More and more projects we work on, demand included statistics for the end-user could see some analytics for his or her activities and so on. Especially that is vital for mobile applications. Collecting statistics is what programmers take care about. But statistics’ visualization is the challenging task for designers. Today we decided to collect the main visualization techniques, outstanding examples and hints how to visualize the data.

We will start with the basics. Data visualization is both an art and a science. Here you have to present an information that has been abstracted in some schematic form, including attributes or variables for the units of information. It has a **purpose** to make a complex data more:

- accessible
- understandable
- attractive
- usable.

Let’s compare it with making numbers and maths formulas look pretty for the artist and understandable for the child. And to create something great you have to understand the **basic techniques to present the data**:

## 2D Area

2D area types of data visualization are usually geospatial, meaning that they relate to the relative position of things on the earth’s surface.

**1. 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.

**2. 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.

**3. 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 the spatial pattern.

## Temporal

Temporal visualizations are similar to one-dimensional linear visualizations but differ because they have a start and finish time and items that may overlap each other.

**4. 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.

**5. 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.

**6. 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 visualization.

**7. Pie Chart**

A pie or circle chart is divided into sectors to illustrate numerical proportion; the arc length and angle of each sector are proportional to the quantity it represents.

**8. Histogram**

A histogram is a data visualization 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 visualizes 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 visualizations 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 the said relationship.

Now you know the main types. And if you will be able to choose the right technique for your data, you also need to understand the proper way to represent it with this technique. Here’s a little hint about **what type of technique will better fit your data**:

Chart via Digital Inspiration

But that’s not the end yet! To create an outstanding data visualization you have to:

- Make your data
*interactive* *Reveal the trends*with your visualization- Use
*animation*! This modern tendency will help you to better show the contrast or the changes in a timeline - Use
*real images*Credits to Payman Taei

- Use
*metaphors*to present a complex idea - Stimulate user’s
*imagination* - Tell a
*story*, show a*process* - Provide
*access to a raw data*(make your visualization interactive to show the raw data when users navigate to a particular part of it)

Main **characteristics** of good data visualization. Graphical displays should:

- show the data
- avoid distorting what the data has to say
- present many numbers in a small space
- make large data sets coherent
- encourage the eye to compare different pieces of data
- reveal the data at several levels of detail, from a broad overview to the fine structure
- serve a reasonably clear purpose
- be integrated with the statistical and verbal descriptions of a data set

More things to inspire:

- Here you will find more alternative visualization techniques.
- You can find a lot of the examples of data visualization on Pinterest.
- A collection of links to useful materials on data visualization.
- List of the most interesting infographics from Tableau.
- 100 infographic examples from Siegemedia. Each infographic on the list is annotated with an icon that allows you to subsegment by the types that interest you the most.
- Data visualization tools to investigate and study their examples.

We hope, this material will be useful for your future data visualization!

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