Interactive Data Visualization Definition

Interactive data visualization refers to the use of software that enables direct actions to modify elements on a graphical plot.

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FAQs

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What is Interactive Data Visualization?

Interactive data visualization refers to the use of modern data analysis software that enables users to directly manipulate and explore graphical representations of data. Data visualization uses visual aids to help analysts efficiently and effectively understand the significance of data. Interactive data visualization software improves upon this concept by incorporating interaction tools that facilitate the modification of the parameters of a data visualization, enabling the user to see more detail, create new insights, generate compelling questions, and capture the full value of the data.

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Interactive Data Visualization Techniques

Deciding what the best interactive data visualization will be for your project depends on your end goal and the data available. Some common data visualization interactions that will help users explore their data visualizations include:

• Brushing: Brushing is an interaction in which the mouse controls a paintbrush that directly changes the color of a plot, either by drawing an outline around points or by using the brush itself as a pointer. Brushing scatterplots can either be persistent, in which the new appearance is retained once the brush has been removed, or transient, in which changes only remain visible while the active plot is enclosed or intersected by the brush. Brushing is typically used when multiple plots are visible and a linking mechanism exists between the plots.

• Painting: Painting refers to the use of persistent brushing, followed by subsequent operations such as touring to compare the groups.
• Identification: Identification, also known as label brushing or mouseover, refers to the automatic appearance of an identifying label when the cursor hovers over a particular plot element. 
• Scaling: Scaling can be used to change a plot’s aspect ratio, revealing different data features. Scaling is also commonly used to zoom in on dense regions of a scatter plot.‍
• Linking: Linking connects selected elements on different plots. One-to-one linking entails the projection of data on two different plots, in which a point in one plot corresponds to exactly one point in the other. Elements may also be categorical variables, in which all data values corresponding to that category are highlighted in all the visible plots. Brushing an area in one plot will brush all cases in the corresponding category on another plot.

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How to Create Interactive Data Visualizations

Creating various interactive widgets, bar charts, and plots for data visualization should start with the three basic attributes of a successful data visualization interaction design - available, accessible, and actionable. Is there sufficient source data available to meet your data visualization goals? Can you present this data in an accessible manner so that it is intuitive and comprehensible? Do your data visualization interactions provide meaningful, actionable insights?

The general framework for an interactive data structure visualization project typically follows these steps: identify your desired goals, understand the challenges presented by data constraints, and design a conceptual model in which data can be quickly iterated and reviewed.

With a rough, conceptual model in place, data modeling is leveraged to thoroughly document every piece of data and related meta-data. This is followed by the design of a user interface and the development of your design’s core technology, which can be accomplished with a variety of interactive data visualization tools.

Next it’s time to user test in order to refine compatibility, functionality, security, the user interface, and performance. Now you are ready to launch to your target audience. Methods for rapid updates should be built in so that your team can stay up to date with your interactive data visualization.

Some popular libraries for creating your own interactive data visualizations include: Altair, Bokeh, Celluloid, Matplotlib, nbinteract, Plotly, Pygal, and Seaborn. Libraries are available for Python, Jupyter, Javascript, and R interactive data visualizations. Scott Murray’s Interactive Data Visualization for the Web is one of the most popular educational resources for learning  how to create interactive data visualizations.

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Benefits of Interactive Data Visualizations

An interactive data visualization allows users to engage with data in ways not possible with static graphs, such as big data interactive visualizations. Interactivity is the ideal solution for large amounts of data with complex data stories, providing the ability to identify, isolate, and visualize information for extended periods of time. Some major benefits of interactive data visualizations include:

• Identify Trends Faster - The majority of human communication is visual as the human brain processes graphics magnitudes faster than it does text. Direct manipulation of analyzed data via familiar metaphors and digestible imagery makes it easy to understand and act on valuable information. 

• Identify Relationships More Effectively - The ability to narrowly focus on specific metrics enables users to identify otherwise overlooked cause-and-effect relationships throughout definable timeframes. This is especially useful in identifying how daily operations affect an organization’s goals.

• Useful Data Storytelling - Humans best understand a data story when its development over time is presented in a clear, linear fashion. A visual data story in which users can zoom in and out, highlight relevant information, filter, and change the parameters promotes better understanding of the data by presenting multiple viewpoints of the data.

• Simplify Complex Data - A large data set with a complex data story may present itself visually as a chaotic, intertwined hairball. Incorporating filtering and zooming controls can help untangle and make these messes of data more manageable, and can help users glean better insights.

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Static vs Interactive Data Visualization

A static data visualization is one that does not incorporate any interaction capabilities and does not change with time, such as an infographic focused on a specific data story from a single viewpoint. As there are no tools to adjust the final results of static visualizations, such as filtering and zooming tools in interactive designs, it is essential to give great consideration about what data is being displayed.

A static visualization is more suited for less complex data stories, building relationships between concepts, and conveying a predetermined view than encouraging exploration and increasing user autonomy. Static designs are also significantly less expensive to build than interactive designs. Deciding whether to build a static or interactive data visualization depends on customer preference, data story complexity, and ROI.

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Examples of Interactive Data Visualization

Interactive data visualizations are being used with increasing frequency, encouraging the development of more creative designs and providing valuable insight in complex, real-world issues. Here are some popular and successful interactive data visualization examples:

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“COVID-19 Pandemic” by HEAVY.AI

Zooming, filtering, and brushing capabilities are incorporated into this interactive map data visualization, providing an intuitive environment in which users can easily identify and explore trends across specific time frames. This dashboard is updated daily with the latest data.

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“Most Valuable Sports Franchises” by Column Five

This data visualization provides a more comprehensive view of each team’s history by incorporating identification elements, allowing viewers to see the number of years each team has competed and the number of championships won.

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“What’s Really Warming the World?” by Bloomberg Business

This interactive design paints an intriguing and thorough data story, visually depicting theories related to Earth’s rising temperature over a determined period of time. Scrolling down reveals layer after layer of data that can be digested and used to help identify relationships and draw conclusions.

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“US Political Donations” by HEAVY.AI

See United States political donations broken down by political party, recipient, party by date, and by geocoded locations on this interactive, color coded map. Zoom in to see a more granular view of the most relevant regions or input a specific zip code.

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Does HEAVY.AI Offer an Interactive Data Visualization Solution?

HEAVY.AI Immerse is a browser-based, interactive data visualization client that works seamlessly with HEAVY.AIDB and Render to create an immersive data experience. Immerse generates SQL queries to the HEAVY.AI backend at the click of a button, and uses instantaneous cross-filtering to dramatically reduce the time to insights and expand an analyst's ability to find previously hidden insights. Analysts can even hand write SQL queries to effortlessly create new dashboards, charts and graphs.