university of california santa barbara...university of california santa barbara the effectiveness of...

UNIVERSITY OF CALIFORNIA

Santa Barbara

The Effectiveness of the Williamson Act: A Spatial Analysis

A Dissertation submitted in partial satisfaction of the

requirements for the degree Doctor of Philosophy

in Geography

by

Jeffrey Alan Onsted

Committee in charge:

Professor Keith Clarke, Chair

Professor Helen Couclelis

Professor David Carr

Professor David Cleveland

September 2007

The dissertation of Jeffrey Alan Onsted is approved.

_____________________________________________ David A. Cleveland

_____________________________________________ David Carr

_____________________________________________ Helen Couclelis

_____________________________________________ Keith Clarke, Committee Chair

June 2007

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Copyright © 2007

by

Jeffrey Alan Onsted

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ACKNOWLEDGEMENTS

It would have been difficult indeed to conduct the research required to complete this

dissertation without the assistance of others. Though many have offered their

valuable time and expertise, the first and most obvious person upon which to bestow

my gratitude would be my advisor and mentor, Professor Keith Clarke. Despite being

on sabbatical during the final year of my research he still made himself easily

accessible, seemingly annihilating the distance between us. His guidance, support,

and unflagging enthusiasm and positivity were bulwarks against the tide of despair

that can so often assail those of us in the trenches of their dissertation. Professor

Helen Couclelis has been another mentor of mine. She has kept me honest by

keeping me mindful of the importance of theory as well as the wealth of criticism

leveled at the seemingly ubiquitous assumption that GIS and modeling can explain all

things in the world. For his effusive comments, generous offers of time, and an

inspiring example of courage in the face of even the worst of adversity, I would like

to thank Professor David Carr. I must also thank Professor David Cleveland since he

has brought a valuable perspective beyond the field of Geography and his great

attention to detail has been invaluable in my revisions.

As far as others who have assisted me, I must thank Ms. Kristin Hart. Her brilliant

and productive assistance with the Assessor’s database editing and the creation of

PowerPoint animations proved essential. Of course, I would also like to thank all of

those public servants who have provided me with data and sound advice. Though this

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list is numerous I must especially single out Mr. Michael Hickey and Mr. Roland

Hill.

Other graduate students have also proved an essential resource and, in a manner only

they can provide, inspiration as well. These include Dr. Noah Goldstein, Mr. Nick

Gazulis, and Dr. Chuck Dietzel. Mr. Thomas Pingel owes my thanks for his

inexhaustible wisdom in all areas technical and his constant willingness to help me.

Mr. Michael Vergeer should also be singled out for his unswerving idealism and

commitment to teaching, which always inspired me when I was in doubt.

Of course, my family: Mom, Ron, Laura, Gerry, Tony, Angie, and all the kids have

served as an anchor of love and support all throughout graduate school. Without

them, and all my dear friends too numerous to list, I could never have made it.

Thanks also to new extended family: Wayne, Fran, and Maggie for giving me a home

away from home. As for my fiancée Kiki, I can only say that her love, loyalty, and

constant sweetness to me, especially during the tumultuous final throes of my

dissertation, has humbled me and offered me fresh hope for what love can be.

Lastly, I would like to dedicate this dissertation to my late Father, William G. Onsted.

He was the greatest man I ever knew and, though he has long since passed, I still

consult his remembered wisdom and take inspiration from his zeal for life. And since

he was also a strong supporter of education, I dare hope that he would be proud of

me.

v

Jeffrey A. Onsted

Doctoral Candidate [email protected]

805-681-9278

EDUCATION Ph.D. in Geography. UC Santa Barbara. “Effectiveness of the Williamson Act: A

Spatial Analysis” (July, 2007) M.A. in Geography. UC Santa Barbara. “SCOPE: A Modification and

Application of the Forrester Model to the South Coast of Santa Barbara County (June, 2002)

B.A. in Urban Studies and Planning with a Minor in Environmental studies. UC San Diego. (June, 1995)

EXPERIENCE Assistant Professor, Florida International University. Joint Appointment between

Department of Environmental Studies and Department of International Relations and Geography. (August, 2007 – Present)

Trainee and Graduate Student Researcher, NOAA Sea Grant Program. Through USC, working with Ms. Gail Osherenko and Dr. Keith Clarke examining retention of Coastal Zone agriculture in California. (April, 2004 – June, 2007)

Graduate Student Researcher, UC Santa Barbara, Department of Geography. Worked in the GeoVisualization Lab. Duties included: analyzing 2-D studies already created, and creating two new-3D models for use in experiments. One is a constellation of points using Vizard and the other an urban landscape using ArcScene. (September 2003-March 2004)

Teaching Assistant, UC Santa Barbara, Department of Geography. Spring 2003 - Physical Geography: Land Surface Processes (Sundbeck) Spring 2001 - Introductory Human Geography (Montello) Fall 2000 - Urban Geography (Couclelis) Spring 2000 - Introductory Human Geography (Proctor) Winter 2000 - Geography Planning and Policy Making (Couclelis) Fall 1999 - Geography of the Information Society (Couclelis)

Field Researcher, UC Santa Barbara, Department of Geography. Conducted GPS and GIS research in Belize and Guatemala with the Meso-American Research Center. Duties included reconciling different GIS layers, using GPS in the field, assisting with editing of grant proposals and reports, as well as participating in meetings and workshops with stakeholders in the Mayan Forest Region. (May 2002)

Graduate Student Researcher, UC Santa Barbara, Department of Geography.

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Worked with Dr. Keith Clarke, assisting him with the South Coast Outlook and Participation Experience (SCOPE) for Santa Barbara County. Duties included: porting the initial model (designed at Prescott College) from PowerSim to Stella modeling languages, reducing size and complexity of model, challenging and changing its assumptions and parameters appropriately, adding additional elements to model, preparing and presenting PowerPoint Presentations to County stakeholders, create necessary environment for model to be simulated in a user-friendly interface through the Internet (currently off-line), and the writing up of the final report. Work included the continuing improvement of the model, outreach to the community, and discovering methods of coupling this model with the Clarke Urban Growth Model (SLEUTH). This culminated in the Regional Impacts to Growth Study (RIGS), the results of which came directly from the SCOPE model. Authored half the RIGS Appendix and helped write and edit sections of report dealing with model and its results. Have appeared on local television explaining the model’s workings and its results. Continuing to provide ad hoc support for the ongoing modeling work. (June 2000 – September 2003)

Environmental Policy Analyst, Science Applications International Corporation (SAIC), Tysons Corner, VA. Environmental Health and Sciences Group. Collaborated in the construction of an EPA urban modeling questionnaire designed for distribution to the various businesses and institutions that designed these models (i.e., MEPLAN, DRAM/EMPAL,etc.) Worked with the EPA's National Agricultural Compliance Assistance Center. Duties included: Internet searches for a wide variety of information useful to the Center; assisting with creation and editing of both the Crop Sector Notebook and the Livestock Sector Notebook; many other miscellaneous tasks including the composing of reports. Assisted with the Title V Air Permit Project by visiting state environmental offices, collecting copies of permits, and entering data. Played a major role in completing the deliverable draft of EPA's Internal Audit Policy Survey. Role included participation in data entry, Q/A, Access Queries, and Report generation. Provided financial research and analytical support for Local Government Sector Profile. Worked on EPA's Oil Refinery Compliance Study. Duties included: visiting state and regional environmental protection offices to procure relevant documents; identifying and analyzing compliance information concerning the constituent refineries, and entering information into a database. (August 1997 – September 1999)

Quality Assurance Specialist, CACI (Information Technology Services and Products). Arlington, VA. Responsible for ensuring correct document analysis for a large commercial litigation suit. Assisted Project Manager with the generation of reports and provided ideas for the evolving methodology of CACI's approach to capturing and presenting the information. (January 1997 – August 1997)

Research Assistant, Renew America, Washington, DC. Worked with the Program

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Director to research, identify, and verify successful environmental programs. Other duties included: assisting with outreach to environmental, business, government, and community groups; and assisting with overall coordination of awards program. (June 1996 – December 1996)

Intern, San Elijo Lagoon Conservancy, Encinitas, CA. Assisted the Executive Director by taking notes at meetings, procuring and photocopying documents from the county, calling appropriate government officials to secure information involved with various issues, speaking before the City Planning Commission, and laboring within the lagoon itself (which included removing invasive species and building channel catches to reduce erosion.) (Winter 1995)

PUBLICATIONS, PRESENTATIONS, POSTERS, AND REPORTS Wu, X., Y. Hu, H. He, R. Bu, J. Onsted, and F, Xi. 2007. “Using multiple methods

to evaluate the performance of SLEUTH in the Shenyang metropolitan area.” (in Revision)

Onsted, J. and K. Clarke. 2007. “The Impact of Policy on Land-Use and Land Cover Change.” Paper, Framing Land Use Dynamics II. Utrecht, The Netherlands. April 18– 20.

Onsted, J. 2006. “California’s Coastal Zone Management Program: Retaining Agricultural Land in the Face of Urban Growth” Poster Presentation, California and the World Ocean’s Conference. Accepted but not shown. September 18.

Onsted, J. 2006. “Assessing the long-term viability of the Williamson Act in California.” The International Conference on The Future of Agriculture: Science, Stewardship, and Sustainability Sacramento, CA. August 7-9.

Onsted, J. 2006. “Suburbs: Farming on the Fringe: Can Tax Incentives Save California’s Farmlands?” The Next American City 11: 23-25

Onsted, J. 2006. “California’s Coastal Zone Management Program: Retaining Agricultural Land in the Face of Urban Growth” Presentation, Sea Grant Trainee Symposium. USC. Los Angeles, CA. April 10.

Onsted, J. 2006. “Effectiveness of a Differential Tax Assessment Program for Farmland Conservation in Tulare County, California”. Presentation, Association of American Geographers. Chicago, IL. March 7 – 11.

Onsted, J. 2004. “Effectiveness of the Williamson Act: An Exploratory Study” Presentation, Association of American Geographers. Philadelphia, PA. March 14-19. Santa Barbara Economic Community Project. 2003. South Coast Regional Impacts of Growth Study. (I authored the appendix and provided most of the modeling support)

Onsted, J. 2002. “SCOPE: The South Coast Outlook and Participation Experience.” Poster, Association of American Geographers. Los Angeles, CA. March 19-23.

AWARDS/HONORS Awarded Dangermond Travel Grant for Conference in Europe, 2007 Sea Grant Traineeship (competitive fellowship) Awardee 2004-2007 Received 4th place in AAG Geography Bowl, 2002

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http://convention.allacademic.com/aag2004/view_paper_info.html?pub_id=2968&part_id1=22611

Department of Geography Out-of-State Fee Fellowship (UCSB) 1999, 2000, 2001, 2002

Department Block Grant Fee Fellowship (UCSB) 1999, 2000, 2001, 2002 Department of Geography Teaching Fellowship (UCSB) 1999, 2000 Provost's Honor List, Muir College (UC San Diego), 1994 Offered Winslow Scholarship (University of Maryland at College Park), 1991 National Merit Scholar Commendation (High School) 1990

GRANTS NOAA Sea Grant (I helped edit proposal) (2005 – 2006) CPRC (California Policy Research Center) (I was primary author) (2004 – 2005) SPF (Shoreline Preservation Fund)(I was primary author) (2005) NIMA (National Image and Mapping Agency) (2003 – 2004) NSF, UCIME (National Science Foundation’s Urban Change Integrated Modeling

Environment) (2000 – 2003)

SERVICE Reviewer, Environmental Management Reviewer, International Journal of Geographic Information Science Reviewer, Environment and Planning B Session Chair, AAG Annual Meeting, Philadelphia, PA (2004) Guest Lecturer, Geography 176 C (GIS Design and Applications) at UC Santa Barbara (2006) Guest Lecturer, Western Kentucky University’s Geography Department. Gave lecture on Folk Culture versus Popular Culture to a Human Geography Class. (2006) Invited Lecturer, Kansas State University's Geography Department. Talk entitled, "Examining the Effectiveness of California's Land Conservation Act". (2006) Guest Lecturer, Economic Geography Class at Santa Barbara City College. (2005,

2006) Participant, in inaugural meeting of the prospective UC Environmental Politics/Policy Institute at UC Santa Cruz. (2005) Guest Lecturer, Goleta Sustainable Food Systems for the Environmental Studies Department at UC Santa Barbara. (2004, 2005, 2006) Guest Lecturer, Coastal and Ocean Law and Policy class for the Environmental

Studies Department at UC Santa Barbara (2004) Guest Lecturer, Urban and Regional Modeling and Planning class (Geography 184 C)

for Geography Department at UC Santa Barbara. (2003) Representative, for the South Coast modeling team, with presentation and

demonstration at GIS Day, held at the Santa Barbara County Government Building. Presentation was broadcast on local government television. (2002)

Lecturer. Presented hour-long presentation and demonstration of South Coast model

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and local planning issues to over 100 local residents during an adult education class at Santa Barbara City College. (2001)

Representative, of the Geography Department to the Graduate Student Association. (2001-2002)

Mentor. Initiated an intra-departmental mentoring program at UCSB Geography for incoming graduate students. Administered this program ever since and have personally been a mentor to five graduate students. (2001 – Present) Mentor. Helped guide students who are placed on academic contract after their first

quarter at UCSB. Duties included assisting with self-assessment, motivation skills, time organization, positive thinking, future goals, and assistance in accessing campus resources. (2001)

Volunteer, with Campus-Wide Orientation for incoming graduate students at UCSB. Entertainment Committee, for Geography Department. (2000 – present) Volunteer, with Summer Bridge program for incoming freshman from under-

represented high schools at UCSB. (2000)

AFFILIATIONS Association of American Geographers

-Coastal Specialty Group -GIS Specialty Group -Graduate Student Affinity Group -Population Specialty Group -Regional Development and Planning Specialty Group -Rural Geography Specialty Group -Spatial Analysis and Modeling Specialty Group -Urban Geography Specialty Group North American Cartographic Information Society American Congress on Surveying and Mapping National Geographic Society Subscriber: Journal of Land Use Science

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ABSTRACT


by

Jeffrey Alan Onsted

The Williamson Act is the flagship conservation program protecting California's

world-renowned farmland. This act, though, is an incentive-based, voluntary

program that is easily compromised by the landowners' prospects of cashing in on

development dollars. This differential tax assessment method for protecting land

exists nationwide but it still cannot compete with the vast amounts of money that

developers can offer these landowners. This temptation is exacerbated by difficulties

farmers on the periphery of urban areas already face. These include suburban

complaints of their operations, trespassers, and marauding dogs and cats, to name a

few. This paper outlines just how effective the Act has been in protecting these

farmlands near urban areas by tracking parcels’ entry into and exit from this Act in

the path of urban expansion, then using this data to create a future scenario of

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Williamson Act lands using a cellular automata (SLEUTH) model. These results

create a probabilistic future land availability landscape that is used, in turn, as an

excluded layer for urban growth scenarios in the future.

By using SLEUTH’s built-in metrics and comparing them with similar modeling

efforts, the value of integrating Williamson Act data, rather than ignoring it or

treating it coarsely, is demonstrated. Exploring different urban growth scenarios in

the future that correspond to Williamson Act policy options offers not only image

outputs but additional metrics as well. This innovative use of SLEUTH allows for a

more robust urban growth simulation and also allows policy makers to explore

various angles of Williamson Act implementation. For instance, though continuing

the current method of Williamson Act administration still results in continual loss of

farmland, abolition of the Act results in far more. Scenario exploration, therefore,

suggests that permanent contracts offer the best protection for farmland. This

dissertation has applications to any geographic region employing differential

assessment programs and can lead to further exploration of exclusion layer

forecasting as well as concomitant coupling opportunities with traditional urban

models.

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TABLE OF CONTENTS

I. Statement of the Problem ......................................................................................... 1

A. Significance of Project .......................................................................... 1

B. Project Outline ...................................................................................... 4

1. Research Questions……………………………………..…4

2. Methods……………………………………………………5

II. Context of the Study................................................................................................ 8

A. Historical Land Use…………………………………………………...8

B. California Farmland………………………………………………….10

C. Farmland Protection Program………………………………………..18

D. Williamson Act Implementation and Eligibility……………………..22

E. Williamson Act Criticism……………………………………………27

F. Theories of Growth…………………………………………………..30

G. Modeling Theory and Criticism……………………………………..35

H. Modeling Approaches……………………………………………….39

I. SLEUTH Applications………………………………………………47

III. Methods and Data………………………………………………………………59

A. Data Background…...………………………………………………59

B. Data Acquisition……………………………………………………61

C. Data Rendering……………………………………………………..66

D. Modeling…………………………………………………………...70

E. Coefficients……………………………………………………….…78

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F. Growth Rules………………………………………………………..80

G. Self-Modification Parameters………………………………………82

IV. Results………………………………………………………………………….87

A. Part 1: (Text)………………………………………………………..87

1. Introduction…………………………………………..….87

2. Examining the Past………………………………………89

3. Williamson Act Forecasting………………...…………..91

4. Urban Forecasting…………………………..…….……..97

5. Conclusion…………………………………..…………..104

B. Part 2 (Maps, Figures, and Tables)…………………………………105

1. Part 1:Exploratory Maps………………………………..106

2. Part 2: Input Images…………………………………….113

3. Part 3: Output Images…………………………………...117

4. Scenario Images…………………………………………127

a) Tulare…………………………………………….127

b) StanMerc…………………………………………134

V. Conclusions........................................................................................... ..……...141

A. Research Questions Revisited…………………………………..…141

B. Future Research…………………………………………………….145

References………………………………………………………………………….149

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LIST OF FIGURES

Figure 2-1. FMMP Survey Area-2002 ................................................................... ..14

Figure 2-2. Number of Farms and total agricultural acreage in CA: 1950-2005....... 17

Figure 2-3. California Government Code Section 51220…………………………....23

Figure 3-1. Uncertainty in urban models over time………………………………….63

Figure 3-2. FMMP Definitions of Important Farmland Categories………………….66

Figure 3-3. FMMP Definitions of Important Farmland Categories………………….72

Figure 3-4. Metrics output for prediction runs using SLEUTH……………………..86

Figure 4-1. FMMP Survey Area-2002 ................................................................. ..105

Figure 4-2. Tulare excluded.wac ......................................................................... …113

Figure 4-3. Tulare.urban.2002.wac.…………………….………………………….114

Figure 4-4. Tulare.excluded.c.……………………………………………………..115

Figure 4-5. Tulare.excluded.nowac……………….………………………………..116

Figure 4-6. 2003 Tulare County Williamson Act run………………………………117

Figure 4-7. 2030 Tulare County Williamson Act run ………..…………………….118

Figure 4-8. Excluded.bauc………………………………………………………….119

Figure 4-9. Stanmerc.Excluded.Wa………………………………………………...120

Figure 4-10. Stanmerc.urban.2002.wa……………………………………………...121

Figure 4-11.

Stanmerc.excluded.new……………………………………………….122

Figure 4-12. Stanmerc.excluded.nowanew………………………………………...123

Figure 4-13. StanmercWA 2003……………………………………………………124

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Figure 4-14. Stanmerc.wa.2030…………………………………………………….125

Figure 4-15. Stanmerc.excluded.baunew2............................................................... 126

Figure 4-16. a) Tulare in 2003…………………………...…………………………127

b) Strict Adherence to WA, 2030……………………………………..127

c) Business As Usual, 2030…………………………………………...128

d) Abolition of the WA, 2030……………………… ……………...…128

Figure 4-17 a) Tulare County WA Classification, 2002……………………………130

b) Tulare County WA Classification, 2030 .......................................... 130

Figure 4-18 a) Tulare County Land Use, 2002........................................................ 131

b) Tulare County Land Use, Strict Adherence to the WA, 2030………131

c) Tulare County Land Use, Business As Usual, 2030…..……..……...132

d) Tulare County Land Use, Abolition of the WA,

2030…………..…..132

Figure 4-19. Tulare County Land Converted to Urban by type and

by scenario, 2030………………………………………………………………133

Figure 4-20. a) Stanislaus and Merced Counties, 2003.....…………………………134

b) Strict Adherence to WA, 2030…………………………………….134

c) Business As Usual, 2030…………………………………………..135

d) Abolition of the WA, 2030……………………… …………….….135

Figure 4-21 a) Stanislaus and Merced Counties WA Classification, 2002………..137

b) Stanislaus and Merced Counties WA Classification, 2030 ............ 137

Figure 4-22 a) Stanislaus and Merced Land Use, 2002..................................... .…138

b) Stanislaus/Merced Land Use, Strict Adherence to the WA, 2030...138

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c) Stanislaus/Merced Land Use, Business As Usual,

2030…..……….139

d) Stanislaus/Merced Land Use, Abolition of the WA, 2030………….139

Figure 4-23: Stanislaus and Merced Counties Land Converted to Urban by type and

by scenario, 2030………………………………………………………140

LIST OF TABLES

Table 2-1: FMMP Acreage Changes California: 2000-2002..................................... 17

Table 2-2: Williamson Act Enrollment Acreage ....................................................... 29

Table 3-1: WA Modeling Layers............................................................................... 69

Table 3-2: Urban Modeling Layers ........................................................................... 70

Table 3-3:Metrics That Can Be Used to Evaluate the Goodness of Fit of SLEUTH .75

Table 3-4: Routines and Results for Calibrating SLEUTH for Tulare County……...76

Table 3-5: Stanislaus and Merced Counties…………………………………………77

Table 4-1: Summary Statistics Table for Tulare County......................................... 129

Table 4-2: Summary Statistics Table for Stanislaus / Merced Counties ................. 136

LIST OF MAPS

Map 2-1: Visalia 1986 Metropolitan Area Land Use (Tulare County) ..................... 18

Map 2-2: Current and Former Williamson Act Lands in Visalia Metropolitan Area.26

Map 4-1: Western Tulare County, 2002 .................................................................. 106

Map 4-2, Visalia and Tulare 1986 ........................................................................... 107

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Map 4-3, Visalia and Tulare 2002………………………………………………….108

Map 4-4: Stanislaus and Merced Counties, 1984…………………………….……109

Map 4-5: Stanislaus and Merced Counties, 2002………………………………….110

Map 4-6: Modesto Metropolitan Area, 1984 ........................................................... 111

Map 4-7: Modesto Metropolitan Area, 2002 ........................................................... 112

Chapter 1: Statement of the Problem

Significance of Project

When Americans reflect upon California and its economy, thoughts usually turn to

Hollywood and the high-tech industries of Silicon Valley, rather than to the State’s

acres of avocados and endless miles of cattle ranches. Many visualize the Midwest as

the nation’s most important agricultural region, but it is California that is the most

agriculturally productive state in the union. In spite of this, California also has one of

the fastest growing populations. Given these two colliding realities, the protection of

California’s agricultural lands becomes paramount. Although there are a number of

Federal, State, and local programs in place to protect farmland in California, the

Williamson Act is responsible, by far, for the most acreage. But how effective has this

land conservation act been over its 40-year history? This research seeks to more fully

understand the impact the Williamson Act has had on the protection of agricultural

lands over time, and also to assess the Act’s continuing long-term viability in the face

of tremendous population pressure.

As far as permanent protection measures go, California does not lead the way in

agricultural conservation easements. That is why it is important to investigate the

effectiveness of the Williamson Act. If it fails to really halt urban sprawl and protect

farmland then nearly all of the

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State’s farmland could eventually be at risk. Also, since nearly every state uses

something similar to the Williamson Act1 it is vital to discover just how effective

voluntary, incentive-based programs are in a general way. Of course, every locality

offers a different zoning and ordnance setting and this should be addressed when

trying to analyze the act. It should be noted, however, that current zoning may make

less difference when predicting growth than one might assume (See Chapter 3).

It is also essential to explicitly examine just what the California legislature’s

reasoning is concerning the formation, promulgation, and enforcement of the

California Land Conservation Act. After all, one must be sure what the Act was

intended to accomplish before assessing its success or failure. Under Section C of the

Act’s declaration (See Figure 2-3), the legislature discourages the “premature and

unnecessary conversion of agricultural land to urban uses.” Therefore the

Williamson Act never intended to halt all development on agricultural lands.

Nevertheless, the wording of the Act, “premature and unnecessary”, is placed sphinx-

like before policy makers both state and local, as well as academics because,

ultimately, this vague raison d’etre must be realized through the specific policy

provisions of the Act as well as oversight and regulation. As the State is not the true

implementer of the Williamson Act, this is interpreted differently in different counties

and at different times. This dissertation, consequently, is not equipped to answer the

question: “Has the Williamson Act prevented the premature and unnecessary

1 Referred to by the American Farmland Trust as “Differential Assessment” programs (AFT website).

2

conversion of agricultural lands to urban uses?” This is a normative judgment and,

therefore, unanswerable in a quantitative sense. What this dissertation aims to

achieve is a framework of past maps and future scenarios through which others,

perhaps even policy makers, may explore this normative question. If they find the

answer troublesome, then perhaps they may initiate change.

A thorough investigation of the Williamson Act bears much more than academic fruit

since, periodically, the State considers whether or not to terminate its subvention

payments to the localities enrolled. This is particularly tempting given the current

belt-tightening climate2. If subvention payments cease, the financial burden for

continuing the program falls on the local governments, many of which are unlikely to

continue it unsubsidized. Also, since there is a great deal of land and money at stake,

any policy decision concerning the act should be as informed as possible. Currently,

there are about 16.6 million acres under Williamson Act contract, well over half of all

private agricultural land. Although this is actually more acreage than was in the act

ten years ago, that fact alone can be misleading. Understanding the geographic

history of adoption and withdrawal from the act offers a more vivid picture (See Map

2-2). Though many new parcels have joined, many others have left and some

developments even sit atop former Williamson Act lands. The several counties

studied in this research reflect this, as much of the prime irrigated farmland tends to

3

be on the flat plains surrounding the burgeoning urban areas and therefore lies in the

path of development. This presents a conundrum for the administration of the

Williamson Act. First, those areas most likely to be developed, ceteris paribus, are

those areas near other developed areas3. Second, Prime Farmland tends to be closest

to these developed areas and, thus, are the very parcels in the greatest danger

(Kuminoff, et. al., 2001; Sokolow and Spezia, 1992). Third, those Williamson Act

parcels nearest to developed areas are more likely to non-renew4 (See Map 2-2). So,

if all this is true, then the Williamson Act is best at protecting those parcels that are

farthest from developed areas which are not only under less threat of development but

tend to be less valuable lands in terms of their agricultural worth.

Project Outline

Research Questions

With the preceding in mind, the following research questions and hypotheses will

guide this dissertation.

• Question: Is the Williamson Act (both specifically and as representative of

other differential assessment programs) useful for modeling of urban spread?

2 At the time of this writing (2007) the Governor of California has offered a revised budget to the CA Legislature that suggests removal of the $39 million in Subvention payments to local governments, an act that would all but destroy the Williamson Act. 3 See SLEUTH Model publications: E. A. Silva and K. C. Clarke, 2002; Clarke, K. C., and L. Gaydos, 1998; Clarke, K. C., Hoppen, S. and L. Gaydos, 1997 4 Although not always for reasons of development, see California Department of Conservation, “The Williamson Act: 1991-1993 Status Report”, California Resources Agency, July, 1994.

4

• Hypothesis: The Williamson Act’s inclusion in urban growth modeling results

in greater accuracy than its exclusion.

• Question: Do spatial variables predict a parcel’s removal from the WA?

• Hypothesis: The same geographic phenomena that predictably apply

development pressure on undeveloped lands also apply pressure on

landowners to leave the Williamson Act.

• Question: How can knowledge of these spatial variables be used to model the

future of WA termination?

• Hypothesis: By designating Former WA lands as urban for the purposes of

SLEUTH’s nomenclature, a future landscape of the WA can be forecast with

accuracy comparable to urban growth forecasting.

• Question: How can WA forecasts be used to influence urban growth

modeling?

• Hypothesis: A probabilistic excluded landscape can be created in a WA

modeling run and fed into a traditional urban modeling routine.

Methods

To answer these questions, the following tasks were performed:

5

► A comparison of the development patterns historically for three counties:

Tulare, Stanislaus, and Merced (See Figure 2-1 for reference).

By populating a Geographic Information System with maps from the California

Department of Conservation’s Farmland Mapping and Monitoring Program (FMMP)

the net growth of urbanized lands and other metrics were evaluated including: edges,

clusters, cluster sizes, and spread. This shed much needed light on the effect the

Williamson Act has on urban growth. These maps also reflect the value of the

farmlands in these counties according to the FMMP’s hierarchical classification

system: Prime Farmland, Farmland of Statewide Importance, Unique Farmland,

Farmland of Local Importance, and Grazing Land.

► An analysis of each county to determine what effect urban growth has on the

adoption, non-adoption, non-renewal, and cancellation of parcels in the

Williamson Act

► Creation of a tool that will allow the Department of Conservation, the State

Lawmakers, as well as local interests and academics to see animated maps of

different farmland conversion scenarios according to various policy options.

This research offers both methodological and practical advances. First, this work has

successfully forecast future Williamson Act patterns so that appropriate

administrators may see the direction in which this voluntary measure is heading in

their respective jurisdictions. Second, building upon the former, this dissertation

utilizes a cellular automata model to create a probabilistic excluded layer that can

6

then be used for urban growth modeling. Such a technique has, to the author’s

knowledge, never been performed.

In the chapters that follow many of the points mentioned in this Introduction will be

expanded. In Chapter 2, a thorough contextualization is offered so that the audience

may see the evolution of theories and techniques that have lead to this dissertation as

well as the role it plays in the field overall. The third Chapter offers an account of the

methods applied and the data utilized for the realization of this research. Chapter 4

engages the specific results of the work along with pertinent additional analysis.

Finally, the fifth Chapter examines how effective this dissertation has been in

answering its research questions and what the implications of this project are for

future modeling investigations as the future of the Williamson Act.

CHAPTER 2: Context of the Study

7

Historical Land Use

Urban sprawl is a relatively new phenomenon. For several thousand years, cities and

towns, at least in Europe, were lonely islands in the broad swaths of countryside that

abruptly appeared outside the city gates. This is, of course, no longer the case

everywhere, particularly in the US. During the last several hundred years and at an

accelerating pace, cities have been advancing and the farmland and open spaces have

been falling back (Vitousek et. al., 1997). Ramankutty and Foley report that, world-

wide, 2.6 million square miles of natural lands have been converted to other uses

(mostly agricultural) over the last few centuries (1999). The détente between city and

country is over. The war though, if it may be called that, is not. There remains a

great deal of agricultural land in the world and developed areas still make up only a

tiny fraction of the Earth’s surface, though much of the world’s land is neither

picturesque nor of much direct use to human beings. Consequently, flat well-drained

lands, comprising only a modest proportion of the Earth’s land, are in direct

competition with expanding cities since these lands are also ideal for development.

Though agriculture has been responsible for the greatest land cover change, altering

what was once wildlands and forest, in the last one hundred years there has not only

been a tremendous increase in population but also a reorienting of the world’s

population from predominantly rural to half urban (Knickerbocker, 2007). This shift

has had a corresponding effect on land use. Cities have expanded and built upon the

lands that formerly surrounded them. This transition is particularly dramatic in the

8

United States, where roughly 80% of the population now lives in urban areas

(Acevedo, 1999). This was not always so.

Since colonial times, the Eastern portion of the US has undergone several

transformations. First, its forests gave way to farms that were settled by early

European pioneers. Later, many of these farms were reclaimed by forests as

populations became more dependent on secondary and tertiary economic activities

and agriculture migrated westward. This is particularly true in New England and

other portions of the Northeast (New York Department of Environment

Conservation). The western portion of the US, on the other hand, still exhibits a

landscape that reflects a great deal of primary economic activity. In fact, agricultural

development has altered much of western North America. For instance, the draining,

irrigation, and damming of California’s Central Valley has transformed it from

wetlands to now mostly croplands (Golden State Museum).

As the remaining arable lands are plowed or protected as open space, the days of vast

agricultural expansion in California are drawing to a close. However, finding the right

balance of farmland, wildlands, open space, and developed land is the new challenge.

There is disagreement, obviously, on just how to achieve this. Many contend, as

discussed later, that it is of the greatest urgency that we protect farmland, particularly

in California, while others claim the rate of conversion is so slow there is no reason

for panic (Plaut, 1980; Fischel, 1982; Gustafson and Bills, 1984; Kuminoff et. al,

2001).

9

California Farmland

California’s vanishing farmland is not an unpopular topic of study. With its 2002

market of agricultural products valued at nearly $26 billion, California is the most

productive agricultural region in the country, if not the world. It is not surprising,

therefore, that the conversion of these fertile lands to urban uses has many groups--

from policy-makers and non-profit organizations to academics and private citizens--

alarmed, and with good reason. The state has already lost well over 11 million acres

of farmland since its peak in the 1950’s5 6. What these numbers don’t tell us,

however, is the way agriculture has been redistributed throughout California in the

last 50 years. After World War II, a building boom began squeezing farmland out of

the coastal regions into new lands colonized in the Central Valley (Sokolow and

Spezia, 1992). This boom continues today and now even threatens this agricultural

cornucopia, since recent studies suggest the Central Valley’s ability to keep the

agricultural economy afloat will be greatly challenged by tremendous population

pressure and low density sprawl (Teitz, et. al., 2005). Unfortunately, as this new

farmland disappears there is no new valley to rescue California’s agricultural

economy (Sokolow, 1997).

More input-intensive farming methods and greater labor availability have

compensated for some of this lost acreage (Medvitz, 1999). However, it is now being

5 But not a concomitant loss in agricultural productivity, see Kuminoff et. al, 2001 and Medvitz, 1999.

10

suggested that productivity will eventually decline as the best farmland continues to

be nibbled away by sprawl and as water becomes an increasingly scarce commodity

(Medvitz, 1999). In fact, new farming operations have become impossible to begin in

some areas of the state since the cost of installing a new water meter has become

prohibitively expensive (Lane, 2005).

Also, farming on the urban fringe is more difficult than in the hinterlands and this

exacerbates the problem. Homeowners complain about farming noises, odors, and

chemicals, while farmers have to deal with trespassers, theft, and mischievous pets

(Handel, 1999).

Even with Right-to-Farm ordinances in place, constant bickering with neighbors is

often the last straw for edge farmers (Hvolboll, 2005). This disconnect has much to

do with expatriated urbanites valuing farmland for its open space aesthetic rather than

for its more utilitarian functions as a place of business and food production. It has

been observed, for instance, that the suburban public raises the alarm about the loss of

farmland not for economic or food security issues but for the open space that these

vanishing parcels afford them (Logan and Molotch, 1987).

The money gained by selling their farms to developers coupled with the difficulty

involved in farming on urban peripheries act as both carrot and stick to pull and push

6 The total loss of farmland is greater since this is only net loss (lost agland – new agland)

11

farmers over the line and into selling their land. As Eric Hvolboll (2005), a Santa

Barbara County avocado farmer and eighth generation Californian put it, “There are

not many farmers so invested in their work that they can justify the opportunity cost

of not selling…Why risk your life, doing a dangerous job, working 60 hours a week,

when you could make more money doing nothing?”

To be fair, though, not all farmers near urban areas are giving up on their trade, even

in Santa Barbara County. In urban Goleta, for instance, there are a number of

working farms still in business. Though none are in the Williamson Act, by

intensifying their agricultural practices in the form of more greenhouses and less row

crops, or by direct marketing, many of the farmers have been able to maximize the

use of their land and stay afloat financially (Santa Barbara County Planning and

Development, 2002). Another advantage pointed out by both the owners and the

managers of the farms has been the low transport costs and a proximal advantage to

the many farmers’ markets stretching along the Coast from Santa Barbara to Los

Angeles (Ibid). Fairview Gardens, one of the oldest organic farms in Southern

California, now surrounded by development, is a conservation easement that not only

proves that urban agriculture can exist but actually utilizes its twelve acres as an

educational farm, teaching the public about the benefits of urban agriculture.

Nevertheless, even these savvy and persistent edge farmers admit their farms may not

last the test of time (with the exception of Fairview Gardens) and cite development

pressures as a constant concern.

12

This pressure is not confined to California, but is a problem throughout much of the

Country. To systematically examine the issue, the American Farmland Trust (AFT)

has identified the 127 Major Land Resource Areas (MLRAs)7 containing high quality

farmland in areas of rapid development (Sorensen et. al, 1997). AFT also ranks these

MLRAs according to an index that takes into account both the area’s agricultural

value as well as local development pressure. California, unfortunately, has three

MLRA’s that are among the top 20 in this index. These include the Central Valley

(considered number one), the central California Coastal Valleys (15th), and Imperial

Valley (17th). The most recent data show an average yearly loss of 260,000 acres

between 1992 and 20028. (National Agricultural Statistics Service (NASS))

Furthermore, recent modeling efforts predict the Central Valley’s population will

triple by 2040, with as much as 1,035,000 acres of additional farmland converted to

urban uses, including over 600,000 acres of prime or statewide important quality

land9 (Bradshaw et. al., 1998. See Figure 2-1 for an overall look at California’s land).

Figure 2-1: FMMP Survey Area-2002 (CA Dept. of Conservation)

7 The US Department of Agriculture has divided the US into 181 Major Land Resource Areas (MLRAs) 8 This covers conversions of every type, not just urbanization. 9 Farmland of statewide importance is the second most valuable farmland type, according to the USDA’s classification system. This category is similar to prime but with minor defects. Go to http://www.consrv.ca.gov/DLRP/fmmp/mccu/map_categories.htm for more details.

13

It should be noted, though, that the majority of agricultural parcels in California are

not currently on the urban fringe and are consequently not in imminent danger of

conversion to urban uses (Sokolow and Spezia 1992; UC Davis 1989; Kuminoff, et.

al., 2001). In fact, at first glance the urbanization rates appear so low that some

argue there may really be no urgency at all. From 1992 to 2002 there were

14

approximately 280,000 acres converted from agricultural uses to urban uses10 (CA

Dept. of Conservation). Considering that this is out of a 1992 pool of 30 million acres

of agricultural land11, that is an average annual conversion rate of 0.093 percent. If

this is in fact the case, is the Williamson Act, or any agricultural preservation

measures, needed at all? The answer seems to be yes, for two reasons. First, these

values don’t suggest the acceleration over this decade in urbanization. The period

1992 to 1994 saw 30,000 acres paved over while the 2002-2004 figures report 94,000

acres converted, a three-fold increase in rate (Ibid. See Table 2-1 and Figure 2-2

below). This is an alarming trend for those with an interest in California’s agricultural

future. Second, most of the best farmland rings urban areas and is in the direct path of

expanding land-hungry cities (See Map 2-1). California's Farmland Mapping and

Monitoring Program's statistics reveal that over 85,000 acres of prime farm land were

urbanized during this decade and, given a base of 4.3 million acres in 199212, prime

land averaged an urbanization rate of nearly 0.2 percent a year, almost three times

faster than the rate for more remote grazing lands (Ibid). Therefore, those farms

closest to urban centers, which tend to be the most prime and productive, are

disproportionately threatened by urban sprawl than more distant farms.

10 There is no universally agreed upon figure. The FMMP and the NASS give different figures for California. Also, there are other categories to which agricultural lands can be converted besides urban and these are not reflected here. 11 NASS’s 1992 figure. Since FMMP does not inventory all lands the NASS figure for total farmland is given instead. FMMP’s total comes out to 26 to 28 million acres. 12 FMMP’s subcategory figure.

15

While the urbanization of farmland is arguably the most irrevocable conversion, the

overall transition of farmland to other uses (including non-productive lands, open

space, or extreme low-density rural developments not considered “urbanized”) has

removed a great deal of these lands from use in the last 50 years. During the 1950s

(1950 – 1960) there was actually an increase of 1.3 million acres of agricultural lands

in California as the investment of new farmlands actually eclipsed the elimination of

others. The ‘60’s saw a reversal of this trend as California experienced an average

loss of 220,000 acres a year. The 1970’s saw even more eradication, with 2.8 million

acres lost. This trend accelerated in the 1980’s as 300,000 acres a year were, on

average, removed from agriculture. The nineties saw a slight deceleration, with 2.8

million acres lost. However, as the first decade of the 21st century has progressed,

there has been an average of 320,000 acres lost per year, on track to be the most

devastating decade for farmland removal since records have been kept (See Figure 2-

2 for all years from 1950 to 2005).

Table 2-1: FMMP Acreage Changes California: 2000-2002

16

Figure 2-2: Courtesy of NASS

Number of Farms and total agricultural acreage in California: 1950-2005

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

1950

1956

1962

1968

1974

1980

1986

1992

1998

2004

Number of farmsAcres (in thousands)

Map 2-1: Visalia 1986 Metropolitan Area Land Use (Tulare County)

17

Farmland Protection Programs

There are a number of arguments for the preservation of farmland in any locality.

First, prime farmland is disappearing across the Earth due both to poor farming

practices, which erode topsoil, as well as from expanding land-hungry cities. Urban

areas are often found in flat, well-drained areas that are ideal for agriculture as well,

causing competition between the two uses. Underdeveloped nations are, tragically,

expected to become ever-increasing consumers of US agricultural products as this

18

dearth becomes compounded by staggering population growth, another economic and

moral incentive to protect our farmland. Also, with dangerous diseases such as the

Avian Influenza looming on the horizon, food security at multiple geographic scales

seems prudent. Third, farms tend to cost local governments less than the taxes levied

on them while housing developments cost more, given the high cost of infrastructure

and services that must be provided (California State Assembly, 2001). Fourth, farms

often provide an attractive viewshed and can even promote tourism, whether it’s the

wine country of California or the Amish country of Pennsylvania. Fifth, farms are

places of business and they provide jobs as well as support ancillary services such as

agricultural equipment suppliers, veterinarians, feed and fertilizer consultants, even

accountants. Lastly, though they use a tremendous amount of water and oftentimes

pesticides as well, many farms provide services to the environment by protecting

topsoil, providing open space, preventing runoff, and even acting as critical pieces of

habitat, services not easily rendered by yet another big box mall (Pennsylvania

Farmland Preservation Association). In fact, Boody et. al. (2005) have found that not

only does agriculture have less of an economic impact on the taxpayer but, when

employing environmentally friendly techniques, can improve the local ecology

without incurring additional expenses, in some cases even improving farm

profitability.

Despite the relatively recent trend toward urban sprawl, agricultural conservation has

been pursued for decades, beginning during the Depression when the Federal

Government’s 1936 Soil Conservation Act took an active interest in protecting

19

farmland (USDA website). The 1960’s saw many state governments following suit,

including California. Today, conservation measures extend from the federal level all

the way down to the local level. A short tour of them allows for a greater

understanding of the policy context within which the Williamson Act functions.

At the Federal level a suite of programs under the aegis of the 2002 Farm Bill form

the central source of money. Rather than being hands-on, this bill instead offers

money to those who apply for conservation easements. The Farmland Protection

Program, for instance, provides $597 million in matching funds to qualified entities

(states, counties, etc.) over 6 years. The previous Funding Bill from 1996 only

provided $52 million. There are also the Wetland Reserve and the Grasslands Reserve

programs of the new Farm Bill that provide funds for farmers to protect these

portions of their property. Although there are other aspects of the Farm Bill that assist

farmers in their environmental efforts, these are the only programs designed to

directly and permanently protect farmland and other sensitive private lands. Whatever

the reasons, though, for the promulgation of farmland conservation measures, those

that are in place must actually accomplish their goals to justify their expense.

California has several state programs of its own that are designed to conserve

farmland. The first, Purchase of Agricultural Conservation Easements (or PACE), is a

loose description of many different states’ attempts to protect farmland directly by

paying farmers to permanently preserve their land. California’s version at the state

level is called the California Farmland Conservancy Program, or CFCP, and it has

20

yielded roughly 24,000 acres thus far (California Department of Conservation (CDC)

website). Similar efforts at the local level have acquired approximately 53,000 more

(AFT website). This may seem like a good deal of acreage, but when put in contrast

with California’s total private farmland acreage of 30 million acres13, this provides

only about 0.26 percent guaranteed perpetual protection of California’s agricultural

lands. Some acreage is counted twice, as it receives both local and state funds, taking

that percentage down even further. In contrast, the state of Maryland in 2003 had 2.1

million acres of farmland (NASS) with 282,000 acres in a similar state-level program

and 209,000 acres held in local programs, (AFT website) yielding perhaps as much as

23 percent permanent protection (Ibid). California’s Williamson Act, on the other

hand, covers a great deal more acreage than the CFCP but, as shall be seen, this

protection is not permanent.

Williamson Act Implementation and Eligibility

The California Land Conservation Act of 1965 (See Figure 2-3), better known as the

Williamson Act, permits local authorities to enter into contracts with private

landowners with the goal of restricting the land use of specific parcels to agriculture

or open space. Under the Act, landowners receive lower use-based property tax

13 NASS’s 1992 figure. Since FMMP does not inventory all lands the NASS figure for total farmland is given instead. FMMP’s total comes out to 26 to 28 million acres.

21

assessments and not the full market value of the land. Local governments then receive

an annual compensation for lost tax revenues from the state via the Open Space

Subvention Act of 1971. Since the financially strapped State periodically examines

the Act for elimination,14 it is pertinent to ask the questions: what are the benefits of

the Williamson Act; have the conservation goals of the Act been met (in light of the

caveats above); and how does this relate to where urbanization and land development

have taken place in the State? More broadly, what is the long-term viability of

voluntary incentive-based conservation programs in the face of inexorable

development pressure? Since they are not unique to California’s policy culture and, in

fact, are used in many of the United States (CDC, 2003), a thorough knowledge of the

specific geographical manifestations of these applications of policy is important.

These effects are difficult to predict, though, since the Williamson Act attempts to

conserve land not through coercion, but through incentives. Nevertheless, as Chapter

3 will demonstrate, though it is difficult to predict a future Williamson Act regulatory

landscape, this dissertation offers an important contribution in the form of a

methodological solution for not only forecasting the Williamson Act but also using

these results to affect urban growth modeling.

Figure 2-3, from California Government Code Section 51220:

51220. The Legislature finds: (a) That the preservation of a maximum amount of the limited

14 At the time of this writing (2007) the Governor of California has offered a revised budget to the

CA Legislature that suggests removal of the $39 million in Subvention payments to local governments, an act that would all but destroy the Williamson Act.

22

supply of agricultural land is necessary to the conservation of the state's economic resources, and is necessary not only to the maintenance of the agricultural economy of the state, but also for the assurance of adequate, healthful and nutritious food for future residents of this state and nation. (b) That the agricultural work force is vital to sustaining agricultural productivity; that this work force has the lowest average income of any occupational group in this state; that there exists a need to house this work force of crisis proportions which requires including among agricultural uses the housing of agricultural laborers; and that such use of agricultural land is in the public interest and in conformity with the state's Farmworker Housing Assistance Plan. (c) That the discouragement of premature and unnecessary conversion of agricultural land to urban uses is a matter of public interest and will be of benefit to urban dwellers themselves in that it will discourage discontiguous urban development patterns which unnecessarily increase the costs of community services to community residents. (d) That in a rapidly urbanizing society agricultural lands have a definite public value as open space, and the preservation in agricultural production of such lands, the use of which may be limited under the provisions of this chapter, constitutes an important physical, social, esthetic and economic asset to existing or pending urban or metropolitan developments. (e) That land within a scenic highway corridor or wildlife habitat area as defined in this chapter has a value to the state because of its scenic beauty and its location adjacent to or within view of a state scenic highway or because it is of great importance as habitat for wildlife and contributes to the preservation or enhancement thereof. (f) For these reasons, this chapter is necessary for the promotion of the general welfare and the protection of the public interest in agricultural land.

To summarize Figure 2-3 above, the Williamson Act allows both the state and the

parcel to “enter into a contract in which each accepts certain costs in return for other

benefits” (USDA website). Enrollment, though voluntary both for the locality as well

as the landowner, is much easier than a withdrawal. For example, in a participating

county or city, a potential candidate need only file an application with the local

government, usually the county planning department. Then, if the land is eligible, it is

usually accepted. Under normal conditions the contract automatically renews each

23

year. To withdraw from the program requires an initiation of non-renewal on the part

of the landowner, setting into motion a nine-year phase out period whereby taxes are

gradually shifted back from use value to market value rates.

All agricultural lands in the state are eligible for Williamson Act contracts, provided

the landowners have enough acreage to meet the minimum parcel requirements and

the county (or city) has designated the land under which the parcel falls as an

agricultural preserve. Every county also has its own rules and regulations regarding

the size and type of land that is eligible. However, the State mandates (except in very

unusual circumstances) that each parcel not only be located in an area zoned for

agricultural preserves by the local government but that these zones must also be no

less than 100 acres. This acreage can be counted across two or more contiguous

parcels or even two non-contiguous parcels as long as they are under the same

ownership.15 It is further stipulated that within these preserves prime agricultural land

must be in parcels of no less than 10 acres and non-prime land in parcels of no less

than 40 acres. However, additional open space lands are also acceptable for contracts.

Rather than carefully designated, Agricultural zones are usually assigned ad-hoc as

various farmers request Williamson Act exemptions (Ken Trott, personal

communication). As far as subvention payments, the State does not simply pay back

the exact amount that the locality has lost. Rather, the local governments are

compensated according to a uniformly standard and simple system: $5 per acre for

15 For more information see the following website:

24

prime land16 outside the three mile sphere of influence for each incorporated city and

$1 for each non-prime acre of land outside this sphere; and $8 per acre for all

Farmland Security Zones within the incorporated area or its three mile sphere of

influence (CA Government Code Section 16140-16154). The totals these

subvention payments bring to counties rarely, if ever, equal the tax revenue these

local governments have foregone. California counties are relatively fortunate,

however, since most states offer no subventions whatsoever to localities that offer

similar programs (AFT, 2003).

Map 2-2: Current and Former Williamson Act Lands in Visalia Metropolitan Area

25

http://www.consrv.ca.gov/DLRP/lca/basic_contract_provisions/index.htm

Although the overall quantity of land is well known, just where these contracted lands

are located is of even greater importance (See Map 2-2). Land use lawyer Susan

Petrovich claims, “the Williamson Act is very effective for non-prime parcels like

ranches out in remote areas because it keeps development from leapfrogging into the

rural areas and keeps it closer to already developed areas” (personal communication).

A formal study conducted in 1989 lends great support to her claim (Williamson Act

Study Group, 1989). Consequently, there is less conversion in lands distant from

urban clusters, but those areas have always offered less attraction for speculators or

developers than those on the city fringe, and arguably require less strenuous measures

of protection. Unfortunately, these distant parcels are mostly comprised of less

valuable grazing lands while the best farmland tends to be closer to the city center

16 Prime farmland is defined by meeting two conditions: one, the land has been irrigated in the last 4 years and two, it meets the various soil criteria as defined by the USDA. More information can be

26

(See Map 2-1). Thus, the lands just outside of California’s cities are prime both for

farmland and real estate, a competition that agriculture just cannot win. As Goleta

farmer John Lane, manager of Lane Farms says, “Once a neighborhood is built next

to a farm it is only a matter of time before the farm succumbs to development”

(2005).

Williamson Act Criticism

Differential assessment programs, such as the Williamson Act, are supposed to

redirect these tendencies of farmers to act in their own economic best interest by

making farming less expensive on the urban periphery and elsewhere. Taxes are low

for those who wish to keep farming while for those who do not wish to continue or do

not have children or other family interested in working the land, no amount of tax

breaks can compel them not to sell. But there is much middle ground for those who

can be swayed one way or the other. This is a crucial piece of understanding, at least

rhetorically. Where is the breaking point for each farmer? How many tax breaks are

necessary for farmers to remain farming rather than sell out, statistically speaking?

This work lends itself to this continuing debate concerning farmers as agents and how

they act in times of growth. It also weighs in on appropriate policy structuring for

remedying the issue of prime farmland loss.

found at http://www.consrv.ca.gov/DLRP/fmmp/overview/prime_farmland_fmmp.htm

27

Differential Assessment programs are in place to help prevent farmland conversion

but many have pointed out they are not a long-term solution. In “A Panacea That

Wasn’t,” John Dean argued that prime farmland has the least comparative tax

advantage since the Williamson Act taxes farmers based on their farm income and not

on the value of their land (1975). Though prime land farmers are taxed more than

ranchers, it is also important to point out the real estate worth of these lands is usually

higher due to their usual nearness to urban areas and a topography ideal for

development. They may pay more than ranches under the Williamson Act but they

certainly pay less than they would without the Act. The more important comparison

lies between the advantage of paying taxes on productive farmland or selling that

land for tremendous profit (Dresslar, 1979). This comparison often renders edge

farmers a greater advantage than remote ranchers. Distant intensively used

agricultural lands would, however, fall victim to Dean’s cited disadvantage. All who

have studied the Williamson Act recognize this major dilemma. Nevertheless,

despite the criticisms, solutions have been intractable (Sokolow, 1990).

The lack of centralized administration is another fault found by scholars. John

Dresslar (1979) called this a major failing and insisted the State take steps to alleviate

the problems resulting in local regulation of the Williamson Act. Though this

recommendation was made nearly thirty years ago it still has not been implemented,

though some of his others were.

28

Alvin Sokolow, widely recognized as one of the leading scholars studying the

Williamson Act, gave the Act a mixed endorsement in 1990. On the one hand, he did

admit that it helped keep certain farming operations solvent. On the other hand,

however, he claimed it did not have an appreciable overall effect on the conversion of

agricultural lands to urban lands. Williamson Act enrollment numbers, therefore, can

be misleading (See Table 2-2) since there are many new and distant farmlands joining

to make up for those on the urban fringe leaving, most likely as a precedent to

development.

Table 2-2: Williamson Act Enrollment Acreage

Total Reported Acreage* Fiscal Year Calendar Year Total Reported Acreage*

1990-91 1990 15,969,159 1991-92 1991 15,946,783 1992-93 1992 15,942,758 1993-94 1993 15,952,365 1994-95 1994 15,952,144 1995-96 1995 15,908,538 1996-97 1996 15,812,511 1997-98 1997 15,889,804 1998-99 1998 15,925,301 1999-00 1999 15,977,116 2000-01 2000 15,936,437 2001-02 2001 16,344,433 2002-03 2002 16,504,721 2003-04 2003 16,560,132

*Totals include both continuing term and nonrenewal Williamson Act contracted land, as well as a small amount of other enforceably restricted non-Williamson Act acreage. Note: “nonrenewal” refers to land that is undergoing the process of non-renewal at that time step only, it does not include those lands that have completed this process sometime in the past.

29

Perhaps Peter Brand says it best, “On the world stage California is a major

agricultural power, but at the local level the agricultural industry offers almost no

resistance to the forces that may eventually destroy it (1995).”

In light of these criticisms, this research has important applications to policy. For

instance, by examining the following attributes of each parcel for each time step:

opting in or opting out; distance from urban area; prime farmland or other; we can

begin to understand how the particular implementation of this policy might be altered

with a view towards greater more efficient realization of its stated goals. By making

the tax breaks greater for prime farmland and less for non-prime farmland, for

instance, the state could help channel growth away from the prime farmland and

towards non-prime farmland.

Theories of Growth

The phenomenon shown in Map 2-1, prime farmland surrounding cities, is not a

recent trend. Even two hundred years ago, Johann Heinrich von Thunen, (1783-

1850) noted that more intensive agricultural lands tend to be closer to the city while

ranching and other less intensive agricultural activities tend to be located farther

away (Von Thunen, 1966).

He theorized that, in an equal plain stretching in all directions, there is an observable

pattern of rings radiating outward from the city where the intensity of agriculture

decreases with distance from the city center. Though geographically applicable, his

30

theory is essentially economic: as one travels farther from the city center land prices

decrease and therefore, less intensive uses of the land are made affordable. As the

thinking goes, a 5000 acre cattle ranch on the border of a large city is just not

economically feasible. However, a five-acre greenhouse parcel that concentrates on

expensive flowers would be. It is natural then to speculate on what could be more

profitable than a five-acre greenhouse parcel. The answer is most likely the creation

of an urban parcel, perhaps a grocery store where agricultural goods are distributed

on a comparatively small lot (perhaps an acre) to a large population base. Since Von

Thunen’s model of land use is temporally static this work can help build upon his

original ideas. However, if it were not, and we continue to expect farmers to act as

economic agents, then we would see those parcels on the urban fringe eventually

becoming urbanized. This would widen the urban footprint and then a wave of more

intense land use could ripple across the landscape. Of course, farmers do not always

act as economically rational agents and many farmers love their life’s calling.

Nevertheless, it is easy to imagine a childless farmer retiring, selling the farm to

developers, and retiring in luxury somewhere.

Thomas Malthus (1798) proffered a theory of population and agriculture even before

von Thunen. He claimed the human population will continue to grow exponentially,

exploiting more land and growing increasing amounts of food until room and

resources are exhausted (Ibid). There would then be an “adjustment” period, which

would involve a great famine and a plummeting of the population to sustainable

levels. His offer of abstinence as an alternative to this dystopia has, thus far, found

31

too few adherents throughout the world to appreciably affect the high rate of

population growth. Nevertheless, his theory was generally accepted, with only a few

objections, until Ester Boserup’s landmark work, The Conditions of Agricultural

Growth (1965). Boserup argues against Malthus’s reasoning and makes a

fundamentally opposite claim. Rather than agricultural innovation driving population

growth, as Malthus contends, it is population growth that creates agricultural

ingenuity. People do the least amount of work necessary to get the food they need,

she asserts. This least effort involves the greatest amount of land (Boserup has

divided the world’s farming systems into 5 hierarchical classes based on the amount

of time land remains fallow). It is only when population increases and available land

is all in use that people, in general, begin to intensify farming practices on the land.

Over 40 years ago Alonso (1960), revising von Thunen, pointed out the peculiarity of

Western urbanization. Instead of the rich living on the inside of the city, with the

poor living in surrounding squatter settlements, like we see in much of the developing

world, the wealthy in the West live on the urban periphery, where land is cheapest

and they can live at lower densities. This Western hunger for the urban-rural

boundary and the associated lower density living, that Americans in particular equate

with a high quality of life, is a large driver of suburbanization in the US. Of course,

our reliance on the automobile as our principle means of travel cannot be extricated

from our hunger for the urban periphery and the resulting and relatively even spread

of American cities, as opposed to along subway routes, represent this. Brian Berry

(1970) argues that the automobile and the abundance of American roads have not

32

caused urban sprawl but simply have facilitated our demand for low-density

residential land. Jackson (1985) disagrees and claims that our suburbanization is

based on our car culture, with Los Angeles as the epitome of automobile-directed

sprawl. Regardless of which came first, the chicken or the egg, everyone agreed by

the mid-1980s that America was a suburban nation (Ibid). Between 1950 and 1970

nearly 11 million people in America moved to the suburbs, while many others were

simply born there (Ibid).

As far as agricultural impact, it was during the late 1970s that the Department of

Agriculture first announced that three million acres of prime farmland were being lost

each year to suburban sprawl. In that time D. Berry (1978) suggested that much

farmland, in anticipation of conversion, would cycle out of production, causing a

ripple effect. Thus the emergence and recognition of the “impermanence syndrome”

(Lopez et. al., 1988), whereby farmers amidst the suburbs, realizing their farming

days are numbered, begin speculating about future development on their land.

Consequently, they reduce investment into their farming infrastructure and/or do not

replace failing or broken equipment, accelerating the land’s productive obsolescence,

and therefore strengthening the farmer’s desire to sell. Also, market forces can,

without a mitigating regulatory regime like the Williamson Act, drive property taxes

so high as to make farming the land an untenable use (Ibid). However, one aspect of

the market somewhat assuages this phenomenon. As the city advances towards the

farms, transportation costs to and from the market served by the farmer are reduced

(Ibid). Nevertheless, this is rarely enough, regardless of the regulatory regimes and

33

endemic market realities, to offset all of the previously mentioned difficulties and

temptations faced by edge farmers. One need only observe the disappearance of

farms along the urban periphery to confirm this, a retreat without which sprawl often

could not proceed.

R. F. Muth (1961) attempted to systematically examine this dynamic when he created

his now well-known urban-rural conversion model. Essentially, inside the urban

boundary, the market value of land exceeds the use value of land for agricultural

purposes and vice versa. However, by incorporating these aspects of speculation,

Lopez et. al., (1988) have suggested the boundary may advance more quickly than

Muth’s model explicitly allows. For instance, if speculators expect the urban-rural

line will be advancing then they anticipate an increase in the market value of their

land. If this expectation is higher than the current use value of their land, then they

will hasten this conversion more quickly, much in the same way that the perception of

a stock market crash can actually cause one.

Modeling Theory and Criticism

In geography as well as economics, models have often been built and consulted to

forecast things to come. In this dissertation, therefore, to tease out the patterns of

Williamson Act termination in the context of urban growth and to extend this into the

future, a model will be needed. Since this research does not simply use regression, as

this lacks a sophisticated understanding of the dynamic variables involved, a more

34

data intensive and spatially explicit tool is used instead. However, whenever scholars

deign to model a geographical phenomenon, they must face the many criticisms of

such an approach. These warnings were very nearly successful in burying modeling

of this type. Once Douglas Lee, Jr. wrote his “Requiem for Large-Scale Models” in

1973, many believed urban and land use modeling had finally spent the momentum it

gained from the quantitative revolution of the 1950s. Lee insisted these ambitious

models were nothing more than black boxes, were not based on soundly applied

urban theory, and were so complicated that even their originators did not understand

the connections between what went in and what came out. Indeed, modeling did

seem to whither under the continuous attacks from alternative approaches, at least in

the United States. However, many of these criticisms, particularly Lee’s, have been

attenuated by great leaps in technological innovation and a certain degree of

advancement in urban and land use theory, though many would argue not nearly

enough. In 1985, for instance, Harris officially claimed that Lee’s criticisms were

outdated and, in fact, fallacious from the start (1985). Undaunted, model critics have

continued to insist that GIS and urban modeling is an antediluvian attempt to

continue a now largely discredited quantitative revolution (Tuan, 1971; Pickles,

1999). Or, as Mercer and Powell (1972) claimed, the decades long devotion to

positivism stripped geography of scholars, leaving behind only computer technicians

and number crunchers. Couclelis, mindful of these attacks, has suggested theoretical

constructions of space (proximal) and implementations (geo-algebra) that would

render GIS and associated modeling environments more relevant to planners (1991,

1997). Wegener, in answer to model critics, insists that the proof of GIS and urban

35

model relevance lies in their continued application to real-world scenarios (1994).

Some have taken a middle path, validating Lee’s and others criticisms not by

abandoning modeling but by building a more thoughtful mousetrap. John Landis, in

the creation of his CUF model (1995) as well as Alberti (1999) would fall into this

category. Tayman (1996), for better or worse, argues that large-scale models are here

to stay not only because of greater advances in computing but because policy-makers

now insist upon their creation.

Though Lee’s requiem did not sound the death knell for large-scale modeling,

disappointing many humanist scholars, it did officially usher in the era of skepticism

in their use. Nevertheless, cellular automata (CA) models answer much of this

critique since they are usually based on simple rules that only through their

application result in complexity and emergence. CA models are also visually useful

and easily explainable and it is for these reasons, along with others, that they have

eclipsed not only systems dynamics models (Forrester, 1969) for geographic use in

general but for other related fields. Cellular automata models, unlike many of the

models being attacked in the past, do not necessarily suffer from large-scale black

box issues. First, CA are spatially explicit, and the model outcome is the totality of

many small and relatively simple micro-simulations. Second, unlike the

comprehensive model undertakings of the past, which provoked so much vitriol from

Lee (1973) and others, CA have rules that are usually simple but result in complexity

only in their application. Much like the game of chess has simple rules that allow for

tremendous complexity in strategy and tactics, CA’s simple rules allow for greater

36

transparency and understanding of results. Third, CA is easily rendered probabilistic,

rather than deterministic, such as a Systems Dynamics approach. This stochasticity

offers greater flexibility in results and allows for greater statistical analysis resulting

from the various conditions the modeler creates. Therefore, since CA is the model

platform of choice for this project, a proper understanding of micro-verus-macro

simulation as well as cellular automata versus agent based modeling is appropriate.

Beginning with theory, Helen Couclelis wrote a paper nearly twenty years ago

entitled, “Cellular worlds: a framework for modeling micro-macro dynamics” (1985)

where she confronts the problem of assuming that all macro-level phenomena emerge

solely from an aggregation of micro-level phenomena. She also cites the allocation

of macro effects amongst the agents as one other great difficulty. For example, if

there is a known increase in population (perhaps supplied by a macro-scale

population sub-model) how should housing demand be distributed amongst the

individual parcel managers across the extent of the modeling environment? Surely,

distributing this demand without knowing exactly which areas are increasing in

population dilutes the model’s accuracy. This pervasive problem “concerns our

capacity to explain the relationship between the constitutive elements of social

systems (people) and emergent phenomena resulting from their interaction (i.e.

organisations, societies, economies)” (Goldspink, 2004). Nevertheless, urban

modelers are determined to bridge this gap between the micro and the macro. Before

this is addressed, though, let us examine just what these two approaches involve in

more detail.

37

One of the main reasons for the nature of these two different approaches concerns the

disciplines that chiefly utilize them. Micro-simulation has evolved from the

approaches of the social sciences and, therefore, has yielded models that deal with the

behavior of individuals and how these aggregate into land use changes (Verburg

et.al., 2005). Two of the most commonly used micro-level approaches are agent-

based simulations (ABS) and micro-economic approaches. An agent is “a real or

abstract entity that is able to act on itself and on its environment; which can, in a

multi-agent universe, communicate with other agents; and whose behavior is the

result of its observations, its knowledge and its interactions with other agents”

(Sanders et al. 1997, as excerpted from Verburg, et al. 2005). Agents are given

certain motivations and other such endowments that affect their behavior and are then

set loose in an environment. ABS’s, when constructed effectively, can yield

fascinating results in the form of emergent properties, i.e., higher level effects that

cannot be observed or predicted from watching these agents in isolation. And not all

of these effects are positive. See Couclelis (1989) for an example where the micro

actions of individuals maximizing their utility collectively emerge into negative

externalities. It should be noted here that cellular automata (CA) are not necessarily

agents, though agents are automata. According to Benenson and Torrens (2005),

however, all CA fail to meet the criteria of agency since a) they do not have their own

agenda and b) they do not anticipate the future. However, if cells represent decision-

making landowners then this discussion may need to be refined further. These

characterizations concerning agents versus automata are still being debated by

38

modelers and theorists (Clarke and Couclelis, personal communication).

Nonetheless, it is contended here that most cellular automata are a form of non-agent

micro-simulation since they act individually from effects in local neighborhoods. It is

also possible to witness emergent behavior since it may not be obvious from the rules

programmed into the cells what kind of results will be produced on the macro level.

This leads to micro-economic models of land use change. These usually take on the

perspective of landowners trying to maximize profit or utility from their land.

However, as Verburg and his associates point out (2005) these models do not scale up

very well to the macro level.

Modeling Approaches

A good example of a micro-level model would be the NELUP (National

Environmental research council Land Use Programme) extension (Oglethorpe and

Calaghan, 1995). According to Agarwal et. al. (2000), this model is at a high level

of Human Decision-Making (HDM) complexity since it treats individual farmers as

micro-economic agents who are trying to maximize their profit and, in so doing,

affecting land use.

Macro-level models, on the other hand, tend to either employ macro-economic theory

or use the systems approach. SCOPE, a model that this author helped to build, is an

example of a macro-level model (Onsted, 2002). Built in STELLA, it uses a systems-

dynamics approach to model changes in jobs, population, housing, and even quality

39

of life throughout the South Coast of Santa Barbara County. What it could not do

though, which was of frustration to many audience members in a long-winded tour

amongst the populace, was allocate growth to particular locales within the South

Coast. This is, of course, a major drawback. Macro-scale models do have the

advantage, however, of incorporating more modules with greater ease since they can

be mathematically related within such frameworks as system dynamics. Which is not

to claim making them accurate and validly constructed is an easy task. In the end,

SCOPE is limited in its lack of specificity. It offers many different outputs, but none

of them are easy for a user to personalize (i.e., what will happen in my

neighborhood?) For planners, these model results, if they are to be believed, are

challenged to transform them into useful planning-relevant tools (See Couclelis,

1991).

It should be noted here, though, that not all macro-level land use models are

aspatial. For example, the Mertens and Lambin (1997) univariate spatial model

relates a series of landscape metrics to the pattern of deforestation in Cameroon and

then uses statistical correlation to make predictions about future deforestation. By

overlaying a remotely sensed image of land cover with another GIS layer of cultural

and natural variables this correlation is structured. Although highly mathematical, as

in the case of systems models, it does involve spatial inputs and outputs. This model

should not be mistaken, however, for a cellular automata, even though it uses raster

images and pixel outputs, since it employs a very different methodology.

40

There are also models that try to take the best of both worlds by integrating both

the micro-level simulation techniques as well as the macro-level simulation

techniques. This is difficult, though, for several reasons. First, if the macro-level

model is aspatial then distributing its results across a spatial field involve specious

and arbitrary decisions. Second, macro-level spatial models often have units of

measurement or resolutions that are too coarse to tease out processes that micro-level

simulations can discover. Third, micro-simulations often use too small an extent to

account for the processes existing at the macro-level and, therefore, fail to properly

account for the context in which their model is running. Fourth, it is dangerous to

aggregate separate micro-simulations together and proclaim a theory of their totality

since there may be emergent properties for which the simple sum of the sundry parts

can’t account (Verburg et. al. 2005).

A germane example of a model that attempts this integration is explained in a

paper written by Dawn Parker and Vicky Meretsky (2004). Their paper intends to

address the gap between macro-level land use change models and empirically derived

micro-level agent based models (ABM). As they point out, macro-oriented models

take such factors as number of jobs and population growth and use this to allocate a

new land use composition with new acreages listed across multiple categories.

Micro-scale models, on the other hand, have the advantage of precise spatial

allocation but when trying to account for broad macro-forces must use coarse

measures that usually fail to “capture the dynamic feedbacks between land-use

41

patterns, spatial location, and land-use composition” (Parker and Meretsky, 2004,

section 1.2).

Their model uses an endogenously derived rent for urban land based on the

quantity of developed land and its pattern. This pattern is derived from micro-scale

decisions of parcel owners. This, in turn, is then accounted for in the endogenously

generated price-mechanisms and this feeds back to the parcel agents. One of the most

innovative aspects of their model is their metrics of edge-effect externalities, which

they use to feed into the macro-level aspects of the model. Similar to SLEUTH, these

measurements take into account such things as: landscape composition, number of

patches/mean patch size, the average deviation of patch shape from the minimum

edge/area ratio (a circle), as well as others.

Though the possibility of land pattern metrics as the conducting rod between macro-

level modeling and micro-level modeling is intriguing, (Mertens and Lambin, 1997)

this dissertation required only a viable CA with which to answer its questions.

Nevertheless, there are other models that could be employed other than SLEUTH or

any CA for that matter. LUCAS, for instance, is possible because it employs

transition probability matrices (LUCAS website) (similar to SLEUTHs deltatrons

(Dietzel and Clarke, 2004)) and is spatially explicit. However, LUCAS has certain

extraneous elements for agricultural conversion purposes (like habitat impact) that

could prove cumbersome. CUF is also a good choice because it has various

42

submodels that allow for greater complexity and it includes zoning (Landis, 2001).

One of CUF’s major drawbacks, though, is it its lack of temporal complexity. “What

If?” is a GIS-based software package where users explore different development

scenarios and see the probable growth patterns and socio-economic consequences that

result (Klosterman, 2001). METROPILUS is actually a combination of Putman's

DRAM (which locates households) /EMPAL (which locates employers/employees)

along with an ArcView GIS output and is currently used in six major metropolitan

areas (Putman, 2001). CUF II, also by Landis, goes one step further with multiple,

rather than binary, land uses, allows different land uses to compete against each other

for sites, accounts for redevelopment and infill, and has been historically calibrated

(Landis, 2001). CURBA, again by Landis, is the California Urban and Biodiversity

Analysis Model (Ibid). While CUF II focuses on urban land uses, CURBA is more

concerned with the habitats in rural areas (Ibid). INDEX (developed by Criterion

Planners/Engineers) is a model that uses GIS to estimate the impact certain land use

decisions will have on community indicators (Allen, 2001). Finally, Li and Yeh

(2000) used a constrained CA they developed themselves to model sustainable urban

development in China, particularly the preservation of prime farmland. It is

interesting to note that different regulatory regimes may be better suited to different

approaches. For instance, China enjoys more centrally controlled planning and,

therefore, could have a better chance of implementing from the top-down the

sustainable ramifications the model suggests. In the United States, on the other hand,

land use planning is so decentralized that each locality suffers from a certain tragedy

of the commons, where each local government must invest in the conviction that

43

compact development and preservation of prime farmland, even when inconvenient,

will be to everyone’s benefit, including those not under its jurisdiction. Despite these

tempting alternatives then, SLEUTH, as an explicit HDM agricultural conversion

model, is more than adequate for the task at hand.

Kuminoff and Sumner’s “Modeling Farmland Conversion with GIS Data” (2001) is

also an approach worth expanding. In their study, they used both econometrics as

well as GIS tools to discover what SLEUTH (Silva and Clarke, 2002; Clarke and

Gaydos, 1998; Clarke et. al., 1997) bears out in a methodical fashion: the greatest

predictor of agricultural conversion is proximity to the edge of urban lands as well as

population growth. It has little to do with aggregate stock values of land. This shows

the importance of geographical specificity when conducting policy analysis,

particularly land use policy. In other words, geography matters. Of the six variables

they analyzed in their search for positive correlations with agricultural conversion,

the economic variables either showed negative or negligible correlation. They also

wrote that local zoning laws seem statistically irrelevant to the nature of urban

expansion and agricultural conversion17. SLEUTH, also, has been validated without

regard to specific zoning laws, apart from lands that are completely off limits (i.e., an

excluded layer). Instead, it relies upon organic growth rules that are universally

applicable to any region (Silva and Clarke, 2002; Clarke and Gaydos, 1997; Clarke

and Gaydos, 1998). The great success of SLEUTH’s applicability both domestically

and internationally speaks well for its employment in this project but also discredits

44

the idea that zoning, with its ad-hoc relevance, has any long-term effect on the shape

and direction of growth (SLEUTH’s operational rules are discussed at greater length

in subsequent chapters). As those who study such matters can attest, zoning is either

changed to fit the political exigencies of the time or, even more simply, exceptions

are made for convenience without any change to the master plan. Well-meaning

though they are, zoning efforts are no match for the forces of urban growth and

sprawl. A simple test of this fact can be made by examining old master plans for a

community and comparing these with what is found there today; many places zoned

for agriculture or other less intensive land uses are now either residential

neighborhoods or big box malls. The only zoning classification that SLEUTH

previously incorporated is outright protection, i.e., an excluded layer such as a park or

other easement. This project explores a more nuanced excluded landscape based on

the particular nature of the Williamson Act.

As for Kuminoff and Sumner, it is unclear how their development restrictions

quantitatively took the Williamson Act into account. Although there could be many

reasons why aggregate numbers could lend the appearance of impotence to the Act,

details must be explored spatially before it can be determined whether or not

restrictions are making a difference, and in what way.

17 Although they provide caveats to this surprising claim. See Kuminoff and Sumner, 2001.

45

This dissertation picks up where they left off. In their conclusions section they

remark: “The importance of edge effects as a determinant of farmland conversion and

of increased urbanization may be of particular interest to city planners and farmland

preservation organizations.”

This call to additional investigation is answered in this project since their research

lends credence to the assertion that no single factor is as significant in farmland

conversion than propinquity. This dissertation demonstrates this by showing the

feedback between urban growth and Williamson Act termination. The cellular

automata platform, in this case SLEUTH, has been indispensable in accomplishing

this.

SLEUTH Applications

Since SLEUTH is indeed the CA being used for this project, it is important to render

it in a broader context of modeling via a framework. C. Agarwal, et. al. (2000) have

designed just such a framework that compares land use/ land change models across

three dimensions: time, space, and human-decision making. These three dimensions

are ascribed two attributes: scale and complexity. Time scale refers to both time step

and duration. The time step is the shortest period of time between observable

phenomena. In SLEUTH’s case this would be one year. The duration is the length of

time that the model runs. The space scale refers to resolution and extent: SLEUTH’s

46

resolution is one cell size (for example, 30 meters by 30 meters) and the extent is the

area being modeled (the South Coast, for example).

These concepts are relatively easy to digest. Agarwal et. al., though, proposed

something new in the examination of the human-decision making: “agent” and

“domain.” (p.5) These terms are meant to be analogous to resolution/time step and

extent/duration. The agent is the unit that will be making decisions and the domain is

the universe in which the agent can act. For instance, in SLEUTH, the agent (though

not necessarily a “true” agent as discussed on page 39) is a cell and the domain would

be the zone being examined.

Now that the attribute of scale across the three dimensions is understood attention

should be focused on complexity, which, across time, is measured by the number of

time steps and model duration. The greater the number of time steps and the longer

the duration, the greater the complexity. Also, the highest complexity involves time

lags and feedback. SLEUTH has many time steps and a long duration (but no time

lags) so it gets a fairly high score on this dimension. Spatial complexity is measured

by how “spatially explicit” (p. 2) a model is. Spatial interaction, where one agent (or

cell in this case) is influenced by neighboring agents, is the most complex and this is

what is seen in SLEUTH.

Human-decision making (HDM) complexity spans the range from none (like in the

case of a systems dynamics model) all the way to high complexity where not only are

47

the agents making decisions within their domains but multiple agents can aggregate

their behaviors to affect higher level processes and higher level properties affect the

HDM of the lower-level agents. SLEUTH is relatively low on this scale since it does

not explicitly account for HDM. Rather, cell behavior is determined through Monte

Carlo simulation based on neighboring cells. Although all urban development

implies HDM, some models are more overt in this accounting than others.

The authors state “the ultimate goal of human-environment dynamic modeling (is to

be) high in all three dimensions.”(p. 10) With this in mind, there are several desirable

traits that an agricultural land conversion model should have. Most of these are

already contained in SLEUTH which, by itself, would be relatively adequate for

modeling farmland conversion. However, the modifications undertaken in this

research now more precisely account for the fluid nature of agricultural protection

policies and how they are shaped by farmers’ choices. It is further averred that these

modifications have improved the model in several ways.

First, and most importantly, SLEUTH is imbued with greater spatial complexity since

each county undergoes an additional modeling cycle analogous to an urban growth

run: Williamson Act termination and spread. SLEUTH currently has an excluded

zone that is off limits to development but this zone is static throughout the duration of

the model run. Although SLEUTH currently has the capacity to have “weighted

resistance” to development this still must be programmed in by the user and is

difficult to relate to policy (Dietzel and Clarke, 2004). The greater spatial complexity

48

results from addition of new rules. Cells that are currently in the Williamson Act are

excluded. However, the proximity of urban edges influences these cells to leave the

Williamson Act. Once they leave, they are then subject to the same phenomena that

the original SLEUTH model bears (Slope, Land use, Exclusion, Urban,

Transportation, Hillshade).

The greatest improvement made in SLEUTH is the addition of HDM. During the

Williamson Act modeling (see Chapter 3 for more details), cells reflect the individual

decisions of landowners to leave the Williamson Act, which needs no approval from

government agencies. These agents simulate HDM by acting in their own interests to

leave the Williamson Act, often in the hopes of selling their farmland to developers.

This builds the pathway to this research’s most important contribution to knowledge:

a technique for not only forecasting future differential assessment landscape’s (such

as the WA) but also a way of using these forecasts to construct probabilistic excluded

layers for use in traditional urban growth models.

SLEUTH is not an untested application. In fact, it has been employed by researchers

all over the world: from Albuquerque, New Mexico to Yaounde, Cameroon

(Gigalopolis website). The use of SLEUTH to forecast changes in the Williamson Act

builds upon the work of these innovators. The model was designed by Keith Clarke of

UC Santa Barbara, who was inspired by the modeling of fire spread. His partnership

with Len Gaydos of USGS, along with others who helped write the code, allowed for

SLEUTH to not only become well-known amongst researchers but amongst decision-

49

makers at all levels of government. National television even displayed some of

SLEUTH’s results on programming concerned with the explosion of urban sprawl

across the American landscape. Nevertheless, and much to SLEUTH’s credit, an

exhaustive accounting of its numerous applications is not necessary since they have

varied more along a geographic dimension rather than a methodological one. Also, a

full explication of SLEUTH’s operational underpinnings will take place in Chapter 3.

What is prudent, though, is a tour of the model’s evolution and how various

innovations by both practitioners and designers have laid important groundwork for

the work realized in this dissertation.

As of this writing there have been at least 32 cities or regions around the world that

have benefited from SLEUTH forecasts. Though for the most part SLEUTH has been

applied as a straightforward urban growth or land use change model (it can be used in

either capacity), there have been those who have created new uses for SLEUTH while

applying them to their region.

Arthur et al., for instance, coupled SLEUTH with an urban runoff model when

running simulations for Chester County, PA (2000; Arthur, 2001). Specifically, the

land use change portion of SLEUTH was run and each year of land use output was

then used to generate urban runoff responses. However, this coupling, rather than

being mutual, is actually unidirectional since these runoff impacts do not inhibit or

encourage urban growth. Nevertheless, Arthur’s work is considered one of the first

couplings of SLEUTH with a physical module capable of exploring land use change’s

50

impact on the environment. Arthur also took inspiration from SLEUTH’s Monte

Carlo uncertainty outputs and claimed that, ideally, a similar probability grid for

evapotranspiration and stormwater runoff could be created. However, she did not

fully implement a tenable methodology for Monte Carlo incorporation and was

therefore left with choosing only the final map output for the final Monte Carlo

simulation as input for her hydrology module or using coarse coupling methods that

made aggregate use of the Monte Carlo runs. As will be seen later, this dissertation

discovers a way to use probabilistic Monte Carlo simulations for its Williamson Act

termination landscape.

SLEUTH has also been coupled with other urban growth models in order to more

accurately determine biodiversity loss from various growth scenarios. Cogan et. al.,

(2001) compare urban growth forecasts amongst Landis’ model (California Urban

Futures or CUF), SLEUTH, and a coarse simplistic model (GAP boundary) to

determine differences in threat to both species and habitat. This work is important

not only for its ability to tease out important planning ramifications of model choice

but also for illustrating the importance of scale selection in both data input and model

output during result interpretation. Again, these two models are more loosely

coupled than tightly bound since SLEUTH urban growth and land use change are

used not to alter the operation of CUF but to examine its effect on CUFs attributes,

e.g., species habitat. Though more a combination of data than code, this effort did

much to demonstrate the benefits of data combination. This dissertation assembles

51

data from several different sources as well, which will be explained further in Chapter

3.

Solecki and Oliveri (2004), similar to Arthur, coupled SLEUTH with air quality

impact assessment models. However, in this case, they downscaled several narrative

scenarios garnered from the Intergovernmental Panel on Climate Change’s Special

Report on Emissions Scenarios (IPCC’s SRES) to create SLEUTH’s growth

parameters. Broken down into a pessimistic scenario (replete with increasing reliance

on automobiles for transportation and low-density sprawl) as well as a more

optimistic alternative (infill, higher-density, less reliance on automobiles) these two

possibilities drove two different parameterizations of SLEUTH. There was also a

third, based on SLEUTH’s normal calibration routine, resulting in a business-as-usual

forecast. These maps are then output as storytelling components to the SRES in the

New York Metropolitan region. Though the authors admit the difficulty in using

macro-scale narrative descriptions to adjust parameters on a local scale, (the possible

specious reasoning plaguing such attempts is discussed above) nevertheless SLEUTH

provided an appropriate tableau on which to realize visually and locally the various

futures of the IPCC. For instance, the authors used SLEUTH’s probabilistic

Excluded Layers in order to decrease infilling for the sprawl scenario. Though they

did this through trial and error rather than rigorous calibration, these sort of dynamic

excluded probabilities can be approached more systematically, which will be

discussed further in Chapter 3.

52

Porto Alegre City, Brazil was used by Leao et. al. (2004) to couple SLEUTH with a

multi-criteria model of landfill suitability assessment. The authors here make an

argument that SLEUTH’s CA functionality answers Couclelis’ (1997) observation

that GIS and urban models have a theoretical gulf between them that makes

interoperability problematic. This boils down to different constructs of space: GIS

uses absolute space and urban models use relative space. Couclelis suggests that

proximal space could bridge this gap with its strong use of neighborhoods (as found

in CA) as they are invested both with properties of absolute and relative space.

Their methodology makes use of SLEUTH’s ability to not only forecast an amount of

urban growth but also its spatial allocation component. This allows the waste

disposal submodule to output an amount of landfill needed to serve a population at

any given time but SLEUTH’s results also help to decide where to locate these new

sites. They have even integrated the NIMBY phenomenon along with more common

distance decay parameters (transport costs) for this suitability analysis.

Faculty and students at UC Santa Barbara have been on the forefront of not only

innovative uses of SLEUTH but also its experimentation and improvement. The most

important endeavor in that regard is the addition of the Land Use Change Modeling

component. With their use of “deltatrons,” Candau and Clarke (2000) took SLEUTH

from a binary urban growth model to a system that can simultaneously predict urban

growth and the change of one land use class to another (A full diagnostic of

SLEUTH’s functions will follow in the next Chapter). Herold et. al., (2003) also

53

incorporated into SLEUTH greater and more varied spatial metrics from which to

evaluate model fit with past data. They also provide a more sophisticated and caveat-

laden discussion of the proper use (and misuse) of metrics and their sensitivity to both

temporal and spatial scale. They even suggest the possibility of using metrics as a

policy tool for intelligent growth, once a better understanding is gained from current

research. SLEUTH was even used to more accurately understand past growth, rather

than forecasting future growth (Goldstein, et. al., 2004). The authors also compared

SLEUTH against an agent-based simulation to examine which bore the greater utility

in understanding past growth. They determined that SLEUTH (as representative of a

CA) had greater flexibility than an ABS, though the ABS approach also proved

useful.

As for the future use of and research in SLEUTH and other CA, there are already

emerging realms of inquiry. First, since model calibration (discussed at length in

Chapter 3) is as much art as science this makes systematization difficult (Benenson

and Torrens, 2005). Many are at work to remedy this (Dietzel and Clarke, 2004). The

lack of agreed upon conventions of metric evaluation is, in this author’s opinion, the

major compounding factor. Other novel uses include the injection and substitution of

“urban objects” into the field (Benenson and Torrens, 2005). These can include

houses, streets, or other objects of infrastructure and can alter the properties of

surrounding cells and even alter their shape. The agglomeration of cells together with

the same properties is another field of budding research. This is of particular use

when using CA at the cadastral level where parcels are of different size and shape

54

(Sembolini, 2000). Though this particular approach was not used here, due to

technical limitations, it could be of great use in the future. For instance, by directing

all cells of a parcel to leave the WA at the same time (or to join it) a more accurate

calibration is achievable as well as a more accurate forecast. Future efforts, which

this author intends to undertake, will hopefully utilize such cutting edge

methodology.

By broadening the use of space, Benenson and Torrens (2005) suggest the possibility

of employing CA at different spatial scales. This would allow different phenomena

and states to be reactive according to different sized neighborhoods. By redefining

the use of time, asynchronous CA, where each cell runs on its own time clock and not

all cells are available for state changes simultaneously, has the potential to greatly

influence the results of an otherwise synchronous CA and, in fact, may lead to greater

possibilities of emergence (Ingerson and Buvel, 1984; Schonfisch and de Roos,

1999).

Though this project builds upon all of the creative and innovative struggles of those

that preceded it there is one, in particular, upon which this undertaking most strongly

builds. Michel B. Tietz, Charles Dietzel (of UCSB), and William Fulton, under the

auspices and funding of the Public Policy Institute of California (PPIC) wrote a report

entitled, “Urban Development Futures in the San Joaquin Valley” (2005). By

consulting planners and decision-makers, the team designed four different scenarios

that were then run in SLEUTH. The scenarios were: 1. Accommodating Urban

55

Development, which is the baseline scenario. 2. Prime Farmland Conservation 3.

High-Speed Rail and 4. Automobile-Oriented Growth.

Though this dissertation does not explore transit-based scenarios it does examine

alternative farmland futures, though with a different methodology. Their Prime

Farmland Conservation alternative simply excludes all Prime Farmland from

development, without taking into “explicit account…such factors as zoning, local

growth control, or other public policy.” (p. 42) However, this reflects no shortcoming

of the team but the nature of SLEUTH itself. This CA uses organic growth rules in

order to predict future urban growth and land use change and the only regulations or

zoning for which it accounts are excluded lands, i.e., lands off limits to development.

Though, as discussed earlier, there is a functionality to create probabilistic, rather

than binary, excluded layers there is no easily quantifiable correspondence between

zoning and the creation of these probabilities.

Teitz et. al. also did not take into account the Williamson Act protected lands, though

they comprise tremendous acreage. Nevertheless, the team indeed calibrated the San

Joaquin Valley with past data and, therefore, their approach is sound. However, by

adding not only a richer, excluded, dataset but also an approach and use of SLEUTH

that allows for a projected change in Williamson Act lands, this dissertation can offer

a more sophisticated analysis of not only urban growth and land use change, but

alternative futures of protected farmland itself. Since policies similar to the

Williamson Act are by no means endemic to California, this dissertation can offer this

56

nascent yet effective method of dynamic excluded layers for use by others, to more

accurately reflect the possible urban growth landscape.

This dynamic excluded layers approach, being more time intensive than the static

approach, can only be justified if it produces more accurate results. A formal

comparison, then, between the work performed in the PPIC Report (2005) and this

dissertation is offered in the following Chapter. County by county, the legitimacy

and efficacy of this methodology against that used by the PPIC team will be

demonstrated.

57

CHAPTER 3: Methods and Data

This undertaking required not only a great deal of data but also a great deal of time to

effect as well. After the data were collected and rendered they had to be tested and

repaired when necessary. The lengthy process of calibration, and in some cases

recalibration, was then conducted before prediction could take place. After

prediction the results were analyzed and the writing process began. By far, though,

the greatest amount of time spent was on the collection and rendering of the data, so

it is that phase which should be addressed first in this chapter.

Data Background

The amount of data necessary to conduct this research should not be surprising since

most models are data-hungry and the quantity and quality of data can greatly

influence results (Lee, 1973). Therefore, before setting out in earnest to collect these

data, the different counties of California were explored and the author considered not

only the possible data they had to offer, but the particular status of their WA

programs and the growth pressure they have been under in past decades. The first

criterion was GIS shapefiles that offered fields relevant to the Williamson Act. There

was great variability amongst counties along these lines so this exploration led to a

selection of Tulare, Stanislaus, and Merced Counties. All three are in the Central

58

Valley, which has become the new locus of population growth in California, drawing

would-be homeowners priced out of the coastal market as well as newcomers from

out of state (Teitz, et. al., 2005). Also, all three counties had comprehensive data

concerning the Williamson Act, as was discovered from an initial exploration of the

Williamson Act data collected by Dr. Charles Dietzel. Though these initial

examinations were not as valuable as Assessor’s data, they were actually culled from

the same database and therefore gave an approximation of the kind of temporal and

spatial detail that might be expected from actual Assessor’s data. There were more

than three counties that fell into this category within the Central Valley. However,

the final three were settled on because a) Tulare County contains Farmland Security

Zones (FSZs), allowing a geographical examination of this new option, which many

counties have not yet employed; b) Merced County has only recently adopted the

Williamson Act and so urban growth before the Williamson Act as well as after could

be observed; and c) Stanislaus County has no Farmland Security Zones but has

employed the Williamson Act for decades and also shares a long border with Merced

County, allowing a contrast between one county in the Williamson Act and one

county not. Other counties in the Central Valley were not chosen because they did

not offer comprehensive data regarding the WA, either in format or content. By

gravitating towards those counties with more meticulous data and sophisticated

methods of recordkeeping, such as GIS shapefiles instead of paper maps, it is possible

the results could be biased. Therefore, although it is not suggested the results from

this dissertation are meaningless for other counties or regions employing the WA,

every region and jurisdiction has its own “digital DNA” and therefore variability in

59

results should be expected. Nonetheless, though the results may vary across

geographic regions, it is here averred that the approach offered in this research and

the difference between scenarios has relevance for not only any county in California

employing the WA, but any area utilizing differential assessment programs as tools

for land conservation.

Data Acquisition

Since the Land Use module of SLEUTH is being used for the purposes of this

dissertation the requisite land use data was collected. Though land can, of course, be

classified along a number of dimensions the Williamson Act orients itself with a

concern towards farmland typologies so likewise has the landscape in this manner

been classified according to farmland production. This was made more convenient by

the fact the California Department of Conservation (DOC) not only offers an online

repository of shapefiles classified by farmland types, found with its Farmland

Mapping and Monitoring Program (FMMP), but they also administer the Williamson

Act itself (CA DOC, 2006). By using this FMMP classified data, therefore, the risk of

classifying lands in categories that are not recognized by FMMP or are not

compatible with the rubric set forth in the Act itself, e.g., Prime and Non-prime, etc.

was eliminated.

The FMMP shapefiles exist biennially, generally from 1984 to 2002, the latest year

used in this research. Tulare County, however, only has data reaching back to 1986

60

Once all of the shapefiles were collected for the years 1984 through 2002, at biennial

intervals for all three counties, one could see urban growth throughout the years and

its conversion of surrounding farmlands. By creating a PowerPoint slide with each

map layer occupying the same place on each slide, an animation of sorts was created,

which is an excellent canvass for exploring map data with the most powerful tool

available, human eyes (Clarke, personal communication).

Since this research models significantly into the future it should also be based on

actual data reaching significantly into the past. Therefore, in this research, SLEUTH

runs as far into the future as data reaches into the past, realizing a certain temporal

symmetry. This is not based on the assumption of linear rates of change. If that

were the case then a simple regression model would be all that’s needed to forecast

growth. Instead, this temporal symmetry is needed to control for uncertainty. The

further one either hindcasts or forecasts from one single data point at the present, the

greater the uncertainty (Goldstein et. al, 2004). This uncertainty can be mitigated,

however, by using multiple data points as well as by modeling into the future

moderately (Ibid). These concepts are best exemplified in figure 3-1 below,

borrowed from Goldstein et. al., (2004). The reader should notice less uncertainty

over the same time period in Fig. 3-5b, due to the presence of multiple data points.

The area between data points is curved to account for the gradual increase in

uncertainty that occurs when leaving one data point but approaching another. Since

there are no data points in the future the uncertainty increases linearly and is not

attenuated by approaching data points.

61

Fig. 3-1: Uncertainty in urban models over time. In (a), only data for the present are included. For (b), three historical data sets are included in the modeling. (Figure and caption from Goldstein et. al., (2004))

Therefore, to create another even more distantly past layer for these counties, data

provided by Charles Dietzel as part of a Public Policy Institute of California (PPIC)

project (Teitz et. al., 2005) was attended to. The details of their data collection

methods can be found in their report (Ibid). However, their data provided a) the road

layers necessary to run SLEUTH, b) the DEM providing Slope information and c) the

Hillshade layer for display purposes. Dietzel also provided an urban layer dating

back to 1974, but no land use. Easier comparisons between results is another

advantage that lay in using the same data as the PPIC team. Of course, FMMP data

was mixed with their data in order to allow for the exploitation of the Land Use

portion of the model.

62

Land use is only half of the story to be told in this dissertation, though. The other

half concerns outlining which lands are in the Williamson Act and which are not, a

characteristic abstruse for even the most powerful satellites. By exploiting a

combination of Assessor’s data as well as Department of Conservation files, a portrait

of present and past Williamson Act landscapes could be painted (See Map 4-3 for an

example). This was accomplished by matching Assessor Parcel Numbers (APNs)

between the documents and the Assessor’s database. This record was then edited to

reflect a) whether the parcel is or was in the WA b) the parcel’s entry date into the

WA c) the parcel’s exit date from the WA and d) the parcel’s reason for exiting the

act. The documents declare a number of various mechanisms by which a landowner

may exit the Act. These sundry methods were aggregated into three main categories.

First, and most common, was non-renewal. This aspect of the Act, discussed in

Chapter 2, allows landowners to phase out their enrollment over a nine-year period.

During this time, as they are slowly ramped back up to normal taxing levels, they still

may not develop their land. The second, less common for number of parcels but

sometimes comprising tremendous acreage, is what has been termed by the author

“involuntary removals.” These include cases of eminent domain, public acquisitions,

rescissions, and other singular incidents that consist of the government brokering a

termination of Williamson Act enrollment for the property. The last category is

cancellation. This is initiated on the part of a landowner who is unwilling to wait

nine years in order to develop his or her land. These landowners must pay an

enormous fee but are rewarded with instant freedom from the Act’s restrictions.

63

Many APNs listed on the older State documents no longer exist in current Assessor’s

rolls. This mismatching increased the further back in time the documents reached.

Of course, this should be expected since county assessor’s offices often must, for

either new bookkeeping reasons or, more often, for reasons of parcel subdivision,

revise APNs. Attempts were made to track down these missing APNs.

Unfortunately, despite being availed of all the resources at the various Assessors’

offices, there remains a number of APNs that could not be located. In total, there

were 3938 acres of former Williamson Act lands in Tulare County unaccounted for

and 10,582 acres in Stanislaus County that could not be found. Merced County,

being new to the Act, had no missing APNs.

This underestimation, unfortunately, affected the Former Williamson Act (FWA)

model runs. More FWA blobs would mean greater possibilities for additional

growth. Therefore, even though the model runs suggest a great deal of parcels

leaving the WA in the future, if all of the FWA were tracked and integrated into input

data there would be even greater termination of contracts. It is somewhat safe,

however, to assume the missing acreage is located mostly near urban areas since

APNs are often revised due to either subdivisions or other lot-line adjustments taking

place (See Chapter 4). Therefore, since much of the recovered missing acreage lies

on currently developed land then it is likely that many of these unaccounted for

parcels are also inside city limits. Because these areas tend to already be bereft of

current WA contracts, then much of the ability of these parcels to encourage WA

termination may be greatly decayed by distance. Therefore, it is assumed that these

64

missing acres, if found, would not radically alter the model results, particularly since

their location may remain a permanent mystery. When examining maps of former

Williamson Act lands shown in Chapter 4, though, this missing acreage should be

kept in mind. Nonetheless, with the Assessor’s data edited properly for both counties

the already assembled land use data was ready to be combined with the Williamson

Act data.

Data Rendering

FMMP data and the Assessor’s data from all three counties were in different

coordinate systems and projections. Therefore, the Assessors’ data, which were all in

the State Plane projection, were converted into the California Albers Coordinate

projection used by the FMMP as well as the PPIC team for their data. Of course, the

Assessor’s layers do not line up perfectly well but with a little spatial adjustment they

were more than adequate (See Chapter 4 for various maps displaying these overlays).

Once this was accomplished the data could be displayed. This allowed different

patterns of both urban growth and Williamson Act change to become apparent.

Figure 3-2, FMMP Definitions of Important Farmland Categories (CA DOC)

Important Farmland Categories

About 90% of the FMMP's study area is covered by US Department of Agriculture (USDA) modern soil surveys. A classification system that combines technical soil ratings and current land use is the basis for the Important Farmland Maps of these lands. In areas where no soil survey is available, a series of Interim Farmland definitions have been developed to allow land use monitoring until soils data becomes available.

65

http://www.consrv.ca.gov/DLRP/fmmp/pubs/soil_criteria.pdf

IMPORTANT FARMLAND MAP CATEGORIES

The colors and letters above are used to depict categories described below. The minimum mapping unit for all categories is 10 acres unless specified. Smaller units of land are incorporated into the surrounding map classifications. Prime Farmland (P) Farmland with the best combination of physical and chemical features able to sustain long term agricultural production. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date. Download information on the soils qualifying for Prime Farmland. More general information on the definition of Prime Farmland is also available. Farmland of Statewide Importance (S) Farmland similar to Prime Farmland but with minor shortcomings, such as greater slopes or less ability to store soil moisture. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date. Download information on the soils qualifying for Farmland of Statewide Importance. Unique Farmland (U) Farmland of lesser quality soils used for the production of the state's leading agricultural crops. This land is usually irrigated, but may include nonirrigated orchards or vineyards as found in some climatic zones in California. Land must have been cropped at some time during the four years prior to the mapping date. Farmland of Local Importance (L) Land of importance to the local agricultural economy as determined by each county's board of supervisors and a local advisory committee. Download a complete set of the Farmland of Local Importance definitions in PDF format. Grazing Land (G) Land on which the existing vegetation is suited to the grazing of livestock. This category was developed in cooperation with the California Cattlemen's Association, University of California Cooperative Extension, and other groups interested in the extent of grazing activities. The minimum mapping unit for Grazing Land is 40 acres. Due to variations in soil quality, smaller units of Grazing Land may appear within larger irrigated pastures. Urban and Built-up Land (D) Land occupied by structures with a building density of at least 1 unit to 1.5 acres, or approximately 6 structures to a 10-acre parcel. This land is used for residential, industrial, commercial, construction, institutional, public administration, railroad and other transportation yards, cemeteries, airports, golf courses, sanitary landfills, sewage treatment, water control structures, and other developed purposes. Other Land (X) Land not included in any other mapping category. Common examples include low density rural developments; brush, timber, wetland, and riparian areas not suitable for livestock grazing; confined livestock, poultry or aquaculture facilities; strip mines, borrow pits; and water bodies smaller than forty acres. Vacant and nonagricultural land surrounded on all sides by urban development and greater than 40 acres is mapped as Other Land. Beginning in 2002, the pilot Rural Land Mapping Project provides more detail on the distribution of various land uses within the Other Land category in four San Joaquin Valley counties.

66

http://www.consrv.ca.gov/DLRP/fmmp/mccu/prime_soils.htm


http://www.consrv.ca.gov/DLRP/fmmp/overview/prime_farmland_fmmp.htm



http://www.consrv.ca.gov/DLRP/fmmp/pubs/Local_definitions_00.pdf

http://www.consrv.ca.gov/DLRP/fmmp/pubs/Local_definitions_00.pdf

http://www.consrv.ca.gov/DLRP/fmmp/rural_land_mapping.htm

Water (W) Perennial water bodies with an extent of at least 40 acres. The main purpose for creating these maps initially was to explore them visually.

Since this project began with a simple interest to explore maps such as these, it was

helpful to finally examine the dynamic interplay between urban growth and

Williamson Act change. A series of gifs were created from the GIS project that

corresponded to the two-year intervals used by the FMMP (First and last years are

displayed in Chapter 4). For the now edited Assessor’s database, snapshots were

captured for each biennial period for the Williamson Act as well. These display both

the parcels that are in the WA, the parcels that are not, as well as those parcels that

were formerly in the WA. It should here be noted that the Assessor’s data for each

County reflects the year it was made and, therefore, in these maps from the past the

parcel outlines are not entirely accurate. In general, the further back in time from the

date of the Assessor’s data the fewer number of parcels actually existed. It was

deemed prohibitively difficult to find old Assessor’s maps that have been digitized, if

any even exist. Also, in those cases where subdivision has taken place on a former

WA parcel, all of the constituent parcels are treated as former WA parcels.

Though animation is difficult to convey in the pages of a dissertation, in this research

the gifs were carefully placed in a PowerPoint presentation so they lined up exactly

with each other. Then, by advancing through the presentation the maps would

animate and one could see the changes taking place. As the next Chapter illustrates,

it appeared visually that, indeed, those parcels leaving the WA were not randomly

distributed (Clarke, personal communication) and that, in fact, a case could be made

67

that the same phenomena cited by Clarke et. al. (1996a, 1996b, 1997, 1998) as the

causal factors in urban growth (Slope, proximity to urban areas, proximity to roads)

are also relevant for the spread of former Williamson Act (FWA) lands. The

differences in degree that each factor has on FWA parcels can, of course, be

discovered and quantified with SLEUTH. Armed with properly rendered data it was

time to delve into the modeling proper.

Modeling

The most time-intensive aspects of SLEUTH, and indeed in much of modeling, are

the data acquisition, rendering, and input. Since SLEUTH accepts data only in the

form of grayscale GIFs, each GIF must be derived from grids all in the same

projection and with the same number of rows and columns. In order to achieve this

all the necessary layers needed to be built. Tables 3-1 and 3-2 below display all of

the different GIFs used. The organization of the table has been borrowed from Teitz

et. al., (2005).

Table 3-1: WA Modeling Layers

Data Type Source Collection Method Description Resolution

Slope California Spatial Information Library (www.gis.ca.gov) (As provided by Dr. Dietzel)

Clipped and reclassified from San Joaquin for Tulare and Stanislaus / Merced

Digital Elevation Map that provides Slope information for one time period

30 m2

Land Use County Assessor’s data and DOC documents

Editing of Assessor’s data with DOC documents and research at Assessor’s offices

Offers 9 different WA status classifications for two time

+/- 40 feet

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http://www.gis.ca.gov/

periods for 3 counties

Urban FMMP and USGS (offered by Dietzel) and Assessor’s data (for WA lands)

Aerial Photography (FMMP) Download (Me). MSS Imagery for USGS. Editing of Assessor’s data with DOC documents and research at Assessor’s offices.

Urban areas and Former WA lands are joined together to act and grow as an urban layer

1.5 buildings / acre for FMMP 30 m2 for USGS

Excluded Assessor’s data Areas not in WA are marked via editing of Assessor’s data

Lands not in the WA

+ / - 40 feet

Transportation Paper maps from Earth Science and Map Library or UC Berkeley. (Provided by Dietzel)

Subtraction from current Cal Trans GIS layer via date matching

Road networks for 4 time-periods classified according to accessibility

100 m2

Reciprocal Excluded Layer Background (Instead of Hillshade)

California Spatial Information Library (www.gis.ca.gov) (Provided by Dietzel) Assessor’s data for WA Lands.

Downloaded by PPIC team and then provided by Dietzel. Then clipped and reclassified from San Joaquin for Tulare and Stanislaus / Merced

This begins as a typical Excluded Layer (Publicly owned lands) but it also has WA lands accounted. It is overwritten with a probabilistic WA removal landscape that is then used for the urban runs.

100 m 2 for Publicly owned lands + / - 40 feet for WA lands

Table 3-2: Urban Modeling Layers Note: Originally aggregated by PPIC team in one-degree blocks. Then merged for all of California then clipped for San Joaquin Valley. Then clipped again and reclassified for both Tulare and then Stanislaus / Merced. Year Source Collection Method Description Resolution Slope California Spatial

Information Library (www.gis.ca.gov) (As provided by Dr. Dietzel)



30 m2

Land Use FMMP Aerial Photography Offers 6 10 acre

69



(FMMP) Download (Me)

possible Land Use classes for each county for two time periods

MMU

Urban FMMP and USGS (offered by Dietzel)

Aerial Photography (FMMP) Download (Me). MSS Imagery for USGS

Displays urban areas for the three counties for four time periods

1.5 buildings / acre

Excluded California Spatial Information Library (www.gis.ca.gov) (Provided by Dietzel) Assessor’s data for WA Lands.

Downloaded by PPIC team and then provided by Dietzel and WA. For calibration, 2002 public lands and WA lands used. For prediction, excluded landscape created through WA modeling module

Publicly Owned Lands for one time period as well as WA lands for calibration. Publicly owned lands and probabilistic modeled WA landscape for prediction

100 m 2 for Publicly owned lands + / - 40 feet for WA lands

Transportation Paper maps from Earth Science and Map Library or UC Berkeley. (Provided by Dietzel)

Subtraction from current Cal Trans GIS layer via date matching

Road networks for 4 time-periods classified according to accessibility

100 m2

Hillshade California Spatial Information Library (www.gis.ca.gov) (As provided by Dr. Dietzel)



30 m2

There are two tables displayed above, rather than one, because there are two aspects

being modeled. First, WA removal is simulated, and then the results are used to

create a probabilistic excluded layer for the urban growth and land use modeling

effort.

70



By examining the assembled maps and displaying simultaneously the growth of

development and the spread of Former Williamson Act (FWA) parcels it became

clear that the spread of FWA parcels, rather than being random, actually appeared to

follow a similar pattern to urban growth. In particular, proximity to urban areas and

transportation corridors appeared to have strong correspondence with leaving the Act

(See Maps in Chapter 4). Therefore, it is justified to use SLEUTH in order to

calibrate the data and offer a metrics output that would quantify what was being

observed. A score comparable to an urban growth run in the same area would

indicate a similar response to the urban growth stimuli of slope, urban areas, and

transportation corridors. For instance, Teitz. et. al., (2005) yielded a high Lee-Sallee

score of .32958 in their coarse calibration of Tulare County (See Figure 3-3 below).

Figure 3-3: The Lee-Sallee index (excerpted from Clarke et. al., 1996) shown below measures the average difference of growth between the control data years and the simulated years. This is specifically expressed by the ratio: the average of the intersection of known urban extent and simulated urban extent over the average of their union.

An FWA coarse calibration conducted in this dissertation yielded a Lee-Sallee high

of .44139, significantly higher than theirs (See Tables 3-4 and 3-5). However, it

should be noted that, in order to realize an accurate simulation of SLEUTH’s edge

effect, all Former WA lands as well as urban lands were treated as one monolithic

71

class. This was done in order to faithfully recreate the conditions that appear to

influence FWA spread. Since proximity to urban areas is one aspect that has been

asserted to indeed be important in FWA spread then the only way to model this,

within the confines of SLEUTH’s architecture, is to treat them the same. In this way,

new FWA lands can be created near urban areas as well as other FWA lands. This,

of course, affects the results, but refusal to combine them enervates urban areas’

effect on the creation of FWA parcels.

In a sense, the author has sought to fool SLEUTH into thinking it is simply modeling

urban growth and land use during the WA runs. But instead of modeling urban

growth it is modeling FWA spread. It would take a great deal of time to actually alter

SLEUTH’s underpinnings in such a way as to avoid the unfortunate necessity of this

conflation. Nevertheless, the combination of these two elements allows for a full

expression of SLEUTH’s growth rules. It should also be made clear that there is a

radically different Excluded Layer in use for the Williamson Act run (See Figures 4-2

and 4-9). In this case, only Williamson Act lands are available for FWA spread and

all other lands (including urban lands) are off-limits. Consequently, this can be

thought of as a “reciprocal excluded layer” since it is somewhat complementary with

the more traditional excluded layer (see Chapter 4) used in an urban growth

simulation. All those Williamson Act lands unavailable for development in the urban

growth simulation are, in this case, the only lands available for spread. By examining

output images in Chapter 4 it can be seen that FWA lands have spread tremendously.

72

Although Lee-Sallee was employed in order to compare metrics with the PPIC Report

(Teitz, et. al., 2005) a different metric was actually used to select between calibration

rounds. In fact, PPIC co-author Dr. Charles Dietzel offered a workshop whereby he

suggested using a product of seven of the built-in metrics and so it was decided to

utilize his research in this project ((2004), See Table 3-3 below for all the metrics

SLEUTH provides for use in calibration). After each round of calibration would be

complete the control stats output was converted into an Excel File. Then, what will

be termed the “Dietzel” (2004) product field was added and then sorted in a

descending order according to that field. However, for the sake of comparison,

Tables 3-4 and 3-5 display side-by-side the Lee-Sallee scores of each round of

calibration in order to compare with Teitz et. al. (2005). There was also another

difference in the approach. The PPIC team (Teitz et. al., 2005), though they

themselves used hierarchical resolution steps during calibration, gave commentary on

this approach’s problems. Since research has shown this method to “lead to

parameter sets that do not as accurately describe the growth of the system as a

calibration at full data resolution (Dietzel, 2004)” (excerpted from Teitz et. al., 2005,

p.94) the author opted to persist with full resolution throughout the entire calibration

process for each county, WA and urban simulation.

Table 3-3

73

Metrics That Can Be Used to Evaluate the Goodness of Fit of SLEUTH (as excerpted

from Teitz et. al, 2005) Note: Bold indicates additional metric added by author.

Metric Name

Description

Product

All other scores multiplied together

Compare

Modeled population for final year/actual population for final year, or IF Pmodeled > Pactual { 1 – (modeled population for final year/actual population for final year)}

Pop Least squares regression score for modeled urbanization compared to actual urbanization for the control years

Edges Least squares regression score for modeled urban edge count compared to actual urban edge count for the control years

Clusters Least squares regression score for modeled urban clustering compared to known urban clustering for the control years

Cluster size

Least squares regression score for modeled average urban cluster size compared to known average urban cluster size for the control years

Lee-Sallee

A shape index, a measurement of spatial fit between the model’s growth and the known urban extent for the control years

Slope Least squares regression of average slope for modeled urbanized cells compared to average slope of known urban cells for the control years

% urban Least squares regression of percentage of available pixels urbanized compared to the urbanized pixels for the control years

X-mean Least squares regression of average x_values for modeled urbanized cells compared to average x_values of known urban cells for the control years

Y-mean

Least squares regression of average y_values for modeled urbanized cells compared to average y_values of known urban cells for the control years

Rad Least squares regression of average radius of the circle which encloses the urban pixels

“Dietzel” Product of Compare, Pop, Edges, Clusters, Slope, X-Mean, Y-Mean (Dietzel, 2004)

Table 3-4 Routines and Results for Calibrating SLEUTH for Tulare County

PPIC Report (Teitz et. al., 2005)

WA Runs Urban Runs integrating 2002 WA into Excluded Layer

Coarse: Monte Carlo iterations = 3 Total no. of simulations = 3,125

Growth Parameters Range Step Diffusion 1–100 25 Breed 1–100 25 Spread 1–100 25

Coarse: Monte Carlo iterations = 4 Total no. of simulations = 3,125 Growth Parameters Range Step Diffusion 1–100 25 Breed 1–100 25 Spread 1–100 25

Coarse: Monte Carlo iterations = 3 Total no. of simulations = 3,125 Growth Parameters Range Step Diffusion 1–100 25 Breed 1–100 25 Spread 1–100 25

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Slope resistance 1–100 25 Road gravity 1–100 25

Resulting Metrics Lee-Sallee = 0.32958

Slope resistance 1–100 25 Road gravity 1–100 25 Resulting Metrics Lee-Sallee = 0.44139

Dietzel Score = 0.616536

Slope resistance 1–100 25 Road gravity 1–100 25 Resulting Metrics

Lee-Sallee = 0.67246 Dietzel Score = 0.86165

Fine: Monte Carlo iterations = 5 Total no. of simulations = 2,160 Growth Parameters Range Step Diffusion 1–10 5 Breed 1–15 5 Spread 15-35 5 Slope resistance 1–50 10 Road gravity 1–50 10


Fine: Monte Carlo iterations = 7 Total no.of simulations = 7776 Growth Parameters Range Step Diffusion 1–50 10 Breed 1–50 10 Spread 75-100 5 Slope resistance 25-75 10 Road gravity 1-50 10

Resulting Metrics Lee-Sallee = 0.3491 Dietzel Score = 0.619798

Fine: Monte Carlo iterations =5 Total no. of simulations = 7776 Growth Parameters Range Step Diffusion 75–100 5 Breed 5-100 5 Spread 0-25 5 Slope resistance 0-50 10 Road gravity 50-75 5 Resulting Metrics

Lee-Sallee = 0.58525 Dietzel Score = 0.905062

Final: Monte Carlo iterations = 7 Total no. of simulations = 3,456 Growth Parameters Range Step Diffusion 1–3 1 Breed 1–3 1 Spread 20-25 1 Slope resistance 1–50 10 Road gravity 1–10 2


Final: Monte Carlo iterations = 8 Total no. of simulations = 7776 Growth Parameters Range Step Diffusion 1–25 5 Breed 1–25 5 Spread 85-100 3 Slope resistance 50-75 5 Road gravity 1-25 5 Resulting Metrics Lee-Sallee = 0.33685 Dietzel Score = 0.637348

Final: Monte Carlo iterations=8 Total no. of simulations = 7776 Growth Parameters Range StepDiffusion 85-100 3 Breed 80-90 2 Spread 0-10 2 Slope resistance 30-40 4 Road gravity 50-60 2 Resulting Metrics Lee-Sallee = 0.5786 Dietzel Score = 0.895966

Highest Values in Final Calibration:

Unknown

Highest Values in Final Calibration: Growth Parameters Final Value Diffusion 1 Breed 25 Spread 97 Slope resistance 65 Road gravity 1

Highest Values in Final Calibration: Growth Parameters Final Value Diffusion 85 Breed 82 Spread 1 Slope resistance 46 Road gravity 52

Self-Modified Parameter Value (SMP) (All Cases) Growth Parameters Final Value Diffusion 2 Breed 4 Spread 45 Slope resistance 1 Road gravity 2

Self-Modified Parameter Value (SMP) Growth Parameters Final Value Diffusion 1 Breed 33 Spread 100 Slope resistance 43 Road gravity 3

Self-Modified Parameter (SMP) Value Growth Parameters Final Value Diffusion 99 Breed 96 Spread 1 Slope resistance 1 Road gravity 57 Resulting Metrics Lee-Sallee = 0.56208

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Dietzel Score = 0.080813

Table 3-5 Stanislaus and Merced Counties

PPIC Report (Teitz et. al., 2005) Stanislaus

PPIC Report Merced

Stanmerc WA Urban Runs integrating 2002 WA into Excluded Layer

Coarse: Monte Carlo iterations=3 Total no. of simulations = 3125 Growth Parameters Range Step Diffusion 1–100 25 Breed 1–100 25 Spread 1–100 25 Slope restnc 1–100 25 Road gravity 1–100 25


Coarse: Monte Carlo iterations = 3 Total no. of simulations = 3125 Growth Parameters Range Step Diffusion 1–100 25 Breed 1–100 25 Spread 1–100 25 Slope restnc 1–100 25 Road gravity 1–100 25


Coarse: Monte Carlo iterations=4 Total no. of simulations = 3125 Growth Parameters Range Step Diffusion 1–100 25 Breed 1–100 25 Spread 1–100 25 Slope restnc 1–100 25 Road gravity 1–100 25 Resulting Metrics Lee-Sallee = 0.52815 Dietzel Score = 0.505561

Coarse: Monte Carlo iterations = 4 Total no. of simulations = 3125 Growth Parameters Range Step Diffusion 1–100 25 Breed 1–100 25 Spread 1–100 25 Slope restnc 1–100 25 Road gravity 1–100 25 Resulting Metrics Lee-Sallee = 0.63475 Dietzel Score = 0.683694

Fine: Monte Carlo iterations=5 Total no. of simulations = 4500 Growth Parameters Range Step Diffusion 1–20 5 Breed 1–20 5 Spread 15-35 5 Slope restnc 25-50 5 Road gravity 50-100 10 Resulting MetricsLee-Sallee = 0.35935

Fine: Monte Carlo iterations = 5 Total no. of simulations = 5400 Growth Parameters Range Step Diffusion 1–25 5 Breed 1–25 5 Spread 15-35 5 Slope restnc 1-100 25 Road gravity 1-25 5 Resulting Metrics Lee-Sallee = 0.26585

Fine: Monte Carlo iterations=7 Total no. of simulations = 7776 Growth Parameters Range Step Diffusion 50-100 10 Breed 50-100 10 Spread 0-50 10 Slope restnc 0-25 5 Road gravity 0-50 10 Resulting Metrics Lee-Sallee = 0.51495 Dietzel Score = 0.767879

Fine: Monte Carlo iterations = 7 Total no. of simulations = 7776 Growth Parameters Range Step Diffusion 25-75 10 Breed 0-50 10 Spread 0-20 4 Slope restnc 0-25 5 Road gravity 50-100 10 Resulting Metrics Lee-Sallee = 0.73359 Dietzel Score = 0.768089

Final: Monte Carlo iterations=7 Total no. of simulations = 3456 Growth Parameters Range Step Diffusion 1–10 2 Breed 1–10 2 Spread 20-30 5 Slope restnc 40-50 2 Road gravity 80-100 5 Resulting Metrics Lee-Sallee = 0.34541

Final: Monte Carlo iterations = 7 Total no. of simulations = 3456 Growth Parameters Range Step Diffusion 1–5 1 Breed 1–5 1 Spread 18-23 1 Slope restnc 25-100 15 Road gravity 1-25 5


Final: Monte Carlo iterations=8 Total no. of simulations = 3456 Growth Parameters Range Step Diffusion 50-75 5 Breed 50-75 5 Spread 5-15 2 Slope restnc 5-15 2 Road gravity 25-50 5 Resulting Metrics Lee-Sallee = 0.45644 Dietzel Score = 0.829079

Final: Monte Carlo iterations= 8 Total no. of simulations = 5184 Growth Parameters Range Step Diffusion 25-50 5 Breed 0-25 5 Spread 0-4 1 Slope restnc 0-15 3 Road gravity 75-100 5 Resulting Metrics Lee-Sallee = 0.73405 Dietzel Score = 0.777984

Highest Values in Final Calibration: NA

Highest Values in Final Calibration: NA

Highest Values in Final Calibration: Growth Parameters Value Diffusion 60 Breed 65 Spread 13 Slope resistance 7 Road gravity 40

Highest Values in Final Calibration: Growth Parameters Value Diffusion 40 Breed 10 Spread 3 Slope resistance 8 Road gravity 85

SMP Value (All Cases) Growth Parameters Final Value Diffusion 2 Breed 7 Spread 54



SMPValue (All Cases) Growth Parameters Final Value Diffusion 37 Breed 9 Spread 3

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Slope resistance 29 Road gravity 100



Slope resistance 5 Road gravity 86 Resulting Metrics Lee-Sallee = 0.67033 Dietzel Score = 0.380748

Before continuing with an accounting of the particular approach used in this

dissertation it is now incumbent upon the author to briefly address SLEUTH’s

architecture. In order, the following pillars of its infrastructure will be addressed:

coefficients used by SLEUTH, its growth rules, and finally its self-modification

utility. Please note that greater detail and equations can be found on the Gigalopolis

website (from which the explanations below also borrow).

Coefficients

SLEUTH uses five different coefficients to describe and measure growth.

These are the very same coefficients that took so much time to calibrate and

re-calibrate, though this outcome depends greatly on the SMPs used in the

process. The coefficients include dispersion (initially referred to as

“diffusion” in the SLEUTH literature), breed, spread, slope, and road gravity.

The dispersion value affects two aspects of growth, spontaneous and road-

influenced. For spontaneous growth, dispersion controls the number of times

a pixel will be selected randomly for possible urbanization. At the same time,

the dispersion value also determines the maximum pixel distance from a cell

that can be searched for a road, allowing for further urbanization.

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The breed coefficient influences both new spreading center growth and

road-influenced growth. A pixel already selected for spontaneous growth is

assigned an additional probability of becoming a new spreading center with

the breed variable. It also determines the number of searches that will be

made from an urbanized pixel during the road influenced growth portion of a

SLEUTH run.

Spread affects just edge growth by assigning a probability that a pixel in

the 3 x 3 neighborhood of a spreading center will generate additional urban

pixels. The slope coefficient, on the other hand, affects all types of growth by

forcing the model to consider the slope each time it examines a pixel for

possible urbanization. (There is a critical slope value, above which

urbanization is impossible, but this addressed in the SMPs.) The higher the

slope coefficient value, the less likely steeper slopes are to urbanize, and vice

versa. In essence, it describes the importance of slope in the urbanization

process. High values declare its importance while a value of one would

render it almost totally irrelevant. Finally, road gravity assigns a maximum

search distance from a pixel for a road from which to generate further

urbanization.

Growth Rules

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As was discussed in the tour of coefficients above, there are four growth steps for

urban growth and two for “deltatron” dynamics used in land use change, which will

be addressed last. Spontaneous growth is the simple random urbanization of any

pixel anywhere on the lattice that is not already urbanized or in an excluded area. It

is controlled by the dispersion coefficient as well as the slope coefficient per the

explanation given previously. New Spreading Center Growth, controlled by the

breed coefficient and the slope coefficient, is next and determines whether or not any

of the newly urbanized cells created from the preceding spontaneous growth step will

become new spreading centers. It should be noted that this is conditioned by

availability of cells around the original cell for urbanization. Step three is edge

growth, whereby the pixels adjacent to all spreading centers, new and old, are

assessed a probability for urbanization based on the spread coefficient but also

conditioned, as always, by the slope coefficient. The final step, road influenced

growth, is determined by the breed coefficient, the dispersion coefficient, slope

coefficient, and, of course, the road gravity coefficient. Essentially, the road gravity

coefficient determines the maximum road search radius for an urbanized pixel. If a

road is found, then the closest pixel to the urbanized cell, but also adjacent to the

road, is temporarily urbanized. From this location a search is conducted along the

road, a “road trip”, with distance examined determined by dispersion. If any

available pixels are discovered then they are subject to random urbanization. Finally,

if this newly urbanized pixel has two adjacent neighbors that are also available then

they too become urbanized.

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Although the land use change mode of SLEUTH was used for this dissertation, both

in the WA simulation and conventional land use change, this was more for the

purposes of examining the types of land lost rather than explicitly forecasting

Anderson level I land use change throughout the test counties. This was explained in

the beginning of this Chapter. Nevertheless, a brief explanation of the land use

change mode and its requisite use of “deltatrons” is in order. With two land-use maps

of the same area but two different time periods we can generate a difference matrix

for each pixel. SLEUTH will also calculate the average slope for each land class. As

far as predictive modeling, this is accomplished through the use of “deltatrons”,or

bringers of change (Candau, et. al., 2000), which don’t only initiate change but also

act as placeholders, marking where change has taken place and what kind. They also

keep track of lifecycles for a pixel’s land class so they may enforce spatial and

temporal autocorrelation. By using the urban growth aspect of SLEUTH as the driver

for the degree of land use change, the deltatrons execute their functions during two

phases of change. In phase one, random pixels are selected, with the number

contingent upon urban growth results, and the slope of these pixels are compared to

the slope of two other randomly selected land use classes. The two closest in slope

are then put into a transition probability matrix based on the difference maps created

and this is used to create land cover change. In step two, secondary land use changes

are created as a direct result of the earlier random changes. Clarke describes an

example thusly, “A single pixel may change from forest to urban. In the next step,

land immediately adjacent to the buildings may be cleared of trees for gardens and

farming (1997).” Therefore, through the search of each altered pixels neighborhood a

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new transition probability matrix is created for associated growth and once again

change is enforced upon the prediction output.

Self-Modification Parameters

Self-modification parameters (SMPs) are the last aspects of SLEUTH’s growth

architecture that need to be addressed. Along with “boom and bust”, explained

below, there are three other additional user-controlled aspects of growth found in the

scenario-file along with the more dynamically functional SMPs. These are: a) slope

sensitivity, b) critical slope and c) road gravity sensitivity. Critical slope, briefly

addressed earlier, is simply the percent slope beyond which urbanization is

prohibited. The model does not discover this number on its own so the modeler must

explicitly define this. Consulting planning commission documents is one method for

achieving this step. However, like in the case of Tulare, slope is invoked (Tulare

County General Plan) in a manner to guide types of development rather than

forbidding development altogether. For instance, on page 5-8,

“Maximum Density: 1 DU/5 Acre if average cross slope is less than 30 percent. 1 DU/10 Acres if average cross slope is 30 percent or greater.”

Therefore, given that there is very little development beyond 30 percent or even

lower (especially given that the MMU for FMMP urban classification is 1.5

dwellings per acre), it was decided to defer to precedent, particularly as set by Clarke

(personal communication). Their critical slope was set at 25 percent, through a

process of trial and error, and, consequently, this is the value used here as well.

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Slope sensitivity and road gravity sensitivity are simply additional multipliers to be

used with the their respective coefficients. Unfortunately, as discussed earlier in this

Chapter, there is, as of yet, no scientific process embedded within SLEUTH to

calibrate and discover these values, other than trial and error of course. Again,

therefore, Clarke (personal communication) and his values were deferred to. This

dissertation, it should be stated, is hopefully part of a trend of greater divulgence

concerning SMPS. In the literature review conducted in this research, no accounting

of these values in other applications was found. Since they greatly affect the

calibration as well as the prediction, offering coefficients without the concomitant

SMPs renders fellow researchers hamstrung, whether in the search for precedent in

their own efforts, or their ability to capitalize and expand upon the work of the

original researcher. It was fortunate, therefore, that at least one application (Clarke,

personal communication) was found upon which to justify the SMP selections.

However, as stated previously, these figures were not formally published but obtained

upon request personally.

The most important components of the SMPs are the “boom and bust.” These were

designed to simulate more realistically the growth cycles of regions. In the early

portion of a growth cycle, when there is an abundance of developable land,

urbanization is more rapid and, when it exceeds a certain level (defined by the critical

high) a “boom” can begin, which is a positive feedback that encourages even more

rapid growth. On the other hand, when a system is saturated due to lack of land or is

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depressed with low growth for other reasons the growth rate can drop below the

critical low, at which time its growth is depressed even further by a multiplier less

than 1.0. Without these self-modification parameters SLEUTH could not achieve the

S curve that typifies actual growth in the world. Linear growth would result instead.

Once the WA runs were completed and a probabilistic excluded layer based on

patterns of Williamson Act termination was created (see Chapter 4 for images),

predictions according to three basic scenarios were executed: Strict Adherence to

current Williamson Act contracts, Business As Usual, and the Abolition of the

Williamson Act. These three scenarios are differentiated only by their respective

Excluded Layers. Output images for each of the scenarios are displayed in Chapter 4.

Strict Adherence to the Williamson Act (Strict for short-hand) is a scenario that

provides an extreme end of one spectrum. This future assumes that every WA

contract in existence in the year 2002 will persist until the end of the modeling runs in

2030. Therefore, the Excluded layer used for urban growth in this scenario is the

same used for calibration.

Business As Usual is the most sophisticated of the scenarios and exemplifies the

greatest contribution to knowledge offered in this work, since it utilizes the

probabilistic excluded layer created in the WA runs. The WA runs themselves,

incidentally, consist of one scenario only, since they have only one Excluded layer.

In fact, the WA runs’ greatest purpose is the realization of this business as usual

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excluded layer for urban growth. Of course, they also offer a future regulatory

landscape in their own right.

The Abolition of the Williamson Act (or No WA for short) is actually quite a simple

scenario. This excluded layer is identical to that used by Teitz et. al. (2005). It

should be remembered, however, that the coefficients were derived using a different

excluded layer and, therefore, the application of the excluded layer used for the PPIC

report results in a great deal more land being available for development than was

assumed in calibration. One advantage, therefore, of WA integration during the

calibration phase is the ability to explore WA policy alteration during prediction.

Figure 3-3: Metrics output for prediction runs using SLEUTH (as excerpted from Gigalopolis website) run: a run consists of a single set of coefficient values and is executed MONTE_CARLO_ITERATIONS number of times from start to stop year year: the representative date for a growth cycle index: control year number sng: the number of new urban pixels generated from spontaneous growth sdg: the number of new urban pixels generated from new spreading center growth sdc: relic data type no longer used og: the number of new urban pixels generated from edge growth rt: the number of new urban pixels generated from road influenced growth pop: the total number of urban pixels area: the total number of urban pixels (same as pop) edges: the total number of urban/non-urban pixel edges clusters: the total number of urban clusters xmean: the average urban pixel column value ymean: the average urban pixel column value rad: the radius of the circle which encloses the urban area: (pow ((area / pi), 0.5)) slope: average slope of urbanized cells cl_size: average urban cluster size diffus: dispersion_coefficient value spread: spread_coefficient value breed: breed_coefficient value slp_res: slope_coefficient value rd_grav: road_gravity_coefficient value

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http://www.ncgia.ucsb.edu/projects/gig/v2/About/data_files/scenario_file.htm#monte_carlo

http://www.ncgia.ucsb.edu/projects/gig/v2/About/bkStrCycle.htm

http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwSpontaneous.htm

http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwNewCenter.htm

http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwEdge.htm

http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwRoadInflu.htm

http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwCoef.htm#dispersion

http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwCoef.htm#spread

http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwCoef.htm#breed

http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwCoef.htm#slope

http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwCoef.htm#road_grav

%urban: Percent of the number of urban pixels divided by the total number of pixels in the study area (nrows*ncols) minus the number of pixels that are completely excluded from urban growth: ((100.0 * urbancount) / (total_pixels - (noncount+road pixels)) %road: Percent of the number of road pixels divided by the total number of pixels in the study area (nrows*ncols) minus the number of pixels that are completely excluded from urban growth: ((100.0 * roadcount) / (total_pixels - noncount)) grw_rate: Percent of the new urban pixels in one year divided by the total number of urban pixels: (100 * num_growth_pix / pop) leesalee: a shape index, a measurement of spatial fit between the model's growth and the known urban extent for the control years. In predict mode this value will always be zero (0):

where A is modeled and B is actual urban area. grw_pix: total number of new urban pixels

SLEUTH’s built-in metrics (see Figure 3-3) are then displayed to offer a sense of

total growth as well as dispersion, cluster, etc. Also, total land use acreages are

compared across the different scenarios. All of these output images, tables, maps,

and metrics are divulged in the following chapter.

CHAPTER 4: Results Part 1 (Text)

Introduction

The maps, figures, images, and tables that comprise this Chapter are the marrow of

the results gathered in this dissertation. Together, they continue the story begun in

the previous Chapter. Since incorporating the Williamson Act into the modeling

exercises was there shown to improve output metrics, then modeling future scenarios

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http://www.ncgia.ucsb.edu/projects/gig/v2/Imp/imModeFunction.htm#predict

could proceed. Preceding the images in this Chapter is explanatory text that provides

a companion narrative to the materials offered in the second half of the Chapter. The

most important images to examine are Figures 4-8 and 4-15 as they are a distillation

of the most essential contribution to knowledge offered in this dissertation: a method

for forecasting future excluded layers and using them to forecast urban growth.

In order to explore the various urban growth scenarios it was first necessary to

conduct a forecast of Williamson Act growth and terminations, the methodological

details of which were offered in Chapter 3. Though in the future there could be more,

in this case, there is only one Williamson Act scenario that required a SLEUTH

forecast: Business As Usual. This situation is predicated upon the assumption that

current Williamson Act administration and regulations will continue on to the year

2030. (See Figures 4-6 thru 4-8 and 4-13 thru 4-15.) The other two Williamson Act

scenarios either removed all Williamson Act protections (Abolition of the WA) or

assumed permanent protection of existing parcels (Strict Adherence). Each of these

three scenarios yielded an Excluded Layer that was then used in the traditional urban

modeling process.

To avoid confusion, though there are indeed three different scenarios involving the

WA, these scenarios are used to alter the excluded landscape of the urban growth

runs, not the WA itself. Of course there could be changes made to the WA excluded

layer (not to be confused with the urban growth excluded layer, see Figures 4-2 and

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4-9 for WA excluded layers) in order to secure forecasts that correspond to different

policy options. For instance, if only certain WA parcels (like those of a certain

acreage threshold) were allowed to terminate their contracts then a different excluded

layer could be used for WA termination forecasting. Though this was not explored in

this dissertation, it is an area ripe for future research. For urban growth runs, each

scenario corresponds to a different excluded layer. There is no other difference

amongst the three forecasts. All were run with the same SMPs as well as growth

coefficients, within the respective counties.

This chapter offers a number of different maps, image outputs, and tables. Since each

figure and table was crafted carefully to fit on each page, maximizing the size of the

image yet efficiently taking up page space, the explanatory text for the results will

precede the maps, images, and tables, which are found at the end of the Chapter.

Examining the Past

As Chapter 3 explained, this dissertation research necessarily went through an

exploratory phase, where the first maps were created for the different time periods.

These maps’ value lay in the power of displaying both urban growth as well as WA

growth and termination (See Maps 4-1 through 4-7). They display what the author

suspected all along: WA termination is non-random. Using the most important data

exploration tool known, our eyes, (Clarke, 2000) it could be seen that those areas that,

87

according to SLEUTH, are more likely to urbanize also seemed to be valuable

predictors for WA contract termination. Also interesting were those parcels that were

tracked and displayed from protected farmland, to unprotected farmland, to

developed land. This land cycle speaks directly to the long-term efficacy of the WA.

Since animation is difficult to display on paper, only the years 1984 (1986 in Tulare’s

case) and 2002 are shown. This is enough, however, to notice the differences over

time. Several things should be easily discovered through their perusal. First, a

significant amount of urban growth occurred in both geographic areas from 1984 to

2002. Second, there was both a significant amount of parcels leaving the WA and, in

some cases, a considerable number of lands joining the WA. The simultaneity of

these processes gives the impression, when only examining overall numbers, that the

act enjoys a static presence in these counties. On the contrary, there is a great deal of

coming and going and the conveyance of this through these maps gives credence to

the far greater utility derived from the examination of phenomenon geographically

rather than through aggregate numbers alone. Third, those lands leaving the WA tend

to be near urban areas as well as roads. Though there are exceptions to this, many of

these are in fact due to extenuating circumstances. For instance, there exists an

enormous parcel in the extreme East of Tulare County (not displayed in Map 4-1 but

revealed in Figure 4-2). This parcel, owned by the National Forest Service and in an

area extremely remote, was nevertheless in the WA for a number of years. It left the

WA, but not for the purpose of housing developments. It is still currently owned by

the NFS and is not developed. Also, in Stanislaus County (See Map 4-5) the large

and numerous red parcels in the Southwest of that County were owned by the State of

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California and removed from the Act through eminent domain for the purpose of

creating Henry W. Coe State Park. Hence, it was not a typical landowner decision

but a government decision and it was not for the purpose of development but a

necessary step in order to create the park.

Therefore, those parcels that most seem to confound the theory and the statistics that

WA parcel termination shows a predictability very similar to urban growth, are those

parcels most likely to not only be publicly acquired but also to not be destined for

urban growth. In this research, however, these outliers were kept and allowed to alter

the results. This was decided upon because it was necessary to create a WA

landscape in the future, both those parcels that left through landowner decisions as

well as those that left due to public acquisition or other such involuntary measures.

Only modeling and forecasting voluntary removals would paint an incomplete picture

of the WA. However, in the future a different approach could be employed that

parses out the different reasons for leaving and results in a more fastidious accounting

of the various mechanisms for contract termination.

Williamson Act Forecasting

Being convinced that there was an opportunity to model WA termination based on the

same criteria for urban growth, despite the difficulties associated with government

removals cited above, it was time to supply SLEUTH with the input images it needed

to run. Not all of the images are shown in this Chapter though a representative few

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have been selected for display. In particular, only the urban inputs as well as the

excluded layers are shown. Figures 4-2 and 4-9 reveal the excluded layer for the WA

termination forecasting. Since only those parcels currently in the WA can actually

terminate their contracts, all non-WA lands are excluded from Former WA growth

(FWA). In these images, the color black, corresponding to 0 in the grayscale, is open

for contract termination and all grayscales with a value 100 or greater are excluded.

All black shown in this image are 2002 WA lands. The next images (Figures 4-3 and

4-10) are the 2002 “urban” input images for the WA runs. The word urban is used

even though, in actuality, the gray in this image is both urban land and FWA land in

the year 2002. The two were combined for reasons cited in Chapter 3. In the urban

layer, the value 0, appearing black, indicates non-urban land. The urban excluded

layer for Tulare County bears a certain reciprocity with the WA excluded layer for

Tulare County, hence the term “reciprocal excluded layers.” Specifically, those lands

not available for FWA growth are the only lands available for urban growth and those

lands off-limits for urban growth are those available for FWA growth. In the future

these reciprocal excluded layers (RELs) could be used to more tightly couple these

two models so they may exchange excluded layers each year, rather than through

final results only.

Figures 4-4 and 4-11 are the urban growth excluded layers for the strict adherence

scenario. The black (0 in the grayscale) are those lands not currently in the WA (in

2002). The two other shades of gray are both off-limits to development in this

scenario but are presented as different colors in order to differentiate WA lands from

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National Forest lands and parklands. The Abolition of the WA scenario uses the

excluded layers shown in Figures 4-11 and 4-12. These figures simply ignore the

WA and so only treat parks and National Forest lands as off-limits to development.

The final scenario, Business As Usual, cannot proceed until the FWA growth

modeling occurs.

As discussed above, the Williamson Act termination modeling, or FWA growth

modeling, has only one excluded layer (Figures 4-2 and 4-9, respectively)

corresponding with its one scenario. For Tulare County, Figures 4-6 and 4-7 show

one Monte Carlo simulation using the coefficients generated through the calibration

process described in the previous chapter. The bright pastel colors, though somewhat

lacking aesthetically, were used to strongly differentiate these outcomes from the

traditional urban modeling outcomes, which use more subdued colors. One hundred

Monte Carlo simulations, similar to that shown in Figure 4-7, were run and the

probabilities were output in grayscale on top of the urban excluded layer

corresponding to strict adherence of the WA (i.e., all lands in the WA as well as all

public and parklands are shown as off-limits). This is displayed in Figure 4-8. The

fact that much of the image is as white as the background does not disrupt the

modeling process, since white (or 255 in the grayscale) is considered excluded. This

process creates the excluded layer for the third urban growth scenario: Business As

Usual, or the continuation of the current WA administration into the future. As

explained in Chapter 3, the number of times in the 100 Monte Carlo simulations a

particular parcel leaves the WA gives it a darker shade, i.e., a lower value on the

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grayscale. This lower value then corresponds with greater availability for

development, with 0, or pitch black, representing no resistance to development

whatsoever, aside from possible Slope incompatibilities. For Tulare County, a

comparison of the Western portion of the County, as shown in figures 4-4 and 4-8,

reveals significant differences. Many areas currently under WA contract but near

urban areas left in nearly all of the 100 Monte Carlo simulations in Figure 4-8. There

is a distance decay effect moving away from the roads and urban areas that appears as

fuzziness between the black and the more solid outlying gray areas. Stanislaus and

Merced counties (hereafter referred to as StanMerc) had more dispersed WA

termination forecasting, resulting in wider patches of mid-level grays, rather than the

quicker decays found in Tulare County (Compare Figures 4-11 and 4-15). Also, the

diminished levels of WA termination in these two counties when compared to Tulare

can be attributed to the fact that Merced County has had zero terminations as of 2002,

affecting the overall results for the combined two counties. The reason for Merced’s

peculiar WA persistence is its very recent participation in the Act itself. It began its

administration of the program in 1998.

As for the actual WA forecasting itself, one Monte Carlo run is offered for the year

2030 for both geographic areas. This gives a general idea of what 2030 could look

like, but that the fact that the image is only one Monte Carlo simulation is important

to keep in mind. Therefore, the specific locations of the forecast FWA lands for this

one Monte Carlo simulation should be thoughtfully compared with the overlaid

probabilistic Excluded Layer that was created from 100 Monte Carlo simulations.

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Nevertheless, even one simulation can offer insight. For Tulare County, Figure 4-7

presents a possible future of the WA. Though there is considerable acreage that is

added to the WA program, represented by dark green turning to light green and tan

turning to baby blue, a great deal is removed, as portrayed by the spread of red pixels.

As in urban growth, this spread is primarily near other urban and FWA areas as well

as along roads. Figures 4-17 a) and b) give a graphical representation of the amounts

of land in these two images in the form of a pie chart. Interestingly, when viewing

this land classification change with aggregate numbers alone, as was discussed in

Chapter 1, it would appear by looking at WA non-prime farmland that there was little

change. In fact, there was a dynamic interplay of non-prime acreage both leaving and

entering the WA in this forecast, rather than the stasis that these numbers alone might

suggest. As for FWA and Urban land, that more than triples between 2002 and 2030.

By examining the land use charts in Figure 4-18, the amount of FWA land alone can

be deduced as going from approximately 1.8% of Tulare County’s land to roughly

10% of all of Tulare County’s land. That is more than a five-fold increase. These

numbers were derived by assuming that the amount of urban land at the start of the

modeling cycle (Figure 4-18 a)) can be subtracted from the FWA and urban acreage

found in Figure 4-17 a), yielding 1.8%. The amount of Urban land in the Business As

Usual scenario for 2030 (Figure 4-18 b)) is 4.5 % that, when subtracted from 14.4%

is nearly 10%. This approach is inexact as these two modeling processes were run

separately, but it is a convenient way of differentiating between the FWA and urban

land that, as discussed earlier, were combined for methodologically expedient

reasons. It is also important to note that over the 28 years displayed in the two pie-

93

charts the amount of farmland that is neither in the WA nor was ever in the WA is an

ever-shrinking pool of acreage. Over half of the unprotected non-prime farmland in

2002 joined the WA while over half of the unprotected prime farmland joined as well.

As for which types of farmland left the WA, the charts reveal that this is

overwhelmingly prime farmland. Although 1.1% of all of Tulare County’s land in

2030, around 34,000 acres, is newly contracted prime WA farmland, the amount

leaving the WA far exceeds this value. The total amount of protected prime farmland

drops by nearly a third: 200,000 acres. That is only the net loss, however, since the

34,000 acres above were added during this process. The gross loss is 234,000 acres.

Only one fourth of the total 320,00 acres leaving the WA are non-prime, with a very

small fraction being other Land.

StanMerc, on the other hand, bears high dispersion values, but low spread values, and

very low slope resistance. Consequently, there is a very scattered effect for WA

contract termination (Refer to Tables 3-4 and 3-5 in Chapter 3 for reference). An

examination of 4-14 and 4-15 demonstrates the effect these coefficients have on the

growth of FWA land. Tulare County FWA results (see figures 4-7 and 4-8), on the

other hand, portray a vastly different pattern of growth. First, Tulare County FWA

has a maximum spread of 100 (Table 3-4) while also maintaining low dispersion and

considerable slope resistance. These last two factors, in combination, have a

straitening effect on FWA growth, and along with high spread, cause a very

concentrated growth of FWA land around areas already urban or FWA.

94

The total amount of FWA and urban land is more than doubled for StanMerc

throughout the modeling cycle (Figures 21 a) and b)). Of course, much of Merced

County was unprotected at the beginning of the modeling cycle and these lands are

excluded from becoming FWA. This also has a limiting effect on growth. Though

unprotected prime acreage in StanMerc declined very slightly, 74,000 prime acres

also left the WA during this time. Again, as with Tulare County, some of this FWA

and urban land was, in 2002, developed land already. By comparing the difference

between Figure 4-21 a) and 4-22 a) StanMerc is revealed to be 5.5% urban (120,000

acres) and 3.6% FWA (80,000 acres) in the beginning of the modeling cycle. By

2030, using the same method described above for Tulare County, StanMerc’s urban

acreage is 130,000 acres and its FWA acreage is 279,000 acres. Though the gains in

developed land were modest, FWA acreage more than tripled. Of those lands leaving

the WA, 126,000 were non-prime while 74,000 were prime. However, because many

parcels join the WA during this time, the net loss of protected non-prime farmland is

46,000 acres and only 4000 acres for prime. Also, since so much land was

unprotected in the beginning of the modeling run, even by 2030 fully 36% of

StanMerc’s agricultural land is forecast to not be protected nor to have ever been in

the WA (i.e., also not part of the FWA category). The WA added, but did not net,

80,000 acres of non-prime farmland and 71,000 acres of prime farmland. As with

Tulare County, the loss of other land is minor.

95

Urban Forecasting

As useful as modeling WA change may be, it still does not necessarily describe

changes on the ground. However, it does create a regulatory landscape that can affect

land cover, which will now be discussed. To reiterate Chapter 3, there are three

different scenarios under discussion for each geographic area. The only difference

between the three scenarios is the excluded layer. These scenarios are: 1. Business

As Usual (Continuance of current administration of the WA, excluded figures 4-8 and

4-15) 2. Strict Adherence to the WA (Present lands are frozen into the future,

excluded figures 4-4 and 4-11) and 3. Abolition of the WA (all current WA lands

become open for development, excluded figures 4-5 and 4-12). The final Monte

Carlo run for the years 2003 and 2030 for each of these scenarios and geographic

areas are displayed in Figures 4-16 a) – d) in the case of Tulare County and 4-20 a) –

d) for StanMerc.

Tulare County quite clearly shows increased growth, particularly in the Business As

Usual as well as the Abolition of the WA scenario. Strict Adherence, not

surprisingly, shows the least amount of difference with 2003. Profound growth along

major roads is another striking feature of these output images. By referring back to

Table 3-4 in the previous chapter, the final coefficients derived through calibration

can be seen for Tulare County urban growth. Using what has been discussed

concerning the nature of growth in SLEUTH and the particular role each of these

coefficients play, the output images for the different scenarios can be contextualized.

First, the tremendous growth along the roads is not only a function of the moderately

96

high road-gravity coefficient (57) but also the breed coefficient, bearing a very high

value of 96, and the dispersion (or diffusion) value, which is 99. Together, these

three values have a multiplicative effect on development along the roads. To review

Chapter 3, dispersion determines the maximum distance from a cell that a road search

can be conducted. Breed determines the number of searches that can be made from

an urbanized pixel during road influenced growth while road gravity itself determines

the maximum search distance from a pixel to search for a road, where further

urbanization can take place. The high dispersion coefficient is also the explanation

for the large number of clusters to be found during the spontaneous growth phase.

This becomes less apparent with greater WA retention since far-flung dispersed

development is not possible with all of those lands excluded. An examination of

Table 4-1 allows a comparison of Tulare County’s three different scenarios with the

base year of 2002, along a number of SLEUTH’s built-in metrics. As far as clusters,

even with Strict Adherence, the number of clusters more than doubles, while the

average cluster size is reduced in half. However, the radius for all the urban areas

only increases by 12 %. In the Business As usual scenario the number of clusters

increases more than seven fold, with average clusters less than a third the size they

were in 2002. The radius, meanwhile, increases by 42%. Finally, in the Abolition of

the WA scenario, the number of urban clusters increases twelve-fold with their

average size nearly one-sixth what they were in 2002. The urban radius increases

62% in this forecast.

97

Figures 4-18 a) thru d), offer pie charts of the different land breakdowns for not only

the three scenarios but for the base year of 2002 as well. Future urbanization ranges

from 18,000 acres for the Strict scenario to 111,000 for the Abolition scenario.

Business As Usual is closer to Abolition in its acreage lost to urbanization, with

71,000 acres, than it is to Strict enforcement. In the Strict Scenario, urban land

consumes 10,500 acres of non-prime farmland, 6500 acres of prime farmland, and

1200 acres of other Land. In Business As Usual, non-prime farmland is slightly more

favored as it comprises 47% of the lost acreage, with prime farmland making up 37%

and other Land equaling 15%. For reasons explained in Chapter 3, the various

farmland categories were kept static between 1984 and 2002, while only urbanization

changed. Therefore, there are no transitions between these various land use classes.

Consequently, all land use change is unidirectional, from the other three classes to

urban.

Figure 4-19 offers a two-dimensional examination of the land classes lost to

urbanization across the three different scenarios. It is helpful to simultaneously

peruse the 2030 image outputs of each of these scenarios while examining this figure,

in order to maximize the benefits of both the geographical and mathematical. In each

scenario, non-prime farmland is the greatest land use class lost to urbanization.

However, the percentage lost is different for each scenario. In the Strict scenario the

breakdown for prime, non-prime, and other land is as follows: 36%, 57%, and 7%,

respectively. For Business As Usual, these figures are: 36%, 48%, and 16%,

respectively. Finally, for the Abolition of the WA, the following results: 36%, 56%,

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and 8%, respectively. The consistency of prime farmland’s proportion of land lost in

each scenario is the most significant fact to be ascertained from these figures. It

would appear that the proportion of available prime farmland, given the different

excluded layers as well as slope conditions, remains approximately equal across the

different scenarios. There is greater variability, on the other hand, between non-

prime farmland and other Land across the scenarios. Since much of the other Land

that is not public lands is actually in the WA and somewhat distant from urban areas,

the Strict scenario does not allow for much of its conversion. Therefore, the low

figures both in absolute numbers and percentages should not be surprising. Also, as

explained in the previous chapter, SLEUTH was designed to follow an S-curve of

urban growth. As available land disappears, growth slows down as it asymptotically

approaches full build-out. With such a decrease in available land, the actual rate of

urbanization is less.

The difference between Business as Usual and the Abolition of the WA concerning

the conversion of other Land may seem unexpected since Business As Usual actually

has more Other Land converted than the Abolition of the WA. However, this may be

due to a reduction in the amount of farmland available for development in the BAU

scenario, causing a greater demand for Other Land. Also, the variance inherent in

stochastic modeling may cause an aberrant figure due to the nature of that particular

Monte Carlo run.

99

Stanmerc, throughout all three scenarios, displayed far more restrained growth.

Figures 4-20 a) thru d) offer the output images for 2003 as well as the three scenarios

while Table 4-2 summarizes the output statistics. The cluster increase for Business

As Usual as well as Strict is very modest. The Abolition of the WA, on the other

hand, offers a tripling of the number of clusters. As far as average cluster size, the

Strict scenario leaves this value virtually unchanged from 2002. Business As Usual

shows a slight decrease in average size while the Abolition of the WA results in an

average cluster size only half as large as 2002.

In comparison to Tulare County, an examination of Figures 4-22 a) thru d),

demonstrates StanMerc’s more moderate growth, even during the Abolition of the

WA scenario. With the exception of the Abolition of the WA, the differences

between the scenarios and 2002 are not extreme. Even in the Abolition scenario,

however, developed land does not even double. Tulare County, on the other hand,

demonstrated more than double the urbanized land for even the Business As Usual

scenario when compared with its baseline 2002 year.

Figure 4-23 portrays the breakdown of acreage lost to urbanization across the three

scenarios. The most important figures to notice are the strikingly similar acreages

lost between the Strict Scenario and the Business As Usual scenario. As with Tulare

County, the Strict scenario has slightly more Other Land lost than Business As Usual.

This is also most likely due to similar reasons. A revisiting of the WA termination

modeling, along with an understanding of StanMerc’s urban growth coefficients,

100

sheds light on this issue. Since StanMerc’s WA terminations had a great dispersion

throughout the current WA landscape, the corresponding lowered resistance to the

forces of development was also dispersed (See figures 4-14 and 4-15). WA

terminations had a low spread value so WA lands near urban areas were not selected

for termination anywhere near as often over the course of the 100 Monte Carlo

simulations as they were for Tulare County. By comparing figure 4-15 with Figure 4-

8, the differences can be seen visually. While Tulare County has a great deal of

alloyed development resistance near urban areas, this falls off sharply with distance

from urban areas as well as roads. StanMerc, on the other hand, reveals its dispersed

WA termination nature through somewhat darker shades of gray spread more evenly

across the landscape. This works in tandem with StanMerc’s significantly lower

urban growth coefficients (See Tables 3-4 and 3-5). Consequently, StanMerc’s low

spread value (3) coupled with a relatively unenervated excluded layer near urban

areas causes minimal new growth around current development. Also, though

StanMerc has a reasonably strong dispersion value (37), most of the undeveloped

areas still offer reasonable resistance to development. Therefore, many of these cells

selected for possible development have an excluded value strong enough to prohibit

this from taking place. As a result, the difference between Strict Adherence and

Business As Usual is rather negligible. The Abolition of the WA, however, does

indeed encourage growth in StanMerc. As discussed earlier, this is not only because

lands previously selected for possible urbanization but denied due to exclusion are

now selected and available, but also because the greater availability of land affects

101

SLEUTH’s “boom and bust” self-modification parameters. Thus, greater growth is

encouraged due to the tremendous supply of available land.

Conclusion

The sum total of all the maps, figures, and tables offered in this Chapter shed light on

the past growth of these two regions of the Central Valley as well as offer possible

glimpses of their future. The effect of the WA on urban growth, whether it is

completely removed or softened over time by gradual contract termination, is clear in

both areas. With what has been examined in Chapter 3 as well as this Chapter, it is

hoped that SLEUTH has now been shown to be extremely valuable for modeling the

WA over time. The following and final Chapter will revisit the questions invoked in

Chapter 1 and explore how well they have been answered with these results as well as

possible future work that could build upon this dissertation.

102

Chapter 4: Results Part 2 (Maps, Figures, and Tables) Figure 4-1:

103

Part 1: Exploratory Maps

Map 4-1: Western Tulare County, 2002

104

Map 4-2, Visalia and Tulare 1986

105

Map 4-3, Visalia and Tulare 2002

106

Map 4-4: Stanislaus and Merced Counties, 1984

107

Map 4-5: Stanislaus and Merced Counties, 2002

108

Map 4-6: Modesto Metropolitan Area, 1984

109

Map 4-7: Modesto Metropolitan Area, 2002

110

Part 2: Input Images

111

Figure 4-2: Tulare excluded.wac. (Used for the Williamson Act Excluded Layer)

Gray are non-WA lands and are excluded. Black are lands in the WA. White is

water.

Figure 4-3: Tulare.urban.2002.wac

112

Gray is urban and Former WA lands. Black are lands that are neither urban nor

Former WA lands.

113

Figure 4-4: Tulare.excluded.c (Excluded Layer used for Strict Adherence Scenario)

Dark Gray are public lands. Light Gray are WA lands. Both are excluded. Black

are lands already developed or available for development. White is water.

114

Figure 4-5: Tulare.excluded.nowac (Excluded Layer used for Abolition of the WA scenario)

Gray are public lands and other protected areas off-limits to development. Black

are those lands open to development. White is water.

Part 3 Output Images:

115

Figure 4-6: 2003 Tulare County Williamson Act run

LANDUSE_CLASS= 0, Unclass is black LANDUSE_CLASS= 1, Former Williamson Act and Urban is red LANDUSE_CLASS= 2, Non-Prime non-WA is tan LANDUSE_CLASS= 3, Prime non-wa is dark green LANDUSE_CLASS= 4, Williamson_Act Non-Prime is light blue LANDUSE_CLASS= 5, Williamson_Act Prime is light green LANDUSE_CLASS= 6, Farmland Security Zone is light purple LANDUSE_CLASS= 7, Water is dark blue

LANDUSE_CLASS= 8, Other Land is dark purple

Figure 4-7: 2030 Tulare County Williamson Act run

116

LANDUSE_CLASS= 0, Unclass is black LANDUSE_CLASS= 1, Former Williamson Act and Urban is red LANDUSE_CLASS= 2, Non-Prime non-WA is tan LANDUSE_CLASS= 3, Prime non-wa is dark green LANDUSE_CLASS= 4, Williamson_Act Non-Prime is light blue LANDUSE_CLASS= 5, Williamson_Act Prime is light green LANDUSE_CLASS= 6, Farmland Security Zone is light purple LANDUSE_CLASS= 7, Water is dark blue LANDUSE_CLASS= 8, Other Land is dark purple

Figure 4-8: Excluded.bauc. Also, Tulare_cumcolor_urban_2030. This output becomes the excluded layer for the input.

117

White is completely off-limits. Solid Gray is WA lands. Fuzzy Gray is land with probability between 0 and 100 for being open to development. The darker the gray the more available the land is. Figure 4-9: Stanmerc.Excluded.Wa

118

Gray are non-WA lands and are excluded. Black are lands in the WA. White is water. Figure 4-10: Stanmerc.urban.2002.wa

119

Gray is urban and Former WA lands. Black are lands that are neither urban nor Former WA lands. Figure 4-11: Stanmerc.excluded.new (The excluded layer used for the Strict adherence scenario)

120

Dark Gray are public lands. Light Gray are WA lands. Both are excluded. Black

are lands already developed or available for development. White is water. Figure 4-12:Stanmerc.excluded.nowanew (The excluded layer used for the Abolition of the WA scenario)

121

Gray are public lands and other protected areas off-limits to development. Black are those lands open to development. White is water. Figure 4-13: StanmercWA 2003

122

LANDUSE_CLASS= 0, Unclass is black LANDUSE_CLASS= 1, Former Williamson Act and Urban is red LANDUSE_CLASS= 2, Non-Prime non-WA is tan LANDUSE_CLASS= 3, Prime non-wa is dark green LANDUSE_CLASS= 4, Williamson_Act Non-Prime is light blue LANDUSE_CLASS= 5, Williamson_Act Prime is light green LANDUSE_CLASS= 6, Farmland Security Zone is light purple LANDUSE_CLASS= 7, Water is dark blue LANDUSE_CLASS= 8, Other Land is dark purple Figure 4-14: Stanmerc.wa.2030

123

LANDUSE_CLASS= 0, Unclass is black LANDUSE_CLASS= 1, Former Williamson Act and Urban is red LANDUSE_CLASS= 2, Non-Prime non-WA is tan LANDUSE_CLASS= 3, Prime non-wa is dark green LANDUSE_CLASS= 4, Williamson_Act Non-Prime is light blue LANDUSE_CLASS= 5, Williamson_Act Prime is light green LANDUSE_CLASS= 6, Farmland Security Zone is light purple LANDUSE_CLASS= 7, Water is dark blue LANDUSE_CLASS= 8, Other Land is dark purple Figure 4-15: Stanmerc.excluded.baunew2

124

White is completely off-limits. Solid Gray is WA lands. Fuzzy Gray is land with probability between 0 and 100 for being open to development. The darker the gray the more available the land is.

SCENARIO IMAGES

125

126

TULARE Figure 4-16: a) Tulare in 2003. brown is urban, yellow is prime, orange is non-prime farmland and green is other land.

b) Strict Adherence, 2030 brown is urban, yellow is prime, orange is non-prime farmland and green is other land.

c) Business As Usual, 2030 brown is urban, yellow is prime, orange is non-prime farmland and green is other land.

d) Abolition of the WA, 2030 brown is urban, yellow is prime, orange is non-prime farmland and green is other land.

127

Table 4-1: Summary Statistics Table for Tulare County

year sng sdg og rt pop area edges cluster

s xmean y Base200

2 200

2 6845 6845 3295 308 193.7

NOWA 203

0 87 15

110

7 211789

21789

2 1306

8 3630 192.1

BAU 203

0 41 70 64 151391

61391

6 9291 2263 193.6

Strict 203

0 1 0 5 2 8633 8633 4744 777 196

Table Key: (as excerpted from Gigalopolis website) sng: the number of new urban pixels generated from “spontaneous” growth sdg: the number of new urban pixels generated from “new spreading center” growth og: the number of new urban pixels generated from “edge” growth rt: the number of new urban pixels generated from “road influenced” growth pop: the total number of urban pixels area: the total number of urban pixels (same as pop) edges: the total number of urban/non-urban pixel edges clusters: the total number of urban clusters xmean: the average urban pixel column value ymean: the average urban pixel column value rad: the radius of the circle which encloses the urban area: (pow ((area / pi), 0.5)) slope: average slope of urbanized cells cl_size: average urban cluster size

Figure 4-17 a) Tulare County WA Land Classification, 2002

128





129

Tulare WA Status: 2002

4.0%

4.5%

2.0%

17.9%

18.9%

0.2%

0.1%

52.3%

FWA and Urban

Non-WA Non-PrimeNon-WA Prime

WA Non-Prime

WA Prime

FarmlandSecurity ZonesWater

Other Land

b) Tulare County WA Land Classification,

Tulare WA Status: 2030

14.4%

2.0%

0.9%

17.8%

12.4%

52.2%

0.1%0.1%

FWA and UrbanNon-WA Non-PrimeNon-WA PrimeWA Non-PrimeWA PrimeFarmland Security ZonesWaterOther Land

Figure 4-18: a) Tulare County Land Use 2002

Tulare Land Use: 2002

Non-Prime Farmland

29.4%

Other Land56.0%

Water0.2%

Prime Farmland

12.3%

Urban2.2%

Figure b) Tulare Land Use Strict Adherence to WA, 2030

Tulare Land Use: 2030 (Strict)

Urban2.8%

Prime Farmland

12.0%

Non-Prime Farmland

29.1%

Other Land55.9%

Water0.2%

c) Tulare Land Use Business As Usual, 2030

130

Tulare Land Use: 2030 (BAU)

Urban4.5%

Prime Farmland

11.4%

Non-Prime Farmland

28.3%

Other Land55.6%

Water0.1%

d) Tulare Abolition of the WA, 2030

Tulare Land Use: 2030 (No WA)

Urban5.8%

Prime Farmland

10.9%

Non-Prime Farmland

27.4%

Other Land55.7%

Water0.2%

Figure 4-19: Tulare Land Converted to Urban by type and by scenario, 2030

131

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Tulare Land Urbanized by Type and by Scenario: 2030

StrictBAUNo WA

Strict 6,532 10,435 1,264BAU 25,656 34,044 11,398No WA 40,475 61,987 8,649

Prime Farmland

Non-Prime Farmland Other Land

132

STANMERC Figure 4-20: a) Stanislaus and Merced Counties, 2003 brown is urban, yellow is prime, orange is non-prime farmland and green is other land.

b) Strict Adherence to WA, 2030 brown is urban, yellow is prime, orange is non-prime farmland and green is other land.

c) Business As Usual, 2030 brown is urban, yellow is prime, orange is non-prime farmland and green is other land.

133

d) Abolition of the WA, 2030 brown is urban, yellow is prime, orange is non-prime farmland and green is other land.

134

Table 4-2: Summary Statistics Table for Stanislaus / Merced Counties

year sn

g sd

g og rt pop areaedge

s cluster

s xmean ymean

2002 629

2629

2 3215 503 382.5 257.5 NoW

A 203

0 35 11 58 8949

5949

5 5381 1537 387.8 267.2

BAU 203

0 1 0 11 0673

8673

8 3422 566 383.6 259.3

Strict 203

0 1 0 10 0669

3669

3 3415 546 383.4 259.4

Table Key: (as excerpted from Gigalopolis website) sng: the number of new urban pixels generated from “spontaneous” growth sdg: the number of new urban pixels generated from “new spreading center” growth og: the number of new urban pixels generated from “edge” growth rt: the number of new urban pixels generated from “road influenced” growth pop: the total number of urban pixels area: the total number of urban pixels (same as pop) edges: the total number of urban/non-urban pixel edges clusters: the total number of urban clusters xmean: the average urban pixel column value ymean: the average urban pixel column value rad: the radius of the circle which encloses the urban area: (pow ((area / pi), 0.5)) slope: average slope of urbanized cells cl_size: average urban cluster size

135





136

Figure 4-21 a) Stanislaus and Merced Counties WA Classification, 2002

StanMerc WA Status: 2002

9.1%

31.2%

11.6%

31.7%

12.2%

1.0%

3.1%

FWA and UrbanNon-WA Non-PrimeNon-WA PrimeWA Non-PrimeWA PrimeWaterOther Land

b) Stanislaus and Merced Counties WA Classification, 2030

StanMerc WA Status: 2030

18.7%

27.6%

8.4%

29.6%

12.0%

1.0%

2.7%

FWA and UrbanNon-WA Non-PrimeNon-WA PrimeWA Non-PrimeWA PrimeWaterOther Land

Figure 4-22 a) Stanislaus and Merced Land Use, 2002

StanMerc Land Use: 2002

Urban5.5%

Prime Farmland

24.2%

Non-Prime Farmland

61.0%

Other Land8.2%

Water1.0%

b) Stanislaus/Merced Land Use, Strict Adherence to the WA, 2030

137

StanMerc Land Use: 2030 Strict

Urban5.9%

Prime Farmland

24.2%

Non-Prime Farmland

60.8%

Other Land8.0%

Water1.0%

c) Stanislaus/Merced Land Use, Business As Usual, 2030

StanMerc Land Use: 2030 BAU

Urban6.0%

Prime Farmland

24.2%

Other Land8.1%

Water1.0%

Non-Prime Farmland

60.8%

d) Stanislaus/Merced Land Use, Abolition of the WA, 2030

138

StanMerc Land Use: 2030 No WA

Urban8.4%

Prime Farmland

23.7%

Non-Prime Farmland

59.2%

Other Land7.6%

Water1.0%

139

Figure 4-23: Stanislaus and Merced Counties Land Converted to Urban by type and by scenario, 2030

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

StanMerc Acres Urbanized by Type and Scenario: 2030

StrictBAUNo WA

Strict 438 4,475 3,599BAU 458 5,784 3,079No WA 9,931 39,268 13,111

Prime Farmland

Non-Prime Farmland Other Land

140

CHAPTER 5: CONCLUSIONS

Research Questions Revisited

With the results offered in the previous chapter, along with the information presented

in Chapters 1 through 3, a number of different conclusions can be drawn. Also, as in

much academic investigation, new questions have been invoked that will stimulate

further research.

In the first chapter, questions were asked, and the subsequent chapters toured the

context, methodologies, and results involved in answering them. A brief recounting

of the questions and hypotheses is in order before turning to the conclusions proper.

• Question: Is the Williamson Act (both specifically and as representative of

other differential assessment programs) useful for modeling of urban spread?

• Hypothesis: The Williamson Act’s inclusion in urban growth modeling results

in greater accuracy than its exclusion.

By comparing the metrics used in this research with those used in the PPIC report

(Tietz et. al, 2005) those used in this dissertation proved closer to 1.0. These metric

values were higher even though different metrics were being used to selected

coefficient ranges between rounds of calibration (See Tables 3-4 and 3-5). Most

importantly, the only difference between the two approaches was this author’s

141

inclusion of the Williamson Act in the Excluded layer used in the rounds of

calibration.

• Question: Do spatial variables predict a parcel’s removal from the WA?

• Hypothesis: The same geographic phenomena that predictably apply

development pressure on undeveloped lands also apply pressure on

landowners to leave the Williamson Act.

A piece of land’s proximity to urban areas and to roads, as well as its slope, are all

considered to play a role in applying pressure on this land to develop. The creation

and examination of maps displaying these phenomena as well as the WA over time

demonstrates that indeed those leaving this voluntary Act tend to do so according to a

manner predictable by these stimuli. Since the calibration process used for the

Former WA modeling resulted in metric values greater than those used for urban

growth by the PPIC team (Tietz et. al, 2005), using the variables above to model this

landscape is justified. (See Tables 3-4 and 3-5)

• Question: How can knowledge of these spatial variables be used to model the

future of WA termination?

• Hypothesis: By designating Former WA lands as urban for the purposes of

SLEUTH’s nomenclature, a future landscape of the WA can be forecast with

accuracy comparable to urban growth forecasting.

142

Since it was determined that the same geographical phenomena that apply pressure on

lands to develop also apply pressure on lands to leave the WA, an urban growth

model, such as SLEUTH, is well-suited to forecast this future landscape. The

creation of appropriate excluded layers as well as naming Former WA lands as urban

lands, per SLEUTH’s requisite naming conventions, allows this process to proceed.

• Question: How can WA forecasts be used to influence urban growth

modeling?

• Hypothesis: A probabilistic excluded landscape can be created in a WA

modeling run and fed into a traditional urban modeling routine.

Historically, the “H” in SLEUTH has represented the Hillshade input layer. This

image has always been inert and serves only as the background for urban growth

displays. It can easily be removed and replaced with other images. Therefore, by

replacing the Hillshade layer with the Excluded layer used in the traditional urban

modeling calibrations, as well as creating a gray scale probability output in the

scenario file, a composite image is created during the FWA forecast over the 100 MC

iterations. This image can then be used as an Excluded layer for a Business As Usual

scenario for urban growth forecasts. Also, simply removing the WA from

consideration as well as assuming total persistence of current lands creates two other

scenarios based on Williamson Act administration variability.

As for the future of the Williamson Act itself, the results suggest that the voluntary

nature of the Act is both a blessing and a curse. It is a blessing because the lack of

143

coercion creates no friction with farmers and is often the only way for many of them

to remain solvent. Therefore, participation rates are enormous. Nevertheless, it is a

curse because farmers can still leave when they choose, though under most

circumstances it takes nine years. The future scenarios suggest that the rate of non-

renewal will eventually eclipse new enrollments leading to less and less protection of

those lands most vulnerable to development. In Tulare County, this is most readily

apparent. In the StanMerc region, this is not as clear since there are still so many

lands that have yet to join the WA. Nonetheless, it would only be a matter of time,

though further in the future than Tulare, that saturation will have taken place and then

contract terminations will begin to reduce the acreage every year thereafter.

Despite the eventual ineffectiveness of the Williamson Act that this model forecasts,

there still remains a remarkable difference between the Business As Usual scenario

and the Abolition of the WA scenario. Though the results suggest an eventual

weakening of the Act, its Abolition causes profoundly more farmland loss. As

mentioned in Chapter 2, the California Governor’s budget revisions have suggested

discontinuing subvention payments for the Williamson Act. This would effectively

destroy the Act as only a few extremely wealthy counties would assume the cost

themselves. As for the Strict Adherence scenario, though unlikely, there is precedent

for such permanence. In Eastern Long Island the differential assessment programs in

place there are enduring and transfer from owner to owner (Major M. Peguero,

personal communication). The tenability of implementing similar permanence in

California is, at least given the current endemic political and economic realities,

144

unlikely. Nevertheless, this dissertation can allow those in power to examine its

contents and perhaps make decisions armed with greater information.

Future Research

Any new piece of research, particularly as a graduate student, will usually contain a

certain degree of trial and error as well as a steep learning curve. This dissertation

was no exception. Consequently, throughout the course of this research a number of

lessons were learned as well as interesting questions raised that could lead to further

investigation.

First, there are issues of resolution that, if addressed, could help advance the accuracy

of these approaches. In particular, there is a parcel-to-pixel problem associated with

the Former Williamson Act modeling. Unlike urbanization, which can and does

occur one building at a time, Williamson Act parcels are either in or out of the Act

and these occurrences can happen instantly. Though the model is calibrated with data

that reflect these changes, future forecasts of a Williamson Act landscape are less

blocky and more refined than a true future cadastral landscape would be. (See

Figures 4-6 and 4-7 for comparison.) Therefore, future approaches would do well to

experiment with a more coarse resolution for the FWA forecasting than for the actual

urban growth modeling.

Second, there are time-delay effects that exist in the administration of the WA that are

more easily abjured in traditional urban modeling. For instance, though it is true that

145

a decision is made to build a home on a piece of land before the home is actually

built, this sort of time-delay does not need to be specifically addressed in a cellular

automata model such as SLEUTH for two reasons. First, SLEUTH is not an explicit

Human Decision Making model since it is specifically trying to capture urban growth

rather than the decision to urbanize amongst actors. Second, there is no universally

agreed upon time-delay between a decision to urbanize and actual development. The

Williamson Act, on the other hand, has an explicitly defined Non-Renewal condition

of termination. To review, this states that once a landowner requests to leave the

WA, there is a nine-year phase out period until that piece of land is officially

removed from the WA and available for urbanization. Since there are also other

methods of leaving the WA more or less instantly (cancellation, eminent domain,

public acquisition, etc.) it is challenging to combine these two types of WA

termination in the same modeling environment. SLEUTH, in this case, does not have

a user-defined option to allow for this sort of time-delay limbo between states.

Therefore, in the case of Non-Renewal, the code would need to be changed to

explicitly account for this time-delay between the decision to leave the Act and actual

removal. Also, experimenting with modeling non-renewal separately from other

forms of removal could not only help isolate this issue but also result, in some cases,

in greater accuracy since many remote WA parcels are purchased for the purposes of

park creation or other non-urban amenities, and therefore serve to confound the

rationale for using SLEUTH’s geographical phenomena (urban proximity, roads, etc.)

as criteria for WA removal.

146

This dissertation’s use of a cellular automata to forecast a regulatory landscape that

controls which lands may or may not be developed is, to this author’s knowledge,

novel. This could, however, be only the beginning of a new line of inquiry and use

for cellular automata models. Let us begin with the idea that, over an infinite period

of time, all lands on Earth will either be developed or protected from development. If

this is true than forecasting urban growth alone is asymmetrical. In every community

there is competition over land between environmentalists and developers, in a race to

develop or protect before the other side secures the fate of the land. By

systematically discovering, if at all possible, those factors relevant for the forecasting

of newly protected land then we may correct this current imbalance in modeling

priorities. In the case of the WA, this forecasting was made more amenable due to

the great similarity between factor’s influencing both Williamson Act removal and

urban growth. Nevertheless, a new cellular automata approach that addresses this

competition between protection and development would allow for a more refined

look at the future. A modeling environment that allows these two phenomena to

influence each other at each time step would be better still, whether they be coupled

models or housed in the same program.

Most modelers strive to make their tools more relevant to planners and planners

endeavor to improve their communities. Moving towards a modeling tool that offers

a myriad of scenarios, based on soundly tested theory and techniques could be

salutary to both. This dissertation is a step in that direction and this author intends to

continue research towards that end.

147

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