As title. The data is sourcing map of several goods: Country A import X volume of goods N from country B. I only have ~10 countries to show on the visualization.

The obvious image comes to mind is kindof like the flight map, but it does not indicate volume.

How can I achieve such visualization?

I just happened on this visualization. Can anyone suggest software tools that aid in the creation of such visualizations?

https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F52c85a01-11ad-4e14-89fb-248e40b248c0_1480x1295.jpeg
Via https://twitter.com/george__mack/status/1665059477802893315, https://www.gurwinder.blog/p/30-useful-concepts-spring-2024

https://preview.redd.it/u0cueolmboyc1.jpg?width=1480&format=pjpg&auto=webp&s=466beafbdf11357bcc99b4bed32a31b2c631a574

The 2024 VAST Challenge is an open call for novel visualizations of knowledge graph. The challenge is run in conjunction with the IEEE VIS Conference. Data is free to download and can be used for any purpose. You can submit an entry if you are interested in attending and presenting at IEEE VIS in Florida in October.

Download data here: https://vast-challenge.github.io/2024/

Conference: https://ieeevis.org/year/2024/welcome

There are three distinct challenges focused on identifying bias, geo-temporal patterns, and changes over time in business relationships.

source of data: kaggle

https://preview.redd.it/idv327xq8axc1.png?width=800&format=png&auto=webp&s=55075422c4a2c63d73b6883416d5d6ce4a726912

Does someone know of a tool available that can convert a simple table (To, From, Product) on the left to a pictorial diagram on the right?

https://preview.redd.it/4mxadndd4owc1.png?width=2805&format=png&auto=webp&s=26b6a1c9e21da33e4b896dbfedfc501f2efb313c

Hello! We are a group of researchers from HCDE. We are looking to recruit participants with experience designing data visualizations of race and other demographic data for a study on the impacts of the positionality, beliefs and biases of visualization designers on their design process and the visualizations they create.

If you are interested, please take a few minutes to complete our screening survey: https://uwashington.qualtrics.com/jfe/form/SV_55QbBhwtvyR41hk. If you meet the eligibility criteria, we’ll contact you with additional information. Please share this with folks who might be interested! If you have any questions, please feel free to reach out! Thank you!

As data visualization enthusiasts, we understand the importance of having access to high-quality, reliable data sources. I recently stumbled upon a platform called sCompute that I believe could have interesting implications for the data visualization community.

sCompute is a decentralized marketplace that connects data providers with data consumers, facilitating the exchange of high-quality datasets across various domains. The platform places a strong emphasis on data quality, integrity, and ethical sourcing.

I wrote an article that explores the potential benefits of using sCompute for sourcing data for visualization projects:

• Access to a wide range of high-quality datasets from verified providers
• Decentralized approach to data sharing and monetization
• Emphasis on data quality and integrity, crucial for creating accurate and meaningful visualizations
• Potential for collaboration and data sharing within the data visualization community

While the article primarily focuses on the machine learning applications of sCompute, I believe the platform's focus on high-quality data sourcing is equally relevant to data visualization.

Article link

I'm curious to hear your thoughts on platforms like sCompute and their potential impact on the way we source and utilize data for our visualization projects. Have you used similar platforms before? How do you think access to high-quality, diverse datasets could enhance the insights we derive from data visualizations?

I'd love to start a discussion on how we can leverage platforms like sCompute to improve the quality and variety of the data we use in our data visualization work, and how this could lead to more meaningful and impactful visual stories.

Please share your experiences, insights, and examples of how high-quality data has made a difference in your data visualization projects.

Please explain me what is going wrong with my code. The correlation not showing in each cells of the heatmap even I already had "annot"

Hello everyone,

I'm developing a dashboard to visualize data from multiple IoT sensors, each measuring different phenomena (e.g., temperature in Celsius, humidity in percentage, Co2 in ppm). I am exploring ways to represent this diverse data on a single chart without using multiple y-axes, as it can get overly complicated when more than two sensors are involved.

I'm considering using normalization to scale all measurements to a common percentage range (0-100%). Here’s a particular challenge I am facing: When a user specifies a preferred unit of measurement for the y-axis on the chart, should the system restrict sensor selection to only those sensors operating within the specified unit, thereby maintaining direct comparability of data? Alternatively, should the system employ a normalization approach, converting all selected sensor outputs to a common scale (e.g., 0-100%), thus allowing the inclusion of diverse sensor types regardless of their native units?

I'm looking for practical advice on how to manage this in a way that is both straightforward for users and scientifically correct. How do you usually handle this situation? I’d greatly appreciate any insights, experiences, or recommendations you could share!