) Peas Green Carrots Tomatoes (assumed 5% refuse) Tomatoes Beets roots) (regular; Cabbage (regular) Cabbage Broccoli (heads) Broccoli Collard Greens (kale %) Greens refuse Collard Dark Green Leafy Lettuce (leaf lettuce) Lettuce Leafy Dark Green Spinach Corn Cucumbers Eggplant Green peppers (Bell) Green Onions (regular) Zucchini

138 0.014 0.005 0.020 0.008 0.206 0.342 0.190 0.060 0.034 0.030 0.021 0.003 0.011 0.014 0.011 0.017 0.013 Multiplier Area Area 0.00199 0.00080 0.00288 0.00115 0.03038 0.05047 0.02799 0.00883 0.00502 0.00449 0.00315 0.00042 0.00167 0.00208 0.00169 0.00257 0.00187 serving sq m for 1 sq m for 0.025 0.025 0.025 0.025 0.103 0.103 0.103 0.110 0.110 0.110 0.037 0.037 0.037 0.037 0.037 0.037 0.037 % of total % of total servings 8.68 3.40 2.05 3.69 12.56 31.35 21.68 12.45 21.90 24.48 11.79 88.38 22.30 17.85 22.03 14.44 19.83 sq m servings / 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 cups / serving 3.40 2.05 3.69 12.56 62.69 17.36 43.36 12.45 21.90 24.48 11.79 88.38 22.30 17.85 22.03 14.44 19.83 cups / sq m 91 36 36 30 89 82 145 145 150 140 128 180 136 104 149 160 124 g per cup (Kolbe, 2013) (Kolbe, Small Market Farm Yield Figures Figures Yield Small Market Farm CROP Dark Green (10% of servings) Green Dark (31% of servings) Starchy (33% of servings)Red & Orange Other (26% of servings) Broccoli (heads) Broccoli (kale %) Greens refuse Collard (leaf lettuce) Lettuce Leafy Dark Green Spinach Corn (bush) Peas Green (irish) Potatoes squash) squash (winter Butternut Carrots (assumed 5% refuse) Tomatoes Beets roots) (regular; (regular) Cabbage Cucumbers Eggplant peppers (Bell) Green Onions (regular) Zucchini

139 6.781 6.780946936 1 sq m yield (Kolbe, 2013) (Kolbe, Small Market Farm Yield Figures Figures Yield Small Market Farm CROP 0.70069785 0.70069785 0.70069785 0.169523673 0.169523673 0.169523673 0.169523673 0.745904163 0.745904163 0.745904163 0.251863743 0.251863743 0.251863743 0.251863743 0.251863743 0.251863743 0.251863743 1 sq m yield (Kolbe, 2013) (Kolbe, Small Market Farm Yield Figures Figures Yield Small Market Farm CROP Dark Green (10% of servings) Green Dark (31% of servings) Starchy (33% of servings)Red & Orange Other (26% of servings) Broccoli (heads) Broccoli (kale %) Greens refuse Collard (leaf lettuce) Lettuce Leafy Dark Green Spinach Corn (bush) Peas Green (irish) Potatoes squash) squash (winter Butternut Carrots (assumed 5% refuse) Tomatoes Beets roots) (regular; (regular) Cabbage Cucumbers Eggplant peppers (Bell) Green Onions (regular) Zucchini

140 Appendix C: Whole Parcel Data

141 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Licking Franklin Delaware Franklin Fairfield Fairfield Delaware Delaware Delaware Franklin Franklin Fairfield Category B B B B B B A A A A A A A A A A A A 4.275125 4.351923 4.208683 4.6921153 4.2750998 4.3519211 4.3519063 4.8195348 4.9278789 4.4050846 4.5794482 4.0064034 5.3657427 5.8417034 7.2026548 4.6655574 9.7191696 19.8826084 PA_RATIO 100.598732 100.598526 1137.72583 661.737793 128.1449738 103.6401367 103.6473846 100.5984268 342.0409241 2630.931152 2719.609131 648.7633057 269.7611389 767.2456665 261.0939941 832.2258911 238.2833252 1620.135376 PERIMETER_ METERS 745.8745728 587.7105103 587.7857666 534.3460083 534.3442993 534.3469238 5036.698242 356706.0625 304574.0625 20069.94727 4533.665039 20446.03906 1997.628784 24951.11914 1752.006714 601.0754395 148187.0156 20117.07422 AREA_METERS 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip DISTRICTS LOT TO 39.99 A* TO RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 RESIDENTIAL LAND: 30 VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY PARK OWNED BY Other Vacant UNPLATTED VACANT, UNPLATTED VACANT, R-RES VACANT Other Vacant Other Vacant Land Vacant R-RES VACANT 500 Residential vacant 500 Residential vacant SINGLE FAMILY SINGLE FAMILY R-RES VACANT SINGLE FAMILY 9.99 ACR* DWELLING ON DWELLING ON PLATTED land UNPLATTED LAND: 0 TO TO LAND: 0 UNPLATTED 9.* 204 204 204 204 204 123 124 129 129 111 119 119 119 120 102 129 130 203 SiteID Study Parcel_ID 570-263379 570-289538 570-289539 570-289540 570-289541 184-003184 41721001001000 220-002118 222-002133 0460015100 41934002018007 31922001078000 31922012049000 063-149358-00.000 0090188120 222-002153 0040113213 070-005749 142 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B B B B B B B B B B B B B 4.299274 4.280828 4.3519654 4.3519378 4.5419111 4.5793004 4.5698953 4.8796468 4.8229051 4.8795056 5.0600977 5.1001101 4.9940691 5.0344057 5.2268491 5.2660413 5.3050256 5.3428545 4.5029912 4.8590441 4.5312552 4.2782865 4.3417168 3.9519019 4.3519282 4.3518381 PA_RATIO 131.909317 170.977951 221.730423 100.5962982 100.5973587 115.8438187 129.8353729 140.7367706 142.0413971 151.8522034 156.7871246 157.1792908 160.4643707 166.9713593 170.5855713 174.2637787 177.5491486 180.8006592 253.6773987 127.2401581 111.8434753 112.9654388 109.7432709 112.0478058 100.5965729 100.5997543 PERIMETER_ METERS 883.472168 534.3097534 534.3278198 650.5316772 941.3708496 803.8737183 948.4243164 847.3310547 991.3447876 1032.450317 964.8790283 989.9155273 1117.827759 1148.122437 1070.040772 1095.079712 1120.114014 1145.126221 3173.666992 2082.326904 609.2341309 697.1905518 638.8999023 803.8858643 534.3218384 534.3777466 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT VACANT LAND VACANT 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 204 SiteID Study Parcel_ID 570-289543 570-289551 570-289552 570-289553 570-289554 570-289555 570-289556 570-289557 570-289558 570-289559 570-289560 570-289561 570-289562 570-289563 570-289564 570-289565 570-289567 570-289568 570-289569 570-289570 570-289571 570-289572 570-289573 570-289574 570-289575 570-289542 143 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B B B 4.501873 4.351933 4.4148183 6.2837577 4.6976547 5.0794244 4.0412626 4.5945749 4.3062444 4.2751417 4.3519268 4.3519292 4.3518538 4.1985426 4.1253676 9.0052176 PA_RATIO 96.7539597 82.9246368 21.3282242 134.7081299 127.6040726 267.1192932 855.7086792 548.5014648 103.6455841 100.5974426 100.5969696 100.6000443 100.5976181 131.3693237 129.1196747 4896.664551 PERIMETER_ METERS 266.525116 27.8532085 895.3671875 835.4171143 237.0820313 3233.317139 34686.55859 16224.01172 587.7607422 534.3314209 534.3258667 534.3770142 534.3317871 979.0183105 979.6242676 295673.4063 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE LandUseDescrip LOT LOT MUNICIPALITIES MUNICIPALITIES SINGLE FAMILY SINGLE FAMILY RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 TO RESIDENTIAL LAND: 0 MUNICIPALITIES MUNICIPALITIES EXEMPT PROPERTY OWNED BY VACANT, UNPLATTED UNPLATTED VACANT, UNPLATTED VACANT, SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY OWNED BY VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL 19 TO 4 APARTMENTS: RENTAL UNITS UNITS RENTAL LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED 9.99 ACR* 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED 209 209 209 207 208 208 209 204 204 204 204 205 205 205 206 204 SiteID Study Parcel_ID 070-002788 070-000469 070-014466 610-166633 560-168616 560-168617 070-000467 570-289577 570-289578 570-289579 570-289580 050-009015 050-009740 560-147986 010-290546 570-289576 144 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B B B B B B B B B B B 4.275198 9.8567638 7.0509195 4.2907729 4.3025336 4.2755494 4.2793632 4.2768912 4.2753048 4.2753029 4.2752671 4.2752242 4.3074932 4.6138382 4.3766479 4.2426224 4.2426162 3.9702978 4.3519058 4.3519154 4.3519106 4.2751241 4.2751236 4.5266247 PA_RATIO 56.995636 98.1113129 103.806076 104.701622 100.591095 107.9119949 104.4486084 103.8064728 103.8580093 103.7326355 103.6582184 103.6564178 103.6552811 103.6508713 113.3407135 114.2993164 100.5913773 106.2042465 100.5931091 100.5931091 100.5932083 103.6414948 103.6415024 119.1202469 PERIMETER_ METERS 33.4360161 562.149231 534.288147 193.6184235 632.5114746 589.3276367 589.5713501 590.0599365 587.5874634 589.1002197 587.8591919 587.8393555 587.8363037 587.7979736 590.8233032 603.4581909 682.0309448 562.1507568 715.5460815 534.2905273 534.2904663 587.7191772 587.7194214 692.5039063 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC LandUseDescrip MUNICIPALITIES MUNICIPALITIES VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY OWNED BY COMMERCIAL VACANT VACANT COMMERCIAL LAND 211 211 211 211 211 211 211 211 211 211 211 211 211 211 211 211 211 211 211 211 211 211 211 209 SiteID Study Parcel_ID 010-144397 010-283139 010-283140 010-283141 010-283142 010-283143 010-283144 010-283145 010-283146 010-283147 010-283150 010-283151 010-283152 010-283155 010-283162 010-283163 010-283164 010-283165 010-283166 010-283167 010-283168 010-283169 010-003747 070-014467 145 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B B B B 5.417747 4.685801 4.7084751 4.3470154 4.6842833 4.0334105 4.6278601 4.2788892 4.2560072 4.2561626 4.0232601 7.1079674 7.3728461 4.6826477 4.0606742 4.0563464 4.2241368 PA_RATIO 79.7564774 100.821228 76.2247086 83.3615875 82.2656097 108.8239517 119.1580963 119.5948639 156.8602142 387.0830383 121.6857986 101.8548965 101.8685837 110.6298447 370.9456177 101.5142975 193.6448975 PERIMETER_ METERS 1302.09668 572.854187 2723.51416 403.4703064 646.6651611 645.1555786 6828.447754 391.0092468 691.3840332 555.1903076 572.7421265 756.1159668 106.8860931 469.9714355 421.4398804 411.3074341 2101.532959 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT VACANT LAND VACANT LAND VACANT SINGLE FAMILY RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 VACANT, UNPLATTED UNPLATTED VACANT, LAND VACANT SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY LAND VACANT VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL LAND DWELLING ON PLATTED DWELLING ON PLATTED 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON DWELLING ON UNPLATTED LAND: 0 TO TO LAND: 0 UNPLATTED TO LAND: 0 UNPLATTED 9.* 9.* 213 213 213 212 213 213 211 211 211 211 211 212 212 212 212 212 211 SiteID Study Parcel_ID 040-000304 040-000320 040-000323 080-003033 040-000096 040-000285 010-283183 010-283185 010-283186 010-283187 010-283188 080-001385 080-001722 080-002004 080-002141 080-002782 010-283182 146 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B B B B B B 4.31707 4.320127 4.2436681 4.4728889 7.0705605 4.0574474 4.1528573 4.9538765 4.0513043 4.2443089 4.2949138 4.3124185 4.7522564 4.7099862 4.7086854 4.1533165 4.1534996 6.2248688 14.9590282 PA_RATIO 176.8181 107.656456 110.2662888 1092.715698 579.5477295 1488.842651 194.2324371 404.3100891 698.5041504 571.6825562 106.1059494 106.1068192 107.0373764 118.8025208 119.6446609 120.0686264 186.8933105 187.0014496 1143.377686 PERIMETER_ METERS 675.1537476 5335.898926 16788.12305 44339.41016 1899.099243 2187.507324 6660.990723 29726.80273 18142.44727 603.2349243 621.8733521 610.3482056 616.0686646 624.9591064 645.2787476 650.2191162 2024.871582 2027.036743 33737.97656 AREA_METERS 0 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE LandUseDescrip LOT RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 TO RESIDENTIAL LAND: 0 VACANT LAND VACANT UNPLATTED VACANT, UNPLATTED VACANT, LAND VACANT VACANT LAND VACANT MUNICIPALITIES MUNICIPALITIES VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY OWNED BY VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND LAND 9.99 ACR* 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED 214 214 214 214 214 214 214 214 214 214 214 214 213 213 213 213 213 214 213 SiteID Study Parcel_ID 010-162080 190-000003 190-002301 190-004807 190-004810 010-005506 010-005506 010-161897 010-161917 010-161923 010-161942 010-162069 040-000507 040-000684 040-006619 040-006625 040-001224 010-005506 040-000500 147 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B B B B B 5.2363238 4.4333625 4.9515266 4.2368479 5.5089498 4.1579213 8.5550709 4.5558696 4.7824187 7.5503349 4.2831383 4.5964227 4.1589351 4.1138129 4.1157618 4.6812587 6.5775695 4.8886342 PA_RATIO 1414.625 122.217186 105.8213882 335.3217163 408.5654907 175.6536255 165.2751312 766.0482788 200.0216064 169.1057434 198.8560944 315.6199341 196.4851532 166.4784851 177.5996857 268.3351746 319.3026428 1134.362549 PERIMETER_ METERS 1637.67273 408.4077148 759.9728394 4586.117188 9299.030273 1016.661987 1580.019531 27342.32617 28272.85938 1749.277222 501.6308899 2155.527588 4715.078125 2232.005371 1862.018189 3285.715332 2356.536377 53842.92578 AREA_METERS 1 0 0 0 1 1 1 1 1 1 0 1 0 0 0 0 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PUBLIC PRIVATE LandUseDescrip LOT LOT RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 VACANT LAND VACANT LAND VACANT UNPLATTED VACANT, VACANT LAND VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL EXEMPT PROPERTY TOWNSHIPS OWNED BY EXEMPT PROPERTY TOWNSHIPS OWNED BY LAND LAND LAND 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 218 218 218 216 216 217 215 215 215 214 214 214 214 214 215 215 216 214 SiteID Study Parcel_ID 010-112150 010-112150 010-112151 010-115697 010-246854 010-150662 110-000178 110-005244 110-000099 190-004812 190-004813 190-004814 190-004815 190-004816 110-000078 110-005243 010-006048 190-004811 148 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B 4.7477508 4.9744115 4.7417908 4.8638887 4.6336355 4.5584879 4.6604028 4.7851286 4.9427395 6.0103898 4.6715822 4.4475141 4.6110396 11.2815924 PA_RATIO 93.1010666 97.5827789 95.3516159 75.0681763 38.2119522 37.2292709 104.9058304 106.9913559 114.8166122 100.3954926 203.8221893 109.6029434 938.6876221 266.3124695 PERIMETER_ METERS 24391.4082 44.2762604 73.8182526 65.1885605 384.5328979 444.7498779 509.1111145 557.2399292 469.4449768 1999.223389 438.4291687 397.0716553 491.7099609 3249.787598 AREA_METERS 1 1 1 1 1 0 1 1 1 1 0 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT LOT LOT WORSHIP SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 TO RESIDENTIAL LAND: 0 SINGLE FAMILY SINGLE FAMILY VACANT, UNPLATTED UNPLATTED VACANT, VACANT, UNPLATTED UNPLATTED VACANT, UNPLATTED VACANT, CHURCHES, PUBLIC VACANT COMMERCIAL LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 9.99 ACR* 9.99 ACR* 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED 218 218 218 218 218 218 218 218 218 218 218 218 218 218 SiteID Study Parcel_ID 010-112573 010-112571 010-112568 010-112534 010-112556 010-112557 010-112559 010-112564 010-112523 010-112211 010-112159 010-112296 010-112296 010-112331 149 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B B B B 4.915029 4.9393158 4.9252424 4.8856764 4.8727522 4.3710155 4.9007778 4.9353414 3.9995525 4.9375272 4.8079777 4.8672638 4.7907395 4.9494972 4.6426406 4.1633449 4.2226443 PA_RATIO 97.550148 93.5681229 122.800354 92.5922394 80.9425354 60.2083893 116.4591141 116.6135864 114.5729141 115.0657654 126.5755844 117.2372971 106.2640228 155.3621674 102.0297546 106.5512619 116.0569611 PERIMETER_ METERS 557.62677 572.270752 555.9213867 560.5870361 549.9389648 838.5620117 463.5942993 1508.925293 427.0067139 378.7311096 401.6854248 494.6660156 549.8196411 624.2335205 397.7581177 377.9799805 203.3035889 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT LOT LOT LOT ON PLATTED LOT LOT ON PLATTED VACANT LAND VACANT LAND VACANT VACANT LAND VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY TWO-FAMILY DWELLING TWO-FAMILY DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 218 218 218 218 218 218 218 218 218 218 218 218 218 218 218 218 218 SiteID Study Parcel_ID 010-113160 010-113183 010-113157 010-113158 010-113159 010-113117 010-113128 010-112723 010-112980 010-112718 010-112705 010-112715 010-112606 010-112607 010-112608 010-112610 010-112591 150 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B 4.476984 4.494873 4.5821199 4.2093062 4.2238636 4.4929152 5.5653634 4.9896698 4.5077028 4.5786133 4.9914556 4.4791255 4.4782248 4.6095495 PA_RATIO 97.4542847 99.3055801 98.7473831 98.9184189 98.4196548 98.1655045 99.7883911 99.3687668 96.9589539 110.3616486 109.3080215 119.1469498 108.8211975 103.2258759 PERIMETER_ METERS 483.052948 452.3441162 687.4091797 669.7061157 492.0126343 458.3305359 475.6455383 481.5526733 479.4339905 459.6739502 427.6837769 496.3334961 492.3659668 442.4445496 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT LOT LOT LOT LOT LOT WORSHIP VACANT LAND VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY CHURCHES, PUBLIC DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 218 218 218 218 218 218 218 218 218 218 218 218 218 218 SiteID Study Parcel_ID 010-113524 010-113526 010-113537 010-113523 010-113505 010-113503 010-113355 010-113582 010-113588 010-113340 010-113544 010-113564 010-113249 010-113539 151 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B 5.459444 4.497436 4.569912 4.3244328 4.3196034 4.7142429 4.5009074 18.361433 4.6998987 4.8843255 4.5479808 5.0655346 9.6853552 4.6021614 PA_RATIO 99.2931442 672.729248 97.4235077 97.9244843 102.1854553 102.4394608 188.1316223 194.0784302 100.7165298 202.6384888 171.6861725 177.5040436 331.0933533 251.1786957 PERIMETER_ METERS 1263.73999 558.3671875 562.4019775 1592.574097 486.6742859 501.5006409 1342.355347 1858.946289 1235.553223 1523.278442 4272.187988 672.5653687 448.1297607 459.1629944 AREA_METERS 1 1 0 0 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 TO RESIDENTIAL LAND: 0 TO RESIDENTIAL LAND: 0 TO RESIDENTIAL LAND: 0 TO RESIDENTIAL LAND: 0 SINGLE FAMILY SINGLE FAMILY VACANT, UNPLATTED UNPLATTED VACANT, UNPLATTED VACANT, UNPLATTED VACANT, UNPLATTED VACANT, UNPLATTED VACANT, VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL LAND DWELLING ON PLATTED DWELLING ON PLATTED 9.99 ACR* 9.99 ACR* 9.99 ACR* 9.99 ACR* 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 218 218 218 218 218 218 218 218 218 218 218 218 218 218 SiteID Study Parcel_ID 010-113858 010-114847 010-114848 010-116128 010-113856 010-113603 010-247209 010-247210 010-247211 010-247377 010-255634 010-113590 010-113601 010-113589 152 COUNTY Franklin Franklin Franklin Delaware Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B B 4.86659 4.252737 4.930007 4.0133858 5.6692891 4.0114722 5.6631684 5.3670588 5.2614331 4.8302898 4.8524976 4.8285208 4.8319125 4.8490348 4.8609056 PA_RATIO 156.769043 563.2072144 130.4710541 102.6725235 128.9493713 100.9501572 194.8955231 441.0371399 157.2940521 156.6734924 157.2928162 157.2208252 156.2553253 156.5363464 181.9915314 PERIMETER_ METERS 317.756958 1058.72229 1037.04126 19693.12305 1057.841919 327.9829407 919.3934937 1318.654541 7026.551758 1060.420776 1042.462402 1061.181152 1030.908569 1045.225464 1362.722168 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT CEMETERIES VACANT LAND VACANT Land Vacant GRAVEYARDS, RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY VACANT, UNPLATTED UNPLATTED VACANT, COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND LAND LAND LAND LAND LAND LAND LAND 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED MONUMENTS, AND MONUMENTS, 222 220 221 221 219 220 222 222 222 222 222 222 222 219 218 SiteID Study Parcel_ID 010-088338 020-003236 31834301062000 250-002430 010-201608 020-001374 010-088367 010-089739 010-089740 010-089741 010-089745 010-089746 010-089814 010-181629 010-255769 153 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B 6.579452 5.327549 4.7916226 4.5577159 4.4141579 6.9283462 5.6542206 4.2680001 5.8817277 4.5623927 4.1408968 5.8139744 12.3783197 PA_RATIO 22.8678093 47.9224396 165.8932495 109.9571838 116.6255798 127.8749161 594.5421753 336.5904236 822.3253784 204.4562988 214.6503754 418.6784668 1035.138672 PERIMETER_ METERS 12.0800762 71.8343735 1198.649536 425.9830627 654.7766113 839.2184448 7363.871094 6219.487793 19546.88477 2008.240234 2687.042236 1144.031128 31699.38672 AREA_METERS 1 1 1 1 1 1 1 1 1 0 0 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT LOT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 TO RESIDENTIAL LAND: 0 TO RESIDENTIAL LAND: 0 VACANT LAND VACANT SINGLE FAMILY VACANT, UNPLATTED UNPLATTED VACANT, LAND VACANT UNPLATTED VACANT, UNPLATTED VACANT, SINGLE FAMILY SINGLE FAMILY COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 9.99 ACR* 9.99 ACR* 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 224 223 222 222 222 224 224 226 226 226 226 226 222 SiteID Study Parcel_ID 060-001269 080-002607 010-091215 010-090737 010-090769 060-003833 060-007531 140-002095 140-002392 140-002400 140-004069 140-004316 010-089844 154 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B B B B B B B B B B B 4.786396 5.521996 4.9462214 4.8167491 4.8908277 4.4919958 4.7294869 4.7822161 4.9606113 4.3075871 4.8542981 5.4063487 4.8869743 5.2365041 4.9019785 4.3807449 4.3264642 PA_RATIO 1847.9729 1851.27771 144.2411194 141.1734161 140.2003479 137.8177032 112.2792664 110.9116745 110.2093506 194.7701263 107.6371155 115.2824554 140.9488678 802.1801147 1484.490967 136.7553558 140.0196228 PERIMETER_ METERS 530.175415 716.239502 850.4153442 859.0090942 821.7382202 941.3070068 563.6001587 537.8932495 1244.092041 470.8186951 843.0830078 22015.86133 143503.6875 80365.97656 142117.7813 974.5244751 1047.397583 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PUBLIC PUBLIC PUBLIC PRIVATE PRIVATE LandUseDescrip LOT THE A* WORSHIP EXEMPTIONS ACADEMIES (PRIV* ACADEMIES VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT VACANT LAND VACANT LAND VACANT LAND VACANT CHARITABLE HOMES FOR (HOSPITALS, EXEMPT PROPERTY OF STATE OWNED BY EXEMPT PROPERTY COLLEGES OWNED BY EXEMPT PROPERTY OF STATE OWNED BY LAND VACANT SINGLE FAMILY SINGLE FAMILY EXEMPT PROPERTY COUNTIES OWNED BY CHURCHES, PUBLIC VACANT COMMERCIAL LAND DWELLING ON PLATTED DWELLING ON PLATTED OHIO OHIO 228 228 228 228 228 228 228 228 228 228 227 227 227 228 228 226 228 SiteID Study Parcel_ID 010-069627 010-069658 010-069714 010-072900 010-072901 010-059898 010-059917 010-065673 010-069557 010-069623 010-151260 010-151262 010-151264 010-059282 010-059498 140-003965 010-059565 155 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category B B B B B B B C B B B B B C C C B 5.2158899 8.5759773 9.8086576 5.0727515 4.0931096 4.0005507 5.0475826 5.0951753 4.8198538 4.6069007 4.6729174 4.6452913 4.1357098 4.9685187 4.8024259 4.6459432 11.0534086 PA_RATIO 242.70578 76.879158 48.5373497 78.4970016 104.4187927 693.9950562 1022.913635 1118.367065 128.0791626 121.4265137 146.8024445 237.2480621 109.9098434 123.3653717 150.8250885 151.6394958 151.5994873 PERIMETER_ METERS 1064.75 880.076355 137.737442 400.7749023 6548.554688 10875.73828 10237.08398 637.4846802 3680.617676 845.8623047 2168.138672 520.0025024 717.0820313 1041.767822 1065.611206 239.4216461 267.1682129 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT STRUCTURES ACADEMIES (PRIV* ACADEMIES (PRIV* ACADEMIES MUNICIPALITIES MUNICIPALITIES EXEMPT PROPERTY OWNED BY LAND VACANT SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY COLLEGES OWNED BY EXEMPT PROPERTY COLLEGES OWNED BY VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL RETAIL OTHER VACANT COMMERCIAL VACANT COMMERCIAL PARCELS ZERO-VALUED LAND LAND LAND LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED 230 228 228 228 229 229 301 301 230 228 228 228 228 301 301 228 228 SiteID Study Parcel_ID 010-099176 010-244168 010-244168 010-069600 010-092184 010-124603 010-002886 010-006368 090-007474 010-073077 010-087865 010-087867 010-087868 010-009952 010-010650 010-072902 010-244168 156 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C C C 5.8700123 4.7557893 4.0045729 4.1009088 3.9511142 4.6448126 4.0445137 6.1532931 6.1519165 4.1300735 4.6094694 4.2154202 4.9701157 4.0722198 4.5526404 12.4010773 PA_RATIO 94.9610214 55.7979889 53.0630188 45.7911987 50.6676445 40.8006516 112.7365799 153.5166321 238.0548859 124.0634613 979.8768921 502.8643494 1886.813599 948.0432739 913.1236572 101.0766602 PERIMETER_ METERS 80.317009 39242.5625 368.8516235 398.6993408 194.1443329 1401.363037 3630.070313 713.4299927 172.1278992 25358.74805 1644.306763 94066.96875 52691.52344 118.0000763 413.5888367 154.8099823 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip ACADEMIES (PRIV* ACADEMIES ACADEMIES (PRIV* ACADEMIES EXEMPT PROPERTY COLLEGES OWNED BY LAND VACANT VACANT LAND VACANT EXEMPT PROPERTY COLLEGES OWNED BY OFFICE ELEVATOR ELEVATOR OFFICE ELEVATOR ZERO-VALUED PARCELS PARCELS ZERO-VALUED PARCELS ZERO-VALUED VACANT COMMERCIAL VACANT COMMERCIAL PARCELS ZERO-VALUED VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND LAND LAND LAND LAND LAND STORIES) STORIES) STORIES) STORIES) BUILDING (3 OR MORE BUILDING (3 OR MORE 301 301 301 301 301 301 301 302 303 303 303 301 301 301 302 301 SiteID Study Parcel_ID 010-022364 010-023417 010-034531 010-034722 010-054962 010-093665 010-289794 010-210762 010-221294 010-233785 010-281337 010-025164 010-030385 010-052141 010-066224 010-021770 157 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C 5.232985 5.8876758 4.5342951 4.0108089 4.4914651 5.5845437 8.1974649 5.2194352 3.8840101 4.3822527 4.3534451 4.8510671 3.9944503 4.0209179 PA_RATIO 212.788269 2528.891846 609.4506226 566.2423096 258.5026245 1290.599609 772.3580933 505.4500122 173.5943298 206.1509552 207.7171173 225.1731567 745.9871826 294.5522156 PERIMETER_ METERS 19931.5332 184489.9531 18065.81055 3312.484619 53408.16797 8877.246094 9377.982422 1100.454468 2817.150147 2246.724121 2389.072022 2154.560303 34877.76953 5366.290527 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip RETAIL STRUCTURES RETAIL SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY DETACHED SMALL COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND LAND LAND LAND LAND LAND LAND LAND LAND LAND DWELLING ON DWELLING ON UNPLATTED LAND: 0 TO TO LAND: 0 UNPLATTED TO LAND: 0 UNPLATTED (UNDER 10000 SQU* 9.* 9.* 305 303 304 304 304 305 305 305 305 305 305 305 305 303 SiteID Study Parcel_ID 010-147199 010-289267 010-146721 010-233779 190-000023 010-146555 010-147168 010-147202 010-147204 010-147205 010-233787 010-251074 010-265880 010-289266 158 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C C 4.265367 4.341136 5.810307 4.4410963 4.3951879 4.7210031 5.2042031 4.7182584 6.3964314 4.2091589 6.3322153 5.0403242 4.9554811 4.7923951 21.4720707 PA_RATIO 67.881218 659.166687 91.6206589 93.9683685 93.7990875 36.5269394 728.0025635 1820.542969 773.6112061 1191.809937 142.1851349 732.1829834 663.7913208 389.5174866 120.6394882 PERIMETER_ METERS 27435.375 1162.76355 39.5210457 233.6247406 19494.94141 122375.1875 26883.26758 34716.70313 461.3962402 1141.084473 23380.63672 3783.925537 572.8778076 359.5768433 383.0821838 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 EXEMPT PROPERTY OF STATE OWNED BY EXEMPT PROPERTY OF STATE OWNED BY LAND VACANT LAND VACANT LAND VACANT UNPLATTED VACANT, SINGLE FAMILY COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND LAND LAND LAND LAND LAND LAND 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED OHIO OHIO 305 305 305 307 307 308 308 309 310 311 311 311 311 311 305 SiteID Study Parcel_ID 010-280872 520-105164 520-207098 010-103196 010-103226 610-199344 610-269029 273-002707 590-192108 010-002090 010-004516 010-004812 010-004934 010-006144 010-266223 159 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C C C 5.241364 4.838408 7.5940924 13.234334 5.8670816 4.9927073 5.1317978 6.6391158 4.9316897 4.5812893 4.8545761 4.9528818 5.2217226 6.7783327 5.0485759 5.0467467 PA_RATIO 73.9835587 93.0516815 91.4871292 82.3427963 92.7407303 94.0692749 123.936409 99.4797974 112.0739594 121.9826279 116.4741745 121.9162445 131.9768066 119.4674606 118.8271179 114.7449646 PERIMETER_ METERS 31.2511673 217.7999115 541.6357422 394.1077881 596.2809448 661.3877563 323.8014221 369.8652039 344.1347656 323.0544739 364.9541931 360.7278137 563.3395386 307.3161316 388.2683411 516.9448853 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT THE A* THE A* THE A* EXEMPTIONS EXEMPTIONS EXEMPTIONS VACANT LAND VACANT CHARITABLE HOMES FOR (HOSPITALS, VACANT LAND VACANT LAND VACANT CHARITABLE HOMES FOR (HOSPITALS, VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT CHARITABLE HOMES FOR (HOSPITALS, SINGLE FAMILY COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED 311 311 311 311 311 311 311 311 311 311 311 311 311 311 311 311 SiteID Study Parcel_ID 010-056773 010-260374 010-045398 010-048840 010-050565 010-050901 010-020606 010-022400 010-027811 010-029061 010-038372 010-041990 010-042001 010-045153 010-047519 010-007599 160 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C C 5.0066509 4.7820139 4.8018603 4.5914526 5.1482897 4.8995175 4.6853957 4.8616123 4.2398324 5.1168175 5.2637396 4.6242709 4.5833864 4.6151714 5.1334305 PA_RATIO 99.9972763 83.0357895 98.6718826 67.7415543 92.1959686 113.560585 111.3969727 101.3476486 178.7891388 158.6845703 129.4308472 109.9051361 113.9256516 121.6184387 121.8279495 PERIMETER_ METERS 398.9162292 301.5150146 538.1801758 461.8347168 387.5265198 191.1629333 1456.095093 1065.390259 931.9190674 461.3559875 468.4402771 397.5001831 704.0862427 696.8161621 489.3728638 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE LandUseDescrip LOT LOT LOT WORSHIP VACANT LAND VACANT MUNICIPALITIES MUNICIPALITIES SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY LAND VACANT VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY OWNED BY SINGLE FAMILY SINGLE FAMILY COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL CHURCHES, PUBLIC 19 TO 4 APARTMENTS: VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL RENTAL UNITS UNITS RENTAL LAND LAND LAND LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 313 312 312 313 313 312 312 313 313 313 312 312 312 311 313 SiteID Study Parcel_ID 010-061950 050-000413 050-000860 010-061790 010-061989 050-000366 050-000862 010-014512 010-129769 010-129776 050-000023 050-000205 050-000332 010-026400 010-061754 161 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C C C 5.25278 4.9449077 4.8520789 4.6038241 4.9332066 5.1813684 5.3078856 5.2384577 5.3130326 5.2613692 4.3316674 4.8295889 4.2884998 4.8526869 5.1986113 4.9653983 PA_RATIO 98.4132996 75.0450821 99.9914017 130.2812958 116.5613098 109.5051117 111.5019302 110.9814758 111.8623505 111.1206207 111.7913055 364.9658813 253.8746033 312.6594238 112.6288147 163.7867279 PERIMETER_ METERS 694.1411133 411.3882446 265.7094421 558.2776489 446.6628723 450.5949707 437.1775818 455.9959717 437.4257507 451.4591675 7098.953613 428.6520996 3504.513672 4151.246582 469.3797302 1088.051025 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE PRIVATE PUBLIC LandUseDescrip LOT THE A* ON PLATTED LOT LOT ON PLATTED WORSHIP EXEMPTIONS VACANT LAND VACANT VACANT LAND VACANT CHARITABLE HOMES FOR (HOSPITALS, VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL CHURCHES, PUBLIC EXEMPT PROPERTY TOWNSHIPS OWNED BY VACANT COMMERCIAL DWELLING TWO-FAMILY LAND LAND LAND LAND LAND LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED 318 318 313 313 313 313 313 313 313 318 318 315 317 313 313 313 SiteID Study Parcel_ID 010-009681 010-009884 130-000512 130-000513 130-000514 130-000515 130-000516 130-000517 130-003190 010-002329 010-007082 100-002478 080-010681 130-000506 130-001617 130-001742 162 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C C 4.583354 5.2405629 4.6492481 5.3921328 5.1654625 4.7801642 4.0006013 4.3303781 5.3250198 4.4917445 4.0988507 4.2475276 4.0516858 4.8044395 5.1081924 PA_RATIO 99.73629 33.682148 127.121666 78.1748276 66.3668823 81.0554733 89.6905975 114.5706787 103.2847977 126.3954544 115.4719925 100.7867584 104.7136154 150.5510101 113.7668304 PERIMETER_ METERS 62.8821144 496.017334 477.9594727 493.5229797 549.4671631 499.7293701 444.5506897 685.1027832 1208.692261 569.8974609 302.9031677 262.1673584 430.9441528 400.2148438 382.9367371 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT THE A* THE A* EXEMPTIONS THE A* THE A* EXEMPTIONS EXEMPTIONS EXEMPTIONS VACANT LAND VACANT LAND VACANT CHARITABLE CHARITABLE HOMES FOR (HOSPITALS, CHARITABLE CHARITABLE HOMES FOR (HOSPITALS, SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT VACANT LAND VACANT LAND VACANT LAND VACANT CHARITABLE HOMES FOR (HOSPITALS, VACANT LAND VACANT LAND VACANT CHARITABLE CHARITABLE HOMES FOR (HOSPITALS, COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED 318 318 318 318 318 318 318 318 318 318 318 318 318 318 318 SiteID Study Parcel_ID 010-049296 010-051569 010-044431 010-043071 010-039287 010-020079 010-020227 010-021453 010-022588 010-027857 010-037542 010-025589 010-026957 010-027594 010-019119 163 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C 6.215754 4.493237 4.894114 4.6720033 4.8007851 5.4204373 4.0790286 6.3084188 6.8491597 4.9670038 4.8738751 5.7878141 4.8871269 5.8199539 PA_RATIO 61.3148346 87.4061279 33.4161797 92.1096954 74.8416138 88.9034195 98.5445862 97.4903259 66.6836166 88.4755478 66.4650345 106.6687927 129.5879211 115.8281174 PERIMETER_ METERS 67.1119919 396.802948 172.2363739 294.5012207 331.4814453 571.5581055 213.1914063 285.9916687 277.4390869 320.3671265 408.8055115 132.7422333 327.7474365 130.4209595 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PUBLIC PUBLIC PUBLIC PUBLIC PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip THE A* EXEMPTIONS STRUCTURES AND LOTS STRUCTURES AND LOTS STRUCTURES AND LOTS STRUCTURES AND LOTS MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY VACANT LAND VACANT LAND VACANT CHARITABLE HOMES FOR (HOSPITALS, VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL GARAGE, PARKING GARAGE, PARKING GARAGE, PARKING LAND LAND 318 318 318 318 318 318 319 319 318 318 318 319 319 318 SiteID Study Parcel_ID 010-290217 010-011138 010-035496 010-050120 010-050120 010-057638 010-004687 010-013441 010-056099 010-060785 010-094941 010-013442 010-020119 010-055188 164 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C C C C 4.082685 4.205852 4.6246705 4.5979652 4.6063371 4.4783125 4.3842854 4.8739071 5.4763589 5.8234701 4.7180824 5.6635303 5.7384315 5.9351053 5.7231641 5.7738829 10.9825182 PA_RATIO 93.2572327 72.5534058 79.4685593 79.3744583 75.1916046 48.6705284 69.5620422 67.0658798 65.4838409 79.9161911 67.4638214 66.8637772 128.5219574 178.9991913 130.9678345 134.1091919 133.7632294 PERIMETER_ METERS 40.1181412 486.936615 521.7630005 246.1240387 298.7165833 296.9272766 281.9096375 123.2351227 695.3447266 1811.315796 149.9752045 126.4458618 286.9052734 141.8951111 135.7673798 549.0914307 536.7073364 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip STRUCTURES AND LOTS STRUCTURES AND LOTS STRUCTURES AND LOTS STRUCTURES AND LOTS STRUCTURES AND LOTS VACANT LAND VACANT PARKING GARAGE, GARAGE, PARKING GARAGE, PARKING GARAGE COMMERCIAL VACANT COMMERCIAL COMMERCIAL COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL GARAGE, PARKING VACANT COMMERCIAL GARAGE, PARKING VACANT COMMERCIAL COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND LAND LAND LAND LAND LAND LAND WAREHOUSES WAREHOUSES WAREHOUSES WAREHOUSES 319 319 319 319 319 319 320 320 319 319 319 319 319 319 319 319 319 SiteID Study Parcel_ID 010-043025 010-049810 010-049811 010-049983 010-052964 010-095797 090-000446 090-001829 010-044126 010-049809 010-270662 010-020467 010-025758 010-030185 010-051790 010-054852 010-020347 165 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C C C C C C 5.79424 5.021914 6.1028576 4.3520927 4.3381186 4.3787999 4.2758222 5.0023923 6.7591777 6.9800243 4.0057392 4.0927014 9.7979469 4.1185722 5.0990109 4.8186526 5.0230122 5.0796824 14.0588474 PA_RATIO 73.644249 89.1874695 115.466507 47.9102974 39.2075844 71.1028595 69.0908966 93.9715958 97.6258087 94.6870651 93.9475861 113.7983932 114.8722229 108.7552948 308.1309509 307.3530884 151.4774475 118.9208298 156.9648895 PERIMETER_ METERS 2813.72876 50.2423553 31.5519562 881.354248 25.5784779 56.4946709 355.347168 213.5704651 683.7163696 701.1762695 695.3474731 646.9364014 3764.715088 916.9389648 1470.906006 281.4156494 339.6419678 410.4668274 342.0567017 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip WORSHIP WORSHIP WORSHIP WORSHIP VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL CHURCHES, PUBLIC CHURCHES, PUBLIC CHURCHES, PUBLIC CHURCHES, PUBLIC LAND LAND LAND LAND LAND LAND LAND LAND 320 320 320 320 320 320 320 320 320 320 320 320 320 320 321 320 321 321 321 SiteID Study Parcel_ID 090-001949 090-001950 090-001951 090-001952 090-002179 090-002182 090-003791 090-004687 090-004688 090-005353 090-005414 090-005533 090-006727 090-008339 010-062499 090-001948 010-062561 010-062562 010-062584 166 COUNTY Franklin Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Delaware Delaware Delaware Franklin Franklin Franklin Franklin Franklin Category C C C C C C C C C C C C C C C C C C C C C 4.368103 4.7944498 4.0298533 4.7927299 4.7927299 4.7927299 4.6964951 4.7003841 5.5936584 4.7816901 4.7827039 4.9628029 4.9663067 5.1306539 5.3779125 4.0374875 4.4924078 4.2059412 5.0596304 4.7849379 11.2536192 PA_RATIO 52.1953239 91.4623871 91.7855225 63.3011131 94.4049225 102.1956787 102.2568588 102.2568588 102.2568588 104.0247192 103.9638901 102.4501419 102.5119629 119.5230408 119.7495956 151.9025726 621.6184082 108.5138779 121.6163788 116.2351151 124.0812531 PERIMETER_ METERS 66.0542297 13360.4248 732.867981 763.744873 672.449585 454.3468628 167.7585602 455.2175293 455.2175293 455.2175293 490.5975342 489.2133484 269.2504883 459.0524292 459.4117432 580.0283813 581.4080811 876.5663452 245.8104095 617.1415405 348.1381836 AREA_METERS 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT WORSHIP WORSHIP WORSHIP WORSHIP R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT Land Vacant Land Commerical Vacant Land Vacant VACANT LAND VACANT SINGLE FAMILY CHURCHES, PUBLIC CHURCHES, PUBLIC CHURCHES, PUBLIC CHURCHES, PUBLIC C - COMMERCIAL C - COMMERCIAL DWELLING ON PLATTED DWELLING ON PLATTED VACANT LAND VACANT LAND VACANT 324 324 324 324 324 324 324 324 324 324 324 324 325 325 326 321 321 321 321 321 321 SiteID Study Parcel_ID 0531023200 0531031100 0531111700 0531112200 0531112300 0531113500 0531117900 0531137500 0531143200 0531143500 0531147100 0531147200 31834112016001 31834103024000 51943223004000 010-062585 010-062678 010-063056 010-063186 010-087382 010-274577 167 COUNTY Licking Licking Licking Licking Licking Licking Licking Licking Licking Licking Licking Licking Licking Delaware Delaware Delaware Delaware Licking Delaware Category C C C C C C C C C C C C C C C C C C C 4.684341 4.258975 4.3412828 4.3409452 4.3409567 4.4056983 4.2139354 4.3361878 5.0620899 4.2002859 4.2083998 4.7487531 4.7487788 4.3679075 4.2522831 4.3680177 4.1083474 4.1699505 11.9359999 PA_RATIO 53.736454 60.945652 99.6876526 99.6878281 99.6877899 55.9149818 52.8613129 126.310997 34.7537384 78.9836044 104.9518814 183.6147156 180.5486145 132.2358856 132.2378082 129.0657806 129.0638733 114.4042587 126.8268433 PERIMETER_ METERS 64.237236 527.288208 527.369873 527.3656616 567.4804688 1898.625122 142.4817963 109.0476227 904.3251953 1797.124512 163.0437164 775.4244995 775.4385376 873.1229248 205.4192352 873.0530396 369.6065063 752.7011719 112.9028168 AREA_METERS Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip counties counties land land land land land land 680 Charitable exempts Vacant Land Vacant Land Vacant Land Vacant 680 Charitable exempts Vacant Land Vacant Vacant Land Vacant 400 Commercial vacant vacant 400 Commercial vacant 400 Commercial vacant 400 Commercial vacant 400 Commercial vacant 400 Commercial vacant 400 Commercial 500 Residential vacant 500 Residential vacant 500 Residential vacant 500 Residential vacant by own prop 620 Exempt by own prop 620 Exempt land land land land 327 327 327 327 327 327 327 327 327 327 327 327 326 326 326 327 327 326 326 SiteID Study Parcel_ID 054-214176-00.000 054-210102-00.000 054-206178-00.000 054-202686-00.000 054-202782-00.001 054-202116-00.000 054-202380-00.000 054-203262-00.000 054-208974-00.000 054-206556-00.000 054-215880-00.000 054-202782-00.000 51943210012001 51943215015000 51943210012000 054-202782-00.002 054-202806-00.000 51943208006000 51943211012000 168 COUNTY Franklin Franklin Licking Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D C D D D D D D D D D D D D D D D 4.2686434 4.6480551 4.8395724 4.8148837 5.4070182 4.9018512 5.0645647 4.4371543 4.9270911 4.3480291 5.4066997 5.4745393 4.5562639 5.4408193 4.2778816 5.4351811 5.6380658 12.9891748 PA_RATIO 75.015213 62.399437 421.046051 97.1933365 91.0106964 95.5957489 84.1740799 92.0438461 100.2034683 135.3869476 112.4346085 140.6786804 143.4082489 135.4848785 134.7191162 134.9559326 142.3348083 139.9030304 PERIMETER_ METERS 49.0932465 847.163269 341.302124 308.8296204 464.7538757 7569.116699 407.4753723 626.9572754 526.1138916 771.5646973 464.1603699 205.9569397 627.9385986 605.5685425 615.2553101 462.9477234 685.7948608 615.7351685 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip municipalit ON PLATTED LOT LOT ON PLATTED WORSHIP WORSHIP VACANT LAND VACANT VACANT LAND VACANT VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT 640 Exempt prop own by by own prop 640 Exempt VACANT COMMERCIAL CHURCHES, PUBLIC CHURCHES, PUBLIC VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL DWELLING TWO-FAMILY VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND LAND LAND LAND LAND LAND LAND 402 402 402 402 402 402 402 402 402 402 402 402 402 402 402 327 402 402 SiteID Study Parcel_ID 010-029384 010-029638 010-000646 010-001325 010-010157 010-010220 010-015463 010-020853 010-021428 010-023559 010-024011 010-028587 010-032879 010-036361 010-038057 054-258866-00.000 010-015464 010-016099 169 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D 4.880404 4.591239 5.1044035 4.8517928 5.0730171 4.0130882 4.6613255 5.5125966 4.9615135 4.8458409 4.8908095 4.0128989 5.3997135 4.2111511 5.7276635 4.0910153 PA_RATIO 43.003582 44.2221756 96.7104263 69.7259979 149.766571 72.2774658 91.4761276 96.6838837 96.1304245 81.9489365 108.9803772 100.2045822 134.1766052 114.8938293 109.2611542 139.7694397 PERIMETER_ METERS 75.0568314 1032.31189 212.215683 401.258667 397.3215027 498.6376953 390.1597595 301.8783875 592.4356079 356.3503723 551.8638306 443.4537964 573.8591309 409.4398804 104.2815704 595.4830933 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PUBLIC PUBLIC PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip RETAIL (WALKUP) (WALKUP) RETAIL MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES VACANT LAND VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY LAND VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL OVER APARTMENTS VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT LAND VACANT LAND LAND LAND LAND LAND 402 402 402 402 402 402 402 402 402 402 402 404 404 402 402 402 SiteID Study Parcel_ID 010-039425 010-041077 010-043787 010-049443 010-054431 010-057122 010-061533 010-002819 010-015466 010-025631 010-049625 010-002539 010-002654 010-038162 010-048328 010-049442 170 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D D 4.875936 4.8711281 4.8251419 4.5206614 4.8886218 4.2333026 4.0003061 4.5531421 4.0648665 6.9822817 5.7811918 4.3478699 7.1506782 4.9092035 5.4046764 5.2956295 14.5822363 PA_RATIO 7.282259 6.9695539 54.6983261 84.5005493 77.6202087 78.8654633 72.6017761 52.3220749 63.1229286 68.6066132 80.2380447 85.1965866 111.7538223 103.8494415 106.7475586 105.4348068 106.8291779 PERIMETER_ METERS 3.3139486 2.0431204 22.1352119 526.3391113 128.5073395 349.3937073 252.1022339 347.0684509 254.2565002 165.6827545 221.2145081 340.9425049 210.7760162 125.9117355 301.1773376 380.5648193 406.9533081 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT ON PLATTED LOT LOT ON PLATTED WORSHIP WORSHIP STRUCTURES AND LOTS STRUCTURES AND LOTS VACANT LAND VACANT VACANT LAND VACANT VACANT LAND VACANT LAND VACANT LAND VACANT VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY PARKING GARAGE, GARAGE, PARKING VACANT COMMERCIAL CHURCHES, PUBLIC CHURCHES, PUBLIC VACANT COMMERCIAL VACANT COMMERCIAL DWELLING TWO-FAMILY VACANT COMMERCIAL LAND LAND LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 404 404 404 404 404 404 404 404 404 404 404 404 404 404 404 404 404 SiteID Study Parcel_ID 010-047976 010-023067 010-023712 010-010859 010-048220 010-056575 010-057873 010-069958 010-256842 010-030918 010-039730 010-039731 010-046941 010-022943 010-008848 010-030919 010-038755 171 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D 4.911026 4.6697493 5.0103264 4.7164693 4.0122447 5.2492604 5.1684079 5.2826929 6.0877128 4.8678732 4.8723984 4.2342014 5.1716914 5.3507233 5.5260835 7.3644319 PA_RATIO 69.419548 72.6418228 87.4008484 98.0696182 29.0378399 109.166832 42.0760231 108.3662949 105.1729813 105.1348419 109.3835068 108.3963547 127.1420135 169.4349213 115.2393036 144.0695496 PERIMETER_ METERS 22.752037 901.644043 66.1917953 382.706543 241.9838257 467.7958679 343.3976135 299.3565674 401.4321594 360.0438843 396.0796814 504.9229736 501.9909058 487.1748657 1002.722717 434.8769226 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE PRIVATE PRIVATE LandUseDescrip RENTAL UNITS UNITS RENTAL LOT ON PLATTED LOT LOT ON PLATTED MUNICIPALITIES MUNICIPALITIES VACANT LAND VACANT LAND VACANT VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY OWNED BY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL DWELLING TWO-FAMILY 19 TO 4 APARTMENTS: MEDICAL CLINICS AND VACANT COMMERCIAL 40+ APARTMENTS: RENTAL UNITS UNITS RENTAL LAND LAND LAND LAND DWELLING ON DWELLING ON PLATTED DWELLING ON PLATTED OFFICES UNPLATTED LAND: 0 TO TO LAND: 0 UNPLATTED 9.* 407 417 407 417 417 417 417 426 407 407 407 426 436 441 407 407 SiteID Study Parcel_ID 010-010874 010-062278 010-047635 010-062098 010-062122 010-062291 010-062749 010-023173 010-010894 010-017190 010-042963 010-034988 590-132244 590-221937 010-008956 010-000184 172 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D 4.5929961 4.9326177 4.8234062 4.6598096 4.6583748 4.5904217 4.1008549 4.1686392 7.3757663 4.6123624 4.5293188 4.5846586 4.5696669 4.5691795 4.6075916 PA_RATIO 94.049736 96.0808868 87.6057816 95.7550812 94.0305557 94.0547714 97.5699692 104.3781509 100.8535919 101.1391907 102.8974457 248.9296265 233.9543762 1896.422241 102.6664886 PERIMETER_ METERS 3149.73291 437.6045532 315.4355469 468.2863464 468.4318237 471.3788147 502.4629211 3684.719238 66108.26563 495.4622803 446.9488831 420.6529846 423.6357422 423.6807251 448.4194031 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PUBLIC PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT WORSHIP ACADEMIES (PRIV* ACADEMIES (PRIV* ACADEMIES ACADEMIES (PRIV* ACADEMIES SINGLE FAMILY SINGLE FAMILY EXEMPT PROPERTY COLLEGES OWNED BY EXEMPT PROPERTY COLLEGES OWNED BY EXEMPT PROPERTY COLLEGES OWNED BY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL CHURCHES, PUBLIC LAND LAND LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 442 442 442 442 442 441 441 442 441 442 442 442 442 442 442 SiteID Study Parcel_ID 010-079528 010-060755 010-060756 010-061193 010-061195 590-159025 590-159029 010-060754 590-158975 010-073108 010-073235 010-073321 010-073329 010-079334 010-079501 173 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D D D 5.019875 5.3760161 5.3457279 4.0196671 4.6786404 4.8764014 4.4184036 5.0456324 4.6242547 4.2704802 4.6072969 5.0848761 4.4246306 5.0324368 5.0277615 4.9895787 4.9064069 4.9117918 PA_RATIO 95.788887 86.909111 99.9846573 86.6090622 95.6224747 98.5511169 94.6525574 96.3922729 96.7904892 108.2883148 108.4334641 162.5098114 108.2789459 117.2577972 100.0609055 114.1416245 107.8935699 116.5706329 PERIMETER_ METERS 315.447998 714.388855 457.541748 405.7346497 411.4460754 1634.477783 456.6961975 600.5612183 540.0727539 427.5987854 397.3224182 354.8701782 594.6166382 353.7594604 367.5657959 376.3029175 564.4834595 313.0766907 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT WORSHIP WORSHIP WORSHIP VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT CHURCHES, PUBLIC VACANT COMMERCIAL CHURCHES, PUBLIC CHURCHES, PUBLIC LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 454 454 454 459 459 442 454 442 459 442 459 459 459 459 459 459 459 459 SiteID Study Parcel_ID 010-083129 010-083145 010-083211 010-013256 010-015191 010-076381 010-083125 010-097708 010-021023 010-079596 010-019655 010-020039 010-020232 010-021293 010-026230 010-053637 010-057993 010-064045 174 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D 4.681675 4.630363 4.610817 5.200469 4.6307564 4.2137218 4.9081335 4.3578997 4.8954034 4.6653504 4.6813722 4.6336784 5.1150255 5.3968496 5.0639305 PA_RATIO 39.299572 39.108036 99.353569 37.4203796 115.502739 99.2133102 101.731926 97.1955872 97.2264099 93.8679504 104.411644 113.2913971 101.1709137 100.1734161 112.9138107 PERIMETER_ METERS 78.8650742 64.1125259 80.5336914 395.565979 598.5356445 466.9913025 556.6825562 452.2424622 450.4230347 467.3617859 437.7372131 440.6188049 444.6443481 343.6047974 403.1000366 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT LOT LOT LOT LOT VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 459 459 459 459 467 459 459 459 459 467 459 459 467 467 459 SiteID Study Parcel_ID 010-078030 010-094542 010-094543 010-185127 010-026161 010-070990 010-067659 010-065898 010-065900 010-032073 010-065862 010-065897 010-030924 010-034358 010-065745 175 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D 4.9734612 4.8863511 4.7127161 15.624073 9.8420229 4.8967113 4.0002379 4.0414305 4.9667454 4.5365973 4.1507001 5.1793017 4.1200161 4.6506133 4.5982237 13.4792738 PA_RATIO 87.224556 96.8418045 70.9837418 31.7759476 88.5621796 69.0650635 37.3040161 43.2653961 115.6674347 133.8435364 102.6671066 126.7661972 103.4677582 548.3171387 519.4668579 487.5315857 PERIMETER_ METERS 20.6409302 10.4238539 26.2532959 86.9640121 307.5813904 560.3416138 422.2631226 327.1052856 114.6070099 726.1912842 512.1558838 932.7477417 399.0870361 17711.92773 12476.57031 11241.52734 AREA_METERS 1 1 1 0 0 1 0 0 0 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT & ASSEMBLY: 0%-10% & ASSEMBLY: OFFICE * MUNICIPALITIES MUNICIPALITIES EXEMPT PROPERTY OWNED BY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT VACANT LAND VACANT SINGLE FAMILY LIGHT MANUFACTURING COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL 19 TO 4 APARTMENTS: SINGLE FAMILY SINGLE FAMILY RENTAL UNITS UNITS RENTAL LAND LAND LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 467 472 482 482 482 482 482 482 480 467 467 467 472 472 467 480 SiteID Study Parcel_ID 010-027563 570-103599 010-005065 010-005136 010-034523 010-061669 010-092052 010-094824 010-065343 010-042715 010-042891 010-061076 570-104097 570-109980 010-034654 010-025938 176 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D 8.42381 5.153203 4.9666271 5.2262535 5.1132512 5.1186814 5.1233721 5.0732918 5.0255094 5.0044403 5.0205169 4.9593606 5.0272207 5.1192932 10.1233778 14.3282146 PA_RATIO 30.8965511 58.9885445 39.9704132 124.1970291 129.8900909 131.5701141 131.6325226 131.4645691 122.5899963 123.0098724 123.1332779 130.9759369 174.1115417 123.3424225 122.3343658 127.5285416 PERIMETER_ METERS 37.8724289 33.9534912 22.5143871 625.3156738 617.6907959 662.0948486 661.3178101 658.4239502 583.8891602 599.1282349 605.3964233 645.9940186 147.6627502 618.5482178 592.1627197 620.5757446 AREA_METERS 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT THE A* THE A* THE A* EXEMPTIONS EXEMPTIONS EXEMPTIONS VACANT LAND VACANT LAND VACANT LAND VACANT CHARITABLE CHARITABLE HOMES FOR (HOSPITALS, VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT LAND VACANT CHARITABLE HOMES FOR (HOSPITALS, CHARITABLE CHARITABLE HOMES FOR (HOSPITALS, COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL GARAGE COMMERCIAL LAND LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED 482 482 482 482 482 482 482 482 482 482 482 482 482 482 482 482 SiteID Study Parcel_ID 130-005578 420-291126 420-291128 420-291129 130-005040 130-011773 130-011872 420-291123 420-291125 010-258901 010-258902 130-000583 130-003205 130-003898 130-007169 010-258900 177 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D D 4.850059 4.838037 4.0588641 4.9512272 4.9098697 4.1231756 4.7181334 5.5306187 9.4296103 4.8722358 4.8353071 4.8784828 5.5294127 4.5017228 4.0166612 4.8461604 4.8552012 PA_RATIO 54.1800728 92.6014328 91.1038361 51.0244522 108.393364 74.4129257 99.9083557 95.0458908 85.8952484 84.7795715 125.3677292 100.0780258 100.4931717 101.6896057 125.6768112 113.4162369 113.3091812 PERIMETER_ METERS 62.2743568 547.715332 178.1846008 349.7918396 344.2971497 153.1415558 527.7945557 513.8354492 425.7781677 420.4816589 431.4542542 386.3834839 434.4937439 516.5974731 364.0665588 445.5043945 544.6477661 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT VACANT LAND VACANT LAND VACANT MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY SINGLE FAMILY SINGLE FAMILY APARTMENTS: 4 TO 19 TO 4 APARTMENTS: COMMERCIAL VACANT COMMERCIAL VACANT COMMERCIAL COMMERCIAL VACANT COMMERCIAL RENTAL UNITS UNITS RENTAL LAND LAND LAND WAREHOUSES WAREHOUSES WAREHOUSES DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 492 492 492 488 488 488 488 488 488 488 488 488 488 492 488 488 484 SiteID Study Parcel_ID 010-012714 010-013655 010-005137 010-010137 010-016227 010-023203 010-024563 010-024564 010-024592 010-025366 010-031885 010-039687 010-039903 010-002370 010-039119 010-040353 010-057457 178 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D D D D 4.994101 5.019135 4.5727997 5.0159645 4.9629173 4.7302222 5.4746137 5.4217787 5.1986704 4.8349957 5.0142951 5.2582636 4.7696161 4.7154942 4.9124098 4.8929963 5.2278028 4.9501805 5.0904555 PA_RATIO 73.6764755 91.6531372 80.7374649 84.1570206 108.844574 95.6344757 88.7288666 90.7541428 92.5852127 99.0461578 92.6245575 109.6607361 118.6916046 115.1420898 110.2806244 110.9942474 106.9736099 102.6919632 103.3252182 PERIMETER_ METERS 471.1875 520.243042 330.783844 418.615387 350.114563 259.5932312 333.8764038 488.2341919 291.3312683 470.0380859 451.0084229 262.0572205 541.5444336 354.0588379 341.3058472 358.0415955 358.9523926 458.8161316 412.0020447 AREA_METERS 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT LOT VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT COMMERCIAL VACANT VACANT COMMERCIAL VACANT COMMERCIAL LAND LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 493 493 493 493 493 493 493 492 493 493 493 492 492 492 492 492 492 492 492 SiteID Study Parcel_ID 010-010018 010-010523 010-004097 010-016449 010-018780 010-026251 010-031820 010-069347 010-013141 010-013323 010-015995 010-024033 010-028102 010-029704 010-029707 010-030803 010-049207 010-069346 010-014086 179 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D D D 4.615478 4.6926746 4.6207018 4.3307576 4.6510515 4.6204939 4.6594453 4.6814752 4.6524239 4.6134963 5.3413382 5.3572125 4.7553892 4.4375458 5.1368957 4.3555493 4.2486725 4.2199607 PA_RATIO 66.686615 85.1470184 83.1106186 87.1435699 119.2550812 121.1892166 121.3389206 125.4976273 124.4413452 123.8458633 124.5418167 125.5546494 123.7425995 281.5942993 294.0387573 137.7881012 126.6490631 131.9678345 PERIMETER_ METERS 237.109848 536.708313 645.8220825 689.4369507 689.5795898 335.1479492 737.7244873 713.2814331 699.8381348 716.5917969 740.6366577 240.6773682 335.8138123 4026.817871 3276.480957 1000.777466 888.5819702 977.9561157 AREA_METERS 0 0 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT MUNICIPALITIES MUNICIPALITIES VACANT LAND VACANT LAND VACANT EXEMPT PROPERTY OWNED BY VACANT LAND VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY RESIDENTIAL LAND: 0 TO TO RESIDENTIAL LAND: 0 VACANT LAND VACANT LAND VACANT SINGLE FAMILY VACANT LAND VACANT COMMUNITY SHOPPING VACANT, UNPLATTED UNPLATTED VACANT, LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY DWELLING ON PLATTED DWELLING ON PLATTED 9.99 ACR* DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED CENTER 495 495 493 495 495 495 495 495 495 493 493 493 493 495 495 495 495 495 SiteID Study Parcel_ID 140-003482 140-003483 010-033632 010-012989 140-003453 140-003454 140-003455 140-003456 140-003480 010-044173 010-046723 010-093732 010-035927 010-130056 010-212855 010-212856 010-212857 140-003452 180 COUNTY Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D D 4.751452 4.2606606 4.0653229 4.2377095 4.0950942 4.5887256 4.8715734 4.8612695 4.6066494 4.9891152 5.0178199 4.7112694 5.0637999 5.3038421 5.0429435 4.5388284 15.5201445 PA_RATIO 85.723259 99.1810455 110.1079559 124.5362473 110.0078812 130.8311005 122.4247284 233.5154724 141.4150848 140.7429199 106.3263931 121.1021881 103.5082016 103.3758926 104.5343552 101.6777344 103.1400452 PERIMETER_ METERS 691.088501 424.432312 418.298584 667.8569946 938.4291992 673.8841553 1020.690796 711.7936401 226.3809204 842.6610718 838.2116699 500.7598267 430.4299316 492.3140259 403.1796875 349.6843262 356.7055359 AREA_METERS 0 0 1 1 0 0 0 0 1 0 1 1 1 1 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip LOT LOT LOT LOT LOT STRUCTURE STRUCTURE VACANT LAND VACANT LAND VACANT LAND VACANT VACANT LAND VACANT LAND VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY VACANT LAND VACANT SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY SINGLE FAMILY LAND VACANT LAND VACANT OTHER RESIDENTIAL OTHER RESIDENTIAL OTHER VACANT COMMERCIAL LAND DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED DWELLING ON PLATTED 497 497 497 504 495 495 495 497 495 504 504 504 495 504 504 504 504 SiteID Study Parcel_ID 010-119724 010-119790 010-119798 010-000369 140-003576 140-004852 140-007405 010-119719 140-003497 010-021271 010-023767 010-080668 140-003486 010-003625 010-029257 010-078467 010-080591 181 COUNTY Licking Licking Licking Licking Licking Licking Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Fairfield Franklin Franklin Franklin Category D D D D D D D D D D D D D D D D D D 4.03234 4.0849466 4.0116024 4.9340281 4.1932487 5.1266437 4.9975357 4.9975224 4.4531674 4.0533748 4.2326236 4.2403841 8.7551651 6.0333281 4.0152202 5.0431852 5.1483817 4.1255546 PA_RATIO 50.5246582 65.9617615 77.4130096 99.2591248 76.5572128 68.5800934 51.9338036 39.2703209 68.8362885 79.5555725 116.8063278 115.6717987 115.6719971 138.2269287 109.6908035 111.5570908 103.0173416 100.6958542 PERIMETER_ METERS 560.324585 93.8631973 264.493866 152.9797821 270.3638916 368.5645447 560.4404907 223.0005798 535.7265625 535.7312012 963.4900513 261.5686646 156.9682312 341.8853455 167.2944641 417.2639465 382.5442505 371.8574829 AREA_METERS 1 1 1 Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PRIVATE PUBLIC PUBLIC PUBLIC LandUseDescrip land MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES MUNICIPALITIES R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT R-RES VACANT EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY EXEMPT PROPERTY OWNED BY C - COMMERCIAL C - COMMERCIAL C - COMMERCIAL 500 Residential vacant 500 Residential vacant vacant 400 Commercial 500 Residential vacant 500 Residential vacant land land land land VACANT LAND VACANT LAND VACANT 509 509 509 509 509 508 508 508 508 508 508 508 508 509 504 504 508 504 SiteID Study Parcel_ID 054-252144-00.000 054-270162-00.000 054-264408-00.000 054-281298-00.000 054-261804-00.000 0532050500 0532050600 0532050700 0532052000 0532052100 0533044400 0532056900 0533166600 054-266472-00.000 010-054903 010-063572 0532050400 010-013007 182 COUNTY Licking Licking Licking Licking Category D D D D 16.29002 5.6773229 4.4515653 8.7569199 PA_RATIO 97.469101 111.4731445 114.1820831 238.8167114 PERIMETER_ METERS 35.8006325 385.5256958 657.9160156 743.7495117 AREA_METERS Public_ Water Pub_Priv PRIVATE PRIVATE PRIVATE PRIVATE LandUseDescrip 500 Residential vacant 500 Residential vacant 500 Residential vacant 500 Residential vacant 500 Residential vacant land land land land 509 509 509 509 SiteID Study Parcel_ID 054-262644-00.000 054-261372-00.000 054-261372-00.000 054-262644-00.000 183 Appendix D: Study Site Data

184 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pub_Parcels 3 0 0 0 0 1 0 0 0 1 3 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 Priv_Parcels 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 161005.1563 Pub_Area 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31729.60156 53353.83594 5036.698242 9586.105469 152.2307739 1567.826172 Priv_Area 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31729.60156 53353.83594 161005.1563 5036.698242 9586.105469 152.2307739 1567.826172 Total_Area 3 0 0 0 0 1 1 0 0 1 3 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 Total_Parcels A A A A A A A Category A A A A A A A A A A A A A A A A A A A A A A 129 128 127 126 125 124 123 122 121 120 119 118 117 116 115 114 113 112 111 110 109 108 107 106 105 104 103 102 101 SiteID

185 0 1 3 1 0 0 0 0 1 0 0 0 0 0 2 0 1 0 0 0 2 0 0 0 1 0 0 0 0 Pub_Parcels 2 0 5 0 3 1 1 2 2 1 3 3 6 0 3 2 1 1 2 1 0 1 21 12 63 19 10 29 36 Priv_Parcels 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 676.772522 637.4846802 9043.326172 3735.482666 14959.68164 56199.46094 19239.26172 3925.821045 Pub_Area 0 0 0 0 12.0800762 27.8532085 266.525116 1925.48938 40.3706741 1280.851318 26559.65039 38958.79688 13655.19336 10764.11621 1057.841919 1247.376465 5242.120117 66030.54688 408.4077148 3653.655762 30058.64453 107048.3594 8955.250977 10583.25586 14183.39551 1116.824097 4834.944824 28686.83203 745.8745728 Priv_Area 0 0 0 12.0800762 5042.64502 27.8532085 266.525116 40.3706741 1280.851318 27197.13477 9043.326172 42694.27734 13655.19336 10764.11621 16017.52344 1247.376465 5242.120117 66030.54688 408.4077148 3653.655762 86258.10938 107048.3594 28194.51172 10583.25586 14183.39551 4834.944824 2602.261963 28686.83203 745.8745728 Total_Area 2 3 6 0 3 1 2 2 2 1 3 5 6 0 5 2 1 1 3 1 0 1 22 12 63 19 11 29 36 Total_Parcels Category B B B B B B B B B B B B B B B B B B B B B B B B B B B B A 229 228 227 226 225 224 223 222 221 220 219 218 217 216 215 214 213 212 211 210 209 208 207 206 205 204 203 202 130 SiteID

186 0 0 4 3 0 0 0 0 0 0 0 0 5 0 0 0 2 0 1 0 0 0 2 0 0 0 0 0 0 Pub_Parcels 7 6 2 0 0 1 0 1 7 1 1 2 0 3 5 2 2 19 27 12 12 10 17 19 24 15 21 13 15 Priv_Parcels 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.6941342 1400.439697 6134.499023 1469.334717 4592.564453 696.4284668 Pub_Area 0 0 0 0 80.0496674 4647.90918 14192.3418 1493.274658 4130.984863 10264.42188 3171.239746 3020.129395 4100.212891 5735.010742 10157.10156 7098.953613 3200.633057 7013.770996 4953.246094 6867.075684 3783.925537 20885.69141 1264.381104 89204.47656 49690.48828 88864.80469 19814.53516 5996.185059 2210.122803 Priv_Area 0 0 0 11664.8623 80.0496674 4647.90918 14192.3418 6871.77002 1493.274658 4130.984863 9305.738281 3020.129395 4100.212891 5735.010742 11626.43652 7098.953613 3200.633057 11606.33594 4953.246094 3783.925537 20885.69141 1264.381104 696.4284668 89204.47656 49690.48828 88864.80469 19814.53516 5996.185059 2210.122803 Total_Area 7 6 2 0 0 1 0 1 7 1 1 2 2 3 5 2 2 19 31 15 12 10 17 19 29 17 22 13 15 Total_Parcels Category D D D C C C C C C C C C C C C C C C C C C C C C C C C C B 407 404 402 327 326 325 324 323 322 321 320 319 318 317 316 315 313 312 311 310 309 308 307 305 304 303 302 301 230 SiteID

187 0 0 3 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 3 0 0 0 0 1 0 0 Pub_Parcels 9 8 0 4 1 2 0 3 8 0 0 4 0 1 0 0 1 0 1 0 5 10 18 14 13 12 22 22 14 Priv_Parcels 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 66.2072525 832.3069458 335.2444763 560.5002441 323.7988281 4696.554688 Pub_Area 0 0 0 0 0 0 0 0 0 582.65625 4578.59668 3675.865234 2479.890625 2850.006104 3301.857178 12756.80371 4334.496582 5014.832031 527.9155273 8179.714356 28080.16602 2642.516846 8142.384277 2908.965088 5470.427246 382.7648621 541.1801758 901.8545532 1648.504761 Priv_Area 0 0 0 0 0 0 0 0 0 582.65625 3203.01709 3675.865234 2479.890625 3682.312988 3301.857178 12756.80371 4913.841309 4334.496582 5014.832031 527.9155273 8179.714356 28080.16602 8142.384277 2908.965088 5794.226074 5079.319824 541.1801758 968.0618286 1648.504761 Total_Area 9 0 4 1 2 0 3 9 0 0 4 0 4 0 0 1 0 2 0 5 10 11 18 15 13 12 22 22 15 Total_Parcels Category D D D D D D D D D D D D D D D D D D D D D D D D D D D D D 509 508 504 498 497 495 493 492 488 484 482 480 478 472 467 461 459 456 454 449 442 441 438 437 436 429 426 421 417 SiteID

188 4.0064034 4.8195348 6.1367002 4.4050846 4.4845629 19.8826084 MeanPAR_Priv ...... 15798.2627 1752.006714 4533.665039 5036.698242 356706.0625 62791.34375 MeanSizePriv ...... 4.9278789 MeanPAR_Pub ...... 304574.0625 MeanSizePub ...... 4.0064034 4.8195348 6.1367002 4.4050846 4.9278789 4.4845629 19.8826084 MeanPAR ...... 15798.2627 1752.006714 4533.665039 5036.698242 356706.0625 304574.0625 62791.34375 MeanSize ...... 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 SiteID

189 4.6545 6.579452 5.4583116 5.6168556 6.5554624 4.8894973 4.0114722 4.9610128 5.3142462 5.1035061 5.2363238 6.0739808 5.6295409 5.0210419 4.6264644 4.2902498 5.0668163 4.7711625 4.4504099 4.0412626 5.0794244 4.1619549 4.5928984 9.7191696 4.6921153 MeanPAR_Priv . . . . 12.0800762 618.562561 27.8532085 266.525116 2022.423096 640.4256592 4551.730957 11417.11719 897.6955566 1057.841919 623.6882324 4172.603027 1313.680542 408.4077148 9979.668945 10174.58887 7583.946289 966.1137085 1770.618164 655.9554443 25455.28516 979.3212891 872.1140747 601.0754395 745.8745728 MeanSizePriv . . . . 5.0727515 5.0084858 5.4063487 4.0133858 5.7331018 6.2248688 4.6121397 9.0052176 MeanPAR_Pub ...... 637.4846802 121995.8125 22015.86133 19693.12305 28099.73047 33737.97656 1962.910523 295673.4063 MeanSizePub ...... 4.6545 6.579452 5.4407864 5.0084858 5.6168556 6.3639431 4.8894973 4.0124292 4.9610128 5.3142462 5.1035061 5.2363238 6.0739808 5.6709652 5.0210419 4.7717738 4.2902498 4.7711625 4.8849459 4.4504099 4.0412626 5.0794244 5.7763758 4.5928984 9.7191696 4.6921153 MeanPAR . . . 12.0800762 618.562561 27.8532085 266.525116 640.4256592 1959.471314 121995.8125 4551.730957 13183.57422 897.6955566 10375.48242 623.6882324 4172.603027 1313.680542 408.4077148 9979.668945 17344.64648 7583.946289 3945.374023 1770.618164 1178.737427 25455.28516 99210.67969 872.1140747 601.0754395 745.8745728 MeanSize . . . 130 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 SiteID

190 6.133389 5.254283 4.746994 5.714015 4.341136 4.547863 4.7965493 5.5209336 5.2189903 4.3751206 5.4184999 5.5219412 5.1309643 4.8072109 4.8104959 4.8526869 4.3316674 5.0571694 6.3322153 6.0911579 4.6873126 4.6819148 9.2771854 4.5424237 12.8406143 MeanPAR_Priv . . . . 33923.75 21865.9668 401.9576721 239.5844269 486.0507507 633.1663208 464.7908936 510.5015869 887.3592529 7118.495606 302.4260254 443.0599976 456.3399963 4151.246582 7098.953613 467.5847473 707.6065674 375.8834534 3783.925537 1151.924072 23380.63672 44799.67969 13501.52734 609.0394897 2924.378174 MeanSizePriv . . . . 4.585197 4.7790346 5.4915657 4.6269493 4.6242709 5.3308992 MeanPAR_Pub ...... 481.3817749 3039.993164 293.8669128 2296.282227 397.5001831 17589.04883 MeanSizePub ...... 6.133389 5.254283 4.746994 4.341136 4.547863 4.7965493 5.5209336 5.1372104 4.4559035 5.4184999 5.5219412 5.1309643 4.8072109 4.9279218 4.8526869 4.3316674 5.0065551 5.6644812 6.3322153 6.0911579 4.6819148 5.3308992 4.6873126 9.2771854 4.5424237 12.8406143 MeanPAR . . . 33923.75 682.725647 21865.9668 401.9576721 239.5844269 485.4483032 1114.531738 464.7908936 510.5015869 887.3592529 7118.495606 302.4260254 443.0599976 428.3273926 4151.246582 7098.953613 707.6065674 376.8660278 1151.924072 3783.925537 17589.04883 23380.63672 44799.67969 13501.52734 609.0394897 2924.378174 MeanSize . . . 230 301 302 303 304 305 307 308 309 310 311 312 313 315 316 317 318 319 320 321 322 323 324 325 326 327 402 404 407 SiteID

191 4.730032 6.1558638 5.0864801 4.1646967 4.9273839 5.2223988 4.8544178 4.9840798 5.2486258 4.7181334 6.8495531 5.3596802 4.2342014 5.3507233 4.4562845 4.8397307 4.6266475 4.8550129 4.9520416 7.3644319 MeanPAR_Priv ...... 368.91745 825.215271 901.644043 382.706543 395.8665771 369.0024109 421.9755249 1061.870117 360.2065125 424.4360657 527.7945557 378.1152344 329.6207275 1002.722717 13810.00879 574.2271729 453.6114197 727.0886841 405.0176086 466.4475098 MeanSizePriv ...... 4.7723737 4.6510515 5.1716914 5.0456324 4.8863511 5.2150869 MeanPAR_Pub ...... 66.1917953 390.5552368 335.1479492 540.0727539 560.3416138 24314.23828 MeanSizePub ...... 4.730032 6.1558638 5.0864801 4.1646967 4.8851085 4.9618778 5.2223988 4.8544178 5.2486258 4.7181334 6.8495531 5.3596802 4.7029467 5.3507233 4.4562845 4.8397307 4.8550129 4.6545796 4.9447427 5.7524233 MeanPAR ...... 825.215271 476.880188 395.8665771 369.0024109 413.4063416 366.6661377 1061.870117 360.2065125 424.4360657 527.7945557 378.1152344 329.6207275 483.9179077 1002.722717 13810.00879 574.2271729 727.0886841 405.0176086 459.3755188 18331.35547 MeanSize ...... 417 421 426 429 436 437 438 441 442 449 454 456 459 461 467 472 478 480 482 484 488 492 493 495 497 498 504 508 509 SiteID

192 0.5234118 Sc1PubPerc ...... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 84272 Sc1PubArea 0 0 0 0 0 0.0226917 Sc1PrivPerc ...... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 720 Sc1PrivArea 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 720 84272 Scen1Area 0 0 0 0 0 0.5234118 0.0226917 Scen1Perc ...... 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 SiteID

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224