Choosing the right type of visualisation depends on one needs to
show (comparison, distribution, composition, or relationship), how much detail
the audience needs, and what information the audience needs in order to be
successful.
Although the amount of data visualisation options may feel
overwhelming, whichever one will choose will be much more comprehensible than
raw numbers alone.
2D Area
2D area types of data visualisation are
usually geospatial, meaning that they relate to the relative position of things
on the earth’s surface.
- Cartogram: A cartogram distorts the
geometry or space of a map to convey the information of an alternative
variable, such as population or travel time. The two main types are area
and distance cartograms.
- Choropleth: A choropleth is a map with
areas patterned or shaded to represent the measurement of a statistical
variable, such as most visited website per country or population density
by state.
- Dot Distribution Map: A dot
distribution or dot density map uses a dot symbol to show the presence of
a feature on a map, relying on visual scatter to show spatial pattern.
Temporal
Temporal visualisations are similar to
one-dimensional linear visualisations, but differ because they have a start and
finish time and items that may overlap each other.
- Connected Scatter Plot: A
connected scatter plot is a scatter plot, a plot that displays values of
two variables for a set of data, with an added line that connects the data
series.
- Polar Area Diagram: A polar area diagram is similar
to a traditional pie chart, but sectors differ in how far they extend from
the center of the circle rather than by the size of their angles.
- Time Series: A time series is a
sequence of data points typically consisting of successive measurements made
over a time interval, such as the number of website visits over a period
of several months.
Multidimensional
Multidimensional data elements are
those with two or more dimensions. This category is home to many of the most
common types of data visualisation.
7. Pie Chart: A pie or
circle chart is divided into sectors to illustrate numerical proportion; the
arc length and angle of each sector is proportional to the quantity it
represents.
8. Histogram: A histogram
is a data visualisation that uses rectangles with heights proportional to the
count and widths equal to the “bin size” or range of small intervals.
9. Scatter Plot: A scatter
plot displays values for two variables for a set of data as a collection of
points.
Hierarchical
Hierarchical data sets are orderings of
groups in which larger groups encompass sets of smaller groups.
10. Dendrogram: A
dendrogram is a tree diagram used to illustrate an arrangement of clusters
produced by hierarchical clustering.
11. Ring Chart: A ring or
sunburst chart is a multilevel pie chart that visualises hierarchical data with
concentric circles.
12. Tree Diagram: A tree
diagram or tree structure represents the hierarchical nature of a structure in
graph form. It can be visually represented from top to bottom or left to right.
Network
Network data visualisations show how
data sets are related to one another within a network.
13. Alluvial Diagram: An
alluvial diagram is a type of flow diagram that represents changes in network
structure over time.
14. Node-Link Diagram:
A node-link diagram represents nodes as dots and links as line segments to show
how a data set is connected.
15. Matrix: A matrix chart
or diagram shows the relationship between two, three, or four groups of information
and gives information about said relationship.
Reference: 15 Most Common Types of Data
Visualization accessed at https://info.datalabsagency.com/blog/data-visualization-news/15-most-common-types-of-data-visualisation
