Enhancing Urban Sustainability with Data, Modeling, and Simulation Proceedings of a Workshop (2019) / Chapter Skim
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3 Advances in Data, Modeling, and Simulation
3 Advances in Data, Modeling, and Simulation
Pages 15-40
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3 Advances in Data, Modeling, and Simulation DATA, MODELING, AND SIMULATION Key Questions David Maier, Portland State University, Moderator Moderator David Maier, Portland State University, explained that speakers would discuss recent advances in technology related to the use of big data, modeling, and simulation that might be useful for urban sustainability. He presented the following key questions as a guide for the discussion: • What advances have been made in data, modeling, and simula tion for air and water quality, network analysis and mobility, and traffic modeling?
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Sensors were placed on top of Google Street View cars to measure methane, and then maps of methane leaks across various U.S. cities were published.
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The use of machine learning, deep learning, and natural language processing tools for integrating data can also support decision making around public health issues and inform public health policy. Disease forecasting, course of action analyses, and modeling of complex disasters aim to capture and represent massively interdependent systems in simulation, which is safer and more cost-effective than running disaster experiments in the real world.
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1 For more information about National Planning Scenario Number One, see https://www. fema.gov/national-planning-frameworks, accessed March 12, 2019.
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FIGURE 3.1 A synthetic representation of an urban system can be particularly useful in preparing for disasters or in developing new policy. SOURCE: Bryan Lewis, University of Virginia, presentation to the workshop, January 30, 2019, with credit to Henning Mortveit, Ph.D.
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C2SMART: Data-Driven Transportation Modeling and Simulation Kaan Ozbay, New York University Kaan Ozbay, New York University, emphasized the importance of transportation systems in cities and provided an overview of uses of simulation for transportation systems. Ozbay's C2SMART University Transportation Center2 works on modeling, simulation, and data-driven 2 For more information about the C2SMART University Transportation Center, see http:// c2smart.engineering.nyu.edu, accessed March 12, 2019.
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The C2SMART University Transportation Center is also engaged in work with modeling and simulation of connected vehicles in an effort to support the New York City Department of Transportation's work in that area. Ozbay described the value of open source tools such as MATSim, an agent-based modeling tool being used in research on redesigning citywide 3 For more information about MATSim, see https://matsim.org, accessed March 12, 2019.
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With all of these innovations, Ozbay continued, an urban data observatory (i.e., warehouse) is needed to incorporate these data and use them in conjunction with various kinds of simulation and decision-making tools.
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Goodings emphasized that this community is responsible for taking action that could lead to real change. INNOVATION IN GEOSPATIAL DATA SOURCES AND SPATIOTEMPORAL ANALYSIS Hurricane Harvey: A Real-Time Role for Data Scientists Katherine Bennett Ensor, Rice University, Moderator Katherine Bennett Ensor, Rice University, explained that Hurricane Harvey demonstrated a real-time role for data scientists -- the Urban Data Platform mentioned in Chapter 2 contains the only existing archive of
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She said that it is critical to understand the questions that data address, the dependence structure of data, the uncertainty associated with a scenario or decision, and the reproducibility of results. Big Data and Urban Science: Advancing Sustainability with High-Resolution Spatiotemporal Data and Data-Driven Modeling Constantine Kontokosta, New York University Constantine Kontokosta, New York University, explained that his work uses large-scale, high-resolution geospatial data to build data-driven models that address specific problems in urban operations, policy, and planning, particularly in sustainability and resilience.
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To work with city data, data integration along spatiotemporal dimensions as well as across sectors and domains is necessary. Kontokosta's team developed a data-mining algorithm that retrieves data from 40 city agencies and other data sources for place-based studies (see Figure 3.4)
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This can be used to better understand hot spot locations and the patterns of energy dynamics across the city, which helps to target policies appropriately. The next project he discussed focused on using geospatial data to understand mobility and behavior.
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Price showed a 30 cm image over a port from space to demonstrate just how much detail can be extracted from satellite imagery. These images can then be used to create digital elevation models.
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© 2019 Maxar Technologies. DigitalGlobe partnered with PSMA Australia to map the continent using satellite imagery, machine learning, and crowdsourcing.
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Kontokosta responded that much appropriate skepticism exists among city agencies and community organizations regarding work with large-scale geospatial data and machine learning algorithms. Having a conversation about transparency is important because it ties into all of the other questions about privacy, trust, and expectations for local government.
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Kontokosta added that his team works in data-rich environments and looks for generalizability of an approach rather than generalizability of results. PRIVATIZATION OF DATA AND DATA PRIVACY: TWO SIDES OF ONE COIN Aniruddha Dasgupta, World Resources Institute, Moderator Following Kontokosta's discussion of localized data and Price's discussion of massive geospatial data, Aniruddha Dasgupta, World Resources Institute, explained that the next step is to discuss how all of this work produces public goods or helps make decisions for the public good.
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For example, who frames -- and then makes -- the very difficult judgment calls about trade-offs among multiple dimensions of privacy, data quality, risk, and cost, at both broad policy levels and at more specific technical levels? Transparent multiway communication in these governance areas can be crucial to the reduction of information asymmetries and improvement of efficiency.
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Data sources included designed data, administrative data, opportunity data, and procedural data, all of which provide different challenges with respect to privacy, confidentiality, security, and privatization. The discovery to find local housing data revealed more than 50 different potential data sources, including commercial, local, and state sources.
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Keller encouraged individuals who may be engaging with the ethical dimensions of data science research to take IRB training. She also shared a number of instances when it makes sense to waive informed consent, as detailed in the Federal Policy for the Protection of Human Subjects: • The research involves no more than minimal risk to the subjects; • The research could not practicably be carried out without the requested waiver or alteration; • If the research involves using identifiable private information or identifiable biospecimens, the research could not practicably be carried out without using such information or biospecimens in an identifiable format;
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Keller concluded by commenting that the data revolution is changing the focus of the privacy discussion from the masking and suppression of data to maintain confidentiality to building trust, policy, and governance around data practices; this is in itself a revolution in both data and society (see Keller, Shipp, and Schroeder, 2016)
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Hawes explained that new data sources raise new privacy concerns relating to data access and sharing, data release, and transparency. He observed that it has become more difficult to obtain data, owing to recent proliferation of state and local privacy laws, improved agency awareness of existing legal requirements, greater scrutiny of agency data practices, and changes in agency risk tolerance.
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Technologies such as secure multiparty computation offer a way to leverage disparate data that may have different regulatory 8 The U.S. Department of Education Student Privacy website is https://studentprivacy.
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DATA USE EXPERIENCES ACROSS CITIES Data Access and Innovation for Cities Amanda Eichel, Global Covenant of Mayors for Climate and Energy Amanda Eichel, Global Covenant of Mayors for Climate and Energy, described the Global Covenant of Mayors for Climate and Energy as an alliance of more than 9,000 cities (more than 7,000 of which are in Europe) that have agreed to take on climate change consistent with the Paris Climate Agreement targets.
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Response to Data Access and Innovation for Cities Jessica Seddon, World Resources Institute, Discussant Jessica Seddon, WRI, observed that the Global Covenant of Mayors' initiatives demonstrate effective ways to bring new data to bear on increasingly urgent and complex problems. Seddon explained that the WRI Ross Center for Sustainable Cities works mostly in the Global South with lower-information cities (i.e., cities without historic investment in statistical systems and national data)
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Ultimately, the Global Covenant of Mayors hopes to automate a scenario planning or policy scenario planning process for cities. Caetano de Campos Lopes, Citizens' Climate Lobby, noted that information to benchmark return on investment is not available at the local level, and he wondered if there are any ongoing projects to address this issue.
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10 The Greenhouse Gas Protocol for Cities is a standard and set of tools to measure greenhouse gas emissions, build emissions reduction goals and strategies, and track progress comprehensively. Scope 3 emissions refers to the emissions from the upstream and downstream supply chain for a city, outside of Scope 1 (i.e., direct emissions within the city)
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