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Kaohsiung, Taiwan_EiJ Paraconference

Misria
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The project "Researching Kaohsiung Archive: practice and reflection" is a collaborative effort with the UCI team addressing the global environmental injustice record in Kaohsiung, Taiwan, particularly focusing on the slow and accumulative harm caused by petrochemical development. Under the Environmental Injustice Global Record (EIGR) website, the Kaohsiung Archive serves as a trans-disciplinary platform for data archiving and communication. Since joining in 2021, the team has collected and visualized diverse data sources, engaging stakeholders in collaboration. The archive development involves addressing key questions related to environmental justice studies, fostering brainstorming and reflection. The project acts as a boundary object, connecting local and international communities, providing an information infrastructure for social dialogue,and aiming to contribute to a sustainable transformation discourse on the risks of petrochemical developments in Kaohsiung. The long-term impact on academic production method and knowledge dissemination remains to be seen, but the project aspires to inspire co-creation, cross-border cooperation,and innovation to empower civil society and enhance environmental justice governance.

The creation of the Kaohsiung Archive begins with a series of questions, utilizing the Environmental Justice (EJ) study framework applied to Hawaii as a guide. These questions delve into the influence of industries on environmental governance and advocacy, exploring the strategiesemployed. The process involves collaborative efforts to answer these questions, fostering brainstorming, debates, and reflections on characterizing the setting and revealing environmental injustice within the case study.

Following workshops and fieldwork in Kaohsiung, the project evolved to formulate narrative structures for mapping and visualizing environmental injustice in the region. Objectives include outlining Kaohsiung's features, focusing on petrochemical-related air pollution and industrial transformation issues, and designing relays to illustrate the challenges faced by fence-line communities and showcase potential action initiatives.

The project's progression involves tracking the issue, identifying and categorizing stakeholders, as well as gathering information and experiences from various parties. Stakeholder claims are sorted out, and efforts are made to find common action goals. Discursive risk analysis is conducted, examining environmental monitoring issues around petrochemical facilities. For instance, in Dashe, there is a focus on the discursive gaps between local residentsand petrochemical workers, revealing disparities in perceptions of air quality and expectations regarding governmental control.

Source

Tu, Wen Ling. 2023. " Researching Kaohsiung Archive: Practice and Reflection." In 4S Paraconference X EiJ: Building a Global Record, curated by Misria Shaik Ali, Kim Fortun, Phillip Baum and Prerna Srigyan. Annual Meeting of the Society of Social Studies of Science. Honolulu, Hawai'i, Nov 8-11.

Kaohsiung, Taiwan

Misria

The project "Researching Kaohsiung Archive: practice and reflection" is a collaborative effort with the UCI team addressing the global environmental injustice record in Kaohsiung, Taiwan, particularly focusing on the slow and accumulative harm caused by petrochemical development. Under the Environmental Injustice Global Record (EIGR) website, the Kaohsiung Archive serves as a trans-disciplinary platform for data archiving and communication. Since joining in 2021, the team has collected and visualized diverse data sources, engaging stakeholders in collaboration. The archive development involves addressing key questions related to environmental justice studies, fostering brainstorming and reflection. The project acts as a boundary object, connecting local and international communities, providing an information infrastructure for social dialogue,and aiming to contribute to a sustainable transformation discourse on the risks of petrochemical developments in Kaohsiung. The long-term impact on academic production method and knowledge dissemination remains to be seen, but the project aspires to inspire co-creation, cross-border cooperation,and innovation to empower civil society and enhance environmental justice governance.

The creation of the Kaohsiung Archive begins with a series of questions, utilizing the Environmental Justice (EJ) study framework applied to Hawaii as a guide. These questions delve into the influence of industries on environmental governance and advocacy, exploring the strategiesemployed. The process involves collaborative efforts to answer these questions, fostering brainstorming, debates, and reflections on characterizing the setting and revealing environmental injustice within the case study.

Following workshops and fieldwork in Kaohsiung, the project evolved to formulate narrative structures for mapping and visualizing environmental injustice in the region. Objectives include outlining Kaohsiung's features, focusing on petrochemical-related air pollution and industrial transformation issues, and designing relays to illustrate the challenges faced by fence-line communities and showcase potential action initiatives.

The project's progression involves tracking the issue, identifying and categorizing stakeholders, as well as gathering information and experiences from various parties. Stakeholder claims are sorted out, and efforts are made to find common action goals. Discursive risk analysis is conducted, examining environmental monitoring issues around petrochemical facilities. For instance, in Dashe, there is a focus on the discursive gaps between local residentsand petrochemical workers, revealing disparities in perceptions of air quality and expectations regarding governmental control.

Tu, Wen Ling. 2023. " Researching Kaohsiung Archive: Practice and Reflection." In 4S Paraconference X EiJ: Building a Global Record, curated by Misria Shaik Ali, Kim Fortun, Phillip Baum and Prerna Srigyan. Annual Meeting of the Society of Social Studies of Science. Honolulu, Hawai'i, Nov 8-11.

NIEHS Dashboard Data Sources

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GitHub Repository

“To empower additional modeling efforts, the complete time series of all daily PVI scores and data are available at https://github.com/COVID19PVI/data. “

12 Key Indicators

“[The authors] assembled U.S. county- and state-level datasets into 12 key indicators across four major domains: current infection rates (infection prevalence, rate of increase), baseline population concentration (daytime density/traffic, residential density), current interventions (social distancing, testing rates), and health and environmental vulnerabilities (susceptible populations, air pollution, age distribution, comorbidities, health disparities, and hospital beds).”

Three types of modeling

“Our modeling efforts directly address the discussion in [6], by contextualizing factors such as racial differences with corrections for socioeconomic factors, health resource allocation, and co-morbidities, plus highlighting place- based risks and resource deficits that might explain spatial distributions. Specifically, three types of modeling efforts were performed and are regularly updated. First, epidemiological modeling on cumulative case- and death-related outcomes provides insights into the epidemiology of the pandemic. Second, dynamic time-dependent modeling provides similar outcome estimates as national-level models, but with county-level resolution. Finally, a Bayesian machine learning approach provides data-driven, short-term forecasts. “

Blackness and PM 2.5

“With respect to factors affecting COVID-19 related mortality, we find that the proportion of Black residents and the PM2.5 index of small-particulate air pollution are the most significant predictors among those included, reinforcing conclusions from previous reports[7]. An increase of one percentage point of Black residents is associated with a 3.3% increase in the COVID-19 death rate. The effect of a 1 g/m3 increase in PM2.5 is associated with an approximately 16% increase in the COVID-19 death rate, a value at the high end of a previously reported confidence interval from a report in late April 2020[7] when deaths had reached 38% of the current total.”

Machine learning and prediction

“To accurately predict future cases and mortality, it is necessary to account for the fluid nature of the data. Accordingly, we developed a Bayesian spatiotemporal random-effects model that jointly describes the log-observed and log-death counts to build local forecasts. Log-observed cases for a given day are predicted using known covariates (e.g., population density, social distancing metrics), a spatiotemporal random-effect smoothing component, and the time- weighted average number of cases for these counts. This smoothed time-weighted average is related to a Euler approximation of a differential equation; it provides modeling flexibility while approximating potential mechanistic models of disease spread. The smoothed case estimates are used in a similar spatiotemporal model predicting future log-death counts based on a geometric mean estimate of the estimated number of observed cases for the previous seven days as well as the other data streams. The resulting county-level predictions and corresponding confidence intervals are shown (Fig. 1)."

Source: https://www.researchgate.net/publication/343642027_The_COVID-19_Pandemi…

US NIEHS Dashboard Creators and Curators

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Skylar W. Marvel1, John S. House2, Matthew Wheeler2, Kuncheng Song1, Yihui Zhou1, Fred A. Wright1,3, Weihsueh A. Chiu4, Ivan Rusyn4, Alison Motsinger-Reif2*, David M. Reif1*

Affiliations:

1 Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA.

2 Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA.

3 Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA

4 Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77845, USA.

US NIEHS Dashboard Types of Data

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“Data sources in the current model (version 11.2.1) include the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC) for emergency response and hazard mitigation planning (Horney et al. 2017), testing rates from the COVID Tracking Project (Atlantic Monthly Group 2020), social distancing metrics from mobile device data ( https://www.unacast.com/covid19/social-distancing-scoreboard), and dynamic measures of disease spread and case numbers ( https://usafacts.org/issues/coronavirus/). Methodological details concerning the integration of data streams—plus the complete, daily time series of all source data since February 2020 and resultant PVI scores—are maintained on the public Github project page (COVID19PVI 2020). Over this period, the PVI has been strongly associated with key vulnerability-related outcome metrics (by rank-correlation), with updates of its performance assessment posted with model updates alongside data at the Github project page (COVID19PVI 2020).”

Source: https://ehp.niehs.nih.gov/doi/10.1289/EHP8690

US NIEHS Dashboard Motivations

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Empowering local actoors

“We present the PVI Dashboard as a dynamic container for contextualizing these disparities. It is a modular tool that will evolve to incorporate new data sources and analytics as they emerge (e.g., concurrent flu infections, school and business reopening statistics, heterogeneous public health practices). This flexibility positions it well as a resource for integrated prioritization of eventual vaccine distribution and monitoring its local impact. The PVI Dashboard can empower local and state officials to take informed action to combat the pandemic by communicating interactive, visual profiles of vulnerability atop an underlying statistical framework that enables the comparison of counties and the evaluation of the PVI’s component data.”

US NIEHS Dashboard References

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US NIEHS Dashboard Visualization

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Built with toxicology knowledge

“The software used to generate PVI scores and profiles from these data is freely available at https://toxpi.org”

General visualization capabilities

“The interactive visualization within the PVI Dashboard is intended to communicate factors underlying vulnerability and empower community action [...] The visualization and quantification of county-level vulnerability indicators are displayed by a radar chart, where each of the 12 indicators comprises a “slice” of the overall PVI profile. On loading, the Dashboard displays the top 250 PVI profiles (by rank) for the current day. The data, PVI scores, and predictions are updated daily, and users can scroll through historical PVI and county outcome data. Individual profiles are an interactive map layer with numerous display options/filters that include sorting by overall score, filtering by combinations of slice scores, clustering by profile similarity (i.e. vulnerability “shape”), and searching for counties by name or state (Additional functionality is detailed in the Supplement). User selection of any county overlays the summary Scorecard and populates surrounding panels with county- specific information (Figure 1). The scrollable panels at left include plots of vulnerability drivers relative to the nation-wide distribution across all U.S. counties, with the location of the selected county delineated. The panels across the bottom of the Dashboard report cumulative county numbers of cases and deaths; timelines of cumulative cases, deaths, PVI score, and PVI rank; daily changes in cases and deaths for the most recent 14-day period (commonly used in reopening guidelines[6]; and predicted cases and deaths for a 7-day forecast horizon.”

Visualizing comparison and "peer counties"

“the multi-criteria filtering capabilities in the Dashboard were used to find a “peer county” for comparison. “

Source: https://ehp.niehs.nih.gov/doi/10.1289/EHP8690 and https://www.researchgate.net/publication/343642027_The_COVID-19_Pandemi…

Beck, Nyah E. | Winter 2023 EiJ Annotations

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  1. How has CRPE adapted its strategies and approaches in response to changes in the political and social landscape, and what are the organization's priorities for the future?

  2. What advice would you give to individuals or organizations interested in working on environmental and social justice issues in low-income communities and communities of color, based on CRPE's experiences?

  3. How does CRPE measure the impact of its work, and what data or metrics does the organization use to track progress?

  4. Can you provide examples of some of CRPE's most significant successes in addressing environmental and social justice issues, and the strategies that were employed to achieve these successes?