Data Visualization Charts
Data Visualization Charts Definition
Data visualization charts are graphical representations of data that tell a story using symbols in order to improve the understanding of large amounts of data. Visual data metaphors such as charts effectively engage human perceptual processes and amplify human cognition more so than semantic data alone. Data visualization charts and graphs transform enormous volumes of dense, unfocused data into comprehensible, meaningful visuals from which valuable, otherwise hidden insights can be revealed.
What are the Different Types of Data Visualization Charts?
There are many different types of data visualization charts that can improve our understanding of large, complex data sets. But each data visualization chart serves a different purpose, not every chart is appropriate for every project, and choosing the wrong type of charts for data visualization can make understanding the data even more confusing. Choosing the best data visualization chart depends on the types of data being analyzed and the types of questions being asked.
There are five main types of charts in data visualization. Below are the best types of charts for data visualization for each category:
Temporal -- These data visualizations are linear, one-dimensional, and typically feature standalone or overlapping lines that have a start time and a stop time. Some temporal data visualization chart examples include:
- Scatter plots: this chart shows two variables in the form of data points, with the physical orientation of each point determined by the value of the variable. A popular scatter plot variant is the data visualization bubble chart, in which the area of each data point bubble represents a third value.
- Polar area diagrams: area charts are essentially data visualization line charts that fill the space between the x-axis with colors, which helps visually communicate part-to-whole relations.
- Time series sequences: using lines, steps, or column charts, time series presents data points at successive time intervals, with the horizontal axis representing time and the vertical axis representing the measured values.
- Timelines: timelines depict the chronological sequence of events on a timescale.
- Line graphs: line chart data visualizations present data as points connected by a continuous line, measuring a variable over an interval of time.
Hierarchical -- These charts order groups within larger groups. The best data visualization chart types for hierarchical data include:
- Tree diagrams: a tree diagram depicts a hierarchy of tasks and subtasks, or parents and children.
- Ring charts: a ring chart data visualization, or sunburst diagram, depicts hierarchy with a series of concentric rings, in which each ring corresponds to a level in the hierarchy.
Network -- These charts show how datasets relate to one another within a network:
- Matrix charts: a matrix diagram depicts the relationships between two or more groups of elements in grid format. There must be at least two variables assigned to the X- and Y-categories. Variables beyond the first two are denoted with different colors.
- Node-link diagrams: node-link diagram is the graphical convention for grahi drawing, with vertices represented as disks, boxes, or textual labels, and edges represented as line segments, polylines, or curves.
- Word clouds: also known as a tag cloud, word clouds show the most used words in a text, or the most searched words, as physically larger or smaller depending on how often each appears. Larger words indicate higher usage.
- Alluvial diagrams: also known as flow diagrams, alluvial charts depict changes in network structures over time.
Multidimensional: These charts depict at least two or more variables and create 3D visualizations with several concurrent layers:
- Pie charts: a pie chart data visualization uses a single circle divided into “slices,” each slice representing a numerical proportion of the whole circle’s value.
- Venn diagrams: a venn diagram uses circles, each representing a different variable, that are overlapped to show the logical relation between sets.
- Stacked bar graphs: Data visualization bar charts use horizontal columns to show numerical comparisons between categories. A waterfall chart is a type of multidimensional bar chart that uses floating bars to illustrate how an initial value is affected positively and negatively by different factors. Bullet graphs, another type of visualization using bars, features a single primary measure, layered with different colors to indicate actual value, target value, and ranges.
- Histograms: histograms depict the distribution of data over a continuous interval or particular period of time, estimating where values are concentrated with the use of vertical bars on a horizontal line.
Geospatial -- Spatial data visualizations overlay data points onto maps. The best charts for data visualization in geospatial cases include:
- Flow map: Linear symbols depict movement of something from an origin to a destination, with the width of the line proportionate to an increase or decrease in the amount of flow.
- Density map: density mapping indicates the concentration of a feature with an increase of decrease in the number of data points in a given area.
- Cartogram: cartograms distort the real boundaries of a geographic region in order to convey alternate variables, which will either inflate or deflate the boundaries proportionate to its numeric value.
- Heat map: magnitude of a phenomenon are depicted as color in two-dimension; categorized as either cluster or spatial.
Why are Data Visualization Charts Important?
The importance of visual storytelling is greater than ever as humans are confronted with the continuously expanding data deluge. Human perceptual processes are more effectively engaged with the use of interactive, visual metaphors than solely with numerical values and text. And statistically, engagement is increased significantly with the incorporation of visual elements into content.
Charts, graphs, and visuals convey information faster than a vast spreadsheet or dense report. Visual metaphors provide a universal language that can communicate with people of any spoken or written language. Data visualization charts make data easier to consume, which helps people quickly derive valuable insights, improve decision making, uncover hidden patterns and relationships, identify upcoming trends, facilitate visual analytics, and improve collaboration.
Does HEAVY.AI Offer a Data Visualization Chart Solution?
HEAVY.AI Render is a server-side engine designed for rendering data visualization charts, such as pointmaps, scatterplots, and polygon visualizations of massive datasets. This gives users zero-latency, exploratory interaction with complex visualizations. HEAVY.AI helps you interactively query, visualize, and power data science workflows over billions of records, helping you find hidden insights beyond the reach of mainstream analytics.