HomeMy WebLinkAboutCC Reso 2024-12 - Establishing VMT as Standard Measurement for CEQA RESOLUTION NO. 2024-12
A RESOLUTION OF THE CITY COUNCIL OF THE CITY OF UKIAH, CALIFORNIA,
APPROVING A CITYWIDE POLICY ESTABLISHING VEHICLE MILES TRAVELED (VMT) AS
THE STANDARD OF MEASUREMENT FOR POTENTIAL VEHICLE TRAFFIC IMPACTS AND
A METHODOLOGY FOR EVALUATION OF FUTURE PROJECTS CONSISTENT WITH THE
CALIFORNIA ENVIRONMENTAL QUALITY ACT (CEQA)
WHEREAS:
1. The California Environmental Quality Act (CEQA) was enacted by the State of California in
1970 to ensure the long-term protection of the environment and requires public agencies to
analyze and disclose the effects of their actions on the environment; and
2. The State of California as a whole, and City of Ukiah residents specifically, have experienced
adverse environmental and economic effects associated with climate change, such as
prolonged wildfire seasons and firestorms, and accompanying days-long power outages and
related Public Safety Power Shutoff events; rising temperatures; mudslides; severe drought,
property destruction; and damage to infrastructure; and
3. Marginalized communities including people of color, immigrants, indigenous communities,
low-income people, those with disabilities, and the unhoused are disproportionately affected
by climate change impacts and must be supported in the transition to a sustainable
environment and economy; and
4. On June 15, 2022, the City Council adopted Resolution No. 2022-44, declaring a climate
emergency and making a call to action to restore a safe climate; and
r 5. Public agencies in California have historically attempted to combat traffic congestion by
relying on a metric known as "Level of Service" (LOS) standards. Developed in the post-war
US specifically for highway travel, the LOS standard assesses the relationship between traffic
speed, volume, and density, putting a priority on how well automobiles flow through a street
network; and
6. The LOS standard fails to combat congestion in the long run because it considers all vehicles
equall: a single person in a car is assessed as equivalent to 50 people in a bus, even though
50 people in a single vehicle contribute far less to congestion than 50 people in 50 vehicles.
The convenience of a short term,free-flowing roadway only encourages more single-occupant
automobiles; and
7. Policies such as use of the LOS standard that prioritize use of the single occupant automobiles
result in expensive road improvements and encourage urban sprawl to the detriment of other
mobility alternatives, such as walking, biking, and public transit; and
8. According to the California Air Resources Board (CARB), emphasis on prioritizing single-
occupant automobile convenience has resulted in transportation becoming the leading source
of Greenhouse Gas (GHG) pollution in California, of which passenger vehicles represent the
largest single source of transportation GHG emissions in California; and
9. In 2013, the State of California Legislature passed, and Governor Brown signed, Senate Bill
(SB) 743 (Steinberg). SB 743 helps reduce GHG emissions overall by promoting integrated
land uses that facilitate transportation through many forms, such as bicycles, walking, public
transit, and carpooling; and
10. In furtherance of its intent, SB 743 directs the Governor's Office of Planning and Research
(OPR) to produce CEQA guidance for cities to reduce automobile travel by replacing use of
the LOS standard in the transportation analysis required under CEQA with a Vehicle Miles
Traveled (VMT) standard, or another measure that "promote(s) greenhouse gas emissions
reduction, development of multimodal transportation networks, and a diversity of land uses";
and
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11. OPR develops CEQA Guidelines to interpret. CEQA statutes and published court decisions,
including several appendices to the CEQA Guidelines that contain forms and guidance for
lead agencies for performing environmental review; and
12. Public agencies are encouraged to develop standards and procedures to implement CEQA
Guidelines, such as replacing LOS in the transportation analysis under CEQA with VMT by
adopting local CEQA thresholds of significance; and
13. Statewide implementation of the SB 743 requirements went into effect on July 1, 2020,
pursuant to which California cities began implementing the new CEQA guidance on applicable
projects; and
14. State law allows lead agencies to set VMT thresholds of significance based either on local or
regional per capita averages; and
15. When a public agency develops a local threshold of significance, CEQA Guidelines require
the threshold of significance be adopted through a public review process and supported by
substantial evidence; and
16. General Plan Goal ENV-8 directs the City of Ukiah to "Achieve carbon neutrality by or before
the year 2045"; and
17. General Plan Goal MOB-2 directs the City of Ukiah to "Reduce vehicle miles traveled (VMT)
to and from residences,jobs and commercial uses in Ukiah"; and
18. General Plan Policy MOB-2.1 directs the City of Ukiah to "support development and
transportation improvements that help reduce VMT below regional averages on a "residential
per capita" and "per employee" basis; and
19.Adopting a VMT policy will facilitate General Plan goals and policies to reduce GHG emissions
and bring the City of Ukiah's transportation analysis methodology in line with State and City
goals pursuant to SB 743 and the General Plan; and
20. CEQA Guidelines Section 15064.7(B) directs the City to adopt thresholds of significance
based on substantial evidence by ordinance, resolution, rule or regulation through a public
process; and
21. On behalf of the City, GHD prepared four Technical Memoranda providing supporting
documentation for appropriate VMT Thresholds in the City of Ukiah; and
22. The GHD Technical Memoranda would require that the City follow the framework suggested
by the Governor's Office of Planning and Research (OPR) in its Technical Advisory on
Evaluating Transportation Impacts in CEQA; and
23. On April 17, 2024, the City of Ukiah City Council considered the VMT policy, at which time all
those in attendance were given the opportunity to speak on this proposed policy, and all
comments were reviewed and considered.
NOW, THEREFORE, BE IT RESOLVED AS FOLLOWS:
A. The City Council of the City of Ukiah accepts and adopts the Technical Memoranda prepared
by GHD providing supporting documentation for appropriate VMT Thresholds in the City of
Ukiah. The Technical Memoranda are incorporated into this Resolution as Exhibit A.
B. The City Council of the City of Ukiah approves a SB 743 CEQA VMT Policy that shall read as
follows:
Section 1: CEQA Findings
The VMT Policy is exempt from review under the California Environmental Quality Act (CEQA)
pursuant to Public Resources Code Section 21065 (definition of a CEQA "project"), CEQA
Guidelines Section 15064.7 (requirements for adopting thresholds of significance), CEQA
Guidelines Section 15061(b)(3) (common sense exemption), CEQA Guidelines Section 15037
Actions by Regulatory Agencies for Protection of Natural Resources, and CEQA Guidelines
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Section 15308 Actions by Regulatory Agencies for Protection of the Environment. CEQA
Guidelines Section 15307 and 15308 are applicable because the proposed VMT Policy will fulfill
the City's implementation of SB 743 which is intended to address climate change impacts that
pose an immediate and growing threat to California's economy, environment, and public health.
Section 2: CEQA VMT Thresholds
The VMT Threshold of Significance shall be 4% better than (below) the Countywide average.
Unless exempt as described in this Policy, this VMT threshold standard shall apply to all General
Plan amendments, long-range plans, discretionary development applications, and transportation
projects.
Section 3. CEQA Land Use VMT Screening Criteria Exemptions
The following screening criteria shall exempt General Plan, long-range plans, and discretionary
development applications from a VMT analysis:
TYPE SCREENING CRITERIA
Small Projects Projects that meet the existing CEQA categorical
exemptions. Class 1 exemption, for small expansions of
existing uses, and Class 3, for new small projects, as
_ specified in CEQA Guidelines §15301 and §15303
Residential Uses All residential eLojects
Employment Uses in Areas with a Proposed employment uses in zones with a total diversity
Diverse Mix of Land Uses score at least 4% better than the Countywide average.
Local-serving Retail Neighborhood shopping centers with a total gross leasable
area of up to 125,000 square feet, with multiple tenants
typically anchored by a supermarket or drugstore; and
single tenant local-serving retail projects of 50,000 square
feet or less.
Projects in Proximity to Major Transit Projects within one-half(1/2) mile of a transit stop with 15
Stops minute or less headways, unless the project has a Floor
Area Ratio (FAR) of less than 0.75, reduces the supply of
affordable housing or includes more parking than required
under the zoning code.
Transportation Projects Roadway, transit, bicycle, and pedestrian projects that do
not lead to a measurable increase in vehicle travel.
Section 4. Amendments to CEQA Land Use VMT Screening Criteria Exemptions
The VMT CEQA Screening Criteria Exemptions listed in Section 3 of this Policy are intended to
identify most, but not all potential exemptions.
Interpretation for General Plan, Long-range Plans, and Discretionary Development Applications:
Interpretation of VMT exemptions for project types not specifically listed above shall be conducted
by the Community Development Director or designee.
Interpretation for City-initiated Transportation Projects: Interpretation of VMT exemptions for
project types not specifically listed above shall be conducted by the Public Works Director or
designee.
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PASSED AND ADOPTED, this 17"day of April, 2024, by the following roll call vote:
AYES: Councilmembers Rodin, Orozco, Sher, and Vice Mayor Crane
NOES: None
ABSENT: Mayor Duenas
ABSTAIN: None
Dougl F rani, Vice Mayo
ATTEST:
Kristine trawler, City Clerk/CMC
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EXHIBIT A
TECHNICAL MEMORANDA:
( 1 ) September 15, 2022
(2) October 3, 2022
(3) September 11 , 2023
(4) February 20, 2024
(1) �
Technical Memorandum
September 15, 2022
• Craig Schlatter,City of Ukiah Contact No. (707)463-6219
•• Jim Harnish, Mintier Harnish cschlatter@cityofukiah.com
• Don Hubbard,TE,AICP 11196303
Project Name City of Ukiah General Plan Update
• SB-743 Methodology
1 . Introduction
This memorandum describes the proposed methodology for assessing transportation impacts in Ukiah
consistent with SB-743 and current CEQA Guidelines. SB-743 addresses a range of topics and aims to
better promote statewide policies that(a)combat climate change by reducing greenhouse gas emissions
and particulates; (b)encourage infill development and a diversity of uses instead of sprawl; and (c) promote
multi-modal transportation networks, providing clean, efficient access to destinations and improving public
health through active transportation. As part of implementing SB 743, revisions to CEQA Section 15064.3
that describes specific considerations for evaluating a project's transportation impacts went into effect
statewide on July 1, 2020.
CEQA gives lead agencies broad discretion over analytical methodologies. CEQA Guidelines
§15064.3(b)(4), which is new with SB-743, reads:
"Methodology. A lead agency has discretion to choose the most appropriate methodology to
evaluate a project's vehicle miles traveled, including whether to express the change in absolute
terms, per capita,per household or in any other measure.A lead agency may use models to
estimate a project's vehicle miles traveled, and may revise those estimates to reflect
professional judgment based on substantial evidence. Any assumptions used to estimate vehicle
miles traveled and any revisions to model outputs should be documented and explained in the
environmental document prepared for the project. The standard of adequacy in Section 15151
shall apply to the analysis described in this section."
No particular methodology or metric is mandated; the choice is left to the lead agency. In making this
choice, an agency should bear in mind what sort of criteria the legislature had in mind for
determining the significance of transportation impacts goals of SB-743. These were expressed in
PRC§21099(b)(1), "Those criteria shall promote the reduction of greenhouse gas emissions, the
development of multimodal transportation networks, and a diversity of land uses."
The methodology described in this memo is based on the one developed for the Sacramento Blueprint
Project, the groundbreaking study of how smart growth policies could lead to reductions in vehicle-miles
traveled (VMT).The Blueprint Project represented a sea change in how transportation impacts were
analyzed, because it demonstrated that conventional travel demand models have inherent blind spots that
make them insensitive to the effects of residential and employment density, neighborhood design, and a
diversity of land uses in close proximity to one another(the 3 D's). It went a step further and developed
procedures external to a traffic model to forecast the effects of the 3 Ds on travel behavior. This work won a
host of awards including US-EPA's National Award for Smart Growth Achievement, FHWA's Transportation
Planning Excellence Award, the American Institute of Architects' Presidential Citation, and AMPO's
National Award for Outstanding Achievement in Metropolitan Transportation Planning.
The Power of Commitment
11196303 1
2. Description of the Methodology
The methodology consists of determining the land use characteristics of each neighborhood and then
assessing the potential for interacting with complementary land uses through non-auto trips. Data shows
that when housing is in close proximity to retail and services uses people will walk or bike to those uses at
least some of the time, and even if they drive the trips will be short(i.e. low VMT trips). Similarly, the
likelihood of people walking or biking to work, rather than driving, depends on the distance between their
homes and workplaces. So measures of proximity are also measures of the potential for VMT reduction.
The steps in the methodology are shown in Figure 1. These are:
Inputting Land Use Data
1) The study area, the city of Ukiah and its vicinity,were divided into in hexagons. The size of the
hexagons was based on survey data of typical distances for walking trips by Americans. The idea
being that land uses in a given hexagon would be within comfortable walking distance of
complementary land uses in the six adjacent hexagons.
2) The existing land uses in each hexagon were then grouped into three categories as follows:
• Residential, measured in households
• Retail, measured in jobs.This category includes services such as banking and beauty
salons that typically attract more trips by customers than commute trips by employees
• Non-retail, also measured in jobs. This includes office, industrial, and agricultural jobs
where the majority of trips are made by employees rather than customers.
Figure 2, Figure 3, and Figure 4 show the existing distribution of households, retail jobs, and
non-retail jobs in Ukiah, respectively.
Computing Diversity Indicators
3) The land uses in each hexagon are then combined with the land uses in the six adjacent hexes to
represent the diversity of land uses available within walking distance to people in the hexagon.
4) The potential for interaction with complementary land uses was then estimated using three diversity
indices, each representing a different type of transaction:
• Jobs/Housing Diversity,which represents a person's ability to walk to their place of
employment. In traffic forecasting this type of trip is termed a home-based work(HBW)trip.
• Retail/Housing Diversity,which represents a person's ability to walk for shopping trips. In
traffic forecasting this type of trip is termed a home-based other(HBO)trip.
• Job/Mix Diversity,which represents the interaction between retail and non-retail uses. For
example, office workers walking to nearby restaurants or coffee shops. In traffic forecasting
this type of trip is termed a non-home-based work(NHB)trip.
The formulas for these indices are as follows:
Jobs/Housing Diversity=1-[(b*HHs-EMP)/(b*HHs+ EMP)]
Jobmix Diversity= 1-[(c*REMP-NEMP)/(c*REMP + NEMP)]
Retail/Housing Diversity= 1-[(b*HHs-REMP)/(b*HHs + REMP)]
Where:
HH = Number of households
REMP= Number of Retail and Service Jobs
11196303 2
NEMP= Number of Non-Retail Jobs
EMP =Total number of jobs (i.e. REMP + NEMP)
b =total regional employment/total regional households
c=total regional non-retail jobs/total regional retail jobs
d =total regional retail/service employment/total regional households
These formulas produce scores for individual hexagons that range from -1 to 1, with a score of 0
indicating an ideal mix of land uses and scores of-1 and 1 indicating that only one of the land uses
is present.
The ideal mix of land uses, found in the formulas as "a", "b", and "c", was determined from the
county-wide mix of the three land uses types. The rationale for this is the fact that land uses tend to
balance when viewed over a large area. For example, government jobs may be concentrated in
one area and industrial jobs in another, while residences and shops are distributed among various
other communities, but when taken as a whole the housing, retail, and non-retail uses in a region
tend to occur in the correct proportions for that particular type of region.
5) The scores for the three types of diversity were then mapped out. These maps can be used by City
staff to identify which parts of the city have a good balance of land uses and which might benefit
from zoning that would promote a better mixing of land uses.
Computing City-Wide Score
6) For some purposes, such as evaluating general plan alternatives, it is useful to be able to compute
a combined diversity score for the study area as a whole. The first step in doing this is to convert
the diversity scores from the-1 to 1 range used in the scores for individual hexagons into their
absolute values, with 0 again indicating a perfect mix of uses and 1 indicating no mix at all (i.e. a
single land use type). If this were not done, then the scores of, say, over-retailed and under-retailed
neighborhoods in different parts of the city would cancel each other out, when in fact both have a
poor land use balance.
7) The three types of diversity are not equally important for every hexagon because the number of
HBW, HBO, and NHB trips depends on the land uses in the hex. The table below shows the
number of trips of each type generated by each of the three land use categories:
Trip Generation Rate
Trip Purpose Household Retail Job Non-Retail
Job
Home-Based Work 2.2 1.2 1.7
Non-Home-Based 1.0 8.1 1.9
Home-Based Other 5.9 8.2 0.8
Total 9.0 17.5 4.4
8) The land uses for each hex are then multiplied by the trip generation rates and used to compute the
percentage of total trips in each trip category. Figure 5 shows the total trips generated by hex zone.
9) The three individual scores for each hexagon are then combined into an individual score for each
hexagon using the trip types as weighting.
10) The scores for the individual hexagons are then combined using the number of trips generated by
the hex to weight their contribution to the city-wide score. Note that this means that the inclusion of
vacant hexagons outside of the city will have no effect on the outcome;they generate no trips and
so their scores will be weighted at zero.
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3. Results
Figure 6, Figure 7, and Figure 8 show the three diversity scores for existing land uses. Figure 9 shows
the total diversity by zone for existing land uses. These figures show several things:
• Much of the city core scores quite well, between-0.30 and 0.30, on jobs/housing diversity(see
Figure 6).This indicates the success that Ukiah has achieved in enabling people to live and work
in close proximity.
• The edges of the city do not score as well on jobs/housing diversity(see Figure 6). However, this
does not hurt the city's overall score as much as Figure 6 might imply, because there are relatively
few jobs and residences in those areas. This is indicated by the small size of the circles in the
hexagons in Figure 6.
• The city as a whole is over-retailed in relation to its population, due to the fact that it serves as the
main retail destination for a large surrounding area (see Figure 7).This has implications both for
sales tax revenues (good)and VMT(bad).
• Figure 8 shows that,with the exception of the city core (the light-colored hexagons), retail and
non-retail jobs tend to be concentrated in different parts of the city(the red and blue hexagons in
the figure). This limits their potential for interaction that does not involve driving.
4. Advantages of the Methodology
This methodology offers a number of practical advantages:
a) Ease of Use: It does not require expensive software and special training to use, as is the case with
most traffic models. City staff can evaluate projects using the Excel program already found on their
computers.
b) Nuanced, Informative Results: Unlike other methodologies, whose output is a just a number saying
the VMT is high or low, this methodology provides a clear indication of the underlying causes of
high or low auto use. For example, it might show the analyst that a proposed housing project is in
a location that lacks local shopping opportunities and might be improved with the addition of
locally-serving retail.
c) Appropriate Scale: While this methodology cannot substitute for a convention traffic model for
forecasting over large geographic areas(entire counties), it is likely to provide a more accurate
representation of travel behavior in a small town than is possible with a conventional model.This is
because traffic models incorporate certain necessary simplifications, such as centroid connectors
and frictionless intersections, that are inconsequential when forecasting long trips but are highly
distorting when forecasting trip-making over small areas.With a total area of less than 5 square
miles, Ukiah is the sort of compact,walkable city better suited to a proximity-based model than a
trip-based model.
5. Thresholds
CEQA analyses performed under SB-743 require the use of thresholds, as was the case for the LOS-based
analyses they replaced. We recommend that the City establish three types of thresholds, namely:
1) Thresholds for Screening by Size: CEQA offers categorical exemptions for very small projects
from having to do EIRs, both because of the negligible impact they are likely to have and because
the expense of performing an EIR might make small projects unviable. We recommend that the
City use the Class 1, for small expansions of existing uses, and Class 3,for new small projects,
exactly as they are written in CEQA Guidelines§15301 and §15303. Some jurisdictions are
experimenting with converting the thresholds in the CEQA guidelines,which as measure in square
11196303 4
feet, into some sort of equivalent in vehicle trips per day.We do not recommend this, because it
involves a series of assumptions that may prove difficult to defend and in any case offers no
significant advantages for Ukiah over the sections as written.
2) Thresholds for Land Development Projects: The methodology described in this memo is
intended for use in analyzing land use projects. As with other aspects of SB-743, there is a lot of
uncertainty regarding how the thresholds should be set. OPR's Technical Advisory suggested using
a threshold requiring a 15% reduction in VMT over existing conditions. The 15% number originated
in CARB's California's 2017 Climate Change Scoping Plan, where it was computed as the average
reduction needed to achieve the State's GHG reduction goals. However, this state-wide average
may not be appropriate for all jurisdictions, and CARB has more recently set higher targets in some
MPO areas and lower targets in others. For example, CARB's latest plan calls for a 19% reduction
in VMT for the four largest MPOs' down to less than 10%for some of the smaller MPOs. Since
Ukiah is not in an MPO region,we suggest using the targets for Shasta RTA, since it is both the
geographically closest MPO and demographically most similar MPO to Ukiah. CARB set the target
for Shasta RTA at a 4% reduction in average per-capita VMT.
3) Thresholds for Transportation Projects: As stated earlier, it is the intent of SB-743 that lead
agencies use, criteria that"... shall promote the reduction of greenhouse gas emissions, the
development of multimodal transportation networks, and a diversity of land uses." (PRC
§21099(b)(1)). We recommend setting a threshold that explicitly focuses on balancing
transportation modes within the city.An example would be:
A project's impacts shall be deemed significant if it results in a percentage increase in
road capacity higher than the percentage increase in bicycle or multi-use capacity."
Note that this threshold would make all active transportation projects presumptively less-than-
significant. It would not preclude the City from undertaking road expansion projects, but it would
mean that such projects would need to include expansion of the bicycle facility system as well. As
written, it would require a 1%-to-1%expansion of the two systems, but it could easily be tweaked to
require a 1%-to-2% or 3% expansion of bike facilities to help that system catch up with the facilities
offered to cars.
6. Using the Methodology for Individual Projects
To use this methodology for an individual land use project is similar to that used for evaluating the General
Plan, except that instead of computing a score for the entire city, you would only compute the score for the
hex where the proposed project will be constructed. This score will reflect the proposed project's
interactions with all the other land uses2 within typical walking distances. The idea is to see whether or not
the project moves the neighborhood it is in towards the"Goldilocks"spot where the three main types of land
uses are in perfect balance.
The Goldilocks framing is best illustrated with a hypothetical example of a developer proposing to build a
residential project in a hex agon that,with its neighboring six hexagons, currently has 500 dwelling units,
500 retail jobs, and 500 non-retail jobs. The developer would like to build 2,000 additional dwelling units,
but their EIR will include a reduced-impact alternative with only 500 additional dwelling units.
City staff would note the diversity indices for existing conditions, then add 2,000 households and note the
results. They would repeat the procedure for 500 households. When they tabulate the results,they would
get a table like the one shown below.
SCAG,MTC,SANDAG,and SACOG
2 This should include both existing and already-approved land uses
11196303 5
Jobs/ Jobmix Retail/ Total ova Improvement
Project Alternative Housing Diversity Housing Diversity in Diversity
Diversity Diversity Score
Existing City Average 0.27 0.24 0.35 0.30
Existing Project Hexagon -0.32 0.33 -0.49 0.40
+500 DUs 0.02 0.33 -0.18 0.20 51%
+2,000 DUs 0.44 0.33 0.27 0.32 22%
The right-most column in the table shows that this project would improve the land use balance in the
neighborhood, and that the reduced-impact alternative would be superior to the developer's preference in
terms of land use diversity. The analyst could stop there, and conclude that under either alternative the
project would have less-than-significant impacts on the area. However, if they took the analysis one step
further and input a range of project sizes into the spreadsheet, they could get a more nuanced feel of the
interactions at work in this location.
Figure 10 shows the Jobs/Housing diversity for different numbers of households, given that 500 retail and
500 non-retail jobs are within walking distance. The black dot shows existing conditions.With 500
households,this area has fewer DUs than would be optimal for this amount of employment. However,the
reduced-impact alternative(blue dot)would result in a nearly ideal mix of jobs and households. The
developer's preferred alternative of adding 2,000 DUs (red dot)would over-shoot the ideal; the area would
go from having too few DUs to having too many for the number of jobs nearby.
Figure 11 repeats the range analysis, but this time for Retail/Housing Diversity. The black dot shows that
the area is over-retailed in proportion to the number of nearby homes. The blue dot shows that the reduced
impact alternative pushes the balance in the right direction, but the area would remain over-retailed. The
red dot shows that the area would go from having too little retail for the area it serves to having too little.
Nevertheless, it would be closer to the ideal mix than existing conditions.
Figure 12 completes the analysis by showing the combined score for a range of project sizes. The shape
changes because the combined score uses absolute values, with zero indicating a perfect mix. The most
interesting thing about this figure is that it shows that the Goldilocks project size would be about 900
additional DUs(so 1,400 DUs in total if you include the existing 500). This would result in the optimal
amount of residential development for an area with 500 retail and 500 non-retail jobs.
Someone may wonder why the score in Figure 12 does not go down to zero for the optimal residential
amount. The reason is that the interaction between the retail and non-retail jobs,the JobMix Diversity, is not
affected by the number of households nearby. Since the proportions of retail and non-retail jobs is in this
case not ideal, an ideal score cannot be achieved in this location; at least not without tinkering with the
amount of employment.
This example illustrates the key advantages of this methodology. In just an hour or two, using just a
spreadsheet, a City staff person could evaluate a proposed project's effects on land use balance and
opportunities for non-auto trips, and thus its effects on VMT.The analysis would not only reveal how the
project would alter the land use balance but also points towards ways to optimize the project.
11196303 6
Figure 1: Methodology Flowchart
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11196303 7
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�WalnutAven a—d 348 s 349 350
■ w o N
� N � East Perkins Streeter •
m �
324
31�9 320 321 322 323 . 325 326 327 3
d.
■ 0 5
■
_ � 301 ■■ ■
296 297 29$ Q29 WeseM�ttSt300 y astGobbi Street 302 + 303 4
E oa
V '
.. _..+ ■
_ ■ / o ■ `^
268 269 270 271 ■272 273� 2no 74' 27.5 c 2�76 277 I 278 2
m �
M
-now= ..
24! ■
245 246 247 248 249 250 I 251 252 H 5 Talmage Road 255
220 221 222 221
224 225 L226 227 228M Commerce Drive 230ow
F
�_ � •■� ' 229 ■
• 4
O
.Q
198 e 1,9.9 • 200 203 1 204 ■.I 205
d
74■ ■■1,75.. i 176 180-- • 181 182
0 1
c ■
+, ow m 157 • 158� 159
Retail Jobs •o- y
433 `134 • 1�35,• 1
obs
•■ f
obs ■• \'
obs )
110 ■ =:���■.�112�
3 Jobs ■��
3 Jobs
1 Jobs s6 s7
3 Jobs
42 Jobs
i
427 428
46" ■ ' '468 469 470
441 442 443 444 } 445 446 ♦t 447
■ ■�^ Empire Dive
■
o w
416 417 418 419 420 • 421 42.2
■
Low Gap Road, o ■ `• •
•
■
392 i 33 • 394 395 396 �I 397 398 399 400
A
0 Wawa
368 369 370 371 F 372 c ; 374 ,
y � 373
CYPrQSs Avenue � c
� n
344� 345 346 347 c y = 349 350 351 r
■ walnut-Avenue
■
East Perkins Street
`•°• y 324
319 320 321 322 323 325 326 % 327 3
■ z
■ 0 5
y
296 297 298 299 `\\Street y 301
WeS., 300 a astGobbiStreet 30 �303 4
E ■
268 269 270 271 272 273 274' 275 c 276 277 1278 2
■• ■
m �
F z 253 4 'rr• ■
245 246 247 248 249 250 } 251 252 y Talmage Road 25 255
■ ll erce Drive'230 4 ;F 220 221 222 223 224 225 L226 22,7 228M Com
_.._ J '
■. ■1� 229
198 e199 200 203 204 I205 0 206
I �
k:
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o I
c ■
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ova 157 1584 159
Non-Retail
Y�
133 `134 ' 1-35,. 1
lobs
•� f
Jobs
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110
5 Jobs
3 Jobs ■
3 Jobs 86 87
37 Jobs
291 Jobs
i
427 428
46, ' '468 469 470
1�■
441 442 443 444 445 446 ♦t 447
■
.. �,� .
� Empire D..nve
■
■ in ■
416 ■ 4,17 418 Al4 420 • 421 42.2
U ■
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392 i 393 394 395 396 ■\ 397 1 398 399 400
2 . .� ■�
368 369 370 371 y 372 373 = ■I 374 1
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w � D
= m �
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• Walnut Avenue d 348 0
■ ��. g
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m
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31,9 320 321 322 323 g 324 325 326 • 327 3
' 3
tn.• 0 7
s
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268 269 270 271 272 273■ 2740 275 c 276 277 1278 - 2
I ■ no
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245 246 247 253 248 249 250 251 252 �, Talmage Road 255
r
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229 y. ■ t
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198 e 19� 200 203 i 204 205
d
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o I
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•y 0
Y
Total Trips Y!'
433 134 1135.. 1
trips •
35 trips � • • �: ■• \'
331 trips
110
468 trips ■—1,1?�
740 trips
1,308 trips { 4 86 87
- 2,198 trips
-4,251 trips
:::428::::: I
......4 .. ............ c. .. ......
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■ -� •■
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■
1 I 372 � - r■■�■
368 370 371 •
• Q O
■ 369 374
I373 ' 1
■ 346 350
_- 344 345 347
348 324 349
325
319r—••■■320 322 323 - ■ 327
rS! 21 0 • Q
■ I 321 ` 3
326
s� ■
SNOWS
■
303■ •
0 0 &,-A0 b ,
296 ■ 297 298 299 304,
300 301 -302276
I
......... .......... ......... ■.— .—..—... ■
.......... .......... ........ ■ —.• ■
�268::::::: ::269...... ..270::::: : O 2O ' ��■ I O ,.2
271 1272 ■ ■•0274 27.5 277 ■ 278
�i ■
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249 ■ 251 ■
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:■:::: '.:":..: :.:::::::::::::: :::::::::::::::: 248 250 I 254 255
253 W
:::■ .. ...■::::. 229
:...■...................... ::::::.......... :: ■ 230 �.
::223 ...... ::224:::::: O O
:..��............ .... :�.............. .... 225 1226 227
::198...... ::�199 O i .I 205
::1:::::: i'�y'i' :. :::::::: 203 204 ■
.■..... ::■::' • ■
I ......:::::::::: .■ ......: ■'1
:■'i i174v,• :�7�:�:`.��.�:1:76:::... O =ter 181 ::■ 182
■
................ ................ ................ 180
-
157.::: :: :::158',. :::159::::::
... .......... ..... �:/ ... ................
�:::
-� '� :::133:::::::' 134:.......••4135':'::: ::::::.I
y ... ............
0 0 - 0.09 Total Trips —_ Annexation "° ..."' ■
p . �.
Areas
0 0.10 - 0.19 ... r%........
.90 Trips 0 0.20 - 0.29 s �-. City Y Limits
��■.�::: :: :.
0 9 Y :86:::::::::::::87::::
- -0.80 0 0.30 - 0.39 Hi hwa
- -0.70 0 3,245 Roads : ' ''�_ i
0.40 - 0.49
- -0.60
0 0.50 - 0.59 n t A '
� •I
�•� / • _
t y 467 • 468 469
■•� �.1'[•.hex, ,�*�, �. - ■
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441 442 443 — ■1 445 446 447
418 ■ w� '
�' `#� ■ '■ 420 ■
416 ■r' ♦♦`421 4: 'F
419
■ ♦ .........
■
O ♦O
■ :::400::::::
O
392 I 393 I■ 397- ■398�• • 399
■ 396 /■'f ••�r
■ ok
37 �i T■ r••_•♦
I368 370 371 •_..J
r - O a
■ 369 ' 374
373
344 I O 346 347 350 351 ;
o O
'� .r. .� ■ 345 ■�■
348 324 -9 325
319 320 322 323 O- ♦r 327
j ■ I 321 ` 3
I\ I 326
■._■I ■ ■
303■
o ■ ■-AQ b
296 0 297 298 299 302 ` 304,
. I 300 301276
I ,
.......... .......... ........ ■ ��: ' ■
�268::::::: ..... ...... ::270::::: ■ 273 ■ ..2
■ O O ■ I O -�
............ 271 1272 ■ 274 275 277 ■ 278 "
249 ■ 251 ■
:245::::::: ':246:::::.: 247::::::: O ■ O
:■:::: '.:":..: :.:::::::::::::: :::::::::::::::: 248 250 1254 255
253 �■
.......
2.:2..0..�■::::-�::::::::2..2:1.:::.:.:.:.: ::::::.......
.... ..... ...... .......
:: ... 224:::....... ■
230
222::.: ... O
-.- . - :A--�� .. .... 225 1226 O7,...■.::::: --� - L•■■ ■ 205 y..
::198+:::::: :: :199 ' 200::::::: O
......' :. :::::::: 203 204 ■
..... ............................. ... ■ ■
:I:::::::::::::: ■ ::I ::::::::::::
...I::
■-:-i174�,.�-: ::�:�:'175:::::_ :...■:�::?6 ... ■r 181 ::■ '182::::::.
180
.::: 158' :::159
............
t
- - . '° •�- i ,�. :::::::133 ::: 134::::::::it�4135':'::: ::::::1
ng Diversity 0-0.09 Total Trips Annexation Areas y � /►:::::::
).90 0.10-0.19 ■_� City Limits
- -0.80 0.20-0.29 Trips 0 Highway s...........:::aa::::
- -0.70 0 0.30-0.39 Roads
3,245
- -0.60 0 0.40-0.49
492 493
427 :......
�.... ..
411
■■ :.r..:::::..
r• O
467 468 469
........... ..... . .
■ O 447::::::: t
441 442 443 444 ■ 445
■ 418 419 ■ 420 w` :.�..:::::::
■ ■ ::�22.......
416 I 417 O •■ Am'-(^�11 ■ O 421 "�""""
. I■
■ ■I O 399`. :::400:::::
q0
392 I 393 ■ ■398 � . .....
■
37'
370 371 •I
• ■ O
■ 368 369 ' 374 • ' j
373 fl
■ 348 ■
344 11 345 346 351
O
■r� 347 350 �■
I � 324 349 325 •\~••
319���■ 320 322 326 • 327
■ • O
i r I 324 ` 3
r 323
■
O ■ �+
296 ■ 297 298 299 • 303 304
300 302 I `
301
......... .......... ......... ......... ■■-J■��■■ ■ ■ ■ ■
278
268::::::: ::269:::::: ::270::::: ::271:':.::: ■ 273 '■ I O � ..2
............ .... :::: 1272 ■ 274 275 277 ■
....M:M ..i ■■�■■ 276 ' r�
..:...... :".... ..... ..
252 253
':246:::::.: :::247::::::: 249 .■`
:■::.'.':::::::: ::::::::: 248::::::: 250 I 251254 4255
�.
■ 227 228 229 230
:220 221: :::: ::::::.222::.::: :: :::223':::::: O
i...M666;'�'�'... ................ .... 224 225 1226
:, .:::::. ..L .
:...:::::: .. I�.. 205
198...... 1199 ' 200::::::: p p
...... 03 204
. ........... .... .......... ....
180 182
•
: 157� 158
O • .0 • O
! \ � 159
`O
4 133 034 135
sity 0 - 0.09 Total Employment —_ Annexation 110 191 1.2
9 0.10 - 0.19 — Areas \�
- -0.80 0.20 - 0.29 Jobs City Limits :::86:::::::::::::87::::
-0.70 0 0.30 - 0.39 0 Highway
0 1,340 Roads `
■493/ v
427
O O
428 f.■'� _
I: '/46-
I40 ■
467 f 469 470
i
•.`
y
446
•A■14r O • O O f'7 O
441 442 443 ' 444 ■ 445 f 447
w�
■ 417 418 ■ .• f
■
416 420■r ♦♦,r4,21 422 ?
� ■ � 396 a I■ ♦
•! a
■ 393'^■ 394 ■I L...
■
O ■ • ♦ O O
392 I 395 I� 397 v398 ■. 399 ♦ 400
■ 0'ftw►
I369 370 371 • .373J• 374
■ 0 0 : 4
■ 368 '
■ 346 349 ■
■ "347 348 350
O0
344 ■ 345 351
324
■r 321 322 T 323 326
o O O 0 O
319 320 ` 327
■ 325 ■
301
O �.
299 ■■J 304
O a 0ro p
296 ■ 297 290 302 `;,03 �■
i■ -=� ■■I.■r_274 275 276 277
■
O 0 O ■ I ■� O • • I O
268 269 270 271 1272 273■ ■♦ .
-
■ 251 ■ �■
■� O O O ■ O L6n■
■ 245 246 247 248 249 250 I 254• �i255
■ ■■�■■�■■r�■■r�■■.�■■ 252
■ ■ ■ 227 229 ■ �. ■
223, ■ 1 .^u
O� O O 2 O O I O O I O r
220� �221� r 222 ■ 224 225 226 ■ 230
"�■ ■7 f♦ 228 '
I ■. a �.■ /
01 o o ••� 0
198 f 199— r200� 203 204 `205
� • 206 �
■■�■■ ■■�■■.�i O ■ O ■ O
174 175 176 180 ' 181 I 182
158 /
57 ■ 159
e ■�
: . . ♦O ••
133' 134 135
Mir
�. ` ■
�V
110 2)1
■_■ o
y Total Trips _411� ♦�
" Annexation Areas
■_� City Limits �l Trips Highway 86 *..Ono 87
0�0 Roads .. -,�raw� ;�id►• •
3,245
Figure 10: Jobs/Housing Diversity for Hypothetical Project
0.60
m
Lo 0.40
in +2,000
0.20 +500
d
Perfect Mix
> 0.00
eo
0 500 1,000 1,500 2,000 2,500 3,000 3,500
-0.20
'N
3
X -0.40 Existing
o -0.60
-0.80
-1.00
-1.20
Number of Households
Figure 11: Retail/Housing Diversity for Hypothetical Project
0.60
0.40
+2,000
a
L
u 0.20
Perfect Mix
0.00
L
a 0 S00 1 1,500 2,000 2,500 3,000 3,500
p -0.20
C +500
v, -0.40
0
.ra
o.6o Existing
4J
-0.80
-1.00
-1.20
Number of Households
11196303 16
Figure 12:Jobs/Retail Diversity for Hypothetical Project
0.80
d
`0 0.70
u
H
0.60
c
0.50 Existing
0.40 +2,000
0 o.30 +500
0.20
I
0.10
Perfect Mix
0.00
0 500 1,000 1,500 2,000 2,500 3,000 3,500
Number of Households
11196303 17
(2) OWN
Technical Memorandum
October 3, 2022
Craig Schlatter,City of Ukiah Contact (707)463-6219
M No.
. • • Jim Harnish, Mintier Harnish cschlatter@cityofukiah.com
Colin Burgett - • - . 11196303
Project City of Ukiah General Plan Update
Name
• SB-743 Methodology
This memorandum augments the prior memorandum provided by GHD (dated September 15, 2022)
concerning the proposed SB-743 methodology with the following additional information relevant to
upcoming discussions concerning the proposed SB-743 methodology:
• Comparison of the citywide land use diversity score with the regional rate for Mendocino County;
• Screening threshold recommendations by type of project; and
• Example application of the proposed diversity index methodology to hypothetical projects on the
edges of Ukiah.
1 . Comparison with Countywide Land Use Diversity
Table 1 provides a comparison of the existing citywide and countywide land use diversity scores, based on
the distribution and proximity of households, retail and non-retails. The diversity score ranges from 0.00 to
1.00, with lower scores (close to 0.00)representing diverse conditions, and higher scores (closer to 1.00)
representing less diverse conditions.
As shown, under existing conditions, the Countywide diversity index is 0.50, while the City of Ukiah score is
0.30, indicating that the diversity of land uses in Ukiah that is superior to the Countywide average. This
reflects the fact that a large portion of Ukiah residents live relatively close to work and/or local services,
generating lower rates of vehicle miles traveled (VMT).
Table 1: Land Use Diversity Score (City & County)
Area Existing Total Land Use
Diversity Score
City of Ukiah 0.30
Mendocino County 0.50
Note:The diversity score ranges from 0.00 to 1.00,with lower scores(close to 0.00)representing diverse conditions,
and higher scores(closer to 1.00)representing less diverse conditions.
—} The Power of Commitment
11196303 1
2. Screening Recommendations
Local agencies may identify screening thresholds to quickly identify when a project should be expected to
cause a less-than-significant impact without conducting a detailed study. The screening thresholds may be
based on characteristics including project size, location, transit availability or provision of affordable
housing, consistent with guidance provided by the Governor's Office of Planning and Research (OPR).
Table 2 summarizes recommended screening thresholds by project type. Taking into account the unique
travel characteristics of Ukiah, all residential projects in Ukiah could be screened from further analysis as
recommended below, because (1) residences located in Ukiah generate low rates of home-based VMT per
Capita compared to the rest of Mendocino County(roughly half the Countywide average based on the
Mendocino COG travel demand model)given greater proximity to jobs and services; and (2) Ukiah has a
jobs/housing imbalance, with an excess of jobs relative to the number of households, that results in most
Ukiah jobs being filled by non-resident commuters; therefore the provision of housing projects in Ukiah
would increase the likelihood that that a larger portion of workers employed in Ukiah may also reside in
Ukiah, thus reducing VMT given shorter commute lengths.
Table 2: Screening Recommendations
Type of Project Recommended Screening Thresholds for City of Ukiah
Small Projects Projects that meet the existing CEQA categorical exemptions: Class
1 exemption, for small expansions of existing uses, and Class 3,for
new small projects, as specified in CEQA Guidelines§15301 and
15303
Residential Uses Residential' projects.
Employment UseS2 in Areas with Proposed employment uses in zones with a total diversity score at
a Diverse Mix of Land Uses least 4% better than the Countywide average
Local-serving Retail Neighborhood shopping centerS3 with a total gross leasable area of
up to 125,000 square feet, with multiple tenants typically anchored by
a supermarket or drugstore; and single tenant local-serving retail
projects of 50,000 sq. ft. or less.
Projects in Proximity to Major Projects within one-half(0.5) mile of a transit stop with 15 minute or
Transit Stops less headways, unless the project has a Floor Area Ratio(FAR)of
less than 0.75, reduces the supply of affordable housing, or includes
more parking than required under the zoning code.
Transportation Projects Roadway, transit, bicycle, and pedestrian projects that do not lead to
a measurable increase in vehicle travel.
All residential projects in Ukiah may be expected to result in less than significant transportation impacts relevant to VMT and SB-743,
because((1)residences located in Ukiah generate low rates of home-based VMT per Capita compared to the rest of Mendocino
County(roughly half the Countywide average based on the Mendocino COG travel demand model)given greater proximity to jobs and
services;and(2)Ukiah has a jobs/housing imbalance,with an excess of jobs relative to the number of households that results in most
Ukiah jobs being filled by non-resident commuters;therefore the provision of housing in Ukiah would increase the likelihood that that a
larger portion of workers employed in Ukiah may also reside in Ukiah,thus reducing VMT given shorter commute lengths.
2 The screening threshold for employment uses would be applicable to proposed commercial land uses in which most daily trips would
be generated by employees(not customers),such as proposed office projects.
s Neighborhood shopping centers of up to 125,000 square feet as defined by the International Council of Shopping Centers(ICSC)
U.S.Shopping Center Classification and Characteristics,provide 30,000 to 125,000 square feet of gross leasable area,typically
anchored by a supermarket and/or large drugstore with a trade area of 3 miles or less.
11196303 2
3. Hypothetical Projects at Edge Locations
In response to questions about the likely impacts for potential projects at"edge locations" near the City
limits, in areas that are currently undeveloped, several hypothetical projects were considered as examples
for upcoming discussion. Table 3 summarizes the potential impact findings, based on the comparing the
diversity score for the Project area (Hex Zone)with the Countywide average. The diversity score for
individual Hex zones takes into account the land use diversity of adjacent Hex zones. The methodology for
assessing interconnectivity between adjacent Hex zones may also be refined for Ukiah to further
incorporate multi-modal characteristics and travel amenities. Figure 1 shows the Hex Zone locations.
Table 3: Hypothetical Projects at Edge Locations
Total Diversity Score for
Hypothetical Location Project Zone Potential Impact Finding based
Project (Hex Zone) Existing on Comparison with Countywide
No Existing plus average
Project
Industrial near
1 North edge of 492 Ne 0.23 Less than significant
Ukiah (undeveloped)
Significant requiring mitigation(s).
For projects requiring mitigations:
mitigation options specific to the
City of Ukiah could be identified as
Retail this analysis methodology is
>150,000 sq ft refined further. Potential
2 near South 181 0.87 0.93 mitigations could include provision
edge of Ukiah of multi-modal improvements (such
as potential connections to a
planned bicycle/pedestrian path
along Airport Road that will
connect the South edge of Ukiah
with downtown).
°Project impacts would be considered less than significant if the total diversity score for the project zone under Existing plus Project
conditions would be at least 4%lower than the Countywide average of 0.50
11196303 3
L .
501
�..
a O. 4O •O' O 14707 ,
476Z� -
r iO• O
450• 451 452 453
♦•`• • 492
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-- •493f♦- --
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6] O
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O O '� O O O 45418
421 422
419 r
396
I.
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o � O I_,0, . O O � O .
392 1395 I 397 399••' .99 409
.1 •V
72
1
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i 346 347 _348 350
O
344 345 3.7
1210
321 322 2,3230 O _---326
319 320 O O
327 328
325
301 0 302
276
277
� 9 O 304 � 30'ti_ ..
j 296 . 297 298 I 303 �ti r
25 - • 2]8 ♦�. I t
0 0 o �•o •o. ��.' Q, O I O
271 6
268 269 270 1272 2]3. -_ 280
i
\ I 251 253
�Y
� 245 246 247 248 249 259 254
252 t
O� O O O O O •O mow'
O
220— 222 224 225 I226 .�i 230
.. 228 - — —
M
19� 199 .200— 203 \ 204 -- 205
•�.. •4• f O _ I 0
174 175 176 180 t 181 I 82
158
t4 o
t 157 `• \159
♦
i
.� �•. 136
' `O •'.- O
133 1134 135
Legend - - _ 10 •♦ )
Total Diversity Total Trips =Annexation Areas 111 /♦
0-.1 0 0 City Limits v
Trips as �_ 87
11- 0 Highway
1 .21-30 Roads ,
—M .31- 4 O 3,245
-IN 0.41-0.5 O 6,490
�0.51-0.6
0.61-0.7
0.71-0.8 O 9,735
0.81-0.9
=0.91-1
P.perslza ANSle CITY OF UKIAH Project No. 11196303
0 500 1,0W 1,590 2,000 N Revision No. -
GENERALPLAN UPDATE 2040
Date 09/1912022
us Peep �
Map Pml.d.,:Lamh.rl Cordonnal Conic HEX ZONE LOCATIONS
AM-1lDaWm:North Amancan,963
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M, 83 Sb@Plan.California II FIPS O402 Feel�nerc�mwsxam 114P mtSsnrr,e.aoa�ciswapswo�♦masao3__tatroptionznnamie rzozzaeoninasaos t.napeonrzoxym.apn oau swme.cryeaunaery.mzmtionwea:rniritierxam�sn,rzozz wona imm.m eannwicewrapn¢scnareap.pmomon
(3) i"'MEMIN
Technical Memorandum
September 11, 2023
• Craig Schlatter, City of Ukiah, GHD Contact No. +1 916 245-4226
•• Todd Tregenza, GHD; Colin Burgett, Donald.hubbard@ghd.com
GHD
• Don Hubbard, TE,AICP; Paige Peel, Project No. 11196303
GHD
ProjectCity of Ukiah General Plan Update
• VMT Option 2 Smart Growth Index: Expanding SB-743 Approach to Include Multimodal Networks
1 . Introduction
Building upon the methodology approved by the City of Ukiah's City Council to establish a transportation impact
framework consistent with SIB 743 and current CEQA guidelines, this memorandum describes the proposed
methodology for including an additional component to consider multimodal networks within the City. Limitations
of the proposed methodology, and initial findings are presented herein. The methodology and guidance
presented herein has been developed for use by the City in establishing an interim ordinance for updating the
City Council-approved SIB 743 implementation policy to include assessments of the effects of multimodal
networks.
1.1 The VMT Problem
Assembly Bill (AB) 32, or Global Warming Solutions Act of 2006, set the State of California's first greenhouse
gas emission (GHG)target which called on the state to reduce emissions to 1990 levels by 2020, and required
the California Air Resources Board (CARB)to develop regulations and scoping plans to meet the state's GHG
reduction goals. In 2016, SIB 32 expanded upon AB 32 and set the goal of achieving a 40 percent reduction in
emissions from 2020 levels.
The transportation sector has long been the single largest source of carbon pollution in the state. In 2020, the
transportation sector accounted for 36.8% percent of the state's GHG emissions. Of the emissions associated
with the transportation sector, 69.3% is associated with single passenger vehicle use. Single passenger vehicle
use alone is thus responsible for 25.5% of the State's total emissions. An additional 8.8 percent of the State's
total emissions are associated with heavy vehicles, meaning that on-road vehicle use accounts for 34.3 percent
of the State's total GHG emissions.'
California Air Resources Board(2022).California Greenhouse Gas Emission Inventory-2022 Edition. Data available at:
https://ww3.arb.ca.gov/cc/inventory/data/data.htm
This Technical Memorandum is provided to foster internal discussion in relation to technical matters associated with the subject and does not represent a final approved
position on any matter.
The Power of Commitment
11196303 1
1.2 SB 743 as a Response
SB-743 gives lead agencies broad discretion over analytical methodologies. CEQA Guidelines §15064.3(b)(4),
which is new with SB-743, reads:
"Methodology. A lead agency has discretion to choose the most appropriate methodology to
evaluate a project's vehicle miles traveled, including whether to express the change in absolute
terms, per capita, per household or in any other measure. A lead agency may use models to
estimate a project's vehicle miles traveled, and may revise those estimates to reflect professional
judgment based on substantial evidence. Any assumptions used to estimate vehicle miles traveled
and any revisions to model outputs should be documented and explained in the environmental
document prepared for the project. The standard of adequacy in Section 15151 shall apply to the
analysis described in this section."
No particular methodology or metric is mandated; the choice is left to the lead agency. In making this
choice, an agency should bear in mind what sort of criteria the legislature had in mind for determining
the significance of transportation impacts goals of SB-743. These were expressed in PRC §21099(b)(1),
"Those criteria shall promote the reduction of greenhouse gas emissions, the development of multimodal
transportation networks, and a diversity of land uses."
Previous research has shown that a neighborhood's environment affects travel behavior and further research
has quantified the effects of smart growth practices on travel behavior. 2 This was done as part of the
Sacramento Area Council of Governments (SACOG) Blueprint Project—the ground-breaking study of how
smart growth policies could lead to reductions in vehicle-miles traveled (VMT). The Blueprint Project
represented a sea change in how transportation impacts were analyzed, because it demonstrated that
conventional travel demand models have inherent blind spots that make them insensitive to the effects of
residential and employment density, neighborhood design, and a diversity of land uses in close proximity to
one another(the 3 D's). It went a step further and developed procedures external to a traffic model to forecast
the effects of the 3 Ds on travel behavior. This work won a host of awards including US-EPA's National Award
for Smart Growth Achievement, FHWA's Transportation Planning Excellence Award, the American Institute of
Architects' Presidential Citation, and AMPO's National Award for Outstanding Achievement in Metropolitan
Transportation Planning. The methodology described in this memorandum is largely based on the research
born of The Sacramento Blueprint Project, as well as methodologies for accessing multimodal network quality
and connectivity and studies of key deterrents to active transportation use.
2. Methodology
Design Parameters
In setting out to design a tool to replace traditional travel demand modeling for SB-743 analysis, our team kept
these key design parameters in mind:
• The tool must be capable of analyzing and measuring the components of SB-743's description of
impact criteria, namely: "Those criteria shall promote the reduction of greenhouse gas emissions, the
development of multimodal transportation networks, and a diversity of land uses." (PRC§ 21099(b)(1))
• The tool should provide useful information beyond what can be obtained using a travel demand model
alone.
• The tool must be of practicable use in-house by agency staff with limited resources. So:
o The tool should use data that is readily available.
2 Making Travel Models Sensitive to Smart Growth Characteristics, D.Hubbard and G.Walters,2006.
11196303 2
o It should be possible to input the background data once (the initial set-up by consultants) and
thereafter only need input data for the project being analyzed (by agency staff).'
o The tool should work on software already available to most planning and public works
departments in California, rather than on specialized modeling software.
o Run times should be relatively short—minutes rather than hours.
o It should be easy to use; ideally staff can learn it in the course of a single afternoon.
While input calculations are derived from a series of GIS-based analysis steps, the need for quantitative
analysis on widely available software led to the decision to use a spreadsheet-based tool. The next section
describes the characteristics of the tool developed and the associated methodology.
2.1 Analysis Steps
The base methodology employed in this analysis consists of determining the land use characteristics of each
neighborhood and then assessing the potential for interacting with complementary land uses through non-auto
trips.
Data shows that when housing is in close proximity to retail and service uses people will walk or bike to those
uses at least some of the time, and even if they drive the trips will be short (i.e., low VMT trips). Similarly, the
likelihood of people walking or biking to work, rather than driving, depends on the distance between their
homes and workplaces. So, measures of proximity are also measures of the potential for VMT reduction.
The steps in the methodology are shown in Figure 1. Note that Figure 1 is provided following Section 2.1, while
the remaining figures referred to in this memorandum are provided at the end of the document.
These steps are:
Calculating Land Use Data
1) The study area, the city of Ukiah and its vicinity, were divided into hexagons. The size of the hexagons
was based on survey data of typical distances for walking trips by Americans. The idea being that land
uses in a given hexagon would be within comfortable walking distance of complementary land uses in
the six adjacent hexagons.
2) The existing land uses in each hexagon were then grouped into three categories as follows:
• Residential, measured in households.
• Retail, measured in jobs. This category includes services such as banking and beauty salons
that typically attract more trips by customers than commute trips by employees.
• Non-retail, also measured in jobs. This includes office, industrial, and agricultural jobs where
the majority of trips are made by employees rather than customers.
Figure 2, Figure 3, and Figure 4 show the existing distribution of households, retail jobs, and non-retail
jobs in Ukiah, respectively.
3) GIS software was used to identify the 6 hexagons adjacent to every hexagon in the study area.
Accounting for Biking Opportunities
Bicycling is not currently a widely used mode in Ukiah 4. Nevertheless, it is useful to have it represented in the
tool so that the effects of improved bicycle infrastructure can be evaluated. This may include projects done as
mitigations for other projects that have significant VMT impacts. The effect of biking is to geographically extend
3 Background data may need to be periodically updated in the future,if the background data inputs become outdated based on local or
regional residential or employment growth.This can be done in GIS at the parcel level and editing the input data,or by identifying the
hexagon zones in which growth has occurred and applying a growth factor as appropriate.This may require assistance from a consultant,
depending on the technical capacity and/or access to software of the involved City staff.
^The American Community Survey found that only 0.7%of commute trips were made by bicycle.
11196303 3
a person's ability to travel without driving. However, this is limited by the fact that only a fraction of people are
willing to travel by bicycle, and they will only bike if the route is relatively safe and comfortable.
To assess multimodal network quality and connectivity and identify the routes that are most likely to be
perceived as safe and comfortable within the City of Ukiah, a Bicycle Level of Traffic Stress (LTS)was
completed. The BLTS analysis completed as part of this proposed methodology employs the level of traffic
stress methodology described in the Oregon Department of Transportation (ODOT) "Analysis Procedures
Manual Version 2, Chapter 14, Multimodal Analysis," (October 2020), which is based on based on the paper,
Low Stress Bicycling and Network Connectivity, Report 11-19, published by the Mineta Transportation Institute
(MTI) (May 2012). The LTS methodology as reported by ODOT's latest Multimodal Analysis Procedure Manual
includes updates to the methodology that was originally published by MTI. The updated methodology includes
analysis criteria for new bicycle facility types that have become more popularly used since the original report
was published and considers additional infrastructure types not analyzed under the original MTI methodological
approach.
The LTS methodology considers a variety of roadway infrastructure characteristics including but not limited to
the following: number of travel lanes (i.e., road width), posted/prevailing speed, roadway functional
classification or Average Daily Traffic(ADT), and degree of separation from the roadway. The LTS analysis
was completed at the City-level for the existing and proposed bicycle networks, as well as the county-wide-level
for comparison purposes and developing thresholds of significance.
Using the results of the level of traffic stress analysis, the hexagons connected via the low-stress network
(facilities scored as LTS 1 or 2)were identified for each source hexagon.
These factors are incorporated into the next steps in the diversity indices methodology as described below:
4) The hexagons that any given hexagon's land uses can interact with were extended to include non-
adjacent hexes that are connected by a path along the low-stress network. For example, in Figure 5
shows that six non-adjacent hexagons are accessible from hex zone 9004 via the low-stress bicycle
network.
5) The land uses in these additional hexagons were then added to the land uses interacting with the land
uses in Hex 9004. However, the land uses in the low-stress neighbors were treated as less accessible
than the land uses in the adjacent hexagons. A factor of 0.56 was applied to reduce the effect of non-
adjacent homes and jobs. In other words, if there were 100 retail jobs in the low-stress neighboring
hexes, they would be treated as equivalent to 56 jobs in a hexagon connected via the low-stress
network.
The 0.56 factor came from research5 that found that:
"A majority(56%) of the region's population fit in the Interested but Concerned category—
thought to be the key target market for increasing cycling for transportation. The analysis
indicates that reducing traffic speeds and increasing separation between bicycles and motor
vehicles, such as through cycle tracks, may increase levels of comfort and cycling rates."
Computing Diversity Indicators
6) The land uses in each hexagon are then combined with the land uses in the six adjacent hexes, and
the land uses accessible through the low-stress bicycle network, to represent the diversity of land uses
available within walking and biking distance to people in the hexagon.
7) The potential for interaction with complementary land uses was then estimated using three diversity
indices, each representing a different type of transaction:
• Jobs/Housing Diversity, which represents a person's ability to walk to their place of
employment. In traffic forecasting this type of trip is termed a home-based work(HBW)trip.
5 Four Types of Cyclists?Examining a Typology to Better Understand Bicycling Behavior and Potential, Dill and McNeil,2012.
11196303 4
• Retail/Housing Diversity, which represents a person's ability to walk for shopping trips. In traffic
forecasting this type of trip is termed a home-based other(HBO)trip.
• Job/Mix Diversity, which represents the interaction between retail and non-retail uses. For
example, office workers walking to nearby restaurants or coffee shops. In traffic forecasting this
type of trip is termed a non-home-based work (NHB)trip.
The formulas for these indices are as follows:
Jobs/Housing Diversity=1-[(b*HHs-EMP)/(b*HHs + EMP)]
Jobmix Diversity = 1-[(c*REMP-NEMP)/(c*REMP + NEMP)]
Retail/Housing Diversity= 1-[(b*HHs-REMP)/(b*HHs + REMP)]
Where:
HH = Number of households
REMP = Number of Retail and Service Jobs
NEMP = Number of Non-Retail Jobs
EMP = Total number of jobs (i.e., REMP + NEMP)
b = total regional employment/total regional households
c= total regional non-retail jobs/total regional retail jobs
d = total regional retail/service employment/total regional households
These formulas produce scores for individual hexagons that range from -1 to 1, with a score of 0
indicating an ideal mix of land uses and scores of-1 and 1 indicating that only one of the land uses is
present.
The ideal mix of land uses, found in the formulas as "a", "b", and "c", was determined from the county-
wide mix of the three land use types. The rationale for this is the fact that land uses tend to balance
when viewed over a large area. For example, government jobs may be concentrated in one area and
industrial jobs in another, while residences and shops are distributed among various other
communities, but when taken as a whole the housing, retail, and non-retail uses in a region tend to
occur in the correct proportions for that particular type of region.
8) The scores for the three types of diversity were then mapped out. These maps can be used by City
staff to identify which parts of the city have a good balance of land uses and which might benefit from
zoning that would promote a better mixing of land uses.
Computing Citywide Score
9) For some purposes, such as evaluating general plan alternatives, it is useful to be able to compute a
combined diversity score for the study area as a whole. The first step in doing this is to convert the
diversity scores from the-1 to 1 range used in the scores for individual hexagons into their absolute
values, with 0 again indicating a perfect mix of uses and 1 indicating no mix at all (i.e., a single land
use type). If this were not done, then the scores of, say, over-retailed and under-retailed
neighborhoods in different parts of the city would cancel each other out, when in fact both have a poor
land use balance.
10) The three types of diversity are not equally important for every hexagon because the number of HBW,
HBO, and NHB trips depends on the land uses in the hex. The table below shows the number of trips
of each type generated by each of the three land use categories:
11196303 5
Table 1: Trip Generation Rates by Trip Purpose
Trip Generation Rate
Trip Purpose Household Retail Job Non-Retail
Job
Home-Based Work 2.2 1.2 1.7
Non-Home-Based 1.0 8.1 1.9
Home-Based Other 5.9 8.2 0.8
Total 9.0 17.5 4.4
11) The land uses for each hex are then multiplied by the trip generation rates and used to compute the
percentage of total trips in each trip category.
12) The three individual scores for each hexagon are then combined into an individual score for each
hexagon using the trip types as weighting.
13) The scores for the individual hexagons are then combined using the number of trips generated by the
hex to weight their contribution to the city-wide score. Note that this means that the inclusion of vacant
hexagons outside of the city will have no effect on the outcome; they generate no trips and so their
scores will be weighted at zero.
Computing Countywide Score
Using the same methodological steps above, the existing countywide score was computed to compare against
the existing citywide score to aid in establishing thresholds of significance.
11196303 6
Figure 1: Methodology Flowchart
1 Key
Divide Analysis
Areainto GIS Task Data from Research
Hexagons
Computation •
2
Low-Stress ouseholds
Bicycle
Network Retail Jobs
Non-Retail
Jobsforeach
t
Hexagon 3tify Low- Identify
Stress Adjacent
ighbors Hexagons
tRed
10
Bicycle Use Land Uses in Low-Stress
Land Usesin Non Home-Based
uctionFactor Neighboring Hexes Adjacent Hexagons Home-Based Other
Home-Based Work
6 Trip-Gen Rate by
Aggregate Land Uses in each Hexagon,its Adjacent Hexagons,and the Land Use
Hexagons Accessible via the Low-Stress Bicycle Network
7 11
Retail/Housing Non Home-Based
JobMix Diversity Home-Based Other
Compute
Jobs/Housing Total Home Based
Diversity Work Tripsfor
each Hex
8 9 12
Retail/Housing
JobMix• - Weightingfor
JobMix Diversity each Hexagon
Jobs/Housing Absolute Values
Diversity Map of Jobs/Housing
Diversity by Hex
13
Combined .-
11196303 7
2.2 Advantages of the Methodology
This methodology offers a number of practical advantages:
a) Ease of Use: It does not require expensive software and special training to use, as is the case with
most traffic models. City staff can evaluate projects using the Excel program already found on their
computers.
b) Nuanced, Informative Results: Unlike other methodologies, whose output is a just a number saying the
VMT is high or low, this methodology provides a clear indication of the underlying causes of high or
low auto use. For example, it might show the analyst that a proposed housing project is in a location
that lacks local shopping opportunities and might be improved with the addition of locally serving retail.
c) Appropriate Scale: While this methodology cannot substitute for a convention traffic model for
forecasting over large geographic areas (entire counties), it is likely to provide a more accurate
representation of travel behavior in a small town than is possible with a conventional model. This is
because traffic models incorporate certain necessary simplifications, such as centroid connectors and
frictionless intersections, that are inconsequential when forecasting long trips but are highly distorting
when forecasting trip-making over small areas. With a total area of less than 5 square miles, Ukiah is
the sort of compact, walkable city better suited to a proximity-based model than a trip-based model.
2.3 Limitations of the Methodology
As is the case with any tool, there are situations where the smart growth score is useful and other situations
where it either cannot be used or where some other methodology might be better suited to the task. The main
limitations to the tool are:
• It can only be used to analyze land development projects. Transportation projects require a different
analytical methodology.
• It was designed for use in analyzing smaller jurisdictions that are not part of a large, contiguous urban
area. In small, relatively isolated towns trips tend to be either long, to another jurisdiction, or short,
within the jurisdiction. In such circumstances a focus on balancing land uses within the jurisdiction can
reduce the longer trips by substituting local destinations in place of more distant ones. Moreover, the
short distances within the jurisdiction create the potential for a significant percentage of trips to be
made by non-auto modes. Conversely, a city that is part of a large contiguous urban area is likely to
have a lot of medium-length trips that are not represented well in this tool. A conventional traffic model
would work better in that case.
• When considering bicycle improvements in this methodology, in order to see a significant effect on the
smart growth score for a given hexagon or the jurisdiction's overall score, proposed improvements
must result high-quality, low-stress facilities that improve network connectivity. If there are few
proposed improvements that address key high-stress barriers, there will be little effect when
considering the multimodal network component in this methodology.
• The smart growth score is not suitable for places with substantial fixed-route transit use, because
transit is not represented in the tool. Fixed route transit plays such a minor role in the transportation
networks of most small jurisdictions in California6 that its inclusion is not worth the additional complexity
it would entail. The dial-a-ride services that are sometimes available are analogous to auto trips and so
are already represented in the model.
s It does not show up at all in the Replica data for jurisdictions in Mendocino County.
11196303 8
3. Results
3.1 Citywide Level of Traffic Stress
A Bicycle LTS analysis was completed for both the existing bicycling network in Ukiah, as well as the planned
bicycling network as shown in the City's 2015 Bicycle and Pedestrian Master Plan (BPMP). Proposed bicycle
facilities recommended in 2015 BPMP included as part of the planned network in this analysis include Class I
Multi-Use Paths, Class II Bike Lanes, Class II Buffered Bike Lanes, and traffic signalization. Class III Bike
Routes do not have an effect on level of traffic stress and are analyzed the same as mixed traffic conditions.
The LTS results were utilized to identify high-quality, low-stress routes within a 1.5-mile bicycling distance of a
given source hex that a majority of the population would feel comfortable using'. The hexagons that are
connected via the low-stress network were added to the land use inputs, as described in Section 2.1. The land
use interaction/diversity added by the additional hexes connected via the low-stress network is discussed in
further detail in Section 3.2.2. This section describes the citywide LTS results and highlights an example of the
low-stress neighbors added by the connectivity assessment.
Figure 5 presents the existing LTS results for Ukiah's bike network and highlights hex#9004 as an example.
The dark green hexes show the immediately adjacent neighbor hexes that are already considered in the
existing diversity score. The light green hexagons show the 18 hexes beyond the immediately adjacent
neighbor hexes that are accessible via the existing low-stress network within a 1.5-mile network distance of
example hex 9004.
While the existing network does provide some additional connectivity to the land use within a given source hex,
improvements in bicycle infrastructure in the City of Ukiah would result in lower levels of traffic stress and
higher degrees of connectivity. This would further improve the potential land use interaction between a source
hex and another hex in the city with more diverse land use types. In turn, the additional connectivity would
improve the land use diversity scores within that hex and, depending on the magnitude of new potential land
use interactions, the city as a whole.
As shown in Figure 5, various north/south corridors in the downtown area create a high-stress barrier that
inhibits bicycle trips between the eastern and western portions of the town, including along State Street north
and south of the recently completed Downtown Streetscape Improvements from Henry to Mill Streets, and
South Dora Street, for example. While there are other possible low-stress connections, a bicyclist would be
forced to take more circuitous and detoured routes if the lowest-stress paths are preferred, which could deter
people from choosing to bike. The addition of a few more low stress bike facilities in both the north-south and
east-west directions barrier may result in a significant increase in biking between the areas east of Main Street
and the areas west of State Street by offering high quality, direct routes between key areas of the City.
Figure 6 presents the LTS results for the planned bicycle network in Ukiah. Key improvements associated with
additional low-stress bicycling routes include the Phase II extension of the Downtown Streetscape
improvements along State Street, a buffered bicycle lane along Bush and Dora Streets, a Class I Path
connecting Low Gap Rd and Bush Street, near Pomolita High School, and extensions of the Great Redwood
Trail Class I Path segments to the northern and southern City limits. Using hex#9004 as an example again, an
additional 24 hexes are accessible via the low-stress network.
3.2 Smart Growth Scores
The following sections discuss the components that make up the VMT Option 2 "Smart Growth Score"
methodology proposed for SB 743 implementation in the City of Ukiah with the effect of the City's low-stress
bicycling network considered. The estimated distribution of existing land uses, as well as the results for
diversity indices analyses of the existing condition and existing condition with low-stress neighbors are
Geller's Four Types of Bicyclists research indicates that 56%of the population would be comfortable riding on facilities at LTS 1 and LTS
2.
11196303 9
presented herein. Additionally, the overall scores are reported for the City of Ukiah and Mendocino County as a
whole.
3.2.1 Existing Diversity Scores
Figure 7, Figure 8, and Figure 9 show the three diversity scores for existing land uses, before the low-stress
neighbors are incorporated. These figures show several things:
• Much of the city core scores quite well on jobs/housing diversity(see Figure 7). This indicates the
success that Ukiah has achieved in enabling people to live and work in close proximity.
• The edges of the city do not score as well on jobs/housing diversity (see Figure 7). However, this does
not hurt the city's overall score as much as Figure 7 might imply, because there are relatively few jobs
and residences in those areas (see Figure 2, Figure 3, and Figure 4).
• Figure 8 shows that, with the exception of the city core, retail and non-retail jobs tend to be
concentrated in different parts of the city. This limits their potential for interaction that does not involve
driving. This could be an opportunity area for the City to encourage a more balanced jobs mix in these
locations.
• The city as a whole is over-retailed in relation to its population, due to the fact that it serves as the main
retail destination for a large surrounding area (see Figure 9). This has implications both for sales tax
revenues (good)and VMT (bad).
3.2.2 Existing with Low-Stress Neighbors Diversity Scores
Figure 10, Figure 11 and Figure 12 show the three diversity scores for existing land uses with the added effect
of the neighbor hexes that are accessible via the City's low-stress bicycling network. These figures highlight a
few effects of considering low-stress bicycle network connectivity on the city's land use diversity scores.
• While much of the city core scored relatively well on jobs/housing diversity in the existing scenario,
many hexagons show further improved scores in the City core, with even more hexagons moving
toward the ideal score of 0. Further, the geographic extent of hexes scoring well on jobs/housing
diversity is extended beyond the City core to cover much of the City(See Figure 10).
• The furthest edges of the City still do not score as well on jobs/housing, however, again, there are
relatively few jobs and residences in those areas (see Figure 2, Figure 3, and Figure 4).
• Figure 11 shows that jobmix diversity has improved through the central city areas, with the most
significant differences seen on the north and west sides of the City, where the most low-stress
connections can be seen (see Figure 5).
• The city remains over-retailed in relation to its population (see Figure 12), and there are still a few key
hexes with high concentration of retail and trips (for example, hex 8887 and hex 8759). However, the
retail/housing diversity scores have improved over much of the central City areas when the low-stress
bike network is considered because the potential for land use interaction has increased fairly
significantly with the added low-stress neighbor hexagons included in the diversity calculations.
3.2.3 Overall Diversity Scores
In the previous methodology adopted by the City, city- and countywide overall scores were reported using this
0 to 1 scale, with 0 being the ideal score. However, in this memorandum, the overall diversity score results are
converted to a 0 to 100 scale, with 100 being the optimal score, and reported as such in the tables below. The
diversity indices results shown in the maps referred to in the previous section still use a -1 to 1 scale, with 0
being the ideal score, because this is needed to highlight which side of the spectrum a poor land use mix leans
toward (more non-retail vs. retail, for example).
11196303 10
Table 2 present the citywide and countywide diversity scores by each diversity category, as well as the trip-
weighted overall diversity. As shown, and discussed previously, the City scores best on jobs/housing diversity,
with a score of 77.0, and worst on retail/housing with a score of 65.7. The total weighted score for the City is
70.1, while the Countywide average is 50.0, making the City of Ukiah 40.1 percent better than the county
average.
Table 2:Existing Diversity Scores
Di ersity Scores Total % Better
Jurisdiction Jobs/ Job Mix Retail/ Weighted than
Housing Diversity Housing Score County
Diversity Diversity Average
City of Ukiah 77.0 72.7 65.7 70.1 40.1%
Countywide Average 57.6 54.5 44.8 50.0 0.0%
Existing Plus Low-Stress Neighbors Diversity Scores (Existing Bike Network)
Table 3 presents the city and countywide diversity scores with low-stress neighbors associated with the existing
low-stress bike network considered. As shown, when the connectivity of the existing low-stress bike network is
considered, the City's total weighted score improves from 70.1 to 81.0, making the citywide score 61.7 percent
better than the countywide average.
Table 4 presents the city and countywide results when the low-stress neighbors associated with the existing
and planned bike network is considered. As shown, the city's overall score increases to 82.5, making the
citywide score 64.6% better than the Countywide average when the connectivity associated with the existing
and planned bike network is considered. If the City were to include additional low-stress bike facilities in the
planned network, these results could improve even more significantly.
Table 3:Existing Plus Low-Stress Neighbors Diversity Scores(Existing Bike Network)
Diversit Scores
Jobs/ Retail/ Total % Better
Jurisdiction Job Mix Weighted than County
Diversity
Housing Diversity Housing Diversity Score Average
City of Ukiah 82.9 82.8 79.2 81.0 61.7%
Countywide Average 60.6 59.7 50.4 55.0 9.2%
Table 4:Existing Plus Low-Stress Neighbors Diversity Scores(Planned Bike Network)
Diversity Scores
Jobs/ Retail/ Total % Better
Jurisdiction Job Mix Weighted than County
Diversity
Housing Diversity Housing Diversity Score Average
City of Ukiah 82.4 87.6 79.4 82.5 64.6%
Countywide Average 60.5 61.3 50.5 55.4 9.9%
11196303 11
4. Additional Tool Developments
Beyond expanding the methodology to include the multimodal network component, the user interface of the
spreadsheet-based tool was improved for clarity and to allow the user to easily assess the effect of individual
proposed projects. The latest version of the tool includes a dashboard component where the user can enter the
hexagon to which a proposed project belongs along with the estimated number of households, and retail and
non-retail employment. These inputs are connected to output tables on the dashboard that provide the citywide
diversity scores by each category, as well as the total trip-weighted scores, and the % better than the
Countywide average. These outputs are calculated for the existing and plus project conditions, both before and
after the multimodal component is considered.
11196303 12
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11196303 13
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11196303 14
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COY
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11196303 15
F gu e 5 Existing Condition £Ta and Connectivity Results
EepE
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| y> i \ , .
> b Js , (low stress'.
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Js#44trstress
Existing Crossing U
w,
JsI (low stress)
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eo Js2%_a_4
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An nexaticGkms
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11196303 16
Figure 6: Planned Network LTS and Connectivity Results
k { ! Lege+d
o ° 'o�A o o`!•y Existing Overall LTS
LTS 1 (YoW Stress)
— --- T
T-* n 4 —', 1-\ l LTS 2"stress)
LTS 3{high stress) _
LTS 4{high stress)
+r— Proposed Net#oTk Crossing
LTS
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�7
r = =-q - Y • LTS 2(low stress)
- _ 4` ;' - 1�_ c x _= • LTS 3(high stress)
t, • LTS 4(high stress)
+ = qc� Annexation Areas
q q �
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, q .• yy Hex Zones
47
0
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L - ;IZ6 6
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f ) _7' —i
11196303 17
Figure 7: Existing Jobs/Housing Diversity
neI nhrea Ho east i
-- x't iirmrlr "x'
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ii I
11196303 18
Figure 8: Existing Job Mix Diversity
r_-X Area A east
......................
... .......
II II II II II.
041p
=E:10
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gs
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11196303 19
Figure 9: Existing Retail/Housing Diversity
nArea 6 H east
997
-
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:'�-0.59--0.SG 0 0.51-0.70 �7,298-76,945
-0.49--DAD
0 031-0.80
.�-0.39--0.M 0.81-0.90 t:J city Limbs
4.29--0.21} _0-91-1.00(More •:i Mnexatlan Areas
-0.19--0.16 housing) Hlghway
-1'19-']r70 0 PloHwEehaldE —Roads
11196303 20
Figure 10: Existing Plus Low-Stress Neighbors Jobs/Housing Diversity
nex Am Ho easL
I-V
}4 IIII II II II II II I' __
IN
'x
L 1
x
i j-�1 9BB9 f f ee-1
-413
II 9631
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i
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I II II III Il�li;;II II II II III IIII II II it lll4 III II II II II II IIIII II IIGiI -T
%-'<If, iQn93 94 'k} '{ r .—
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—
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.�-0.29--0.2a 0.91-1.00iMofe •:iAmnexatlonAieas
i�-0.19--0.1 a Houeing] H4hway
0-0.09-GAD 0 No Houeenalde —ROWS
101'�
11196303 21
Figure 11: Existing Plus Low-Stress Neighbors JobMix Diversity
nEx Area 6 he Hal east
r
F
I I-
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--- {I III II III�YEI
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11196303 22
Figure 12: Existing Plus Low-Stress Neighbors Retail/Housing Diversity
nay AM ne a east �
x". -"c
41
i
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0 0.71-0.80 �
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-0.99-9.00 0 Na Hause18 U —Roads
1�1
11196303 23
MWOR
(4) i `q
Technical Memorandum
February 20, 2024
• Craig Schlatter, City of Ukiah Contact No. +1 (707)463-6219
•• Don Hubbard &Todd Tregenza, GHD cschlatter@cityofukiah.com
• Colin Burgett& Paige Peel, GHD 11196303
ProjectCity of Ukiah General Plan Update
• Step-by-step Instructions for VMT Option 2 (Smart Mix tool)
1 . Background
This memorandum provides instructions for assessing project impacts using the City of Ukiah "Smart Mix tool"
created by GHD. The "Smart Mix tool" has been provided to the City as an Excel spreadsheet file.
2. Impact Threshold
Table 1 presents the existing city and countywide diversity scores with multi-modal characteristics included (as
described in the October 2023 Council presentation. As shown, the citywide score of 81.0 is 57.9% better than
the Countywide average of 55.0.
Impacts would be considered less than significant if a project were to result in a diversity score of 56.8 or higher
(therefore at least four percent better than the County average).
Individual land use projects will be evaluated based on their effect on the Diversity score for the applicable
project area (hex zone)that the proposed project is located in. Alternatively, citywide projects (such as General
Plan updates or Specific Plans, etc.) may be evaluated based on whether or not the citywide score remains
56.8 or higher.
Table 1:Existing Diversity Scores&Significance Threshold
Di ersity Scores
Total % Better
Jurisdiction Jobs/ Job Mix Retail/ Weighted than County
Diversity
Housing Diversity Housing Diversity Score Average
City of Ukiah 82.9 82.8 79.2 81.0 57.9%
Countywide Average 60.6 59.7 50.4 55.0 0.0%
Significance Threshold 56.8 4.0%
The Power of Commitment
11196303 1
3. Instructions for Assessing Project Impacts using Smart
Mix tool
The user interface of the spreadsheet-based tool was updated to allow the user to easily assess the effect of
individual proposed projects at both the zone and citywide level under"plus Project" conditions. Figure 1
provides a map of Ukiah hex zones (also shown within the spreadsheet on the tab titled "MAP OF UKIAH HEX
ZONES"). The tool includes a sheet where the user can enter the zone to which a proposed project belongs
along with the proposed number of households, and or anticipated retail and non-retail employment associated
with commercial uses. These inputs are connected to output tables provide the resulting diversity scores at
both the zonal and citywide level. These outputs are shown for the existing and plus project conditions.
The spreadsheet tool includes a tab titled "Instructions"that provides the following instructions for assessing a
development project using the tool:
workbookPrior to Step 1: Ensure
Formulas . . . . Calculation sub-tab.
Click the Calculation Options drop-downensure
Step In the STEP 1 - ENTER PROJECT tab: enter the project location based
on the ID number of the applicable hexagon zone(s)the proposed
project is associated with (see tab titled "MAP OF UKIAH HEX ZONES")
in column B, and enter the proposed project land use inputs in columns
C, D, and E for the associated the hexagon zone(s). To run the
workbook calculations, either save the file, or navigate to Formulas tab
in Excel toolbar ribbon (referred to in Step 1), then Calculation sub-tab,
then click Calculate Now.
Step In the STEP 2 -VIEW RESULTS BY ZONE tab: view the results for the
applicable hexagon zone(s). Project impacts are considered LESS
THAN SIGNIFICANT if the project area (hex zone) score under Existing
plus Project conditions is 56.8 or higher. (Note: these results by zone
are applicable to site-specific development projects. Citywide projects
such as General Plan updates or Specific Plans may instead be
assessed based on the City of Ukiah average under Existing plus
Project conditions, which is shown on the STEP 1-ENTER PROJECT
tab).
11196303 2
Figure 1: Hex Zone Map
v01
Lrpend
a' r
Existing Overall LTS
° LTS 1 (law stress;
n�=- _G i •S
•' -- ; ° LTS 2(low stress;
• -� r oo a LTS 3(high stresu:
_ LTS 4(high stresu,
01, Existing Crassinp LTS
00
00 o
\O ....... • LTS 1 (low stress;
--- � JC -g' U O � • LTS 2(low stress;
C
o 00 _o • LTS 3(high stresu;:
C O •Q 0 o v 0 o • LTS 4(high stresu;:
C
— ' °o� c1.• °°. } 8 i •_ �'_—a An nexatic n Areas
00- /° .°o o° City Limits
00000 p 0 ° 0 0 = o�
01 r i 0 Hex Zores
OQ° 000 0o O° ° O—o. O b �l
f I
f— — — --- 00000, o 0 o:_a: o o°c
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11196303 3