GUDP San Jose Research White Paper

Last updated: January 21, 2016

Student and teaching team representatives at San Jose City Hall. Table of Contents

Abstract Introduction

Background Problem Statement Project Goals

Table 1. Student projects completed in 2015­2016 research phase. Project Team

Sustainability Figure 1. Sustainability diagram.

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Water Table 2. Projected water deliveries. Table 3. Predicted supply and demand for drought years. Table 4. Projected per capita use, multiple dry years vs. normal year (gpcd). Table 5. Indoor water use vs. retrofit indoor water use. Table 6. Projected water deliveries given retrofit home. Table 7. Predicted supply and demand for drought years given retrofit home. Table 8. Projected per capita use given retrofit home. Equation 1. Rainwater capture. Table 9. Rainwater capture analysis ­ average, annual rainfall. Table 10. Rainwater capture analysis ­ drought year, annual rainfall.

Solid Waste Figure 2. Trash diverted from landfills. Figure 3. Environmental, social, and economic impacts of solid waste. Figure 4. End­to­end solid waste lifecycle. Figure 5. Waste indicators. Figure 6. Heat map of residential waste density per capita. Figure 7. Waste impact model.

Housing Figure 8. Housing demand for 2040. Figure 9. Basic layouts for single­family and multi­family house used in analysis. Figure 10. Process flow diagram for residential building life cycle. Figure 11. Ratio of environmental impacts, single­family vs. multi­family. Figure 12. Comparison of global warming potential across building lifecycle.

Resilience Figure 13. Resilience diagram. Figure 14. System diagram of urban resilience. The green and red arrows represent hypothetical increases (green) and decreases (red) in different types of capital.

Critical Scenario Analyses: Natural Hazard Risk Earthquakes

Figure 15. Earthquake risk analysis procedure using the URF. Figure 16. Annualized earthquake loss per capita in San Jose.

Floods Figure 17. Predicted 100 year flood economic loss ($).

Community Vulnerability to Hazards Table 11. ABAG Community Vulnerability Indicators.

Community Health Methodology Health Score

Figure 18. Community health score. Nutritional Health

Table 12. Nutritional health summary statistics for Santa Clara County.

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Figure 19. Obesity rates by zip code in San Jose. Environmental Health

Table 13. Environmental health summary statistics for Santa Clara County. Figure 20. Asthma rates by zip code in San Jose.

Mental Health Table 14. Mental health summary statistics for Santa Clara County. Figure 21. Incidence of severe psychological distress by zip code in San Jose.

Public Health Table 15. Public health summary statistics for Santa Clara County. Figure 22. Influenza and pneumonia death rates by neighborhood in San Jose.

Vibrancy Figure 23. Vibrancy diagram. Figure 24. Virtuous cycle of downtown growth. Figure 25. Virtuous cycle of transit­oriented development.

Public Transit Figure 26. Primary and intermediate stops. From the data used to calculate the score of each station, we could see that the density of land uses and patterns varies geospatially. For instance, home density varies geospatially (Figure 28). Also, pedestrian and biker activity varies geospatially (Figure 29). Pedestrian activity is concentrated downtown, whereas, biker activity is distributed throughout the city. Figure 32. VTA Light Rail Routes. Figure 33. Variables measured at each transit stop. Figure 34. Variables measured at each transit stop (continued). Table 16. Light rail stop score. Figure 36. Station scores and walking zones. Figure 37. Station scores and home and job density.

Focused Growth Figure 38. Focused Growth in San Jose. Figure 39. Locations of newly built housing in San Jose since 2008. Figure 40. Comparison of resident race: new development areas vs. San Jose average. Figure 41. Comparison of resident age: new development areas vs. San Jose average. Figure 42. Comparison of resident income: new development areas vs. San Jose average. Figure 43. Comparison of resident income over time.

Conclusions

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Abstract This White Paper offers a cursory assessment of sustainability, resilience, and vibrancy in the City of San Jose, which was conducted by an interdisciplinary project team of students in a 10­week course at Stanford in 2015. Students developed a framework to provide tools and indicators for monitoring urban system performance through the three holistic lenses of sustainability, resilience, and vibrancy. They then pursued various projects within this framework to varying degrees of completion. Methods and metrics used in these projects include trend analysis, network analysis, life cycle assessment, risk analysis, and more. In general, results from the projects suggest that opportunities exist for strengthening the City’s sustainability, resilience, and vibrancy vis­a­vis its targets as indicated in the Envision San Jose 2040 General Plan. The limitations and assumptions of the projects are also described in this report. Supplementary materials, such as spreadsheets and GIS shape files, are provided in the appendices online at gudp.stanford.edu. The framework and projects will continue to be developed in future iterations of the course through collaboration with the City of San Jose.

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Introduction

Background The Global Urban Development Program (GUDP) course is offered by Stanford University’s Department of Civil & Environmental Engineering as part of a new Sustainable Urban Systems initiative. GUDP engages interdisciplinary teams of undergraduate and graduate students in real­world challenges presented by municipal city partners. Designed as an immersive project­based experience, the course spans the entire academic year (3 ten­week quarters) and is divided into three phases: Project Research, Project Definition, and Project Development. In the summer of 2015, GUDP formalized a partnership with representatives of the City of San Jose Planning Department, including Michael Brilliot, Jared Hart, and Kimberly Vacca. This consortium of partners has since expanded to include representatives from the Department of Transportation, Office of Economic Development, Environmental Services Department, and other departments and offices, as well as nonprofits and community organizations like SPUR. In the fall of 2015, GUDP accepted fifteen students to form this year’s “Project Team.” This White Paper represents the culmination of the team’s Project Research Phase.

Problem Statement In 2011, the City of San Jose produced its Envision San Jose 2040 General Plan, which lays out the city’s values and its long­term goals for land­use and municipal policy. According to the first 1

4 Year Review Progress Report in 2015, “while the City has advanced many of the General Plan’s goals, some performance measures remain relatively inconclusive or unchanged at this time… Some of the primary General Plan implementation challenges include raising [San Jose’s] jobs to employed residents ratio, increasing the percentage of affordable housing, and implementing mixed­use development in Urban Villages.” As part of its Annual Review and 4 2

Year Review processes, the City is looking for analytical tools and dashboards to better understand why certain General Plan goals have not been achieved.

Project Goals The Project Team developed a holistic framework for assessing the City’s performance towards its General Plan goals from a systems perspective, which they believe may help to identify interrelated challenges and leverage points for decision­making. The framework focuses on the universal goals of Sustainability, Resilience, and Vibrancy, as defined below:

1 Envision San Jose 2040 General Plan: https://www.sanjoseca.gov/DocumentCenter/Home/View/474 2 4 Year Review Progress Report: https://www.sanjoseca.gov/DocumentCenter/View/47785.

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Sustainability: Managing the flow of ecological, economic, and social capital in/out of the city within the limits of carrying capacities. For the City, sustainably managing ecological capital may include regulating water supply, renewable energy, and stormwater in its infrastructure. Sustainably managing economic and social capital may include analyzing the fiscal impacts of land use and the jobs to employment ratio.

Resilience: The capacity of the city to survive, adapt, and grow in the face of acute shocks and chronic stresses. Seismicity, sea level rise, and other hazards require the City to improve the robustness and redundancy of its infrastructure and the socioeconomic capital of its communities.

Vibrancy: The competitive attraction of economic capital and social activity to the city. Wherever the City invests in strategies like placemaking, focused growth, leadership in innovation, and environmental stewardship, it is actively building its own vibrancy to attract economic capital and social activity.

The General Plan’s values and strategies offer specific goals that can be viewed within the framework of Sustainability, Resilience, and Vibrancy. In other words, by focusing on systems­level characteristics, the framework allows the Project Team to integrate San Jose’s diverse structures, flows, and interactions into a cohesive systems performance, akin to health in the human body. The Project Team’s work comprises only a limited portion of what can be assessed with the framework. An overall outline is presented below, with specific projects (developed by the students) shown in bold. The rest of this section will cover the methodology and results of these indicator projects.

Table 1. Student projects completed in 2015­2016 research phase.

Sustainability Resilience Vibrancy

Ecological 1. Water 2. Solid Waste

Economic 3. Housing

Critical Scenario Analyses 4. Earthquake Risk 5. Flood Risk

Internal Capital Analyses 6. Community Vulnerability 7. Health

8. Public Transit 9. Focused Growth

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Project Team

McKenzie Andrews (andrews7@stanford.edu) is a senior majoring in Economics with a minor in Modern Languages. Before Stanford, she worked for the National Security Language Initiative for Youth in Taiwan through the US State Department. Her summers working at the Inter­American Development Bank in DC and interning in San Francisco in a public finance investment bank, spurred her interest in economic development.

Luke Babich (lbabich@stanford.edu) is a senior majoring in political science through the department’s research honors track. He uses natural language processing and automated text analysis to explore the role of media in the Sino­US rapprochement. He is Co­President of the Forum for American/Chinese Exchange at Stanford (FACES), a student­led organization based at Stanford's Freeman Spogli Institute.

Parker Barnes (parkerb@stanford.edu) is a second­year graduate student pursuing an MA in Business and Environmental Science. Prior to Stanford, Parker worked for the Boston Consulting Group (BCG) in Sydney, Australia, in BCG's Energy and Natural Resources practice. Before BCG, Parker worked for Trina Solar, one of China’s largest solar panel manufacturers, in Shanghai.

James Bradbury (jbradbur@stanford.edu) is a senior pursuing a BA in Linguistics, with fairly wide­ranging interests outside that field ranging from software engineering and data science to political thought and urban policy. Within GUDP, he focuses on data analysis, visualization, and housing policy. He plans on a career in journalism or the technology industry.

Marianne Dang (mldang@stanford.edu) is junior majoring in Architectural Design. Her experience at Stanford has always been widely varied. She’s impassioned by a spectrum of topics, from environmental science, debate/speech, philosophy, cosmology, and architecture. She hopes to cultivate herself as a designer with intention and purpose.

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Danielle Dobos (ddobos@stanford.edu) is a senior pursuing a BA in economics. She is interested in economic growth and urban development, particularly in an international context. While at Stanford, she has worked in the US government and foreign ministries, at the World Bank Group, and in management consulting. She plans to work in economic consulting after graduation, and hopes to pursue a policy career.

Owen Goldstrom (okgold@stanford.edu) is a 2nd year master's student in the Atmosphere / Energy program, with a specific focus on waste­to­energy. Before returning to school, he worked at PG&E in the Energy Efficiency department, where he learned about the energy technologies, markets, and regulation. Last year, Owen participated in the ETC (Energy Transformation Collaborative), with a focus on sustainable transportation.

Rommy Joyce (rjoyce@stanford.edu) is a graduate student in the Sustainable Design and Construction Master program. Before Stanford, she worked at the Sandia National Laboratories (SNL) as a year­round electrical engineering intern. At Stanford, she helped to develop Keewi, a management system that helps to reduce electricity consumption and to change consumer behavior.

Jillian Kilby (jkilby@stanford.edu) is pursuing an MA in Public Policy and an MBA. After Stanford, she will build an infrastructure incubator that will pick­up broken government projects and deliver them. Typical projects include road, rail, bridge, and public transit projects that have been on the drawing board for years. Prior to Stanford, Jillian owned and operated a civil engineering and project management company in Australia.

Sidharth Kumar (skumar11@stanford.edu) is currently pursuing an MA in Mechanical Engineering. His focus is in design, both in mechanical design as well as in human factors. He did his undergrad at Stanford as well, studying product design and engineering. He has a wide variety of project and work experiences, ranging from the oil industry to educational teaching tools.

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Jack Lundquist (jlundqui@stanford.edu) is a junior pursuing a B.S. Civil Engineering. He has worked previously with the City of Oakland’s Resilience Initiative. His interests lie at the intersect of natural, manmade, and societal systems in urban networks. In the future, he hopes to explore how modern computing technologies, data analytics, and urban informatics can help us understand complex urban systems.

Maria R. Martinez (mmart174@stanford.edu) is a second year graduate student pursuing an MA in Structural Engineering. She has had large internship experiences in Structural Engineering, including the design and engineering of the Interstate 95 bridge expansion. She has also been exposed to performance­based earthquake design while at Stanford.

Jorge L. Meraz (jmeraz@stanford.edu) is a second year graduate student pursuing an MA in Environmental Engineering and Science. While at Stanford he is focusing on issues of water resources and quality. His interest in sustainable urban systems began during his time abroad in Beijing, China.

Brittany Morra (bmorra@stanford.edu) is pursuing an MA in Sustainable Design and Construction in Civil and Environmental Engineering. Her main topics of interest are Sustainable Urban Systems, Energy Retrofitting for Historic Structures, and Health and Design. Her professional goal is to improve new, existing, and historic development to make the urban environment sustainable for people and the environment.

Leopold Wambersie de Brouwer (leopoldw@stanford.edu) is a Senior pursuing a BA in Environmental Systems Engineering, Urban Track. His interests lie in Civil Engineering, Architecture, and all things Urbanism. A Belgian citizen who's lived most of his life in Rio de Janeiro Brazil, Leopold's international upbringing affects his outlook on cities and their potential.

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Sustainability

The General Plan highlights sustainability as a key goal, building on the existing goals of the 2008 Green Vision. San Jose aims to become a leader in urban sustainability by “minimizing 3

impacts on resources, and ensuring that the City is able to maintain the infrastructure and services necessary to sustain its economy and quality of life.”

To inform the General Plan, we measured sustainability as the flow of environmental, economic, and social capital in and out of San Jose. We attempt to look at these issues on a per capita basis as much as possible for two reasons. The first reason is that population growth, worldwide and in San Jose, represents a major sustainability issue. In recognition of this, San Jose is already taking very positive steps to address this issue with form­based zoning planning. The second reason is that measuring sustainability on a per capita basis will make it easier for the city to produce actionable strategies with community participation.

The following diagram illustrates our systems view of sustainability, focusing on the flows of capital in and out of the urban system, which is represented by a cube. The actionable results of such of a framework target minimization of the inputs and outputs, as well as self­sufficiency, which is represented by the cyclic arrows within the cube. This could include such strategies as urban agriculture, wastewater reuse, etc.

Figure 1. Sustainability diagram.

By modeling any one system and measuring its inputs, outputs, and self­sufficiency through the sustainability framework, we can better identify pain and leverage points for improving the entire urban system’s sustainability. One possible outcome is the exploration of more targeted and measurable strategies to complement the strategies defined in the Green Vision and the General Plan. Among San Jose’s many service systems and sustainability targets, we focused on waste and water. A separate team also conducted a life cycle assessment comparing the environmental impacts of single­family and multi­family housing.

3 San Jose Green Vision: http://www.sanjoseculture.org/Index.aspx?NID=1417

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Water 4

Author: Jorge Meraz In regards to water usage, we focused on the General Plan’s Goal MS­3: Water Conservation and Quality. The analysis focuses mainly on aspects of water conservation, but could, in the future, include aspects of quality as well.

The analysis projects water­use trends based on 2010 Urban Water Management Plans (UWMPs) from each of the three water retailers that distribute water to San Jose. The goal from this analysis is to highlight projected water­use trends as they relate to residential water consumption. Residential water consumption was assumed to include Single Family, Multi­Family, and Irrigation demand use.

Irrigation demand was predicted based on values obtained from the San Jose Municipal Water System 2010 UWMP. In addition to assumptions for irrigation demand, the analysis assumed that Single Family and Multi­Family water deliveries were predominantly for indoor uses. In addition to water use, water supply was also included in the analysis, especially within the context of a drought. Given a normal, non­drought year, it is expected that supply will be able to sufficiently meet demand. However, as outlined in the UWMPs, supply is expected to decrease during periods of drought. The analysis looked at how supply would be affected during periods of drought for each of the water retailers’ service areas, and combined them to get a complete picture for the city of San Jose. The analysis assumes that no surface water is being imported or groundwater pumped to meet demands when a deficit exists.

In addition, we included an analysis of potential rainwater capture for the city. Total building footprint served as a proxy for this analysis. Moving forward, an economic analysis may also be included, especially as it relates to savings at the consumer level (i.e. residential level). Combined, the three water retailers – Great Oaks Water Company, San Jose Municipal Water System, and the San Jose Water Company – deliver water to a population that is larger than that of the city. In order to best represent water deliveries for the city of San Jose, 2010 census data, along with 2010 total population for all service areas, were used to determine what fraction of the population receiving water resides within city boundaries. The fraction was then applied to subsequent population years and carries through to bulk calculations for water deliveries (i.e. annual totals). Table 2 summarizes total water deliveries, highlighting residential water deliveries and projected daily per capita use.

4 See the Sustainability Appendix: Water_Methodology.xlsx.

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Table 2. Projected water deliveries.

Water Use Sectors 2015 2020 2025 2030 2035 Single­‑Family Residential (MG) 22606 22901 23204 23519 23844 Multi­‑Family Residential (MG) 4578 4826 5009 5202 5449 Irrigation (MG) 7220 6769 6381 6011 5695 Totals, annual, Residential (MG) 34404 34496 34594 34733 34989 Totals, annual (MG) 51283 52133 53060 54161 55324 Fraction Residential Use 0.67 0.66 0.65 0.64 0.63 Totals, daily (MG) 141 143 145 148 152 Totals (gpcd) 137 130 124 119 115 Totals, Residential (gpcd) 92 86 81 76 73

Table 3 summarizes supply and demand while experiencing multiple years of a drought. The table focuses on potable water deliveries only, excluding recycled water and losses. For years 1 and 2 of a multiple year drought, it is expected that supply be sufficient to meet demands; however, year 3 projects that demand will be greater than supply.

Table 3. Predicted supply and demand for drought years.

Year 2015 2020 2025 2030 2035

Year 1

Supply Totals 55,326 56,220 57,199 58,278 59,431 Demand Totals 51,283 52,133 53,060 54,161 55,325 Difference 4,042 4,087 4,139 4,117 4,106 Difference as % of Supply 7 7 7 7 7

Year 2

Supply Totals 55,021 55,995 57,053 58,223 59,467 Demand Totals 51,283 52,133 53,060 54,161 55,325 Difference 3,738 3,862 3,993 4,062 4,142 Difference as % of Supply 7 7 7 7 7

Year 3

Supply Totals 45,150 45,938 46,795 47,743 48,750 Demand Totals 51,283 52,133 53,060 54,161 55,325 Difference 6,133 6,195 6,265 6,418 6,575 Difference as % of Demand (12) (12) (12) (12) (12)

Table 4 summarizes projected daily per capita usage given a normal, non­drought year. In addition, the table also includes projected daily per capita usage given a multiple dry year events. Attention should be focused on year 3 of a multiple year drought, where a decrease in per capita use is required.

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Table 4. Projected per capita use, multiple dry years vs. normal year (gpcd).

Year 2015 2020 2025 2030 2035

Year 1

per capita normal year 137 130 124 119 115 per capita dry year 148 141 134 128 123 difference 11 10 10 9 9 % surplus 8 8 8 8 7

Year 2

per capita normal year 137 130 124 119 115 per capita dry year 147 140 134 128 123 difference 10 10 9 9 9 % surplus 7 7 8 8 7

Year 3

per capita normal year 137 130 124 119 115 per capita dry year 121 115 110 105 101 difference 16 15 15 14 14 % decrease use required 12 12 12 12 12

Table 5 focuses on indoor water use and the extent to which that can be decreased if a home were retrofitted with high efficiency plumbing (i.e. low flow showerheads, toilets, etc.). This analysis was done using San Francisco Public Utilities Commission’s (SFPUC) Non­Potable Water Calculator.

Table 5. Indoor water use vs. retrofit indoor water use.

Indoor Water Use

Residential 2015 2020 2025 2030 2035 Total, annual (MG) 27184 27727 28214 28721 29294 Total, daily (MG) 74 76 77 79 80 Total (gpcd) 73 69 66 63 61

Indoor Water Use ­ Retrofit Home

Residential 2015 2020 2025 2030 2035 Total, annual (MG) 16325 17427 18610 19814 21006 Total, daily (MG) 45 48 51 54 58 Total (gpcd) 44 44 44 44 44 Table 6 serves a similar purpose to that of Table 2, summarizing total water deliveries, highlighting residential water use and projected daily per capita use. The difference between the tables is that Table 6 combines Single Family and Multi­Family use into one category, Residential. In addition to combining the sectors, the projected water deliveries were updated given that all homes be retrofit.

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Table 6. Projected water deliveries given retrofit home.

Water Use Sectors 2015 2020 2025 2030 2035 Residential, Indoor (MG) 16325 17427 18610 19814 21006 Irrigation (MG) 8811 8261 7787 7336 6951 Totals, annual, Residential (MG) 25137 25688 26397 27151 27957 Totals, annual (MG) 40425 41833 43456 45254 47037 Fraction Residential Use 0.58 0.58 0.58 0.57 0.57 Totals, daily (MG) 111 115 119 124 129 Totals (gpcd) 108 105 102 100 98 Totals, Residential (gpcd) 63 60 59 57 55 Table 7 serves a similar purpose to that of Table 3, summarizing supply and demand while experiencing multiple years of a drought. The table focuses on potable water deliveries only, excluding recycled water and losses. For years 1, 2, and 3 of a multiple year drought, it is expected that supply be sufficient to meet demands.

Table 7. Predicted supply and demand for drought years given retrofit home.

Year 2015 2020 2025 2030 2035

Year 1

Supply Totals 55,326 56,220 57,199 58,278 59,431 Demand Totals 40,425 41,833 43,456 45,254 47,037 Difference 14,901 14,387 13,743 13,024 12,394 Difference as % of Supply 27 26 24 22 21

Year 2

Supply Totals 55,021 55,995 57,053 58,223 59,467 Demand Totals 40,425 41,833 43,456 45,254 47,037 Difference 14,596 14,162 13,597 12,969 12,430 Difference as % of Supply 27 25 24 22 21

Year 3

Supply Totals 45,150 45,938 46,795 47,743 48,750 Demand Totals 40,425 41,833 43,456 45,254 47,037 Difference 4,726 4,105 3,340 2,489 1,714 Difference as % of Supply 10 9 7 5 4

Table 8, as with Table 4, summarizes projected daily per capita usage given a normal, non­drought year. In addition, the table also includes projected daily per capita usage given a multiple dry year events. For years 1, 2, and 3 of a multiple year drought, a decrease in per capita usage is not expected, given that all homes are retrofit.

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Table 8. Projected per capita use given retrofit home.

Year 2015 2020 2025 2030 2035

Year 1

per capita normal year 108 105 102 100 98 per capita dry year 148 141 134 128 123 difference 40 36 32 29 26 % surplus 37 34 32 29 26

Year 2

per capita normal year 108 105 102 100 98 per capita dry year 147 140 134 128 123 difference 39 35 32 29 26 % surplus 36 34 31 29 26

Year 3

per capita normal year 108 105 102 100 98 per capita dry year 121 115 110 105 101 difference 13 10 8 5 4 % surplus 12 10 8 5 4

The following analysis, for rainwater harvest, shows how much rainwater can be collected for both a normal year and during a year of drought. The San Jose Municipal Water System 2010 Urban Water Management Plan was used to determine average annual rainfall and average rainfall during a drought year. San Jose receives close to 15 inches of rain annually during a non­drought year. Rainwater analysis for a drought year was done using the extreme single­year drought of 1976. Volume of rainwater captured was given by using the Natural Resources Defense Council’s methodology:

Equation 1. Rainwater capture.

Vgal = Square Feet of Roof x % of Total Roof Area Used x Inches of Rain x 1 ft/12 in x 7.48 gal/ft3 x 0.8 Capture Efficiency

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Table 9. Rainwater capture analysis ­ average, annual rainfall.

Building Footprint

(ft2)

% Total Rooftop Area

Rain (inches)

Conversion Factor (ft/in)

Conversion Factor (gal/ft3)

Capture Efficiency

Volume (gal)

Volume (MG)

Harvest 7.40E+08 1 15 0.08 7.48 0.8 5.53E+09 5533

Table 10. Rainwater capture analysis ­ drought year, annual rainfall.

Building Footprint (ft2))

% Total Rooftop Area

Rain (inches)

Conversion Factor (ft/in)

Conversion Factor (gal/ft3)

Capture Efficiency

Volume (gal)

Volume (MG)

Harvest 7.40E+08 1 7 0.08 7.48 0.8 2.58E+09

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Using the information within these tables, we will work to develop an environment in which users are able to see different outcomes of water consumption based on different water use strategies. The goal with said environment will be to reinforce water conservation efforts, especially within the residential water use sector. In addition to the initial analysis, further analysis for each of the water retailers can be found in the appendix. Further analysis includes a more granular approach to projected water deliveries. The analysis, included within each water retailers’ respective sheet, projects annual water use given non­retrofit homes, retrofit homes, and supply that is impacted during periods of drought. The analysis assumes that, during non­drought years, supply will exactly equal the demand needed when homes are not retrofit. Projected water deliveries were calculated based on data provided within each respective UWMP. Given that these analyses are based on a number of assumptions, subsequent analysis of the data could be focused on combining the new, more granular data and comparing it to new UWMP’s that are expected to be released in the following year. In addition, the analysis could be repeated to provide an up­to­date assessment.

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Solid Waste 5

Authors: Parker Barnes, Owen Goldstrom, Sidharth Kumar San Jose’s General Plan for 2040 has defined a target to become a “zero waste” city by 2022. This means achieving 100% diversion of solid waste from local landfills, up from today’s diversion rate of 73% (see below). As the General Plan explains, “the ultimate goal of zero 6

waste is to contribute to a greener community,” which represents a philosophical, social and aesthetic desire to position San Jose as a leader in urban sustainability.

Figure 2. Trash diverted from landfills.

Complementing San Jose’s overarching “zero waste” target,the General Plan defines five other objectives related to solid waste:

Goal MS­5 – Waste Diversion: Divert 100% of waste from landfills by 2022 and maintain 100% diversion through 2040

Goal MS­6 – Waste Reduction: Reduce generation of solid and hazardous waste. Goal MS­7 – Environmental Leadership and Innovation: Establish San Jose as a

nationally recognized leader in reducing the amount of materials entering the solid waste stream.

Goal MS­8 – Environmental Stewardship: Establish San Jose as a local, regional, and statewide model for responsible management of resources.

Goal MS­9 – Service Delivery: Operate a municipal solid waste management system that maximizes efficiencies in service delivery while protecting the environment, public health, and safety.

5 See Sustainability Appendix: Waste Supplement folder. 6 Green Vision Goal 5: https://www.sanjoseca.gov/index.aspx?NID=2950.

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While “zero waste” is an excellent goal in the near term, it does not necessarily reflect the full range of environmental, social and economic impacts that solid waste has in San Jose (as depicted in the figure below).

Figure 3. Environmental, social, and economic impacts of solid waste.

Accordingly, we decided to take a broader, system­wide perspective on San Jose’s solid waste management in order to quantify the impact and efficacy of the overall system. To do this, we looked at the end­to­end solid waste “lifecycle”, as depicted below. We further sought to define relevant “indicators” and metrics that might be used to understand the impacts and performance of each step in the lifecycle.

Figure 4. End­to­end solid waste lifecycle.

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For the first step of the lifecycle, we defined two indicators that can be used to track the progress made by San Jose in reducing the amount of waste generated within its borders:

Waste Density: the amount of waste generated, collected or disposed per day in a given area (e.g. city neighborhood or block), measured in tons of waste per day per square­mile (tons/day/sq.mile).

Waste Metabolic Rate : the rate of waste generated / disposed / recycled / composed 7

per hour of human activity, with total human activity (“THA”) defined as the total amount of hours of the “system” per year (THA = Population * 24 hours/day * 365 days/year)

Figure 5. Waste indicators.

7 “Waste Metabolic Rate” indicator has been adapted from a study, “A multi­scale analysis of urban waste metabolism: density of waste disposed in Campania,” by Giacomo D’Alisa, Maria Frederica Di Nola, and Mario Giampietro, Journal of Cleaner Production (2012).

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For both indicators, we conducted geospatial analyses to estimate the value of each indicator at the level of neighborhoods (see example map below).

Figure 6. Heat map of residential waste density per capita.

To estimate the environmental impacts of the remainder of the solid waste lifecycle, we developed a quantitative “waste impact model” that calculates the green house gas (GHG) emissions for each step, including collection, processing, material recovery and end­of­life.

Figure 7. Waste impact model.

This model will allow for greater understanding and analysis of the solid waste system. For example, how will increasing trash diversion from landfill impact GHG emissions (i.e. diverting 100% of trash from landfill)? What are the GHG impacts of a more efficient collection or

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processing, reduced volume of materials, or to a change in the type of materials entering the system?

In order to calculate the avoided GHG from recycling and composting, we referenced the International Council for Local Environmental Initiatives Recycling and Composting Emissions Protocol. The protocol estimates the metric tons of carbon dioxide equivalent (MTCO2e) 8

avoided from using recycled inputs instead of virgin inputs, fertilizer production displacement credit, and avoided landfill disposal.

Once the model has been finalized, we can run a variety of scenarios to estimate the environmental impact of different waste­related policy interventions. Scenarios that can be run include:

1. Increase diversion of trash from landfill to 100% a. Additional diversion attributed to 50% recycled materials and 50% organic

materials b. Additional diversion attributed to 100% recycled materials c. Additional diversion attributed to 100% organic materials

2. Increase efficiency of collection by 25% 3. Reduction in total volume of materials by 15%

a. Reduction in materials attributed to 5% garbage, 5% recycling and 5% organic materials

Based on the outcomes of these scenarios, we can make recommendations to the city as to which areas of additional regulations, programs and investments might provide the greatest reduction in MTCO2e from the solid waste system. In addition, we can recommend a set of data collection processes, which will allow this model to be used going forward, and to enhance the efficiency of collection and accuracy of model inputs.

8 "Recycling and Composting Emissions Protocol Version 1." 2013. 7 Dec. 2015 <http://californiaseec.org/resources­guidance/ghg­inventories­community­scale/recycling­and­composting­emissions­protocol­version­1/at_download/file>

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Housing 9

Authors: Brittany Morra, Parker Barnes Demand for housing in San Jose will increase as the city continues to grow. A projected increase of approximately 130,000 households is expected by 2040, and the city does not currently have the housing stock to support this population growth. Moving forward, it may be helpful to have a comprehensive picture of the housing demand in San Jose; this can be achieved by comparing housing price and current household incomes (assuming 30% household expenditure on housing) with projected city growth, and comparing that to the quantity and price of the existing housing stock.

Figure 8. Housing demand for 2040.

Based on the current stock and future demand, assuming the new households are distributed by the same percentages as the current income brackets, San Jose may wish to increase its housing stock by approximately 40% (127,000 units). Here, we examined the life cycle impacts (LCA) of single­family vs. multi­family housing in San Jose. With such a large demand for housing between now and 2040, San Jose could consider the impacts of building one housing type over the other if the city wants to decrease its environmental impact moving into the future.

9 See Sustainability Appendix: Housing Supplement folder.

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The “functional unit” of the LCA study was the total energy and resources required to house 300 San Jose residents in either (a) a single mid­rise apartment building (with 96 units) or (b) 96 detached single­family homes, each over the 50­year lifespan of the building (entire building “lifecycle”). Three different software packages ­ Simapro, Revit and CalEEMod ­ were used to quantify the environmental impacts of each building.

Figure 9. Basic layouts for single­family and multi­family house used in analysis.

Figure 10. Process flow diagram for residential building life cycle.

The results of the LCA study show that the global warming potential (as measured in kg of CO2 eq.) of a single­family house is ~75% higher than a multi­family residence over the 50­year lifespan of the buildings. This relationship is also true for other impact categories, including smog: single­family is 72% higher, ecotoxicity is 64% higher, and eutrophication is 56% higher. The vast majority of these impacts (92­94%) are related to the use­phase phase of the building (energy and fuel consumption), followed by the embodied energy within the building materials

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(~4­5%) and the transportation and landfilling of building rubble (~2%). In terms of lifecycle costs, the group of 96 single­family houses was estimated to be 96% more expensive than the multi­family residence over 50 years, with the largest costs from electricity (26­29%), land (12­26%), and fuel (gas) (17­19%).

Figure 11. Ratio of environmental impacts, single­family vs. multi­family.

Figure 12. Comparison of global warming potential across building lifecycle.

Based on this analysis, the city may wish to consider pursuing a higher­density housing strategy when zoning future residential developments. This could involve the development of mixed­use, multi­story buildings such as the one modeled in this study. For single­family houses, the authors recommend imposing restrictions that seek to mitigate the additional environmental impact of single­family housing. This could include mandating distributed­generation of electricity (e.g. rooftop solar) and more energy­efficient appliances (e.g. HVAC systems).

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Resilience Author: Jack Lundquist Resilience is defined by 100 Resilient Cities (a Rockefeller Foundation initiative at the forefront of resilience thinking) as the capacity for a city to survive, adapt, and grow from the chronic stresses and acute shocks it faces. In fact, an inability to respond to critical shocks and stresses in a timely and effective manner can prevent cities from focusing on its ambitious goals in other domains, such as in sustainability and vibrancy. For example, a large earthquake in San Jose will cause significant structural, economic, and human losses. This will force city agencies to devote more time and resources to recovering from those losses, which means that they have less time and resources to devote to implementing the key growth and development strategies described in the General Plan. A more resilient San Jose is one that has taken steps to mitigate risk by having a recovery plan, which enables the city to recover quickly after disruptions, and continues to grow to meet the vision laid out in the General Plan.

Figure 13. Resilience diagram.

However, it can be challenging to incorporate resilience thinking into the planning process due to its seemingly invisible nature. It is hard to understand how resilient a community is without witnessing it undergo a recovery process, at which point the opportunity to build resilience has been lost. This is why it is important for cities to incorporate resilience thinking into their planning process. If resilience is not incorporated into the planning process, then all other policies aimed at improving the economic, social, and cultural lives of a city’s citizens may be less effective. However, if resilience is incorporated into the planning process, the city stands to benefit from the increased connections, initiatives, and opportunities that follow: a benefit referred to as the Resilience Dividend. 10

Because of limited time and resources, we knew that our initial work would present a woefully incomplete picture of the city’s overall resilience to numerous shocks and stresses. Whereas our

10 For a more in­depth look at resilience and the resilience dividend, please refer to “The Resilience Dividend,” one of the seminal texts on resilience written by Judith Rodin (head of the Rockefeller Foundation and its 100 Resilient Cities Initiative).

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other primary indicators (sustainability and vibrancy) can be thought of with respect to one isolated system (e.g. the sustainability of San Jose’s waste disposal system), resilience is an indicator that is dependent on the interconnected nature of these systems and the cascading impacts a disruption to one system might have on others. For example, a critical disruption like a large earthquake could cause immense amounts of structural and infrastructure damage, but that damage could also severely impact a community’s mobility due to disruptions in the transportation network, which could further impact health outcomes (if people are unable to reach a hospital) and economic outcomes (if business­owners are unable to reach their employees or the location of their business). These cascading impacts are incredibly important to understand when thinking about planning for resilience, as they are ultimately what pose the greatest challenge to recovering efficiently after a disruption. Therefore, we felt that it was important to develop a framework for understanding resilience that was designed to be flexible and modular, so that additional measurements of loss due to these cascading impacts could be captured in later analyses. The Urban Resilience Framework (URF) we developed aims to achieve this flexibility by thinking about resilience using “systems thinking”. With respect to the URF, the system in question is the urban system itself. The urban system is itself actually an aggregate system that accounts for the vast quantity of social, economic, physical, and environmental systems that act together within the urban boundary. However, when looking at the resilience of these systems to critical disruption we are primarily interested in understanding how vulnerable these systems are ­ in other words, how much damage these systems would likely sustain during and after a disruption, and how well these systems could regain their original capacity. We have defined this capacity as some stock of capital each system is made up of: houses, trees, industries, roads, people, and so forth. Therefore, to understand urban resilience, one has to look closely at how these capital change during and after a critical disruption. Furthermore, we have defined a 11

critical disruption as the reaction that occurs between a combination of external threats or opportunities and a set of internal urban capital. These external conditions can also be divided into the four categories of urban systems defined above. This is an overview of the genesis of the Urban Resilience Framework (URF), which is visualized below using a system diagram.

11 In addition to looking closely at the flux of urban capital after a critical disruption, it is also sensible to look closely at which systems are the pain points most important in catalyzing the cascading impacts of a disruption (e.g. infrastructure damage contributing to social and economic losses) in order to determine where the real leverage points for building resilience exist.

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Figure 14. System diagram of urban resilience. The green and red arrows represent hypothetical increases (green) and decreases (red) in different types of capital. On paper, this framework may seem redundant. All it does is considers practices within urban risk analysis and formally defines how these practices can be used to measure resilience, but in the field of risk management, practitioners have been implicitly applying this framework for years! We contend that by explicitly defining those practices in a very general sense, the Urban Resilience Framework is able to synthesize a vast number of different kinds of analyses (be they economic, engineering, or sociological) into a unified way of thinking about resilience. This is extremely important because it allows stakeholders across the city who are engaged in the management of different urban capital (e.g. housing, roads, parks, social services) to work together using a comprehensive and holistic language about the vast, disparate but ultimately interconnected resilience challenges they face, asking questions like:

1. What elements of the urban fabric (communities, infrastructures, economies, etc.) are most vulnerable to disruption?

2. Which elements are most important in ensuring the city’s resilience? 3. How can the city best use its limited time and resources to build resilience?

It may not be immediately apparent how the framework in its current form can help facilitate this conversation. However, this framework can be developed into an operational dashboard that visualizes these different impacts geospatially and allows stakeholders to model the impacts of resilience­building initiatives by modifying the initial stocks of urban capital and/or comparing the impacts these changes would have on capital losses during various critical resilience scenarios. We hope that in future iterations of this research phase, students will, in addition to conducting more analyses of critical resilience scenarios, be able to develop such tools. In addition to developing the URF, our team also performed impact analyses of two different critical resilience scenarios (floods and earthquakes) and analyzed the vulnerabilities of two different internal forms of social capital: human health and hazard response capacity (community vulnerability to hazards). The next few sections go into detail about the methodologies and results of these analyses, as well their potential value within the URF.

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Critical Scenario Analyses: Natural Hazard Risk The URF is a framework that looks at urban resilience with respect to the critical disruptive scenarios facing the city. When it comes to defining these scenarios, the ones that are particularly salient are natural hazards, including earthquakes, floods, landslides, and fires. In particular, the Bay Area is in one of the most seismically active regions in the country, which makes a nuanced, and comprehensive understanding of the impacts earthquakes can have on the urban landscape an immediate priority when planning for resilience. Therefore, one of the first projects our team undertook was an earthquake impact analysis using the HAZUS­MH tool, developed by the Federal Emergency Management Agency (FEMA) to serve as a standardized methodology for measuring the impacts of various hazard events. In addition, we also performed a flood impact analysis using a simplified methodology derived from the HAZUS­MH methodology. In the future, we also plan to expand our hazard risk analyses to include landslide and fire impact analyses. Because our analysis was based on the HAZUS­MH methodology, the question of whether this methodology is in line with the URF’s methodology is quite relevant. Thankfully, HAZUS­MH looks at hazards as external threats that act on different sets of urban systems to produce estimates of a few different capital losses ­ specifically, structural damage, human loss, and the economic loss associated with these losses. Therefore, this analysis is directly in line with URF methodology, making it capable of being built into an online dashboard that visualizes different capital losses geospatially and allows for the modification of initial conditions.

Earthquakes 12

Author: Maria Martinez Introduction Earthquakes are potentially the most catastrophic and likely critical resilience scenarios faced by the City of San Jose. The General Plan addresses earthquake hazard, suggesting policies that enforce stringent structural criteria on new development, requiring retrofits of particularly vulnerable structures, specifying the importance of minimizing risk to new public infrastructure, and encouraging public education about emergency preparedness and risk mitigation. However, certain important questions about the city’s resilience to earthquakes remain: Are there other buildings outside of those specified for retrofit that are particularly vulnerable? Is there existing infrastructure that is particularly vulnerable? Is any infrastructure critical in responding to an emergency (e.g. communications infrastructure) or in ensuring the normal functioning of city agencies and communities at large? What communities are most vulnerable to displacement in an earthquake, and how can the city best engage and educate them about emergency

12 See Resilience Appendix: Earthquake_Hazard_Methodology.pdf.

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preparedness? These questions are important when determining where to further focus resilience­building efforts so that the goal (as defined in the General Plan) of “minimiz[ing] the risk of injury, loss of life, property damage and community disruption” can best be met. While our research at this phase focused purely on modeling damages, casualties, and economic losses due to earthquakes, we hope that these metrics can be used as the springboard for future students to conduct analyses that measure community displacement, characterize how critical various types of infrastructure are to emergency response and recovery and how structurally vulnerable they are. Additionally, we hope that in the future these analyses can be incorporated into an online dashboard that, using the URF as a foundation, can visualize different capital losses (e.g. infrastructure damaged, people displaced) and their relationships, and can model the impacts of different resilience­building policies by allowing stakeholders to edit the initial stocks of capital (e.g. sturdiness of infrastructure, community preparedness, etc.) and run earthquake simulations to see how losses would change. Methodology Our team performed this analysis using FEMA’s HAZUS­MH software. Please refer to FEMA’s website for a more in­depth methodological discussion of its HAZUS tool . 13

Figure 15. Earthquake risk analysis procedure using the URF.

We can actually visualize our research process using the URF diagram. Essentially, the HAZUS tool asks for a few inputs to perform its analysis: ground shaking maps that characterize the earthquake hazard, a map of population density and a map of the building stock that includes data characterizing their economic value and structural characteristics. HAZUS then determines the expected structural damage for a set of probabilistic earthquake scenarios, predicted level of casualties and an annualized expected loss (AEL) based on the cost of structural damage, casualties and sheltering individuals whose homes may have been destroyed.

13 FEMA HAZUS: http://www.fema.gov/hazus

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Results According to our analysis, which can be further reviewed in the Appendix, the City of San Jose had a total AEL of $325.3 million, with a per capita AEL of $130. These results were modeled in ArcGIS and are visualized below. From these results we can observe how the risks are maximized in areas with high seismic hazard (the closer to the faults the larger the risk) and high building exposure. Also, the greater the population density, the larger the amount of losses due to displacing or sheltering. Structural damage was not an explicit output of our analysis but can be inferred by looking at the map visualizing AEL, as AEL is determined in a large part by structural damages.

Figure 16. Annualized earthquake loss per capita in San Jose.

Of course, there is still much more work to be done to refine and expand this research. In the future we hope to be able to visualize structural losses on their own, as well as to model and visualize damage to critical infrastructure. Furthermore, we hope to incorporate the community vulnerability dataset (described in detail in a later section) to determine the relative rates of displacement. Finally, we plan on merging all of these analyses into an online dashboard using the URF as its foundation for presenting resilience challenges and solutions.

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Floods 14

Author: Jack Lundquist Introduction Flood hazard is another natural hazard that, historically, has levied significant costs to San Jose. However, flood hazard has impacts that are significantly different than those of earthquakes. Floods, at least in San Jose, don’t usually result in human losses or structural damage. They do have economic impacts through the damage of building contents and could potentially disrupt critical transportation, communications and water infrastructure. But the biggest difference between flood hazards and earthquake hazards is that floods have a significant impact on the city’s environmental capital. Floods damage the city’s aquatic ecosystem, both by creating more polluted runoff as well by contributing to siltification processes that are already making the watershed of which San Jose is a part more and more uninhabitable. Additionally, this siltification process also reduces the bearing capacity of the watershed, further increasing flood risk. Therefore, although floods do not pose nearly as significant a risk to economic, physical and structural capital as earthquakes, they do pose a larger risk to the city’s environmental capital ­ specifically, its already vulnerable watershed. Based on this characterization, a few key questions about the city’s resilience to flooding emerges: What pieces of critical infrastructure are vulnerable to disruption during flooding, and what implications do these vulnerabilities have on the city’s environmental, social and economic capital? Which parts of San Jose’s urban watershed are being damaged most from runoff pollution and siltification, and what impact would a flood play in that process? We focused on quantifying economic losses in the instance of the “100 year flood” in order to demonstrate that a tool using the URF as its foundation could visualize economic impacts of earthquakes and floods together to motivate a comparative risk analysis. In the future we hope students will attempt to tackle some of the more complex questions about San Jose’s resilience to flooding and will be able to synthesize those findings with others in a dashboard­style tool based on the URF. Methodology Given the complexity of the HAZUS tool, our team only had the time and resources available to perform one hazard impact analysis using its methodology. However, we still aimed to perform a more cursory impact analysis of flood risk, using a simplified version of the HAZUS methodology and focusing on economic losses. While one can still gain from our analysis some understanding of how economic impacts due to flooding differ throughout San Jose, because of

14 See Resilience Appendix: Flood Hazard Supplement folder.

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significant methodological differences we concluded that a comparison of economic losses from earthquakes and floods using our research would be inaccurate. In order to estimate the economic impact of flooding in San Jose, we decided to examine the 100 year flood scenario, as it seemed to be the baseline upon which flood design criteria are established in California as well as in San Jose. Therefore, we focused our analysis only on buildings that fell within the 100 year flood boundary. To calculate economic loss, we assumed that the primary source of economic loss in a flood would be the loss of contents in the building. We assumed this because we did not think the 100 year flood in San Jose would cause significant structural damage. Of course, losses may occur in the form of mold or bacteria that linger after the flood has submerged, as well as long­term losses that come from the disruption of business, but we decided to assume they were negligible in comparison to the loss of contents. Secondly, we assumed that the 100 year flood would only reach the first floor of buildings, meaning that only contents on the first floor of the building would be lost. This assumption came from the same intuition about the severity of the flood used to make the first assumption. Finally, we assumed that all buildings of each building type (residential, industrial, commercial, agricultural) would have the same economic loss per square foot. Based on these assumptions, we created a spreadsheet to calculate an average value for economic loss per square foot of each building type based on HAZUS tables. We then multiplied this value by the square footage of each building type (sorted by census tract and looking exclusively within the floodplain) to determine our metric for economic loss. This output is visualized below.

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Figure 17. Predicted 100 year flood economic loss ($).

There are a few major methodological limitations to our flood risk analysis. First, flood damage was assumed to be constant across the 100 year floodplain. In reality, more damage would occur in areas of lower elevation. Because of this, areas like Alviso that are at low elevation will have underestimated risk values. Second, the assumption that all buildings of a specific type have the same loss per square foot should result in less variability between census tracts, as the analysis does not take into account the potential for more or less valuable buildings of a specific type. However, because the main determinant of economic impact is still building density this should not severely impact our relative results. But unfortunately, even though this map would likely resemble closely the distribution of economic loss during a flood, the actual values of economic loss are skewed and therefore unable to be compared to the AEL figure derived in our earthquake analysis. In the future, we hope to use HAZUS to test the results of our analysis, as well as to unify it methodologically with our earthquake analysis so that a comparative hazard risk analysis could be carried out. Furthermore, we hope to expand our research towards the questions posed in the introduction, with a focus on infrastructure and environmental capital.

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Community Vulnerability to Hazards Author: Jack Lundquist In the section detailing our earthquake impact analysis, we noted that an important capital loss not accounted for in our research was that of people being displaced from the city. This displacement is likely due to an inability for citizens to rebound from the immense economic (and potentially psychological) impact of an earthquake that may have destroyed their homes and weakened them and their family members during and after the quake. Determining any realistic measurement of this complex phenomena requires a much more in­depth understanding of the social capital different communities have in San Jose and how these differences may cause vulnerabilities. We were able to find an existing dataset that contains many important indicators of community vulnerability to hazards. In 2014, the Association of Bay Area Governments (ABAG) in conjunction with the Bay Conservation and Development Council published a dataset entitled “Community Vulnerability Indicators.” This dataset is incredibly powerful to our group, as it 15

defines vulnerability in the same context our group does (i.e. with respect to natural hazards), contains measurements of each indicator, an aggregate indicator representing net vulnerability (where each indicator is given either a 0 or 1, resulting in an aggregate indicator ranging from 0 to 10), and thorough methodological justification contained in the technical report. For these reasons, we decided to use ABAG’s “Community Vulnerability Indicators” as our own indicator for Community Vulnerability to Hazards. The figure below describes each indicator in detail. In the future, we hope this dataset could be used to identify a measurement (proxy or otherwise) for displacement during a critical hazard scenario ­ an important loss of social capital for San Jose.

15 http://resilience.abag.ca.gov/wp­content/documents/housing/Final%20Report/StrongerHousingSaferCommunities_TechnicalReport.pdf

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Table 11. ABAG Community Vulnerability Indicators.

Indicator Measure Measure needed to score a 1

Housing cost burden % household monthly housing >50% of gross monthly income

>15%

Transportation cost burden % household monthly transportation costs >5% of gross monthly income

>15%

Home ownership % not owner occupied housing

Mean +1 standard deviation

Household income % households with income less than 50% AMI (area median income)

>30%

Education % persons without a high school diploma >18 years

Mean +1 standard deviation

Racial/Cultural Composition

% non­white >70%

Transit dependence % households without a vehicle

>10%

Non­English speakers % households where no one ≥ 15 speaks English well

>20%

Age ­­ Young children % young children under 5 years

Mean +1 standard deviation

Age ­­ Elderly % elderly over 75 years >10%

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Community Health Author: Danielle Dobos Community health is an important measure of community wellbeing and contributes to a sustainable city environment. Santa Clara County ranks among one of the healthiest communities in California, boasting medical facilities such as Kaiser Permanente, Stanford Hospital, and Santa Clara Valley Medical Services that provide high­quality care to county residents. Despite high state rankings, San Jose falls below the county average on a number of key metrics. This section aggregates information from the Santa Clara Department of Public Health, Kaiser Permanente’s Community Health Needs Assessment, and the California Health Interview Survey (CHIS) to produce a community health indicator for San Jose. The indicator is comprised of four policy­relevant health categories assigned equal weighting: (1) nutritional health, (2) environmental health, (3) mental health, and (4) public health and communicable diseases. The indicator we developed has not been integrated into the URF yet, but future analysis of hazards should utilize the health metrics considered below to estimate health impacts as changes in social, human capital. For example, an analysis of fire risk could be performed and the social and economic losses due to increased particulate matter in the air causing respiratory diseases could be one of the outputs measured.

Methodology San Jose’s community health score represents a normalized average of the four health categories. For each category, we collected the most pertinent statistics where data was available for all cities in Santa Clara County. We then normalized these statistics to a mean of 0 and a standard deviation of 1, and averaged the metrics to create the four indicator scores, which are averaged in turn to create the overall health score. As a result, San Jose’s final community health score is inherently benchmarked against the rest of Santa Clara County. We also reported intra­city differences for each health metric. We included gradient maps depicting the health outcomes by neighborhood (or zip code where neighborhood level is not available) for each metric. This data is specific to San Jose and highlights the wide health disparities that exist within San Jose despite overall high community health scores.

Health Score According to our methodology, San Jose’s overall health score is ­0.33, which means it is one­third of a standard deviation below average health outcomes when compared to other cities in Santa Clara County.

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Figure 18. Community health score.

Nutritional Health Nutritional Health refers to health outcomes with a strong correlation to nutrition and physically active lifestyles. San Jose falls below the county average in three important nutritional health metrics: (1) the number of heart disease deaths per 100,000, (2) the number of diabetes deaths per 100,000, and (3) Obesity rates (BMI > 30) among those 18 and older.

Table 12. Nutritional health summary statistics for Santa Clara County.

Metric SCC Min SCC Max San Jose Avg.

County Avg.

Data Source

Heart disease deaths (Per 100,000 people)

99.3 188.1 126.2 119 Santa Clara County Public Health Department, 2008­2012; U.S. Census. 2010

Diabetes deaths (Per 100,000 people)

7.8 42.7 25.4 22.7 Santa Clara County Public Health Department, 2008­2012; U.S. Census. 2010

Obesity Rates (% of the 18+ population)

13.1% 28.6% 20.5% 18.9% California Health Interview Survey, 2011­2012

Note: Figures highlighted in red are above the county average San Jose also experiences large disparities in obesity rates and associated health outcomes within city borders. The downtown area has the highest obesity rates with approximately one quarter of the population with a BMI > 30 compared to the relatively healthier outlying regions.

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Figure 19. Obesity rates by zip code in San Jose.

Environmental Health Environmental Health refers to health outcomes with a strong correlation to environmental factors such as air and water quality. San Jose performs around the average in two important environmental health metrics: (1) the number of chronic respiratory disease deaths per 100,000 and (2) the percent of the population ever diagnosed with asthma

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Table 13. Environmental health summary statistics for Santa Clara County.

Metric SCC Min SCC Max San Jose Avg.

County Avg.

Data Source

Chronic respiratory disease deaths (Per 100,000 people)

17 41.9 28.8 25.3 Santa Clara County Public Health Department, 2008­2012; U.S. Census. 2010

Diagnosed with asthma (% of the 18+ population)

12.7% 16.5% 14.4% 14.6% California Health Interview Survey, 2011­2012

Note: Figures highlighted in red are above the county average Within San Jose, higher asthma rates and associated respiratory illnesses occur in the southwestern regions of the city, away from downtown and the city center. There are a number of potential confounding factors here, including higher median household incomes in the outlying regions of the city. Wealthier households are more likely to visit medical professionals and receive an asthma diagnosis.

Figure 20. Asthma rates by zip code in San Jose.

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Mental Health Mental Health refers to the emotional wellbeing and general happiness of a population. While it is difficult to measure the happiness of a city, San Jose has average rates of mental disorders and episodes of severe psychological distress among Santa Clara County. The city is home to five full­service hospitals and a robust mental health services delivery system that likely lead to these positive outcomes.

Table 14. Mental health summary statistics for Santa Clara County.

Metric SCC Min SCC Max San Jose Avg.

County Avg.

Data Source

Experienced severe psychological distress (% of the 18+ population)

3.0% 7.3% 6.0% 5.9% California Health Interview Survey, 2011­2012

Note: Figures highlighted in red are above the county average The percentage of the population who have experienced severe psychological distress is concentrated in the downtown area and southwest side of the city ­­ the poorest and wealthiest regions of San Jose, respectively. Both of these regions have access to adequate emergency care and hospitalization in the case of severe psychological distress.

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Figure 21. Incidence of severe psychological distress by zip code in San Jose.

Public Health Public health refers to communicable diseases, such as influenza or sexually transmitted infections, that spread person­to­person through a population. San Jose has higher rates of influenza and pneumonia than the county average, although mortality rates from communicable diseases remain extremely low for all cities.

Table 15. Public health summary statistics for Santa Clara County.

Metric SCC Min SCC Max San Jose Avg.

County Avg.

Data Source

Influenza & pneumonia deaths (Per 100,000 people)

10.9 23.3 16 14.8 Santa Clara County Public Health Department, 2008­2012; U.S. Census. 2010

Note: Figures highlighted in red are above the county average

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There is not enough information available on the mortality rate from influenza, pneumonia, and other communicable diseases by neighborhood to draw insights from intra­city variation. Overall, San Jose enjoys high levels of civilian health and a well­developed healthcare system.

Figure 22. Influenza and pneumonia death rates by neighborhood in San Jose.

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Vibrancy Author: James Bradbury What makes a city a great place to live, work, and be, besides things we expect from every city like safety, resilience, basic services, and sustainability? Lots of things do: good public transit, a walkable downtown, attractions, and a qualitative sense of energy—the feeling that your city is full of interesting people and places.

Figure 23. Vibrancy diagram.

All of this contributes to a set of self­reinforcing, virtuous cycles. When you can get around your city on foot or by bike, you are more likely to stop when you pass an interesting shopfront. You are more likely to spend their time and money at places in the city. When the city’s people become a commercial base for its businesses, those businesses can invest and improve themselves. They can transform themselves into destinations—places that people would really want to walk and bike to.

Figure 24. Virtuous cycle of downtown growth.

Urban art, transit systems, mixed­use development, all empower people to move and spend time in their city. They connect the places where people meet, work, eat, shop, and live. They make a city vibrant.

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Another example of a virtuous cycle of vibrancy is related to transit­oriented development: development near transit leads to better use of transit and more transit investment. There is immense potential for San Jose to take advantage of this cycle over the next few years and decades; two major ways to do so are through more convenient public transit and smart, focused growth.

Figure 25. Virtuous cycle of transit­oriented development.

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Public Transit Authors: Rommy Joyce, Leopold Wambersie As San Jose’s resident and workforce populations continue to grow, the General Plan has deemed transit­oriented developments one of its main priorities. To accommodate a forecasted 130,550 new households and 146,680 new jobs through to 2040 , San Jose has initiated new 16

plans to support smart growth that locates new housing close to jobs and transit. In addition, this type of encouragement of the use of public transit was accompanied by policies that promote the improvement of the reliability and accessibility of public transit to job opportunities and essential services. In order to measure progress towards these goals, it is important to assess existing public transportation conditions.

Figure 26. Primary and intermediate stops.

We worked to evaluate access to public transportation to primary destinations, such as home and work, and intermediate stops, such as recreational facilities and other essential services. Public transit is one of the elements of a virtuous cycle. Therefore, we looked into the quality of public transportation in San Jose. We focused on the VTA light rail network, which is the backbone of San Jose’s transit efforts and as such deserves special attention. We decided that studying the use and makeup of the land surrounding stations was an important step in assessing the network’s convenience. This work ties into Transit Oriented Development, where

16 Association of Bay Area Governments and Metropolitan Transportation Commission. “Draft Bay Area Plan – Forecast of Jobs, Population, & Housing,” March 2014.

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the focus is specifically on developments near stations. In this analysis, the stations considered are within the city limits. To assess the performance of each station, we created a score. This score was based on accessibility, connectivity, frequency of service, and human activity. These variables will be described in more detail below. Once we calculated these variables, we assigned a score between 0 and 100 to each of of them; then, we calculated the overall score for each transit stop. This score can provide a quantitative tool to compare the performance of these stations. This will help us to understand what works and what doesn’t, as well as areas of interest and areas which require more attention. For accessibility, we considered access to land areas, access to job opportunities, and the number of households living near a station. For connectivity, we considered the number and type of public transit near these stations. For frequency, we calculated the number of trains per hour. Finally, for human activity around each station, we calculated the number of pedestrians and bikers at the intersection surveyed. The dataset used to calculate these variables includes:

Bicycle and Pedestrian data from the Metropolitan Department of Transportation Longitudinal Employer­Household Dynamics data from the US census Number of business establishments data from the US census in San Jose VTA Light rail weekdays schedule and connections VTA Light rail stops A street map of San Jose

To calculate some variables, we did a network analysis (Figure 27), generating regions which are accessible within reasonable walking distances from stations. All variables were assigned a coded weighting based on the distance from each station. For example, the ¼­mile walking zone is more heavily weighted than the other walking zones. Since these variables are weighted, we expect certain stops will perform better than others based solely on the side of their walking zones as generated by the network analysis.

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Figure 27. Areas within ¼­mile, ½­mile, and ¾­mile of VTA stations, created via network analysis.

From the data used to calculate the score of each station, we could see that the density of land uses and patterns varies geospatially. For instance, home density varies geospatially (Figure 28). Also, pedestrian and biker activity varies geospatially (Figure 29). Pedestrian activity is concentrated downtown, whereas, biker activity is distributed throughout the city.

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Figure 28. Relative household (left) and relative job (right) density throughout San Jose.

Figure 29. Relative bicycle (left) and relative pedestrian (right) density throughout San Jose.

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By using the walking regions created, we obtained a value for each variable at each walking zone we analyzed. Figure 30 and 31 display the total number of business establishments and households that can be accessed by each VTA light rail stop in San Jose.

Figure 30. Total number of business establishments near transit stop.

Figure 31. Total number of households near transit stop.

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By intersecting the variables mentioned before with the walking zones, we can better understand how the character of a station’s region changes as we progress along the transit lines from North to South. The following independent variables were measured at each transit stop (Figures 32­34):

Jobs around station Employee household around station Number of pedestrians at intersections surveyed Number of bicycles at intersections surveyed Floor space (commercial or residential) in square miles Space dedicated to pedestrians (sidewalks and plazas) in square miles Area accessible by foot in square miles Area accessible by bike in square miles

Figure 32. VTA Light Rail Routes.

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Figure 33. Variables measured at each transit stop.

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Figure 34. Variables measured at each transit stop (continued).

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Whereas one may expect a smooth curve up as we near downtown and down as we move away, some interesting questions are raised by the data:

Why are there so many jobs near the Tasman and Orchard stations? Why are there so many households around Hostetter station Why are there so many bikes relative to pedestrians around Almaden, or Montague

Stations? These stations and groups of stations are unique and can present interesting case studies as to what is working well. The next step would be to get boots on the ground and go to these stations to further investigate. As we mentioned before, we assigned a score between 0 and 100 to each transit stop. Points are awarded based accessibility, connectivity, frequency of service, and human activity around each station. For accessibility and human activity, the maximum points are awarded to the variables within a ¼­mile distance from stations. The points decrease as the number of variables and the distance from public transit stops decrease. We summed the value of all the calculated variables to generate the transit stop score. Figure 35 shows the final results in ArcMap and Table 16 shows the values for each light rail station.

Figure 35. Light rail stop score plotted on map.

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Table 16. Light rail stop score. 17

# NAME SCORE

1 Almaden Station ­­

2 Alum Rock Station 24.3

3 Baypointe Station 27.7

4 Berryessa Station 21.7

5 Blossom Hill Station 8.6

6 Bonaventura Station 14.5

7 Branham Station 8.2

8 Capitol Station 19.6

9 Champion Station 11.3

10 Cisco Way Station 13.7

11 Civic Center Station 25.4

12 Component Station 24.9

13 Convention Center Station 40.6

14 Cottle Station 12

15 Cropley Station 32.2

16 Curtner Station 23.8

17 Children's Discovery Museum Station* ­­

18 Gish Station 26.6

19 Great America Station 6

20 Great Mall/Main Station 15

21 Hostetter Station 21

22 I880/Milpitas Station 7.7

23 Japantown/Ayer Station 37

24 Karina Station 19.3

25 Lick Mill Station* ­­

26 McKee Station 14.2

27 Metro/Airport Station 18.3

17 Asterisk indicates limited information.

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28 Montague Station 9.5

29 Oakridge Station 16.5

30 Ohlone/Chynoweth Station 16

31 Old Ironsides Station 11

32 Orchard 15

33 Penitencia Creek Station 19.5

34 River Oaks Station 18.3

35 Saint James Station 54.9

36 Saint James Station 50.2

37 Paseo de San Antonio Station 81.9

38 Paseo de San Antonio Station 65.3

39 San Fernando Station 28.5

40 San Jose Diridon Station 26.8

41 Santa Clara Street Station 66.2

42 Santa Clara Street Station* 68.1

43 Santa Teresa Station ­­

44 Snell Station 10.6

45 Tamien Station 8

46 Tasman Station 22.9

47 Virginia Station 9.1

48 Race Station 19.5

49 Fruitdale Station* ­­

50 Bascom Station 18.4

51 Downtown Campbell Station 10.8

52 Winchester Station 8.5

53 Hamilton Station 9.2

From the results obtained, we can see that transportation quality is uneven. To understand these results, we need to analyze the walking zones we used in our analysis as well as the variables considered to calculate each score. From Figure 36, we can see that low scores seem to be related to areas that are less accessible. By increasing access to these stations and the

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walkability in those areas, the scores of those stations could improve. However, the solution for these low scores is much more complex. For instance, there are certain stations that perform poorly but seem be very accessible according to the network analysis we performed around each station.

Figure 36. Station scores and walking zones.

Figure 37 shows that the stations with low scores that have large walking zones either have a large number of people living near a station or large number of people working near a station. In order to improve these scores, we need a holistic analysis, which can help to improve the city’s vibrancy as well.

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Figure 37. Station scores and home and job density.

Running these through the ‘On the Map’ online census data platform reveals that 8% of people living near a station also work near a station in San Jose, while 24% of people living near a station also work near a station in San Francisco. Since vibrant cities have a considerable amount of people living and working near stations, it could be worthwhile to focus on land use in the surroundings, zoning, and development in order to improve the vibrancy of the city.

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Focused Growth Authors: James Bradbury, Luke Babich With the General Plan’s Major Strategy 3 (Focused Growth) and Goal H­4.3, excerpted below, the City has decided to embrace a “smart growth” perspective and use its power to regulate land use to try to focus new housing construction in mixed­use, transit­accessible developments located in City­designated growth areas while avoiding additional sprawl or harm to existing neighborhoods. This strategy marks a significant shift from the City’s historic growth patterns, and is central to San Jose’s efforts to become a denser, more vibrant, and more livable city. H­4.3 Encourage the development of higher residential densities in complete, mixed­use,

walkable and bikeable communities to reduce energy use and greenhouse gas emissions.

MS­3 [...] A Major Strategy of the Envision General Plan is to focus new growth capacity in

specifically identified “Growth Areas,” while the majority of the City is not planned for additional growth or intensification. [...] residential growth capacity is provided through the conversion of older commercial areas to mixed­use, including sites previously identified for housing development within North San José and the new commercial sites made available for mixed­use development within the Envision General Plan Urban Village areas. [...]

The City is currently conducting the 4­Year Review of its 2040 General Plan, but the portions of the most recent progress review report that cover housing are relatively limited in scope, with an emphasis on the City’s legal obligations under ABAG and Federal affordable­housing mandates. We decided to assess quantitatively the implementation of Focused Growth goals with respect to new housing developments during the 2007­2014 period covered by the just­completed Housing Element. Our assessment relied on cross­referencing annual development reports that document major completed construction projects with the City’s online development permit database. We measured progress towards Focused Growth using a three­part metric, composed of the fraction of new housing built each year that is a) located in growth areas, b) located in mixed­use developments, and c) located near transit. In the aftermath of the near­complete halt in development that succeeded the financial crisis of 2008, and with the approval in 2011 of the General Plan, the data show an immediate and substantial effect of focused growth on development patterns. In 2012, just one year after the General Plan’s Growth Areas were finalized, more than 90% of new housing units in major developments were located there, while a similar fraction were located in areas the City defines as “near light rail;” each of these fractions is about twice the 2008­2009 average. It is likely that

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this near­instantaneous change demonstrates not an impossibly fast developer reaction to a brand­new planning situation, but rather that decisions about where the city should locate growth areas and where developers should build in 2012 were made around the same time for similar reasons. The story is less positive with respect to mixed­use development, which is a major component of the Focused Growth strategy. While the trend is encouraging, only a quarter of new housing in major developments completed in 2014 was built as part of mixed­use complexes. Furthermore, according to the most recent 4­Year Review progress report, no mixed­use developments have yet been permitted in urban villages, the main type of growth area in the General Plan, because developers and officials are waiting for completed urban village specific plans. The situation for transit­oriented development is also mixed; while the fraction of housing built near light rail has declined from its 2012 peak, this could in principle be the welcome result of development priorities shifting to other forms of transit, including in anticipation of upcoming Bus Rapid Transit and BART lines.

Figure 38. Focused Growth in San Jose.

A geospatial assessment of focused growth shows a number of notable patterns. Developments in growth areas tend to be larger in terms of number of units, with the most concentrated center for development overall located in the North San Jose employment zone, the largest city­designated growth area. Another important growth center is Santana Row, home to the three mid­size green circles at center left, demonstrating the tradeoffs between development in

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places which are already popular, dense, and walkable and a single­minded focus on transit accessibility.

Figure 39. Locations of newly built housing in San Jose since 2008.

Focused Growth Demographics The Focused Growth plan was put in place in part to restrain residential development. Left unchecked, developers would build more of the sprawling single­family units that blanket much of San Jose. These residences are extremely profitable for developers, who have no shortage of moneyed buyers in the Bay Area’s over­heated housing market. But they are extremely problematic for the city: excluding younger or less­wealthy residents, closing off opportunities for

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badly­needed commercial development, damaging San Jose’s ethnic and socioeconomic diversity. Even more important than curbing construction of single­family homes, Focused Growth is intended to propel the construction of homes and neighborhoods that contribute to a more vibrant society in San Jose. That is, it is not merely meant to cut short the old development path, but to establish a new one. Mixed­use, transit­oriented development will encourage residents to travel through their neighborhoods, growing the consumer base for San Jose’s shops and restaurants. This, in turn, yields better local businesses and a stronger tax base for the City, which can be used to provide public services and further propel the creation of denser and more­walkable urban corridors. The city has many policy tools to support the focused growth agenda, but the ultimate success of the General Plan depends on buy­in from both developers and new residents – actors that the city can influence, but can never directly control. Even if developers are forced to stop building outside of designated growth zones or away from light­rail lines, they won’t necessarily be incentivized to start building in a more urban or vibrant way. Likewise, the city hopes to attract working millennials who want to work and shop close to home, and who can therefore fuel demand for the kind of development envisioned in the focused growth plan. But while the city can push for residences designed to appeal to this demographic, whether they actually move in to newly developed areas depends on whether millennials actually buy into the city’s new vision of life in San Jose. With that in mind, we set out to develop a second set of metrics for the Focused Growth plan. While the data described above measure the changes in residential development, in the second stage of our analysis we measured how such changes are reshaping the demographic profile of San Jose. Our approach offers insight into how the housing policies put in place with the General Plan are already influencing the choices made by new residents, and to trace them back to decisions made by developers to either circumvent or conform to the city’s vision. The metrics in the last section allow planners to assess whether Focused Growth policies are being carried out; leveraging the metrics in this section, planners can evaluate whether Focused Growth is actually achieving its long term goal of fostering vibrant urban communities in San Jose. Methods For our analysis in the previous section, we cross­referenced San Jose’s online development permit database with annual reports that document the completion of major construction projects, compiling a new dataset with IDs and geographic information on all recently­completed residential developments in San Jose. The dataset also contained a list of all residential addresses associated with each development project, which was the starting point for the second stage of our quantitative analysis.

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A number of websites offer reverse­address lookup services, allowing users to match an address with the name of the individual living there. We created an automated program to query all free and publicly available address lookup sites with each address from our dataset, then compare and aggregate the results. Next, we obtained a voter database maintained by Catalist, a Washington, D.C.­based firm that provides data consulting services to left­wing political groups, including Obama’s 2008 presidential campaign. Catalist aggregates information from voter files, consumer data, and demographic records into a vast and highly­detailed database. Matching our person­address pairs against Catalist’s data, we obtained statistics on the sub­population of San Jose residents who have moved in to new residential developments. Our approach will tend to understate the populations of certain groups. Voter files are the foundation of Catalist’s database, and while the company also collects information from auto licensing records and other sources, voters will be overrepresented compared to nonvoters. Lower­income individuals or individuals facing a language barrier are also less likely to leave a paper trail of the kind collected by Catalist, and their numbers will also be understated. However, we have no reason to expect the dataset’s bias to be different or more dramatic in new development areas than in San Jose as a whole. In this way, any bias will not compromise our ability to use Catalist data to estimate trends and draw contrasts between the population of San Jose and the sub­population of residents moving in to new developments. Further, our estimates of San Jose’s racial demographics track closely with official US Census statistics, lending validity to the measures generated with Catalist data. Results The ethnic profile for residents of new developments is similar to that of San Jose City as a whole, but with several marked differences. While Asians make up about a third of the San Jose population, in our subsample they are a majority, accounting for almost half (45%) of the residents of new developments. This difference is caused mostly by a much smaller Hispanic population in the new developments: while Hispanics make up a little more than a fourth of the San Jose population, they account for only 16% of our subsample. New developments also have a small Caucasian population relative to the city, 35.9% as compared to 40.7%. The growing Asian population in San Jose’s new residences is consistent with trends for the Bay Area as a whole, although our data suggests that the trend is particularly significant in San Jose.

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Figure 40. Comparison of resident race: new development areas vs. San Jose average.

Figure 41. Comparison of resident age: new development areas vs. San Jose average.

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The age distribution for residents of new developments closely corresponds with that of San Jose overall, except for one demographic: 18­29 year­olds make up only 6.9% of new­development residents, as compared with 16.9% for the city as a whole. Working­age millennials are conspicuously absent from San Jose’s growth zones, with a greater proportion of 30­50 year­olds making up the difference. With respect to millennials, the City is facing a challenging chicken­and­egg problem. Millennials are looking for walkable neighborhoods with shops and entertainment centers that are easily accessible from their home. They are attracted to mixed­use and transit­oriented development and simultaneously encourage more of such development, as Houston saw in 2014, when an influx of working millennials launched a major boom of commercial construction in the city. San Jose has pushed for denser, more transit­oriented and mixed­use development in part to bring more millennials in to focused growth zones, and catalyze this cycle, but without a growing millennial population already in place, it is difficult to change development practices. However, our data show that new development projects in the city are failing to attract this demographic. We believe the problem lies in path­dependency of San Jose’s housing market. First, on the consumer­side, San Jose’s reputation as a place to live has been defined by its suburbs and single­family dwellings. Consumer perceptions of the city will inevitably lag behind real changes on the ground; San Jose needs to not only push forward denser and more urban development, but to actively and creatively market these changes and rebrand itself to young workers moving to the Bay Area. Without a strong demand from millennials already in place, risk­minimizing housing developers will continue building in the ways they always have. We conducted several small­scale case studies of recent development projects in our dataset to explore how developers are actually responding on the ground to the City’s Focused Growth plan. While it is difficult to establish concretely whether the case is mean or outlier, one project at Corde Terra Circle shows that there is strong resistance to building more urban and walkable development. The project passed itself off as “mixed­use” by creating a recreational center within a ring of mostly single­family residences, and met density requirements by building an elder care facility adjacent. The Corde Terra development is attractive and walkable – but only for the small group of residents who live within it. It is less a new model of urbanism than a slightly denser kind of sub­urbanism, still highly insular and disconnected from the city as a whole. Developers will not build homes designed to appeal to a demand that does not already exist; with no shortage of wealthy buyers for suburban­style homes, they will continue to follow the letter but not the spirit of the Focused Growth plan, slowing the pace of urban developments that will appeal to millennials. Rather than rely on developers to change their practices and create demand among millennials, the City should reorient itself to stimulate demand directly. First, it should escalate efforts to market San Jose as an attractive place for young people to live. Second, it should focus more

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strongly on engaging corporate partners in the focused growth discussion. Businesses, especially tech companies that employ millennial workers, can be a powerful force to push for smart growth because offering good working conditions in desirable areas is part of how they compete for talent. Having corporate stakeholders committed to a project will make it much less risky for developers, and there is a lot of space for the City to contribute meaningfully towards bringing these two groups together.

Figure 42. Comparison of resident income: new development areas vs. San Jose average.

Figure 43. Comparison of resident income over time.

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With regard to income, our data are more optimistic. The first of the two figures above shows the income distribution for residents of new developments as compared with the overall income distribution for San Jose. The two distributions appear to closely correspond, and both slant heavily towards the very­rich. However, disaggregating the sample of new­development residents reveals a very different trend. The second figure breaks our subsample down into three groups: residents of homes completed between 2007 and 2011, the four years prior to the release of the San Jose General Plan; residents of homes completed between 2011 and 2015, the four years after the Plan was published; and residents of homes completed within the last year. While the income breakdown of the first group reflects that of San Jose overall, residents of more recent development show much more socioeconomic diversity. It is also worth noting that new low­income residents, those entering the city in the last few years, are especially likely to be under­represented in the Catalist database, since they have had less time to accumulate a paper trail that would tie them to the city. If anything, the dramatic trends above are likely understating the City’s success at providing housing for low­income residents. We suspect that San Jose’s remarkable success in this regard is related to the City’s Inclusionary Housing Ordinance, a policy released shortly before the rollout of the General Plan which mandated that all new residential developments of 20 or more units must include housing for low­ and moderate­income residents. The ordinance faced a legal challenge from the California Building Industry Association that placed a freeze on its implementation ­­ only in June of 2015 was the policy finally upheld by the California Supreme Court and the City given liberty to begin applying it. However, San Jose has many levers at its disposal to speed up or slow down the permit approval process. It is likely that in the wake of the City’s development re­orientation, after the release of the ordinance and General Plan, San Jose began using its policy tools to increase pressure on developers to include inclusionary housing in their plans.

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Conclusions While the research methodologies, tools, and projects presented above are far from complete, the Project Team in this year’s iteration of GUDP has built a strong foundation for future developments in collaboration with the City of San Jose. While the Project Team will conclude their efforts in the Research Phase with this report and continue on to focus projects as defined by the city partners, the Research activity will be resumed in Fall of 2016 with a new team of students and teaching assistants. Those in the City of San Jose who are interested in supporting or guiding further research directions are encouraged to contact the GUDP teaching team. Future steps as proposed by the Project Team for future students may include:

Generally updating all socioeconomic data once the results of the 2015 Census are available.

Updating the water supply dashboard with new data received from the water suppliers once it is available in 2016.

Conducting an economic analysis of water supply costs to the city and to the consumer based on land use patterns and geographic regions.

Updating the solid waste density indicators and solid waste impact model with the complete data from waste service providers in the city.

Completing the GHG reduction analysis of the solid waste impact model and comparing alternative Green Vision strategies to 100% landfill diversion.

Using the methodologies developed with the solid waste system to analyze other ecological systems like food, water, energy, wastewater, etc.

Completing a half­finished project involving fiscal policy and visualizing municipal taxes through a “taxpayer’s receipt” dashboard.

Refining the Urban Resilience Framework through further hazard analyses. Exploring the relationship between health and the built environment. Refining the methodologies from the Public Transit stop score project to correlate with

measured ridership. Applying the methodologies from the Public Transit stop score project to other public

transit systems like buses, bus rapid transit, and future BART service. Refining the methodologies from the Focused Growth demographics project to better

understand residency patterns in new neighborhoods. Updating the Focused Growth development project to include new construction data

from 2016. Many more projects can be explored, along with greater use of tools such as GIS network analysis, programming methods within GIS, statistical analysis, risk analysis, etc. We also

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expect that more data from a variety of local, state, and federal sources will be available to students in future projects. We would like to acknowledge the following city partners who have contributed their time and effort to support our academic activity: Michael Brilliot, Jared Hart, and Kimberly Vacca from the San Jose Planning Department, who acted as the primary city representatives for the student activity; Doug Moody, Rosalynn Hughey, Wayne Chen, Chris Burton, Jessica Zenk, Kathy Leveque, Nanci Klein, Michael Liw, Salvador Alvarez, Barry Ng, Kevin Miller, Erica Garaffo, Adam Marcus, Michael Gonzales, and Ruth Cueto who participated in feedback meetings and presentations by the students and teaching team. We would like to thank the following mentors and advisors from other institutions who supported the academic activity: Laura Tolkoff and Kristy Wang from SPUR, who presented to the students and hosted the students for a feedback meeting; Rosey Jencks from SF Water who presented to the students; Professor Richard Kos and Professor Ginette Wessel from San Jose State University who presented to the students and hosted the students for a feedback meeting; Professor Tang Ya from Sichuan University who presented to the students and led a parallel academic activity at Sichuan University. We would like to thank the following mentors and advisors from Stanford University who supported the academic activity: Professor Ray Levitt, Lecturer Michael Bennon, Associate Professor Eduardo Miranda, Professor Bruce Cain, Researcher Preeti Hehmeyer, Lecturer Dehan Glanz, Professor Mark Jacobson, Professor Craig Criddle, Assistant Professor Michael Lepech, Professor Thomas Hansen, Researcher Sebastien Tilmans, Lecturer Douglas Abbey, Researcher Rishee Jain, Professor Renate Fruchter, Researcher Camilo Gomez, Lecturer Jospeh Kott, Lecturer Eloi Laurent, Visiting Scholar Bruce Cahan, Lecturer Stace Maples, and Lecturer David Medeiros. The teaching team for the 2015­2016 academic year includes Professor James Leckie, Consulting Professor Jie Wang, Lecturer Glenn Katz, Lecturer Sandy Robertson, Lecturer Charlotte Stanton, PhD student Rob Best, and Lecturer Derek Ouyang. The appendix to the research white paper can be accessed at gudp.stanford.edu. For general inquiries about GUDP, please contact Derek Ouyang at douyang1@stanford.edu.

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