EPA/600/R-13/112
United States ARS/295385
Environmental Protection Juiy20i3
Agency www.epa.gov/research
Investigating Historic Parcel
Changes to Understand
Land Use Trends
A Methodology and Application
for the San Pedro River Watershed
RESEARCH AND DEVELOPMENT
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Investigating Historic Parcel Changes
to Understand Land Use Trends
Methodology and Application
for the San Pedro River Watershed
Charlotte C. Ely1, William G. Kepner2, Dave C. Goodrich3, and Maliha S. Nash2
Corresponding Author, U.S. EPA Region 9, Water Division
2U.S. Environmental Protection Agency, Office of Research and Development, Landscape Ecology Branch
3USDA-Agricultural Research Service, Southwest Watershed Research Center
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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Acknowledgements
This research was made possible by the U.S. Environmental Protection Agency's (EPA)
Regional Research Partnership Program (RRPP), which provides training opportunities for
regional technical staff to work directly with Office of Research and Development (ORD)
scientists in the ORD laboratories and centers. I'm very grateful for the opportunity, and to the
EPA Region 9 and ORD management and staff who supported the project.
I also owe a considerable debt of gratitude to the ORD Las Vegas Laboratory's Landscape
Ecology Branch and to the United States Department of Agriculture (USD A) Agricultural
Research Service's (ARS) Southwest Watershed Research Center. Both agencies provided
generous logistical support and their staff generously contributed their time and expertise.
I'm very thankful to the Cochise County Archives and Information Technology staff.
Without their advice, generosity, and impeccable management of historic documents and
geodata, this project could not have been completed.
I am also grateful to William M. Rodgers, whose 1965 research inspired this study. Upon
receiving his postgraduate degree, Rodgers accepted a teaching position in UC Denver's
Geography Department. In honor of the late professor, UC Denver annually awards a scholarship
in his name.
Dr. Britta G. Bierwagen (Physical Scientist, EPA/ORD, Global Change Research Program,
Washington, D.C), Dr. Russell L. Scott (Research Hydrologist, U.S. Department of Agriculture
Agricultural Research Service, Southwest Watershed Research Center, Tucson, AZ), and Dale
Turner (Conservation Planner, The Nature Conservancy, Tucson, AZ) peer-reviewed the report.
Thank you, Britta, Russ, and Dale for your suggestions and corrections.
Lastly, I am most appreciative of my mentors and co-authors, for their guidance and support
throughout all phases of this project.
in
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IV
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Notice
This report has been peer reviewed by the U.S. Environmental Protection Agency Office of
Research and Development and the USDA Agricultural Research Service and approved for
publication. Mention of trade names or commercial products does not constitute endorsement or
recommendation by EPA nor USDA/ARS for use.
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VI
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Table of Contents
Acknowledgements iii
Notice v
List of Figures ix
List of Tables xi
List of Appendices xiii
List of Acronyms and Abbreviations xv
Abstract 1
Introduction 1
Methods 5
Results 16
Discussion 25
Conclusion 29
Appendix A 30
Appendix B 31
Appendix C 42
Appendix D 43
References 45
vn
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Vlll
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List of Figures
Figure 1. Map of San Pedro River Watershed
Figure 2. Landscape Change from Perennial Grassland to Mesquite Woodland in a
Semi-Arid Rangeland
Figure 3. Land cover in the Upper San Pedro River Watershed using Landsat
MSSandTM
Figure 4. Location Map of the Study Area 4
Figure 5. Landholdings in Upper San Pedro River Valley, 1900 and 1964 5
Figure 6. Photographs of Cochise County Archives 6
Figure 7. 1964 County Assessor Tax Roll Record 6
Figure 8. Plat Books Entries Showing Landholdings in Township 23, Range 21 in
1913 and 1964 7
Figure 9. 1964 Alpha Index to Assessment and Tax Roll 8
Figure 10. 1940, 1950, and 1964 Summary of Acreage Changes in Upper San Pedro
River Valley 8
Figure 11. Parcels within the Study Area, 2012 10
Figure 12. Pie Charts Displaying Percentage of All Holdings for 1940, 1950,
1964 and 2012 11
Figure 13. Books and Book-Maps in the Study Area 13
Figure 14. Segment of Scanned 1971 Tax Roll Record 14
Figure 15. Changes in Total Number of Parcels and Average Parcel Size (1971 - 2012) 16
Figure 16. Acreage Trends (1882 - 2012) 16
Figure 17. Acreage Trends Using ICLUS Housing Density Categories (1971 -2012) 17
Figure 18. Book-Map Trends, Changing Percentage (1971 -2012) 18
Figure 19. Book-Map Trends, Changing Number (1971 -2012) 18
IX
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List of Figures (cont.)
Figure 20. Upper San Pedro Land Use, 1971 20
Figure 21. Upper San Pedro Land Use, 1981 21
Figure 22. Upper San Pedro Land Use, 1991 22
Figure 23. Upper San Pedro Land Use, 2001 23
Figure 24. Upper San Pedro Land Use, 2012 24
Figure 25. Land Use Change in Sierra Vista Southeast and Tombstone (1971 and 2012) 25
Figure 26. Population and WWTF Capacity in Sierra Vista, AZ (1971 - 2012) 27
Figure 27. Number of Parcels and WWTF Capacity in Sierra Vista, AZ (1971 - 2012) 27
Figure 28. WWTF Capacity and Book-Maps in Sierra Vista, AZ (1971 - 2012) 28
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List of Tables
Table 1. 2012 Land Holdings Analysis 11
Table 2. Explanation of ICLUS Housing Density Categories 15
Table 3. Decadal Acreage Trends Using ICLUS Housing Density
Categories, 1971 -2012 17
Table 4. Number and Percentage of Book-Maps Falling into ICLUS HD Categories,
1971 -2012 18
Table 5. Decadal Trends in Population, Parcels, Book-Maps, and WWTF capacity
in Sierra Vista, AZ 27
XI
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Xll
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List of Appendices
Appendix A. Books and Book-Maps within the Study Area 30
Appendix B. Changing Number of Parcels within Study Area Book-Maps 31
Appendix C. Land Jurisdiction in Study Area 42
Appendix D. Example Tax Roll Records for 1971, 1981 and 1991 43
xni
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XIV
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Acronyms and Abbreviations
AGWA Automated Geospatial Watershed Assessment
APN Assessor Parcel Number
BLM U.S. Bureau of Land Management
CFR Code of Federal Regulations
EPA U.S. Environmental Protection Agency
GIS Geographic Information System
HD Housing Density
ICLUS Integrated Climate and Land Use Scenarios
IPCC Intergovernmental Panel on Climate Change
IT Information Technology
MGD Million Gallons a Day
MSS Multi-spectral Scanner (Landsat)
NEPA National Environmental Policy Act
NHD National Hydrology Dataset (USGS)
PLSS Public Land Survey System
SPRNCA San Pedro Riparian National Conservation Area
SRES Special Report on Emissions Scenarios
STORET Storage and Retrieval Database
SWAT Soil and Water Assessment Tool
TANA TeleAtlas of North America
TM Thematic Mapper (Landsat)
USGS U.S. Geological Survey
USPP Upper San Pedro Partnership
WWTF Wastewater Treatment Facility
xv
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XVI
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Abstract
Long-term land use and land cover change, and the associated impacts, pose critical challenges
to sustaining healthy communities and ecosystems. In this study, a methodology was developed
to use parcel data to evaluate land use trends in southeast Arizona's San Pedro River Watershed.
Changes to parcel size are examined decade by decade, for two intervals: from 1882 to 2012, and
from 1971 to 2012. Graphs are used to depict decadal parcel trends for both intervals. Parcel
density maps additionally illustrate decadal trends for the 1971 to 2012 interval. The parcel
density maps and graphs employ housing density categories developed by the Environmental
Protection Agency's Integrated Climate and Land Use Scenarios project. The purpose of this
study is to 1) improve and describe a methodology for evaluating land use trends using parcel
data; 2) display land use trends in a portion of the San Pedro Watershed using parcel data; and 3)
discuss the implications of the analysis for evaluating environmental impacts with modeling
tools and for assessing indirect effects as required by the National Environmental Policy Act.
Introduction
The San Pedro River is considered one of the
last free flowing, undammed rivers in the American
Southwest; it flows intermittently between two
deserts and through two countries (Figure 1),
supporting tremendous biodiversity and providing
an important stopover along the central migratory
flyway. Changes to ground- and surface water
quality and quantity on both sides of the border
have raised serious concerns about watershed
sustainability. A particular focus in the Upper San
Pedro River Watershed is long-term water supply
reliability and impacts to the country's first
National Riparian Conservation Area, the San
Pedro Riparian National Conservation Area
(SPRNCA). Despite pioneering water management
approaches and collaborative partnerships, "the
overall situation in the regional aquifer is not
improving; rather, it continues to get worse" (USPP
2011).
The impact of urbanization on the San Pedro
River watershed is a significant driver of declining
water quality and quantity (Me et al. 2011). Yet,
few researchers have analyzed the area's changing
urban landscape. The purpose of this study is not
only to show land use trends in a portion of the San
Pedro River Watershed, but also to improve and
describe a methodology that could be used to
chronicle growth-induced land use change in
watersheds across the country.
Figure 1: The San Pedro River flows 230 km (—142 mi) from its
headwaters in Cananea, Sonora, Mexico to its confluence
with the Gila River in Arizona. The watershed is within
the Madrean Archipelago, also known as "Sky Islands."
This area is one of the most biologically diverse in the
world (Koprowski 2005, Skroch 2009). The geographic
convergence of two major mountain ranges (the Rocky
and the Sierra Madre) and two vast deserts form the
foundation for ecological interactions found nowhere
else (Skroch 2009). Hydrology data from USGS NHD;
Administrative boundaries from AZTANA; Ecoregions
from NHEERL; Mexican hydrology data and
administrative boundaries from Kepner et al. 2003.
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Land use change can have devastating impacts on the landscape. The consequences of human
modification of the Earth's surface for extraction of natural resources, agricultural production,
and urbanization may even rival those that are anticipated via climate change (Vitousek 1994,
Vorosmarty et al. 2000, Chapin et al. 2002, DeFries and Eshleman 2004, Brauman et al. 2007,
Whitehead et al. 2009).
Understanding and mitigating the consequences of future land use change require knowledge
of past trends and impacts. Historic reference conditions can provide resource-managers with
baseline "snap shots" capable of informing and directing the management and implementation of
present day projects and planning. Evaluating management decisions using only current
conditions belies potential impacts (Covington and Moore 1992). Without knowledge of past
projects and their consequences, how can we evaluate whether present management will lead to
significant environmental impacts in the future?
Preferably, reference conditions would be based on undisturbed environments. However,
most environments have been impacted and modified by both modern and aboriginal humans
(Swanson et al., 1993). Arguably, all environments could be described as "disturbed" or
"produced nature" (Smith 1996). It is less important that a reference condition be "pristine" than
that it be simply available and that subsequent changes to that baseline can be evaluated using
consistent and measurable criteria.
Comparing conditions across large landscapes and assessing cumulative environmental
impacts over time has been challenging. Before the launch of remote sensing satellites in the
early 1970s, past and present conditions could be compared using archival literature and
photography. Since then, remote imagery has increasingly been used to chronicle change.
Despite certain limitations, both datasets have been used to produce compelling analyses of
landscape change in the arid Southwest.
Vegetation change in the American West has been a subject of concern throughout the
twentieth century (Humphrey 1958, Hastings and Turner 1965, Branson 1985, Grover and
Musick 1990, Bahre 1991, Bahre and Shelton 1993, and Turner et al. 2003). Most of the
evidence for vegetation change is provided from a series of matched photographs - a method
called repeat photography - beginning in the late 1800s and early 1900s (Figure 2). However,
there are serious drawbacks in using this technique to assign change over this period of history.
' • lr»
Figure 2: Landscape change from perennial grassland to mesquite woodland in a semi-arid rangeland (Santa Rita Mountains south of Tucson,
Arizona) from 1903 (left) and 1941 (right) (from Kepner et al. 2002)
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As some authors, e.g. Bahre (1991), point out, the field of view in older photographs is
usually oblique and covers little total area, which limits their usefulness in determining change in
plant occurrence over large regional areas. Secondly, the historic photographic series are usually
separated by large periods of time, often captured more than a decade after the sites were first
disturbed by human activity. Lastly, repeat photography has largely been used for qualitative
comparisons and little progress has been made in quantifying and characterizing change using
this dataset. Although several studies have addressed specific aspects of vegetation change in the
Southwest, few have attempted to synthesize the cumulative impacts over large regional or
watershed areas.
Important advances in the integration of remote imagery, computer processing, and spatial
analysis technologies have been coupled to landscape ecology theory to study the distribution
patterns of communities and ecosystems (Kepner et al. 2000 and 2002). Landsat imagery, for
example, has been used to evaluate the human and environmental processes affecting distribution
patterns over time (Figure 3). The combination of these technologies contributes to our ability to
characterize large areas; it also provides predictive models for alternative future scenarios, which
can lead to a more robust comparative analysis of impacts relative to alternative courses of
management action (Kepner et al. 2004).
Forest
Oak Woodland
Mesquite Woodland
Grassland
Desertscrul)
Riparian
Agriculture
Urban
Figure 3: Land cover in the Upper San Pedro Watershed using Landsat MSS and TM (Kepner et al. 2002)
There are limits, however, to how much change can be detected employing remote imagery
and spatial analysis technologies. For example, satellite images vary in scale related to pixel size
and spectral resolution, which can complicate the generation of cohesive and comprehensive
mosaics. Furthermore, their availability is limited. For instance, the earliest Landsat imagery
dates back only to 1972.
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Archival ownership records also provide information for understanding reference conditions,
and may help facilitate the analysis of photographs and satellite images. While such records -
notably county Treasurer and Assessor documents - may be limited as well, our research
suggests property records (i.e., parcel data) may nonetheless provide important insight into
historic land use trends, fill data gaps, and corroborate the findings of other change-detection
methodologies.
For the purpose of this report, the results are restricted to a portion of the Upper San Pedro
River Watershed that was studied by William R. Rodgers, a University of Arizona graduate
student in the mid-1960s. Our study area encompasses the same rectangular section of the
watershed Rodgers defined as the Upper San Pedro River Valley and mapped using the Public
Land Survey System (PLSS) (Figure 4).
Upper San Pedro Watershed
Lower San Pedro Watershed
Study Area
SPNCRA
Figure 4: Location Map of the Study Area. The study area was defined using the Public Land Survey System (PLSS); the grid shows historic
township boundaries. Also shown is the San Pedro Riparian National Conservation Area (SPRNCA). Administered by the U.S. Bureau
of Land Management (BLM), SPRNCA protects approximately 64 km (—40 mi) of the river corridor. Hydrology data from USGS
NHD; Administrative boundaries from AZTANA; PLSS boundaries from Cochise County; SPNCRA boundaries from Kepner et al.
2003.
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Methods
At the age of 55, Brigadier General William M. Rodgers retired from the Army and enrolled
in the Geography program at the University of Arizona. In 1965, Rodgers submitted his thesis,
titled "Historical Land Occupance of the Upper San Pedro River Valley Since 1870." The study
relied heavily on documents provided by the Cochise County Treasurer and Assessor. Rodgers
described using Tax Rolls from 1882 through 1964 to analyze the changing extent, number, and
acreage of parcels, decade by decade. He drew detailed landholding maps using the Public Land
Survey System (PLSS). Figure 5 shows the 1900 and the 1964 landholding maps from Rodgers'
study. In Figure 5, the diagonal lines show how much of each section and how many of a
Township's 36 640-acre sections were occupied for each of the years examined.
TIUS
RliC
fig, 2b,—I.tftKI Hii]<11ngi» In upper San Pertr* RTrer
l^fi-'* (noi tnelucjn^ ^pantch ir-nnta)-1
y, 1 _i . - - L ein>.i Haldi.xLgs in Upper jsara
(not Including Spaniah
KLTCT Val
= 1 S*CtlQTl (6*10 ^crss) of township.
Figure 5: Landholdings in Upper San Pedro River Valley, 1900 and 1964 (Rodgers 1965)
The historic tax rolls are stored in large binders, organized by year and, from 1935 to 1970,
by Tax Roll Number, which is also referred to as the Assessment Number. For example, the
1964 Tax Roll binder "35015 - 35441" contains all tax records with assessment numbers ranging
from 35015 through 35441. Before 1935, the Tax Roll was organized by year and,
alphabetically, by owner last name. Today, the binders are stored in the Cochise County
Archives (Figure 6).
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Figure 6: Cochise County Archives space savers (left) and the row containing the 1964 Tax Roll binders (right)
Each Tax Roll record provides an assessment number, the name and address of the owner, the
taxes due, and, sometimes, the property's legal description, acreage, and location, defined using
PLSS coordinates (e.g., Township 21, Range 22, southeast quarter of section 35) (Figure 7).
ASSESSMENT and TAX ROLL
Cochise County, Arizona, 1964
TAX RECEIPT
NUMBER
Figure 7: Scanned Image of 1964 County Assessor Tax Roll Record (Courtesy of Cochise County Archives, 2012)
If a property spanned multiple sections in multiple townships and/or ranges, the Tax Roll
record - if complete - would list the acreage owned within each area (e.g. Township 21, Range
22, southeast corner of section 32, 160 acres; Township 22, Range 22, northern half of the
northwest quarter of section 2, 80 acres). Rodgers was interested only in properties within the
Upper San Pedro Valley, which he defined as Townships 18 through 24 and Ranges 19 through
23 (Figures 4 and 5). As the Tax Roll was not organized geographically and as many of the Tax
Roll records had missing and/or incomplete acreage and location information, it seems more than
likely Rodgers would have also relied on County plat books.
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The County's oldest plat book dates from 1913, and the most recent from 1964. The plat
books were organized by Township, Range, and Section. The properties within a given section
were listed one by one and included only two additional pieces of information: who owned it and
its acreage (Figure 8). There is no unique assessment number associated with each entry in the
plat books. Rodgers would have needed to take great care in identifying an individual property.
Since large properties consisted of land in multiple sections, ranges, and/or townships, the
owner's name would appear multiple times in the plat books. Once Rodgers knew who occupied
land in his study area, he would have been able to track that individual down in the Tax Roll and
then accurately describe the acreage of a single property.
OWNSH1P^RANCE^/
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TOWNSHIP 23, RANKE 21
f ' .
All
XL'- . . -
SECTION i
•tif 4 Si3S t 3|~»jE i KJSSaaSE 477.54. Pboants Title i Trust Co., Trasta*
Lots 1-2 & S|MK . •- - 1
In SS5¥3»SS of See. 1 *
lo ;it:ux»Nt; of sec. la-Sy uta
Be*;. 13'J* K * IOC' 3 ct »» Cor. . i
of SEj-of Sec. 3-£10C'-£ZQQ*-
IIOO'-SSQQ* to Beg. (-••'- -'•-
la SK3S!(S^-By MSB 2.2'/u
Beg. at 31 Cor. of SEi-E130'-
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V,. C. Ogurek, etux
Phoenix Titl« a Trust Co.. Trustae
K Till* 4 Troat Co.. Trustee
SECTION *
47-;.f>4 Phoenix Title 1 Tr^3t Co., Trwste*
Ibo Eva tioson SraiahsT
In Si of ^ot 4 In Wt-b*p " 1.5C7 Phoenlit Tit
HW cornir ot -ot 4-io3fa,70'-
Figure 8: Scanned Plat Books entries showing landholdings in Township 23, Range 21 in 1913 (left image) and 1964 (right image)
(Courtesy of Cochise County Archives, 2012)
Examining holdings in 1940, 1950, and 1964 would have required an additional step because,
from 1935 to 1970, the Tax Rolls were organized by assessment number, not by owner name. In
order to connect a property owner to a specific piece of property during those years, Rodgers
likely referenced the "alpha indices," which are organized alphabetically by owner last name
(Figure 9). Adjacent to the owner name, the alpha index lists the unique assessment number
associated with that person's property. By referencing a given year's alpha index, Rodgers could
have tracked down a Tax Roll record using the assessment number. To examine parcels in 1940,
he may have done this as many as 325 times; for the year 1964, as many as 831 times. He did not
describe his methods in detail, and they remain an impressive mystery - particularly for those
years that lack plat books (before 1913) and Tax Roll binders (before 1886).
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Figure 9: 1964 Alpha Index to Assessment and Tax Roll
However he obtained the data, the result was
an analysis of the changing number and size
of properties over an 80-year period. Rodgers
not only described where people settled
within the watershed, but how large those
settlements were. The acreage of a property
can provide insight into how the land was
used, especially when coupled with
additional data. For example, Rodgers also
documented the changing cattle population
decade by decade. To analyze the changing
trends in the size of landholdings, he grouped
properties into four categories: 0-159 acres,
160 - 319 acres, 320 - 999 acres, and 1000
acres and up.
These size categories not only refer to the PLSS (i.e., a 640-acre section divided by 4 equals
160 acres), but probably to the Homestead Acts. The original 1862 Act granted 160 acres, or a
quarter of a section. Later iterations increased the allotted acreages. The 1909 amendment, for
example, increased the size of homesteads to 320 acres in western (i.e. arid) states (BLM 2013).
For each year he examined, Rodgers counted the number of landholdings within each category,
calculated their sum acreage, determined what percentage of all holdings the sum acreage
represented, and, lastly, established the average property size in each category. Figure 10 shows
the tables and hand drawn pie charts for the years 1940, 1950, and 1964.
TABLE IV
CATEGORIES OF LAND HOLDINGS IN UPPEB SAN PEDRO RIVER
VALLEY BY NUMBER, ACREAGE, PERCENTAGE Of TOTAL
HOLDINGS, AND AVERAGE SIZS 1930-1964"
roar
194O
1950
196*1
Aeroago of
holdings
0-159
160-319
320-999
1000*
Sp Grantv
Tol a 1
0-159
160-319
320-999
1OOO +
Sp Grants
Total
0-159
160-319
320-999
IOOO«
Sp Grunt*
Total
No.
us
71
10O
28
y
]'2f,
244
47
69
'10
3
399
674
73
47
34
3
831
A. i i-.«,v
10,127
13,820
46,180
122,137
51,572
243,836
7,593
8,383
31,644
151,409
51,572
256,601
14,297
14,672
26,660
175,188
51,572
282,389
% of all
holdings
4.1
7-2
24.0
50.9
21.5
2.7
3-5
14.7
59.0
20. 1
:..i
5.2
9-5
62.0
18.2
Avnrogo ulicn
of holding*
in ncrna
81
194
462
4,362
31
I7K
579
3,785
21
LiOl
567
5,153
Source: "Cochiae County Tax Roll* 19*0-1950, Aiseaiora Tax
Holl 1964
1950
Fig. 28.—Privately Owned Land in Upper San Pedro River
Valley by Sis* at Holding. 1940-1964*
0-159
acres
16O-319
acres
320-999
acre*
1000*
acre*
Spanish
Source: *Cochis« County Ta^c Rolls 1940-1950, A»»e»«or» Ta
Boll 1964
Figure 10: 1940, 1950, and 1964 summary of acreage changes in Upper San Pedro River Valley (Rodgers 1965)
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The number of properties (column 3, i.e. the column with the heading "No.," in Figure 10)
does not represent the total number of landholdings within the watershed during that time; rather,
it appears to be the sample size Rodgers used. His thesis is quiet on this matter. The difficulty in
establishing every unique property's location and size likely prevented Rodgers from obtaining
the true total. He calculated "percentage of all holdings" (column 5) using "Total Acreage"
(column 4, last rows for each year examined). The pie charts at right display this calculation. For
example, in 1964, the properties greater in size than 1,000 acres covered a cumulative acreage of
175,188; the total acreage of all properties at that time was 282,389; the "percentage of all
holdings" for properties of that size was 62%, i.e. (175,188/282,389)*100.
Today, the Cochise County Information Technology (IT) Department has mapped each
property using customized Geographic Information System (GIS) software, thus simplifying the
tasks of displaying, querying, and analyzing land use trends. The IT Department provided the
authors with a geodatabase that contained property information for the entire county. The
geodatabase included the precise geographic location and size of each landholding. With this
information, all properties within the study area, i.e., all those properties with their "centroid"
within Townships 18S though 24S, and Ranges 19E through 23E, could be identified. Figure 11
shows the landhol dings within the study area for the year 2012. Including public and mining
land, there are 37,360 individual parcels.
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Figure 11: Parcels within the Study Area, 2012. Parcel data provided by Cochise County IT department; Hydrology data from USGS NHD
In order to complete the survey that Rodgers began, the same analysis was performed for the
2012 data. The 37,360 parcels were grouped into the four size categories. The number of parcels
within each category were counted, the sum acreage calculated, the percentage of all holdings
determined, and the average property size established (Table 1). Rather than, as Rodgers did,
calculate "percentage of all holdings" using "Total Acreage," "Number of Holdings" was used
instead. Figure 12 displays the results of those calculations for 1940, 1950, 1964 and 2012. With
the goal of continuing Rodgers' decade-by-decade analysis, the authors sought to additionally
obtain parcel data for 2001, 1991, 1981 and 1971. However, the parcel record for those decades
was not available in the same format as the record for 2012. 2011 marked the first year the
County mapped all parcels using GIS software. To analyze the previous decades and map the
changes, an alternative approach was needed.
10
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Categories of Land Holdings in Upper San Pedro River Valley by Number, Acreage, Percentage of Total
Holdings, and Average Size in 2012.
Acreage of Holdings
0-159
160-319
320-999
1000+
Total
Number of Holdings
36,891
170
209
90
37,360
Total Acreage
142,231
36,489
110,499
464,278
% of all holdings
98.74%
0.46%
0.56%
0.24%
100%
Average size (ac)
3.86
214.64
532.78
5,158.65
Table 1: 2012 Land Holdings Analysis (Data Courtesy of Cochise County IT Department, 2012)
IO-J.SQ
1160-319
11000 +
1940 (Data from Rodgers, 1965)
1950 (Data from Rodgers, 1965)
1964 (Data from Rodgers, 1965)
2012 (Data from Cochise County, 2012)
Figure 12: Percentage of all holdings using "Total Acreage" for 1940, 1950, 1964 and 2012. For example, in 2012, there were 90
properties as large as 1,000 acres or more, and a total of 37,360 properties: (90/37,360)*100 = 0.24%.
By 1965, each record in the Cochise County Tax Roll possessed two unique identifiers: the
assessment number and an Assessor Parcel Number (APN). The APN is a unique identification
number used in a system of tracking parcels called an "Assessor Map-based" system. Under this
system, the assessment map itself is incorporated into the parcel identifier (IAAO 2012). The
parcel identifier (e.g. the APN) refers to three units. For Cochise County, these three units are the
book, the map, and the parcel number. Within the study area, each "book" possesses a unique
three-digit number (e.g. 101, 102); generally its area coincides with old PLSS designations, often
covering the same area as two or more townships. To identify which books fall within the study
area, Microsoft Excel's MID function was used to isolate the first three characters of every
unique property's APN in the 2012 dataset. Excel's "remove duplicates" function then revealed
the unique numbers. Excluding mining parcels, there are 17 books.
11
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Within each book are multiple "maps," which represent a smaller geographic area and
possess a unique two-digit number, 1 through 99. Combining the "book" number with a "map"
number gives the "book-map" number. There are a total of 541 book-maps within the study area
(Appendix A). However, parcel data were not collected for every book-map, for several reasons,
including that some book-maps lie within federal or state lands that do not contain private, non-
mining parcels. For example, the Coronado National Forest and the U.S. Army's Fort Huachuca
together encompass over four dozen book-maps (Appendix C). Furthermore, parcel data were
not collected from the same number of book-maps every year. In 1971, parcel data were
collected from 325 book-maps; from 398 in 1981; from 415 in 1991; from 420 in 2001; and from
427 book-maps in 2012. This is because not all book-maps have always contained parcels. As
the number of housing developments increased, so did the number of book-maps containing
private, non-mining parcels (Appendix B).
Finally, the "parcel number" refers to a specific piece of real property within a book-map. A
parcel number is generally a three-digit number (e.g. 001). If the property has been subdivided
multiple times, a letter may be added (e.g. OOlAorOOlB). Atypical APN would be "10101001"
(i.e., Book 101, Map 01, Parcel number 001). While the Tax Roll began including the APN in
1965, it wasn't until 1971 that it began to be organized by the APN rather than the assessment
number. For example, the 1971 Tax Roll binder "101-01-001 to 106-39-149" contains all tax
records for parcels 10101001 through 10639149. In other words, in 1971, the County began
organizing the parcel records geographically. Each book and book-map refer to a specific area
(Figure 13). Some books are not shown in Figure 13 because the study area contains relatively
small portions of their area not visible at the map's resolution.
12
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I I Book-maps
Figure 13: Books and Book-Maps in the Study Area (Book-Map data provided by Cochise County IT department; Hydrology data from
USGSNHD)
13
-------
With improved mapping technologies and the County's use of more modern book keeping
procedures, the acreage analysis Rodgers made for 1882 through 1964 could be completed for
1971 through 2012. Additionally, detailed maps illustrating parcel density trends could be
constructed. To analyze and display parcel density in the study area, the changing number of
private, non-mining parcels within relevant book-maps for one year of each decade were tracked.
The first dataset is from 1971, as it marked the first year the Tax Roll was organized by APN,
and the remaining datasets are from the subsequent decades (1981, 1991, 2001, and 2012).
Collecting data for 1971, 1981, and 1991 required tracking not just the APN, but also the
antiquated assessment number. While the assessment number assigned to a unique property
changes every year and while it does not include geographic information, following the
assessment numbers proved to be useful. Assessment numbers advance numerically. The first tax
record within the first Tax Roll binder for any given year is "1" and each record follows in
succession. To count the number of parcels within a particular book-map, one must note the
assessment number at the beginning of a book-map (e.g. 1621 for book-map 10201 in the year
1971), flip through the Tax Roll binder's pages, note the last number (e.g. 1628), and calculate
the difference (for this example, 8). In this way, the changing number of parcels within particular
book-maps for the years 1971, 1981, and 1991 were tracked. Figure 14 shows a segment of the
first page of the 1971 Tax Roll record. Appendix D provides examples of multiple records for
1971, 1981, and 1991.
To obtain acreage data the years 1971, 1981, and 1991, unique parcels were randomly
selected from the dataset. Microsoft Excel's RandBetween function was used to generate the
random sample of assessment numbers for properties within the study area. Those particular
properties were located in Tax Roll binders, and their parcel size recorded. This was done for 1%
of parcels for each year: In 1971, 99 of 9,035 parcels were sampled; in 1981, 183 of 18,016
parcels were sampled; and in 1991, 228 of 22,786 were sampled. For properties within
subdivisions, the acreage was almost always omitted from the Tax Roll. For these properties,
parcel size was assumed to be a generous 0.25 acres, which is the size of a "suburban" housing
unit, as defined by the Integrated Climate and Land Use Scenarios (ICLUS) (USEPA 2009).
ASSESS/VENT AND 1AX iOLL FOR THE
COUNTY Of -"ist , ARIZONA FOR THE YEAR
Figure 14: Segment of scanned 1971 Tax Roll Record (Courtesy of Cochise County Archives, 2012). Identifying annotations added.
The County began maintaining the Tax Rolls electronically in 1996. The IT Department was
easily able to provide a spreadsheet listing all the properties within the study area for the year
2001. There were 29,319 private, non-mining parcels. However, obtaining the acreage
information for those properties was not as easy. The legal descriptions were missing. The
County compared the 2001 list of parcels to a 2002 list. Where APNs matched, the parcel size
from the 2002 data was ascribed to the 2001 data. Using this method, the County extracted
acreage data from the legal descriptions of over 10,000 parcels.
14
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As with the Tax Roll records from 1971, 1981, and 1991, the 2001/2002 records for
properties within subdivisions almost always lacked acreage information. The 2012 data were
used to determine the acreage of parcels within subdivisions. Where the APNs matched, the 2012
subdivision acreage was ascribed to the 2001 parcels. For the remaining properties within
subdivisions, parcel size was assumed to be 0.25 acres. Eventually, nearly 97% (or 28,308) of
the parcels in the 2001 dataset were assigned acreage.
Having determined the number and sizes of parcels throughout the study area and within
particular book-maps for 1971 through 2012, the acreage analysis not only picked-up where
Rodgers left-off but could also be incorporated into to more contemporary investigations, such as
ICLUS. The four acreage categories used in the Rodgers study reflect the splitting and
combining of original homesteads, and the regional shift from a largely rural and agricultural to a
more suburban and service-based community and economy. Examining land use trends using
ICLUS Housing Density (HD) categories further refined the analysis, and expanded its utility.
The ICLUS project dataset has been identified as ideal for projecting watershed-wide
development into the future because its national-scale HD scenarios are consistent with the
Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios
(Nakicenovic and Swart, 2000) greenhouse gas emissions storylines. ICLUS uses four categories
for HD representing rural, exurban, suburban, and urban land uses (Bierwagen et al. 2010;
USEPA 2009; USEPA 2010).
Density Category
Urban
Suburban
Exurban
Rural
Acres Per
Housing Unit
0.25
0.25-2
2-40
>40
Housing Units
Per Acre
>4
0.5-4
0.025-0.5
0.025
Hectares Per
Housing Unit
0.1
0.1-0.81
0.81-16.19
>16.19
Housing Units
Per Hectare
>10
1.23-10
0.06-1.23
0.06
Table 2: Explanation of ICLUS Housing Density (HD) Categories. ICLUS uses changes in HD to project changes in impervious surface cover,
which can be used to examine impacts to water quality.
Decade-by-decade parcel data were analyzed using the ICLUS HD categories to create maps
illustrating parcel concentration changes over time. The Cochise County book-map dataset
included book-map area in square meters. The area of each book-map was converted to acres,
and then divided by the number of parcels within that particular book-map. For each decade, the
book-maps were then classified as urban (less than 0.25 acres/parcel), suburban (0.25-2
acres/parcel), exurban (2-40 acres/parcel), or rural (more than 40 acres per parcel), resulting in
five distinct maps (Figures 20-24). For book-maps with no associated private, non-mining parcel
data (e.g., Fort Huachuca, the Coronado National Forest, book-maps encompassing undeveloped
land, etc.), the number of parcels per acre was assumed to be zero, i.e., rural because those book-
maps contained fewer than 0.025 housing units per acre.
15
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Results
Between 1971 and 2012, the number of private, non-mining parcels within the study area
increased from 9,035 to 36,511. The overall change increased consistently from 1971 to 2012,
with a rate of 657 parcels per year (dashed line in Figure 15). However, the decadal change had a
different trend. The rate of increase was the highest between 1971 and 1981 (898 parcels/year),
lower but still high between from 1981tol991 and from 1991to 2001 (477 and 653 parcels/year,
respectively), and an increasing but smaller rate from 2001 to 2012 (654 parcels/year). Between
1971 and 2012, the average parcel size dropped from 37.98 to 8.01 acres.
40
S 35
30
=-
•8
•_
01
J3
25
20
15
10
•3 5 -I
-Total Number of Parcels
•Average Parcel Size
•y = 0.657x-1285; R2 = 0.992
40
35
30
25
20
15
10
g
a
2.
ai
1960
1970
1980
1990
2000
2010
2020
Figure 15: Decadal Changes in Total Number of Parcels and Average Parcel Size for 1971-2012
Parcels with an area of 159 acres or less increased by almost 10% over the 41-years, representing
nearly 99% of all parcels by 2012 (Figure 12; Figure 16). Figure 16 incorporates data from the
Rodgers study to show acreage trends over the last 130 years. Between 1882 and 2012, the
number of parcels with an area of 159 acres or less jumped from 16.98% to 98.74%; parcels with
an area between 160 and 319 acres dropped from 71.70% to 0.47%.
.
3 3
£3 O
Figure 16: Decadal Trends Using Rodgers Acreage Categories: 1882 - 2012.
16
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Using the ICLUS housing unit sizes to analyze parcel data between 1971 and 2012 provides
greater insight into land use changes. Over the course of 41 years, the number of "urban" parcels
increased by over 36%, while parcels in all other acreage categories decreased: "suburban"
parcels by -15%, "exurban" by -8%, and "rural" parcels by -10%. Figure 17 shows these
acreage trends graphically. Table 3 details the percentage of parcels falling into the ICLUS
housing unit categories for each decade.
Urban (Less than .25 acres)
Suburban (Between 0.25 & 2 acres)
Exurban (Between 2 & 40 acres)
Rural (Greater than 40 acres)
Figure 17: Decadal Acreage Trends Using ICLUS Housing Density Categories, 1971 - 2012.
Year
1971
1981
1991
2001
2012
Urban
(Less than 0.25 acres)
3.03%
0.55%
3.49%
36.96%
39.41%
Suburban
(Between 0.25 & 2 acres)
51.52%
61.75%
64.63%
36.10%
32.47%
Exurban
(Between 2 & 40 acres)
32.32%
23.50%
24.89%
23.86%
25.73%
Rural
(Greater than 40 acres)
13.13%
14.21%
6.99%
3.08%
2.39%
Parcel
Sample Size
99
183
229
28,200
36,511
Total Number
of Parcels
9,035
18,016
22,786
29,319
36,511*
Table 3: Decadal Acreage Trends Using ICLUS HD Categories, 1971 - 2012. (*Note: Of the 37,360 parcels within the study area (Figure 11),
849 were public and/or mining parcels and therefore excluded from the ICLUS analysis).
As explained in the methods section, only the 2012 dataset included geographic information
for each parcel. Mapping changes over time required tracking the changing number of parcels
within individual book-maps. Figures 18 and 19 show decadal book-maps trends between 1971
and 2012. Table 4 details the number and percentage of book-maps falling into the ICLUS HD
categories for each decade. Over the course of 41 years, the area of land classified as "urban"
increased by 2.82%, the area classified as "suburban" by 8.35%, and the area classified as
"exurban" by 11.95%. The area of land classified as "rural" decreased by 23.13%.
17
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Percentage of Book-maps falling into
1 of 4 ICLUS HD Categories
f-f\r\f ^^^"ITrK^n H **c«? *Vi.jr\ ft T-^s •sr-iws r»^r natv»j»l\
60%
50%
40%
30%
/U/o
10% "
Suburban (Blwn. 0.25 & 2 acres per parcel)
Exurban (Btwn. 2 & 40 acres per parcel)
Rural (More than 40 acres per parcel)
. —
0% -1
1970 1980 1990 2000 2010
Figure 18: Decadal Book-Map Trends, 1971 - 2012 (Changing Percentage).
1 Urban Suburban Exurban Rural
1981
1991
2001
2012
Figure 19: Decadal Book-Map Trends, 1971 - 2012 (Changing Number).
Urban
Number of Percentage
Book-Maps of Total
0.92%
2.01%
2.65%
2.86%
Suburban
Number of Percentage
Book-Maps of Total
10.15%
15.08%
16.39%
18.33%
33
60
68
77
79
18.50%
Exurban
Number of
Book-Maps
105
141
163
175
189
Percentage
of Total
32.31%
35.43%
39.28%
41.67%
44.26%
Rural
Number of
Book-Maps
184
189
173
156
143
Percentage
of Total
56.62%
47.49%
41.69%
37.14%
33.49%
Total Number
of Book-Maps
325
398
415
420
427
Table 4: Number and Percentage of Book-Maps Falling into ICLUS HD Categories, 1971 - 2012.
Figures 20 through 24 show land use in the study area for the years 1971, 1981, 1991, 2001,
and 2012. The most significant changes occurred in and around established communities. The
land use in and around the communities of Tombstone, Bisbee, and Huachuca City changed at a
18
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notable pace. For example, the number of parcels within the City of Tombstone's boundaries
increased by 194% while the land designated as rural decreased by 100%. Change was most
pronounced in the central southwestern portion of the study area, near Fort Huachuca and Sierra
Vista. For example, the number of parcels in Sierra Vista's unincorporated counterpart, Sierra
Vista Southeast, increased by 550% while the area classified as rural decreased by 70.84%.
Figure 25 shows detailed maps of Sierra Vista Southeast and Tombstone in 1971 and 2012.
Esparza and Carruthers (2000) have also noted the growth and changing land use in and
around Sierra Vista. This pattern of growth, particularly in regard to its accompanying increase
in water consumption, agitates many. The concern that a rapidly increasing population could
destructively deplete water resources has been repeatedly expressed (American Rivers 1999,
Arias 2000, Browning-Aiken et al 2004, Bredehoeft et al. 1999, Pool and Coes 1999, West and
Vasquez-Leon 2008, USPP 2010, and many others), and periodically litigated - e.g., a 1990s suit
against a U.S. Fish and Wildlife Service non-jeopardy decision, a 2002 suit against Fort
Huachuca's planned expansion (CBD 2013), and a recently filed Superior Court suit seeking to
overturn a state ruling that permitted a new 6,900-home development in Sierra Vista (Davis
2013).
19
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Upper San Pedro Land-Use in 1971
Book-Maps Falling into 1 of 4 Housing Density Categories
In 1971. the study area contained 9.035 parcels.
3.03% of those parcels were less than .25 acres.
51.52% were between .25 and2 acres.
32.32% were between 2 and 40 acres.
13.13 percent were greater than 40 acres.
The parcels were Iocatedin32:i book-maps.
.92% of those books-maps contained more than 4 parcels acre.
10-15% contained between .5 and 4 parcels'acre,
32.31% contained between .025 and .5 parcels, acre.
13.13% contained less Than .025 parcels acre
J Rural
| | Ex urban
| | Suburban
| Urban
^^^— San Pedro River
SanPedroWatershed
1C Ikfflw
_|
Land-Use data derived from parcel data provided by CochiseCounty. Land-Use designations based onEPAICLUS housing density categories.
San Pedro River & Watershed data from USGS NHD.
Figure 20: Upper San Pedro Land Use, 1971
20
-------
Upper San Pedro Land-Use in 1981
Book-Maps Falling into 1 of 4 Housing Density Categories
In 19S1; tie study area contained 18.016 parcels.
55°o of those parcels ^vere less than .25 acres.
61.75%\vere between .25 and 2 acres.
23.50%\vere benveen2 and40 acres.
14_21%\vere greater than 40 acres.
The parcels were located in 39S book-maps.
2.01% of those boots-maps contained more than 4 parcels acre
15.OS% contained between .5 and 4 parcels acre.
35.43%containedbettveen .025 and .5 parcelsacre.
47.49% contained less than .025 parcels acre
j Rural
| Exurban
Suburban
| Urban
• SanPedroRiver
SanPedro Watershed
10 Mil«
_|
Land-Use data derived from parcel data provided by Co chiseCounty. Land-Use designations based onEPAICLUS housing density categories.
San Pedro River & Watershed data from USGS NHD.
Figure 21: Upper San Pedro Land Use, 1981
21
-------
Upper San Pedro Land-Use in 1991
Book-Maps Falling into 1 of 4 Housing Density Categories
In 1991T the study area contained 22:?S6 parcels.
3.49% of those parcels were less than .25 acres.
64.63%'were between .25 and2 acres.
24.89%were between2 and4Q acres.
6.99% were greater than 40 acres.
The par eels were Iocatedin415 book-maps.
2.65% of those books-maps contained more than 4 parcels acre.
16.39% contained between .5 and4 parcels acre.
39-23% contained between .025 and -5 parcels acre.
41.69% contained less than ,025 parcels acre
I I Rural
Ex urban
Suburban
Urban
SanPedroRiver
S anPe dro Watershed
5 10
i I
Land-Use data derived from parcel data provided by CochiseCountv. Land-Use deasnations based onEPAICLUS housing density categories.
San Pedro River & Watershed data from USGS XHD.
Figure 22: Upper San Pedro Land Use, 1991
22
-------
Upper San Pedro Land-Use in 2001
Book-Maps Falling into 1 of 4 Housing Density Categories
In2QGl: Ihe study area curtained 297319 parcels.
3
-------
Upper San Pedro Land-Use in 2012
Book-Maps Falling into 1 of 4 Housing Density Categories
In 2012. the study area curtained 36.Ml parcels.
39.41% of those parcels were less than .25 acres.
32.10%wen between.25 and2 acres.
25.73% were between 2 and 40 acres.
2.39% were greater than40 acres.
The par eels were located in42? book-maps.
3.75% ef those books-maps contained more than 4 parcels acre.
IS.50% contained between .5 and 4 parcels acre.
44 26% contained between 025 and 5 parcels acre
33.49% contained less than .025 parcels acre
Rural
| Exurban
Suburban
Urban
SanPedroRiver
SanPedro Watershed
! 10 MilB
I |
Land-Use dsta deiiwdfem parcel data provided tytochseCouny. Land-Use deasnations basedocEFAlCLUS housing denatycategones.
San Pedro River & \\ateisbeddatafrcm USGS N'HD.
Figure 24: Upper San Pedro Land Use, 2012
24
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Land Use Change in Two Upper San Pedro Communities:
Sierra Vista Southeast and Tombstone, 1971 and 2012
I I Tombstone
I Sierra Visla Southeast
~ San Pedro
I Watershed
Sierra Vista Southeast, 1971
Sierra Vista Southeast. 2012
R"™' The number of parcels within Sierra Visla Southeast increased by 550%.
Exurban i\w area classified as rural decreased by 29%.
— Suburban [be number of parcels within the C'ity of Tombstone increased by 194%.
^| Urban The land designated as rural decreased by 14.29%.
Tombstone. 1971
Tombstone, 2012
Land Use data delivered from parcel data provided by Cochise County. Land use designations based on EPA ICLUS housing density categories.
San Pedro River and Watershed data from USGS NHD. Arizona administrative boundaries from TANA
Figure 25: Land Use Changes in Sierra Vista Southeast and Tombstone, 1971 and 2012
Discussion
Historic parcel data have been used to show land use trends in a portion of the Upper San
Pedro River Watershed, and the methodology has been described. As a methodology for
evaluating land use change, analyzing parcel data is promising but has serious limitations. Some
challenges include:
• The difficulty in amassing representative historical acreage data;
• The discrepancy between what exists on paper versus reality, i.e., parcels that have been
subdivided may not have been developed;
• The substantial amount of time needed to collect the data.
Incorporating findings from other change detection methodologies (such as archival
photographs and remote imagery) could make historic parcel data analysis more accurate.
Differences and similarities in findings could offer important insight into the efficacy of each
approach, and provide more reliable reference conditions to use in environmental decision-
making.
25
-------
Despite its limitations, using historic parcel data to examine land use change can be quite
useful. Pairing historic parcel data with other historic and localized parameters could allow
environmental managers to qualify the relationship between changing land use and other
environmental and cultural trends at the community scale. For example, if paired with
demographic and water quality data, environmental managers could use unique and scaled
historic baselines to calibrate planning tools designed to explore plausible future impacts of
different scenarios to a specific watershed, or even specific areas within a watershed.
The Automated Geospatial Watershed Assessment (AGWA) tool has recently been used to
characterize the hydrologic impacts of growth in the San Pedro River Watershed (Burns et al.
2013). However, rather than rely on historic population data unique to the watershed, the
analysis drew from nation-wide population projections. Instead of using historic water quality
data unique to the watershed, the analysis integrated the Soil Water Assessment Tool (SWAT)
component of AGWA with national scenarios provided from ICLUS.
As they are available, incorporating historic data into AGWA could potentially provide more
accurate and relevant projections for smaller scale analyses versus the basin scale as was
reported by Me et al. (2011). For example, between 1971 and 2012, the number of parcels in
Tombstone increased 194%. The number of "urban" and "suburban" book-maps increased by
40% and 110%, respectively; and the number of "exurban" and "rural" book-maps decreased by
12.5% and 100%, respectively. In that same period, the population increased by 11% (U.S.
Census Decennial, 1970 & 2010). Models used to forecast probable landscape changes could
incorporate such trends.
Historic water quality data are available through EPA's STOrage and RETrieval (STORET)
data "warehouse," a repository for water quality data, including biological, chemical, and
physical parameters. Within two miles of Tombstone, there are ten monitoring stations with a
combined 1,023 water quality records, the earliest dating from 1952 (EPA STORET 2012).
These records might also reveal useful trends that could be incorporated into models such as the
AGWA watershed modeling system.
Environmental managers could also use historic baselines, including the data generated from
this study, to assess the impacts of projects. NEPA requires that the "indirect" effects of federally
funded projects be analyzed and described in environmental documents, such as Environmental
Assessments and Environmental Impact Statements. Federal regulations state that, "indirect
effects may include growth inducing effects and other effects related to induced changes in the
pattern of land use, population density or growth rate, and related effects on air and water and
other natural systems, including ecosystems" (40 CFR § 1508.8(b)).
The lack of historic data often requires decision makers to assess a project's indirect effects
without sufficient context or background. How well can a NEPA reviewer assess the growth-
inducing impacts of, for example, a wastewater infrastructure expansion project without knowing
the historic relationship between wastewater treatment capacity, population, land use, and other
environmental parameters? Pairing historic parcel data and the resulting land use trends analysis
with historic population and wastewater treatment capacity data could help evaluate an
expansion project's potential impact on land use patterns.
26
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The City of Sierra Vista provides an example. Between 1971 and 2012, the number of
parcels in Sierra Vista (including Sierra Vista Southeast) increased -549%, from 1,591 parcels to
10,327 parcels; the percentage of "urban" and "suburban" book-maps increased by -800% and
-112%, respectively. The number of "exurban" and "rural" book-maps decreased by -8% and
-75%, respectively. In that same period, the population increased by -780% (U.S. Census
Decennial, 1970 & 2010), and wastewater treatment capacity increased -566%, from 0.6 million
gallons a day (MGD) to 4 MGD (SEAGO 1978, 2012). Table 5 lists the decadal changes.
Figures 26, 27, and 28 display the historic relationships graphically.
•
1970
1980
1990
2000
2010
Population of
Sierra Vista
6,689
24,937
42,220
52,123
58,685
Number of
parcels
1,591
3,909
5,548
8,406
10,327
Book-Maps
% Urban
0.00%
3.36%
5.65%
6.40%
8.00%
% Suburban
19.23%
30.25%
33.06%
38.40%
40.80%
Book-Maps
% Exurban
46.15%
48.74%
48.39%
45.60%
42.40%
Book-Maps
% Rural
34.62%
17.65%
12.90%
9.60%
8.80%
Wastewater
Capacity
(MGD)
.6
.6
2.9
4
4
Table 5: Decadal Trends in Population, Parcels, Book-Maps, and WWTF capacity in Sierra Vista, AZ.
I960
1970
1980
1990
2000
2010
2020
Figure 26: Population and Wastewater Treatment Facility (WWTF) Capacity in Sierra Vista, AZ: 1971 - 2012
1960
1970
1980
1990
2000
2010
2020
Figure 27: Number of Parcels and WWTF Capacity in Sierra Vista, AZ: 1971-2012
27
-------
Book-maps (% Urban)
Book-maps (% Suburban)
Book-maps (% Exurban)
Book-maps (% Rural)
'WWTF capacity (MOD)
1960
1970
1980
1990
2000
2010
2020
Figure 28: WWTF Capacity and Book-Maps in Sierra Vista, AZ: 1971-2012
Assuming the complex factors that affect everything from wastewater flow to subdivision
development continue at the same rate as they did between 1971 through 2012, the trends
displayed in Figures 26, 27, and 28 could be used to develop a scenario that projects how
expanding Sierra Vista's WWTF would affect the area's pattern of land use1.
The analysis suggests that for every increase of 1 MOD in Sierra Vista's wastewater
treatment capacity, 1) the population would increase by nearly 13,000 people; 2) the number of
parcels would increase by roughly 2,150 units; and 3) the land use would become roughly 1%
more urban, 6% more suburban, and nearly 7% less rural. Using these assumptions, a NEPA
reviewer analyzing the indirect effects of expanding Sierra Vista's WWTF from the current 4
MGD to a hypothetical 6 MGD could consider the addition of 26,000 more people, 4,300 more
parcels, and a landscape that would become 2% more urban, 12% more suburban, and 14% less
rural.
Determining whether or not these changes "significantly affect the pattern and type of land
use.. .including altering the character of existing residential areas" (40 CFR § 6.207 (a)(3)(xi)) is,
of course, a more complicated endeavor that would require additional information. But
incorporating a future scenarios analysis into an indirect effects assessment could provide a more
reliable basis from which to make predictions than the present approach, which has been
criticized as inconsistent and imprecise (Mandelker 2010).
1 Many of the properties in Sierra Vista Southeast rely on private wells and septic systems. While the expansion of the Sierra Vista WWTF would
not necessarily directly serve its unincorporated counterpart, the expanded capacity would accommodate a greater number of individuals and
businesses within the facility's core service area. Growth in central Sierra Vista would very likely catalyze growth in Sierra Vista Southeast, the
historical development of which has been tied to its incorporated neighbor.
28
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Conclusion
"Upper San Pedro River Valley" parcel size and location trends between 1882 and 2012 were
evaluated using 1) assessor records, and 2) the 1965 research completed by William M. Rodgers.
Land use trends between 1971 and 2012 were produced using 1) assessor records, 2) the book-
map geodatabase provided by Cochise County IT staff, and 3) EPA's ICLUS HD categories.
ICLUS HD categories were used for a number of reasons, including their use in a similar EPA
research effort (Burns et al. 2013), the relative simplicity of their reclassification to a product
supported by modeling tools (e.g. AGWA), and the significant science behind the product (IPCC
and SRES consistent storylines).
The analysis shows substantial land use change, particularly in and around Sierra Vista.
However, the changes seem relatively minor compared to the linear increase in the total number
of parcels and people. Perhaps this is because the watershed includes large amounts of land
where development is either restricted or limited (Appendix C). The analysis, in other words,
shows increased density. While typically indicative of a more sustainable community (Burchell
and Mukherji 2003), population-driven urbanization could exhaust the one source of drinking
water (the local aquifer) and the aquifer-dependent San Pedro River. There is particular concern
over how new development could affect water rights the BLM holds to maintain flows through
SPRNCA. This concern again emerged in the courts in May 2013 when the BLM filed suit to
prevent the proposed 6,900-unit development in Sierra Vista (Davis 2013).
Evaluating the relationship between land use trends and water consumption patterns (i.e.
changing number and capacity of wells, depth to aquifer levels, and river flow) could also help
calibrate forecasting tools, inform indirect effect analyses, and generally help communities
within the Upper San Pedro Watershed navigate contentious projects and environmental
management quandaries. Such an analysis would benefit many communities in arid and semi-
arid geographies. Rapidly rising populations place considerable pressure on finite water
resources throughout the Southwest. Southwestern communities could greatly benefit from
looking to their past to understand future impacts to water and other vital resources.
The methodology described could be applied well beyond the San Pedro. Communities
across the Country - including the vast majority of municipalities within California, Arizona,
New Jersey, New York, Virginia, and Nevada, as well as an unknown percentage of
municipalities within Georgia, Kentucky, Maryland, and Vermont - have used the assessor map-
based system (USDA 1979). While many of these communities have likely incorporated GIS to
more reliably track parcels, it is also likely that today's parcel identification numbers reflect
yesterday's map-based system and that the approach defined here could be widely replicated.
Alternative futures analyses allow us to consider various scenarios, and to develop strategies
that better prepare society to confront the challenges ahead. In the face of climate change,
economic instability, and resource scarcity, futures analyses can help protect our most vulnerable
people and places. As possible, such analyses should be based on local historic trends. At the
very least, the usefulness of present models should be judged by their ability to generate
simulations that describe known historic conditions. As Richard Powers observed, "the simplest
possible test for any futures game consist(s) in finding out whether it (can) predict the past."
29
-------
Appendix A: Books and Book-Maps within the Study Area
102
10201
10202
10203
10204
10206
10207
10208
10209
10210
10211
10218
10221
10234
10235
10236
10259
103
10337
10338
10339
10340
10341
10342
10343
10344
10346
10347
10348
10350
10351
104
10401 10454
10402 10455
10403 10456
10404 10457
10405 10458
10406 10460
10407 10461
10408 10462
10409 10463
10410 10464
10411 10465
10412 10466
10413 10467
10414 10468
10415 10469
10416 10470
10417 10473
10418 10474
10419 10475
10420 10476
10421 10477
10422 10478
10423 10479
10424 10480
10425 10481
10426 10482
10427 10483
10431 10484
10434 10485
10437
10438
10439
10440
10441
10442
10443
10444
10445
10446
10447
10448
10449
10450
10451
10452
10453
105
10502 10553
10503 10554
10504 10556
10505 10558
10506 10559
10507 10560
10508 10564
10509 10565
10510 10566
10511 10567
10512 10568
10513 10569
10514 10570
10515 10571
10516 10573
10517 10574
10518 10575
10519 10576
10520 10577
10521 10578
10524 10583
10525 10588
10527 10589
10528 10590
10529 10591
10530 10592
10531 10593
10533 10594
10534 10595
10535 10596
10536 10597
10537 10598
10538 10599
10539
10540
10541
10542
10543
10544
10546
10547
10548
10549
10550
10551
10552
106
10601 10655
10602 10656
10603 10657
10604 10658
10605 10659
10606 10661
10608 10662
10609 10663
10610 10664
10611 10665
10612 10666
10615 10667
10616 10668
10617 10669
10618 10670
10619 10671
10620 10672
10621 10673
10622 10674
10623 10675
10624 10677
10625 10678
10626
10627
10628
10629
10631
10632
10634
10635
10636
10639
10640
10641
10642
10643
10645
10646
10647
10648
10649
10650
10651
10652
10653
10654
107
10701 10765
10713 10766
10715 10767
10716 10768
10717 10769
10718 10770
10719 10771
10720 10772
10721 10773
10722 10774
10723 10775
10724 10776
10727 10777
10728 10778
10729 10779
10730 10780
10731 10781
10733 10782
10734 10783
10736 10784
10737
10738
10739
10740
10741
10742
10743
10744
10745
10746
10747
10748
10749
10750
10751
10752
10754
10755
10756
10758
10759
10760
10761
10762
10763
10764
108
10801
10806
10807
10808
10811
10812
10813
10814
10815
10816
10817
10818
10819
10820
10821
10822
10829
10830
10831
10832
10833
10836
10837
10838
10839
10840
10841
10844
10850
10853
10869
10876
10881
10882
10883
109
10901
10902
10903
10904
10905
10906
10907
10908
10909
10910
10911
10912
10913
10914
10915
10917
10918
10919
10921
10924
10925
10928
10930
10932
10933
110
11001
11003
11004
11005
11006
11009
11012
11013
11014
11016
11017
11018
11019
11020
11022
11023
11024
11025
11026
11027
11028
11029
11030
11031
11032
11033
11034
11035
11040
11041
11042
11043
11044
11045
11050
11051
11054
11055
11056
11057
11059
11060
120
12031
121
12101
12102
12103
12104
12105
12106
12107
12108
12109
12110
12111
12112
12113
12114
12115
12116
12117
12118
12119
12120
12121
12122
12123
12125
12126
12127
12128
12129
12130
12131
12133
12134
12135
12136
12137
12139
12140
12141
12142
12143
12144
12146
12147
12149
12150
12151
30
-------
Appendix B: Changing Number of Parcels within Study Area Book-Maps
Book-Map
10201
10202
10203
10204
10206
10207
10208
10209
10210
10211
10218
10221
10234
10235
10236
10259
10337
10338
10339
10340
10341
10342
10343
10344
10346
10347
10348
10350
10351
10401
10402
10403
10404
10405
10406
10407
10408
10409
10410
10411
10412
10413
1971
(# of parcels)
8
1
1
0
1
0
0
0
0
1
0
0
11
o
5
14
0
3
1
4
1
1
0
1
0
0
0
0
1
2
0
14
46
6
1
o
J
102
5
21
104
0
17
18
1981
(# of parcels)
17
5
7
6
1
0
3
3
16
11
8
13
14
4
21
0
8
3
5
1
1
2
12
4
1
2
1
1
8
18
21
52
6
10
14
110
34
49
105
16
18
23
1991
(# of parcels)
22
5
7
10
1
5
4
5
26
14
19
18
19
6
24
13
18
3
6
1
1
2
13
6
1
4
6
1
10
15
29
52
12
12
25
162
150
58
109
26
21
23
2001
(# of parcels)
25
5
7
11
1
5
5
5
34
19
21
23
20
7
29
15
19
o
J
9
1
1
2
13
6
1
5
6
1
10
289
285
66
222
95
34
298
222
115
109
50
23
35
2012
(# of parcels)
26
7
10
14
1
3
6
9
31
48
24
23
45
1
2
28
21
3
11
1
1
2
13
6
1
5
6
4
15
395
373
72
227
106
35
402
247
126
107
85
27
18
-------
Book-Map
10414
10415
10416
10417
10418
10419
10420
10421
10422
10423
10424
10427
10431
10434
10437
10438
10439
10440
10441
10442
10443
10444
10445
10446
10447
10448
10449
10450
10451
10452
10453
10454
10455
10456
10457
10458
10460
10461
10462
10463
10464
10465
10466
10467
1971
(# of parcels)
21
27
10
1
1
6
17
17
8
29
0
5
0
2
3
2
2
1
9
33
1
o
J
6
366
6
9
124
9
7
o
3
1
8
7
o
J
0
0
2
6
o
J
1
13
3
12
5
1981
(# of parcels)
28
43
10
2
2
7
25
25
10
47
0
15
17
7
3
6
2
1
9
61
8
17
8
359
9
10
122
12
6
4
1
10
7
15
0
80
14
2
5
3
17
3
14
6
1991
(# of parcels)
31
76
16
2
2
7
31
26
11
47
0
25
76
8
5
5
2
8
14
67
9
37
10
357
9
19
121
11
7
5
1
85
47
14
0
82
18
2
7
4
17
3
14
6
2001
(# of parcels)
33
74
24
65
81
7
29
27
16
43
16
224
220
8
7
7
2
15
15
81
72
80
15
343
29
24
90
11
7
5
24
122
79
14
5
82
20
2
12
5
18
4
14
7
2012
(# of parcels)
36
75
27
137
149
8
30
27
16
42
259
473
271
13
8
11
11
14
18
99
94
106
25
336
43
27
82
29
13
6
42
138
137
15
5
83
20
2
15
5
20
6
13
10
32
-------
Book-Map
10468
10469
10470
10473
10474
10475
10476
10477
10478
10479
10480
10481
10482
10483
10484
10485
10502
10503
10504
10505
10506
10507
10508
10509
10510
10511
10512
10513
10514
10515
10516
10517
10518
10519
10520
10521
10524
10525
10527
10528
10529
10530
10531
10533
1971
(# of parcels)
4
3
2
1
2
0
0
0
0
0
0
0
0
0
0
0
1
4
422
81
2
15
o
J
9
37
15
23
110
100
2
5
0
0
1
1
1
1
4
1
4
131
5
1
1
1981
(# of parcels)
10
3
3
1
3
46
24
64
24
0
0
0
16
16
0
0
1
7
481
414
22
25
2
51
39
15
24
110
202
140
48
66
47
1
34
o
5
i
11
i
o
J
130
7
1
1
1991
(# of parcels)
22
3
3
1
22
66
183
76
31
18
28
5
179
12
36
33
1
5
501
555
32
43
6
57
43
16
26
111
259
143
51
102
96
1
111
3
1
12
1
6
130
7
1
1
2001
(# of parcels)
23
3
3
1
33
77
236
106
34
45
42
7
187
12
67
35
1
45
500
562
46
51
6
57
42
16
26
111
205
143
339
102
160
1
159
o
3
1
13
1
6
111
8
1
1
2012
(# of parcels)
26
3
3
1
38
83
217
87
34
67
83
7
258
15
95
36
1
646
502
578
53
57
7
58
43
16
28
111
207
329
967
100
168
1
170
4
1
13
1
6
104
10
1
1
33
-------
Book-Map
10534
10535
10536
10537
10538
10539
10540
10541
10542
10543
10544
10546
10547
10548
10549
10550
10551
10552
10553
10554
10556
10558
10559
10560
10564
10565
10566
10567
10568
10569
10570
10571
10572
10573
10574
10575
10576
10577
10578
10583
10588
10589
10590
10591
1971
(# of parcels)
1
1
1
9
7
56
24
1
5
3
1
6
1
3
2
1
8
4
2
1
0
4
0
1
1
0
0
1
o
J
2
2
1
0
1
1
1
1
1
1
1
0
59
24
41
1981
(# of parcels)
1
2
4
14
9
69
31
5
5
5
1
10
1
7
1
1
10
14
2
1
161
4
1
0
1
0
0
1
o
J
2
2
1
0
1
1
1
1
1
1
1
272
58
35
159
1991
(# of parcels)
1
2
4
20
10
78
62
15
7
5
1
12
1
9
1
1
12
13
2
1
160
4
1
0
1
0
0
1
3
2
2
1
0
1
1
1
1
1
1
1
272
56
34
157
2001
(# of parcels)
1
2
4
29
20
87
81
38
17
6
1
17
1
9
1
8
12
13
2
1
160
4
1
0
1
0
0
1
o
J
2
2
1
1
1
1
1
1
1
1
1
272
54
35
155
2012
(# of parcels)
1
2
4
37
67
105
96
50
27
6
1
22
1
10
1
8
13
14
2
6
160
12
1
1
1
1
1
1
o
J
2
2
1
1
1
1
1
1
1
1
1
263
58
35
155
34
-------
Book-Map
10592
10593
10594
10595
10596
10597
10598
10599
10601
10602
10603
10604
10605
10606
10608
10609
10610
10611
10612
10615
10616
10617
10618
10619
10620
10621
10622
10623
10624
10625
10626
10627
10628
10629
10631
10632
10634
10635
10636
10639
10640
10641
10642
10643
1971
(# of parcels)
o
6
0
0
0
0
0
0
0
6
3
6
156
36
94
4
3
4
2
2
7
2
3
12
24
48
196
96
0
157
65
36
51
9
0
10
0
32
9
3
210
0
4
2
0
1981
(# of parcels)
516
74
277
0
1
278
265
209
6
3
10
151
47
31
15
38
3
3
3
11
50
21
22
29
48
198
96
0
177
75
38
50
10
129
10
51
34
9
3
208
60
4
2
0
1991
(# of parcels)
861
72
277
390
3
352
321
290
6
3
11
149
51
47
24
38
4
5
10
9
64
23
24
35
46
195
96
12
182
84
41
54
11
122
13
4
41
13
3
193
61
5
3
0
2001
(# of parcels)
896
72
273
875
105
798
390
473
6
3
30
148
62
58
36
65
3
7
19
15
122
28
65
39
44
185
95
17
183
116
40
57
11
103
15
4
43
14
3
178
58
9
7
18
2012
(# of parcels)
882
68
275
989
659
819
395
473
7
6
117
157
63
75
37
66
2
8
25
293
130
56
84
42
44
184
95
19
192
134
41
59
14
97
16
4
47
21
4
170
58
24
7
31
35
-------
Book-Map
10645
10646
10647
10648
10649
10650
10651
10652
10653
10654
10655
10656
10657
10658
10659
10661
10662
10663
10664
10665
10666
10667
10668
10669
10670
10671
10672
10673
10674
10675
10677
10678
10701
10713
10715
10716
10717
10718
10719
10720
10721
10722
10723
10724
1971
(# of parcels)
2
5
44
154
62
0
81
1
4
4
2
133
18
34
0
21
81
0
108
27
0
11
295
342
137
238
2
12
181
113
0
0
7
0
0
12
110
0
12
8
12
127
28
33
1981
(# of parcels)
2
8
59
158
70
41
115
1
5
4
3
191
23
35
108
24
71
361
108
30
123
13
297
391
138
246
9
25
178
115
80
0
13
0
37
23
110
1
23
18
18
155
29
8
1991
(# of parcels)
2
11
132
159
69
104
117
1
3
6
3
192
24
36
108
25
81
360
108
40
122
23
296
450
138
247
11
33
178
124
80
148
8
2
104
46
111
49
29
23
19
152
39
10
2001
(# of parcels)
o
5
34
77
161
76
159
118
2
4
17
3
196
18
132
108
27
82
408
108
40
122
24
298
527
138
249
72
35
178
125
80
152
15
68
119
76
110
49
34
38
22
162
39
10
2012
(# of parcels)
2
41
96
164
78
159
127
4
4
28
2
202
16
132
108
28
85
410
108
41
123
24
295
527
221
250
595
38
180
172
80
152
226
8
126
107
111
49
35
43
24
164
49
12
36
-------
Book-Map
10727
10728
10729
10730
10731
10733
10734
10736
10737
10738
10739
10740
10741
10742
10743
10744
10745
10746
10747
10748
10749
10750
10751
10752
10754
10755
10756
10758
10759
10760
10761
10762
10763
10764
10765
10766
10767
10768
10769
10770
10771
10772
10773
10774
1971
(# of parcels)
2
0
10
0
13
176
0
0
69
7
9
5
0
7
2
8
0
0
42
4
0
173
69
11
0
0
3
1
13
0
2
0
0
0
0
99
140
245
53
0
0
1
0
0
1981
(# of parcels)
2
0
13
0
27
176
0
162
72
7
12
8
7
7
2
12
23
8
29
7
0
205
329
24
17
9
15
8
42
14
12
48
46
22
0
144
183
291
84
9
153
64
107
145
1991
(# of parcels)
2
2
14
0
27
176
1
240
76
7
13
9
7
16
36
16
24
8
28
14
174
208
345
43
27
20
33
14
47
15
58
63
50
30
0
177
204
292
93
22
146
107
163
145
2001
(# of parcels)
2
2
15
0
27
176
8
243
136
7
20
16
7
20
68
21
18
9
27
378
290
208
414
90
36
46
57
17
55
60
83
86
63
41
0
222
214
298
103
29
145
116
161
144
2012
(# of parcels)
11
6
16
2
27
176
14
245
139
7
24
27
7
21
85
28
18
10
53
190
951
215
421
114
42
63
352
29
65
78
99
99
99
46
119
226
222
299
124
40
145
119
163
144
37
-------
Book-Map
10775
10776
10777
10778
10779
10780
10781
10782
10783
10784
10801
10806
10807
10808
10811
10812
10813
10814
10815
10816
10817
10818
10819
10820
10821
10822
10829
10830
10831
10832
10833
10836
10837
10838
10839
10840
10841
10844
10850
10853
10869
10876
10881
10882
1971
(# of parcels)
0
0
0
0
0
0
0
0
0
0
o
J
4
1
10
0
0
0
0
65
4
1
5
2
2
2
1
1
1
27
91
0
3
2
8
13
6
11
1
1
9
2
0
42
0
1981
(# of parcels)
44
133
173
51
219
1
2
117
124
178
4
3
2
10
12
30
17
1
66
5
1
4
2
7
2
1
1
3
67
89
0
3
2
11
21
13
13
1
1
11
2
1
42
0
1991
(# of parcels)
59
124
155
423
219
1
0
117
124
178
4
3
2
10
14
36
17
4
65
6
1
4
2
11
2
1
1
3
76
86
0
3
3
11
22
13
13
1
1
13
2
1
42
0
2001
(# of parcels)
83
124
155
819
219
1
0
117
124
178
4
o
J
2
11
19
37
35
23
73
6
1
6
2
10
2
1
1
3
99
83
10
3
3
31
23
15
18
1
1
14
2
1
42
0
2012
(# of parcels)
89
124
156
876
220
1
1
117
124
178
12
3
1
48
19
40
37
31
76
9
28
8
4
75
2
1
1
3
112
85
17
6
5
60
27
15
18
1
1
19
2
1
42
1
38
-------
Book-Map
10883
10901
10902
10903
10904
10905
10906
10907
10908
10909
10910
10911
10912
10913
10914
10915
10917
10918
10919
10921
10924
10925
10928
10930
10932
10933
11001
11003
11004
11005
11006
11009
11012
11013
11014
11016
11017
11018
11019
11020
11022
11023
11024
11025
1971
(# of parcels)
0
7
0
4
28
31
21
55
36
45
154
111
91
96
43
94
8
o
J
5
0
0
7
0
0
0
0
5
2
7
2
2
0
0
0
0
0
0
4
31
36
1
1
1
1
1981
(# of parcels)
0
4
2
5
32
38
23
60
41
55
174
133
96
98
46
94
9
3
10
398
0
13
1
1
160
263
11
2
7
2
2
0
0
0
0
0
0
6
31
36
3
1
1
2
1991
(# of parcels)
0
4
2
10
38
38
23
64
40
60
179
143
98
98
43
92
10
93
10
400
0
24
1
1
159
258
15
2
7
93
2
2
7
10
7
8
24
7
31
36
5
1
1
2
2001
(# of parcels)
0
4
3
12
39
40
16
64
41
58
179
140
93
100
41
90
9
o
J
10
392
0
25
1
10
160
258
14
2
9
2
2
2
9
10
9
8
25
18
31
34
5
7
1
2
2012
(# of parcels)
4
11
5
25
39
43
20
73
43
58
192
141
118
105
42
86
11
6
21
378
3
36
1
10
158
221
15
2
18
6
2
2
9
11
11
8
29
146
31
31
5
8
1
2
39
-------
Book-Map
11026
11027
11028
11029
11030
11031
11032
11033
11034
11035
11040
11041
11042
11043
11044
11045
11050
11051
11054
11055
11056
11057
11059
11060
12031
12101
12102
12103
12104
12105
12106
12107
12108
12109
12110
12111
12112
12113
12114
12115
12116
12117
12118
12119
1971
(# of parcels)
1
1
1
2
2
0
0
1
1
1
1
2
1
1
4
4
4
2
2
1
3
12
0
0
0
2
1
4
5
14
23
3
14
10
31
9
2
14
15
18
1
27
14
12
1981
(# of parcels)
1
2
3
2
5
0
0
1
2
1
1
4
2
1
4
4
5
3
2
1
3
13
2
0
0
3
2
5
17
14
45
6
16
12
36
16
3
21
25
40
1
34
52
16
1991
(# of parcels)
1
2
3
4
17
0
0
2
7
2
1
4
2
1
4
4
8
14
2
1
4
17
2
0
0
6
3
8
131
17
48
6
19
16
36
18
5
18
29
45
2
30
51
15
2001
(# of parcels)
1
2
3
2
19
8
22
7
7
3
1
4
2
1
7
6
10
18
2
1
4
19
2
10
235
43
5
13
31
18
48
7
25
16
37
20
8
23
32
76
2
40
49
18
2012
(# of parcels)
1
2
3
4
57
9
26
7
7
3
1
5
2
1
11
7
14
28
2
1
4
20
3
6
25
55
9
12
34
18
56
6
25
17
28
18
8
23
30
89
7
43
50
23
40
-------
Book-Map
12120
12121
12122
12124
12123
12125
12126
12127
12128
12129
12130
12131
12133
12134
12135
12136
12137
12139
12140
12141
12142
12143
12144
12146
12147
12149
12150
12151
12425
12431
12432
12434
12435
12436
12439
12440
12441
12445
1971
(# of parcels)
1
13
7
4
2
6
2
4
11
0
0
4
5
2
7
10
2
1
2
2
1
2
0
0
3
1
1
2
0
2
10
29
0
1981
(# of parcels)
13
44
13
4
15
2
4
16
0
0
4
5
3
8
14
2
1
2
2
3
1
2
1
2
1
2
40
16
3
1
1
2
0
2
10
14
0
1991
(# of parcels)
25
69
20
8
4
14
2
4
23
16
12
12
4
3
8
13
4
4
2
4
3
15
12
1
2
1
2
41
18
3
1
1
2
0
2
9
24
4
2001
(# of parcels)
28
91
20
37
17
2
4
34
16
12
4
6
4
10
16
4
4
2
4
3
1
1
169
2
1
2
41
31
5
1
1
2
0
2
27
29
4
2012
(# of parcels)
33
90
26
47
19
1
7
40
16
12
4
7
4
10
22
6
5
3
9
3
1
1
170
76
1
2
39
44
6
1
2
2
2
2
28
33
4
41
-------
Appendix C: Land Jurisdiction in Study Area
NFS
BLM
U.S.F.S.
Private
U.S. Army
Stale Parks
State Trust
Book-Maps
Land Jurisdiction and book-map data provided by Cochise County. San Pedro River and Watershed data from USGS NHD.
42
-------
Appendix D: Example Tax Roll Records for 1971,1981 and 1991
Scanned 1971 Tax Roll Record (Courtesy of Cochise County Archives, 2012).
43
-------
ASSESSMENT AND TAX ROLL FOR THE
fQK THE YEAR
?oi"s-i«"°«i, iiwuws., a,, « ""«"•"" J-^M*
•Jl.
11.. i«» -^n>—: . ;x.yy/—rt^-Ti
•»-, -, | ! TO J il» — : I»5J : i >«i
WT^
B-^J-i- " "TVe?4., ~t'g|[,"f " •','---''
••' ^S PP^
st.ta W 101
;il -IS
DIAL 142)
"i:
• " 30001 ' I '4flJ
IBJin '— T0#l ~ I ' 1...JS1 T" !
TATE2«HJ2071 : *T
If Si 8! !i
^ '" "" t
MS-^
?v. ,UBi;f
SOI N VE
vslto
E-UCt SP 19
ooS JrSir «Ancntttts-»i-Li)l-
MU^VI ig4 |oi J212
U9DU lU-NVOO l.lilJ'J
HUU I'LL.' i.Ub»LtO H-T
ttPtT" 080 J»SlJC•*!
-U&9£ fcuJgJglM
U9UU 10,t)VuO t.liUO
Scanned 1981 Tax Roll Record (Courtesy of Cochise County Archives, 2012).
ASSESSMENT AND TAX ROLL FOR COCHISE COUNTY ARIZONA
FOR THE YEAR
ECX 5175
UCSPK *c . *Z BSTQ
«.-*|r- — issf1^
' — ~'"*i'iinn
8 i goal
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211
FCJI 5175
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^^Br,, ^;r:--
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^7Hi - ' 1357
3570
IICSOK Aft A2 85703
• 11*:^
. — i.1 ' ~ncc
«um->«L> |«^»
ZZ1
ECX 5175
ILCiOh AR A2 85703
' 10/23/91 97927 IS. 42 '«* ^lj 06/15/92
TinssiVVsi
-
bBQO — *•"
IE
LAuVCKS TITLE OF «,', II '••!'. Coo ' CHASTAIN QCNALD L 5
P 0 BOX 5406 .00 *"< 1250 F FOOTHILLS DR BC-1037
TUC50M I? 857Q3 .DO rr-m SltftfiA VIST* *Z B5635
15. 4Z Wr*i 2D
OOCGDO
SSM
tatte
^J 10/23/91 919T1 I5'8S ^ ^ 06/15/92
TUCSON AZ 65703 .00 :.i .i-ti SIERRA VIStt »Z S5
Eps
^. ]3..J»^ • — -z^^*! — ^« -
- !lC/2!/91 97927 15.42 M "~- I06/1S/9Z
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15
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15
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. CC -
DOt f
•88 S
!; (i c. i
miiu-
itta
P 0 POX 5406 .00 nn 1250 E FOOTHILLS D« IC-1D37 .66 PEN
TUCSON AZ 85703 .00 '".•-; SltRH* VISTA 1Z BS635
15.42 lowi iD.83 TO-
SOOOOD
KT.V J
a ft 6 D oc
?000
^^i j — «™M..«^ — i 5^=s . r issr^Fs
lt,/^1/91 97927 15.42 an " Ct/lS/92
LAgTLRS IITLF OF AZ.1C5915 ^QQ £n CHAST*1N DONALD L
15*42 ™
DQOUDO
BBSSSS
2000
3S j"
Iir:-"1
— •" 1 «-™
~ lb"/°23/91 97927 IS'*ji '** [ ""•' 1 Ob/lS/92
Jlt.B-JlU JJi
&we
-So™
IS
20
"r° ,;:,.
15
5
OC6 i
3.31
12 •-•„
41 it;!
CL ... .
SJ
007 I
JOS'
Scanned 1991 Tax Roll Record (Courtesy of Cochise County Archives, 2012).
44
-------
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