Images or infographics have always been compelling tools however, we prefer interactive data visualization tools for their ability to capture attention and curiosity and make an impression. Thus, the question becomes: How do we communicate to people the information they need in a way that is not only easy to understand but also engaging? Very few people have time to read through a long technical report on climate risk and how it might affect them. With the increasing threat of climate risk, how much more significant do you anticipate data visualization will become? I’m reminded of the writings from the philosopher Timothy Morton, who described climate change as a “hyper-object”: a multifaceted network of interacting forces so complex, and with so many manifestations that it is almost impossible to fully conceptualize it in your head at once.Ĭlimate change is complicated and communicating about the risks it creates is a unique problem. Thus, it’s important to illustrate the relationship between inputs and outputs in a way that is reasonably easy to understand. Once we start accounting for joint outcomes and conditional probabilities, spreadsheets turn into mazes. Understanding risk means understanding what range of outcomes are possible and what it most likely to happen. How can data visualization help change the way insurers confront the challenges of catastrophes? The most crucial aspect of data visualization for insurers is the potential to explore “what-if” scenarios with interactive tools. After a catastrophe, they help us identify areas that need the most to rebuild. Before a catastrophe, these tools help us identify at-risk zones to bolster resilience. We can make spreadsheets of policies and claims, but how do you express the relationships between each row in these spreadsheets? We can use data visualization to show how houses closest to a river are most at risk during a flood or show the likely paths of wildfires through a landscape. Why is data visualization so essential in preparing for and responding to catastrophes? What immediately comes to mind is maps. To further understand data visualization, we sat down with Dr. Michel Leonard, Chief Economist and Data Scientist, Head of the Economics and Analytics Department at the Triple-I, these data visualizations provide an ever-needed way to more effectively communicate these hazards, expanding the knowledge base of insurers, consumers, and policymakers. The Triple-I uses data visualization in its Resilience Accelerator to better illustrate the risks many communities face with natural disasters, particularly hurricanes, floods, and resilience ratings.
Whether for tracking long-term rainfall trends, monitoring active wildfires, or getting out in front of cyber threats, data visualization has proved itself tremendously beneficial for understanding and managing risk. Indeed, a good visualization possesses a narrative, eliminating the extraneous aspects of the data and emphasizing the valuable information.
And, in an age of big data, machine learning, and artificial intelligence, the possible applications of data science and data analytics has only expanded, helping curate information into easier to understand formats, giving insight into trends and outliers. Matrices, histograms, and scatter plots (both 2D and 3D) can illustrate complex relationships among different pieces of data.
Since Tukey’s advancements, data visualization has progressed in extraordinary ways.
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Tukey helped invent graphic displays, including stem-leaf plots, boxplots, hanging rootograms, and two-way table displays, several of have become part of the statistical vocabulary and software implementation. Tukey published his paper The Future of Data Analysis, which advocated for the acknowledgement of data analysis as a branch of statistics separate from mathematical statistics. However, modern data visualization is considered to have emerged in the 1960s, when researcher John W. The origins of data visualization could be considered to go back to the 16 th century, during the evolution of cartography. Such displays help clarify multifaceted data relationships and convey data-driven insights. Simply put, data visualization is the depiction of data through static or interactive charts, maps, infographics, and animations. By Max Dorfman, Research Writer, Triple-Iĭata visualization has become an increasingly important tool for understanding and communicating complex risks and informing plans to address them.