Top 10 Data Visualization Examples of 2024
By Aaron Edgell
Updated on Nov 22, 2022 | 10 min read | 6.4k views
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By Aaron Edgell
Updated on Nov 22, 2022 | 10 min read | 6.4k views
Share:
Human brains process visual information much better than raw facts. Especially when it comes to vast chunks of data with multiple factors that need to be considered. Visualizations such as charts or graphs allow us to analyze trends, data, and patterns rapidly and with ease. If you are a beginner in python and data science, upGrad’sdata science programs can definitely help you dive deeper into the world of data and analytics.
Data is very powerful, especially when we can identify what the data is telling us. With numbers and textual data being hard to analyze and represent, visualizations have played an essential role throughout the decades and even centuries ago.
Let us check how data visualization has allowed us to organize and present data in visual contexts.
Data visualization has always been prevalent throughout history, using more traditional methods for data collection and analytics. Data has been visualized from ancient times, where every piece of information was analyzed manually before being plotted or formulated as a part of a graph. Nowadays, we have business intelligence tools and visualization tools that automatically do the work for us when we feed the required data as input. For example, Tableau and Microsoft’s Power Bi or even Excel are great for visualizations. If you are a beginner in data analytics and data science, upGrad’s data science programs can definitely help you dive deeper into the world of data and analytics. There is a multitude of ways to present visualizations. Here are some important ones:
Data visualization can either be interactive or be static. Though both are extensively used, interactive visualizations are comparably modern and more detailed. If you wish to learn more about the different tools used for building visualizations and various visualization techniques, an advanced program such as upGrad’s Master of Science in Data Science is highly recommended. Now, let’s check out some of the best Data visualization examples in history.
The Cholera Map is an infamous early dot map visualization created by Jon Snow. The visualization involves bar graphs that represent the number of deaths due to Cholera on specific city blocks. The length of these bars and the concentration both show the trends and distribution of the deaths due to this disease. From this, an important discovery was made by analyzing specific households and the deaths connected to them. The households with the highest deaths were drinking the water from the same wells, this was a breakthrough at that time and allowed the authorities to isolate high concentrations of cholera outbreaks by identifying the wells that had been contaminated by sewage systems. The visualization also led to better sewage systems, and more importance was placed on securing wells or other drinking water sources.
This is an interactive visualization from the US Office of Management and Budget. The visualization is presented as a treemap prepared by the White House during Obama’s time in the office. This is one of the best data visualization examples for budgets that separate government programs and areas of interest that can be visually broken down. Even though this 2016 budget treemap is fundamental, it clarifies the budget for each sector rapidly. The map isolates each sector and allows taxpayers to understand where their money is going and what percentage is being spent on each sector in comparison with other sectors.
During the 1850s, the mortality rate for soldiers kept rising and took the lives of many. However, this was not simply due to war but also due to underlying causes such as diseases and lack of hygiene or medical resources. Florence Nightingale built this visualization with the help of William Farr, a statistician based on research commissioned by a Royal Order. Florence Nightingale is one of the most important people in Victorian history. Her medical expertise alongside numerous hospital visits helped in creating this extremely innovative visualization. The data reveals that a chunk of the deaths was due to post-battle wounds, unhygienic medical practices, and diseases rather than deaths during direct combat or battles. This also helped bring out the importance of preventing the underlying causes for most of the deaths during this period.
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Here is another great example of an interactive visualization that allows us to identify trends in federal spending when it comes to Research and Development. This dashboard functions like a tool that helps us examine per the budget allocation for R&D by agencies or context through the Fiscal years between 1976 and 2023. Unlike static visualizations, this dashboard allows users to explore a multitude of combined perspectives.
The U.S. allocated $1.3 million in 2019 as a discretionary spending budget. Discretionary spending, unlike mandatory spending, is an optional part of the budget equation that can be altered by lawmakers. These funds can be discussed and assigned to sectors such as education, defense, or transportation in accordance with budgetary requirements. This visualization has been created by Will Geary, a data scientist who has created this based on discretionary spending every year between 1963 and 2017.
A bit different than the others, but interesting nonetheless, this data visualization example is built using the data on character interactions in the popular franchise, Star Wars. This is a masterpiece built by researcher Kirell Benzi by plotting the relationships between nodes and points representing the interactions in the network. The graph tracks every connection that occurs between the thousands of characters from Star Wars. Every character is projected by a single node that is connected by color-coded lines with similar nodes. For example, the nodes connected by red lines represent the dark side of the force, while the blue lines represent the light side.
This is one of the simpler data visualization examples that allow us to quickly check which countries have GDPs that are less than Amazon’s market capitalization of about $1.7 trillion. In this map, the countries that have a GDP that is less than $1.7 trillion are highlighted with yellow-orange while the others with a greater amount as their GDP are greyed out.
This chart is made by Arthur Buxton to represent the colour palettes that have been used the most by artists through the period between 1895 and 1905. The pie charts in this innovative visualization are proportional to each extensively used colour by each artist. This proportion is in respect to the other preferred colours in their palettes throughout these ten years.
This is one of the more creative, great data visualization examples that showcase the schedule or habits of famous people. This visualization takes each of their activities into account based on the data from Mason Currey’s book, ‘Daily Rituals’. The chart has been broken down by time, and we can easily check how each of these creative personalities spent their time during a certain point in an average day.
Bloomberg Business created this interactive visualization based on the effects on temperature from 1880 to 2005 due to both natural factors and pollutants. This graph follows global warming as it increases the average temperature and how each factor such as land use, greenhouse gases, aerosols, and even natural events contribute to this. For example, greenhouse gases have been one of the leading causes of global warming throughout these years, while aerosols have managed to decrease the temperature during these same years.
The ability of visualizations to project data through various methods and from different perspectives is something that is genuinely valued. There have been many innovative visualizations throughout the ages, and visualizations are getting more advanced or complex as time passes. To truly keep up and use visualizations in sectors such as finance or business, upGrad’s Business Analytics Certification program is highly recommended.
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