HomeMy WebLinkAboutEdina Density StudyCity of Edina Density Study
Spring 2024
Prepared by
Kenton Briggs, Holly Leaf, Johnny Menhennet, Greg Olberding, Jem
Thompson, and Yuping Wu
Students enrolled in PA 8081: Master of Urban and Regional Planning Capstone
Faculty Advisor
Dr. Nichola Lowe, Humphrey School of Public Affairs
Prepared in Collaboration with
Matthew Gabb, City of Edina
The project on which this report is based was completed in collaboration with the City of
Edina as part of a 2023–2024 Resilient Communities Project (RCP) partnership. RCP is a
program at the University of Minnesota’s Center for Urban and Regional Affairs (CURA)
that connects University faculty and students with local government agencies in
Minnesota to address strategic projects that advance local resilience, equity, and
sustainability.
The contents of this report represent the views of the authors, and do not necessarily
reflect those of RCP, CURA, the Regents of the University of Minnesota, or the City of
Edina.
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NonCommercial 3.0 Unported License. To view a copy of this
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City of Edina Density Study
Humphrey School Capstone Report
The Hubert H. Humphrey School of Public AffairsUniversity of Minnesota – Twin Cities
Kenton Briggs
Holly LeafJohnny Menhennet
Greg Olberding
Jem Thompson
Yuping Wu
PA 8081 Capstone Workshop
Planning and Public Policy
Instructor: Nichola Lowe, Professor
Spring 2024
City of Edina
3
Table of Contents
Glossary of Terms
Executive Summary 4
Introduction 6
City of Edina Background 8
Is Density the Right Approach? 18
Which Approach to Density is Most Effective? 26
What Policy Actions are Needed? 40
Final Conclusions 49
Appendix A - Regression Analysis Methodology 54
Appendix B - Transportation Decision Framework 63
Appendix C - Peer Cities Analysis Methodology 72
Appendix D - Scenario Analysis Methodology 78
Glossary of Terms 101
4
The City of Edina (the City) has outlined a series of strategies in its Climate Action Plan (CAP) to
reduce greenhouse gas emissions while achieving economic growth, furthering social equity, and
improving the wellbeing of the local environment.
To help meet the City’s goal of 45% greenhouse gas (GHG) reduction from 2019 levels by 2030, the CAP identifies five strategies related to transportation and land use patterns, three of which are of concern to this study:
TL 1: Decrease community-wide vehicle miles traveled (VMT) by 7% by 2030
TL 2: Double public transit commuter ridership from 3.3% to 6.6% by 2030
TL 3: Increase average population per developed acre by 4% by 2030
This study seeks to better understand the relationship between population density and transpor
tation mode share to determine whether the projected density increases are likely to be sufficient
to meet the City’s transportation goals. Three research questions were asked:
Is density the
right approach?
Which approach to density is most effective?
What policy actions
are needed?
Edina’s climate strategy must involve increasing density, a strategy which has proven effective for reducing VMT, especially in conjunction with other policies. However, a 4% increase in residential density will not be suffi-cient for Edina to meet its CAP goals.
Edina should target density increases around specific nodes and corridors. City-wide density increases are less efficient at encourag-ing sustainable transpor-tation options like walking, rolling, transit, and biking. Recommended zoning strategies can be found on the reverse side.
The City of Edina can take a range of policy actions aimed at allowing and encouraging targeted population density increases while maximizing the benefits of that densi-ty. Policy recommendations can be found below.
Recommended Policy Actions
Land Use
Recategorize Neighborhood Nodes as MixedUse Centers, and MixedUse Centers as Community Activity Centers Achieve Thrive MSP 2040 Transportation Policy Plan density minimums in existing and future transit corridors
Allow commercial uses at corner parcels on France Avenue, with
at least duplexes permitted along the whole corridor
Disallow new construction and/or operation of drivethru enter
prises.
When conducting corridorbased zoning, include abutting lots on parallel streets in addition to the corridor itself Continue to support ADU development
Built Form
Replace maximum building coverage requirement with existing maximum impervious surface coverage requirement Supplement maximum height and density standards with minimum height and density standards.
Reduce minimum lot size to 4,500 square feet Decrease front setback requirements
Adopt a set of objective design standards Reduce height transition requirements
Transportation
Eliminate parking requirements; if infeasible, cap maximums at one per unit at the highest.Study and remediate barriers to active transportation access to transit stops
Update the TDM Policy with objective, pointsbased standards
Executive Summary
Executive Summary
5Executive Summary
Recommended Scenarios
Existing Conditions
“Basic”“Enhanced”“Preferred”
Increases allowable density around
the six “Areas of Change” as defined
in Edina’s Comprehensive Plan
as well as within a quarter mile of
future E Line stations. The bare
minimum of targeted density
increases that will be required to
move the needle on the Climate
Action Plan goals.
Builds on the previous scenario
by adding softer density increases
within 500 feet from the “Areas of
Change” as well as within a quarter
mile of a reimagined “Better Route
46”, which continues west from the
Grandview neighborhood along
Vernon Avenue and Lincoln Drive,
terminating at the future Green
Line Opus Station in Minnetonka.
Adds increased allowable density
within 500 feet of the Regional
Bicycle Transportation Network
as well as within a quarter mile of
major healthcare facilities. This is
the most impactful scenario for
reaching Edina’s Climate Action
Plan goals.
Using the Urban Footprint software, we tested several scenarios
to compare the effectiveness of different zoning strategies. Strategies which employed targeted density increases, around
commercial areas, along transit corridors, and near regional
bicycle trails consistently performed better than citywide
density increases.
We developed three recommended scenarios, combining the most effective strategies. Each of these scenarios builds
upon the last, with the “Basic” scenario representing the bare
minimum of zoning changes required to support a shift toward
sustainable transportation modes in Edina. The “Preferred”
scenario is the one that combines the most effective of all targeted density strategies, and will have a transformative
change toward reaching Edina’s climate goals.
Zoning Scenario Analysis
6
Introduction
Introduction
The City of Edina is a firstring suburb in the Twin Cities metropolitan area. It is a relatively affluent suburb with primarily singlefamily zoning. It is also home to significant commercial, medical, and office facilities which contain jobs for a considerable number of area residents, many of whom
commute from other parts of the metropolitan area.
The City of Edina adopted its Climate Action Plan (CAP) in December 2021. The CAP lays out an ambitious road map to help the City combat climate change as well as achieve broader goals for environmental wellbeing, economic growth, and social equity. The City has committed to reduc
ing greenhouse gas (GHG) emissions in line with goals set by the State of Minnesota and Henne
pin County, as well as those set by the 2015 Paris Agreement. Edina commits to reduce GHG emis
sions across the community to 45% below 2019 levels by 2030 and achieve net zero emissions by 2050. GHG emissions citywide reached 716,715 metric tons in 2019, which sets the Edina target for 2030 at 395,000 metric tons or below.
According to the CAP, the transportation sector accounted for 41% of GHG emissions in the city in
2019. Meeting the CAP goals for GHG reduction will require dramatic changes to the City’s transportation network and land use patterns.
The CAP is envisioned as a “living document”, involving intermittent measurements of progress
and plan revisions throughout the implementation phase. The next planned revisions will occur in
2025. The City of Edina has requested this study to explore the relationship between transportation, land use, and GHG emissions in the community to better understand and meet transportationrelated goals outlined in the CAP. This report provides an analysis of these targets and a set of
recommendations related to policy and programming to help the City meet them.
This report is concerned with the first three strategies for Transportation and Land Use outlined in the CAP:
TL 1 – Decrease communitywide vehicle miles traveled (VMT) by 7% by 2030
TL 2 – Double public transit commute ridership from 3.3% to 6.6% by 2030
TL 3 – Increase average population per developed acre by 4% by 2030
The primary purpose of this study is to understand whether the CAP target for density increases, as outlined in strategy TL 3, are sufficient to meet the targets for VMT and transit commuter ridership (TL 1 and 2, respectively).
7
Research Questions
This report seeks to answer the following questions:
1.Is increasing population density the right approach to reducing VMT and increasing transit
ridership?
2.Which approach to increasing density is most effective: blanket citywide upzoning, target
ed density increases around particular nodes and corridors, or some combination of the
two?
3.What policy actions should be implemented in tandem with these approaches to further
reap the benefits of increased density and bring Edina closer to meeting its CAP goals?
Report Structure and Overview
This report will begin with an overview of the existing and future conditions regarding the City’s
land use, zoning, and transportation infrastructure. We briefly compare Edina to a group of peer
cities, located both within the Twin Cities metropolitan area and nationwide. This section will pro
vide important context for the quantitative and qualitative analyses that we perform throughout the report.
Next comes a section that we have titled, “Is Density the Right Approach?” Here, we seek to val
idate the assumption implicit in our research questions that an increase in population density
will have a significant effect on VMT and transit ridership. We employ two methodologies to this end. The first is a brief literature review of existing scholarship on the relationships between land
use and other factors that influence VMT and mode share. The second is a series of multivariate
regression analyses that seek to quantify the strength of these relationships within the context
of the MinneapolisSt. Paul metropolitan area. In conducting these regressions, we also begin to
answer the question of whether a 4% density increase will be sufficient for Edina to meet its transportation goals.
We then move into a section entitled, “Which Approach to Density is Most Effective?” This is
where we seek to answer the research questions concerning citywide versus targeted density
increases. Here we introduce our Zoning Scenario Analysis, wherein, using a software called Urban Footprint, we study a number of different strategies toward upzoning and increases to population
density, evaluating their relative impacts on VMT, GHG emissions, and transit ridership. Using the
most effective strategies, we recommend three increasingly strong potential approaches to up
zoning.
The following section concerns our policy analysis, titled “What Policy Actions are Needed?”. Here,
we examine the City’s CAP, comprehensive plan, and city ordinances for gaps and shortcomings,
and introduce specific recommendations for policy changes that will help Edina to reach its CAP
goals. Our recommendations fall into four categories: CAP goals, land use and zoning code, built
form ordinances, and transportation policy. These recommendations are designed to inform the City of Edina’s CAP review and amendment process in 2025, the City’s upcoming comprehensive
planning process, as well as other programs and practices.
Finally, we summarize the overall takeaways from our analyses and suggest some areas for future
study. By synthesizing the results of our analyses, we come to crosscutting conclusions that we hope will remain useful and relevant to Edina and perhaps beyond well into the future.
Introduction
8
City of Edina Background
City of Edina Background
In this section, we will examine the current land use, zoning, and transportation characteristics of
the City of Edina. Our recommendations are solidly rooted in the existing conditions within the
City, and account for factors such as neighborhood character and infrastructural capacity.
Land Use & Zoning
Edina is a fully developed city, having been predominantly built out by 1970 and with only 2% of
the city being categorized officially as undeveloped land. The majority of the land area is zoned
exclusively for singlefamily residential use (Figure 2 and Table 1). As such, any significant population increases will require densification of existing residential land. According to the 2018 Comprehensive Plan, this densification is likely to occur in targeted areas of the city where multifamily
and mixeduse developments are allowed. The Comprehensive Plan states that, “approximately 93
percent of the City’s land area will be unaffected by the 2018 Comprehensive Plan Update.”
The 2008 Comprehensive Plan identified 6 ‘Areas of Change’ where redevelopment was anticipated to occur (Figure 1). These areas of change represent much of the City’s current or future land
use for commercial, mixeduse, and highdensity residential. Since the 2008 plan, small area plans
have been developed for each of these ‘Areas of Change’, under community guidance. Our scenar
ios for upzoning strategy are reflective of this strategy, with several of the scenarios analyzed focus on upzoning in and around the identified ‘Areas of Change’. The ‘Areas of Change’ are as follows:
•Wooddale & Valley View
•GrandView
•44th & France
•Greater Southdale
•70th & Cahill
•50th & France
While Edina is projected to grow modestly in population and employment (Table 2), it is important
that most redevelopment in the City be concentrated predominantly as residential or mixeduse with a residential component, as Edina has a strong imbalance between jobs and housing. Despite the overwhelming use of land in the City being dedicated to singlefamily homes, Edina has
more jobs than its population can support. A healthy ratio of jobs to housing units is 1.4, reflecting
that the median household has 1.4 incomeearners. However, as can be seen in Table 2 using 2016
data (most recent year of data when the analysis was completed in 2019, and not just estimatereliant), the jobs:households ratio was 2.35. The implication of this is that many more employees commute into the City than commute out of it, and adding more households than jobs will result
in more Edinaemployed residents being able to live near job centers, reducing local and regional
VMT. Given that the greatest ‘Area of Change’ by land area (Southdale) is also where the prepon
derance of Edina employment lies, the chance to grow household totals in Edina in and around Southdale would be uniquely beneficial for local and regional mode shift, as short trips are most likely to be taken without a vehicle.
9City of Edina Background
Figure 1: ‘Areas of Change’ in Edina
Source: Authors, using Urban Footprint and City of Edina 2018 Comprehensive Plan
10City of Edina Background
Figure 2: Future Land Use Map (residential density classified by units per acre)
Source: City of Edina
11City of Edina Background
Source: City of Edina 2018 Comprehensive Plan, from Metropolitan Council
Table 1: Existing Land Uses in Edina
Use 2005 Acres 2016 Acres Percent Total Acres (2016)2005 - 2016 Change 2005 - 2016 Percent Change
Singe Family Detached 5,434 5,419 53%15 0.3%
Park, Recreational or Preserve 922 972 10%50 5.4%
Golf Course 693 666 7%27 3.9%
Institutional 468 444 4%24 5.1%
Major Highway 401 442 4%41 10.2%
Multifamily 420 433 4%13 3.1%
Office 407 395 4%12 2.9%
Retail and Other Commercial 384 313 3%71 18.5%
Industrial and Utility 337 299 3%38 11.3%
Single Family Attached 261 372 3%11 4.2%
Open Water 261 269 3%8 3.1%
Undeveloped Land 211 216 2%5 2.4%
Mixeduse Commercial &
Other
51 1%25
Mixeduse Industrial 17 0%17
Mixeduse Residential 17 0%17
Mixed Use 2008*26 15 226.9%**
Total 10,225 10,225 100%
*The 2008 plan did not split up mixed use into multiple categories, so comparison is between combined totals of mixed use.**Change in total mixed use
Table 2: Edina Population, Household, and Employment Projections
1970 1980 1990 2000 2010 2016 2020 2030 2040
Population 44,046 46,073 46,070 47,425 47,941 51,804 55,000 60,000 63,600
Households 13,005 17,961 19,860 20,996 20,672 22,309 24,000 27,700 29,800
Employment 20,240 36,061 44,534 52,991 47,457 52,330 51,800 54,000 56,100
Source: City of Edina 2018 Comprehensive Plan, from Metropolitan Council estimates and revised Thrive MSP 2040 forecasts, as of January 2019
12City of Edina Background
Transportation
There has been a long history of transit service in Edina, and the 2020s will support the larg
est expansion potential in the past half century or more. Transit service in Edina began with a
streetcar line running along 44th Street in 1905, connecting northern Edina with Minneapolis
to the northeast, and Hopkins and communities along Lake Minnetonka to the west by 19061. This right of way was abandoned when the service converted wholly to bus operation in 19542.
The right of way can be observed in the figure below, and today is built upon with singlefamily
residential, for the most part. Legacy portions of the right of way within the City of Minneapolis
nearby are still in operation with the historic ComoHarriet Streetcar Line. Bus service along a
second eastwest corridor was also present along 50th St and Vernon Ave as recently as 2019. Prior service into Minnetonka was provided by Metro Transit with Line 46 and Line 146, which
operated along a similar 50th StreetVernon AvenueLincoln Drive alignment, but with an ex
press segment along I35W to Downtown Minneapolis, operated at commute hours. It is only in
the contemporary Covidera reality that Edina has for the first time in the past century not had a
complete eastwest transit route.
Figure 3: 1906 map of trolley service between Edina and Hopkins
Source: Minnesota Streetcar Museum
Recent commuter mode share data (ACS 20182022 5year estimates) indicates that singleoccu
pancy vehicles (SOV) remain the majority of Edina commuters’ mode of transportation at 70%.
Edina residents who worked from home represented 22% of wouldbe commuters (this share is likely higher than indicated, since it includes two years prior to the pandemic).
Public transit, walking, and bicycling each made up around 1% of commuter mode share. This is
significantly lower than the public transit mode share of 3.3% outlined in the CAP, indicating an
overall decrease in public transit commuting since the start of the COVID19 pandemic. This pattern has been seen throughout the metro region, as public transit ridership has trended away from commuter peak hours and towards allday, allpurpose trips. Moreover, future transit service
in Edina namely, the METRO E Line Arterial Bus Rapid Transit (ABRT) route will focus on all
day highfrequency service meant to serve a variety of trip types. It is for this reason that we are
recommending a change in the CAP goal TL 2 from increasing commuter transit ridership mode share to a focus on overall public transit mode share, regardless of trip type.
13City of Edina Background
Figure 4: Commuter mode share in Edina
Source: Authors’ calculations using ACS 2022 5-year estimates
Future Transit
There are two major Metro Transit projects which will improve access to transit in Edina. The
METRO E Line (Arterial BRT) project will originate at Southdale Transit Center and run north
through the city via France Avenue. This will connect the city with the Chain of Lakes area,
downtown Minneapolis, and the University of Minnesota. The improved transit route is part
of Metro Transit’s rollout of an Arterial Bus Rapid Transit network, and will have stops at 44th/France, 47th/France, 50th/France, 54th/France, 58th/France, 62nd/France, and at the Fairview
Southdale Hospital & Medical Center before terminating at Southdale Transit Center, adjacent
to the eponymous mall. These stations are spaced approximately every halfmile, ensuring
no more than a 5minute walk along the corridor for access to any of the stops. This line has a
scheduled opening of 2025.
The second major transit infrastructure project to impact Edina is the Green Line Extension.
While the route will not go through Edina, it will pass closely through neighboring suburbs to
the north and west of Edina (St. Louis Park, Hopkins, Minnetonka, and Eden Prairie), at times
passing as close as a quarter mile from the City limits. This project is anticipated to begin service in 2027, connecting the southwestern suburbs directly to Minneapolis, the University of Minne
sota, and St. Paul. The major benefit this project will bring to Edina is the catalyst for the poten
tial return of a full eastwest transit corridor through the City, connecting through the western
part of Edina to Opus Station in Minnetonka.
14City of Edina Background
These transit expansions will support VMT reduction and increases in transit mode share for all
trips. Smart land use planning and development decisions will be important to make best use
of these transit improvements.
Figure 5: Existing and Planned Transit in Edina
Source: Authors created using Metro Transit data
15City of Edina Background
Edina Peer City Comparison
An analysis of peer cities is used to compare Edina’s standing on several attributes related to the
study’s goals, among them residential density and VMT. The two objectives of this section are to
identify potential correlations between attributes, as well as to identify any potential objectives
and strategies in their plans that can help inform Edina’s planning.
The two groups of peer cities used in this study are provided by the City of Edina. Peers were
identified due to similar demographic, economic, or spatial characteristics with Edina. The first
group contains fellow suburban cities within the MinneapolisSaint Paul metropolitan area,
including all of Edina’s immediate neighbors. The second group is composed of cities in other metropolitan areas within the United States which also share similar characteristics. Any plan
ning and development work done by these cities with regard to density, VMT reduction or any
other topic in this study can be helpful to inform future work in the City of Edina.
Table 3: Edina’s peer cities located in the Twin Cities metropolitan area
St. Louis Park Minnetonka Golden Valley Eden Prairie
Plymouth Richfield Bloomington Eagan
Table 4: Edina’s peer cities located nationally
Littleton, CO
(Denver Metro)
Menlo Park, CA
(Bay Area)
Highland Park, IL
(Chicago Metro)
Lee’s Summit, MO (Kansas City Metro)Bellevue, WA (Seattle Metro)Bethesda, MD (Washington Metro)
Carmel, IN (Indianapolis Metro)Newton, MA (Boston Metro)Shaker Heights, OH (Cleveland Metro)
Figure 6: Population density of Edina and its peers
Source: Authors calculations from 2020 US Census
16City of Edina Background
Figure 7: Percentage of transit commuter mode share for Edina and its peers
Source: Authors calculations from 2018-2022 ACS 5-year estimates
Figure 8: Estimated residential VMT per capita for peer cities
Source: Authors calculations from Urban Footprint estimates
17
As shown in Figures 6 through 8, Edina sits midway among the peer cities in population densi
ty, transit mode share, and residential VMT per capita. One observation that can be drawn from
Edina’s standing is that Edina incorporates elements of an inner ring and an outer ring suburb.
The transit mode share is significantly lower than usual first ring suburbs, and its single family housing ratio is very high and more aligned with an outer ring suburb. Geographically, Edina is an inner suburb and its density is comparable to other inner ring suburbs.
The peer city analysis suggests that most cities have identified transportation and passenger
vehicles especially as a major source of GHG emissions, usually on par with residential energy and electricity needs as the primary emissions sources. Among the various peer cities, most make mention in some format (Comprehensive Plan, Climate Action Plan, etc.) the need to
reduce emissions coming from passenger vehicles. These efforts can be generally broken into
three categories: making vehicles cleaner, reducing auto mode share for transportation, and
reducing the need for travel altogether.
Edina’s efforts to increase density and mixeduse development are variants of the second and
third strategies (increasing origins, increasing access to destinations) and many peer cities have
identified similar strategies as methods to reduce VMT and emissions. Only a few of the peer
cities, such as Menlo Park, have already determined specific VMT reduction targets and plans to reach these targets within a defined timeframe. Fortunately, Edina already has established goals in its CAP that directly address exactly this.
Another major takeaway from the Peer City Analyses is that practically all cities are focusing
their efforts on a specific area or corridor rather than a blanket upzoning within the city. Most targeted corridors are areas where major transit routes pass through making transitoriented development (TOD) the most popular strategy for increased density.
In conclusion, Edina’s efforts toward VMT reduction and strategies to increase density are fur
ther ahead of most of its peer cities. Since most climate action plans and comprehensive plans are targeted for 2030 and beyond and have been adopted within the past decade, there are not many postimplementation evaluations from which we can analyze results. For the rest of the
peer city analysis, see Appendix C.
City of Edina Background
Section References
1. “Como–Harriet Streetcar Line Historical Marker,” Historical Marker, February 12, 2023, https://www.hmdb.org/m.asp?m=38850.
2. “ComoHarriet Streetcar Line,” City of Minneapolis; ComoHarriet Streetcar Line, accessed May 3, 2024,
https://www2.minneapolismn.gov/residentservices/propertyhousing/preservation/landmarks/alphabet
ical/comoharrietstreetcarlinetrolley/.
18
Is Density the Right Approach?
Is Density the Right Appraoch?
The first question we sought to answer is whether the current densification target of 4% is
enough to meet the city’s VMT and transit ridership goals. This question assumes a causal rela
tionship between population density, VMT, and transit ridership: that is, if Edina were to densify its residential population, decreased VMT and increased transit ridership would follow. So before answering our research question, we sought to determine whether this is even a valid assumption. Is
it true that an increase in population density will reduce VMT? Does density increase transit rider
ship? And if so, will a 4% increase be sufficient? This section aims to answer these questions.
In attempting to validate the relationship between population density, VMT, and transit ridership, we found the following:
• Density matters, but it is not all that matters.
• We found a strong linkage between population density and VMT, but other factors such as
household income and access to noncar transportation modes also play a role.
• Population density only boosts transit ridership insofar as it supports improved transit service.
• Destination accessibility and urban design have a considerable impact on transportation patterns.
• Edina’s current target of a 4% density increase will not be sufficient to meet its transporta-
tion goals.
• We estimate that, using population density alone, Edina would need to densify by 18.3% to
achieve a 7% reduction in VMT per capita.
• Increasing population density on its own is not likely to have a significant impact on transit
ridership.
• Highfrequency service on the METRO E Line is likely to boost transit mode share in Edina.
Our methodology for this section was twofold. First, we conducted a literature review of existing
academic studies on the relationship between land use and transportation outcomes. Second, we
conducted two multivariate regression analyses of municipalities in the 7county region served by the Metropolitan Council. Our analyses use a range of explanatory variables, including population
density, to predict VMT and transit mode share. These analyses are discussed in detail below.
Literature review
The relationship between population density and VMT and transportation mode share has been studied for decades, with no broad consensus among researchers. Two factors make studying
these relationships a challenge. First, defining density is complex. The same population density
can be achieved in very different built environments based on the distribution of that density and
the mix of land uses present in a city. Second,there exists an innumerable number of variables
that influence individuals’ transportation choices that are not directly related to density.
19
Contemporary research generally recognizes that density, amongst other elements of the built
environment, influences both VMT and travel behavior. A common framework for understanding
the impact of these factors is often utilized: the 7 Ds.
1. Density: The amount of some variable (population, employment, businesses, etc.) per unit of geographic area.
2. Diversity: The presence and quantity of different land uses in a given geographic area.
3. Design: The layout of streets, intersections, active transportation infrastructure, and public
transit networks. Other built form elements such as transit stop design, streetscaping, and building massing and setbacks may also be included in this category.
4. Destination Accessibility: How easy or difficult it is to reach destinations using various trans
portation modes. .
5. Distance to Transit: The distance (using appropriate transportation networks) from origins
or destinations to the nearest transit stop. This may also be analyzed as overall transit system density, the distance between public transit stops, or the number of public transit stops within a geographic area.
6. Demand Management: Built environment interventions that influence the cost and utility of certain modes, such as the provision or removal of car parking spaces.
7. Demographics: While demographics are not an element of the built environment, this variable is often controlled for when studying the built environment’s impact on travel behavior so as to eliminate confounding sociodemographic factors.
A detailed metaanalysis1 of existing academic research uses the 7 Ds framework to determine
how differences in the built environment influence VMT and transit use. This metaanalysis combines the findings from over 50 articles and reports on the effects of these variables on travel behavior, resulting in average associated impacts on VMT and transit ridership.
Of the 7 Ds, Destination Accessibility and Design are especially impactful with regards to VMT. The
most influential independent variables identified within this report are the average distance to downtown for residents, accessibility by car, and the density of intersections and streets within a given geographic area. Results from this analysis indicate that as the average distance to down
town decreases, so too does VMT. This is likely due to the fact that shorter distances to downtown
result in shorter average trips, and increase the likelihood of being able to access key destinations
by modes other than a personal automobile. An increase in accessibility by car and intersection and street density are also associated with a decrease in VMT, likely due to the shorter average trip distances that increased connectivity provides. With regards to public transit use, Design and
Distance to Transit were found to be the most influential of the 7 Ds. The variable with the great
est impact was distance to the nearest transit stop, with lower average distances being associated
with higher levels of ridership.
In terms of design, a greater density of streets and intersections, as well as a higher ratio of four
way intersections, within a geographic area was associated with higher rates of transit use. This
reflects the relationship between transit riders and pedestrians, as the majority of transit users
access these services by foot. Greater street and intersection density helps to improve connectivity and for pedestrians, enhancing access to transit services.
Each of the built environment variables that were analyzed in this report were found to be inelas
tic, meaning that a 1% change in any of the elements studied resulted in a less than 1% change in
VMT or transit use. This means that it is unlikely that small changes to any single element will be
Is Density the Right Appraoch?
20
sufficient to induce significant changes in travel behavior. The influence of density in particular
is limited, with increases in density being associated with minimal changes in VMT and transit
use. We believe that these findings indicate that in order for Edina to meet the city’s CAP goals, a
variety of built environment elements beyond density must be addressed. Destination accessibility and design in particular should be improved in any attempt to reduce VMT and increase transit ridership within the City of Edina.
Findings from this report indicate that, while density has a statistically significant relationship
with VMT and transit ridership, the impact is modest. Of the variables analyzed, population and job density are some of the least influential factors. Results from this study indicate that a 1% increase in population density is associated with a 0.04% reduction in VMT and a 0.07% increase
in transit use, on average. Based upon these findings, the 4% increase in population density per
developed acre (Edina CAP goal TL 3) will be far from adequate to result in the 7% decrease in VMT
(CAP goal TL 1), or the doubling of transit ridership (CAP goal TL 2). While the findings from this analysis indicate that changes to density alone will be insufficient to achieve Edina’s CAP transportation goals, this report also illuminates factors that can be combined with increases in density
to further reduce VMT and increase public transit ridership.
It is important to note that the findings from this metaanalysis were calculated by aggregating the results from a wide variety of studies. These studies examined various geographic scales, and utilized a variety of different methodologies. While this analysis offers approximate impacts on
VMT and transit use associated with differences in the built environment, actual results for spe
cific locations may differ significantly. As such, an understanding of how increases in density will
impact VMT and transit use within Edina requires a more targeted approach.
We will now turn to our statistical analysis, which applies the above findings to the context of Edi
na and the 7county region served by the Metropolitan Council.
Statistical analysis
We elected to perform a statistical analysis for two reasons. The first is to validate the very premise behind our research question – that is, whether population density has a meaningful relationship
to VMT and transit ridership within the specific context of the Twin Cities metropolitan area. The
second was to better understand the broader array of factors that influence both VMT and transit
ridership. In order to make our analysis most relevant to Edina’s context, we examined these metrics for the 7county Twin Cities metropolitan area as served by the Metropolitan Council.
Our key findings were as follows:
• Population density is associated with lower VMT per capita, as is walking mode share.
• Absent any other interventions, we estimate that Edina would need to increase its population
density by 18.3% to meet its target of a 7% reduction in VMT.
• Population density is associated with higher transit ridership only insofar as it supports in
creased transit service.
We also found relationships between VMT, transit ridership, and several other demographic and transportationrelated variables. We will now provide a brief overview of our methodology, a sum
mary of our findings, and some takeaways to be incorporated into our policy recommendations. A
full methodology, results tables, and more detailed interpretations, can be found in Appendix A.
Is Density the Right Appraoch?
21
Introduction to methods and reasoning
A regression is a statistical model that estimates a relationship between a dependent variable and one or more independent variables. A linear regression with one independent variable is called a
bivariate regression, whereas a regression with multiple independent variables is called a multi
variate regression.
We hypothesized a relationship between the dependent variables of VMT2 and transit ridership3 as a function of several independent variables related to demographics, economics, and transporta
tion attributes. We then undertook a series of bivariate and multivariate regressions, with VMT and
transit ridership as the dependent variables, using each of the localities within the 7county met
ropolitan area as the observations.
Our primary independent variable was population density, which we defined as the municipal
population divided by acres of land zoned as either residential or mixed use. We found that this
residential population density measure was more closely related to VMT and transit mode share
measures than simple population density, which is skewed by nonresidential uses. Another key
independent variable we examined was access to transit, for which we used the proxy measure of transit stop density. This methodology does not account for frequency, speed, connectivity, or
population or employment density in relation to transit stops. These subtleties are important to
keep in mind when interpreting our regression results.
In total, there are 181 localities (i.e., cities and townships) in the area served by the Metropolitan Council. However, we removed several observations from both analyses, for example, due to being
townships rather than cities, or being outliers in one or more key variables. Throughout, we at
tempted to balance our relatively large number of potential independent variables with our rela
tively small sample size.
VMT
Below is the summary of our multivariate regression results with local VMT per capita as the dependent variable.
Is Density the Right Appraoch?
Summary Statistic Value
Number of observations 122
Rsquared 0.3922
Adjusted Rsquared 0.3433
Probability > F 0.0000
Table 5: Multivariate regression summary with local VMT per capita as dependent variable
Source: Author’s calculations, various data souces [see Appendix A]
22
Overall, this model explains just over onethird of the variation in local VMT per capita. This in
dicates that while these variables are important, there are likely a variety of other factors which
influence VMT that are not accounted for in this regression.
Of the variables analyzed in this model, the most important for our purposes is residential popula
tion density. The coefficient here implies that a 1% increase in residential density is associated with
a decrease in about 10.04 vehicle miles traveled per capita, which is 0.38% of current levels. At this
rate, using increased population density alone, Edina would need to increase its density by 18.3% in order to meet the CAP target of a 7% reduction in VMT per capita far more than the 4% density target currently set by the CAP. However, this interpretation is agnostic on the approach the City
takes to achieving this increase in population density. As we will examine the next section of the
report, there could be considerably different VMT outcomes depending on the spatial distribution
of population density.
Walking mode share was also negatively associated with VMT; for every 1% increase in walking
mode share, the model predicts that local VMT will drop by 2.6 miles per capita.
The two incomerelated variables median household income, and highincome status were also both statistically significant. The interplay between these variables is complex, as they control for one another while displaying seemingly contradictory results: median household income is
negatively associated with VMT, while highincome cities are expected to have higher VMT. This
pattern illustrates a “bell curve” shaped trend in household income: VMT is highest among cities
with uppermiddle incomes, while both the lowestincome and the very highestincome cities tend to have lower VMT. This is where more advanced forms of statistical analysis, such as spatial regression, may prove fruitful in better understanding these complex relationships.
Is Density the Right Appraoch?
Independent Variable Coefficient P-value
Log of population per resi-dential acre***-1004.278 0.000
Percent of households own-ing home -1280.269 0.159
Log of local road lane miles per acre -221.0041 0.256
Log of walking mode share***-262.1106 0.010
Work-from-home mode share 2092.638 0.111
Median household income**-.0093618 0.016
Transit stop density*12750.8 0.095
Has transit (binary)48.23469 0.801
High-income (binary)***627.7592 0.007
*significant at p = 0.1 level**significant at p = 0.05 level***significant at p = 0.01 level
Table 6: Multivariate regression summary with local VMT per capita as dependent variable with local
VMT per capita as dependent variable
Source: Author’s calculations, various data souces [see Appendix A]
23Is Density the Right Appraoch?
Compared to VMT, this multivariate regression was able to explain much more of the variation in transit mode share almost twothirds of it, according to the adjusted Rsquared value of 0.6319. However, this was largely driven by just one variable: transit stop density.
It is worth reiterating that transit stop density is merely a proxy for access to transit. Regardless
of the number of transit stops, any significant increase in transit service is likely to boost transit ridership. For example, the upcoming METRO E Line, the BRT route that will serve Edina, will have stations that are spaced further apart than the existing Route 6 in other words, a lower density
of transit stops. But the increased level of transit service, improved speed and reliability, and tran
sitoriented development around BRT stations has historically been associated with increased
levels of transit ridership in the context of the Metro Transit system4. Therefore, we do not recommend using this coefficient to craft specific policy; rather, it underscores the close relationship between improved transit service and transit ridership.
Summary Statistic Value
Number of observations 133
R-squared 0.6614
Adjusted R-squared 0.6319
Probability > F 0.0000
Table 7: Multivariate regression summary with transit mode share as dependent variable
Source: Author’s calculations, various data souces [see Appendix A]
Independent Variable Coefficient P-value
Population per residential acre*0.0008785 0.058
Local road lane miles per acre -0.0328466 0.742
Households with no vehicle**0.1647657 0.019
Walking mode share*0.1377205 0.093
Work-from-home mode share**0.0421348 0.015
Log of median household income 0.0028835 0.599
Transit stop density***0.6286264 0.000
High-frequency transit (bi-nary)**0.0099594 0.033
*significant at p = 0.1 level**significant at p = 0.05 level***significant at p = 0.01 level
Table 8: Multivariate regression results with transit mode share as dependent variable
Source: Author’s calculations, various data souces [see Appendix A]
Transit ridership
Below is the summary of our multivariate regression results with transit mode share as the dependent variable.
24Is Density the Right Appraoch?
Let us now turn to residential population density. Population density is significantly correlated
with transit ridership, but loses significance at the 0.05 level once controlling for transit stop den
sity. This suggests that population density is a significant predictor of transit mode share only
insofar as it supports improved transit service. We recommend a density strategy that emphasizes targeted, corridorspecific interventions that either take advantage of existing transit (i.e., TransitOriented Development) or help to spur additional future service from Metro Transit.
Three other variables were identified as having a positive relationship to transit mode share: per
cent of households without a vehicle, percent working from home, and presence of highfrequency transit. The former two help to explain the variation in transit mode share, but are not closely tied to our recommended policy interventions.. However, the third variable, presence of highfre
quency transit (defined as 15minute allday service or better), has considerable implications for
policy. We find that the presence of highfrequency transit in a locality is associated with a transit
mode share about 1 percentage point higher than localities without highfrequency transit. Edina currently does not have any highfrequency transit. However, with the opening of the METRO E Line in 2025, it will likely enter this exclusive club of municipalities. We expect Edina’s transit mode
share to improve by at least 1 percentage point upon the opening of the E Line, even absent in
creases in population density or further improvements to transit service.
Conclusion
Our literature review and regression models serve primarily to better understand the driving variables behind VMT and transit ridership. We gave particular attention to population density, spe
cifically population density on residential land. Our results are generalizable across most of the
7county area served by the Metropolitan Council, but there are certain actionable insights with
regard to Edina’s goals of reducing VMT and increasing transit ridership.
We found that, controlling for factors such as income, transit service, and mode shares, popula-tion density is an important predictor of VMT, with higher population density being associated
with lower VMT. Edina could likely reduce its VMT per capita by increasing density even without
doing so in a strategic way or pairing it with parallel interventions. However, it is clear that a 4%
increase in density will not be sufficient to meet the city’s VMT or transit ridership goals, as outlined in the City’s CAP.
In order to meet these goals, Edina must increase its population density far beyond 4%. Edina can
further reap the VMT benefits of density by encouraging non-car travel modes and improving
accessibility to major destinations. This might mean building housing within walking distance of major employers, improving pedestrian infrastructure, and designing safer streets.
We did not find that population density was as important of a predictor of transit mode share.
Rather, the level of transit service within the community is by far the most important predictor
of transit mode share. Population density is largely important insofar as it supports and encourages increased levels of transit service. Edina is already anticipating the opening of the METRO E
Line, which will provide an unprecedented level of transit service to the city. A secondary predictor
is the presence of highfrequency transit, defined as 15minute allday service or better. We there
fore recommend that Edina advocate for frequent E Line service and reinstated service on Metro
Transit Route 46 to Opus Station in Minnetonka. However this decision is ultimately up to Metro Transit.
Based upon this analysis, we can conclude that the statistical evidence favors a targeted, node-
and corridor-based density strategy as compared to a blanket citywide policy. While our conclu
sions from the regression analysis speak to the policies that a municipality may enact to reduce
25Is Density the Right Appraoch?
VMT or increase transit ridership on the municipal level, it does not speak to the spatial differences
within a locality that inform these metrics. Indeed, one of our key conclusions is that density works
best when it is supported by other policies. What does this support look like? Our next meth
odology, the Zoning Scenario Analysis, accounts for the different strategies that Edina may take toward increasing density within its borders to achieve more favorable VMT and transit ridership outcomes.
Section References
1. Ewing, Reid, and Robert Cervero. 2010. “Travel and the Built Environment.” Journal of the American Planning Association 76 (3): 265–94. https://doi.org/10.1080/01944361003766766.
2. “VMT” here refers to per capita VMT on local roads. The focus on VMT per capita acknowledges the real
ity that VMT may increase with increased population. We excluded nonlocal classes of road due to the
potential for skewing from major highways running through disproportionately small municipalities. By
focusing only on local roads, we were able to make applestoapples comparisons between municipali
ties and focus on the roads over which these localities exercise a meaningful level of control.
3. “Transit ridership” here refers to transit commuting mode share from the American Community Survey 2022 5year estimates. While this is by far the most robust data source available for transit ridership, it has significant limitations as it does not account for transit usage for noncommuting trips, and does not account for the nuance of commute modes that vary temporally throughout the week or year.
4. According to Metro Transit, ridership on the METRO D Line in 2023 was about twice that of its predeces
sor route, Route 5, in 2022.
26
Which Approach to
Density is Most Effective?
Which Approach is Most Effective?
Considering the conclusions drawn in the previous section regarding density, our focus shifts to addressing the effectiveness of various approaches to densification. These approaches typically
fall into two categories: citywide densification and targeted densification strategies. Consequent
ly, this section aims to determine whether a blanket citywide densification strategy or a targeted
corridorbased densification strategy would better serve Edina’s CAP goals. Moreover, this analysis explores a variety of targeted density strategies, including around specific transportation hubs, the ‘Areas of Change’, and essential destinations. Our analysis indicated that targeted strategies
around specific nodes and corridors, especially the ‘Areas of Change’, transit corridors, active
transportation networks, and healthcare facilities resulted in the best outcomes in meeting Edi
na’s CAP goals.
To evaluate the effectiveness of various densification strategies, we developed several zoning
scenarios focusing on different approaches to densification. These scenarios were analyzed using
Urban Footprint, a software utilized by the Metropolitan Council to model density increases. Urban
Footprint enables us to assess potential changes resulting from zoning changes at the parcel level. Urban Footprint enabled us to obtain projections of Residential Density in Dwelling Units per Acre (Density DU /Acre), CommunityWide Vehicle Miles Traveled (VMT), and Public Transit Mode
Share Percentage (Transit Ridership), which aligns with the three goals outlined in Edina’s CAP.
We also collected estimated Total Greenhouse Gas (GHG) emissions, given that the overarching
goal of Edina’s CAP is to reduce GHG emissions by 45% by 2030 to combat climate change. To provide a more meaningful comparison, we decided to analyze VMT and GHG emissions per capita instead of in total, recognizing that any increase in density would lead to an increase in population
and inevitably leading to a higher total VMT and GHG emissions.
In assessing the effectiveness of the zoning scenarios, we devised an Impact Score formula aimed at identifying the most effective scenarios in terms of VMT/capita, Transit Ridership, and GHG/capita. The Impact Score is derived by initially calculating the percentage change from the base
scenario for Density (DU/Acre), VMT/capita, Transit Ridership, and GHG/capita. Subsequently, the
percentage changes for VMT/capita, Transit Ridership, and GHG/capita are each divided by the
percentage change in Density (DU/Acre) (Figure 9). The resulting score provides an estimateof the effect on VMT/capita, Transit Ridership, and GHG/capita for each percentage increase in density. These Impact Scores played a crucial role in formulating our set of three recommended zoning
scenarios, each comprising the most effective zoning scenarios as determined by the Impact
Score.
27
Since we are using a different methodology compared to what was utilized in Edina’s CAP, we
decided to use the existing conditions rather than 2019 values as a base scenario (Scenario A)
for all subsequent scenarios. Our scenarios can be broadly categorized based on their approach and targeted aspect. The B Scenarios entail different levels of citywide density increases. The C Scenarios focus on targeted density in and around the ‘Areas of Change’ as outlined in Edina’s
2040 Comprehensive Plan. The D Scenarios target Mode Shift, which aims at transitioning au
tomobilebased trips to sustainable transportation modes, including the use of public transit,
biking, rolling, and walking. This is achieved by targeting density around existing or future public transportation routes and pedestrian/bicycle facilities. The E Scenarios concentrate on increasing density around essential destinations such as schools, employment hubs, healthcare facilities, and
parks. The last set of scenarios which represent our recommended scenarios, were developed by
combining the most effective of the scenarios, with effectiveness measured by the Impact Score.
These scenarios have been titled ‘Basic’, ‘Enhanced’, and ‘Preferred’, with each scenario building upon the previous one, with the ‘Preferred’ scenario incorporating the most strategies and being our highest recommendation for Edina. (See Appendix D for Scenario Methodology)
Throughout this analysis, we considered which scenarios would align with Edina’s goals as out
lined in their CAP. Although we used different methodologies, precluding direct comparisons with the set targets in the CAP, we utilized the percentage change in the targets as a benchmark to evaluate our scenarios. In Edina’s CAP, the goals included a 45% reduction in GHG emissions,
a 7% reduction in VMT, a 100% increase in commuter public transit ridership, and a 4% increase
in density. We directly tested whether a 4% increase in density would achieve Edina’s VMT and
transit ridership goals. Additionally, we assessed whether the scenarios would meet the state of Minnesota’s goal of reducing VMT by 20% per capita by 2050 as articulated in Minnesota’s Climate Action Framework.1
It is important to acknowledge several key limitations of Urban Footprint before delving into
the results. Firstly, several goals outlined in Edina’s CAP did not directly correspond to the data provided by Urban Footprint. For instance, Urban Footprint does not specifically calculate Transit Ridership for Commuting Trips only but instead provides Transit Ridership in terms of overall
mode share for all trips. Additionally, we opted to measure density using Dwelling Units per Acre
Figure 9: Impact Score Formula
Source: Authors
Which Approach is Most Effective?
28
(DU/Acre) rather than Population per Residential Acre. This decision was made because zoning
changes would directly impact dwelling units and only indirectly affect population. Furthermore,
to achieve a citywide increase in density, Urban Footprint utilizes dwelling units to increase popu
lation rather than specifying a total population count. As previously mentioned, we are also using per capita versions of VMT and GHG emissions rather than total figures due to the population increase that would occur regardless of the policies implemented.
Secondly, as previously stated, we had to utilize existing conditions today as the basis for our
scenarios due to being unable to make direct comparisons with the CAP due to differing methodologies. Thirdly, Urban Footprint does not permit the addition of additional public transit services into the scenarios. Consequently, future public transit developments such as the E Line and Green
Line Extension, along with other potential public transit improvements, cannot be modeled or
considered when Urban Footprint measures Transit Ridership. Therefore, any scenario results for
Transit Ridership must be interpreted in light of the current public transit routes, stops, and frequency, without improvements to the service itself. However, it is reasonable to assume that if Urban Footprint could measure increased public transit service, then Transit Ridership would likely
be significantly higher. This assumption is particularly valid given that higher density is a prereq
uisite for increased transit service by Metro Transit.2
Finally, it is worth noting that Urban Footprint may not provide a completely accurate representation of reallife scenarios. Zoning classifications do not guarantee the highest possible density
development is built, as this depends on property owners, developers, and decisions made by Edi
na’s Planning Commission and City Council. Additionally, population distribution among differ
ent zoning types is averaged, potentially leading to inaccuracies in population counts, which may impact the overall analyses. Despite these limitations, we believe that Urban Footprint is the best way to evaluate the effectiveness of different zoning changes aimed at increasing density and
their impacts on VMT, Transit Ridership, and GHG emissions.
Which Approach is Most Effective?
29Which Approach is Most Effective?
Figure 10: Base Scenario Map
Source: Authors created with Urban Footprint
Scenario A – Base Scenario
Scenario A is based upon Urban Footprint’s base canvas, which reflects the existing property
conditions and zoning (See Appendix D for Urban Footprint’s Base Canvas Methodology). The
accuracy of the base canvas was verified through three different methods: visual comparison with
Edina’s 2040 comprehensive plan, visual comparison with Google Map’s satellite imagery, and a comparison of estimated population with the 2020 Census data. This scenario serves as the foun
dation of comparison for all other scenarios and establishes the baseline against which the im
pact of various metrics can be assessed (Figure 10).
30
Scenario B – Citywide Density
Scenario B is divided into three different scenarios: B1, B2, B3. The first two scenarios involved
selecting all residential parcels in Urban Footprint, calculating the average density of these par
cels, and increasing that by a certain percentage. B1 involves a 4% increase in density citywide,
which aligns with the Density goal in Edina’s CAP. B2 involves an 8% increase in density citywide, which is double the Density goal set by Edina’s CAP, to explore if there are any multiplier effects.
Scenario B3, at the request of Edina City Staff, focuses on increasing the average density of all R1
parcels by 6.95%, resulting in an average net 4% increase in density citywide.
Subscenarios:
• B1: 4% Increase in Citywide Density (DU/Acre)
• B2: 8% Increase in Citywide Density (DU/Acre)
• B3: 6.95% Increase in R1 Density (DU/Acre) which Averages 4% Increase in Density Citywide
(DU/Acre)
Which Approach is Most Effective?
Scenario Density
change
from base
(%)
VMT/capita
change (%)
VMT
Impact
Score
GHG/capita
change (%)
GHG Im-
pact Score
Transit
Ridership
change (%)
Transit
Ridership
Impact
Score
B-1 3.85%-1.98%0.52 -4.07%1.06 -2.45%-0.64
B-2 7.41%-2.73%0.37 -5.88%0.79 -2.84%-0.38
B-3 3.85%-0.86%0.22 -2.65%0.69 -1.80%-0.47
Table 9: Scenario B Results
Source: Authors’ calculations with Urban Footprint
31Which Approach is Most Effective?
Figure 11: ‘Areas of Change’ with 500-foot buffer
Scenario C – ‘Areas of Change’
Scenario C is divided into two different scenarios: C1 and C2 each focusing on the identified
‘Areas of Change’ outlined in Edina’s 2040 Comprehensive Plan and increasing the density in
their vicinity. In C1, upzoning occurs only within the ‘Areas of Change’. In C2, a 500foot buffer is
established around each ‘Areas of Change’ to further increase density (Figure 11). Both scenarios aim to create vibrant, mixeduse communities where residents can live, work, and shop without
reliance on cars. Additionally, surface parking is minimized, and mixeduse parking structures are
concentrated to optimize land use efficiency. Within the 500foot buffer, residential duplexes are
predominantly zoned, complemented by suburban townhomes in select areas.
Subscenarios:
• C1: ‘Areas of Change’
• C2: ‘Areas of Change’ with a 500ft. Buffer
Scenario Density change from base (%)
VMT/capita change (%)VMT Impact Score
GHG/capita change (%)GHG Im-pact Score Transit Ridership change (%)
Transit Ridership Impact Score
C-1 26.81%-13.89%0.52 -13.04%0.49 12.53%0.47
C-2 32.18%-16.75%0.52 -16.08%0.50 13.64%0.42
Table 10: Scenario C Results
Source: Authors’ Calculations with Urban Footprint
Source: Authors created with Urban Footprint
32Which Approach is Most Effective?
Scenario D - Mode Shift Toward Sustainable Transportation
Scenario D is divided into four different scenarios D1, D2, D3, and D4, all aimed at promoting
mode shift toward sustainable transportation modes to reduce VMT and GHG emissions. Both
D1 and D2 target mode shift toward public transit by allowing dense mixeduse transitoriented
development (TOD) within 500 feet of proposed bus stops and duplexes/suburban townhomes within a quarter mile of these stops. D1 focuses on this zoning strategy along the future E Line
Arterial Bus Rapid Transit (ABRT) line (Figure 12), while D2 extends this approach along a potential
EastWest transit corridor spanning W 50th St/Vernon Ave S/Lincoln Dr (Figure 14). This corridor
is currently partially served by Route 46 which travels along 50th St and Vernon Ave before termi
nating in the Grandview neighborhood. This corridor was previously served fully by Route 46 as recently as 2019 and is reimagined in D2 to restore its previous bus routing and connecting Opus
Station (Green Line Extension) in Minnetonka to 46th St Station (Blue Line) in Minneapolis. With
additional connections to Orange Line, A Line, C Line, D Line, and future E Line, this route would
offer Edina residents convenient transfer points for regional travel. It is worth noting that this
scenario achievesan average density of 19 DU/acre along this reimagined transit corridor, meeting Metro Transit’s density requirement of 15 DU/acre for ABRT service.3 Conversely, D3 targeted
active transportationby increasing zoning alongside the Regional Bicycle Transportation Network
(RBTN) (Figure 13). The RBTN was chosen as it represents a regional approach to active transpor
tation. Finally, D4 integrates all the scenarios, striving to encourage mode share between both
transit and pedestrian/bicycling.
Subscenarios:
• D1: E Line Corridor
• D2: E Line Corridor & EastWest Corridor
• D3: Active Transportation (RBTN)
• D4: Combination of all the other D scenarios
Scenario Density
change
from base
(%)
VMT/capita
change (%)
VMT
Impact
Score
GHG/capita
change (%)
GHG Im-
pact Score
Transit
Ridership
change (%)
Transit
Ridership
Impact
Score
D-1 28.52%-18.62%0.65 -12.30%0.43 12.60%0.44
D-2 44.01%-26.22%0.60 -19.88%0.45 15.64%0.36
D-3 22.20%-13.32%0.60 -13.45%0.61 8.45%0.38
D-4 48.29%-29.31%0.61 -24.47%0.51 16.01%0.33
Table 11: Scenario D Results
Source: Authors’ calculations with Urban Footprint
33Which Approach is Most Effective?
Figure 12: E Line Corridor
Source: Authors created with Urban Footprint
Figure 14: East-West Corridor
Source: Authors created with Urban Footprint
Figure 13: RBTN
Source: Authors created with Urban Footprint
34Which Approach is Most Effective?
Scenario E - Essential Destinations
Scenario E is divided into five different scenarios, all focusing on increasing density around dif
ferent categories of essential destinations. Each scenario within this category involves increasing
allowable density on all residential lots within a quarter mile radius of the associated destinations.
E1 aims to increase density around schools and educational institutions (Figure 15). E2 targets density increases at employment hubs, both retail and office (Figure 16). E3 focuses on increas
ing density around healthcare facilities (Figure 17). E4 centers on increasing density around parks
(Figure 18). Finally, E5 integrates all four categories of essential destinations, aiming to increase
density around each of them.
Subscenarios:
• E1: Public Schools
• E2: Retail & Employment Hubs
• E3: Major Healthcare Facilities
• E4: Major Parks
• E5: Combination of all the other E scenarios
Scenario Density change from base (%)
VMT/capita change (%)VMT Impact Score
GHG/capita change (%)GHG Im-pact Score Transit Ridership change (%)
Transit Ridership Impact Score
E-1 5.08%-0.40%0.08 -2.39%0.47 -0.75%-0.15
E-2 28.17%-18.47%0.66 -21.53%0.76 3.61%0.13
E-3 22.69%-16.96%0.75 -17.32%0.76 2.48%0.11
E-4 19.99%-10.20%0.51 -14.68%0.73 -0.53%-0.03
E-5 27.18%-12.63%0.46 -19.48%0.72 -1.33%-0.05
Table 12: Scenario E Results
Source: Authors’ calculations with Urban Footprint
35Which Approach is Most Effective?
Figure 15: Public Schools
Source: Authors created with Urban Footprint
Figure 16: Retail and Employment Hubs
Source: Authors created with Urban Footprint
Figure 17: Major Healthcare Facilities
Source: Authors created with Urban Footprint
Figure 18: Parks
Source: Authors created with Urban Footprint
36Which Approach is Most Effective?
Recommended Scenarios - ‘Basic’, ‘Enhanced’, ‘Preferred’
The Recommended Scenarios are divided into three different scenarios, ‘Basic’, ‘Enhanced’, and
‘Preferred’. These were developed by analyzing the results of all preceding scenarios using the
impact score formula. Based on which scenarios had the highest impact scores, they were amal
gamated into three different scenarios, each representing a different level of effectiveness. ‘Basic’ incorporates C1 (‘Areas of Change’) and D1 (ELine Corridor) (Figure 19). The‘Enhanced’ scenario
builds upon ‘Basic’ by adding a 500 foot buffer around the ‘Areas of Change’ (C2) and the East
West Corridor (D2) (Figure 20). Lastly, the ‘Preferred’ scenario incorporates the Regional Bicycle
Transportation Network (D3) along with the most promising of the essential destination sce
narios, major healthcare facilities (E3) (Figure 21). The ‘Preferred’ scenario represents our highest recommendation for Edina. These scenarios employ the methods used previously with increased
density in areas of overlap (See Table 9 for Recommended Scenario Results).
Recommended scenarios:
• ‘Basic’: ‘Areas of Change’ (C1) & E Line corridor (D1)
• ‘Enhanced’: ‘Areas of Change’ with 500 ft Buffer (C2) and E Line corridor and EastWest corri
dor (D2)
• ‘Preferred’: ‘Areas of Change’ with 500 ft Buffer (C2), E Line corridor, EastWest corridor, &
RBTN (D4), and Major Healthcare Facilities (E3)
Scenario Density
change
from base
(%)
VMT/capita
change (%)
VMT
Impact
Score
GHG/capita
change (%)
GHG Im-
pact Score
Transit
Ridership
change (%)
Transit
Ridership
Impact
Score
Basic 32.34%-17.29%0.53 -16.37%0.51 13.35%0.41
Enhanced 42.43%-22.09%0.52 -24.41%0.58 13.34%0.31
Preferred 45.75%-25.01%0.55 -27.07%0.59 13.87%0.30
Table 13: Scenario F Results
Source: Authors’ calculations with Urban Footprint
37Which Approach is Most Effective?
Figure 19: ‘Basic’ Scenario Map
Source: Authors created with Urban Footprint
Figure 20: ‘Enhanced’ Scenario Map
Source: Authors created with Urban Footprint
Figure 21: ‘Preferred’ Scenario Map
Source: Authors created with Urban Footprint
38Which Approach is Most Effective?
Discussion of Scenario Results
The Zoning Scenario Analysis yields several important conclusions. Firstly, it suggests that tar-geted densification strategies are more effective than citywide approaches. This is evidenced
by the low impact scores of the B scenarios, which even show a decline in transit ridership. This
phenomenon could be attributed to the dispersion of density across Edina, resulting in reduced accessibility to nonautomotive modes of transportation and consequently lower transit ridership.
Among the targeted density strategies, scenarios focusing on mode share toward sustainable transportation demonstrate the highest impact scores, indicating their effectiveness (Figure
22). Considering the limitation of not being able to add or increase public transit service in Urban
Footprint, the scores for these scenarios could potentially be much higher.
Based on the Zoning Scenario Analysis, a 4% increase in citywide density, as outlined in Edina’s CAP, falls short of meeting Edina’s other goals. This is illustrated by scenario B1, where a 4%
increase in Density would result in only a 1.98% decrease in VMT/capita, a 4.07% decrease in GHG/
capita, and a 2.45% decrease in Transit Ridership. This is compared with a goal of a 7% decrease
in VMT, a 45% decrease in GHG emissions, and a 100% increase in commuter public transit ridership. This underscores the necessity for a higher average density than what a 4% increase would achieve, alongside a more targeted approach to density increases which would be more effective.
In terms of Edina’s CAP goals, the effectiveness of strategies prompts the question of what level of
density would be required to achieve them. For instance, to attain the VMT reduction goal of 7%, dividing it by the VMT/capita Impact score for each recommended scenario (‘Basic’, ‘Enhanced’, and ‘Preferred’) yields a required density increase of 13.1%, 13.4%, and 12.8% respectively. This
is in contrast to the estimated 18.3% increase in density that would be required to reach the
VMT goal if density is not targeted, as found in the regression analysis. That means that if Edina
focused on the most effective strategies, Edina would need around a 13% increase in Density to achieve their VMT reduction goal as outlined in their CAP. If Edina wanted to achieve their GHG reduction goal of 45% via density alone, it would require approximately 88.9%, 78.2%, and 76.1%
increases in density for each of the respective recommended scenarios. That means Edina would
need much more density to fully reach their GHG reduction target of 45%, as no scenario met that
Figure 22: Impact Score by Scenario
Source: Authors’ calculations from Urban Footprint
39Which Approach is Most Effective?
goal. Finally, to reach the transit ridership increase of 100%, the percentages are notably higher
(242.2%, 318.2%, and 329.8% for the respective recommended scenarios), although the absence of
increased public transit service in the modeling suggests that the increase in density would be
less significant. This is further confirmed by our regression analysis which suggests that density only impacts transit ridership insofar as it encourages increased transit service. Edina should collaborate with Metro Transit to encourage enhanced public transit service and align zoning
accordingly to achieve the transit ridership goal in the CAP. Another goal Edina should be aiming
for is the State of Minnesota’s goal of reducing VMT/capita by 20% by 2050. To that end, both
the ‘Enhanced’ and ‘Preferred’ recommended scenarios achieve that goal.
Furthermore, the zoning scenario results reveal several other notable findings. There is significant
overlap between the ‘Areas of Change’, Mode Shift Toward Sustainable Transportation, and Major
Healthcare Facilities density strategies, suggesting that the ‘Areas of Change’ are a solid foun-
dation. Building upon it with a targeted focus on transit corridors and healthcare facilities should put Edina in a good position to achieve their CAP goals. Another result that stood out was how in most scenarios, the GHG/capita percentage decrease was several percentages higher compared
to the percentage decrease in VMT/capita. This is probably due to Urban Footprint considering
electricity and water usage along with VMT in calculating GHG emissions. This underscores the
importance of density not only in reducing VMT but also in minimizing GHG emissions through increased efficiency in electricity and water usage. This indicates that density increases are use-ful not only in meeting the transportation goals in the CAP, but in achieving the broader GHG targets. A final finding when doing the Zoning Scenario Analysis was the lack of mixeduse devel
opment and an overabundance of surface parking lots in Edina, even within the already defined
‘Areas of Change’. This points to a need for Edina to focus on encouraging mixed-use develop-ment areas in targeted areas along with right-sizing parking options through both removal of surface parking and consolidation of parking using parking garages, which can also have mixed
use purposes.
Section References
1. State of Minnesota, “Minnesota’s Climate Action Framework,” 2022, https://climate.state.mn.us/sites/cli
mateaction/files/Climate%20Action%20Framework.pdf.
2. Metropolitan Council, “Density & Activity Near Transit: Local Planning Handbook,” January 2018, https://metrocouncil.org/Handbook/Files/Resources/FactSheet/LANDUSE/DensityandActivityNearTransit.aspx.
3. Metropolitan Council, “Density & Activity Near Transit: Local Planning Handbook,” January 2018, https://
metrocouncil.org/Handbook/Files/Resources/FactSheet/LANDUSE/DensityandActivityNearTransit.
aspx.
40
What Policy Actions are Needed?
What Policy Actions are Needed?
This section seeks to supplement the linear regression analysis and the Zoning Scenario Anal
ysis with an indepth review of Edina’s existing programs and policies related to land use, built
form, and transportation. In assessing these existing policies, we identified several gaps and deficiencies in their framework for transportation and land use. We came to several recommendations that are designed to further bolster the City’s efforts to meet its CAP goals. We reviewed
Edina’s 2040 comprehensive plan, city ordinances, and the CAP itself.
How Edinans Choose to Travel
Our policy analysis and recommendations recognize that vehicle miles traveled and public tran
sit ridership are ultimately the result of decisions made by the residents of Edina. In determining effective strategies to reduce VMT and increase public transportation use, our team developed
a transportation decision making framework (Appendix B) which outlines the primary factors
influencing how an individual chooses one mode of transportation over another. These deci
sions are shaped by a complex interplay of factors, including individual preferences, costbenefit
analyses, environmental considerations, social norms, and the inertia of past habits.
Drawing from this framework, we have identified two primary elements that Edina can influ
ence so as to achieve the city’s CAP goals. First, Edina should improve the viability of sustain
able transportation modes through landuse and builtform interventions. Specifically, the city
should prioritize increasing the supply of origins and destinations within useful proximity to public and active transportation networks. This intervention would have the added benefit of
reducing travel distance (and subsequently VMT) between origins and destinations. Next, Edi
na should improve the attractiveness of sustainable modes through interventions that make
the relative cost, reliability, comfort, safety, and convenience of public and active transportation
competitive with travel by car.
Official Recommendations
Comprehensive Plan
In reviewing the Comprehensive Plan for the City of Edina, emphasis was placed on identifying
land use descriptors, transportation frameworks, and ordinances that are holding back the City
of Edina from realizing greater potential for livable development and sustainable travel. Recommendations below are made to advance equity and development within the City and make mobility more seamless and balanced amongst modes.
1. Redesignate Neighborhood Nodes to Mixed-Use Center, and redesignate Mixed-Use Centers to Community Activity Center.
Aligned with CAP Actions TL 3-1, TL 3-3
Scenario modeling has proven unequivocally that one of the best opportunities Edina has
to increase density in a transitsupportive and lowVMT manner lie in the City’s ‘Areas of Change’. Given that the majority of the ‘Areas of Change’ are along or proximate to transit, and that at least one exists in each quadrant of the City (making for easy active transporta
tion access), we recommend increasing the densities of each ‘Area of Change’. To do so, each
of the nodes at 44th Street/France Avenue, 70th Street/Cahill Road, and Valley View Road/
41What Policy Actions are Needed?
Wooddale Avenue. As it stands, these areas are designated as Neighborhood Nodes. To in
crease the mixeduse nature of these nodes and promote increased residential density, it is
our recommendation that these be redesignated as MixedUse Center. It is our further rec
ommendation that the existing MixedUse Centers at 50th Street/France Avenue and GrandView be redesignated as “Community Activity Center,” as they are important, highlydesired regional destinations and each has quality (and improving) transit access.
These recommendations, informed by our Zoning Scenario Analysis, are also supported
by our transportation decision making framework. Allowing higher density and increased mixeduse development in these locations can increase the supply of origin and destination points within useful proximity to sustainable transportation options. This will encour
age more vitality and strengthened density in line with the CAP goals. The Zoning Scenario
Analysis showed that growth in mixeduses around and within ‘Areas of Change’ was highly
beneficial in progress toward CAP goals.
2. Zone every parcel along France Avenue north of Southdale to permit at least
two housing units, and that corner parcels along the corridor each permit neighborhood-scale retail uses.
Aligned with CAP Actions TL 3-1, TL 3-3, TL 3-7
France Avenue has great value, and even greater potential. Connecting major ‘Areas of Change’ along soontobe excellent transit, it is a logical place to encourage density and create more opportunities for living and working. Given the level of transit service and access
to destinations as discussed with regard to transportation decisionmaking, all parcels along
France Avenue should have a minimum zoned capacity of two units, though we encourage
further allowable residential capacity beyond two. Additionally, each corner lot along France Avenue should permit neighborhoodscale retail. A more fleshed out neighborhood with a range of origins and destinations will encourage transit use. This consistent treatment of the
street will create more of an identity for the corridor with a string of appealing nodes that will
encourage more active transportation to and along France Avenue.
3. When upzoning along corridors, include all parcels that are not on the corridor
but are adjacent (not across the street) to the corridor-fronting parcels, improving
quality of life and adding more origins near corridor amenities.
Aligned with CAP Action TL 3-3
The City at the moment has no development buffers that allow for incremental height in
creases on parcels that are not on major corridors but lie just parallel to them. For example,
development on France Avenue is limited in its potential heights and massing due to the adjacency of singlefamily lots on the rear of the parcels. Given the width of rights of way of streets versus widths between adjoining rear setbacks, it would be prudent to increase
density not just on corridorfacing lots, but additionally on the lots to their rear that have a
frontage on the parallel street to that of the corridor. This has the intended effect of not only
locating twice the redevelopable acreage near transit and mixeduse corridors than can be achieved just with corridor upzoning, but there are many other positive benefits.
Most importantly, the main corridors on which upzoning is to take place are often high
ertraffic streets. Increasing the residential density near but not on these corridors means
that many greater residents will have the opportunity to live on quiet streets, with the result being much less exposure to noise and air pollution. Noise and air pollution are significant
42What Policy Actions are Needed?
elements in the social determinants of health, and the impact to neighborhoods would be
minimized because there would not be singlefamilyzoned lots that back up to multifamily
in any instances, creating less conflict over character and massing while reducing average
exposure to environmental burdens
4. Allow increased density in the Comprehensive Plan within a quarter mile of 50th
Street and Vernon Avenue, and within a quarter mile radius of E Line stops to maximize the potential for service restoration/expansion and achieve meaningful
transit mode share.
Aligned with CAP Actions TL 2-1, TL 2-5, TL 3-3
Metro Transit sets density thresholds by which it makes decisions on where to increase transit service levels. With the potential for service restoration to connect with the Green Line
Extension, it is important that Edina prepare the community with transitsupportive land use
to match its designation by the Metropolitan Council as an “urban” community. According to
the requirements set forth to initiate an increase in transit service (Table 14), local bus routes with high frequency must average 10 residential units per acre within a quartermile buffer along the route. This is the average, so densities in even closer proximity should exceed 10
units/acre if the City is to preserve lower residential densities that don’t front Vernon and
Lincoln. Though 10 units per acre is the floor, Metro Transit targets densities 1560+ units/acre
in the buffer area for its frequent local bus service. Further, along corridors served by Arterial Bus Rapid Transit (E Line), the density minimum is 15 units per acre with targets of 2060+.
Our transportation decision making framework indicates that significant mode shift towards
transit can be expected once density crosses this requirement threshold, driven by the in
crease of potential trip origins and destinations near rapid transit service. This shift has the potential to create a beneficial feedback loop, with increased ridership inducing more service to be added to the community. This is borne out in our Zoning Scenario Analysis that
showed transit corridors performing very successfully for additional community density,
contributing to decreases in VMT/capita and increases in public transit mode share.
Table 14: Thrive MSP 2040 guidelines for minimum average residential density (in dwelling units/acre), from the Transportation Policy Plan. Edina is classified as “Urban”.
Right-of-Way Type Transit Type Geography Urban Center Urban Suburban Suburban Edge / Emerging Suburban Edge
Fixed or
Dedicated
Transitway
Light Rail Transit
Commuter Rail Dedicated BRT
half-mile
radius
50 25 20 15
Highway Transitway (MnPass/HOV)
Highway BRT half-mile radius 25 12 10 8
Shared Rights-of-Way
Arterial BRT quarter-mile radius 15 15 15 15
Local Bus Routes on High Frequency Network
quarter-mile along route 10 10 10 10
43What Policy Actions are Needed?
Source: Metropolitan Council
City Ordinances
Our review of City ordinances focused on built form requirements, parceling, and transportation
demand management (TDM) policy. We found instances where these policies are overly restric
tive with regard to encouraging increased density and reduced VMT, as well as instances where
processes and standards can be streamlined in order to minimize the costs associated with new development.
5. For new multi-unit construction, replace maximum building coverage with the existing requirement for impervious surface coverage to maximize site flexibility
and development potential.
Aligned with CAP Action TL 3-4
Edina should consider replacing building coverage requirements for residential zones with
existing requirements regulating impervious surface coverage. This furthers the City’s work
on supporting ADU development and makes smallscale multifamily feasible. This recom
mendation replaces the limit of 30% building coverage on the lot with the existing 50% impervious surface requirement. By preserving the same limit on impervious surfaces but
relaxing building coverage, there is greater developmental flexibility for new multiunit con
struction and sites may better be able to feasibly reach their development capacity. However,
there is no adverse environmental impact, as sites would see no loss of green space relative
to existing requirements, therefore ensuring no stormwater runoff contamination beyond existing conditions as well.
6. Reduce minimum lot sizes in single-family residential zones to a maximum of
4,500 square feet, to permit at least one lot subdivision on each conforming parcel.
Aligned with CAP Actions TL 3-1, TL 3-3, TL 3-4
The current minimum lot size in Edina for R1 parcels is 9,000 square feet. To enable construction of more affordable homes and increase residential density, Edina should reduce
minimum lot sizes, especially in proximity to transit. Reduced lot sizes near transit allows for
more households per acre, increasing the utility of transit for a greater share of residents,
which is identified in our transportation decision making framework as a critical component
of any effort to increase the use of sustainable modes. This action has the added benefit of of lowering the cost of housing. Smaller lot size is directly correlated with increased home
ownership opportunities across income levels, and greater residential density will decrease
per capita VMT.
7. Develop and pass a set of objective design standards to streamline and depoliticize development while reducing development soft costs to promote
housing growth and affordability.
Edina does not currently have identifiable objective design standards. When architects are
unable to design to predetermined, objective standards, it can lead to uncertainty in the
development process and can increase the time it takes to realize a project. A lack of objective standards increases the probability that antigrowth advocates object to a project and
lengthens development timeline while taking more staff time to administer project propos
als. Additionally, it reduces the incidences of PUD being the primary way to develop at scale
in the City.
44What Policy Actions are Needed?
8. Eliminate parking minimums, or in the case of their continuance, disallow
minimums higher than one per household.
Aligned with CAP Action TL 3-2
Edina should rationalize parking requirements if not outright eliminate them. Parking
requirements induce demand for driving, and they raise the cost of housing and of goods.
In lieu of elimination, consider common sense reform. An example is that offstreet parking spaces are required at a minimum of one per unit for singlefamily housing units, but multifamily requires 1.25 spaces per home. Given the proximity of most current and future
multifamily housing to transit, and the lower average incomes of those in multifamily, it does
not make sense to require extra spaces for multifamily.
The greatest effect of parking requirements is that of increased costs passed on to renters. A reduction of parking provided or required has a direct impact on transportation decisionmaking by reducing the convenience of driving. This decrease in the utility of trips made
by car may be impactful enough to influence the comparison of transportation modes,
inducing more Edina residents to opt for active transportation and public transit.
9. Within ‘Areas of Change’ and in proximity to transit, supplement maximum
standards with minimum height and density standards.
Aligned with CAP Actions TL 3-1, TL 3-3, TL 3-4
When understanding that most growth in the City will be targeted toward ‘Areas of Change’, it is important to ensure that future development takes best advantage of the value of land
and the proximity benefits of transit service that reaches most of the ‘Areas of Change’. To
do so, Edina should prescribe minimum height limits so that greater potential is realized
from colocating more origins and destinations with each other and within close proximity to transit service. This also supports our findings that targeted density performs much better than distributed density. To make the most of targeted density, it’s imperative that land be
used most effectively.
10. Decrease front setbacks in neighborhoods in the walkshed of transit service, and particularly along corridors that connect ‘Areas of Change’.
Aligned with CAP Actions TL 3-1, TL 3-3, TL 3-7
In order to create a more activated streetfront and facilitate communityscale density, Edina should reconsider its rigid front setback requirements. In the case of walkable, transitserved neighborhoods, gentle density can be accommodated without provisioning so much of
the lot to lawns. Porches fronting the street, or entries to homes directly from the sidewalk
contribute to greater visual interest and street activity, while allowing for more flexible site
design and making it more simple to add additional housing units without needing to build up tall.
In addition, the current code allows for the minimum front setback (never less than 30
feet) to be expanded even more if other buildings on the street segment average greater
setbacks. This requirement should be removed. Reduced setbacks promote greater gentle
density, more street interest and vitality, an improved pedestrian experience, and greater site flexibility.
45
11. Remove the requirement to have height limits contingent on nearby zoning heights. Make clear that certain height limits are universal across land use
designation, so that well-located parcels are not underdeveloped compared to potential. Otherwise, limit calculation to only apply to one parcel as a transition
zone.
Aligned with CAP Actions TL 3-1, TL 3-3
The current transition zone between different land use and builtform types make it
infeasible to easily transition in height from lot to lot. This ensures many buildings guided for
higher densities in the ‘Areas of Change’ may not utilize their full zoning envelope if they are
near any buffer. The requirement to not fully utilize development potential in proximity to R1
renders the zoning designation for ‘Areas of Change’ much less useful and limits the density potential in the smartest areas of the City in which to grow. If the height calculation that
takes into consideration the height of neighboring structures is not to be eliminated entirely,
then it should at least reduce from 80 feet to less than 75 feet given that the minimum lot
width is typically 75 feet. This would make it such that just one lot would serve as a buffer
between differentiallyzoned parcels.
12. Provide a streamlined permitting process for garage ADU construction/conversion.
Do not require additional parking for the additional dwelling unit. Design a set of pre-approved plans for garage conversions of varying garage widths from
which homeowners may select in order to speed development and reduce the
associated architectural costs for conversions.
Aligned with CAP Actions TL 3-1, TL 3-2, 3-7
Due to required setbacks and parking minimums, most homes in Edina have significantly
more space on their properties between driveways and garage spaces than is necessary
for vehicle storage. In addition, most garage space represents significant square footage potential sufficient for smallformat dwellings if converted. Given the unlikelihood
that driveway storage is insufficient to meet offstreet parking needs and the desire to
add more density to the City without compromising the built form of neighborhoods,
garage conversions offer exciting potential to add households to the City in a way that is
imperceptible to neighbors. This also supports the City’s leadership on ADU creation and provides a greater diversity of housing options.
13. Disallow the construction and operation of new drive-thru enterprises through the
Code of Ordinances. Existing operation should not be curtailed, but new permits to operate drive-thrus or other auto-oriented uses shall not be granted and in
future redevelopment, concessions should be awarded for lowering the number and size of curb cuts present on the sidewalks.
Aligned with CAP Actions TL 1-1, TL 2-4, TL 3-3, TL 3-4
Drivethru restaurant and beverage operations encourage more trips taken by auto, as
they do vehicle idling. Drivethrus have a number of detrimental impacts. Beyond the clear GHG emissions impacts from vehicle trips and vehicle idling, they take up prime land
in commercial corridors and their curb cuts and asphalt expanses contribute to a hostile
pedestrian environment.
What Policy Actions are Needed?
46What Policy Actions are Needed?
Car washes and other businesses that are intended to serve vehicles without the need for
their operators to step out have the exact same detrimental environmental and land use
consequences. A reduction in curb cuts and vehicle ingress/egress across the sidewalk also
contributes to the improvement in pedestrian and cyclist comfort, incentivizing active travel mode shift.
Climate Action Plan
The Climate Action Plan needs to ensure its goals are consistent and mutuallysupportive. To
that aim, the next update to the CAP must match its density growth targets with 1) regional
lyadopted growth plans, 2) the City’s Comprehensive Plan, and 3) datadriven analysis (sup
ported by this report) that ensures VMT and transit usage targets are achievable with increased zoned capacity. The primary recommendation (Report Recommendation #14) to the Climate
Action Plan is to increase density targets to at least approximately 20%, which matches expect
ed decadal population growth projections and if implemented correctly, would meet CAP VMT
goals.
For recommendations that amend or address clear Climate Action Plan goals but are not clearly under the purview of the Comprehensive Plan or the Zoning Code of Ordinances, the following
additional suggestions to policy and planning are listed below:
15. Study which barriers exist in the street grid that add a significant time/distance penalty for neighborhood access to transit and/or community-serving amenities/
employment. Begin with assessing how to better connect the entire Strachauer
Park neighborhood to the E Line station at France Avenue/62nd Street.
Aligned with CAP Actions TL 1-2, TL 3-3
A disconnected grid creates barriers to walkability and access. There are certain
discontinuous segments of streets that impede access to transit stops. Longer walks and
detours that do not materially affect travel time for vehicles but introduce significant time penalties for those accessing transit harm goals to increase transit and active transportation
mode share, to say nothing of the potential value of E Line (and other) investments that is
left on the table. The City should adopt a plan, perhaps as an update to the Living Streets
Plan, to selectively acquire properties that present significant barriers to accessing transit,
and utilize those properties to construct links that make the grid more permeable.
16. Update the CAP actions addressing the Pedestrian and Bicycle Master Plan with
targets that come with specific annual commitments on expanding safe active travel infrastructure. Obligate the City of Edina to fund implementation of the
Pedestrian and Bicycle Master Plan annually.
Aligned with CAP Actions TL 1-1, TL 1-2
The Pedestrian and Bicycle Master Plan is a great document that seeks to make Edina a
community more safe and comfortable to navigate through active travel. CAP Action TL
12 in particular calls for an acceleration in the pace at which recommended changes are
constructed in the City. Despite that smart call, the goal, among others, does not have an
attached date for construction milestones.A CAP that takes seriously its mode shift goal must prioritize realistic timelines and accountability measures that hold the City to timelines
on improvements to the bicycle network that make the City more interconnected and safe.
47What Policy Actions are Needed?
17. Shift Edina’s CAP goals for transit to a goal for mode share of transit writ large,
rather than just focusing on commute share, and a further action should be added
in “TL 1: Decrease community wide VMT by 7% by 2030” that specifically adds a numeric target to the percentage of trips conducted by active travel that Edina
would like to see.
The COVID19 pandemic radically reshaped the transportation landscape due to rapid
changes to the way in which we work and commute. Public transit ridership has become
much less oriented to peak travel times, and is spread not just through the day, but through the week, with weekend ridership recovering most quickly on lines with frequent service.
In addition, there has been a boom in the use of ebikes. Edina recently enacted a rebate
program for residents in the amount of up to $1,200 to be applied towards the purchase of a
qualified ebike. This,and the forthcoming supplemental rebate for Minnesotans to purchase
ebikes, will increase the attractiveness of active transportation by directly reducing the costs associated with this mode. These interventions will likely lead to, significant
increases in active travel .with room to expand yet further. The existence of a goal for active
transportation mode share can give more weight to the acceleration of Vision Zero, Safe
Routes to School, and Pedestrian and Bicycle Master Plan implementation.
18. Update the Travel Demand Management Policy with a points-based, evidence-
driven checklist to streamline development and make clear the needed
mitigations from the outset. Participation in bulk pass programs should wholly mitigate parking requirements.
Aligned with CAP Actions TL 1-3, TL 1-4, TL 2-6, TL 3-2
Our analysis finds that TDM policies can be an effective tool to influence the transportation mode choice process by incentivizing sustainable transportation options, while
disincentivizing single occupant vehicle trips. Current City TDM policies require a
subjective analysis and approval from staff. To reduce staff requirements associated with
the administration and analysis of TDM plans, and to facilitate more clear development
standards, the introduction of a pointsbased TDM plan that covers both residential and commercial development is recommended.
This policy change would enable a more streamlined tracking process, and help to ensure
consistent outcomes. It would also facilitate more transparent development standards. The
City should consider required participation in bulk transit pass programs through Metro
Transit (Metropass, Residential Pass) as a complete offsetting mitigation strategy that would substitute the entirety of required parking. In transportation decisionmaking, the reduction
of friction of transit use through the removal of fare payment barriers would significantly
incentivize transit travel.
The Policy Review section seeks to identify concrete actions through which updates to the next Comprehensive Plan, Code of Ordinances, and Climate Action Plan can be improved.
Meaningfully, these recommendations are informed and supported by the transportation
decisionmaking framework, which identifies the necessary interventions to effectively
support and promote mode shift away from singleoccupancy vehicles. Whereas Edina 2050
will continue to guide land use and transportation decisions, it’s the Code of Ordinances that regulates the built form of the community and should be modified to reflect best practices
in the field of urban planning: namely a relaxation of stringent requirements that hinder the
City’s ability to grow in vibrancy, particularly around priority growth areas. The Climate Action
48What Policy Actions are Needed?
Plan is admirable in its goals, but must adapt density goals to reflect the reality that VMT
reduction goals (total OR per capita) will not be met without more intense development. That
aside, a growing city needs to assess GHG reduction and VMT reduction goals on per capita
improvement given that fundamentally, people are not pollution and an improvement to average community performance is good not just for Edina but for the region.
49
Final Conclusions
Final Conclusions
In answering our three research questions, we have determined that density increases can be instrumental in achieving Edina’s climate goals, both in regards to transportation and green
house gas emission targets more broadly. However, density alone will not be enough to effec
tively reach the targets outlined in the Climate Action Plan. Additionally, the current density
targets outlined in the CAP are insufficient under any scenario in reaching the sustainable
transportation targets for a reduction in vehicle miles traveled and an increase in transit ridership.
To maximize the effectiveness of density increases in Edina, a targeted density strategy should
be employed: focusing on key commercial nodes, transit corridors, active transportation net
works, and essential destinations such as healthcare facilities. We have outlined three recommended scenarios for increasing allowable density in a strategic way that will be most transfor
mative in reaching Edina’s climate goals. The three recommended scenarios: ‘Basic’, ‘Enhanced’,
and ‘Preferred’ offer progressively more impactful zoning scenarios.
Figure 23: ‘Basic’ recommended scenario
Source: Authors created using Urban Footprint
50
Figure 24: ‘Enhanced’ recommended scenario
Source: Authors created using Urban Footprint
Final Conclusions
51
Figure 25: ‘Preferred’ recommended scenario
Source: Authors created using Urban Footprint
Final Conclusions
52Final Conclusions
Even when employed strategically, zoning changes will require support through other policy ac
tions. We have developed a list of recommended policy actions for Edina that will help to reach
the City’s climate targets alongside the adoption of our recommended zoning scenarios above.
The three Climate Action Plan goals related to Transportation & Land Use that this study investigated should be amended during the 2025 CAP amendment process as follows:
TL 1 – Amend the goal to measure VMT per capita, rather than citywide VMT. Augment
existing actions with active transportation mode share goals that complement transit mode share, and tie action into delivery and funding prioritization for active transportation projects.
TL 2 – Amend the goal to measure transit mode share, rather than just commuter transit mode share.
TL 3 – Align population density targets with resultdriven analysis of growth scenarios that DO meet the VMT reduction and GHG reduction targets. A 4% residential density increase is insufficient to achieve other Cityadopted goals.
Specific recommendations that support the three Transportation and Land Use goals above
should be incorporated into subsequent updates of the Comprehensive Plan, the Code of Ordinances, and the Climate Action Plan itself:
Comprehensive Plan (Land Use)
1. Recategorize Neighborhood Nodes as MixedUse Centers, and MixedUse Centers as
Community Activity Centers
2. Allow commercial uses at corner parcels on France Avenue, with at least duplexes permit
ted along the whole corridor3. When conducting corridorbased zoning, include abutting lots on parallel streets in addi
tion to the corridor itself
4. Achieve Thrive MSP 2040 Transportation Policy Plan density minimums in existing and
future transit corridor
Code of Ordinances (Built Form)
5. Replace maximum building coverage requirement with existing maximum impervious surface coverage requirement
6. Reduce minimum lot size to 4,500 square feet
7. Adopt a set of objective design standards
8. Eliminate parking requirements; if infeasible, cap maximums at one per unit9. Supplement maximum height and density standards with minimum height and density standards.
10. Decrease front setback requirements
11. Reduce height transition requirements
12. Continue support for ADUs, particularly garage conversions13. Prohibit new drivethrus
Climate Action Plan (Overall Goals + Transportation)
14. Align density goals across plans and in support of VMT reduction
15. Study and reduce active transportation barriers
16. Add specific yearly targets for implementation progress on active transportation projects
17. Supplement CAP with active transportation mode share goals18. Update the TDM Policy with objective, pointsbased standards
53
These recommendations emphasize what has already long been known in the City of Edina:
that transportation, land use, and climate policy are deeply interrelated and mutually reinforc
ing. Our ideas are merely a starting point for the many actions the City will need to take in the
coming years to mitigate the effects of climate change. By aligning its goals with empirical reality, targeting density in the places with the most opportunity for positive change, and making strategic policy interventions that promote sustainable lifestyles, Edina can forge a new path
toward a greener and more resilient future.
Special Thanks
Matthew Gabb, City of Edina
Addison Lewis, City of Edina
Marisa Bayer, City of Edina
Andrew Scipioni, City of Edina
Stephanie Hawkinson, City of Edina
Bill Neuendorf, City of Edina
Michael Greco, Resilient Communities Project
Stina Kielsmeier-Cook, Resilient Communities Project
Eric Wojchik, Metropolitan Council
MacKenzie Young-Walters, Metropolitan Council
David Burns, Metropolitan Council
Dennis Farmer, Metropolitan Council
Peter Wilfahrt, Metropolitan Council
Samuel Limerick, Metropolitan Council
Nichola Lowe, Humphrey School of Public Affairs
Yingling Fan, Humphrey School of Public Affairs
Frank Douma, Humphrey School of Public Affairs
Greg Lindsey, Humphrey School of Public Affairs
Angie Fertig, Humphrey School of Public Affairs
Eric Lind, Center for Transportation Studies
Duncan Kay, Urban Footprint
54
Appendix A - Regression Analysis Methodology
Statistical Analysis
In order to better understand the explanatory variables that account for VMT and transit commut
ing mode shares, and to inform our policy recommendations, a key component of our methodolo
gy was to conduct various regression analyses, using the Stata statistical software. These included both a simple regression of population density and VMT per capita at the municipal level, as well
as multivariate regressions wherein population density and various other demographic, econom
ic, geospatial, and transportationrelated variables would be tested for their correlation with both
VMT per capita and transit as a commuting mode share.
The focus of this regression analysis was the Twin Cities metropolitan area, specifically the Minne
sota counties of Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington; that is, the sev
en counties served by the Metropolitan Council. We chose this approach for several reasons. First,
this approach maximizes the comparability between Edina and the other cities in the regression,
whereas crossregional comparisons may have been skewed by a greater quantity of confounding variables. Second, the Metropolitan Council (as well as other, broader entities such as the Minne
sota Department of Transportation) collects and publishes standardized data on variables such as
land use and employment for each of the localities in the sevencounty region. Third, there are 181
total localities in this area, including 140 cities and 41 townships, giving us a relatively large sample
size to draw from. Fourth, this approach allows the results of our analysis to inform planning and policy interventions within the Metropolitan Council’s service area more broadly, including cities
outside of Edina and the Council itself.
Because some data was only available for the cities within the Metropolitan Council area, we
excluded the townships from our analysis. We excluded the communities of Landfall and Hilltop from the analysis, as these are mobile home parks with very high population density per residen
tial acre, creating outliers that are not comparable to Edina. Finally, we excluded the small rural
communities of Miesville, Coates, Nowthen, and North Oaks, due to being outliers in terms of local
roadway miles a key variable that is used to calculate VMT per capita.
Simple Regression
One goal of this regression analysis was to validate what is arguably the key assumption behind
our research question: that an increase in population density is associated with a decrease in
percapita VMT. In order to test this hypothesis, we undertook a series of simple regressions with
population density as the explanatory variable and VMT per capita as the dependent variable, each measured in two different ways.
Our explanatory variable, population density, required both population and area data to compute.
Population data was collected for the 140 localities located fully within the Metropolitan Coun
cil service area based on the 2022 American Community Survey (5year estimates). We used two different methods for calculating population density, and performed the regression analysis using
each one. In the first, we divided the population by the total land area in each locality, to arrive at
a simple population density figure. The second method attempted to control for nonresidential
land uses, by calculating the total area within each municipality that contains residential land use,
as per the Metropolitan Council’s 2020 Land Use dataset. The population was divided by the number of acres of residential land to arrive at a residential population density figure.
Appendix A
55
For our dependent variable, VMT data was retrieved from the Minnesota Department of Transpor
tation’s dataset “VMT by Route System in each City, within each County” as of 2022, which includes
the average daily VMT, annual VMT, and centerline mileage in every locality in the state, disaggre
gated by the class of road. To calculate VMT per capita, we divided the total annual VMT across all classes of road by the total population of each locality in the sevencounty region. As an alternative dependent variable, we found the VMT per capita specifically on roads classified as local
roads. This variable is intended to control for the presence of major highways artificially inflating
the VMT figures for certain smaller municipalities.
Multivariate Regression
In conducting our literature review, we determined that one goal of our report must be to go
beyond a simple conception of density as determining VMT or transit ridership. For that reason,
we elected to conduct a multivariate regression analysis incorporating not only population den
sity, as outlined above, but several other factors as well. Like with the simple regression, the data for these variables were collected to the extent possible for the 140 municipalities fully within the sevencounty Metropolitan Council service area.
We examined the following variables:
• Access to transit: We used density of transit stops as a proxy for access to transit service, which
we hypothesized to be inversely related to percapita VMT. For each municipality, we found the
average number of transit stops per acre. In order to account for crossmunicipality pedestrian
activity, i.e., people who live in one municipality but use a transit stop located in another, we
did so using a quartermile buffer around each municipality. This is especially important where
municipal lines follow major roads, as the transit stop going in one direction might be located
in a different municipality from the transit stop going in the opposite direction. In summary,
we defined access to transit as the number of transit stops within 0.25 miles of a municipality
divided by the area within that municipality plus a 0.25mile buffer.
• Access to vehicle: The proportion of households lacking access to a vehicle was hypothesized
to be inversely related to percapita VMT. This data was drawn from the ACS 2022 5year esti
mates.
• Centeredness index: This is a measure of the concentration or dispersion of population with
in a municipality. We hypothesized that more centralized population distributions would be
associated with lower VMT per capita. This association could have considerable implications for
land use and zoning policy. We replicated the methodology outlined “Measuring Urban Sprawl
and Validating Sprawl Measures,”1 finding the standard deviation of block group densities
divided by the average density of block groups for each municipality in the metropolitan area
containing at least 10 block groups.
• Commuting mode share: We examined the proportion of commuters traveling via singleoc
cupancy vehicle, walking, taking transit, and working from home. This data was retrieved from
the ACS 2022 5year estimates.
• Employment, employers, and wages: We hypothesized that the density of employment with
in a locality would be associated with differences in VMT and mode share. We examined both
the jobs and the establishments (i.e., employers) located within each locality, with the assump
tion that all localities in the sevencounty metropolitan area function as one labor market, and
crossmunicipal commuting is common. In addition, we examined the average wages for jobs
within each municipality, as well as the number of workers living in each municipality. The lat
ter metric was used to normalize the number of jobs, to create a jobstoworkers ratio metric.
Appendix A
56Appendix A
This data by sector was retrieved from the Metropolitan Council’s 2022 Employment dataset.
• Household income: We examined two metrics related to household income among house
holds located within each municipality. The first is median household income per locality. The
second is the proportion of households earning less than $40,000 per year. We hypothesized
that the former would be positively correlated and the latter inversely correlated with percapi
ta VMT. This data was retrieved from the ACS 2022 5year estimates.
• Population density: Based on the results of the simple regression, we determined that the
association between VMT and population density is slightly greater when defining population
density as population per acre of residential land. Population density was retrieved from the
ACS 2022 5year estimates of population per locality, and residential land area was retrieved
from the Metropolitan Council’s 2020 Land Use dataset.
• Vehicle lane miles: Vehicle lane miles were retrieved from the Minnesota Department of
Transportation’s 2022 Centerline Mileage and Lane Mileage by Route System dataset. These
vehicle lane miles were disaggregated by road classification, and we also examined the analo
gous figures with regard to road centerline miles.
• Binary variables: Based on the summary statistics of the above variables, we chose to add
three binary variables to the analysis:
• Has transit: We found that about half of the municipalities in the 7county metro have zero transit stops located within 0.25 miles of their boundaries, and consequently have next to zero commuters using transit. We therefore established a binary variable in which the
municipalities containing active transit stops (or transit stops within 0.25 miles) received a
value of 1 and all other municipalities received a value of 0.
• High-income: We wanted to know if there were significant differences in VMT among municipalities with a relatively high median income. We hypothesized that the degree of median household income is less important than the general character of the city as high
erincome or lowerincome. The median among municipalities of the median household
incomes within the 7county metropolitan area was $103,906. We found that this was a rea
sonable cutoff to consider a city to be highincome. Therefore, a municipality was assigned a value of 1 if it was above this number, or 0 if it was equal to or below.• High-frequency transit: Recognizing the limits of transit stop density as a proxy for tran
sit access, we created a binary variable in which a city received a value of 1 if it contains a
transit stop within Metro Transit’s High Frequency Network2. The municipalities included
in this small group are Bloomington, Brooklyn Center, Burnsville, Columbia Heights, Falcon Heights, Hilltop, Minneapolis, Richfield, Roseville, and St. Paul. We excluded Fridley, which contains one stop on Route 10 at the very northern tip of the route’s highfrequency por
tion. As noted above, we also excluded Hilltop due to being an outlier in several fields.
Methodology
Data for the municipalities in the 7county metro was collected, aggregated, normalized by population and/or area where applicable, and collected in a CSV file. All townships and several cities were removed from the dataset, as discussed above. We then imported this CSV into Stata.
We began by running pairwise correlation (pwcorr) and locally weighted regression (lowess) func
tions between each of the independent variables and both of the dependent variables (i.e., VMT per capita on local roads and transit commuter mode share). The pwcorr function assesses the degree of linearity between the two variables. The lowess function generates a scatter plot with a lo
cally weighted line of best fit, showing the shape of the relationship between the two variables. By
evaluating the outputs of these functions, we determined whether to perform a transformation
function to linearize the independent variable. This determination was made independently for
57Appendix A
each independent variable’s relationship with each dependent variable, so, for example, we might
have made the decision to logarithmize population density in relation to VMT but not in relation
to transit mode share. The pwcorr and lowess functions were then performed on the transformed
variables as a matter of due diligence. For the transformed and untransformed variables that exhibited a high degree of linearity with a given dependent variable, we ran a bivariate regression; however, we did not exclude any variables from our analysis solely on the basis of noncorrelation
in these bivariate models.
It is worth noting that our final number of observations was lower than the total number of municipalities in the 7county metropolitan area. As discussed above, townships and certain municipalities were removed as outliers. In addition because the logarithm of 0 is undefined, this meant
that any municipality with a value of 0 for any logarithmized variable was excluded from the
analysis. This was particularly important for the mode share of alternative commuting modes; for
example, many localities did not have anybody walking to commute, so they were excluded once we logarithmized the walking mode share.
The end result of this process was a list of transformed and untransformed variables which would
be included in each multivariate regression analysis. At this point, we also elected to remove sev
eral variables from each analysis. Most were removed due to anticipated collinearity with other variables; for example, we elected to include road lane miles per capita over road centerline miles per capita due to its somewhat higher degree of linearity and bivariate statistical significance. We
also removed the centeredness index; it showed a very low level of linearity or correlation at the
bivariate level, and its relatively small number of observations would have significantly decreased
the sample size of the multivariate analyses.
Upon arriving at our preliminary list of variables for each multivariate analysis, we underwent an it
erative process in which we ran a multivariate regression with all remaining variables, then tested
for multicollinearity using the vif function in Stata, then removed variables that appeared to be
contributing to multicollinearity. Because it definitionally takes multiple variables to be multicollinear, we chose the variables to eliminate based on their relative importance to the City’s CAP and their relevance to potential policy prescriptions. We considered this process complete, and
the multivariate regression finalized, when the vif function did not return a score for any variable
greater than 5, implying a low degree of multicollinearity. In the case of transit mode share as
the dependent variable, we found that transit stop density was far and away the most important variable for predicting transit mode share, so we experimented with removing this variable from the regression to better understand the impact of other variables. Ultimately, however, we deter
mined that including this variable led to the most statistically robust outcome.
58Appendix A
Regression -Results Tables
Line of fit and bivariate regression results
Dependent
Variable Independent variable Independent variable (long)
lowess
shape
Degree of
linearity
R-square
d P-value Coefficient Unit of coefficient
percapitalocalvmt popperresacre Populationdensity (residential)log
Peracre of residential
land
percapitalocalvmt logresdens
Logofpopulation density
(residential)0.5280 0.2788 0.000 -823.0011
percapitalocalvmt popdens Populationdensity (overall)log Peracre
percapitalocalvmt logpopdens Logofpopulation density (overall)-0.5507 0.3032 0.000 -554.6003
percapitalocalvmt estab_dens Establishment density log Peracre
percapitalocalvmt logestab Logofestablishment density -0.5066 0.2566 0.000 -488.2614
percapitalocalvmt perc_owner Homeownership linear 0.3341 0.1116 0.000 2284.272
Percent of
households
percapitalocalvmt
localroadlanemilesperc
apita Density of local roads(population)linear 0.5447 0.2968 0.000 75668.59 Lanemiles per capita
percapitalocalvmt
localroadlanemilespera
cre Density of local roads(area)log Lanemiles per acre
percapitalocalvmt loglocalroadlmpa Logoflocal road lanemilesper acre -0.4527 0.2049 0.000 -755.199
percapitalocalvmt job_dens Job density log Peracre
percapitalocalvmt logjobdens Logofjobdensity -0.3656 0.1337 0.000 -257.8631
percapitalocalvmt j_w_ratio Ratio ofjobs toworkers log Ratio
percapitalocalvmt logjwratio Logofratio of jobs to workers 0.0503
percapitalocalvmt perc_noveh No vehicle log
Percent of
households
percapitalocalvmt logperc_noveh Logofpercent withno vehicle -0.1802 0.0325 0.065 -217.5806
percapitalocalvmt perc_drovealone Drivingalone linear -0.1542 0.0238 0.075 -1900.035 Percent of commuters
percapitalocalvmt perc_transit Taking transit linear -0.1309 0.0171 0.132 -7330.558 Percent of commuters
percapitalocalvmt perc_walked Walking log Percent of commuters
percapitalocalvmt logperc_walk Logofpercent walking -0.2380 0.0567 0.008 -282.6535
percapitalocalvmt perc_wfh Working from home linear 0.2276 0.0518 0.008 2991.148 Percent of commuters
Dependent
Variable Independent variable Independent variable (long)
lowess
shape
Degree of
linearity
R-square
d P-value Coefficient Unit of coefficient
percapitalocalvmt med_hh_inc Medianhousehold income linear 0.2922 0.0854 0.001 0.0079461 Dollars
percapitalocalvmt hh_under_40k
Households makingless than
$40,000 per year linear -0.3624 0.1314 0.000 -5032.964
Percent of
households
percapitalocalvmt centeredness Centeredness index linear 0.0730 0.0053 0.593 177.368
Centeredness index
(see methodology
section)
percapitalocalvmt transitstopdensity Transit stopdensity log/linear -0.1845 0.0335 0.033 -13620.64 Peracre
percapitalocalvmt logtransitstopdensity Logoftransit stopdensity -0.3583 0.1284 0.002 -127.3323
percapitalocalvmt hastransit Has transit service n/a -0.0983 Binary
percapitalocalvmt highfreq Has high-frequency transit n/a -0.0539 Binary
percapitalocalvmt highincome High-income n/a 0.3115 0.0970 0.000 618.6602 Binary
perc_transit avg_weekly_wage Average weekly wage linear 0.0790 0.366 Dollars
perc_transit popdens Populationdensity (overall)linear 0.6338 0.4018 0.000 0.0050997 Peracre
perc_transit popperresacre Populationdensity (residential)linear 0.5895 0.3475 0.000 0.0029127
Peracre of residential
land
perc_transit percapitavmt VMT -0.0746
Annual VMT per
capita
perc_transit percapitalocalvmt VMTon localroads linear -0.1177 0.0138 0.177 -2.15E-06
Annual VMT per
capita
perc_transit estab_dens Establishments linear 0.4236 0.1794 0.000 0.0001496 Peracre
perc_transit job_dens Jobs linear 0.5407 0.2923 0.000 9.81E-06 Peracre
perc_transit j_w_ratio Ratio ofjobs toworkers linear 0.1810 0.0327 0.037 0.0040893 Ratio
perc_transit
localroadlanemilesperc
apita Density of local roads(population)log Lanemiles per capita
perc_transit loglocalroadlmpc
Logoflocal road lanemilespercapita -0.4482 0.2009 0.000 -0.0144233
perc_transit
localroadlanemilespera
cre Density of local roads(area)linear 0.3122 0.0975 0.000 0.4739549 Lanemiles per acre
perc_transit perc_owner Homeownership linear -0.4511 0.2035 0.000 -0.0552244
Percent of
households
perc_transit perc_noveh No vehicle linear 0.5601 0.3137 0.000 0.5917644
Percent of
households
perc_transit perc_drovealone Drivingalone linear -0.5097 0.2598 0.000 -0.1119112 Percent of commuters
perc_transit perc_walked Walking linear 0.4174 0.1742 0.000 0.5829106 Percent of commuters
Regression - Results Tables
Table 15: Line of fit and bivariate regression results
59Appendix A
Dependent
Variable Independent variable Independent variable (long)
lowess
shape
Degree of
linearity
R-square
d P-value Coefficient Unit of coefficient
perc_transit perc_wfh Working from home linear 0.1220 0.0149 0.162 0.0292857 Percent of commuters
perc_transit med_hh_inc Medianhousehold income log -0.2192 0.0481 0.011 -1.09E-07 Dollars
perc_transit logmedhh_inc Logofmedianhousehold income -0.2530 0.064 0.003 -0.015357
perc_transit hh_under_40k
Households earning under $40,000
per year linear 0.3459 0.1196 0.000 0.0876946
Percent of
households
perc_transit centeredness Centeredness Index none 0.1788 0.032 0.187 0.0146932
See methodology
section
perc_transit transitstopdensity Transit stopdensity linear 0.7504 0.563 0.000 1.011297 Peracre
perc_transit hastransit Has transit service n/a 0.4219 0.1717 0.000 0.0154275 Binary
perc_transit highfreq Has high-frequency transit n/a 0.5466 0.2988 0.000 0.0394626 Binary
perc_transit highincome High-income n/a -0.2219 0.0492 0.010 -0.0080457 Binary
Regression - Results Tables Continued
Table 16:Multivariate regression results with local VMT per capita as dependent variable
Number of observa
tions
122
Rsquared 0.3922
Adjusted Rsquared 0.3433
Probability > F 0.0000
Independent Variable Coefficient P-value
Log of population per resi-dential acre***-1004.278 0.000
Percent of households own-ing home -1280.269 0.159
Log of local road lane miles per acre -221.0041 0.256
Log of walking mode share***-262.1106 0.010
Work-from-home mode share 2092.638 0.111
Median household income**-.0093618 0.016
Transit stop density*12750.8 0.095
Has transit (binary)48.23469 0.801
High-income (binary)***627.7592 0.007
*significant at p = 0.1 level**significant at p = 0.05 level***significant at p = 0.01 level
60
Independent Variable Coefficient P-value
Population per residential acre*0.0008785 0.058
Local road lane miles per acre -0.0328466 0.742
Households with no vehicle**0.1647657 0.019
Walking mode share*0.1377205 0.093
Work-from-home mode
share**
0.0421348 0.015
Log of median household income 0.0028835 0.599
Transit stop density***0.6286264 0.000
High-frequency transit (bi-nary)**0.0099594 0.033
*significant at p = 0.1 level**significant at p = 0.05 level***significant at p = 0.01 level
Table 17: Multivariate regression summary with transit mode share as dependent variable
Appendix A
Number of observations 133
Rsquared 0.6614
Adjusted Rsquared 0.6319
Probability > F 0.0000
61Appendix A
Discussion - VMT Regression
Overall, this model explains just over onethird of the variation in local VMT per capita. While this
certainly goes a long way toward explaining how a city might go about reducing VMT, there is
quite a bit that is left unexplained, and more advanced statistical methods – for example, geospa
tial statistics – may be necessary to better understand these processes.
Most significant of the variables analyzed in this model is residential population density, which is
significantly correlated with VMT at the 0.01 level. The coefficient here implies that a 1% increase
in residential density is equated with a decrease in about 10.04 vehicle miles traveled per capi
ta, which is 0.38% of current levels. At this rate, using increased population density alone, Edina would need to densify by 18.3% in order to meet the CAP target of a 7% reduction in VMT per capi
ta. However, this interpretation is agnostic on densification strategy.
Walking mode share was negatively associated with VMT; for every 1% increase in walking mode
share, we predict that local VMT will drop by 2.6 miles per capita. Currently, the walking mode share in Edina is about 1.16%, or 298 persons. A 1% increase would mean about three more people
commuting by walking, for a total walking mode share of 1.17%. Extrapolating further, if the walk
ing mode share were to double to 2.32%, we would expect local VMT to decrease by about 262
miles per capita.
The two incomerelated variables median household income, and highincome status were
also both statistically significant. It is important to remember that these variables control for
one another that is, median household income is examined separately for higherincome and
lowerincome municipalities. Highincome municipalities, of which Edina is one, are defined as
having a greaterthanaverage median household income among cities in the metro in practice, above $103,906. We found that, controlling for numerous other variables, median household
income is negatively correlated with local VMT: for every additional $1,000 in median household
income, we expect local VMT to drop by about 9 miles per capita. However, we also expect high
erincome municipalities to have greater VMT by almost 630 miles per person. These seemingly
contradictory results illustrate a “bell curve” shaped trend in household income: VMT is highest among cities with uppermiddle incomes, while both the lowestincome and the very highestin
come cities tend to have lower VMT. This is where more advanced forms of statistical analysis,
such as spatial regression, may prove fruitful in better understanding these complex relationships.
Discussion - Transit Ridership Regression
Compared to VMT, this multivariate regression was able to explain much more of the variation in
transit mode share almost twothirds of it, according to the adjusted Rsquared value of 0.6319.
However, this was largely driven by just one variable: transit stop density. When controlling for all
other variables, this was the only independent variable that achieved significance at the 0.01 level.
The coefficient here implies that an increase in 1 transit stop per acre will increase the level of transit mode share by 62.9 percentage points; in more realistic and perhaps more intuitive terms, this
means that to achieve a 1 percentage point increase in transit mode share, a city could add 10.2
transit stops per square mile. In Edina’s case, this would mean about 162 transit stops throughout
the city.
However, it is worth reiterating that transit stop density is merely a proxy for access to transit. In
reality, this estimate of 162 transit stops per 1 percentage point increase in transit mode share is
likely an overestimate, as it does not account for other factors such as frequency of transit service,
speed and connectivity of transit routes, or population and employment density along transit
62Appendix A
routes. For example, the upcoming METRO E Line, the BRT route that will serve Edina, will have
stations that are spaced further apart than the existing Route 6 in other words, a lower density
of transit stops. But the increased level of transit service, improved speed and reliability, and transitoriented development around BRT stations has historically been associated with increased levels of transit ridership in the context of the Metro Transit system. Therefore, we do not recom
mend taking this coefficient as the basis for a policy recommendation; rather, it underscores the
close relationship between improved transit service and transit ridership.
Let us now turn to residential population density, which is not significant at the 0.05 level. Interestingly, this variable does have a highly significant relationship to transit mode share at the 0.01
level when transit stop density is removed from the regression analysis. This demonstrates that
population density is a significant predictor of transit mode share insofar as it supports improved
transit service. This aligns with our recommendations of targeted, corridorspecific densification that either takes advantage of transit (i.e., TransitOriented Development) or spurs additional future service from Metro Transit.
Three other variables were identified as having statistically significant relationships to transit
mode share: percent of households without a vehicle, percent working from home, and presence of highfrequency transit. Each of these had a positive relationship with transit mode share. The former two households without a vehicle, and commuters working from home illustrate demo
graphic trends, but do not lend themselves well to policy recommendations. Carfree households
are typically associated with lower incomes, and remote work is associated with higher incomes.
However, our regression controls these variables for median household income, so their significance perhaps illustrates a certain level of selfselection, wherein people who live in areas where transit is a viable mode choice are comparatively likely to choose to live carfree or to work re
motely.
However, the third variable, presence of highfrequency transit, does lend itself to policy recommendations. We find that the presence of highfrequency transit in a locality is associated with a transit mode share about 1 percentage point higher than localities without highfrequency
transit. Edina currently does not have any highfrequency transit. However, with the opening of
the METRO E Line in 2025, it will enter this exclusive club of municipalities assuming the E Line
meets Metro Transit’s standards for inclusion in the High Frequency Network2. We expect Edina’s transit mode share to improve by about 1 percentage point upon the opening of the E Line, even absent increases in population density or further improvements to transit service.
Section References
1. Ewing, Reid, and Shima Hamidi. “Measuring Urban Sprawl and Validating Sprawl Measures.” National Cancer Institute, National Institutes of Health, Apr. 2014, gis.cancer.gov/tools/urbansprawl/sprawlreportshort.pdf2. High Frequency Network, n.d., https://www.metrotransit.org/highfrequencynetwork
63
Appendix B - Transportation Decision Framework
The Transportation Decision Framework is grounded in a core concept: The car is the competition
when it comes to reducing VMT and increasing public transit commute ridership. In the City of
Edina, like in many American cities and towns, over a century of autocentric transportation planning has positioned the private automobile as the most convenient, and often the default, option for the majority of trips. This is reflected in the dominance of trips made by car within Edina,
which account for 78% of commute trips. Given Edina’s goals of dramatically reducing vehicle
miles traveled (VMT) and increasing public transit commute ridership, addressing the prevalence
of singleoccupant vehicle (SOV) trips is paramount. To shift the balance towards more sustainable forms of transportation—such as public transit, walking, and cycling—alternate modes must be made more convenient, travel by personal automobile must be made less convenient, or ideal
ly, a combination of both strategies must be employed.
Understanding the factors that influence an individual’s transportation mode choice is critical, as vehicle miles traveled and public transit ridership are ultimately the results of decisions made by people. These decisions are shaped by a complex interplay of factors, including individual prefer
ences, costbenefit analyses, environmental considerations, social norms, and the inertia of past
habits. The transportation decisionmaking framework analysis presented here is intended to
serve as a bridge between the quantitative analysis conducted in sections 5 and 6, and the recommended policy interventions in section 9. Specifically, this section provides an overview of various considerations that influence transportation mode choice at the individual level, and provides the
City of Edina with recommended interventions to reduce VMT and increase sustainable trans
portation mode share. These recommended interventions will be further elaborated on in subse
quent segments of the report.
The framework presented is not intended to be comprehensive, as the factors that influence
mode choice, and their relative importance, will differ from individual to individual. Rather, the
goal of this model is to provide insights into key factors that enable or inhibit the use of nonau
tomotive modes, and propose recommendations that will reduce VMT and increase the share of trips made by public and active transportation within Edina. By doing so, it seeks to contribute to achieving the ambitious goals set forth in the City of Edina’s climate action plan.
Consideration 1: Assessment of Mode Viability
Perhaps the most straightforward consideration when it comes to transportation mode choice is simply whether a particular mode can reasonably be used to complete a given journey. This consideration must inherently take into account the origin (starting point) and destination (end
point) of the trip, as well as the infrastructure and systems connecting these two points.
Trip Origin
For a significant number of trips, especially commute trips, an individual’s home is the origin
point of the journey. The decision of where to live is influenced by a wide variety of factors, including housing price, residential preferences, and considerations relating to accessibility (distance to
key destinations, employment opportunities, and transportation facilities).
Appendix B
64
The location of origin points is of critical importance to transportation decision making, due to the
fact that the starting point of a trip directly determines which transportation modes are viable. As
such, any effort to reduce VMT and increase the mode share of public transportation in the City of Edina should prioritize increasing housing density and other common origin points in locations that provide easy access to current and future public transit and active transportation infrastruc
ture. The future E Line Bus Rapid Transit alignment is an area that should be considered for sig
nificant residential upzoning, so as to bring as many residents within a useful distance of this high
frequency transit system as possible.
Trip Destination
The destination of a particular trip is also a key element that influences transportation mode
choice. The list of potential destinations is infinitely large, but some common trip end points in
clude places of employment, school, commercial establishments, and the homes of friends and
family members. Similar to origin points, the location of destinations and their proximity to trans
portation infrastructure directly influences which modes of transportation could feasibly be used to complete the trip.
Infrastructure and Systems Connecting Origin and Destination
The infrastructure and systems necessary to connect origins to destinations are equally important
in determining the viability of any given transportation mode. Examples of transportation infra
structure include roads, street crossings, sidewalks, bike lanes, multiuse trails, transit lines, and transit stops. For a given transportation mode to be considered viable, these systems must link the origin and destination of a trip.
The influence of the presence of specific infrastructure is supported by the regression analysis
conducted in section 5, where transit stop density was found to influence both total vmt and public transit mode share of cities and townships within the Metropolitan Council’s jurisdiction. The presence of highfrequency transit was also found to be statistically significant for public transit
mode share, indicating that the future E Line will likely have a positive impact on public transit
commute mode share in Edina once operational.
At a basic level, if a given mode cannot reasonably connect an individual’s origin to their destination, given the existing infrastructure, then it is highly unlikely that this mode will be chosen.
What is considered ‘reasonable’ is ultimately determined by the individual, but there are time and
distance standards which are generally considered reasonable for large portions of the popula
tion.
• Transit: One quarter mile from local transit service, and one half mile from high frequency
transit, is typically considered a reasonable distance to make use of these systems, with rider
ship of those beyond these thresholds dropping precipitously1.
• Walking: 73% of USA adults feel that short trips (up to .5 miles and up to 10 minutes) made by
walking are reasonable, and 43% feel that trips of 1 mile or more, and twenty minutes or more
are reasonable2.
• Biking: Trips made by cycling are generally 5 miles or less, with the majority of trips ranging
from 1 to 2 miles in distance3.
For automotive travel, the answer to the question of whether driving can connect origin to desti
nation is almost always yes, due to the dominance of car oriented transportation planning in most communities. As it stands, roads connect the majority of origins and destinations, and the speed of automotive travel makes even long distance journeys reasonable. However, for sustainable
transportation modes answering this question is less straightforward.
Appendix B
65
In the case of public transit and active transportation, the location of origins and destinations
plays a vital role in determining which modes can reasonably be used. The further the journey
from origin to destination, the less viable certain modes become. Active transportation is particularly limited based on trip distance, although what is considered a feasible distance will vary by individual. For both active and public transportation, the lack of adequate infrastructure (such as
sidewalks, bike lanes, and transit stops) in useful proximity to an individual’s origin and destination
will generally result in these modes being considered unviable.
In order to achieve the City of Edina’s goals of reducing VMT and increasing public transit commute ridership, more origins and destinations must be brought within useful proximity of public
and active transportation infrastructure, and additional infrastructure should be planned to in
crease sustainable transportation access to more residents and locations.
Appendix B
66Appendix B
Figure 26: Assessment of Mode Viability Flow Chart
Source: Authors
67
Consideration 2: Comparison of Modes
After determining which transportation modes can connect a given origin and destination, these
options can then be compared against one another to determine which is most attractive. In this
comparison, individuals may consider factors such as travel time, effort, comfort, flexibility, reliability, perceived safety, cost, and the directness of the route. While each of these elements may
influence mode choice, the relative importance of each will vary based on factors specific to each
individual (see Consideration 3).
Traditional economic models are often applied to understand this comparison process. While these models have limitations they still may serve as a useful tool for understanding the cost ben
efit analysis of transportation modes at an individual level.
Financial Cost and Time Cost
The cost of a transportation mode can generally be broken down into two categories: the actual
financial cost (cost of a transit pass, cost of gasoline, etc.) and the time cost of the mode. Equa
tions have been developed to help determine the total cost of various transportation modes, and then compare them against oneanother (see appendix B?). These equations often assign
a monetary value to an individual’s time, generally based on hourly pay rates. An assumption of
these models is that individuals are utility maximizing, and will choose the transportation option
that requires the least amount of resources (time and money). Relating to the core premise of this
section, it is generally assumed that the ‘cost’ of modes such as transit and active transportation must be competitive, if not less than, the ‘cost’ of driving in order for widespread modal shifts to
occur.
To calculate the cost of transportation modes, including the value of an individual’s time, the fol
lowing equation (or similar equations) may be applied:
Trip Cost = C + Twdw+Tvdv
Where:
C = monetary cost
Tw = walk/wait time
Dw = marginal disutility of walking/waiting
Tv = invehicle time
Dv = marginal disutility of invehicle time
Appendix B
68Appendix B
An important consideration with regards to these equations is that the relative value of one’s
time can vary significantly. If this value is based upon hourly wages (or annual salary), then high
income individuals will place greater value on transportation modes that reduce the amount of time required to make a trip. This is particularly important in the City of Edina, where the median household income is significantly higher than that of the Twin Cities metropolitan area.
Reliability
The reliability (real or perceived) of transportation modes is an important consideration when
comparing transportation options. If a user cannot reasonably assume that a particular mode
will be available when and where it is needed, then the utility of this mode will ultimately suffer. Reliability is especially important for public transportation, and doubly so when the frequency of
public transit is low. If a potential user is not confident that public transit will be available when
it is scheduled, the propensity to use this mode will likely decrease.
If Edina residents can be sure that public transit is a reliable option, then public transit use will likely increase. The future highfrequency public transit planned for the City of Edina, the E Line,
will ultimately achieve this, improving the reliability of public transit for those who make use of
this infrastructure. The E Line will provide shorter average wait times and the assurance that
transit vehicles will be available often.
Flexibility and Autonomy
The ability of a transportation mode to adapt and accommodate unforeseen circumstances is another consideration that may impact an individual’s mode choice. Unforeseen needs and
emergencies may require additional trips at a moment’s notice. The potential for these circum
stances to arise may lead Edina residents to opt for transportation modes that can adapt to
changing needs and conditions. While these events, by their very nature, are unpredictable, the potential of them occurring may be enough to sway mode choice to the transportation option that provides the greatest degree of flexibility and ability to operate ‘offscript’.
Mode Cost Comparison Example
Wage Assumptions
$24.00 per hour
$0.40 per minute
Car Transit Bike
Monetary Cost (C)$5 $3 $0
Walk/Wait/Park Time 5 minutes 20 minutes 5 minutes
Travel Time 15 minutes 40 minutes 60 minutes
Car Transit Bike
Monetary Cost (C)$5 $3 $0
Walk/Wait/Park Time 5 x $0.40 = $2 20 x $0.40 = 16$5 x $0.40 = $2
Travel Time 15 x $0.04 = 6$0 minutes 60 * $0.40 =$24
Total $130.00 $27 $26
Trip Cost = C + Twdw+Tvdv
69Appendix B
Comfort
The level of comfort associated with different transportation options may also influence mode
choice. For public transportation within Edina, both the experience on board the transit vehi
cle itself, as well as the experience of accessing and waiting at a transit stop, may be consid
ered when analyzing the comfort of the mode. For active transportation,infrastructure and the
surrounding built environment within Edina directly influence the comfort of the journey. The presence of dedicated pedestrian and cycling infrastructure can help to increase comfort, with
levels of comfort generally increasing with the degree of separation from automotive traffic.
Safety
The relative safety, real or perceived, of transportation modes is also a factor that may be consid
ered when comparing options. The safety of active transportation modes is largely dependent upon the infrastructure available, with separation from automotive traffic being a key determinant. The perception that public transportation is unsafe, whether on board transit vehicles
or at transit stops, may also result in lower usage by Edina residents. In order to make sustain
able modes competitive with single occupant vehicles within Edina, significant efforts must be
made to improve the safety of public transit and active transportation.
In order to make sustainable modes of transportation competitive with single occupant vehi
cles within Edina, interventions should be employed which reduce the time and cost of desired
modes, and improve their overall reliability, flexibility, comfort, and perceived safety. While less
politically palatable, efforts to dramatically reduce VMT must also include interventions which lessen the convenience of single occupant vehicles.
Consideration 3: Weighing Personal and Social Factors
Personal preferences, limitations, thresholds, and social influences are also key elements which
impact the transportation mode choice process, often directly influencing the viability of modes
(Consideration 1) and mode comparison (Consideration 2). Table x provides a list of personal and societal factors which may influence the transportation mode decision by Edina residents,
as well as a brief description of each variable. Table x identifies how these various personal and
societal factors may interact with Consideration 2 Comparison of Modes elements.
70Appendix B
Table 18: Personal and Societal Factors influencing Tranportation Mode Choice
Personal or Societal Factor Description
Personal Ability or Lim
itations
Individuals with physical disabilities or limitations may require transportation
modes that offer specialized accessibility features, such as lowfloor buses or ele
vators at train stations. Personal health conditions or fitness levels can also dictate
the suitability of active modes like walking or cycling.
Value of Time An individual's perception of the value of their time can greatly influence the com
parison of modes from a cost and time perspective. Individuals who place a higher
value on their time may opt for faster modes, such as driving, to minimize travel
time.
Economic Status Economic status influences transportation choices by determining what options are financially viable. Higherincome individuals may be able to afford private vehicles more easily, while the initial investment required for this option may be cost prohibitive for lowerincome individuals.
Safety Threshold Each individual has a certain degree of risk tolerance. If the perceived safety of a
particular mode exceeds this threshold then the individual will likely be hesitant to
utilize that particular mode, or may simply consider it unviable.
Comfort Threshold Similar to Safety Thresholds, each individual has a certain degree of discomfort or inconvenience that is considered acceptable. If a particular mode of transportation exceeds this threshold the likelihood of use will suffer.
Trip Specific Require
ments
Tripspecific requirements, such as the need to transport goods or passengers can
influence mode choice. Depending upon the requirements of a trip, certain modes
may be insufficient to accommodate specific needs.
Need for Autonomy/
Flexibility
Some individuals may require higher degrees of flexibility and autonomy in their
day to day life, due to a variety of potential considerations. Individuals who place a
higher degree of value on autonomy and flexibility may only consider transporta
tion modes that can accommodate these needs.
Peer Influences Peer influences can shape transportation preferences, as individuals may choose similar modes to those used by friends or colleagues, whether it's carpooling, biking, or using public transit.
Social Stigmas Social stigmas attached to certain modes of transportation can deter their use;
for example, some may avoid public transit due to negative perceptions, or others
might avoid driving due to environmental concerns.
71Appendix B
Table 19: Comparison of Modes and Personal and Social Factors Interaction
Comparison Consideration
Financial and Time Cost Financial Status
Value of Personal Time
Government/Employer Incentives
Reliability Comfort Threshold
Trip Specific Requirements
Flexibility and Autonomy Trip Specific Requirements
Personal need for flexibility and autonomy
Knowledge of Existing Conditions
Comfort Comfort thresholdStigmas Associated with Transportation Mode
Safety Safety thresholdPhysical Ability Stigmas Associated with Transportation Mode
Convenience Value of Personal Time
Trip Specific Requirements
Additional Consideration
A primary assumption of our Transportation Decision Making Framework and proposed inter
ventions is that individuals are generally rational and seek to maximize utility. In other words, it is
assumed that people will ultimately choose the transportation option that has the lowest relative ‘cost’ and the highest relative benefit. Despite this concept being common in traditional econom
ics and much of the literature on travel behavior, we view this as a key limitation of our model. The
fields of behavioral psychology and behavioral economics have demonstrated that oftentimes
people do not act in a fashion that would be considered ‘rational’ due to a wide variety of factors
such as cognitive biases, past habits, and social influences. This understanding is crucial when designing interventions to promote sustainable transportation, as improving the viability and at
tractiveness of sustainable transportation may not suffice to dramatically change travel behaviors.
However, we believe that improving the viability and attractiveness of desired modes is a neces
sary first step. Once active transportation and public transit is viable and competitive with travel
by car, softer interventions informed by behavioral psychology and behavioral economics may be deployed to maximize the impact of our recommendations.
Section References
1. Kittelson & Associates, Inc., Parsons Brinckerhoff, KFH Group, Inc., Texas A&M Transportation Institute,
Arup, Transit Cooperative Research Program, Transportation Research Board, and National Academies of
Sciences, Engineering, and Medicine. 2013. Transit Capacity and Quality of Service Manual, Third Edition.
National Academies Press. Washington, D.C.: Transportation Research Board. https://nap.nationalacade
mies.org/catalog/24766/transitcapacityandqualityofservicemanualthirdedition.
2. Watson, Kathleen B., Susan A. Carlson, Tiffany HumbertRico, Dianna D. Carroll, and Janet E. Fulton. 2015.
“Walking for Transportation: What Do U.S. Adults Think Is a Reasonable Distance and Time?” Journal of
Physical Activity and Health 12 (s1): S53–61. https://doi.org/10.1123/jpah.20140062.
3. “National Bicycling and Walking Study.” n.d. https://safety.fhwa.dot.gov/ped_bike/docs/case1.pdf
72
Appendix C - Peer Cities Analysis Methodology
Peer City data were obtained from Urban Footprint using the various analysis modules provided.
The criteria are chosen for their potential relevance to this study. For the similarity analysis, each
peer city is ranked based on how close its variables are to Edin. This step was repeated for each of the criterias shown in the table below. These rankings are then averaged and aggregated into a combined ranking where a smaller value indicates that a city is more similar to Edina.
Appendix C
Table 20: Peer City Analysis Comparison Criteria Code
Code Description
A Total Population
B Population Density
C Single Family Housing Ratio
D Travel Mode Share Auto
E Annual Vehicle trips per Capita
F Annual Residential VMT per capita
G Annual GHG Emissions by Vehicles
H Travel Mode Share Transit
73Appendix C
Table 21: Criteria Ranking
Figure 27: Total Population of each peer city
Source: 2018-2022 American Community Survey
Similarity Criteria
Table
A B C D E F G H Total
Edina 1 1 1 1 1 1 1 1 8
St Louis Park 3 9 10 11 6 6 5 11 61
Minnetonka 2 13 3 5 16 13 12 2 66
Golden Valley 15 10 2 2 10 4 4 6 53
Eden Prairie 5 14 7 10 4 14 14 10 78
Plymouth 13 6 11 15 5 8 10 5 73
Richfield 8 18 4 6 12 10 7 18 83
Bloomington 17 5 9 13 3 17 15 12 91
Eagan 7 11 13 12 2 12 11 4 72
University Park, TX 14 19 5 7 18 15 13 7 98
Lee's Summit, MO 19 17 8 9 8 18 19 8 106
Bellevue, WA 20 4 17 19 11 5 8 15 99
Carmel, IN 18 12 6 3 9 16 16 9 89
Menlo Park, CA 10 2 19 17 20 9 18 14 109
Newton, MA 16 15 16 18 17 11 6 16 115
Shaker Heights, OH 12 8 15 4 7 19 17 17 99
Manhattan Beach, CA 9 20 20 16 19 7 2 3 96
Bethesda, MD 6 16 12 20 15 2 3 20 94
Littleton, CO 4 3 18 8 13 3 9 13 71
Highland Park, IL 11 7 14 14 14 20 20 19 119
74Appendix C
Figure 28: Residential Single Family Housing Ratio
Source: Urban Footprint
Figure 29: Drive Alone Mode Share for Peer Cities
Source: 2018-2022 American Community Survey
75Appendix C
Source: Urban Footprint
Figure 30: Average Annual Trips per Capita for Peer Cities
Source: Urban Footprint
Figure 31: Annual GHG Emissions of Peer Cities
76Appendix C
According to Table 21, Edina is generally more similar to fellow suburban cities in the Twin Cities
metropolitan area compared to cities in other regions. Among cities outside of Minnesota, Little
ton, CO is the city that is most similar to Edina. Golden Valley, MN is the most similar in total. In terms of the charts, two results in particular stand out: Edina has a very high ratio of single family to total housing ratio as well as a very high single occupancy vehicle mode share, leading the
statistics among peers in the Twin Cities. These two points are important because the former
suggests that Edina is a great target for strategic increases to density. This is because communi
ties heavily zoned for single family housing (R1) tend to have lower residential densities. The latter showcases that Edina is still very auto dependent and increased transit focus may prove useful to VMT reduction goals called for in the CAP. Among peer cities, the following have their own Cli
mate action plan (CAP) which mentions a reduction of VMT or similar as an explicit goal:
• Minnetonka, MN
• Bloomington, MN
• Bellevue, WA
• Highland Park, IL
• Carmel, IN
• Menlo Park, CA
Minnetonka’s CAP shows that residential electricity usage was the highest source of GHG emission before dropping significantly in recent years, leaving passenger vehicles as the highest
source at 44% (compared to Edina’s 41% emissions share from transportation). In the report, en
couraging shift to a cleaner mode of transportation as well as increased support for EV are iden
tified as “high impact” strategies to reduce travel emissions. Support for telecommuting is also
mentioned as well.
For the outofstate peer cities, Bellevue is working with the Washington state government to
incorporate a commute trip reduction plan (CTR) with the goal of reducing vehicle emissions. A
CTR is only implemented in a handful of US states and its purpose is to encourage employers to
support cleaner modes of commuting or to eliminate transportation emissions altogether with telecommuting support. The plan set the target for VMT to be reduced by 18% and drivealone
auto use to be reduced by 16.3% over 4 years.
Highland Park, IL is slightly different from the cities already showcased in that its residential
electricity usage remains the highest GHG source by far. The plan also mentioned that Highland Park’s emissions are generally lower than the national average. The main strategies proposed to
reduce autobased emissions are support for EV and cleaner modes such as biking and public
transit.
The Carmel, IN CAP incorporates 7 proposed strategies within its transportation section to reduce emissions. At present, only 2 strategies are actively being implemented: one is a bikeshare
program which seeks to encourage mode shift to biking and provide reliable infrastructure for
the mode itself; the other is a focus on increasing mixeduse zoning and higher densities so that
as many residents as possible are within accessible distance of destinations and are able to use
cleaner modes of transportation. One interesting observation with this CAP is that public transit, such as bus and rail, are barely mentioned despite the fact that Carmel is one of the two peer cit
ies reviewed in this study to lack the mode altogether as shown in Figure 7 of the report.
77Appendix C
The Menlo Park CAP targets a VMT decrease goal of 25% by 2030 through zoning changes and
improving urban infrastructure for easier bike access through a new master street plan. The CAP specifically calls for upzoning efforts to be concentrated along existing transit corridors which aligns with the findings and recommendations of this study.
The table below showcases the presence of land use or density changes in the fellow metro area
peer cities’ comprehensive plans:
Peer City Density Changes
Minnetonka Corridor Targeted (I394, Ridgedale, Crosstown)
Source: 2040 comprehensive guide plan | city of Minnetonka, MN. Ac
cessed May 3, 2024. https://www.minnetonkamn.gov/government/departments/communitydevelopment/planningzoning/comprehen
siveguideplan/2040comprehensiveguideplan.
Plymouth Transit Corridor TargetedSource: Comprehensive plan | city of Plymouth, MN. Accessed May 3, 2024.
https://www.plymouthmn.gov/departments/communityeconomicdevelop
ment/planning/comprehensiveplan.
Eagan Both Targeted and Citywide upzoning (Metro Red Line Stations)
Source: “2040 Comprehensive Guide Plan.” City of Eagan Home. Accessed
May 3, 2024. https://cityofeagan.com/2040plan.
Golden Valley Corridor Targeted (I394, Golden Valley Rd Transit corridor)
Source: “2040 Comprehensive Plan.” 2040 Comprehensive Plan | Golden Val
ley, MN. Accessed May 3, 2024. https://goldenvalleymn.gov/191/2040Compre
hensivePlan.
Eden Prairie Transit corridor focused (Metro Green Line SW Expansion)
Source: Aspire Eden Prairie 2040 | city of eden prairie. Accessed May 3, 2024.
https://www.edenprairie.org/citygovernment/departments/communitydevelopment/planning/aspireedenprairie2040.
Richfield Corridor Targeted (Lyndale and 66th, Penn, Cedar, I494)Source: Richfield, MN. Accessed May 3, 2024. https://www.richfieldmn.gov/departments/community_development/planning_and_zoning/comprehen
sive_plan/index.php.
St. Louis Park Around specific neighborhoods, transit corridors, and employment areas.
Source: Comprehensive plan 2040 | St. Louis Park, MN. Accessed May 3, 2024.
https://www.stlouisparkmn.gov/ourcity/comprehensiveplan.
Bloomington Around Transit Corridors
Source: “Forward 2040 Documents.” City of Bloomington MN, July 13, 2023.
https://www.bloomingtonmn.gov/plan/forward2040documents.
Table 22: Density Upzoning Strategies Among Peer Cities
78
Appendix D - Scenario Analysis Methodology
Urban Footprint Software
Overview
Urban Footprint is a software application that allows urban planners to assess the impact of zon
ing changes by creating and analyzing scenarios. This tool integrates numerous regularly updat
ed datasets into a userfriendly map interface. At its core is the Base Canvas that is generated
upon the creation of a project and reflects the existing built environment. Using the Base Canvas as a baseline, Urban Footprint can run different analyses using a set of modules to see how zon
ing changes affect certain metrics. The current analysis modules include Emissions, Energy Use,
Household Cost, Land Consumption, Risk and Resilience, Transportation, Walk and Transit Ac
cessibility, and Water Use. For our study, we used the Base Canvas, Transportation Module, and
Emissions Modules to collect data we analyzed during our Zoning Scenario Analysis. This helped to answer the question of ‘Which Approaches to Density are Most Effective?’ The following is the
methodology used by Urban Footprint to calculate and create the Base Canvas, Transportation
Analysis Module, and Emissions Analysis Module.
Base Canvas
Urban Footprint’s Base Canvas is a geospatial dataset that reflects current land use and demographic information at the parcel or census block level. It serves as the foundation for future
scenario development and analysis within the platform and is updated quarterly. This dataset
includes various attributes like land use, demographics, and development characteristics for
each parcel or census block. The Base Canvas creation process incorporates data from a variety
of sources such as census data, parcel provider data, pointofinterest data, and road data. This process involves steps like standardizing geometries, translating land use codes, allocating values
for attributes like dwelling units and population, and calculating intersection density. The result is
a comprehensive dataset that provides essential context for urban planning and analysis within
Urban Footprint1.
Transportation Analysis Module
Urban Footprint’s Transportation Analysis Module is a comprehensive tool for evaluating the impacts of land use and transportation scenarios. It generates estimates for various metrics, includ
ing vehicle miles traveled (VMT), modespecific trips, transportation costs, and greenhouse gas
(GHG) emissions. The module utilizes the MixedUse Development (MXD) method, which quanti
fies the effects of urban form, transportation, supply, and management policies on travel behav
iors through statistical models based on the “8 Ds.” These factors include density, diversity, design, destination accessibility, distance to transit, development scale, demographics, and demand
management. The MXD model assesses the impacts of scenario modifications on public transpor
tation networks, land use, and regional development patterns. The core of the MXD method has
three key components: trip generation, distribution, and mode choice modeling. These are used
to estimate VMT per household and capita, modespecific trips, GHG emissions, and household costs2. Figure 32 shows the flow between inputs and outputs for the Transportation Analysis.
Appendix D
79
Emissions Analysis Module
Urban Footprint’s Emissions Analysis Module provides estimates for greenhouse gas (GHG) emis
sions linked to passenger vehicle transportation, building energy, and water use. It highlights
differences in GHG emissions across various land use scenarios, influenced by travel behaviors and building programs affecting vehicle miles traveled (VMT), energy consumption, and water usage.
Additionally, the module calculates criteria pollutant emissions from passenger vehicle transpor
tation, including nitrogen oxides (NOx), particulate matter (PM2.5 and PM10), sulfur oxides (SOx),
carbon monoxide (CO), and volatile organic chemicals (VOC). These emissions are calculated
based on average permile factors, offering comparative insights at the project scale rather than localized emissions impacts. The emissions estimation process considers outputs from the Trans
portation, Energy Use, and Water Use modules, along with input assumptions regarding vehicle
performance, transportation fuel emissions, and electricity and natural gas emissions.4 Figure 33
depicts the module’s analysis flow from inputs to outputs for emissions.
Appendix D
Figure 32: Transportation Analysis Flow3
Source: Urban Footprint3
Figure 33: Emissions Analysis Flow5
Source. Urban Footprint4
80
Impact Score
The Impact Score Formula serves as a vital tool in evaluating the effectiveness of zoning scenari
os by using the outputs of the Urban Footprint analyses. It begins by computing the percentage
change from the base scenario for Density (DU/Acre), VMT/capita, Transit Ridership, and GHG/cap
ita. Subsequently, these percentage changes for VMT/capita, Transit Ridership, and GHG/capita are
divided by the percentage change in Density (DU/Acre), as outlined in Figure 27. It is important to note that the percentage change in VMT/capita and GHG/capita are negative as Edina is seeking
to decrease these metrics, not increase them. The resulting Impact Scores offer a rough estimate
of the impact on each metric for every percentage increase in density. As an example, a one per
cent increase in Citywide density that decreases Citywide VMT/capita of 0.5% would receive an im
pact score of 0.50. These Impact Scores were instrumental in shaping our recommended zoning scenarios, highlighting the most effective approaches from our initial zoning scenarios.
The development of the Impact Score stemmed from discussions within the team regarding
scenario zoning methodology. One perspective advocated for a uniform 4% increase in density
citywide to ensure compatibility across all zoning scenarios. Conversely, another viewpoint argued
that different zoning approaches necessitate varying levels of densification, cautioning against artificially constraining scenario zoning to a fixed density increase. The Impact Score emerged as
a solution to this discourse, enabling scenario zoning without limitations while providing a stan
dardized tool for comparing all scenarios based on density.
Appendix D
Scenario Methodology
Scenario A - Base Scenario
Scenario A is based upon Urban Footprint’s base canvas which represents the current property
conditions and zoning (See Appendix D for Urban Footprint’s Methodology). The base canvas was
analyzed for accuracy through three different methods. The first method involved a visual comparison between it and Edina’s 2040 Comprehensive Plan to make sure they mostly aligned with
each other. The second method involved a visual comparison between the base canvas and the
satellite image of Edina as seen on Google Maps. This was used to identify various parcels that
were mismarked on the base canvas. The most common mismarking was of various parcels that
were marked as vacant residential but were instead parks/open spaces or lakes/ponds. The final method involved comparing the estimated population against the 2020 Census estimates and
working with an advisor from Urban Footprint to account difference. The last method was espe
cially important as the base population of Edina was estimated at around 45,000 people com
pared to the 2020 Census which estimated around 53,000 people. Thanks to the advice of the ad
visor, we were able to find two major issues with the base canvas which when corrected brought the total estimated population to around 51,000, which was within what we thought was a good
margin of error (Figure 35).
Figure 34: Impact Score Formula
Source: Authors
81Appendix D
Figure 35: Base Scenario Map
Source: Authors created with Urban Footprint
82Appendix D
Scenario B - City Wide Density
• Scenario B-1 was created to represent a 4% increase in density, aligning with the goal outlined
in Edina’s CAP. To achieve this, a filter was applied to the Base Canvas, targeting all residen
tial parcels within Edina. This process occurred in three steps representing three categories of
residential types, ‘Singlefamily attached,’ ‘Singlefamily detached,’ and ‘Multifamily’ categories.
Each step involved modifying the residential attributes of each respective housing type cate
gory to reflect a 4% increase in dwelling units. This adjustment was calculated by dividing the
current number of dwelling units by the gross acre (acres) to determine the current density,
then multiplying it by 1.04 to signify the 4% increase. Additionally, a square footage per unit
(ft2/unit) metric was necessary, which was determined by using the ‘Mean’ function in the data
table to compute the average parcel area for each respective category. Following this process
across all three housing type categories, the resultant scenario depicted an approximate 4%
increase in density (DU/Acre).
• Scenario B-2 was created similarly to B1 to see what the doubling of Edina’s CAP Density goal
would have on the respective metrics. We also wanted to see if there were any multiplier ef
fects from having a higher density in terms of increasing the metric impact for every percent
age increase in density. The same process was used as B1, except that instead of multiplying
the current density by 1.04, it was instead multiplied by 1.08 to signify an 8% increase in density
citywide. The resultant scenario depicted an approximate 8% increase in density (DU/Acre).
• Scenario B-3 was specifically developed at the request of Edina City Staff, who requested a
scenario to explore the impact of densifying only R1 zoned residential parcels to achieve a 4%
increase in density (DU/acre) citywide. After experimenting with various density percentages, it
was determined that a 6.95% increase in density for R1 zoned residential parcels achieved the
desired 4% overall density increase citywide. Following the methodology employed in other
B scenarios, a 6.95% increase in density was applied to both the ‘Singlefamily attached’ and
‘Singlefamily detached’ categories, which reflects R1 zoned residential parcels. The resultant
scenario depicts an approximate 4% increase in citywide density focused solely on R1zoned
residential parcels.
Scenario C - ‘Areas of Change’
• Scenario C-1 was created by increasing density and diversifying zoning types in Edina’s ‘Areas
of Change’ as identified in Edina’s 2040 Comprehensive Plan (Figure 36). The first step was
uploading the shape of the ‘Areas of Change’ into Urban Footprint to form an outline of the
areas in question. Then parcels adjacent to major roads were upzoned with “Main Street Com
mercial/MU High” with a density of 66.4 DU/acre. Surface parking lots were either removed or
consolidated into “Parking Structure/MU” with a density of 18.0 DU/acre. Finally, parcels not on
the main streets that were residential were upzoned into either “Garden Apartments” with a
density of 25.6 DU/acre, “Suburban Townhome” with a density of 20.2 DU/acre, or “Duplex” with
a density of 8.0 DU/acre.
• Scenario C-2 was created building upon Scenario C1 with the expansion of the ‘Areas of
Change’ by a 500foot buffer (Figure 37). Within these new buffer areas, parcels along major
arterial roads were zoned “Main Street Commercial/MU Low” with a density of 48.0 DU/acre. All
other singlefamily residential parcels were upzoned to “Duplex” with a density of 8.0 DU/acre.
83Appendix D
Figure 36: Scenario C-1 Map
Source: Authors created with Urban Footprint
84Appendix D
Figure 37: Scenario C-2 Map
Source: Authors created with Urban Footprint
85Appendix D
Scenario D - Mode Shift Toward Sustainable Transportation
• Scenario D-1 is centered on increasing density along the future E Line Corridor in Edina (Figure
38). This involved the creation of shapefiles for both the E Line Route and planned bus stops
within Edina using ‘geojson.io’. Following the importation of the shapefiles, buffers of 500 feet
and a quarter mile were generated to guide zoning decisions. Within the 500foot buffer and
along major roads like France Ave., parcels were zoned as “Main Street Commercial/MU High”
with a density of 66.4 DU/acre. Residential parcels situated within the 500foot buffer but not
adjacent to main roads were rezoned as either “Garden Apartments” with a density of 25.6 DU/
acre, “Suburban Townhome” with a density of 20.2 DU/acre, or “Duplex” with a density of 8.0
DU/acre. Lastly, within the quarter mile buffer, all other singlefamily residential parcels were
upzoned to “Duplex” zoning with a density of 8.0 DU/acre.
• Scenario D-2 expands upon scenario D1 by increasing zoning along an EastWest corridor en
compassing 50th St., Vernon Ave., and Lincoln Dr., alongside the future E Line corridor (Figure
39). This corridor corresponds to a reimagined Metro Transit Route 46, stretching from the fu
ture Opus Station (Green Line Extension) in Minnetonka through Edina and to 46th St Station
(Blue Line) in Minneapolis. This is compared to the current Metro Transit Route 46, which goes
from 46th St Station (Blue Line) and terminates in the Grandview neighborhood of Edina. This
reimagined Metro Transit Route 46 follows the same routing of Route 46 as it was in 2019 and
passes through two existing ‘Areas of Change’. To create scenario D2, the previous scenario,
D1, was copied over to serve as the foundation. Then shapefiles were created for a reimagined
Metro Transit Route 46, including a potential route and bus stops. Buffers of 500 feet and a
quarter mile were created based on the potential bus stops. Then the same zoning methodol
ogy as used in D1 for the E Line was employed along the EastWest Corridor. Finally, a check
was done to ensure that the resulting average density (DU/acre) within a quarter mile of the
potentially reimagined Metro Transit Route 46 met Metro Transit’s density requirements of 15
DU/acre for ABRT service. As zoned, the scenario produced an average density of 19 DU/acre
within the EastWest corridor, qualifying for much improved ABRT service from Metro Transit.
• Scenario D-3 focuses on densification around active transportation facilities using the Region
al Bicycle Transportation Network as a proxy (Figure 40). The Regional Bicycle Transportation
Network (RBTN) was chosen as a proxy for active transportation due to it being a highquality
regional network. The scenario was implemented utilizing a shapefile containing the RBTN in
Edina that was provided by Edina City Staff and uploaded to Urban Footprint. Subsequently, a
buffer of 500 feet along the RBTN was created. Within this buffer, all singlefamily residential
parcels were rezoned to “Duplex”, which has a density of 8.0 DU/acre.
• Scenario D-4 combined all of the above Mode Shift Toward Sustainable Transportation scenar
ios using the same zoning strategy (Figure 41).
86Appendix D
Figure 38: Scenario D-1 Map
Source: Authors created with Urban Footprint
87Appendix D
Figure 39: Scenario D-2 Map
Source: Authors created with Urban Footprint
88Appendix D
Figure 40: Scenario D-3 Map
Source: Authors created with Urban Footprint
89Appendix D
Figure 41: Scenario D-4 Map
Source: Authors created with Urban Footprint
90Appendix D
Scenario E - Essential Destinations
• Scenario E-1 was created to test the impacts of upzoning residential lots within a quarter mile
of all public schools in Edina (Figure 42). Within this area, single family detached parcels were
upzoned to duplexes, effectively doubling the density of these lots. The underlying concept be
hind this scenario was to upzone around schools to increase the likelihood that students would
be able to walk to school rather than being dropped off by their parents. Edina’s schools are
a source of pride for the City and we wanted to leverage their strength through the enabling
of more families to live in close proximity to the education and green space that Edina Public
Schools offer.
• Scenario E-2 was created to test the impacts of upzoning residential lots within a quarter mile
of retail and employment hubs in the City (Figure 43). The identified areas were largely similar
to the Areas of Change, except that this scenario expanded them further out, including exist
ing single family detached lots. Within this area, single family detached parcels were upzoned
to duplexes, effectively doubling the density of these lots. Single family attached lots were con
verted to “Suburban Townhome” zoning, with a density of 10.2 DU/acre. Existing multifamily
lots were converted to “Suburban Multifamily”, with a density of 44.3 DU/acre.
• Scenario E-3 was created to test the impacts of upzoning residential lots within a quarter mile
of major healthcare facilities in the City (Figure 44). These identified areas were also largely
similar to the Areas of Change, but not as large an area as scenario E2. As with the previous
scenario, single family detached parcels were upzoned to duplexes, effectively doubling the
density of these lots. Single family attached lots were converted to “Suburban Townhome”
zoning, with a density of 10.2 DU/acre. Existing multifamily lots were converted to “Suburban
Multifamily”, with a density of 44.3 DU/acre.
• Scenario E-4 was created to test the impacts of upzoning residential lots within a quarter mile
of large parks in the City (Figure 45). This scenario resulted in upzoning a large amount of the
current single family detached lots in the City. As with the previous scenarios, single family de
tached parcels were upzoned to duplexes, effectively doubling the density of these lots. Single
family attached lots were converted to “Suburban Townhome” zoning, with a density of 10.2
DU/acre. Existing multifamily lots were converted to “Suburban Multifamily”, with a density of
44.3 DU/acre.
• Scenario E-5 combined all of the above essential destination scenarios using the same zoning
strategy (Figure 46).
91Appendix D
Figure 42: Scenario E-1 Map
Source: Authors created with Urban Footprint
92Appendix D
Figure 43: Scenario E-2 Map
Source: Authors created with Urban Footprint
93Appendix D
Figure 44: Scenario E-3 Map
Source: Authors created with Urban Footprint
94
Figure 45: Scenario E-4 Map
Source: Authors created with Urban Footprint
95Appendix D
Figure 46: Scenario E-5 Map
Source: Authors created with Urban Footprint
96Appendix D
Recommended Scenarios
• The ‘Basic’ scenario was created by integrating Scenario C1 (‘Areas of Change’) and D1 (E
Line Corridor), which were identified as the most effective scenarios based on the Impact
Score results (Figure 47). Initially, the scenario started with a copy of Scenario C1 followed by
implementing the same methodology as previously stated in Scenario D1 to upzone around
the E Line Corridor.
• The ‘Enhanced’ scenario evolved from the ‘Basic’ scenario by incorporating the subsequent
most effective scenarios, namely C2 (‘Areas of Change’ with a 500foot Buffer) and D2 (E Line
Corridor & EastWest Corridor) (Figure 48). Building upon the ‘Basic’ scenario, the ‘Basic’ sce
nario was first copied followed by implementing the same methodology as previously stated
in Scenarios C2 and D2. Since both C2 and D2 were built upon the previous scenarios C1
and D1, themselves integrated into the ‘Basic’ scenario, only upzoning in the 500foot Buffer
and along the EastWest corridor needed to occur.
• The ‘Preferred’ scenario represents our highest recommendation to Edina and combines the
greatest number of effective zoning scenarios. Expanding upon the ‘Enhanced’ scenario, it
incorporates Scenarios D4 (All of the Mode Shift Scenarios) and E3 (Major Healthcare Facili
ties) (Figure 49). To develop this scenario, the ‘Enhanced’ scenario served as the foundational
template. Employing the same methodology outlined previously in Scenarios D4 and E4,
adjustments were made. Given that D4 encompassed all the Mode Shift scenarios and the
‘Enhanced’ scenario already integrated two of the three focuses, only upzoning around the
RBTN needed to occur.
97Appendix D
Figure 47: ‘Basic’ Scenario Map
Source: Authors created with Urban Footprint
98Appendix D
Figure 48: ‘Enhanced’ Scenario Map
Source: Authors created with Urban Footprint
99Appendix D
Figure 49: ‘Preferred’ Scenario Map
Source: Authors created with Urban Footprint
100
Scenario Data
Table 23 comprises all the data, including calculated Impact Scores, for all the scenarios for the
zoning scenario analysis.
Appendix D
Scenario Density
change
from base
(%)
VMT/capita
change (%)
VMT Im-
pact Score
GHG/capita
change (%)
GHG Im-
pact Score
Transit
Ridership
change (%)
Transit Rid-
ership Im-
pact Score
B-1 3.85%-1.98%0.52 -4.07%1.06 -2.45%-0.64
B-2 7.41%-2.73%0.37 -5.88%0.79 -2.84%-0.38
B-3 3.85%-0.86%0.22 -2.65%0.69 -1.80%-0.47
C-1 26.81%-13.89%0.52 -13.04%0.49 12.53%0.47
C-2 32.18%-16.75%0.52 -16.08%0.50 13.64%0.42
D-1 28.52%-18.62%0.65 -12.30%0.43 12.60%0.44
D-2 44.01%-26.22%0.60 -19.88%0.45 15.64%0.36
D-3 22.20%-13.32%0.60 -13.45%0.61 8.45%0.38
D-4 48.29%-29.31%0.61 -24.47%0.51 16.01%0.33
E-1 5.08%-0.40%0.08 -2.39%0.47 -0.75%-0.15
E-2 28.17%-18.47%0.66 -21.53%0.76 3.61%0.13
E-3 22.69%-16.96%0.75 -17.32%0.76 2.48%0.11
E-4 19.99%-10.20%0.51 -14.68%0.73 -0.53%-0.03
E-5 27.18%-12.63%0.46 -19.48%0.72 -1.33%-0.05
‘Basic’32.34%-17.29%0.53 -16.37%0.51 13.35%0.41
‘Enhanced’42.43%-22.09%0.52 -24.41%0.58 13.34%0.31
‘Preferred’45.75%-25.01%0.55 -27.07%0.59 13.87%0.30
Table 23: Zoning Scenario Analysis Data
Source: Authors calculations with Urban Footprint
Section References
1. Urban Footprint, “Base Parcel Canvas Creation,” January 17, 2024, https://help.urbanfootprint.com/methodology-documentation/base-parcel-canvas-creation.2. Urban Footprint, “Transportation Analysis,” January 12, 2024, https://help.urbanfootprint.com/methodol-ogy-documentation/transportation-analysis.3. Urban Footprint, “Transportation Analysis,” January 12, 2024, https://help.urbanfootprint.com/methodol-ogy-documentation/transportation-analysis.4. Urban Footprint, “Emissions Analysis,” March 5, 2020, https://help.urbanfootprint.com/methodolo-gy-documentation/emissions-analysis.5. Urban Footprint, “Emissions Analysis,” March 5, 2020, https://help.urbanfootprint.com/methodolo-gy-documentation/emissions-analysis.
101
Glossary of Terms
Abbreviations
• ADU: Accessory dwelling unit
• ABRT: Arterial bus rapid transit
• BRT: Bus rapid transit
• CAP: Climate Action Plan
• GHG: Greenhouse gas
• Log: Logarithm
• SOV: Singleoccupancy vehicle
• TDM: Travel demand management
• TOD: Transitoriented development
• VMT: Vehicle miles traveled
Glossary of Terms
• ADU (Accessory Dwelling Unit): An ADU is a selfcontained residential unit with its own living
room, kitchen, and bathroom on the same parcel of land as a singlefamily dwelling. An ADU
can be located within, attached to or detached from the main residence.
• Accessibility: With regard to transportation, accessibility refers to the ease or convenience
with which a person can use that form of transportation. Components of accessibility include
distance, quality of infrastructure, and accommodations for people with mobility needs.
• Active transportation: Travel made by walking, rolling, cycling, or otherwise outside of a
motorized vehicle.
• Adjusted R-squared: A form of the Rsquared statistic used in multivariate regressions, which
adjusts the Rsquared value based on the number of independent variables that are used in
the analysis. This is done because, mathematically, adding more independent variables will
always lead to a higher Rsquared value, regardless of if the added independent variables are
effective in explaining the dependent variables.
• Amendment: A formal, substantive change made to a document such as a comprehensive
plan or CAP.
• ‘Area of Change’: Geographic zones defined in Edina’s comprehensive plan, which are expect
ed and encouraged to experience increases in density.
• Arterial bus rapid transit (ABRT): A form of bus rapid transit wherein the buses run on
mixedtraffic streets, instead of in dedicated lanes.
• Binary: In statistical analysis, this refers to a variable where there are only two possibilities, usu
ally denoted as 0 or 1, or true or false.
• Bivariate: See “Regression”
Glossary of Terms
102
• Building massing: See “Massing”
• Built environment: Manmade structures, features, and facilities viewed collectively as an en
vironment in which people live, work, and play.
• Bus rapid transit (BRT): A form of rapid transit that uses buses instead of trains. Typically, BRT
routes have stateoftheart stations spaced further apart than typical bus routes, plus other
interventions designed to make the buses faster and more reliable. Oftentimes this includes
dedicated lanes. A BRT route that does not have a dedicated lane is known as ABRT (Arterial
Bus Rapid Transit).
• CAP (Climate Action Plan): Plan adopted by the City of Edina in 2021 that sets targets for met
rics such as greenhouse gas emissions, as well as a roadmap to achieving those targets.
• Causation: In statistical analysis, the quality of an independent variable directly affecting the
value of a dependent variable.
• Citywide density: This refers to interventions that increase the level of population density in all
parts of the city, as opposed to targeting specific nodes and corridors.
• Coefficient: In statistical analysis, a coefficient is a number that describes the relationship be
tween two variables, such as the slope or yintercept of a line of best fit.
• Commercial: A land use that includes retail and business activities such as stores, restaurants,
and offices.
• Commute: The trip that a person makes between their home and their place of employment
or education.
• Comprehensive plan: A plan formally adopted by a city that outlines the changes they intend
to make to zoning codes, built form ordinances, and other policies.
• Connectivity: In transportation planning, the quality of two or more places or transportation
routes being connected together.
• Correlation: In statistical analysis, the quality of lower values for one variable being distinctly
associated with lower or higher values for a second variable.
• Corridor: A transportation route connecting two or more places, such as a road, a railroad line,
or a walking path.
• Demographics: Statistics that describe the population and people of a given area.
• Density: A measure of the quantity of people or things within a unit of geographic area. See
also: “Population density”
• Dependent variable: In statistical analysis, a variable that depends on another variable.
• Developed acre: An acre of land which has been developed in some way, i.e., includes a build
ing or other manmade structure.
• Driving mode share: The share of people who commute to their place of employment or edu
cation using a personal vehicle. Often divided into “driving alone” and “carpooling.”
• Dwelling unit: A house, apartment, or condominium in which a person may reside.
• Greenhouse gas: Any gas that absorbs infrared radiation in the atmosphere. Greenhouse gas
ses include, but are not limited to, water vapor, carbon dioxide (CO2), methane (CH4), nitrous
oxide (N2O), hydrochlorofluorocarbons (HCFCs), ozone (O3), hydrofluorocarbons (HFCs), perflu
orocarbons (PFCs), and sulfur hexafluoride (SF6).
• Green Line Extension: Also known as the Southwest Light Rail, or Southwest LRT, this is a light
rail line running between Minneapolis and Eden Prairie that is currently under construction
Glossary of Terms
103
and expected to open in 2027.
• E Line: See “METRO E Line”
• Emissions: The release of a substance (usually a gas when referring to the subject of climate
change) into the atmosphere.
• Explanatory variable: See “Independent variable”
• Frequency: The length of time between consecutive buses or trains along a particular route.
• Function: A mathematical relationship in which a value for one variable is associated with ex
actly one value for another variable.
• Independent variable: In statistical analysis, a variable which does not depend on any other
variable.
• Infrastructure: The system of public works in a given place, such as roads, bridges, and utility
lines.
• Intervention: An action taken by a government to change a process or outcome.
• Land use: Land use refers to the total of arrangements, activities and inputs undertaken in a
certain land cover type (a set of human actions). The term land use is also used in the sense of
the social and economic purposes for which land is managed (e.g., grazing, timber extraction
and conservation).
• Linear regression: See “Regression”
• Locality: See: “Municipality”
• Local road: A road that is operated by a municipality or locality.
• Log: See: “Logarithm”
• Logarithm: The exponent that indicates the power to which a base number is raised to pro
duce a given number. In statistical analysis, a logarithmic function is often used to turn a
nonlinear line into a linear line.
• Massing: The size, shape, and bulkiness of a particular building or block.
• Methodology: A process or set of rules used to arrive at a conclusion.
• METRO E Line: An ABRT line running between Edina and Minneapolis that is scheduled to
open in 2025.
• Metropolitan area: A region consisting of a city or cities and its surrounding suburbs.
• Metropolitan Council: A governmental body that controls land use, transit, wastewater man
agement, and other policy areas in seven counties in the MinneapolisSt. Paul metropolitan
area.
• Metro Transit: The transit agency serving much of the MinneapolisSt. Paul metropolitan area,
and a subsidiary of the Metropolitan Council.
• Multi-family: A residential building that contains more than one unit, such as an apartment
building, a condominium building, or a duplex.
• Multivariate: See: “Regression”
• Municipality: The smallest form of government: a village, town, township, or city.
• Mixed-use: A zoning designation that allows buildings that include multiple uses, usually resi
dential as well as commercial or industrial.
• Mode: A form of transportation such as a personal vehicle, public transportation, or active
transportation.
Glossary of Terms
104
• Mode Share: The proportion of trips taken by a certain mode of transportation.
• Mode Shift: The process of shifting a proportion of total trips taken in a personal vehicle to
other modes.
• Net Zero Emissions: Refers to a community, business, institution, or building for which, on an
annual basis, all greenhouse gas emissions resulting from operations are offset by carbonfree
energy production.
• Node: A point in a transportation network where multiple corridors come together.
• Observation: In statistical analysis, a single data point.
• Outlier: In statistical analysis, an observation that contains a value significantly above or below
that of the other data points. Outliers are often excluded from statistical analyses.
• Paris Agreement: 2015 international agreement signed by 195 countries that sets targets for
climate change mitigation.
• Pedestrian: A person walking or rolling as a form of transportation.
• Peer city: A city that has similar qualities to the city in question (in this case Edina).
• Population Density: The number of people residing in a given unit of land area.
• P-value: In statistical analysis, a number from 0 to 1 indicating the probability that a given re
sult is not random.
• Qualitative analysis: Analysis involving numbers, statistics, and mathematics.
• Quantitative analysis: Analysis involving words, ideas, and subjective experiences.
• Regression: A type of statistical analysis that analyzes the relationship between a dependent
variable and one or more independent variables.
• Residential: A land use that includes homes where people can live.
• R-squared: In statistical analysis, a statistic that measures the extent to which a regression
analysis explains the variation in the relationship between two variables. 0 means that the re
gression analysis explains none of the variation, and 1 means that the regression explains all of
the variation, i.e., one can draw a straight line between two variables.
• Sample size: The number of observations in a regression analysis.
• Setback: The distance between a building and the public rightofway (i.e., a sidewalk or
street), often required by law.
• Significant: See “Statistical significance”
• Single-occupancy vehicle (SOV): A motorized vehicle, such as a car, which contains only one
person.
• Statistical significance: In statistical analysis, a result is considered statistically significant if
the pvalue, i.e., the chance of it being random, is less than a certain threshold, usually 0.05.
• Streetscaping: Aspects of street or road design that impact the subjective experience of pe
destrians, cyclists, drivers, and other users.
• Target: A goal that a city intends to achieve for a given metric.
• Targeted density: A strategy to increasing density that prioritizes development along or near
particular nodes and corridors, rather than citywide (i.e., everywhere all at once).
• TDM (Transportation Demand Management): the process by which expected vehicle traffic is
planned for and mitigated to reduce traffic impact of new development and incentivize mode
shift
Glossary of Terms
105
• Transit: Also known as public transportation, a form of transportation, usually run by the gov
ernment, which transports people in shared vehicles at a subsidized cost. Includes buses,
trains, trams, and subways.
• Transit mode share: The share of people who commute to their place of employment or edu
cation using transit, as opposed to driving, walking, cycling, or working from home.
• Transit-Oriented Development (TOD): Development of housing, retail, and other uses that is
centered around a transit station.
• Transportation mode: See “mode”
• Trend: The trend of a quantity measures its change over a time period, with a positive trend
value indicating growth in the quantity, and a negative value indicating a decrease. It is de
fined as the ratio of the change in the quantity over the time period, divided by the initial value
of the quantity, and is usually expressed either as a percentage or a fraction.
• Trip: An instance of a person traveling between two places.
• Trip type: The primary reason or purpose of a trip. This could include commuting, shopping,
running errands, or visiting friends or family.
• Upzoning: The process of amending a zoning code such that greater density and/or a wider
variety of land uses is permitted.
• Variable: In statistical analysis, a quality or quantity that tends to change from observation to
observation.
• Variation: The extent to which the value of a given variable changes from observation to ob
servation.
• VMT (Vehicle Miles Traveled): The total amount of miles traveled by all vehicles in the City,
typically calculated as an annual sum.
• Walking mode share: The share of people who commute by walking to their place of employ
ment or education.
• Zoning: The act or process of partitioning a city, town, or borough into zones reserved for dif
ferent purposes (such as residence or business).
Glossary of Terms
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