By Max Dorfman, research writer, Triple-I
Data visualization has become an increasingly important tool for understanding and communicating complex risks and informing about plans for managing them.
Simply put, data visualization is the depiction of data through static or interactive charts, maps, infographics and animations. Such displays help to clarify multifaceted data relationships and convey data-driven insights.
The origins of data visualization can be traced back to the 16th centuryth century, during the development of cartography. Modern computer visualization, however, is considered to have originated in the 1960s, when researcher John W. Tukey published his article The future of data analysis, who advocated the recognition of data analysis as a branch of statistics separate from mathematical statistics. Tukey helped invent graphic displays, including stem charts, box plots, hanging rotograms, and two-way tables, several of which have become part of statistical vocabulary and software implementation.
Since Tukey’s advances, data visualization has evolved in extraordinary ways. Matrices, histograms and scatter plots (both 2D and 3D) can illustrate complex relationships between different pieces of data. And in an age of big data, machine learning and artificial intelligence, the possible applications of computer science and data analysis have only expanded, helping to gather information into easier-to-understand formats, providing insight into trends and extremes. In fact, a good visualization has a story that eliminates the foreign aspects of data and emphasizes the valuable information.
Whether tracking long-term rainfall trends, monitoring active forest fires or facing cyber threats, data visualization has proven to be extremely beneficial for understanding and managing risks.
Triple-I uses data visualization in its Resilience Accelerator to better illustrate the risks many societies face with natural disasters, especially hurricanes, floods and resilience assessments. With Dr. Michel Leonard at the helm, chief economist and data researcher, head of the economics and analysis department at Triple-I, gives these data visualizations an ever-necessary way to more effectively communicate these dangers and expands the knowledge base for insurers, consumers and politicians.
To further understand data visualization, we sat down with Dr. Leonard.
Why is data visualization so important for preparing for and responding to disasters? What will immediately come to mind are maps. We can create spreadsheets with 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 exposed during a flood or to show the probable paths for forest fires through a landscape. Before a disaster, these tools help us identify risk zones to strengthen resilience. After a disaster, they help us identify areas that need to be built up the most.
How can data visualization help change how insurance companies handle the challenges of disasters? The most crucial aspect of data visualization for insurers is the ability to explore “what-if” scenarios with interactive tools. Understanding risk means understanding what different outcomes are possible and what is most likely to happen. Once we start reporting common results and conditional probabilities, spreadsheets turn into labyrinths. Therefore, it is important to illustrate the relationship between input and output in a way that is reasonably easy to understand.
With the growing threat of climate risk, how much more significant do you expect data visualization to become? I am reminded of the writings of the philosopher Timothy Morton, who described climate change as a “hyper-object”: a multifaceted network of interacting forces that are so complex and with so many manifestations that it is almost impossible to fully comprehend it in your head at once.
Climate change is complicated and communicating about the risks they create is a unique problem. Very few people have time to read through a long technical report on climate risk and how it can affect them. 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?
Pictures 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.
How does Resilience Accelerator fit into the field of data visualization? With Resilience Accelerator, we wanted to explore the interplay between insurance, finance and climate risk, and present our results in an engaging, insightful way. It was our goal from the beginning to develop a tool that would help decision makers, insurers and members of society to find their counties, see their ratings, compare their ratings with neighboring counties and see what measures they should take to improve their ratings. .
What motivated this investment in data visualization – and how can it help change the way societies, decision-makers and insurance companies prepare for natural disasters? It is our job to help our members understand climate-related risks for their business and for their policyholders. Hurricanes and floods are just the first post in a climate risk series we are working on. We want our data to drive discussion about climate and resilience. We hope that the fruits of these discussions are societies that are better protected from the dangers of climate change.
Where do you see data visualization going in the next five to 10 years?
I’m interested to see what’s coming from the latest addition of GPU acceleration to browsers and the transition from internet infrastructure to fiber optics. GPU acceleration is the practice of using a graphics processor unit (GPU) in addition to a central processing unit (CPU) to accelerate processing-intensive operations. Both of these techniques are necessary to create a 3D visualization environment with streaming real-time data.