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Hawai'i, Arizona, Italy, South Africa, Australia

Misria

Astrophysics is a discipline that has a lot to do with environmental justice, even if it doesn’t look like so. Astrophysics research nowadays involves both large cutting-edge infrastructures and a great number of people and institutions, usually at international level. Most of these projects require to be placed in very specific environments, which are not very common on our planet, to function in the best conditions. The territories chosen to host large facilities for astrophysics, as remote as they can be, are not empty. In most cases, they are inhabited (or regularly frequented) by people who are not always involved in the decision process and may see the construction as an invasion of lands they have owned or occupied for centuries. In this context, we believe that what pulls people away from environmental justice advocacy, especially those who do not live in or near these territories, is the lack of information and awareness about this topic, which may cause strongly polarized opinions and harshful discussions on the topic. To try to fill this gap, as science communicators we decided to develop a game-based activity which fosters the debate about this connection. Among our inspirations is the struggle of the protectors of Mauna a Wākea, on the Big Island of Hawai‘i. The mountaintop is a sacred place for Native Hawaiians, who have been fighting to protect their ancestral land from the construction of the Thirty Meter Telescope (TMT). There are many other examples of large astronomical infrastructures and their impact on territories, including in our own country (Italy), some more virtuous than others, that show how the Astrophysics research world is strongly connected to environmental justice. For this activity, we chose the Creative Commons PlayDecide format, which aims to facilitate simple, respectful and fact-based group discussions. The game consists of a different set of cards containing facts about the topic, issues for different interest groups and personal stories of fictional individuals who are involved or affected by the topic. By telling the stories of different characters involved in this kind of situation, we aim to enlarge the debate, fostering the change of perspective of players. We wish that many people around the world download and use the game, either during public outreach activities with schools and the general public or as a self-awareness exercise within the astronomical community. The game does not refer to a specific facility, but we researched study cases related to astronomical observatories in sites such as Mauna a Wākea (Hawaiʻi), Kitt Peak and Mount Graham (Arizona), the Sardinia Radio Telescope in Italy as well as ongoing projects such as the SKA Observatory in South Africa and Australia. In particular, for the story cards, we strived to provide a balance in terms of gender and affected communities, trying as much as we could to avoid stereotypes, in the awareness that we, as the authors of the activity, are a group of white, female astronomers from a G7 country.

Toniolo, Rachele and Claudia Mignone. 2023. "Some students play the PlayDecide activity at a Science Festival in Italy." 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

tschuetz

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

tschuetz

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

tschuetz

“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

tschuetz

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 Visualization

tschuetz

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…

Biomass energy failing Question 4

mtebbe

Biomass energy plants: see themselves as a cost-effective solution for farmers who need to get rid of dead trees and other woody waste that pose wildfire risks without openly burning them; they also produce energy

Utilities companies: looking for the "least-cost, best-fit" source of energy, don't care where it comes from just that it's reasonably priced

Farmers: need cheap ways to dispose of waste