Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments Proceedings of a Workshop (2023) / Chapter Skim
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5 Building Confidence in Data and Institutions
5 Building Confidence in Data and Institutions
Pages 54-63
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From page 54... ...
Jain observed that different cities have different combined factors of importance: while the problem in Chicago related to data infrastructure, organizational structures, and analytical capability, the problem in New Orleans related to leadership, processes, and capacities. To better understand which combination of factors generates causal pathways to cities' use of data and analytics, Jain and his colleagues considered the following questions: What causal condition variables could enable cities to use data and analytics, and how can they be measured 54
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From page 55... ...
that are important for understanding cities' uses of data and analytics. Jain explained that practitioners could benefit from newly generated understanding provided by this type of analysis, with the ability to prioritize factors that drive cities' use of data and analytics; strengthen the perspective of cities as socio-technical units; and shift the focus from physical urban infrastructure to the data and analytics overlay.
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From page 56... ...
Wang suggested that decision makers and the data community use the data that support their decision making and report their experiences about the strengths and weaknesses of those data. He also encouraged decision makers to perform gap analysis to identify opportunities to improve data quality as well as to learn about the best practices of chief data officers (e.g., via MIT's Chief Data Officer and Information Quality Symposium2)
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She partnered with the Pittsburgh Black Worker Center5 and the UrbanKind Institute6 (both Black-led organizations) as well as the Western Pennsylvania Regional 3 Greenlining Institute, 2005, Fairness in Philanthropy, http://greenlining.org/wp-content/ uploads/2013/02/FairnessinPhilanthropyPartIFoundationGivingtoMinorityledNonprofits.
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From page 58... ...
Transportation Asset Management Plan. The enterprise-wide Data Quality Management Plan reveals gaps related to signal systems, which are one of MnDOT's most critical assets; although progress has been made and confidence has been built around some of the data, Cremin said that more time is needed both working in teams and working on data processes to enhance data quality.
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. The 2022 Transportation Asset Management Plan, a federally required plan for state DOTs, revolves around building effective data.
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She proposed scouring existing data used by nongovernmental agencies and nonprofits and partnering with university data centers to help make these policies. She emphasized that because policies are rarely tested on the people who will be impacted by them, it is also important to understand cultural differences and avoid making assumptions about experiences of people in the community -- another reason to ensure that the right people are in the room for decision making.
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From page 61... ...
The program focuses on climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, remediation of legacy pollution, clean water infrastructure, and training and workforce development. The Executive Office of the President, the White House Environmental Justice Advisory Council (WHEJAC)
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From page 62... ...
Key CEJST users include communities, public data users and technologists, and federal program officers; the architecture itself begins with community input. Switzer described the Justice40 Open Source Community, a Github repository12 that is free and available for public observation and reuse under a Creative Commons 1.0 license.
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From page 63... ...
prioritizing data sets that address community vulnerabilities for programs in Justice40. Switzer also discussed Justice40's process to collect participatory data: solicit input from WHEJAC on data needs; crowdsource data set ideas from the open-source community; identify how to assess the data for use; investigate data sets based on identified criteria; help CEQ experiment with methodologies using data sets; launch CEQ's beta methodology on CEJST; solicit public input; and continue to iterate on and develop the tool and methodology with CEQ, WHEJAC, and the public.
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