Related Experiment Video
Updated: Oct 31, 2025
05:23
Visualization of SARS-CoV-2 using Immuno RNA-Fluorescence In Situ Hybridization
Published on: December 23, 2020
6.3K
Learning from lines: Critical COVID data visualizations and the quarantine quotidian
Emily Bowe1, Erin Simmons1, Shannon Mattern1
1The New School, New York, NY, USA.
Big Data & Society
|June 30, 2021
Summary
Artists and data scientists are creating alternative COVID-19 maps and visualizations. These counter-plots encourage critical data analysis and highlight the pandemic's diverse impacts across personal and global scales.
Area of Science:
- Public Health
- Data Visualization
- Sociology
Background:
- The COVID-19 pandemic generated widespread use of data visualizations, including graphs and maps.
- These visualizations often presented a simplified or incomplete picture of the pandemic's complex realities.
Purpose of the Study:
- To describe the functions and significance of counter-plots and subaltern maps created in response to official COVID-19 data.
- To highlight how these alternative visualizations foster critical thinking and represent diverse pandemic experiences.
Main Methods:
- Analysis of artistic and data-driven projects creating counter-narratives to official COVID-19 data.
- Examination of the educational tools and critical perspectives offered by these alternative visualizations.
Main Results:
- These projects provide tools for critically assessing data sourcing and modeling in COVID-19 visualizations.
- They reveal how pandemic spatial logics manifest in local environments and personal experiences.
- Alternative visualizations emphasize the pandemic's impact on community dynamics and individual care practices.
Conclusions:
- Counter-plots and subaltern maps offer crucial alternative perspectives on the COVID-19 pandemic.
- They demonstrate the importance of considering multiple scales, from global trends to local realities and personal geographies.
- These visualizations empower public participation and critical engagement with pandemic data.
Related Concept Videos
01:18Steps in Outbreak Investigation
271
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
271
01:11Residuals and Least-Squares Property
8.2K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.2K
01:25Interpreting Run Charts
2.9K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
2.9K
00:52Pareto Chart
7.3K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
7.3K
01:04Pie Chart
15.1K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
15.1K
01:22Interpreting R Charts
163
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
163