data visualization library

This site aims to provide a comprehensive collection of data visualizations, descriptions, and sources links to learn more about visualization techniques. Organized by function, the top navigation (listing attributes to rank) is available to shortlist the potential options for your charting needs. Use the library as a resource to inspire and produce your own charts, graphs, and maps and after you do - consider sharing the work here on vizipedia.

Data Visualizations vs. Infographics 

Data Visualizations are pictures of data. With the world's information doubling every two years, understanding large data sets to create meaning is more important than ever. Standalone sets of individual data points can appear meaningless. But data visualizations can turn data points into graphical representations and imagery that can produce insightful knowledge from data and bring to the forefront answers to key questions or patterns. Well-executed data visualizations can both empower and enable better decision-making. The meaning within a data set can be more clearly understood with images that allow users to readily spot, process, and develop discoveries.

Unlike information graphics, data visualizations present the data in a raw and uncurated way that does not showcase the headlines but rather allows the reader to create their own headlines and interpretations of the results. A composed newspaper article with answers to who, what, when, where is akin to an information graphic in the same way a spreadsheet of data types is to a data visualization. Depending on the data set and sets of questions, an array of data visualizations can be used to handle the demands to expose answers. In this way, data visualizations are a type of communication method that can succinctly tell various stories of a data set.

Approach To Creating the Categories (Attributes to Rank) 

The identified set of categories, listed as tabs, were derived from a two step approach. First a large variety of visualizations (close to 100 hundred) were collected and defined. Print and several online sources were used to gather and review the visualizations. The full collection was analyzed, reviewed, and discussed to extract the essential function(s) of each visual communication. Second, a qualitative technique of clustering like items into functional groups called affinity diagraming was conducted. This technique allowed us to derive the categories from the ground up. The exercise resulted in the ten categories presented as tabs, starting with Attributes and ending with Rank.