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The ASTROMOVES project captures the career decision-making of astrophysicists and those in adjacent sciences, with particular attention to ‘intersectional’ identities, sex/gender diversity and visible/invisible disabilities. Qualitative interviews were recorded online (due to the Pandemic) and each scientist was assigned an Indigenous Hawaiian pseudonym. This was a subversive move to remind astrophysicists of the enormous debt they owe to the Hawaiian people for the use of their sacred mountain tops. All of the scientists consented to having a Hawaiian name. Seven scientists chose their own pseudonyms, most were Hawaiian place names: Maui, Waikiki, Waiheke, and Holualoa. Two Brazilians likewise chose Indigenous place names: Caramuru and Paraguaçu. The last name chosen was Kū'oko'a. Kū'oko'a is the Hawaiian concept of freedom, of which I was unaware. When questioned by editors, I had to evoke my Oahu birth as my right to use Hawaiian pseudonyms. For my visualizations, I chose to not use the Mercator projection which artificially enlarges Europe, instead I use the Peters projection or equal area map. Thus, Europe is de-emphasized by showing its area relative to the rest of the world. 

Holbrook, Jarita. 2023. "Visualizing Astrophysicists’ Careers." 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

Lozi people living on the vast Zambezi (Barotse/Bulozi) floodplain in Western Province, Zambia, are experienced agriculturalists long adapted to seasonal patterns of flooding and drought. Compounded histories of colonialism, administrative disenfranchisement and neglect, extractive activities, and the concomitant reduction of territory and authority have impoverished the regional ecology and people. Increased climatic variability and other ecological challenges threaten Lozi ways of life, crops, and biodiversity more generally. Fish stock depletion is a major challenge reportedly partially caused by chemical contamination from insecticide treated mosquito nets used for fishing, other poison-based fishing methods, and from a Canadian mining company’s activities there. A Chinese construction company that built the bridge spanning the floodplain reportedly cultivated shrimp and snail species that threaten native fish populations. While seasonal fish bans are in place, their timing does not always correspond with traditional bans, and increased hunger (along with the impossibility of enforcement) renders such bans ineffective. In addition, some fish species have disappeared altogether. Biodiversity losses, which include water serpents deemed to be responsible for maintaining the structural integrity of the river, impact biopolitical life, cultural heritage and TEK. Appeals to UNESCO for Heritage status and WWF projects are underway and may confer some protection, however local suspicion remains due to prior agreement violations (with other bodies).

Liu, Jennifer. 2023. "Mapping otherwise?: weaving waterscape knowledges" 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.

The Covid-19 Pandemic Vulnerability Index (PVI) Dashboard, which relies on the Toxicological Prioritization Index (ToxiPi) to integrate diverse data into a geospatial context.

National Institute of Environmental Health Sciences (NIEHS). COVID-19 Pandemic Vulnerability Index (PVI) Dashboard. 2021. Available online: https://covid19pvi.niehs.nih.gov/ (accessed on 24 July 2021).

The PVI dashboard is included in the CDCD’s Covid-19 Data Tracker as part of the “Unique Populations” tab.  

NIEHS also developed Covid-19 PVI lesson plans for high school students (grades 9 – 12) to learn to examine risk factors associated with Covid-19 using the index. The goals of the curriculum are to provide students with a tool for examining the spread and health outcomes of a pandemic, to promote their awareness of how various factors (biological, social, behavioral, etc.) impact disease spread and outcomes, and to support the development of prevention and intervention strategies that reduce exposures to risk factors and their adverse health impacts. The lesson plans highlight the significance of social and environmental determinants in public health.

Learning objectives of the curriculum include:

• Knowing what a mathematical model is, the purpose of using a mathematical model
• How to examine the social factors contributing to the spread of infectious disease
• How to analyze the environmental factors that contribute to the spread of infectious disease
• Knowing about intervention strategies that could mitigate the impact of infectious disease on public health

The PVI dashboard was also used by anthropologist Jayajit Chakraborty to examine the relationship between Covid-19 vulnerability and disability status in the US. Chakraborty applied the dashboard and data from the 2019 American Community Survey to investigate whether vulnerability to the pandemic has been significantly greater in counties containing higher percentages of people with disabilities in four timeframes from May 2020 to February 2021. Chakraborty found that the percentage of people with disabilities (as well as those reporting other cognitive, vision, ambulatory, self-care and independent living difficulties) was significantly greater in counties with the highest 20% of the PVI. Chakraborty calls for further research to better understand the adverse impacts of Covid-19 on PwDs (people with disabilities).

Chakraborty, J. Vulnerability to the COVID-19 Pandemic for People with Disabilities in the U.S. Disabilities 2021, 1, 278-285. https://doi.org/10.3390/disabilities1030020

The index produces an overall score derived from 12 indicators distributed across four domains (current infection rates, baseline population concentration, current interventions, and health and environmental vulnerabilities. Each vulnerability factor is represented as a slide of a radar chart (see below).

The dashboard can also be used to visualize changes over time in cases, deaths, PVI, and PVI rank (with a line chart and a bar chart), as well as predicted changes in cases and deaths (with a line chart), see below.

Additional visual layers can be added to the PVI map (e.g. number of cases and deaths).

The PVI offers a visual synthesis of information to monitor disease trajectories, identify local vulnerabilities, forecast outcomes, and guide an informed response (e.g. allocating resources). This includes short-term, local predictions of cases and deaths. The PVI dashboard creates profiles (called PVI scorecards) for every county in the United States.

The PVI dashboard can be customized to specific needs by adding or removing layers of information, filtering by region, or clustering by profile similarity. The Predictions panel connects historical tracking to local forecasts of cases and deaths. The dashboard applies an integrated concept of vulnerability composed of both dynamic (infection rate and interventions) and static (community population and health care access) factors.

The statistical modeling supporting the PVI dashboard (generalized linear models of cumulative outcome data) has indicated that following population size, the most significant predictors of cases and deaths were the proportion of Black residents, mean fine particulate matter [particulate matter ≤2.5μm in diameter (PM2.5)], percentage of population with insurance coverage, and proportion of Hispanic residents.

The ToxPi*GIS framework, from which the PVI was built, is a free tool that integrates data streams from different sources into interactive profiles that overlay geographic information systems (GIS) data. This enables people using the tool to compare, cluster, and evaluate the sensitivity of a statistical framework to component data streams. In other words, this enables the integration of data that are not normally compared (data are combined into a matrix comprised of various domains or categories, varying weights and represented by color schemes).

County

Data is drawn from the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC), testing rates from the COVID tracking project (produced by the Atlantic Monthly Group), social distancing metrics from mobile device data, and USA Facts’ measures of disease spread and case numbers.