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Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop (2023)

Chapter: 2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability

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Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
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2

Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability

In order to address infrastructure decisions and sustainability goals, decision makers use a variety of tools, data, and metrics to understand a community’s needs. In a series of presentations and open panels, participants in this session discussed which data are relevant, how the data are connected to services, and how infrastructure performance is measured.

2.1 DATA TO ACTION: USING DATA TO SUPPORT RESILIENCE AND SUSTAINABILITY IN THE CITY OF HOUSTON

Loren Hopkins, Houston Health Department, noted that Houston—with 7 million residents, rising temperatures, multiple bayous, frequent flooding and significant air pollution from vehicles and its port—is particularly challenging for sustainable infrastructure planning. Resilient Houston,1 a mayoral initiative, envisions a city that is a healthy place to live; is equitable, inclusive, and affordable; leads in climate adaptation; grows up, not out; and develops a transformative economy. As part of the initiative, Houston’s Climate Action Plan aims to reduce Houston’s base-year greenhouse gas emissions by at least 40 percent by 2030 and by at least 75 percent by 2040. Houston Complete Communities,2 another

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1 For more information about Resilient Houston, see https://www.houstontx.gov/mayor/Resilient-Houston-20200518-single-page.pdf, accessed August 28, 2022.

2 For more information on Houston’s Complete Communities Initiative, see https://www.houstoncc.org/, accessed August 28, 2022.

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

mayoral initiative, supports specific neighborhoods that would benefit from additional assistance to reach their full potential.

Hopkins explained that the Houston Health Department identifies data for these initiatives; determines risks of flood, heat, and pollution; and prioritizes areas for and types of intervention. Its Data Science Team collates information from syndromic surveillance (911 calls, emergency department visits, and hospitalization data), health records (reportable diseases, vaccinations, and vital statistics), and community partners (academic research groups, local health jurisdictions, and local hospitals) with demographics (age, race, ethnicity, gender, and socioeconomic status) and environmental resources (weather, pollen, flooding, and pollutants).

Hopkins described how the Houston Health Department leverages these data and resources for several ongoing projects. For example, it monitors heat, flooding, and pollution across the city at the granular level—an analysis revealed that the risk of an asthma attack is six times higher in one area of the city than in the rest of city. By monitoring at the granular level, she explained, areas where multiple indicators are found at disproportionate levels can be identified (see Figure 2-1). In another example, the department established a tree-planting initiative that relies on historical neighborhood data for targeted intervention to mitigate the effects of pollution and flooding.3

The Health Department’s many partnerships have been crucial for data collection and analysis. For example, the Flood Inundation and Response System emerged from a partnership with academic research groups. It uses historical floodplain maps and real-time rainfall data to create a predictive model for certain communities at high risk for flooding, which helps to identify locations of people who might need assistance during an evacuation.

Another partnership between the Houston Health Department and academic research groups works to understand asthma attacks in Houston communities, their relation to pollution and climate change, where to target interventions, and how to support asthma care through the use of multidata interpretation (e.g., 911 data, air-monitor data, census tract demographics, medical compliance data, and the Texas Children’s Health Plan vulnerability index).

Hopkins explained that systems that had been piloted in one locality are now being expanded to the broader community. An alert system piloted in local schools with students suffering from asthma has

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3 L.P. Hopkins, D.J. January-Bevers, E.K. Caton, and L.A. Campos, 2022, “A Simple Tree Planting Framework to Improve Climate, Air Pollution, Health, and Urban Heat in Vulnerable Locations Using Non-Traditional Partners,” Plants, People, Planet 4(3):243–257, https://doi.org/10.1002/ppp3.10245.

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
Image
FIGURE 2-1 The Houston Health Department monitors heat, flooding, and pollution at the granular level.
SOURCES: L. Hopkins, Houston Health Department, presentation to the workshop, July 13, 2022. Data courtesy of: H3at.org - CAPA/NIHHIS and D. McClendon, 2020, “Heat Watch Houston & Harris County,” December 3, https://osf.io/yqh5u/; J. Chakraborty, T. Collins, and S. Grineski, 2019, “Exploring the Environmental Justice Implications of Hurricane Harvey Flooding in Greater Houston, Texas,” American Journal of Public Health 109(2):244–250, https://doi.org/10.2105/AJPH.2018.304846; K. Ensor, L. Raun, and D. Persse, 2013, “A Case-Crossover Analysis of Out-of-Hospital Cardiac Arrest and Air Pollution,” Circulation 127(11):1192–1199, https://doi.org/10.1161/CIRCULATIONAHA.113.000027; L. Raun, K. Ensor, J. Pederson, L. Campos, and D. Persse, 2019, “City Specific Air Quality Warnings for Improved Asthma Self-Management, American Journal of Preventive Medicine 57(2):165–171, https://doi.org/10.1016/j.amepre.2019.03.022.
Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

expanded to the larger community; based on the air-monitor pollutant data, an alert is sent on days with conditions similar to those when high asthma attacks occur, and a network of support is provided for families to reduce vulnerability to attacks. The Houston Health Department is also expanding its heat-related illness surveillance, using historical data (syndromic data, environmental and weather data, vital statistics, demographic and socioeconomic status, and 911 data) to predict and warn of heat-related illness and identify who could receive better support. Hopkins underscored the advantage of having a diverse data science team of engineers, medical professionals, statisticians, and public health professionals as well as access to data systems and academic partnerships to enable such initiatives.

2.2 RELEVANT COMMUNITY DATA FOR INFRASTRUCTURE AND SUSTAINABILITY

Brittany Sellers, City of Orlando, Florida, explained that the Orlando’s Office of Sustainability and Resilience, launched in 2007, has more than 100 strategies across seven key areas: clean energy, green buildings, local food systems, zero waste, livability, clean water, and electric and alternative transportation. She described three key questions that her office evaluates to help determine which data are relevant to sustainability and infrastructure and how they can be used.

First, where are you, and where are you going? Sellers suggested starting the decision-making process with a baseline to help determine the end goal—for example, the current state of greenhouse gas (GHG) emissions across a jurisdiction. In Orlando, significant decreases in CO2 emissions have occurred per capita, but it is important to understand where the emissions are high and why. She observed that most GHG emissions in Orlando are from the built environment, not from the transportation sector as many might suspect. An additional consideration is the disproportionate energy burden on lower-income areas with less-efficient buildings. This knowledge has allowed the city to work with local utilities to address this burden with better programs for renters, for example. She emphasized that leveraging census tract data and utility company data is critical to identifying where to allocate resources to reduce emissions and consumption as well as to improving quality of life for business owners and residents.

Second, how quickly do you need to move? Sellers suggested looking at established national and international frameworks, such as the United Nations’ Sustainable Development Goals, to determine how (and how quickly) assets and services provided by the local government could be expanded and/or made more efficient and cost-effective. For example,

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

how many unnecessary cases of asthma is a city comfortable with before taking action to reduce pollutants? She underscored the need to also consider qualitative data to prioritize decision making for what is most important to the community. Quoting researcher Brené Brown, she posited that “maybe stories are just data with a soul.”

Third, what do you need to take to get there? Sellers explained that in Orlando elevating community voices led to the creation of a low-maintenance rooftop garden at a fire station, which at-risk youth use to grow produce. Thus, previously unused space has been transformed to mitigate pollution, offer workforce development, and provide food. She reiterated the value of community engagement in enhancing sustainability and added that an old-fashioned sticker board is an effective and inclusive way to collect input from residents.

2.3 INFLUENCE OF COMMUNITY INFRASTRUCTURE ON SUSTAINABILITY AND RESILIENCE OUTCOMES

Kyle Buck, Environmental Protection Agency Office of Research and Development, described the concepts that drive his modeling work, starting with the individual/community as the origin of the research. He emphasized the importance of being attentive to the distribution and characteristics of this population. The other end of the spectrum of the research, the destination, considers how assets or hazards impact the individual/community. Buck’s research focuses on understanding how the characteristics of the individual/community drive the intended outcomes, and how access to assets or hazards influences outcomes. He pointed out that because projects intended to benefit a particular population often do not achieve the expected outcomes, it is important to consider unknowns, many of which come from infrastructure, to prevent unintended outcomes in the future. He remarked that a better understanding of this infrastructure requires a better understanding of the scale of community benefits. He described the transfer of information and resources as key components of community resilience and sustainability, and noted that defining a community in terms of spatial, social, or economic outcomes gives the data bounds. Higher-scale issues (e.g., governance, how resources are allocated to the community) can also influence these outcomes, but consideration of movement between scales is what is most important in a community study.

Buck stressed that how data are collected and aggregated influences how they will be used in the research design. Data can be categorized in terms of connectivity: the assessment of stressor and benefit distribution within communities relates to sustainability and resilience outcomes. For example, characteristics such as distance, money, language, time,

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

connectivity, and knowledge can act as barriers or opportunities, which highlights the importance of collecting qualitative data to better understand individual residents’ issues.

Buck provided two recent research examples to illustrate the influence of community on infrastructure and sustainability. The first studied neighborhood-level segregation and its influence on broader patterns of sustainability by reviewing the distribution of census tracts and the concentration of minority populations across five levels of segregation. The study revealed that social and economic sustainability at the county level decreased with increasing segregation, but environmental sustainability increased. He pointed out that because these patterns are not the same across all minority groups, it is important to evaluate how specific infrastructure influences outcomes. The second studied how household and neighborhood-level access to infrastructure and amenities (which is not distributed evenly across communities) drives broader resilience outcomes. The study revealed that even with a regional approach (e.g., the creation of a regional recreational alliance), each community has its own priorities (e.g., ecological or social impacts), and data at the parcel level would help to better understand connections.

2.4 BETTER DATA AND MEASUREMENT FOR LOCAL DECISIONS IN TIMES OF CRISIS

Joseph Salvo, University of Virginia, highlighted a myriad of local planning issues (e.g., elderly residential care, commuting patterns, migration, the gig economy) that could be more fully understood with better data. The University of Virginia is working closely with the Census Bureau on the Curated Data Enterprise,4 which could create a “universal frame” for data integration to empower the Census Bureau to use multiple data sources and create more robust measures.

Salvo emphasized that local planners should be both consumers and directors of data. Knowledge of population and the local economy permit an assessment of strategies adopted by local officials that affect the quality of life of residents. He pointed out that the COVID-19 pandemic has increased inequities in the world of work, with some able to move and/or work remotely while others have no such option. The crisis has affected nursing homes significantly, testing their resilience in the face of disaster. The following questions are critical for local planners to develop responsive strategies: How are residents sustaining themselves economically? Are residents relocating as a result of the pandemic, and how is

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4 For more information on the Curated Data Enterprise, see https://biocomplexity.virginia.edu/institute/divisions/social-and-decision-analytics/census, accessed August 28, 2022.

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

that affecting the population? What has happened to the most vulnerable populations?

Salvo presented a case related to the post-pandemic world of work, noting that the gig economy could be better understood by state and local agencies and nonprofits to inform decision making and create more equitable policies. Because many federal data sources have not kept pace with these changes and do not capture current trends, private-sector data could be exploited for a more comprehensive understanding.

Another case, which focused on domestic migration, revealed disparities between people who are tied to specific locations and those who are not and confirmed that events that precipitate movement of people could be monitored in near-real-time so that planners could react accordingly. Several sources about migration patterns are now available on various platforms. For example, an application in New York City captures movement using postal change of address data during COVID-19 and reveals those zip codes that are most affected—essentially, places where people have higher income and thus the ability to move. The integration of multiple sources of data could help to determine whether these moves are temporary or permanent.

A final use case related to vulnerable populations in nursing homes. Salvo explained that a comprehensive view of nursing homes and elder care is being formulated by the Census Bureau in partnership with the University of Virginia to integrate data with different units of analysis, timing, and geography. In this case, the Curated Data Enterprise would provide the data product to local decision makers to integrate census-linked frames (geography, people, jobs, and businesses) and information from multiple agencies on nursing home residents, staff, and owners. This information could then be combined with safety and engineering data from federal agencies and local governments to produce a platform that a local decision maker could use to better understand how to mitigate the effects on a nursing home of a severe climate event.

In closing, Salvo underscored the value of data products that are actionable at a local level, capture data in real time, and are accessible to local governments as events are happening to enable data-driven actions.

2.5 METRICS AND MEASURES OF PERFORMANCE

Bill Robert, Spy Pond Partners, posed the following key questions for decision makers: What metrics are needed to support investment decisions? How do these metrics relate to underlying investment objectives? What data sources are available for calculating the needed metrics? What are common data issues, and how could they be addressed?

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

Robert described several useful resources for transportation data and metrics.5,6,7 These resources offer approaches for defining metrics and using them for decision making as well as prioritizing public transportation investments.

Robert indicated that the selection of metrics should be guided by underlying investment objectives; in NCHRP Report 921, these objectives are mobility, preservation, safety, security, resilience, environment, community, economic development, accessibility, and environmental justice. Each of the objectives requires one or more metrics. For example, the Federal Highway Administration (FHWA) has defined the following metrics for national reporting on the objective of mobility: percent of person-miles that are reliable, the Truck Travel Time Reliability Index, annual hours of peak hour excessive delay, and single-occupancy vehicle travel. For the objective of preservation, FHWA requires reports on percent of pavement and bridges in good/poor condition. For the objective of safety, FHWA expects reports on fatalities and fatality rate as well as serious injuries and serious injury rate. Common data sources for such highway-related metrics include the Highway Performance Monitoring System, the National Bridge Inventory, the National Performance Management Research Data Set, agency traffic performance databases, agency safety databases, and agency infrastructure management systems. Other relevant data sources include the Census Bureau, the Bureau of Economic Analysis, FHWA Highway Statistics, and the National Oceanic and Atmospheric Administration.

Robert discussed five challenges in selecting and using metrics. First, when selecting metrics to support decision making, one must consider the specific application (e.g., system-level monitoring, target-setting, or prioritizing investments). However, sometimes metrics are not well defined, owing to a lack of consensus and a corresponding lack of consistent data sources. For example, despite the importance of resilience, no consensus across agencies exists on the metrics that should be used to quantify it. Second, he said that one should be aware that different data sources have varying levels of detail, and different levels of detail are needed for different applications—for example, fatalities and serious injuries might

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5 National Academies of Sciences, Engineering, and Medicine, 2015, Guide to Cross-Asset Resource Allocation and the Impact on Transportation System Performance, Washington, DC: The National Academies Press, https://doi.org/10.17226/22177.

6 National Academies of Sciences, Engineering, and Medicine, 2019, Case Studies in Cross-Asset, Multi-Objective Resource Allocation, Washington, DC: The National Academies Press, https://doi.org/10.17226/25684.

7 National Academies of Sciences, Engineering, and Medicine, 2021, Prioritization of Public Transportation Investments: A Guide for Decision-Makers, Washington, DC: The National Academies Press, https://doi.org/10.17226/26224.

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

be of concern at a system level, while reduction in crashes might be of greater interest at the project level. Third, issues of data quality and consistency arise when data are assumed to be accurate but are not, data are accurate for some uses but not for others (e.g., probe data are useful for vehicle speed but not for traffic counts), or data are outdated. Fourth, the following questions are key to addressing temporal issues: when are data collected, over what timeframe are data reported, and when are data available for use (e.g., key leading indicators such as seat belt use are often collected only annually)? Fifth, it is critical to review metrics to ensure that they function as expected to avoid unintended consequences. For example, when measuring mobility, focusing solely on vehicle speeds or throughput could result in underemphasizing accessibility to key destinations or alternative modes.

Robert offered two examples of successful state initiatives for defining metrics and using data for decision making. The Virginia Department of Transportation adopted the “Smart Scale” approach to prioritize proposed transportation infrastructure investment biannually, using 14 metrics to address the objectives of safety, congestion, accessibility, environmental quality, economic development, and land use. The Delaware Valley Regional Planning Commission established a process to prioritize proposed investments for both its Transportation Improvement Program and its Long-Range Plan, the metrics for which are aligned with the agency’s 2045 Long-Range Plan. He noted that although both examples focus on transportation, they are transferable to other sectors with infrastructure-intensive applications. He stressed that numerous examples of successful uses of metrics to support investment decisions using a structured, repeatable approach exist. Many challenges remain, however, in defining appropriate metrics and obtaining the data to calculate them.

2.6 CONSIDERATIONS IN MEASURING THE BENEFITS OF INFRASTRUCTURE SERVICES

Anita Chandra, RAND Corporation, discussed approaches to capturing the value of infrastructure services through the lenses of resilience, equity, and well-being. For instance, Chandra and her colleagues developed A Model for Calculating Dividends from Resilience Projects,8 which describes systemic and behavioral issues that present challenges when merging data on infrastructure.

Chandra defined “resilience” as the capacity of a system (a household, a community, or an organization) to prepare for disruptions from outside

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8 C.A. Bond, A. Strong, N.E. Burger, S. Weilant, U. Saya, and A. Chandra, 2017, Resilience Dividend Valuation Model: Framework Development and Initial Case Studies, Santa Monica, CA: RAND Corporation, https://www.rand.org/pubs/research_reports/RR2129.html.

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

the system, to recover from shocks and stresses, and to adapt to and grow from a disruptive experience. The “resilience lens” is an approach to investment and recovery that accounts for risk of loss from a shock or stressor as well as for benefits from resilience projects. These “co-benefits” can be economic, environmental, or social. Therefore, she explained, the “resilience dividend” is a change in the overall system benefits from a resilience project when combining the damage reduction (i.e., change in system benefits during and after shocks) and the co-benefits (i.e., change in benefits during normal times). A Resilience Dividend Valuation Model (RDVM) Framework can be used to determine the value of resilience projects (see Figure 2-2). This model looks at investments made post-shock as well as the augmentation of capital investments against the backdrop of other accruals when building infrastructure elements. All of these contributions could have a benefit in terms of creating income, building consumer demand, and investing in aspects of thriving and flourishing.

Chandra noted that this study of resilience dividends raised questions about the data available to measure resilience benefits. As the infrastructure map of a community changes, the economic development

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FIGURE 2-2 The Resilience Dividend Valuation Model (RDVM) Framework used to value resilience projects.
SOURCES: This visual is adapted from two RAND reports led by C.A. Bond: C.A Bond, A. Strong, N.E. Burger, S. Weilant, U. Saya, and A. Chandra, 2017, “Resilience Dividend Valuation Model: Framework Development and Initial Case Studies,” Santa Monica, CA: RAND Corporation, https://www.rand.org/pubs/research_reports/RR2129.html. Also available in print form; and C.A. Bond, A. Strong, N.E. Burger, and S. Weilant, 2017, “Guide to the Resilience Dividend Valuation Model,” Santa Monica, CA: RAND Corporation, https://www.rand.org/pubs/research_reports/RR2130.html. Also available in print form. This graphic was part of the presentation to the workshop given by A. Chandra, RAND, July 13, 2022.
Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

opportunities, community safety and security perceptions, income-generating potential, and social capital also change––longer-term benefits that are more difficult to measure. Data to measure these longer-term benefits are sparse, which makes it challenging to determine a complete valuation of infrastructure investments. This study of resilience dividends also revealed that different investment strategies have different pathways to benefits, that the resilience of a system depends on behavior and constraints, that data to establish causation are expensive, and that benefits can accrue even in the absence of a shock. She added that procedural, contextual/historical, and distributional elements of equity should also be considered to measure infrastructure investments and services.

Reflecting on the role of cities in helping people to realize their potential and ensuring that all residents thrive and flourish, Chandra described the Wellbeing Project in Santa Monica, California.9 This project explores the connection between the physical and social environment and well-being, with measures such as green-space access, use of and satisfaction with transit, and infrastructure perceptions. “Well-being amenities” include bike paths, trees, and street connectivity, while “well-being detractors” include vacant houses, violent crimes, and fast-food restaurants. This sustainable city framework has guided budget planning for Santa Monica. Chandra emphasized the need to translate data to action; although cities collect much data, data quality and usefulness are often not assessed. She suggested building a culture around data in cities and adopting community mobilization strategies to engage with data about infrastructure and well-being.

2.7 DISCUSSION

Serving as a moderator for the session, Sarah Slaughter, Built Environment Coalition, posed a question about the barriers to identifying and obtaining data as well as about available data that could be overlooked. Hopkins replied that data access agreements and other legal issues often create delays, and problems arise when data that lack history are unusable. She urged cities to create integrated platforms where researchers could more readily access and analyze data. Salvo championed efforts toward data curation, noting issues with the Census Bureau’s different data sources that are not yet integrated. Buck added that finding data sets that align temporally is difficult, for example, and spatial data present technical and privacy issues.

In response to a question from Slaughter about building trust in data, Salvo encouraged planners, through partnerships, to understand what the

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9 For more information about the Wellbeing Project, see https://santamonicawellbeing.org, accessed August 28, 2022.

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

locals need, to react to these priorities, and to determine what the local government already has that could be incorporated into a measure that will be relevant when merged with census data. He suggested leveraging a framework that curates data based on standards of reasonable criteria for a data set.

Slaughter wondered about the potential for federal data to be downscaled to the neighborhood level. Robert responded that although resources are available to look at annualized data across a state, national data sources (e.g., FHWA) are not useful if one wishes to drill down to a neighborhood or project level. Hopkins added that local data are essential in Houston because the city has to react at the zip code level; federal data are not available at this level and are often outdated by several years. Chandra described the challenges of obtaining useful and high-quality asset data: because a municipal government can capture only so much data, more consistent relationships with the private sector would be beneficial. Sellers suggested building relationships with residents, other local governments, and private companies as well as holding routine public meetings for people to express what is important to them, which could improve access to data at the appropriate level.

Slaughter observed that only some data and performance measures relate to federal or state requirements, and she raised the question of the usefulness of required data for specific applications. Robert replied that although federal requirements ensure consistency in data, federally required performance measures are only a starting point; at the project level, high-level definitions are insufficient, and using only federally required data for investment decisions could be hazardous.

Slaughter commented that cities still have to make decisions even when the available data and metrics are not adequate to support those decisions, and Buck pointed out that, in terms of resilience, defining the stressor creates several additional data issues. Some studies cross city, county, and state lines, and each location collects different data in different ways, which complicates data aggregation. As a result, some predictions have to be made about the infrastructure. The added complications presented by social and infrastructure data sets also increase the likelihood of error. Serving as a moderator for the session, Kathy Ensor, Rice University, asked how this error is quantified and how the uncertainty in the data and forecasts is communicated. Buck acknowledged that no standard process exists; he explained that Monte Carlo simulations are run to try to predict the stability of a model and to determine how much each indicator might affect the model stability overall.

Amid such uncertainty, Slaughter inquired about strategies to balance trade-offs and to improve decision making for infrastructure and infrastructure investments. Hopkins indicated that effective prioritization

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×

in Houston is based on targeting specific vulnerabilities at the local level. Sellers cautioned against using proprietary software that partnering communities cannot use, and noted that universally available modeling tools could be used to compare trade-offs between healthy air quality days and expansion of renewable energy in an area and related rate increases. When collaborating with stakeholders, she championed the use of simple 2 × 2 comparisons of “high” and “low” across several factors (e.g., political feasibility, community impact), which are useful for decision making with multiple factors and potential consequences. Buck asserted that communities want to see patterns in data, but people who do not work with data might be confused as to why a hot spot on a map is not a vulnerable area; the scale and context of the analysis are critical. He encouraged talking with the community and other stakeholders, clearly communicating boundaries about which part of the population could be impacted, and carefully defining outcomes. Salvo said that his team built tools for and provided training to empower nonprofits and local community groups in New York City to access data from the census and other sources that can be used to apply for grants for their specific needs. He noted that building this type of infrastructure required up-front resource investment, which is especially worthwhile for large cities that will eventually use the tools without assistance. Chandra highlighted the value of considering an equity perspective as well as the role of the private sector in infrastructure investment decisions. She also encouraged decision makers to think about how new threat information is being consumed more quickly and how that affects infrastructure investment; better private-sector partnerships would also enable data access for those purposes.

Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
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Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
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Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
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Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
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Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
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Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
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Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
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Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
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Page 18
Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
Page 19
Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
Page 20
Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
Page 21
Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
Page 22
Suggested Citation:"2 Relevant Data, Analytics, and Metrics for Infrastructure and Sustainability." National Academies of Sciences, Engineering, and Medicine. 2023. Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26905.
×
Page 23
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The National Academies Board on Mathematical Sciences and Analytics and Board on Infrastructure and the Constructed Environment convened a 3-day public workshop on July 13, 20, and 27, 2022, to explore state-of-the-art analytical tools that could advance urban sustainability through improved prioritization of public works projects. Invited speakers included people working in urban sustainability, city planning, local public and private infrastructure, asset management, and infrastructure investment; city officials and utility officials; and statisticians, data scientists, mathematicians, economists, computer scientists, and artificial intelligence/machine learning experts. Presentations and workshop discussions provided insights into new research areas that have the potential to advance urban sustainability in public works planning, as well as the barriers to their adoption. This publication summarizes the presentation and discussion of the workshop.

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