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Types of Data Visualization

By Priya PedamkarPriya Pedamkar

Home » Data Science » Data Science Tutorials » Big Data Tutorial » Types of Data Visualization

Type of Data Visualization

Introduction to Types of Data Visualization

Data Visualization is defined as the pictorial representation of the data to provide the fact-based analysis to decision-makers as text data might not be able to reveal the pattern or trends needed to recognize data; based upon the visualization, it is classified into 6 different types, i.e. Temporal (data is linear and one dimensional), Hierarchical (it visualizes ordered groups within a larger group ), Network (involve visualization for the connection of datasets to datasets), Multidimensional (contrast of temporal type), Geospatial( involves geospatial or spatial maps) and Miscellaneous. 

What is Data Visualization?

Data visualization is a methodology by which the data in raw format is portrayed to bring out the meaning of that. With the advent of big data, it has become imperative to build a meaningful way of showcasing the data so that the amount of data doesn’t become overwhelming. The part of portraying the data can be used for various purposes, such as finding trends/commonalities/patterns in data, building models for machine learning, or being used for a simple operation like aggregation.

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Different Types of Data Visualization

Data visualization is broadly classified into 6 different types. Though the area of data visualization is ever-growing, it won’t be a surprise if the number of categories increases.

Temporal: Data for these types of visualization should satisfy both conditions: data represented should be linear and should be one dimensional. These visualisation types are represented through lines that might overlap and have a common start and finish data point.
Scatter Plots Uses dots to represent a data point. The most common in today’s world is machine learning during exploratory data analysis. Types of Data Visualization
Pie Chart This type of visualization includes circular graphics where the arc length signifies the magnitude. pie chart
Polar area diagram Like Pie chart, the Polar area diagram is a circular plot, except the sector angles are equal in length, and the distance of extending from center signifies the magnitude. polar diagram
Line graphs Like the scatter plot, the data is represented by points, except joined by lines to maintain continuity. line graphs
Timelines In this way, we display a list of data points in chronological order of time. timelines
Time series sequences In time series, we represent the magnitude of data in a 2-D graph in chronological order of timestamp in data. time series sequence
Hierarchical: These types of visualizations portray ordered groups within a larger group. In simple language, the main intuition behind these visualizations is the clusters can be displayed if the flow of the clusters starts from a single point.
Tree Diagram In a tree diagram, the hierarchical flow is represented in the form of a tree, as the name suggests. Few terminologies for this representation are:

–  Root Node: Origination point.

–  Child node: Has a parent above

–   Leaf node: No more child node.

tree diagram
Ring Charts / Sunburst Diagram The tree representation in the Tree diagram is converted into a radial basis. This type helps in presenting the tree in a concise size. The innermost circle is the root node. And the area of the child node signifies the % of data. ring charts
TreeMap The tree is represented in the form of rectangles closely packed. The area signifies the quantity contained. tree map
Circle Packing Similar to a treemap, it uses circular packing instead of rectangles. circle packing
Network: The visualization of these type connects datasets to datasets. These visualizations portray how these datasets relate to one another within a network.
Matrix charts This type of visualization is widely used to find the connection between different variables within themselves. For example, correlation plot. matrix charts
Alluvial diagrams This is a type of flow diagram in which the changes in the flow of the network are represented over intervals as desired by the user.  

alluvilal diagrams

Word cloud This is typically used for representing text data. The words are closely packed, and the size of the text signifies the frequency of the word.  

world cloud

node-link diagrams Here the nodes are represented as dots, and the connection between nodes is presented. node link diagrams
Multidimensional: In contrast to the temporal type of visualization, these types can have multiple dimensions. In this, we can use 2 or more features to create a 3-D visualization through concurrent layers. These will enable the user to present key takeaways by breaking a lot of non-useful data.
Scatter plots In multi-dimensional data, we select any 2 features and then plot them in a 2-D scatter plot. By doing this we would have nC2 = n(n-1)/2 graphs.          Type of Data Visualization
Stacked bar graphs The representation segment bars on top of each other. It can be either a 100% Stacked Bar graph where the segregation is represented in % or a simple stacked bar graph, which denotes the actual magnitude. stacked bar diagram
Parallel Co-ordinate plot In this representation, a backdrop is drawn, and n parallel lines are drawn (for n-dimensional data). Type of Data Visualization
Geospatial: These visualizations relates to present real-life physical location by crossing it over with maps (It may be a geospatial or spatial map). The intuition behind these visualizations is to create a holistic view of performance. 
Flow map The movement of information or objects from one location to another is presented where the size of the arrow signifies the amount. Type of Data Visualization
Choropleth Map The geospatial map is colored on the basis of a particular data variable. Type of Data Visualization
Cartogram This type of representation uses the thematic variable for mapping. These maps distort reality to present information. This means that on a particular variable, the maps are exaggerated. For example, the image on the left is a spatial map distorted to a bee-hive structure.

 

Type of Data Visualization
Heat Map These are very similar to Choropleth in the geospatial genre but can be used in areas apart from geospatial as well. Type of Data Visualization
Miscellaneous: These visualizations can’t be generalized in a particularly large group. So instead of forming smaller groups for the individual type, we group it into miscellaneous. Few examples are below:
Open-High-Low-Close chart This type of graphs is typically used for stock price representation. The increasing trend is called as Bullish and decreasing as Bearish.      Type of Data Visualization
Kagi-Chart Typically the demand-supply of an asset is represented using this chart.     Type of Data Visualization

Conclusion

From the above types of visualization, we see that broadly there are 6 types of groups. The list above is not an exhaustive list, but a few which are widely used. In the coming times, as and when new types get added to the list, the groups might increase. That’s it for types of visualization. We would continue on what parameters to look at during fixing on the type of visualization.

Recommended Articles

This is a guide to the Type of Data Visualization. Here we discuss the basic concept with different types of Data Visualization in detail. You can also go through our other suggested articles to learn more –

  1. Types of Data Analysis Techniques
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