San Jose State University

Mineta Transportation Institute

Project 2122 May 2023 

Land Use Analysis on Vertiports Based on a Case Study of the San 
Francisco Bay Area 

Wenbin Wei, PhD 
Kerry Rohrmeier, PhD, AICP 
Tifany Martinez 
Michael Winans 
Heungseok Park 

Cover page image of modern helicopter on helipad 

transweb.sjsu.edu 

Mineta Transportation Institute 

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Report 23-13 

Land Use Analysis on Vertiports Based on aCase Study of the San Francisco Bay Area 

Wenbin Wei, PhD 
Kerry Rohrmeier, PhD, AICP 
Tiffany Martinez, Michael Winans, and Heungseok Park 

May 2023 

A publication of theMineta Transportation InstituteCreated by Congress in 1991 

College of BusinessSan José State UniversitySan José, CA 95192-0219 



TECHNICAL REPORT 
DOCUMENTATION PAGE 




1. Report No.
23-13 

2. Government Accession No. 

3. Recipient’s Catalog No. 

4. Title and Subtitle:Land Use Analysis on Vertiports Based on a Case Study of the San Francisco Bay Area 

5. Report DateMay 2023 

6. Performing Organization Code 

7. Authors 
Wenbin Wei: 0000-0001-9813-7555 
Kerry Rohrmeier: 0000-0002-5383-934XTiffany Martinez: 0000-0003-1303-4528Michael Winans: 0000-0002-3110-3024 
Heungseok Park: 0000-0003-4628-2317 

8. Performing Organization ReportCA-MTI-2122 

9. Performing Organization Name and AddressMineta Transportation InstituteCollege of BusinessSan José State UniversitySan José, CA 95192-0219 

10. Work Unit No. 

11. Contract or Grant No. 65A0660 

12. Sponsoring Agency Name and AddressCalifornia Department of Transportation1120 N Street Sacramento 
CA 95814 

13. Type of Report and Period Covered 

14. Sponsoring Agency Code 

15. Supplemental Notes 

16. Abstract 

Vertiport research and development trails in the emerging commercial air taxi sector known as Advanced Air Mobility (AAM).
Published scholarship remains limited as federal, state, and local governments have yet to create or implement policies for therapidly progressing larger AAM ecosystem, which is designed around autonomous electric vehicle takeoff and landing aircraft.
With the potential for frequent low altitude flights, long-range planning must demonstrate awareness, knowledge, and 
utilization of geographic information science to select safe and just vertiport locations. This study summarizes the AAM 
literature and offers planners a set of stakeholder-informed parameters to use in a no-cost preliminary GIS analysis when 
applied to urban, suburban, and exurban site suitability models. Parameters for this case study were identified under the 
considerations of safety, access, and equity for vertiport placement and given a high, medium, or low priority level to determinesite suitability. The goals of this study are to establish a framework for the systematic approach to vertiport site selection and 
to provide recommendations for how a region might plan its AAM network, regulations, or best practices. The approachestablished by this framework would ensure general consistency in AAM land use planning for a region while remaining flexibleenough to allow for other considerations that may differ between regions, such as local zoning or state regulations. The studyalso highlights the importance of integrating a focus on land use planning when implementing AAM, especially as it relates toa case study of the San Francisco Bay Area. 

17. Key WordsAdvanced air mobility (AAM), Land useplanning, GIS, Transportation planning 

18. Distribution Statement 
No restrictions. This document is available to the public through The NationalTechnical Information Service, Springfield, VA 22161. 

19. Security Classif. (of this report) 
Unclassified 

20. Security Classif. (of this page) 
Unclassified 

21. No. of Pages 75 

22. Price 

Form DOT F 1700.7 (8-72) 


Copyright © 2023 
by Mineta Transportation Institute 
All rights reserved. 

DOI: 10.31979/mti.2023.2122 

Mineta Transportation Institute College of BusinessSan José State University, San José, CA 95192-0219 

Tel: (408) 924-7560Fax: (408) 924-7565Email: mineta-institute@sjsu.edu 

transweb.sjsu.edu/research/2122 


ACKNOWLEDGMENTS 


The authors appreciate the great help, support, and guidance from: Dr. Hilary Nixon, the Mineta 
Transportation Institute (MTI) Research Director; Mr. Matthew Friedman, Mr. Patrick Miles, 
and Mr. Tarek Tabshouri, the three Division of Aeronautics Chiefs at the California Department 
of Transportation (Caltrans); and Mr. Nathan Loebs and Mr. Balwinder Tarlok, two Officials 
from the Division of Research, Innovation and System Information at Caltrans. 




CONTENTS 


Acknowledgments Page vi 
List of Figures Page viii 
List of Tables Page ix 
Executive Summary Page 1 
1. Introduction Page 5 
1.1 Research Goal Page 5 
1.2 Study Area Page 5 
1.3 Research Methodology and Approach Page 6 
1.4 Report Organization Page 8 
2. Literature Review Page 9 
2.1 Advanced Air Mobility Page9 
2.2 Vertiport Design Page 12 
2.3 AAM Models Page 13 
2.4 AAM Policy Page 15 
2.5 Transportation and Land Use Page 17 
3. Research Methodology and Modeling Methods Page 20 
3.1 Introduction Page 20 
3.2 General Procedure Page 20 
3.4 Data Format and Access Page 25 
4. Case Studies Page 26 
4.1 Introduction Page 26 
4.2 Urban: San Francisco Page 26 
4.3 Suburban: San Jose Page 29 
4.4 Exurban: Livermore Page 32 
4.5 East Bay & Peninsula Corridors Page 34 
5. Result and Recommendations Page 36 
5.1 Research Results and Implications Page 36 
5.2 Recommendations Page 37 
6. Summary and Conclusions Page 39 
6.1 Research Summary Page 39 
6.2 Further Studies Page 40 
Bibliography Page 41 
Appendix A. GIS Data Portal Layers Page 45 
Appendix B. Requested Data Layers Page 60 
Appendix C. Suitable Site Locations Page 61 
About the Authors Page 63

 
LIST OF FIGURES 


Figure 1. Study Area Map Page 6 
Figure 2. Project Workflow Page 7 
Figure 3. GIS Modeling Roadmap Page 21 
Figure 4. Zoning Aggregation Page 23 
Figure 5. Geoprocessing Workflow Page 25 
Figure 6. San Francisco Suitability Map Page 28 
Figure 7. San Jose Suitability Map Page 31 
Figure 8. Livermore Suitability Map Page 34 
Figure 9. Corridor Suitability Map Page 35 
Figure 10. Summary of Suitability Maps Page36 


LIST OF TABLES 


Table 1. Urban GIS Variables Page 27 
Table 2. Suburban GIS Variables Page 30 
Table 3. Exurban GIS Variables Page 33 
Table 4. Summary of Suitable Sites Page 37 


Executive Summary 


Objective 


The vertiport case study of the San Francisco Bay Area establishes a framework for a systematicapproach to vertiport site selection and recommendations for how a region might plan their 
Advanced Air Mobility (AAM) network using Geographic Information Systems (GIS). This 
approach offers consistency in AAM site selection for a region while remaining flexible enough to 
allow for other local considerations that may differ between regions such as zoning or community 
preferences. 

The study area encompassed the greater San Francisco Bay Area, which for this study included 
Alameda, Contra Costa, San Francisco, San Mateo, and Santa Clara Counties. This broad region 
was chosen to ensure the inclusion of a variety of different urban forms and built environments 
within a region that would likely have broad implementation of AAM where there is an air, rail, 
and transit network. This airspace already consists of commercial air traffic serving major airports 
within the region including San Francisco International Airport (SFO), San Jose International 
Airport (SJC), and Oakland International Airport (OAK), in addition to several smaller regional 
airports which mainly serve light General Aviation (GA) traffic. 

On the landside, San Francisco City and County represents urban areas having compact densities; 
Santa Clara County, which includes the City of San Jose, represents suburban forms with 
considerable low-rise single-family residentials, and Alameda County including the City of 
Livermore, which serves as an exurban employment and tourism destination. Using GIS, the 
research team conducted a suitability analysis to determine appropriate locations for vertiports in 
the Bay Area. 

Ultimately, the goal is to encourage transportation planners and local governments to start 
planning for AAM in a simple, cost-effective way. By layering safety, access, and equity features, 
suitability composite maps are created that inform public engagement and entitlement 
decision-making in California communities. Analyzing the “where” is a valuable first step for 
comprehensive plan development and for regulatory zoning amendment considerations that will 
be needed to permit eVOTL flights and their associated vertiport land use categories. 

Scholarly Literature 

During this study, key literature was released that helped aid and guide the research process. In 
August of 2022, the Ohio Department of Transportation released the nation’s first advanced air 
mobility framework; while the study summarized opportunities and challenges for Ohio to 
embrace AAM, it also served as a guideline for other states. The framework also presented three 
use cases of AAM including cargo/freight delivery, regional air mobility, and emergency services. 


Engineering Brief #105 for Vertiport Design was released by the Federal Aviation Administration 
(FAA) in September 2022, providing interim guidance for the design of vertiports to serve aircraft 
having vertical takeoff and landing (VTOL) capabilities. In addition, given the research location, 
the study considered Caltrans’ California Airport Land Use Planning Handbook and the 
California Aviation System Plan for identifying relevant regulatory requirements appropriate for 
aviation planning and land use compatibility. 

GIS case studies and alternative methodologies were researched to formulate the workflow and 
process in other literature. Rimjha et al. (2021) is most relevant because it too was a case study for 
northern California and offers a larger regional context for a suitability analysis. Fadhil (2018) used 
a weighted linear combination method to locate suitable areas for Urban Air Mobility (UAM) 
ground infrastructure, including two industry interviews to determine variable considerations for 
analysis in the metropolitan cities of Los Angeles and Munich. K-means algorithms are also used 
in numerous analyses to determine site suitability. Jeong, So, and Hwang (2021) applied the 
K-means algorithm to the Seoul metropolitan area focusing on values related to commuter data. 
Across these examples, the selection of considerations was based on the type or scale of analysis 
(such as demand, travel, or region). These studies helped identify parameters used for the GIS 
analysis methodology in this research. 

Methodology & Results 

For the suitability analysis, parameters were separated into three categories—safety, access, and 
equity—for vertiport site placement in the Bay Area. These parameters were identified through 
literature reviews and discussions with key industry and agency stakeholders. Safety is a paramount 
consideration in the aviation industry and will be the primary influence in any AAM framework.
Access is a major consideration of traditional planning and ensures that facilities can be reached 
through a mix of transportation modes which can positively impact its use. Equity is an emerging 
concern of modern planning and considers the equity of impacts that a project might have and 
unequal impacts may stifle a project or create additional considerations on how to ameliorate those 
inequities. The parameters were then assigned to a priority level of high, medium, or low, which 
varied depending on the geographic form at that place (urban, suburban, or exurban).
High-priority factors include those which are essentially non-negotiable, and failure to meet any 
of those factors indicates an unsuitable site. Medium priority factors are more flexible to a degree,
and, while not meeting these factors is not ideal, they do not necessarily indicate an unsuitable site. 
Low priority factors do not factor into the dichotomy of suitability, but are additional 
considerations for how ideal the site is for vertiport placement. 

In a real world setting of site selection, suitability can be determined based on four evaluation 
outcomes. A site which meets all nine factors in the matrix, all parameter categories at all priority 
levels, can be considered a "Pass" and is highly suitable. One that meets the criteria in high and 
medium priorities, but not low priority, can be one that is "Highly Considered." A site that only 
meets the high priority criteria is further downgraded to "Considered," and a site that does not 
meet high priority criteria, regardless of the other parameters it may fit, is considered a "Fail" in 
the suitability analysis. Using geospatial analysis tools, subject parcels that met assigned criteria 
were identified and summated to determine suitable locations for vertiports. 

The compact urban form of San Francisco contains higher population densities and, as a result, 
has a higher output of suitable parcels, at 1392 meeting the priority features. San Jose in its 
suburban form contains only 43 resultant parcels that meet high priority standards. This is reduced 
even more in Livermore in its exurban form, where just three parcels meet high priority standards 
for the three parameters (safety, access, and equity). Additional parcels meet standards for medium 
priorities with conditions that could be developed through zoning, economic development, and 
capital improvements, which would boost more sites into highly suitable conditions. 

Recommendations 

The following recommendations are based on our research findings, which are not limited to 
geographic modeling, but also address implications for how local governments and planners can 
prepare for AAM. 

Getting Started: 

- Determine the appropriate use case(s) for AAM integration within selected study area 
boundaries. 
- Develop a list of non-negotiable “high priority” parameters for vertiport locations. 
- Map existing flight path(s) and engage early with the FAA and local airport authorities. 


Data Acquisition: 

- Ensure there is easy data access across all agency departments (including engineering, 
public works, transportation, recreation, urban forestry, and community development). 
- Review the metadata for geospatial appropriateness and consistency. 
- Maintain a data dictionary and data log to maintain accuracy and currency. 


Data Analysis: 

- Prioritize locations needing intermodality such as hospitals and transit stations. 
- Value proximity to safe pedestrian and bicycle routes, and other micromodal options. 
- Understand that suitability varies by community and that preferences change. 



Land Use Planning: 

- Incorporate GIS site suitability AAM analysis into the Transportation and Land Use 
sections of comprehensive plans. 
- Consider vertiports a form of TOD infill and redevelopment. 
- Add vertiport as a land use category in land development codes or zoning codes. 


Engagement: 

- Add participatory GIS in stakeholder workshops and incorporate virtual reality to lessen 
fears about eVTOL aircraft noise and their aesthetic impacts. 


1. Introduction 

1.1 Research Goal 

The goals of this study are to establish a framework for a systematic approach to vertiport site 
selection and to provide recommendations for how a region might plan their AAM network, 
regulations, or best practices. The approach established by this framework would ensure generalconsistency in AAM land use planning for a region while remaining flexible enough to allow for 
other considerations that may differ between regions such as local zoning or state regulations. 

This project is based on the case study of vertiport site suitability across five counties in the San 
Francisco Bay Area region. The objective is to understand what it means to have a new AAM 
vertiport land use and to create a replicable process for the beginning of geographic planning. Bygenerating a set of prioritized parameters from reputable and free geospatial data, the study outlines 
a simplistic GIS workflow to identify parcels potentially fitting future vertiport development. 
Unlike previous models, this study examines safety, access, and equity in urban, suburban, and 
exurban settings. 

Recommendations are based on the research findings, which are not limited to geographicmodeling but also address how local governments and planners can prepare for AAM. 

1.2 Study Area 

The study area, as shown in Figure 1, includes three representative vertiport destinations as the 
foci of landside GIS analysis including the City of San Jose in Santa Clara County, the City and 
County of San Francisco, and the City of Livermore in Alameda County. However, GIS analysis 
was conducted on all parcels across all five counties (Santa Clara, San Mateo, San Francisco, 
Alameda, and Contra Costa) because the same methodology may be applied to identify suitable 
ground conditions (below air corridors) connecting these three destinations. It was important to 
this study to examine different geographic scales since California’s metros are multinucleated with 
varying densities (and in this case, span urban San Francisco through suburban San Jose to exurban 
Livermore). 


Figure 1. Study Area Map 


1.3 Research Methodology and Approach 

Research was conducted over one year and divided into multiple phases. First, a literature review 
on vertiports is conducted. Second, GIS modeling methods are developed and locational 
parameters for GIS analysis are generated. Third, data acquisition, storage, and GIS analysis are 
conducted. Fourth, the results are interpreted and the data is visualized. Lastly, a summary report 
highlighting a replicable workflow for identifying vertiport parcel (at a site-specific scale) locationsand making recommendations is written . This effort required a mixed-methods and largelyqualitative approach including a review of existing published scholarship, discussions with key 
informants, organizing and leading a roundtable style focus group, and GIS analysis. Early on, an 
overview project workflow was created for ESRI ArcPro GIS software, although the same process 
could be run using open-source QGIS (Figure 2). 


Figure 2. Project Workflow 


With industry, and federal and state governments unveiling new aircraft and planning documents 
frequently during 2022, the study was aided by having two graduate research assistants and one 
undergraduate research assistant tasked with managing data acquisition while the PrincipalInvestigator and Co-Principal Investigator met with industry and government to formulate 
location variables and parameters, which is a critical component of this research. Quarterlypresentations to the Caltrans Division of Aeronautics Directors served a critical advisory role in 
completing this project. The research team participated in two NASA AAM EcosystemCommunity Integration Working Groups held in October 2021. These early gatherings brought 
together expertise in transportation, aviation, aerospace, engineering, and planning, and were a 
great starting point for vertiport location consideration. Combined with existing published 
peer-reviewed literature on previous GIS methods, a list of appropriate landside location variables 
was generated and reviewed by Caltrans aviation, planning, and equity officers. Once the variables 
were prioritized, they were presented to gain scholarly feedback at the American Association of 
Geographers (AAG), at the California State Chapter American Planning Association (APA)
conferences and at the Future of Aviation Tech Transfer Conference. Interestingly, the planning 
audience was largely ignorant of AAM indicating the need for greater cross-collaboration in 
transportation and land use planning. 

The￿esearchh team met with two industry leaders currently developing proprietary geospatial 
modeling software to discuss this study. AirspaceLink has worked with the City of Ontario and 
the State of North Dakota to create a tool for autonomous flight mission planning that also 
includes a risk score based on ground location features. EY-Parthenon is a large consulting 
organization currently building machine learning software for AAM planning that will incorporate 
real-time weather and congestion data. These advanced models can be useful for planning at a 
selected site and for ongoing operations. 


San Jose State University, in partnership with the California Governor’s Office of Planning & 
Research, hosted the first European Union + California Urban Air Mobility roundtable. This 
meeting was hosted by State Director Sam Assefa, and European Union Regional Minister JeroenOlthof. This international focus group gathered experts from federal, state, and local governments, 
industry, and academia with the purpose of addressing how to govern AAM. Notable participants 
included Wisk Aero, Joby Aviation, Archer Aviation, Caltrans, City of San Jose Office of 
Innovation, City of San Jose Department of Planning, and the ministry of the Province of Noord-
Holland (Amsterdam). Following this, Joby Aviation included the research team at its Field Day 
to tour its Marina, and the California research, development, and production facilities. 

1.4 Report Organization 

Besides this introductory chapter, this report is divided into the following chapters: Literature 
Review, Research Methodology and Modeling Methods, Case Studies, Results and 
Recommendations, and a Summary and Conclusions. The literature review provides an overview 
of the scholarly research on AAM (and the subtopic known as UAM) and vertiports. Research 
Methodology and Modeling Methods discusses the basis for this analysis including the research 
design and parameter variables established for geoprocessing. Case Studies includes generatedurban, suburban, and exurban parameter matrices and high-value composite site suitability maps 
identifying urban, suburban, and exurban parcels that may be appropriate vertiport locations. 
Results and Recommendations interprets the case study findings, and the final chapter discusses 
project implications and provides a summary of our research and opportunities for future research. 


2. Literature Review 

2.1 Advanced Air Mobility 

Urban Air Mobility (UAM) is a subset of the Advanced Air Mobility (AAM) ecosystem generally 
referring to a new transportation mode powered by vertical takeoff and landing aircraft (VTOL). 
Electric versions, known as eVTOLs, can eventually be used as autonomous passenger air taxis 
that run on highly automated on-demand systems to transport passengers or cargo at low altitudes 
within 60 miles. UAM aims to transform mobility by alleviating traffic congestion through a 
reduction in single-occupancy automobile use. This will decrease greenhouse gas emissions and 
offer the public a new transit alternative at infrastructure buildout. UAM is a quiet sustainable 
travel mode that efficiently replaces surface transportation, and the typical eVTOL aircraft has 
been designed to accommodate as many as six passengers, though at onset, flights will require a 
licensed pilot. The vertiport is a ground hub serving eVTOLs that can be used for passenger 
boarding and disembarkation, and for aircraft maintenance and charging. Vertiports can be 
considered as a brand-new land use that operates like a hybrid of an airport and transit station. 

In the case of UAM, the industry is advancing faster than policy. Planning for UAM has been 
slow because critics feel the technology does not apply to most consumers nor does it offer a 
considerable benefit to other transit services. It is true that UAM will initially serve small 
populations (especially those in emergency situations), but a well-integrated systems plan for 
landside vertiports and airside corridors means UAM has the potential to safely and equitably 
benefit everyone. UAM and its associated infrastructure will start in “low-complexity, low-
operational tempo operations and [build] toward an environment of higher operational tempo and 
the introduction of UAM airspace structure to mitigate an otherwise higher level of complexity” 
(FAA ConOps 1.0, 2020). Successful implementation of any UAM network requires significant 
engagement and investment from industry and government at all levels, and extensive stakeholder 
and public engagement. 

To date, the most favorable UAM use case is in emergency and fast-response healthcare services. 
There are already examples of drone deliveries, i.e., Unmanned Aircraft Systems (UAS), for 
medical practice. During the coronavirus pandemic, United Parcel Service (UPS) and CVS 
Pharmacy embarked on a joint effort to airdrop prescription medication to a retirement facility in 
Florida during shelter-in-place orders. This same practice was applied in Rwanda and Ghana 
where test kits and medication were air dropped (Goyal & Cohen, 2022). On a larger scale, UAM 
presents new opportunities for eVTOL vehicles to transport emergency patients and dispatchsupplies when timing is critical. However, in its current technological state, UAM applications of 
aeromedical transport are not expected to be any more cost-effective. It is also believed that these 
use cases will positively contribute to public acceptance (Goyal & Cohen, 2022). The Ohio AAM 
framework prepared by the State Department of Transportation expects healthcare providers to be 
early adopters of AAM with the highest likelihood for small package delivery. Utilizing small UAS 
vehicles will still bolster logistical support for a statewide approach needed to fully leverage AAM 
(Ohio DOT, 2022). Assessing demand for UAM infrastructure varies from place to place. UAM 
is likely to be adopted in dense urban areas where populations earn higher than median incomes 
and are faced with longer than average commute durations. Regardless of location, widespread 
adoption, especially in the case of autonomous flight, requires consumer trust in safety—something 
not yet seen with automobiles. 

In 2007, the United Nations released data that more than half of the world’s population lives in 
urban areas with a forecast that the percentage will rise to 60% by 2030 and 67% by 2050 (Swadesir 
& Cees, 2018). Rapid urbanization will be linked to increased congestion, especially worsening in 
global cities. In these cities “air-taxis,” which are expected to rollout as early as 2024, put pressure 
on planners to consider significant changes to the built landscape, and to address potentialenvironmental effects of something truly unique. Further, there is the challenge underlying policy 
to understand future user behavior which can at best be derived from current daily rhythms such 
as time allocations, activity timing, scheduling, and frequency (de Abreu e Silva & Goulias, 2009;
as cited in Chaniotakis et al., 2020). Simply stated, “if people behave the same [with UAM] within 
clusters of individual behaviors, there is a good basis that they would behave the same when it 
comes to aspects such as new forms of mobility and new infrastructure,” (Chaniotakis, et al., 2020). 
A key question is whether the degree of consumer demand is similar in autonomous systems sincethis could change with demographic and economic trends as is the case with aging populations, 
rising fuel prices, increasing health and environmental concerns, and changing consumer location 
preferences that tend to increase demand for more accessible multi-modal locations (Litman & 
Steele, 2017). 

Large cities should see greater demand for eVTOL trips—places such as New York City, Los 
Angeles, and Washington, D.C. have air taxi commuters. More than 33% of all air taxi demand 
trips predicted in a model completed by Haan et al. occur in three combined statistical areas 
(CSAs), while 50% of all air taxi demand occurs in the top six CSAs. This suggests air taxi service 
may be viable only in a handful of places and/or additional port infrastructure investment will be 
needed for UAM adoption in small cities. That is, the potential market might be concentrated in 
large cities because smaller cities might not make financial sense (Haan et al., 2021). It is expected 
that on-demand urban air transportation should be targeted to get people to and from the city core 
along with options to travel elsewhere, if needed. 

Planning for desired travel routes can follow existing major roads and freeways. When comparing 
travel time for on-demand urban air transportation against three of the most common transportmethods in the Melbourne metropolitan area, on-demand urban air transportation was found to 
be the quickest in 88% of all modeled cases. The only occasion where on-demand urban air 
transportation was comparable in terms of speed was when the destination was less than 
10 kilometers from the point of origin. The largest difference showed a three-hour benefit over 
driving with all cases being faster than driving (Swadesir & Cees, 2018). 


With current eVTOL aircraft, the optimal performance is a mission traveling no farther than 
60 miles at a cruising speed of 150 miles per hour, and having a pilot plus four passengers (Uber 
Elevate). Thus, advancement in batteries will be an enabling technology essential for widespread 
usage. With this comes new challenges in the aviation industry, challenges defined by capabilities 
to improve battery strength, achieve full FAA certification, build ground infrastructure, establish 
Air Traffic Management (ATM) procedures, train pilots, and ensure safety. Proper safety applies 
to all parties involved—from passengers, to operators, to the public, and to private infrastructure 
owners (Cokorilo, 2020). Eker et al. (2019) evaluated the feasibility of four security measures for 
public acceptability including: (a) the use of existing FAA regulations for air traffic control; (b) 
establishing air-road police forces; (c) detailed profiling and background checks of eVTOL owners 
and operators; and (d) establishing no-fly zones near sensitive locations, such as military bases, 
power/energy plants, government facilities, and major transportation hubs (Shahriar et al. 2020). 
NASA, the University of California, Berkeley, and the City of Los Angeles are also studying 
airspace operational safety because current ATM systems do not manage low elevation airspace, 
established air corridors are at a high elevation, there will be increased workload, and UAM will 
require extensive safety and maintenance training. Several challenges delay integration with 
existing National Air Service (NAS) operations and urban operations; specifically, there may be a 
higher frequency of operations than seen in existing commercial aviation, there might be higher 
density operations at low altitudes, and varying performance differences are yet to be measured. 
Combined, these factors will stretch current air traffic control system capabilities and drive the 
need for significant changes (Bauranov & Rakas, 2021). To successfully implement UAM systems,
the transportation sector must prepare for a separate Air Traffic Control (ATC) strategy for 
eVTOL aircraft that pays equal consideration to airsides and landsides. The approach controls 
near vertiports are essential for service in populated urban areas (Song & Yeo, 2020, p. 1). 

Vertiports must be placed throughout a city for easy access to destinations or for transfer 
transportation modes. Taking this into consideration—along with no-fly zones that are in place 
due to safety restrictions—it becomes apparent that the air space available for UAM will be quite 
limited (Eissfeldt, 2020, p. 2). No-fly restrictions limit flight over public buildings, correctional 
institutions, hospitals, conservation areas, and military lands. 

A market study of UAM focused on three potential cases—airport shuttles, air ambulances, and 
air taxis—presented to NASA in 2018, indicates that the UAM market faces both technological 
and non-technological challenges (Booz, Allen, & Hamilton, 2018). In terms of non-technological 
constraints, there is real concern over fast safety and security screenings (Mofolasayo, 2020). This 
is a risk factor that must be made relatively nonexistent for transit model adoption. Several 
developed econometric models found that sociodemographic factors, cultural impacts, and affinity 
to automation (including the enjoyment of technology use) heavily influence adoption. In 
particular, the presence of in-vehicle cameras and human operators aid confidence in UAM service 
(Tyrinopoulos & Antoniou, 2020). 


The general public perception of UAM is one of caution. This is mainly due to expected noise 
levels. Aircraft manufacturers, such as Joby Aviation, have reduced noise generation to 40 dBA,
mimicking the sound of wind and thus mitigating this issue, but this may still be too noisy in some 
circumstances, particularly for vertiports located close to residential areas (Swadesir & Cees, 2018). 
Thus, the primary reason people do not want a vertiport in their community is still noise (Cohen,
1996). 

2.2 Vertiport Design 

The term vertiport is a general categorization of next generation heliports necessary to enable 
AAM operations. However, not all takeoff and landing locations have equal capabilities or 
identical roles in the AAM ecosystem. Based on their function, most ‘vertiplaces’ will be 
characterized into one of three categories—vertihubs, vertiports, or vertistops (NASA, 2020). The 
Heliport Design and Vertiport Design Advisory Circulars published by the U.S. Department of 
Transportation provide design specifications for heliports and vertiports dating as early as 1991. 
As defined by the Vertiport Design Guide: 

vertiport landside features such as passenger services (terminal and parking), hangars, 
employee parking, charging equipment, and storage will vary from site to site. Ground space 
required for these features is in addition to the ground space required for airside activities. At 
elevated vertiports, facilities and services may be located on floors one or more levels below 
the level of the airside surface. Landside facilities should be functional, attractive, and capable 
of orderly expansion as future needs develop. (FAA AC Vertiport Design, 1991) 

Early in 2022, the FAA published an Engineering Brief establishing interim draft guidance for 
vertiport design and operation that serves UAM aircraft; the FAA further published a revision to 
this document in September 2022 (FAA EB #105, 2022). The publishing of these subsequent 
guidelines signals to the aviation industry that UAM implementation is expected in the near future,
and therefore the FAA must continue to work with eVTOL manufacturers to establish 
performance standards. In 2022, the Ohio State Department of Transportation (ODOT) 
published the first ever state-level AAM framework plan with recommendations for integration 
based on collaboration with industry partners (ODOT, 2022). 

UAM will require dedicated infrastructure that currently does not exist. Even though UAM is 
initially expected to retool or better utilize existing helicopter infrastructure, there will be a shift to 
vertiports as a predominant ground land use. The site selection process for vertiports becomes 
crucial for effective and safe UAM operation. There are several important factors to consider when 
evaluating vertiport locations, some of which are obvious such as avoiding flight obstructions. 
ODOT calls for an iterative process for lifecycle management in AAM evaluation, exploringsurrounding airspace and land uses. This calls for noise monitoring and approved route planning 
for on-demand and scheduled trips by aircraft type (ODOT, 2022). Vertiports need transmission 
interconnection to sufficient electricity to power eVTOL operations and aircraft, though 
manufacturers are investing in alternative fuels such as hydrogen. Fulton (1969) wrote in Official 
Architecture and Planning how quiet vertiports could act as technological growth points and provide 
city-centre renewal. Ahead of its time, a 1996 report of vertiport characteristics by Peisen et al. 
laid out the physical requirements for vertiports to accommodate passenger load, but based 
standards on airport design. However, for UAM to be successful, the overall travel time must be 
shorter than conventional road travel during peak congestion hours. One way to decrease the 
overall travel time is by reducing transfers though this requires multiple vertiports (Park et al., 
2020). This potential volume of citywide UAM operations generates a need for standardized 
design: “Using standard modular components can reduce construction duration but planning is 
needed to streamline permitting” (NATA, 2019). 

Perhaps the most comprehensive review was conducted by Garrow, German, and Leonard who 
created a database of 800 publications in UAM, EV, and AV literature. A meta-analysis compared 
the overall research related to demand modeling, operations, and integration with existinginfrastructure. Then, comparative analysis identified important factors for design and operation 
for future AAM systems along with gaps in the research that will be important for future study 
(Garrow et al., 2021). 

A holistic approach to vertiport site selection should not only include technical engineeringconsiderations but also pay mind to the land use implications of access, zoning, and mitigating 
negative effects. This ensures UAM results in a net community land use analysis, especially when 
using GIS, which allows for easier identification of specific criteria while also providing for 
flexibility in changing parameters. As an additional benefit of forethought, careful planning can 
mitigate future issues leading to community action which might require immense staff time and 
can result in new ordinances or regulations which restrict the operational levels of a vertiport below 
its planned design. 

2.3 AAM Models 

UAM research methodologies range from those that are demand driven and route analysis to K-
means algorithm clustering at regional scales. Case studies have examined global metros from the 
United States, Australia, East Asia, and Europe. In all applications, the variables were 
predetermined. Knowing UAM demand is of course valuable to the site selection process, but at 
this point, it can only be forecasted. Goyal et al. used a five-step process for demand modeling, 
including: (1) trip generation, (2) scoping, (3) trip distribution, (4) mode choice, and (5) market 
constraints. Alternatively, Winter, Rice, and Lam statistically compared demand modelling to a 
survey of early adopter willingness. Results range by value, fun, wariness, fear, and happiness, and 
suggest there is value in extensive consumer marketing. 

A northern California sensitivity analysis conducted by Rimjha et al. explored commuter demand 
when the calculation of UAM demand was integrated with vertiport placement and travel cost per 
mile. By creating an algorithm for mode choice, the study placed vertiports to maximize UAM 
demand for any given number of vertiports throughout the region. It considered mode alternatives, 
drive alone, and transit options. Findings reveal that UAM success depends on popularity among 
high-income users and operational efficiency. The system would have to be errorless for 
commuting purposes in order to generate profit. Ultimately, wait times will greatly impact UAM 
demand, so the 

system must have policies that lead to a minimum delay; otherwise, the driving alternative quickly 
becomes a more attractive mode for commuters. The construction and operation of a future UAM 
system will be complex and it will require intricate long-term planning. Several policies will be 
needed to promote the system’s success and economic feasibility. Many of these policies are 
implemented in some form for other transportation systems (e.g., aviation or driving), so the 
lessons learned in their implementations will be of use to the UAM system. (Rimjha et al., 2021) 

Designing UAM aircraft requires knowledge of local conditions where the transportation system 
will be implemented. The station network, including elevation, architecture, local climate, and 
demographics strongly influence aircraft type (Ploetner et al., 2020). For this case, a multi-criteria 
analysis was conducted to evaluate alternatives, and an indicator system was developed to assess 
proposed UAM scenarios—from environmental, to transport-business related, and to 
socioeconomic—each having a corresponding desired outcome with defined sub-indicators. To 
estimate future UAM demand in Munich, a current and future mobility demand per household 
with detailed origin to destination routes had to be known. Additionally, a specific mode-choice 
model for both commuters and other users (such as airport passengers) including all other relevant 
alternative modes of transport (such as autonomous vehicles) had to be set up based on stated 
preference surveys (Ploetner et al., 2020). 

A K-mean algorithm analysis was conducted by Jeong, So, and Hwang (2021) for the Seoul 
metropolitan area that focused on commuter data. The commuter demand was estimated from a 
2015 commuting population census conducted by Statistics Korea. The K-means algorithm used 
requires enough data for clustering, so allocations can be made to each area and plotted on a dot 
density map where one point equals 5000 persons. This clustering is repeated many times until 
optimal vertiport locations are observed (Jeong et al., 2021). The key element is parameter “k” 
which should be initially specified, and the entire clustering process is repeated until a cluster center 
location appears. Vertiport locations are then selected using commuter data and the K-means 
algorithm (Jeong et al., 2021). Another methodology using the K-means algorithm and 
comparative analysis was conducted from the Zillow Transaction and Assessment 
Dataset (ZTRAX). This method introduced land use by size and cost constraints for the 
determination of landing sites. This thesis compares the availability of land, effects from splitting, 
consolidation, relocation of vertiports, and the cost per passenger-mile (Tarafdar, 2019). GIS washelpful because it analyzed geocoded (latitude and longitude) data with acreage, county name, floor 
area, and landform type (Tarafdar, 2019). This proved valuable for assessing vertiport suitability 
based on square footage. 


Fadhil (2018) conducted notable GIS research on AAM ground infrastructure. This research 
applied integrated AHP-Delphi analysis built on weighting various criteria and also used statistical 
aggregation of judgments made in group decision-making. This benchmarks a consensus amongexperts engaged in an iterative process (Mu & Pereyra-Rojas, 2017; as cited in Fadhil, 2018). 
Ground infrastructure is one of the critical issues affecting the implementation of AAM (Vascik 
& Hansman, 2017; Uber Elevate, 2016; as cited in Fadhil, 2018). Yet, there are only a few 
published sources on landing equipment. In order to improve the process for factor selection, 
AHP-Delphi and expert interview methods were conducted jointly resulting in a ranking of 
weighted factors (Fadhil, 2018). 

2.4 AAM Policy 

2.4.1 Federal 

AAM is an emerging market with no universal framework or public policy. As AAM technology 
continues to advance, so will FAA regulations. This will spur proactive and reactive policy creation 
as eVTOLs become further integrated into society. Specific governance should be developed to 
address air travel and operations including vertiport safety regulations (California DOT, Caltrans). 
Currently, eVTOL and vertiport standards are based on drones, helicopters, and helipads. 

It is expected that the FAA will act as the principal agency responsible for setting AAM standards, 
especially for safety, as is the case for existing aircraft. The FAA began establishing standards for 
vertiport design when it published Advisory Circular (AC) 150/5390 in 1991, but this document 
was eventually cancelled in 2010 (FAA EB#105, 2022). FAA standards are evolving in 
consultation with manufacturers. These in progress standards are available for public review in the 
Vertiport Engineering Brief (FAA EB#105, 2022). Due to the anticipated high-tempo operations, 
the FAA safety standards in 14 CFR Part 135 are akin to those that generally apply to commercial 
non-scheduled operations typically referred to as private charters or air-taxis. It is important to 
note that, under the Heliport Design Guide Advisory Circular, the FAA cannot regulate private 
heliports but can provide specifications and constitute proper infrastructure for takeoff and landing 
sites. While a vertiport may operate as a privately owned private-use facility, it is also possible that 
the vertiport will be required to comply with federal standards to ensure “adequacy” as defined in 
the regulatory text of Part 135 (NATA, 2019). 

2.4.2 State 

In the decade since the 2013 legislative session, state lawmakers have frequently considered bills 
addressing unmanned aerial systems. State legislatures often debate if and how drone technology 
should be regulated, considering the benefits of autonomous drone use, the privacy concerns, and 
their potential economic impact. At least 44 states enacted laws addressing drones and an 
additional three states adopted resolutions. Common issues include defining UAS, law 
enforcement limitations, and how operations can be flown safely in public (NCLS 2021). These 
regulations are applicable to eVTOLs, but need to be refined and/or expanded for human 
passengers. The FAA’s Engineering Brief #105 Vertiport Design specifies that states’ departments 
of transportation, aeronautics commissions, or similar airport authorities grant approval and, in 
some instances, issue a license to establish and operate landing facilities. Many states have 
determined to what extent the FAA Heliport Design Guide applies and how the FAA guidelines 
are subsequently enforced within their jurisdictional boundaries, ranging from very minimal and 
permissive to intense oversight and restrictive (NATA 2019). In California, the state grants its 
Department of Transportation (Caltrans) full oversight and authority when it comes to regulating 
airports within their jurisdictions. Published standards must be met, including sound studies, land 
use compatibility studies, and California Environmental Quality Act (CEQA) Environmental 
Impact Reports as defined by the Caltrans Airport Land Use Planning Handbook. In 2022, the 
ODOT published its own AAM framework introducing plans for statewide AAM in the coming 
years with an expectation that uncrewed flights will be licensed by Ohio for operations in 2027 
(Ohio DOT, 2022). The framework by Ohio’s DOT is the first of its kind and does an excellent 
job explaining AAM use cases including airport shuttles, emergency services, passenger air taxis, 
and cargo/freight deliveries along with regulatory, industry, and workforce recommendations. 

2.4.3 Regional 

No regional policies have been adopted for AAM air transportation or connection to surface 
transportation. However, AAM integration at the regional scale is essential for achieving a safe 
and accessible multimodal vision. A regional transit network needs efficient flight paths connecting 
the periphery to the urban core, and to neighboring cities and/or employment hubs. Regional 
agencies or metropolitan planning organizations, and the federally mandated and funded 
transportation policy-making organizations foster cross collaboration between local jurisdictions 
and provide a defining voice in establishing roles and responsibilities: “Even though civil aviation 
authorities are accustomed to dealing with all airspace issues, the integration of UAM will require 
city and regional stakeholders to take an active role in shaping some aspect of UAM policydevelopment” (LA DOT, 2022). 

2.4.4 Local 

Local government policies will depend largely on geography. AAM is novel, making an advisorycommittee critical for documenting and determining the wants versus needs of communities. 
Advisors should facilitate focus groups with relevant community advocacy groups representing 
environmental justice, transit advocacy, pedestrian advocacy, civil rights, economic development, 
and community leadership (LA DOT, 2022). This outreach and engagement are essential for 
AAM adoption. Local government is also responsible for California Environmental Quality Act 
(CEQA) compliance and drafting comprehensive plans and zoning codes not currently addressing 
AAM. Some communities are more sensitive to noise, growth, and development, and want to feel 
protected by extensive policies and design guidelines. NASA recommends that local governments 
considering vertiports should engage early with the FAA, who must assess the safety of any 
proposed vertiport location and any impacts to the existing National Airspace System. The FAA’s 
current regulations require a vertiport proponent provide timely notice of intent. Interested parties 
should contact the Airports Regional Office or Airports District Office. Several communities 
enacted new zoning ordinances, changed building and fire codes, and created conditional use 
permitting procedures to expand vertiport development permitting (FAA EB #105, 2022). 

2.5 Transportation and Land Use 

Before building vertiports, as new multidimensional spaces for connecting surface and air 
transportation, it is essential and beneficial to understand how other new transportation modes 
affected their surroundings in the past. In most cities, transportation comes in several forms from 
public transit (including high-speed rail) to on-demand drivers, private automobiles, buses and bus 
rapid transit, and micromodal bicycles and scooters. To reduce harmful greenhouse gas emissions,
cities are investing in ways to reduce single occupancy vehicle trips in favor of Transit Oriented 
Development (TOD). In this way, land use planning helps achieve sustainable transportationgoals. TOD can be expanded to include vertiports which not only builds new infrastructure but 
also creates new green jobs; however, moving aviation off the airport will pose new questions over 
land use compatibility (FAA Land Use Compatibility and Airports, 2022). Until AAM is a reality, 
the most compatible zones are commercial and industrial, vacant lots, and publicly owned sites. 
Initially, residential communities, even downtown, should be excluded to alleviate community 
impact. 

California's land use and transportation problems are co-dependent; problems in any one arena are 
reflected in another. Such co-influences call for a more holistic planning approach (Cervero, 2003). 
This provides a better understanding of the interaction between transportation and location 
phenomena, and thus greater knowledge of the determinants of urban spatial patterns (Putman,
1983). Understanding this interrelationship is essential. Badoe and Miller (2000) found that 
land-use policies emphasizing higher urban densities, traditional neighborhood design, and a 
land-use mix do result in declines of auto ownership and use, while also enhancing transit 
patronage and walking. The debate concerning residential density in determining urban 
transportation “efficiency” or “sustainability” is ongoing. A “pro-density” argument is associated 
with the scholarship of Newman and Kenworthy (1989), among others, in which density seems to 
be the single most important factor for explaining macro differences in transportation and energy 
use in cities (Badoe & Miller, 2000). AAM seems fitting in this case; when eVOTLs become a 
quiet electrified alternative to urban congestion, it can re-center discussion on other environmental 
or social justice effects. 

Environmental equity is of growing concern in planning. Airport development is legally required 
to consider environmental impacts since the adoption of the National Environmental PolicyAct (NEPA) and creation of the Environmental Protection Agency (EPA) in 1970. NEPA 
adheres mainly to direct impacts on the environment from a proposed action, but the EPA’s 
responsibility is reviewing actions of other federal agencies—this was expanded following the 


Department of Transportation’s refusal to release agency comments regarding the Environmental 
Impact Statement (EIS) on supersonic transport (Alm, 1988). The concept of environmental 
justice entered public discourse in the 1980s following studies over proximity to hazardous waste 
landfills surrounding communities of racial or ethnic minorities, and low-income backgrounds. 
Then, in the 1990s, continued evolution of federal policy including the Presidential Executive 
Order 12898, Federal Actions to Address Environmental Justice in Minority Populations and Low-
Income Populations, resulted in a new requirement for specific considerations of environmental 
justice (McNair, 2020). In the following decades, environmental justice was given progressively 
higher importance with many studies and actions. Yet, research continues to reveal disparities in 
environmental justice in the airport context. Correlations exist between environmental concerns 
and the locations of communities of traditionally disadvantaged racial, ethnic, or socioeconomic 
backgrounds (McNair, 2020). 

There is fear that AAM will disproportionately affect those with unequal access or opportunity.
AAM is more likely to cause a negative impact on regions with a lower income due to a lack of 
access and economic factors which can hinder participation in community planning meetings. 
While the concept of accessibility is simple to understand, measuring it is often complex and 
diverse (Kelobonye et al., 2019). Accessibility denotes the ease of reaching destination 
opportunities such as jobs, stores, schools, and health services. Accessibility could refer to people 
or places; it could be measured at the neighborhood or regional scale, and in absolute terms (e.g., 
minutes) or in relative terms (e.g., as indices). While social, economic, and political factors could 
become barriers to access, spatial accessibility is affected by three spatial factors: attributes on origin 
(for example, home location when accessibility refers to places, an individual’s gender, income, or 
other characteristics when accessibility refers to people); attributes on destination (for example, 
number of jobs or size of schools); and spatial separation or transportation linkage between origin 
and destination (Pan et al., 2018). 

Land use patterns affect accessibility because the ability to reach desired services and activities is 
affected by mobility—the amount and type of travel activity (Duranton & Guerra, 2016; Litman,
2003). Different land-use patterns have different accessibility features. Urban areas have more 
accessible land use and more diverse transport systems, but slower and more costly automobile 
travel. Suburban and rural areas have less accessible land use and fewer travel options, but driving 
is faster and cheaper per mile (Litman & Steele, 2017). There is a significant lack of U.S. data 
related to the transport disadvantage of specific socio-demographic groups that can support an 
investigation of transport need and disadvantage (Pyrialakou, Gkritza, & Fricker, 2016).
Transportation equity considerations have limitations. First, they only consider a subset of all social 
equity issues (Litman & Brenman, 2020). For example, the “intangible” transportation outcomes, 
such as walkability or livability, are frequently overlooked, while “tangible” outcomes, such as 
reduced traffic congestion and improved transit coverage, are easier to measure and present to the 
public; thus, they are the focus of social equity considerations and are prioritized over the intangible 
outcomes (Handy, 2008). For effective decision-making it becomes essential to integrate all factors 
into a transportation equity analysis framework. Second, consideration of equity issues is 
challenged by the need to balance the diverse goals of such a project (Guo et al., 2020). As AAM 
transportation infrastructure is incorporated into cities, planners must be cautious of accessibility 
and land use when selecting locations. Designing AAM with multimodal transit requires planners 
and stakeholders to account for accessibility in both physical and ethical senses and for walkability 
to and from these locations. 

Existing research shows that the airport industry is sited in places where communities of 
traditionally disadvantaged backgrounds tend to have higher risk for exposure to negativeenvironmental impacts. Specific examples include a study showing a higher likelihood of fine 
particulate matter from aircraft emissions surrounding the study airport in a community having 
lower income, less education, and non-white ethnic background (Rissman et al., 2013); a noise 
study which found that ethnicity was an important predictor of exposure to aviation noise pollution 
(Ogneva-Himmelberger & Cooperman, 2010); and a study of the planning processes which 
highlighted an inherent power imbalance in the public participation process (McNair, 2020).
Planning can benefit greatly from quantitative measurements. 

One study focused on NEPA development filings at 19 airports between 2000 and 2010, andidentified two shortcomings in the NEPA process for addressing environmental justice. First, the 
unit of geographical analysis is not prescribed but instead simply directs that it should not 
artificially dilute or inflate the affected populations. The study found that the effects to so-called 
protected communities were downplayed due to the large population of those communities in the 
geographic study area (McNair, 2020). In doing so, the reasons behind why these populations were 
clustered in the affected area were overlooked and might include historically racist practices such 
as redlining. Additionally, in some cases, the definitions of “low-income communities” were often 
based on general thresholds rather than local benchmarks which artificially decreased the 
population of affected communities in the environmentally impacted areas (McNair, 2020).
Secondly, there was a tendency to nullify findings from the NEPA process. In two of the airports 
studied, environmental impacts were identified but written off as mitigated though not actionable 
measures. There was also a tendency to reference within the NEPA process the need for an impact 
to be worse than would occur in the no-development scenario, creating an implication that airports 
are free to continue creating unequal effects so long as there was no change to the status quo in 
current operations (McNair, 2020). 


3. Research Methodology and Modeling Methods 

3.1 Introduction 

Transportation facility development historically involved site selection from land available for lease 
or purchase followed by a feasibility analysis during a specified due-diligence period. During the 
feasibility phase, planning professionals had the arduous task of manually compiling disparate 
sources to examine which location(s) yields the most desired outcomes. Usually, this process was 
transformed by web data portals and GIS software. Geoprocessing tools automate the combination 
of spatial data and identify ideal candidate locations quickly. This digitally replaced the use of 
translucent overlays on multiple maps needed to add variables or subtract constraints and expanded 
study areas. 

GIS models are powerful tools for exploring regional extents at site scale variability. By modeling 
parameters, computational processing allows one to test changing conditions or scenarios without 
making costly real-world errors. The dynamic composite maps are data visualizations made 
possible by a relatively simple series of GIS steps. This can be for free using open-source GIS 
platforms for spatial analysis such as QGIS. Therefore, this research not only introduces the 
general procedure for vertiport site suitability analysis but also links to variable data sources and 
steps to carry out procedures. 

3.2 General Procedure 

ArcGIS Pro is capable of data storage, advanced analytics, and data analysis in 2D, 3D, and 4D 
spaces; its maps can be easily shared online, and across devices and operating systems (ESRI). 
Public agencies are increasingly offering GIS portals where geospatial information is readily 
available for download and can be processed by an end user. While these resources are great for 
political and physical features such as zoning or mass transit stations and stop locations, portals 
might not include demographic data. The US Census demographic and socioeconomic tables and 
household (travel) behavior information collected in the American Community Survey are 
available through Census.gov and can be related to locations in GIS. Computational power is 
needed for analysing an entire regions’ vector points, lines, and polygon data, or when creating 
new continuous raster surfaces. 

This research is a vector-based analysis of parcels based on safety, access, and equity variables used 
to determine vertiport site suitability. New transportation systems are extremely expensive, so using 
geospatial models and forecasts prior to capital investment is imperative and frequently referred to 
as a digital twin. The techniques outlined here are purposely unsophisticated because an 
overarching goal of this research is to reduce barriers and improve standardization in community 
AAM planning. 


The novel contribution of this research is a vetted list of available data prioritized for urban, 
suburban, or exurban settings. Instead of applying numerical weights to each variable, this study 
takes an innovative and more flexible, implementable, and realistic approach. Myriad parameters 
or factors must be considered for vertiport development from different perspectives, and each have 
a different importance to the decision-makers. Therefore, it is important to bucket priorities of all 
these considered parameters into high, medium, or low values that can be further refined when 
AAM is able to be tested in situ. Here, variable priority levels were classified from published 
literature, NASA-led working groups, industry demonstration, key informant discussions, and 
focus group insights. 

The general procedure and steps to develop the GIS modeling for site suitability analysis is 
summarized in the roadmap in Figure 3 and is based on the case study of the San Francisco Bay 
Area. 

Figure 3. GIS Modeling Roadmap 


Step 1 Establish Parameters. Parameters are based on the Caltrans mission to emphasizetransportation safety, access, and equity. This step also involves choosing representative samples 
for analysis because there is variability across the San Francisco Bay Area. 

Step 2 Classify Variables. It is recognized that data layers are in fact variables within each of the 
three parameters (safety, access, and equity). Decisions on how variables should be prioritized 
differently in urban, suburban, and exurban places was largely based on the literature review and 
qualitative inquiry. Each variable was assigned in a table as having either high, medium, or low 
importance based on geographic form (i.e., urban, suburban, or exurban). 


Step 3 Data Organization. This is a huge effort in any regional study. Parcel and zoning information 
was gathered from five counties and 52 cities in the Bay Area (see Appendices). There are MPO 
data sources, but it was determined that cities and counties maintain the most up-to-date parcel 
information within their jurisdictional boundaries. Socioeconomic data at the block group scale 
was obtained from the U.S. Census website. All data was logged and stored for free on GoogleDrive; meta-data were reviewed for currency and academic research permission. 

Step 4 Data Aggregation. Data aggregation is needed for efficient geoprocessing. Electric eVTOLs 
are designed to be quiet and without emissions; however, the environmental effects on surrounding 
land uses are still unknown. To be cautious and conservative, parcels with industrial or commercial 
zoning were aggregated and used as a proxy for early vertiport development. 

Step 5 Data Analysis. Site suitability analysis can be done using ArcGIS Online; however, given the 
abundance of the five counties’ parcel data, this study used the ArcPro2.8.2 Desktop version to 
minimize processing time when selecting by attribute or using the join, intersect, and clipgeoprocessing tools. These basic steps are fundamental but must be replicated for each variable 
found in each parameter, and conducted in repeated series. Network analysis was also used for 
connection to transit while buffers were applied to examine proximity to incompatible/sensitiveland uses in the vicinity and for distance to points of interest (POI) and employment anchors.1 
Generate Drive Time Areas was used to calculate drive times and walking distances for several 
variables because it creates a feature class based on travel time and distance. This can include 
driving distances and walking. When working with around 200,000 zoning parcels across five 
counties, the researchers chose to use a Boolean output. A parcel which either was scored as having 
a “high” value or did not meet the criteria was excluded from further study. However, planners 
recognize that places can change, so a site excluded from GIS study cannot be considered 
permanently unsuitable for vertiport development. 

Step 6: Data Visualization. When a parcel scored “high” across all three parameters, it was included 
on the resulting site suitability map. 

1 The FAA published guidelines for land use compatibility for airports, in FAR Part 150, which identifies what landuses are normally considered compatible (FAA, VII-6). These include: agricultural, commercial, and industrial uses. 
Those that are typically considered incompatible are residential areas, schools, and churches. 

3.3 Variables 

As mentioned in Step 4 above, commercial and industrial zoning served as a land-use proxy for 
vertiport analysis. Each county and city have multiple and often different zoning designations for 
commercial (GC, C-1, C-2, etc.) and industrial (I, IC, IB, etc.) uses. Figure 4 depicts the process 
for aggregating zoning data gathered from 57 different agencies. This required extensive data 
collection and repeated reference to municipal zoning codes to define abbreviations. For data 
management, a new field was created to categorize parcels as industrial, commercial, or agricultural 
land (as recommended by the FAA Vertiport Design Circular). This aggregated land use made 
data manipulation easier and helped improve map clarity. 

Figure 4. Zoning Aggregation 


Twenty-seven variables were selected for use in the GIS vertiport site suitability analysis and 
generally included: 

- Parcels large enough to support vertiport development and operations 
- Final Approach for Takeoff and Landings (FATO) and flight paths 
- Location of existing commercial and general aviation airports and heliports 
- Presence of incompatible, or sensitive, neighboring land uses 
- Employment data on job density, presence of largest employers, and LongitudinalEmployer-Household Dynamics (LEHD) 
- Multimodality considerations like bicycle and walking distance to major transportation 
nodes, major employers, or Points of Interest 
- Census statistics on median household income, race, ethnicity, head of household, rent 
versus ownership, and language preference 
- Household Transportation Expenditure 
- Presence of electricity transmission lines and substations 
- Highways and major arterial roads 
- Vacant and underutilized properties 
- Surface parking lots or parking garages with roof access 


Safety is paramount to AAM adoption. Parcels having greater than 250 ft. by 250 ft. surface area 
were selected based on FAA vertiport design standards. These 1.5 acres account for takeoff and 
landing space plus passenger terminal(s) at the vertiport. In the vertiport vicinity, 500 ft. buffers 
were applied to create a safe space around sensitive land uses (including all schools, daycare, 
libraries, community centers, assisted living, and parks). Buffers were also created around 
obstructions such as powerline towers in San Jose and skyscrapers in San Francisco. A 1.5-mile 
buffer was created around existing airports and heliports. 

Access variables were gathered from multiple data sources including LEHD Job Density, Median 
Household Income, and Transportation Consumer Expenditure. For this analysis, census block 
groups are preferred. In some cases, block group values ranged, so an average was determined by 
creating histograms. The Points of Interest (POI) and employment center locations came from 
market research including crowd-sourced information such as Yelp and TripAdvisor. 

The equity variables were gathered using Social Explorer and downloaded at block group scale, 
but it should be noted that this information ultimately comes from the U.S. Census. The 
determination of 30% and 50% of household median income depends on location, and in this case 
came from Bay Area regional housing assessments. 

When these variables are combined during spatial analysis, the result is a composite map showing 
suitability sites across all three parameters (safety, access, and equity). These parcel polygons are 
merged into a single new output dataset. Figure 5 is a geoprocessing workflow for suitabilitymapping. 


Figure 5. Geoprocessing Workflow 


3.4 Data Format and Access 

GIS data used to complete this study was obtained from multiple compressed file formats, though 
information is largely contained as either comma separated value (.csv) spreadsheets or vector 
shapefiles (.shp, .shx, .dbf, .sbn, .xml, and .dbf). The data files used in this study can be accessed 
using this link. 


4. Case Studies 

4.1 Introduction 

The San Francisco Bay Area region is selected as a case study. It has a population nearing 7.8 
million people that includes the Oakland-San Francisco-Hayward Metropolitan Statistical Area 
and the nation’s 10th largest city, San Jose. The region comprises nine counties, but the five selected 
for this study morphologically encompass the South Bay. It is recognized that there are significant 
geographic differences across the region. Therefore, instead of processing a single GIS workflow 
across the entire area, we have obtained more accurate results by breaking the Bay Area into smaller 
representative samples, each having a prioritized variable matrix that more precisely models spatial 
variation. 

In this chapter, results based on the GIS modeling and the variables discussed in the previous 
chapter are mapped by location including urban San Francisco, suburban San Jose, and exurban 
Livermore, as well as the East Bay and the Peninsula corridor. 

4.2 Urban: San Francisco 

The Pacific Ocean, San Francisco Bay, Golden Gate Park, and topography naturally shape San 
Francisco. With limited land, the city has evolved from a historic gold rush seaport into a thriving 
cosmopolitan tourist destination and global technopole for the high-technology employment 
sector (the Salesforce Tower is an example). The city represents a grid pattern with desirable 
population density for a compact form that is desirable in both urban planning and urban design. 
The predominant land use is mid-to high-rise zero lot line mixed-use buildings having a 
commercial ground level with residential units above. The city has significant cultural landscapes 
such as Chinatown and Japantown, but most of the 36 neighborhoods are multicultural 
destinations for sightseeing, restaurants, and shopping. For this reason, the variables in Table 1 
prioritize proximity to transit, walkability, and points of interest; and the pedestrian, cycling, and 
transit-related variables are regarded as high priority. The choice of these variables and the 
determination of their priority levels are made in consultation with Caltrans. 


Table 1. Urban GIS Variables 

Safety Access Equity 
HighUses not allowed within a Above average LEHD job density Block groups with medianPriority 500 ft. radius of Final 
Approach for Takeoff andLanding (FATO):

- Schools (K12,
CommunityColleges,
Presence of largest employers inthe bay area 
2 mile bicycling to at least 1 majortransportation node (BART,
CalTrain, and/or Airport) 
household income levels at or 
below 30% of average 
Large population of singlehead of household 
(apartments can be built insome commercial zones) 
Universities) 

- Parks 
- Libraries 
- Gas Stations 
- Hospitals 
- Day care 
- Assisted Living
 
0.25 mile walking distance to atleast 1 major transportation node 
0.25 mile walking distance to atleast 10+ Points of Interest (POI) 
Block groups having largeproportion of the population 
identifying as Black orAfrican American, AmericanIndian or Alaska Native,
Asian, and Native Hawaiianor Other Pacific Islander 
Minimum Parcel 250 ft. x Block groups having a large 
250 ft proportion of the population 
identifying as Hispanic, 
Latinx, Chicanx 
Medium Airports within ½ nm of Block groups with an aboveBlock groups with median 
Priority FATO average transportation consumerhousehold income levels at or 
expenditure below 50% of average 
Heliports within ½ nm of 
FATO Existing Power Infrastructure Large population of rentersVertiports within ½ nm of 
0.25 mile walking distance to 1 
major employer 
(apartments can be built insome commercial zones) 
FATO 
10 min driving distance to 1 
major employer 
10 min driving time to at least10+ POI 
Low Parking lots or garages with roofPrimary language spokenPriority access other than English 
Block groups with above averagepopulation density 
Block groups with above averagemedian household income 
Proximity to a grand boulevard orarterial 
10-minute driving distance to atleast 1+ major transportation node 

Other desirable variables if available are: 4050 ft. from physical obstruction (such as trees, poles, and wires); approach and 
departure paths; vertical separation based on 8:1 approach and departure surface; above average traffic counts; and above 
average office lease price 


Using the variables above and the workflow described in Chapter Three, as many as 1392 parcels 
were found to be suitable vertiport locations in the City and County of San Francisco (Figure 6). 
This is out of 234,693 total parcels; for a complete address list refer to Appendix C. 

Figure 6. San Francisco Suitability Map 



In San Francisco, any new vertiport must be strategically located for maximum efficiency and 
demand. It is acknowledged that more than one thousand parcels met suitability criteria based on 
GIS outputs, however none are ground-truthed. Still, the results are sensible. North Beach by Pier 
39 has proximity to the bay and many parking garages that could be repurposed, and it is a popular 
sight with nearby employers. The Tenderloin and South of Market are also prime locations given 
their proximity to shopping areas and major employers (including Civic Center Plaza with several 
museums, events halls, and theaters) with an easy connection to Bay Area Rapid Transit (BART). 
Surprisingly, Bayview, a neighborhood farther from jobs and points of interest, was selected 
because of equity variables and adjacency to BART. 

4.3 Suburban: San Jose 

Wide-reaching San Jose is representative of most suburban cities across the American West. The 
predominant land use is one-or two-story single-family residences which, in this case, stretch to 
the east, west, and south, with major employers and points of interest (Levi’s stadium, a performing 
arts center; the Mineta-San Jose International Airport; the Great America theme park; a 
convention center; museums; and San Jose State University) focused in the core and just to the 
northeast. For this reason, variables in Table 2 prioritize walkability and bikeability plus commuter 
drive times; the variable for 10-minute drive times to employment and points of interest is 
considered with high priority, replacing the pedestrian-, cycling-, and transit-related variables 
considered high priority in the case for San Francisco. 


Table 2. Suburban GIS Variables 

Safety Access Equity 
HighUses not allowed within a Above average LEHD jobBlock groups with medianPriority 500 ft. radius of Final density household income levels at 
Approach for Takeoff and 
Landing (FATO): 
Presence of largest employers in the bay area 
or below 30% of average 
Large population of single

- Schools (K12,
CommunityColleges,
Block groups with aboveaverage population density 
head of household 
(apartments can be built insome commercial zones) 
Universities) 
- Parks 
- Libraries 
- Gas Stations 
- Hospitals 
- Day care 
- Assisted Living 

Minimum Parcel 250 ft. x 
Block groups with aboveaverage median household 
income 
Block groups with above 
average consumer 
transportation expenditure 
Presence of powerinfrastructure (high voltage, 
Block groups having largeproportion of the 
population identifying asBlack or African American,
American Indian or Alaska 
Native, Asian, and NativeHawaiian or Other Pacific 
Islander 
250 ft substation) Block groups having a large10-min drive to at least 1+ 
major transportation node 
proportion of the 
population identifying asHispanic, Latinx, Chicanx 
Medium Airports within ½ nm of Proximity to a grand Block groups with medianPriority FATO boulevard or arterial household income levels at 
or below 50% of average 
Heliports within ½ nm of 0.25 mile walking distance to 
FATO 1+ major transportation node Large population of renters(apartments can be built inVertiports within ½ nm of 0.25 mile walking distance to 
some commercial zones) 
FATO 1+ major employer 
10-min drive time to 1+ majortransportation node 
Low 
Priority 
10 min drive time to 10+ POI Primary language spoken 
other than English 
0.25-mile walking distance to10+ POI 

Other desirable variables if available are: 4050 ft. from physical obstruction (such as trees, poles, wires); approach and departurepaths; vertical separation based on 8:1 approach and departure surface; above average traffic counts; and above average office 
lease price. 


Figure 7. San Jose Suitability Map 



In the City of San Jose, 43 parcels were found to be suitable vertiport locations (Figure 7) out of 
459,282 parcels. San Jose is sprawling but has a global reputation as the heart of Silicon Valley 
because of an abundance of major employers with large land holdings (such as Cisco, Adobe, 
NetApp, and PayPal). The city is also linked by the expansive Santa Clara Valley Transit Authority 
light rail system. Suitable locations for a vertiport include Valley Oak Technology Park near Santa 
Teresa VTA station due to ample parking adjacent to State Highway 85 and U.S. Highway 101. 
Redevelopment seems promising, though the San Jose Mineta International Airport flight path 
may pose airside constraints. Another location, although historically and politically contentious 
due to environmental effects, is the opportunity for the redevelopment of Reid-Hillview Airport 
for commercial vertiport use. 

4.4 Exurban: Livermore 

The City of Livermore is a representative example, because it is a small exurban East Bay 
commuter city having at least one major regional employer, the Lawrence Livermore National 
Laboratory, and is a known agritourism destination (including wineries and harvest festivals).
Based on its rural-urban interface, the location variables in Table 3 are adjusted to prioritize drive 
times, which is different from the case in San Francisco, where walkable places with transit are 
prioritized. 


Table 3. Exurban GIS Variables 

Safety Access Equity 
HighUses not allowed within a Above average LEHD jobBlock groups with median 
Priority 500 ft. radius of Final density household income levels at or 
Approach for Takeoff and 
Landing (FATO):

- Schools (K12,
CommunityColleges,
Presence of largest employersin the bay area 
Block groups with aboveaverage population density 
below 30% of average 
Large population of singlehead of household 
(apartments can be built insome commercial zones) 
Universities) 

- Parks 
- Libraries 
- Gas Stations 
- Hospitals 
- Day care 
- Assisted Living 

Block groups with aboveaverage median household 
income 
Block groups with aboveaverage consumer 
transportation expenditure 
Presence of powerBlock groups having largeproportion of the population 
identifying as Black orAfrican American, AmericanIndian or Alaska Native, 
Asian, and Native Hawaiianor Other Pacific Islander 
Minimum Parcel 250 ft. x 
250 ft 
infrastructure (high voltage, 
substation) 
10-min drive to at least 1+ 
major transportation node 
Block groups having a largeproportion of the population 
identifying as Hispanic, 
Latinx, Chicanx 
Medium Airports within ½ nm of Proximity to a grand Block groups with medianPriority FATO boulevard or arterial household income levels at or 
Heliports within ½ nm of 10-min drive time to 1+ 
below 50% of average 
FATO major employer Large population of rentersVertiports within ½ nm of 
FATO 
10-min drive time to 1+ 
major transportation node 
(apartments can be built insome commercial zones) 
Primary language spoken 
other than English 
Low 0.25 mile walking distance to 
Priority 1+ major employer 
0.25 mile walking distance to 
1+ major transportation node 
0.25 mile walking distance to 
1+ POI 
10-min drive time to 10+ 
POI 

Other desirable variables if available are: 4050 ft. from physical obstruction (such as trees, poles, wires); approach and departurepaths; vertical separation based on 8:1 approach and departure surface; above average traffic counts; and above average office 
lease price 


The same GIS workflow was conducted for Livermore as was used in the previous cases, and in 
this output just three parcels out of 51,836 met site suitability parameters (Figure 8). 

Figure 8. Livermore Suitability Map 


At the urban-rural interface there exists more land area but also farther distances to transportation. 
Highways and employers are also located beyond the small-town core. Figure 8 identifies the 
locations suitable for vertiports although visual examination of ESRI aerial imagery shows it under 
construction. The Livermore sites are found to be closest to single-family and multi-family 
housing in the study and highlight the importance of integrating vertiports into residential zones, 
especially for smaller cities. 

4.5 East Bay & Peninsula Corridors 

Ground hubs for vertiports spaced approximately 40 miles apart mean the space between them 
could represent corridors in the East Bay (Alameda County) and along the Peninsula (northern 
Santa Clara County and San Mateo County). If San Francisco, San Jose, and Livermore each had 
vertiports, then a basic assumption would be that the airspace between these points of origin or 
destination would be needed for new low-altitude air corridors. For this reason, the same GIS 
workflow was applied to the landside spanning northern Santa Clara and San Mateo Counties,
and in Alameda County resulting in shaded pink suitability areas (Figure 9). GIS outputs reveal 
ample land is suitable for safe and equitable AAM operations with eVTOLs flying over, largely 
due to extensive agricultural lands. However, from AAM literature, eVTOLs could follow major 
highways (US 101, I-280, I-580, I-680, and I-880) or opt to fly above the San Francisco Bay.2 

Figure 9. Corridor Suitability Map 


2 When air corridor planning the ground space below, the accessibility parameter is no longer applicable 


5. Result and Recommendations 


5.1 Research Results and Implications 


This research demonstrates how GIS can be used to identify safe, accessible, and equitablevertiport locations in urban, suburban, and exurban areas of the San Francisco Bay Area. The 
results contribute to the limited planning scholarship published on AAM to date. AAM is a rapidly 
advancing transportation sector, and it is clear from meetings with stakeholders that considerable 
proprietary geospatial models are currently needed to address the pressing question about where 
vertiports belong in a region. Advanced approaches and models such as machine learning, neural 
networks, and real-time three-dimensional spatiotemporal analyses will no doubt prove useful 
tools for future AAM land and airside operations. However, at this early stage in the AAM 
ecosystem, it is the public, advocates, communities, and local government (lacking resourced GIS 
staff) who can benefit most from using a set of urban, suburban, and exurban matrices which, when 
applied in a simplistic geoprocessing workflow, clearly highlight possible future vertiport locations. 

One of the major results of our research are the site suitability maps, shown in Figure 9, which 
should be used at the start of the long-range planning engagement and can also be used as a rational 
basis for AAM decision-making. 

Figure 10. Summary of Suitability Maps 


San Francisco San Jose Livermore 


The results of our case studies clearly validate and strongly support the usefulness of our GIS 
modeling approach. As indicated in Table 4, our research modeling can effectively narrow largedatasets (parcels covering five counties) to less than 19% of all possible vertiport site locations in 
our case study regions. More suitable sites do not mean more vertiports are needed, just that more 
parcels met the criteria for the geographic setting. San Jose could, for example, need more 
vertiports than San Francisco even though fewer parcels met the high priority parameters for safety, 
access, and equity based on the assumptions and specifications of our current research. As an 
important feature and advantage of the GIS modeling applied in our research, the site selection 
results could be easily adjusted with the modification of the variables and their priority levels in 
the future. In addition to the safety, access, and equity variables considered in this research, we 
expect that more variables from both the supply and the demand side will be added in the GIS 
modeling analysis. The final selections of the vertiport location will also depend on other practical 
issues such as community acceptance, environment concerns, and some special needs. 

Table 4. Summary of Suitable Sites 

San Francisco San Jose Livermore 
Total Parcels 234,693 459,282 51,836 
Suitable Parcels 1,392 43 3 

This research’s case study found that each representative location has at least one underutilized or 
vacant parcel that could be redeveloped as a future vertiport. However, nowhere are vertiportspermitted (yet), and an AAM developer cannot apply for a building permit or zoning map 
amendment until comprehensive plans and land development ordinances are revised to include 
vertiport use. This responsibility falls on local government, community development staff, and 
planning commissions. 

5.2 Recommendations 

Based on the research experiences and lessons that were learned from this project, the following 
recommendations are made for vertiport site selection at different stages, from the perspectives of 
land-use planning, policy, and GIS modeling. 

Getting Started: 

- Determine the appropriate use case(s) for AAM integration within selected study area 
boundaries. 
- Develop a list of non-negotiable “high priority” parameters for vertiport locations. 
- Map existing flight path(s) and engage early with the FAA and local airport authorities. 


Data Acquisition: 

- Ensure there is easy data access across all agency departments including engineering, public 
works, transportation, recreation, urban forestry, and community development. 
- Review the metadata for geospatial appropriateness and consistency. 
- Maintain a data dictionary and data log to maintain accuracy and currency. 


Data Analysis: 


- Prioritize locations needing intermodality such as hospitals and transit stations. 
- Value proximity to safe pedestrian and bicycle routes, and other micromodal options. 
- Understand that suitability varies by community and that preferences change. 


Land Use Planning: 

- Incorporate GIS site suitability AAM analysis into the Transportation and Land Use 
sections of comprehensive plans. 
- Add vertiport as a land use category in land development codes or zoning codes. 
- Consider vertiports as a form of TOD infill and redevelopment. 


Engagement: 

Add participatory GIS in stakeholder workshops and incorporate virtual reality to lessen fears 
about eVTOL aircraft noise and their aesthetic impacts. 


6. Summary and Conclusions 

6.1 Research Summary 


At present, vertiport research cannot keep pace with the rapidly advancing AAM industry. Aircraft 
may be available in just a few years, but very few short-or long-range plans show awareness of, or 
policies to govern, safe, accessible, and equitable vertiport development. This study summarizes 
AAM literature and offers transportation planners a set of prioritized parameters that assign no 
cost to urban, suburban, and exurban spatial analysis. This study’s goals were to establish a 
systematic approach to vertiport site selection using GIS and to provide recommendations for how 
a region might begin planning for AAM. The workflow presented is standard but also flexible 
enough to allow for regional differences. Our case studies consider land use planning for AAM, 
especially as it relates to the San Francisco Bay Area, but our research methodology and modeling 
methods can be adapted and replicated in other places. 

The suitability maps constructed in this research serve as a conversation starting point about AAM 
land use. By creating variable matrices for safety, access, and equity parameters, GIS software can 
automatically consider access to existing multimodal transportation options and identify areas that 
may be equitably harmed by vertiport development. This is the basis for participatory GIS which 
is a powerful tool for effective engagement because the public can visualize vertiports, flight 
corridors, and these actions’ potential consequences. Suitability maps alone cannot sell AAM to 
communities, but it certainly helps, especially coupled with virtual reality and auralization. 

Considerable new knowledge was gained from this research because of the need for qualitative 
information gathered beyond the original scope of work. After a set of parameters were created 
and defined, we accomplished a few qualitative investigations including an extensive literature 
review, participation in industry working groups, meetings with industry professionals, and the 
hosting of a focus group, in addition to preparing for and conducting regional GIS mapping. The 
parameters were then assigned to a priority level of high, medium, or low, which varied depending 
on the geographic form at that place (urban, suburban, or exurban). Using geospatial analysis tools, 
subject parcels that met assigned criteria were identified and summated to determine suitable 
locations for vertiports. 

Ideally AAM and its vertiports will become a new sustainable mechanism to connect regional areas 
challenged by vehicular surface traffic and congestion. As is frequently the case in the San 
Francisco Bay Area, which is separated by water bodies, there are numerous freeways and bridges, 
and it has transit systems that are not seamlessly integrated making AAM a promisingcomprehensive transportation solution. With AAM comes new opportunities for public and 
private partnership to boost business, emergency, and tourism services, and to economically benefit 
local communities. Despite the initial approach to separate vertiports from housing and largely 
residential areas, the adoption of vertiports into these areas can meet new housing demands 
generated by an increased population, and also boost the economic development of these areas. 
Moreover, by incentivizing redevelopment and vitalization with vertiports, spaces can be 
transformed and overall benefit the community. However, failure to plan for AAM may lead to 
inadequate vertiport mitigation or worsen the incompatibilities which led to a history of 
inaccessible and inequitable airport development. Therefore, our research will not only make a 
great contribution to scholarly literature and research methodologies, but also provide strong 
support and practical guidance for vertiport development, land use, and regional planning. 

6.2 Further Studies 

The most significant challenge to GIS research is the availability and granularity of the geospatial 
information needed for the desired analysis. In the case study of this research, only free data were 
used, which precludes the use of potentially valuable information on segmented consumer mobility 
behaviors typically collected from mobile phone GPS records. With this additional data, planning 
vertiport locations would benefit greatly from understanding who, when, and where moves in a 
specific region such as the San Francisco Bay Area. 

Also, additional time to intensively ground truth suitable parcels would be useful. For example, 
Google Street View imagery showed one location under construction which is not suitable for now 
but could be a candidate location for a vertiport in the future. Site visits would also aid researchers 
in obstruction identification, since small items such as wires and poles are hard to identify in aerial 
images, although they occasionally appear as shadows. 

The type of landside study presented in this research is obviously needed for safe, accessible, and 
equitable vertiport placement. However, creating future hubs for ground and air connection 
requires thorough airside analysis too. It is expected that GIS will be equally helpful, if not more 
useful, in the airside and integration analysis. Software tools such as ESRI ArcScene and 3D 
Analyst can be applied to visualize eVTOL movement in corridors and around obstructions where 
LiDAR or point cloud renderings of the built environment exist. 

In the future, more GIS modeling-based research must extend beyond exurban places to examine 
vertiport site suitability across rural regions. While through this research we have gained extensive 
insight by working with industry stakeholders, the next phase should position Caltrans in a 
facilitatory role to liaise research and communications with local governments. For example, a GIS 
suitability mapping training can be carried out in order to refine and improve the application 
process around a more intuitive user experience. This could be a best practice that can advance 
up-to-date knowledge and incorporate future design principles for vertiport and AAM planning 
in general. 


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Below are various data layers used in this GIS case study. Sources are broken down into Zoning, 
Safety, Access, and Equity. 

Appendix A. GIS Data Portal Layers 

File Location File Type Description Location Data Provider 

Zoning 
https://data.acgov.org/datasets/0cac9cf8503841d7975fe5471c5815c4_0/explore 
Feature Layer(CSV/KML/Shapefile) 
Alameda County Zoning 
Alameda County 
Open Data – Alameda County 

https://data.acgov.org/search?collection=Dataset&q=land%20use&sort=name 
Feature Layer(CSV/KML/Shapefile) 
Alameda Parcels 
Alameda County 
Open Data – Alameda County 

https://data.acgov.org/datasets/7b064a13a9234bfba97654007ccbf8e8_0/explore?location=37.680196% 2C121.906442%2C11.00 
Feature Layer(CSV/KML/Shapefile) 
Alameda Census Tract Boundaries 
Alameda County 
Open Data – Alameda County 

https://fremont-ca-opendatacofgis.hub.arcgis.com/datasets/25db2e74c6254091a6f340cf01f8f092_0/explore?location=37.529627% 2C122.012239%2C12.61 
Feature Layer(CSV/KML/Shapefile) 
Zoning – Fremont 
Alameda County, Fremont 
City of Fremont Open DataHub 

https://earthworks.stanfor 
d.edu/catalog/ark28722s7w889 
Feature Layer(CSV/KML/Shapefile) 
Zoning – Hayward Alameda 
County,
Hayward 
Earthworks – Stanford 
Libraries – City of Hayward 
(Author) 

https://data.oaklandca.gov/dataset/OaklandZoning/
ngyq-upwh 
Feature Layer(CSV/KML/Shapefile) 
Zoning – Oakland Alameda 
County,
Oakland 
City of Oakland,
Department of Planning andBuilding, Planning andZoning Division 

https://gis.cccounty.us/Downloads/Planning/ 
Feature Layer(CSV/KML/Shapefile) 
Contra Costa Land Use 
Element 
Contra Costa 
County 
Open Data – Contra Costa 
County 

https://gis.cccounty.us/Downloads/Planning/ 
Feature Layer(CSV/KML/Shapefile) 
Contra Costa Zoning Contra Costa 
County 
Open Data – Contra Costa 
County 

https://www.cityofconcord.org/737/GIS-Maps-Portal 
Feature Layer(CSV/KML/Shapefile) 
Zoning – Concord Contra Costa 
County,
Concord 
Open Data – Concord 

https://milpitas-gismilpitas.hub.arcgis.com/datasets/ba1cd9f57c95468 
Feature Layer(CSV/KML/Shapefile) 
Zoning – Milpitas Santa Clara 
County,
Milpitas 
Open Data – City of 
Milpitas 


File Location File Type Description Location Data Provider 
Zoning 
18ce0451aeebc4d47_0/explore?location=37.429332%2C121.892600%
2C12.47 

https://giscupertino.
opendata.arcgis.com/datasets/Cupertino::
zoning/explore?location=
37.309899%2C122.043900%
2C13.85 
Feature Layer(CSV/KML/Shapefile) 
Zoning – Cupertino Santa Clara 
County,
Cupertino 
Open Data – City of 
Cupertino 

https://datamountainview.
opendata.arcgis.com/datasets/MountainView::zoningdistricts/
explore?location=37.402550%2C122.081350%
2C13.31 
Feature Layer(CSV/KML/Shapefile) 
Zoning – Mountain View Santa Clara 
County,
Mountain 
View 
Open Data – City ofMountain View 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::zoningdistricts/about 
Feature Layer Land Use Zoning Districts Santa Clara 
County, SanJose 
Open Data – City of San Jose 

https://www.cityofpleasantonca.gov/gov/depts/gis.asp 
Feature Layer(CSV/KML/Shapefile) 
Zoning – Pleasanton Alameda 
County,
Pleasanton 
Open Data – City of 
Pleasanton 

https://opengis.cityofpaloalto.org/OpenGisData/ 
Feature Layer (Shapefile) Zoning – Palo Alto Santa Clara 
County, PaloAlto 
Open Data – City of Palo 
Alto 

https://gismoragatown.opendata.arcgis.com/datasets/c18a51d380c840fd999953f6b8e5 dd14_0/about 
Feature Layer (Shapefile) Moraga Zoning (Public) Moraga,
Contra Costa 
County 
Open Data – Moraga, CA 

https://www.geosl.org/datasets/sanleandro::zoning-1/about 
Feature Layer (Shapefile) San Leandro Zoning San Leandro,
Alameda 
County 
Open Data – City of San 
Leandro 

https://www.cityofpleasantonca.gov/gov/depts/gis.asp 
SHP Pleasanton Zoning Pleasanton,
Alameda 
County 
Open Data – City of 
Pleasanton 

https://pleasant-hillspatial-datamappleasanthill.hub.arcgis.com/maps/726d7292263a4b11baed63b22002ee4 8/about 
Feature Layer(CSV/KML/Shapefile) 
Pleasant Hill Zoning Contra Costa 
County,
Pleasant Hill 
Pleasant Hill Open Data 


File Location File Type Description Location Data Provider 
Zoning 

https://data.sfgov.org/Geographic-Locations-andBoundaries/Bay-AreaCounties/s9wg-vcph 
Feature Layer(CSV/KML/Shapefile) 
Bay Area Counties San 
Francisco 
Bay Area 
Open Data – City andCounty of San Francisco 

https://data.sfgov.org/Geographic-Locations-andBoundaries/ZoningMap-ZoningDistricts/3i4a-hu95 
Feature Layer(CSV/KML/Shapefile) 
SF Zoning Counties San 
Francisco 
County 
Open Data – City andCounty of San Francisco 

https://data.sfgov.org/Housing-andBuildings/Landuse/us3sfp9q 
Feature Layer(CSV/KML/Shapefile) 
SF Land Use San 
Francisco 
County 
Open Data – City andCounty of San Francisco 

https://data.sfgov.org/Geographic-Locations-andBoundaries/BuildingFootprints/ynuv-fyni 
Feature Layer(CSV/KML/Shapefile) 
SF Building footprints San 
Francisco 
County 
Open Data – City andCounty of San Francisco 

https://datasmcmaps.opendata.arcgis.com/datasets/coastalzone/explore?location=37.295496%2C122.317500%2C10.55 
Feature Layer(CSV/KML/Shapefile) 
Coastal Zone San Mateo 
County 
Open Data – San Mateo 
County 

https://datasmcmaps.opendata.arcgis.com/datasets/planningzones/
explore 
Feature Layer(CSV/KML/Shapefile) 
Planning Zones San Mateo 
County 
Open Data – San Mateo 
County 

https://datasmcmaps.opendata.arcgis.com/datasets/2014-2022housing-elementopportunity-sites/explore 
Feature Layer(CSV/KML/Shapefile) 
Housing Element 20142022 
San Mateo 
County 
Open Data – San Mateo 
County 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::updateddemographicspopulation/about 
Feature Layer(CSV/KML/Shapefile) 
Updated Demographics – 
Population: includescurrent-year estimates and5-year projections of U.S.
demographic data 
Santa Clara 
County 
City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::freewayramps/about 
Feature Layer(CSV/KML/Shapefile) 
Freeway Ramps: Arepresentation of the edgeof pavement of ramps on oroff a freeway, highway, orinterstate, to determine thefurthest reaches of a 
Santa Clara 
County 
City of San Jose 


File Location File Type Description Location Data Provider 
Zoning 
freeway’s, highway’s, or 
interstate’s width 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::freeway/about 
Feature Layer(CSV/KML/Shapefile) 
Freeway: A representationof the edge of pavement offreeways to determine thefurthest reaches of freeway’s 
width 
Santa Clara 
County 
City of San Jose 

https://data.sccgov.org/Government/Parcels/b6cf8q54 
Feature Layer(CSV/KML/Shapefile) 
Parcels Santa Clara 
County 
Open Data – Santa Clara 
County 

https://data.sccgov.org/Government/LandPolygon/24sy-ym6n 
Feature Layer(CSV/KML/Shapefile) 
Land Polygon Santa Clara 
County 
Open Data – Santa Clara 
County 

https://gisdatasccplanning.hub.arcgis.com/datasets/zoning2/explore 
Feature Layer(CSV/KML/Shapefile) 
Zoning Santa Clara 
County 
Open Data – Santa Clara 
County Planning Office 

https://gisdatasccplanning.hub.arcgis.com/datasets/unincorporated-areas2/explore?location=37.200000%2C121.700000%2C9.86 
Feature Layer(CSV/KML/Shapefile) 
Unincorporated Areas Santa Clara 
County 
Open Data – Santa Clara 
County Planning Office 

https://gisdatasccplanning.hub.arcgis.com/datasets/land-cover2/explore 
Feature Layer(CSV/KML/Shapefile) 
Land Cover Santa Clara 
County 
Open Data – Santa Clara 
County Planning Office 

https://gisdatasccplanning.hub.arcgis.com/datasets/general-plan2/explore?location=37.339120%2C121.798050%2C10.00 
Feature Layer(CSV/KML/Shapefile) 
General Plan Santa Clara 
County 
Open Data – Santa Clara 
County 

https://gisdatacsj.opendata.arcgis.com/datasets/zoningdistricts/explore 
Feature Layer(CSV/KML/Shapefile) 
Zoning Districts San Jose, CA Open Data – City of San 
Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::highpoverty-rate/about 
Feature Layer(CSV/KML/Shapefile) 
High Poverty Rate San Jose, 
CA 
Open Data – City of San Jose 

https://data.sfgov.org/Culture-and-Recreation/Recreation-
Feature Layer(CSV/KML/Shapefile) 
Recreation and Parks 
Properties 
San Francisco 
Open Data – San Francisco 


File Location File Type Description Location Data Provider 
Zoning 
and-ParksProperties/gtr9-ntp6 

https://gisdatacityofpittsburg.opendata.arcgis.com/datasets/cityofpittsburg::copzoning/about 
Feature Layer (Shapefile) 
City of Pittsburg, Zoning 
Pittsburg,
Alameda County 
City of Pittsburg Open dataHub 

https://data.cityofberkeley.info/CityGovernment/ZoningDistricts/2dtu-vge3 
Feature Layer(CSV/KML/Shapefile) 
Zoning – Berkeley 
Alameda County, Berkeley 
Open Data – City of Berkeley 

https://public-gismissioncity.opendata.arcgis.com/datasets/missioncity::city-of-santa-clarazoning/explore?location=37.389796%2C121.921390%2C14.74 
Feature Layer(CSV/KML/Shapefile) 
Zoning – City of Santa Clara 
Santa Clara County, Santa Clara 
City of Santa Clara
Enterprise GIS PublicPortal 

https://sunnyvalegeohubcityofsunnyvale.hub.arcgis.com/datasets/81f57ae1ca49400f8a1a322eae53391c_0/explore?location=37.393990%2C122.023926%2C12.88&s howTable=true 
Feature Layer(CSV/KML/Shapefile) 
Parcels – Zoning Sunnyvale 
Sunnyvale Geohub 

https://data.sfgov.org/Housing-andBuildings/Land use/us3sfp9q 
SHP 
SF – land use (for SQ area) 
San Francisco, CA 
SF Open Data Portal 


File Location File Type Description Location Data Provider 
Safety 

https://gis.data.ca.gov/datasets/5a3754e93bb44583903c7025389b8422_0/about 
Feature Layer(CSV/KML/Shapefile) 
Aviation/AWOS 
California 
California State Geoportal 

https://data.cnra.ca.gov/dataset/nhdmajorfeatures/resource/f7ab90e7-897a-49ff8bba-98035d8c5015 
SHP Major Rivers and Creeks 
California 
CA Natural Resources Agency 

https://data.cnra.ca.gov/dataset/nhdmajorfeatures/resource/33b8464d-8e03-4301acb4-0e753c51d0f7 
SHP 
Major Lakes and Reservoirs 
California 
CA Natural Resources Agency 

https://hub.arcgis.com/datasets/CalEMA::ca-energycommission-gasstations/about 
Feature Layer (Shapefile) 
gas stations – California 
California
CA Energy Commission 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::major-privatedevelopmentfootprints/about 
Feature Layer 
Major Private Development Footprints 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/school/explore?location=37.247152%2C121.852629%2C11.40 
Feature Layer 
School Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/library/explore?location=37.337662%2C121.864349%2C12.76 
Feature Layer 
Library Santa Clara County,
San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::hospital/about 
Feature Layer(CSV/KML/Shapefile) 
Hospitals Santa Clara County,
San Jose 
Open Data – City of San Jose 


File Location File Type Description Location Data Provider 
Safety 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::road-noise/about 
Feature Layer(CSV/KML/Shapefile) 
Road Noise: Boundaries of noise contours from 
General Plan 2020 update 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::airport-noise/about 
Feature Layer(CSV/KML/Shapefile) 
Airport Noise:Boundaries of San Jose 
International airports noise area 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::streets/about 
Feature Layer(CSV/KML/Shapefile) 
Streets: segments representing centerlines of all roadways within San Jose, CA 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::high-voltagepowerlinetowers/about 
Feature Layer(CSV/KML/Shapefile) 
High Voltage PowerlineTowers 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::railroad/about 
Feature Layer(CSV/KML/Shapefile) 
Railroad 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://data.acgov.org/datasets/1197af3bbb5e4aa6be187ee428757a9a_0/explore 
Feature Layer(CSV/KML/Shapefile) 
Airports and heliports in the Bay Area 
Bay Area 
Open Data – Alameda 
County 

https://www.californiaschoolcampusdatabase.org/#download 
GDB 
California Schools & Public Universities 
CA 
California School Campus Database 

https://gis.data.ca.gov/datasets/CHHSAgency::oshpdhealthcare-facilities/about 
Feature Layer(CSV/KML/Shapefile) 
OSHPD Healthcare Facilities 
California 
CA State Geoportal 

https://hifldgeoplatform.opendata.arcgis.com/datasets/geoplatform::child-care-centers/about 
Feature Layer(CSV/KML/Shapefile) 
Child Care Centers 
U.S. 
Homeland 
Infrastructure 
Foundation 

https://www.parks.ca.gov/?page_id=29682 
SHP 
CA State Parks 
California 
California State Geoportal 


File Location File Type Description Location Data Provider 
Safety 
https://data.sfgov.org/Culture-andRecreation/ParkLands-Recreationand-ParksDepartment/42rwe7xk 
SHP 
SF parks and rec 
San Francisco 
San Francisco Open Data 

https://gisdatasccparks.hub.arcgis.com/datasets/SCCParks::santa-claracountypark/explore?location=37.203842%2C121.744361%2C10.54 
SHP 
Santa Clara County Parks 
Santa Clara County 
Santa Clara County Parks GIS Open Data 

https://gis.data.ca.gov/datasets/CDEGIS::￿alifornia-schools2019-20/about 
SHP 
CA public schools 
CA 
California State Geoportal 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::park/about 
SHP 
San Jose local and County Parks 
San Jose 
City of San Jose Open Data 

https://data.acgov.org/datasets/4842a70247ee493eb1d523f176c04483_0/about 
CSV 
Alameda County Parks 
Alameda County 
Alameda County GIS portal 

https://www.parksforcalifornia.org/parkaccess/?overlays1=parks%2Cnoparkaccess&overlays2=parks%2Cparksper1000 
SHP 
California parks (local,county, state, federal) 
California 
California State Parks 

https://services5.arc 
gis.com/bBj6qT5vDKhNkfmk/arcgis/rest/services/Library_Branch_MASTER_ 
WFL1/FeatureServer 
SHP Libraries California library.ca.gov 

https://data.sfgov.org/Housing-andBuildings/Tall-BuildingInventory/5kya-mfst 
SHP 
Tall Building Inventory 
San Francisco 
SF Open Data Portal 


File Location File Type Description Location Data Provider 
Safety 

https://data.acgov.org/datasets/b55c25ae04fc47fc9c188dbbfcd51192_0/about 
SHP 
Parcels 
Alameda County 
Alameda County GIS portal 

https://gisdatacaltrans.opendata.arcgis.com/datasets/d8833219913c44358f2a9a71bda57f76_0/about 

Traffic Volume 

Caltrans Open Data 

https://www.socialexplorer.com/reports/socialexplorer/en/report/cd771e6e-3d5211ed-b79aaf6e515729b6 
csv 

Survey:Market ProfileData 2021 
Data set:Original Tables(ORG)Table: MPD_110. Employment by Sector(Employees) [20] 
5 counties 
Social Explorer 

https://www.socialexplorer.com/data/ACS2020_5yr/metadata/?ds=SE&table=A10009 
csv 
Social Explorer Tables:
ACS 2020 (5-YearEstimates) (SE)
Table: A10009. 
Households by Presenceof People Under 18 
Years by HouseholdType [19] 
6 counties 
Social Explorer 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::parcels/about 
shp 
San Jose Parcels 
San Jose, CA 
San Jose City Data Portal 

https://data.sfgov.org/GeographicLocations-andBoundaries/ParcelsActive-andRetired/acdm-wktn 
shp 
San Francisco Parcels 
San Francisco, CA 
SF Open data portal 

https://oehha.ca.gov/calenviroscreen/maps-data/downloaddata 
Feature Layer (Shapefile) 
Shows the CalEnviroScreen 4.0 results by OEHHA 
California OEHHA 


File Location File Type Description Location Data Provider 
Access 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::transportation-analysiszones/about 
Feature Layer 
Transportation Analysis Zones 
San Francisco Bay Area 
Open Data – City of San Jose 

https://data.smcgov.org/Transportation/Caltrain-Routes/upph-wy44 
Feature Layer(CSV/KML/Shapefile) 
Caltrain Routes 
Bay Area 
Open Data – San Mateo County 

https://data.smcgov.org/Transportation/Caltrain-Stations-andStops/jzd3-rqcd 
Feature Layer (CSV/KML/Shapefile) 
Caltrain Stations and Stops 
Bay Area 
Open Data – San Mateo County 

https://data.smcgov.org/Transportation/BARTLines/ni6s-89wu 
Feature Layer(CSV/KML/Shapefile) 
BART Lines 
Bay Area 
Open Data – San Mateo County 

https://data.smcgov.org/Transportation/BARTStations/pj5k-xgir 
Feature Layer(CSV/KML/Shapefile) 
BART Stations 
Bay Area 
Open Data – San Mateo County 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::downtown-parking-lots/about 
Feature Layer(CSV/KML/Shapefile) 
Downtown Parking Lots 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::averagedaily-traffic/about 
CSV/KML/Shapefile 
Average Daily Traffic collected by travel direction 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/consumerexpenditure/explore?location=37.327185%2C122.002616%2C12.58&showTable=true 
Feature Layer 
Consumer Expenditure Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/￿aliforncommunity-surveycommute-towork/explore 
Feature Layer 
American Community Survey – Commute to Work 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::￿aliforncommunity-surveyhousing/about 
Feature Layer 
American Community Survey – Housing 
Santa Clara County,San Jose 
Open Data – City of San Jose 


File Location File Type Description Location Data Provider 
Access 
https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::updateddemographicsincome/about 
Feature Layer 
UpdatedDemographics – Income 
Santa Clara County, San Jose 
Open Data – City of San Jose 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::vacantland-inventory/about 
Feature Layer(CSV/KML/Shapefile) 
Vacant Land Inventory 
Santa Clara County,San Jose 
Open Data – City of San Jose 

https://www.eia.gov/maps/layer_info-m.php 
SHP 
Power Infrastructure (various SHPs) 
U.S. 
U.S. EnergyInformation Administration 

https://nhts.ornl.gov/ 
CSV/SAS/DBF National Household Travel Survey 
U.S. 
NHTS 

https://www.bts.gov/latch/latch-data 
CSV/SAS 
Local Area Transportation Characteristics for Households Data(NHTS 2017TransferabilityStatistics) 
U.S. 
BTS (Bureau ofTransportationStatistics) 

https://www.bart.gov/schedules/developers/geo 
KML 
Stations and Lines 
Bay Area 
BART Open Data 

https://gis.data.ca.gov/datasets/1f71fa512e824ff09d4b9c3f48b6d602_0/about 
SHP 
National Highway System 
California CA 
State Geoportal 

https://gis.data.ca.gov/datasets/e21246e58c6f46edb39aa5a1639bc2ad_0/about 
SHP 
Hospital Heliports California 
CA State Geoportal 

https://cecgiscaenergy.opendata.arcgis.com/datasets/CAEnergy::￿alifornia-electricsubstations/about 
SHP 
California Electric Substations 
California 
California Energy Commission 

https://cecgiscaenergy.opendata.arcgis.com/datasets/CAEnergy::￿alifornia-electrictransmission-lines/about 
SHP 
California Electric 
Transmission Lines 
California 
California Energy Commission 

https://data.sfgov.org/Transportation/MTAparkingcensus_offstreet/dkzc-uy8h 
SHP 
MTA.parkingcensus_offstreet 
San Francisco 
SF Open Data Portal 

https://lehd.ces.census.g 
CSV 
LEHD Origin-
California 
U.S. Census Bureau 


File Location File Type Description Location Data Provider 
Access 
ov/data/#lodes 
Destination Employment Statistics(LODES) 

https://data.census.gov/cedsci/table?q=population&g=0500000US06001%241500000,06013%241500000,06075%241500000,06081%241500000,06085%241500000&tid=ACSDT5Y2020.B01 003 
CSV 
B01003 TOTAL POPULATION 
5 counties BG 
U.S. Census Bureau 

https://www.socialexplorer.com/tables/ACS2020_5yr/R13188531 
CSV 
A00002 Population Density – ACS 5YR 2020 
5 counties BG 
Social Explorer;U.S. Census Bureau 

https://www.socialexplorer.com/tables/ACS2020_5yr/R13188546 
CSV 
A14006. Median 
Household Income (In2020 Inflation Adjusted Dollars) – ACS 5YR 2020 
5 counties BG 
Social Explorer;U.S. Census Bureau 

https://www.socialexplorer.com/tables/EASI2021/R13188548 
CSV 
Market Profile Data 2021: MPD_117. Consumer Expenditures,Transportation 
5 counties BG 
Social Explorer; U.S. Census Bureau 

https://opendata.mtc.ca.gov/datasets/MTC::transportation-analysiszones/about 
SHP 
Transportation Analysis Zones 
California 
MTC 

https://opendata.mtc.ca.gov/datasets/MTC::plan-bay-area-2040forecastemployment/about 
CSV 
Plan Bay Area 2040Forecast – Employment 
Bay Area 
MTC 

https://opendata.mtc.ca.gov/datasets/MTC::plan-bay-area-2040forecast-land use-andtransportation/about 
CSV 
Plan Bay Area 2040
Forecast – Land Use 
and Transportation 
Bay Area 
MTC 

https://gisdatacsj.opendata.arcgis.com/datasets/CSJ::averagedaily-traffic/about 
SHP 
Average Daily Traffic San Jose, CA City of San Jose open 
data portal 

https://congestion.sfcta.org 
CSV 
Congestion in SF San Francisco, CA San Francisco County
TransportationAuthority 


File Location File Type Description Location Data Provider 
Access 
https://cdn.nar.realtor/sites/default/files/documents/2022-q1commercial-
metromarket-reports-ca-0509-2022.pdf 
Office Lease Pricing Santa Clara County,
San Jose 
National Association of 
Realtors 


File Location File Type Description Location Data Provider 
Equity 

https://www.census.gov/cgibin/geo/shapefiles/index.php?year=2021&layergroup=Block+Groups 
SHP 2021 Census Block 
Groups 
5 Counties U.S. Census 

https://www.socialexplorer.com/data/ACS2020_5yr/metadata/?ds=SE&table=B04001 
CSV 
ACS 2020 (5-Year 
Estimates)
Data Source: Social 
Explorer; U.S. Census 
Bureau 
Data set: Social ExplorerTables: ACS 2020 
(5-Year Estimates) (SE)
Table: B04001. 
Hispanic or Latino byRace (CollapsedVersion) 
5 Counties Social Explorer;
U.S. Census Bureau 

https://www.socialexplorer.com/data/ACS2020_5yr/metadata/?ds=SE&table=B10040 
CSV 
Survey: ACS 2020(5-Year Estimates)
Data set: Social ExplorerTables: ACS 2020 
(5-Year Estimates) (SE)
Table: B10040. 
Residents Paying MoreThan 30% or at least 
50% of Income on 
Selected Home 
Ownership Expenses [3]
Universe: 
Owner-OccupiedHousing Units 
5 Counties Social Explorer;
U.S. Census Bureau 

https://www.socialexplorer.com/data/ACS2020_5yr/metadata/?ds=SE&table=A10009 
CSV 
Survey: ACS 2020 
(5-Year Estimates)
Data set: Social 
Explorer Tables: ACS2020 (5-YearEstimates) (SE)
Table: A10009. 
Households byPresence of PeopleUnder 18 Years byHousehold Type 
5 Counties Social Explorer;
U.S. Census Bureau 

https://www.socialexplorer.com/data/ACS2021_
CSV
Survey: ACS 2021(5-Year Estimates)
5yr/metadata/?ds=ACS2Data set: American 
1_5yr&var=B25003002 Community Survey2021 (ACS21_5yr)
Table: 
B25003. Tenure [3]
Universe: Occupiedhousing units 
5 Counties
Social Explorer;U.S. Census Bureau 

https://data.census.gov/table?tid=ACSST5Y2020.S1101 
CSV American Community Survey 2020: ACS 5-Year 
Estimates SubjectTables 
S1101 – 
HOUSEHOLDS 
AND FAMILIES 
5 Counties – census 
tracts 
U.S. Census 

https://data.census.gov/table?q=B04006 
CSV 
American Community Survey2021: ACS 1-Year 
Estimates Detailed 
Tables 
B04006 – PEOPLE 
REPORTING 
ANCESTRY 
5 Counties – census 
tracts 
U.S. Census 

https://data.census.gov/table?q=S1601 
CSV 
American Community Survey 2021: ACS 1-Year 
Estimates Detailed 
Tables 
S1601 – 
LANGUAGE 
SPOKEN AT 
HOME 
5 Counties – census 
tracts 
U.S. Census 


Some zoning layers were provided via email because agencies require a subject to written agreement 
for data use form, but this is information still free to anyone who requests it and is permitted for 
use in GIS location analysis. 

Appendix B. Requested Data Layers 

File Type Description Location Data Provider/Contact 
SHP Zoning – Dublin Alameda jeff.baker@dublin.ca.gov 
SHP Zoning and Parcels – Livermore Alameda planning@cityoflivermore.net 
SHP Zoning – Union City Alameda planning@unioncity.org 
GDB Zoning – Albany Alameda planning@albanyca.org 
SHP Zoning – Alameda Alameda planning@alamedaca.gov 
SHP Zoning – San Ramon Contra Costa Planning@sanramon.ca.gov 
SHP Zoning – Antioch Contra Costa bpeters@ci.antioch.ca.us 
SHP Zoning – Oakley Contra Costa ruiz@ci.oakley.ca.us 
SHP Zoning – Pittsburgh Contra Costa GISServices@pittsburgca.gov 
SHP Zoning – Brentwood Contra Costa engineering@brentwoodca.gov 
SHP Zoning – Martinez Contra Costa dutyplanner@cityofmartinez.org 
SHP Zoning – Pleasant Hill Contra Costa tfujimoto@pleasanthillca.org 
SHP Zoning – Walnut Creek Contra Costa DutyPlanner@walnut-creek.org 
SHP Zoning – Orinda Contra Costa orindaplanning@cityoforinda.org 
SHP Zoning – Lafayette Contra Costa planner@lovelafayette.org 
SHP Zoning – Hercules Contra Costa smatinpour@ci.hercules.ca.us 
SHP Zoning – San Pablo Contra Costa planning@sanpabloca.gov 
SHP Zoning – El Cerrito Contra Costa planning@ci.el-cerrito.ca.us 
SHP Zoning – Colma San Mateo planning@colma.ca.gov 
SHP Zoning – Los Gatos Santa Clara planning@losgatosca.gov 
SHP Zoning – Saratoga Santa Clara planning@saratoga.ca.us 
GDB Zoning – Morgan Hill Santa Clara planning@morganhill.ca.gov 
SHP Zoning – Los Altos Santa Clara planning@losaltosca.gov 


The following list is the GIS attribute table for parcels identified as suitable for vertiports in 
San Francisco, San Jose, and Livermore. 

Appendix C. Suitable Site Locations 

San Francisco 
1355 Sansome St, San Francisco, CA 94111 
201 Filbert St, San Francisco, CA 94133 
2001 The Embarcadero, San Francisco, CA 94133 
1 Beach St, San Francisco, CA 94133 
100–172 Beach St, San Francisco, CA 94133 
709 Taylor St, San Francisco, CA 94108 
626 Grant Ave, San Francisco, CA 94108 
650 California St, San Francisco, CA 94108 
600 California St, San Francisco, CA 94108 
725 Sacramento St, San Francisco, CA 94108 
731 Commercial St, San Francisco, CA 94108 
710 Grant Ave, San Francisco, CA 94108 
631 Kearny St, San Francisco, CA 94108 
779 Clay St, San Francisco, CA 94108 
720 California St, San Francisco, CA 94108 
1180 Evans Ave, San Francisco, CA 94124 
401 Cesar Chavez St, San Francisco, CA 94124 
20th St, San Francisco, CA 94107 
1270 Missouri St, San Francisco, CA 94107 
2000 McKinnon Ave, San Francisco, CA 94124 
1800 Folsom St, San Francisco, CA 94103 
1750 Harrison St, San Francisco, CA 94103 
2550 Cesar Chavez St, San Francisco, CA 94124 
2525 Marin St, San Francisco, CA 94124 


San Jose 
80 Great Oaks Blvd, San Jose, CA, 95119 
30 Las Colinas Ln, San Jose, CA, 95119 
6203 San Ignacio Ave, San Jose, CA, 95119 
6150 San Ignacio Ave, San Jose, CA, 95119 
6626-6650 Santa Teresa Blvd, San Jose, CA, 95119 
6601-6645 Via del Oro, San Jose, CA, 9511 
6659-6673 Via del Oro, San Jose, CA, 95119 
79 Great Oaks Blvd, San Jose, CA, 95119 
6340-6358 San Ignacio Ave, San Jose, CA, 95119 
30 Great Oaks Blvd, San Jose, CA, 95119 
9 Great Oaks Blvd, San Jose, CA, 95119 
100 Great Oaks Blvd, San Jose, CA, 95119 
6480 Via del Oro, San Jose, CA, 95119 
3150 Almaden Expy, San Jose, CA, 95118 

Livermore 
1510 Holmes St, Livermore, CA, 94550 
1706-1754 N Livermore Ave, Livermore, CA, 94551 
Granada Shopping Center, 1951 Holmes St, Livermore, CA 94550 


About the Authors 

Wenbin Wei 

Dr. Wenbin Wei is a Professor in the Department of Aviation and Technology in the College of 
Engineering at San Jose State University. He is also an Affiliated Professor in the Department of 
Industrial and System Engineering as well as Director of the Human Automation Integration 
Lab (HAIL). Before joining the faculty at San Jose State University, Dr. Wei was a research 
analyst in the Department of Operation Research and Decision Support at American Airlines. He 
earned a Ph.D. from the University of California, Berkeley in transportation engineering and 
management. Dr. Wei has conducted research spanning transportation planning, traffic control 
and management, multimodal transportation systems, rail and high-speed rail transportation,
airport and airline management, unmanned aerial vehicles (UAV), Advanced Air Mobility(AAM), logistics, and supply chain management. Dr. Wei has obtained more than $2 million in 
research grants from FAA, NASA, and Caltrans. His research was published in over 40 articles in 
peer-reviewed journals and conference proceedings. 

Kerry Rohrmeier 

Dr. Kerry Rohrmeier, AICP, is an Assistant Professor in the Department of Urban and Regional 
Planning at San Jose State University and a former practicing land planner. Dr. Rohrmeier earned 
a doctorate at the University of Nevada, Reno in Geography in 2013 and has since taught GIS to 
hundreds of California State University students. Her funded research focus covers Bay Area 
geographies scaling from sites to regions and is published in prestigious geography and planning 
journals. 

Tiffany Martinez 

Tiffany Martinez worked as a graduate research assistant for the entire project and was hired as a 
Transportation Planner at Caltrans following the completion of this study and her MA in 
Geography from San Jose State University. Ms. Martinez also earned her BA in Global Studies 
from San Jose State University. 

Michael Winans 

Michael Winans worked as a graduate research assistant toward the end of this project and brought 
considerable expertise from his role in Airport Operations at San Jose’s Mineta International 
Airport. Mr. Winans is completing his MS in Urban Planning at San Jose State University and 
previously earned a BS in Aviation from San Jose State University. 


Heungseok Park 

Heungseok Park worked as an undergraduate research assistant at the beginning of this project 
before being hired as a Transportation Planner at Kimley-Horn consulting. Mr. Park earned a BSin Aviation from San Jose State University during which he won first place on a student team in a 
national vertiport design challenge sponsored by NASA. 


MTI FOUNDER 
Hon. Norman Y. Mineta 

MTI BOARD OF TRUSTEES 

Founder, Honorable 
Norman Mineta*** 

Secretary (ret.), 
US Department of Transportation 


Chair, 
Will Kempton 

Retired Transportation Executive 


Vice Chair, 
Jeff Morales 

Managing Principal 
InfraStrategies, LLC 

Executive Director, 
Karen Philbrick, PhD* 

Mineta Transportation Institute 
San José State University 

Winsome Bowen 

President 

Authentic Execution, Corp 


David Castagnetti 

Partner 

Dentons Global Advisors 


Maria Cino 

Vice President 

America & U.S. Government 
Relations Hewlett-Packard Enterprise 


Grace Crunican** 

Owner 
Crunican LLC 

Donna DeMartino 

Retired Transportation Executive 


John Flaherty 

Senior Fellow 

Silicon Valley American 


Leadership Form 

Stephen J. Gardner* 

President & CEO 
Amtrak 


Rose Guilbault 

Board Member 
San Mateo County 

Transit District (SamTrans) 


Kyle Christina Holland 

Senior Director, 
Special Projects,TAP Technologies, 
Los Angeles County Metropolitan 
Transportation Authority (LA Metro) 


Ian Jefferies* 

President & CEO 
Association of American Railroads 


Diane Woodend Jones 

Principal & Chair of Board 
Lea + Elliott, Inc. 


Therese McMillan 

Retired Executive Director 


Metropolitan Transportation 
Commission (MTC) 

Abbas Mohaddes 

CEO 

Econolite Group Inc. 


Stephen Morrissey 

Vice President – Regulatory and 
Policy 

United Airlines 


Toks Omishakin* 

Secretary 
California State Transportation 

Agency (CALSTA) 

Marco Pagani, PhD* 

Interim Dean 


Lucas College and 
Graduate School of Business 
San José State University 

April Rai 

President & CEO 


Conference of Minority 

Transportation Officials (COMTO) 


Greg Regan* 

President 

Transportation Trades Department, 
AFL-CIO 


Paul Skoutelas* 

President & CEO 
American Public Transportation 
Association (APTA) 


Kimberly Slaughter 

CEO 

Systra USA 


Tony Tavares* 

Director 
California Department of 

Transportation (Caltrans) 

Jim Tymon* 

Executive Director 
American Association of 
State Highway and Transportation 
Officials (AASHTO) 


* 
= Ex-Officio 


** = Past Chair, Board of Trustees 

*** = Deceased 


Directors 

Karen Philbrick, PhD 

Executive Director 


Hilary Nixon, PhD 

Deputy Executive Director 


Asha Weinstein Agrawal, PhD 

Education Director 


National Transportation Finance 

Center Director 


Brian Michael Jenkins 

National Transportation Security 

Center Director