What are the emerging trends in multivariate data visualization for environmental science?

Based on my experience UMAP always gave much better results and proven to be much more robust compared to t-SNE. But of course at the end of the day it is statistics and it depends on your data, but UMAP seems to be a much better manifold based method.

To emphasize a point: dimensionality reduction should be balanced by a focus on ensuring that variation can still be explained.

Emerging trends in environmental science visualization include interactive tools, spatial representation, machine learning integration, network visualization, temporal analysis, AR/VR exploration, and big data handling techniques for vast datasets.

Emerging trends in multivariate data visualization for environmental science are shaping the way we understand complex environmental systems. From interactive dashboards to 3D geospatial visualizations and the integration of machine learning, these trends empower researchers to gain deeper insights, track changes over time, and collaborate effectively. As we navigate the complexities of environmental data, these innovations play a vital role in addressing environmental challenges and fostering data-driven decision-making.

From what I’ve seen, the big trends in multivariate visualization are immersive technologies, interactive & intelligent dashboards, ML integration, and improved visual communication….

Interactive visualization is revolutionizing environmental science by offering intuitive ways to explore multivariate data. For instance, climate researchers can utilize interactive maps to visualize changing temperature trends over time. By manipulating filters or sliders, one can test various scenarios or focus on specific regions. This not only enriches data comprehension but fosters engagement, enabling scientists and the public to draw insightful conclusions from complex datasets seamlessly.

Interactive visualization is definetely needed at some point in EDA for multimodal and high dimensional data. A statistician needs to modicy filters and configurations and observe the changes to understand the true nature of the data. Therefore, it is essential to learn libraries like streamlit, which is not so complex at all.

Emerging trends in multivariate data visualization for environmental science include multimodal visualization, which enhances understanding by integrating various data formats like text, images, and animations. This approach not only enriches the data representation but also makes it accessible and engaging to diverse audiences, facilitating a more comprehensive interpretation of environmental trends and patterns.

Most convenient way to fuse multimodal data together is to use embeddings. You can embed long paraghraphs of text with powerful language models, you can embed images with pretrained foundational image models etc. Then using proper dimensionality reduction, scaling and some other statistical models if necessary you can display them in a single intuitive graph.

Know who the message is for and adjust it accordingly. It's very common to see people presenting to a room and trying to deliver a message for everyone, this is simply not possible. If you want to create change, target that message for the key stakeholder. Do your research and know what makes the stakeholder tick. Secondly, most stakeholders are not interested in how you tested the data or what a regression model is (well, not normally, anyway). People want to know what the insights mean for them and their job or department. You will not get anywhere simply talking people through a series of graphs and charts.

Key in data storytelling, or any presentation, is understanding the audience and accordingly tailoring a narrative. The story for a fellow data scientist might vary sharply with the story for a functional expert, and informs the visuals that you will prepare. For example a domain expert may not care about F1 scores.

ChatGPT really can help augment your data storytelling based on my experience. ChatGPT4 especially is competent in generating narratives of different tones with good analogies in statistics.

As a data visualization expert, I emphasize the growing significance of data storytelling in multivariate data visualization. Data storytelling is the fusion of art and science, where data, visuals, and narratives converge to convey a compelling and meaningful message. It's the key to transforming complex data into understandable narratives. By understanding your purpose, audience, and crafting a resonant message, you can make data genuinely impactful. The emotional aspect is crucial; connecting data to real-world consequences can drive empathy and inspire action. In today's data-driven world, the ability to transform information into stories with influence is a must-have skill for every data professional.

A fantastic way to ensure your visualisations are colour-blind friendly is to print preview the visualisation in greyscale. This allows you to see the variations in hue rather than colour. By adjusting hue for contrast it makes the visualisation friendly for those with colour blindness and saves on colour printing when printing is necessary. Universal design benefits everyone.

Ethical data visualization is a rising trend in environmental science. It means presenting accurate, honest, and fair data while respecting sources, subjects, and users. In a world where data influences significant decisions, ethical visualization is a moral responsibility. It safeguards against misrepresentation, upholds transparency, and shapes a responsible, just narrative. As data professionals, let's prioritize ethics to build trust, responsible use, and positive change.

Advances in temporal data visualization is key, where changes in data over time can be as important as static values at any given moment. For MVA, visualizing how multiple factors change together over time can reveal trends, cycles, patterns, and anomalies that might not be evident looking at the data in isolation. Advancements: In time series analysis: Enhanced graphing of multiple variables over time improves correlations, causations, and forecasts of future conditions. Dynamic time warping can find similarities between time series and such techniques are becoming more accessible. Visualizing networks that change over time, e.g. food webs/migration patterns, can provide insights into how relationships develop and fluctuate.

I think any statistican or data scientist should get acquinted with big data visualization. At some point for certain tasks downsampling would not be enough and you would need methods to visualize high number of datapoints somehow.

In environmental science, ethical data visualization can work wonders. Picture this: A research team used ethical data presentation to accurately represent a declining bird species. Their visuals sparked conservation efforts, funding, and species recovery. It's proof that ethical data visualization can drive real change and transparency in the environmental sector. Let's embrace its potential.