DATA VISUALIZATION LITERACY
In the recent past, the world has always pursued ways and forms into which they can enhance efficiency and still sustain the value of the object. The need for improved methodology and functionality has also been received in the sectors that use data as their primary source of production. The need for data processing into a summarized data and retain the meaning has been the challenge. Thanks to the innovators that today’s efficiency is not a jargon for people to think of. Data efficiency has been solved more easily and appropriately. This paper explains more about data visualization and visualization literacy.
First, we need to understand the meaning of these terms. Data visualization is the process of representing data either statistically or numerically for easy understanding. Data visualization literacy is defined as the process of translating or interpreting visualized data, patterns, symbols into meaningful information (Wood, Kachkaev, & Dykes, 2018). The visual design in chapter six for the racial dot map would be described in this manner. The project manager needs to understand the sectors where there is a high rate of racial discrimination patterns and related incidences all over the world. This is to understand the main cause of racism in the world.
The other hat is the data scientist, who is responsible for producing all necessary information about racial discrimination. They source their information from the human right departments and secondary data related to this issue. The other hat is the journalist, who is the most important person in propagating the idea of the project manager to reach the design stage (Dmowska, & Stepinski, 2019). The journalist work with the data analyst to seek the appropriate information to deduce the right questions. The journalist prepares questionaries’ such as, what is the source of racial discrimination? how can it be resolved?
The other hat involves computer scientists; they are responsible for creating a great design. The computer scientist with his skills works by compiling every information n gathered concerning racial discrimination and analyses it either by creating various patterns and diagrams. The scientist works by creating life in the data so that it can be appealing in all dimensions (Börner, Bueckle, & Ginda, 2019). The designer then appears to give a taste of his visual view of things. The designer produces designs that will be up to date and designs which will produce answers to the questions developed from the beginning. He ensures the data is well represented through rightful color designs and models. In the racial map, the blue, yellow, and red colors show the different patterns.
Lastly, the technologist appears to give the final view through his competency in the competitive world. The technologist gives the final remarks concerning the designs and the visual data representation. The technologist gives the most recent trends about the viewer’s desire and needs and how to satisfy the larger number (Wood, Kachkaev, & Dykes, 2018). Here more complex designs are used and also reduces the noise in the data visualization, such as avoiding too many wordings and color crashes. In conclusion, the seven hats are a tremendous move in data visualization and literacy in the current data revolution.
References
Börner, K., Bueckle, A., & Ginda, M. (2019). Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments. Proceedings of the National Academy of Sciences, 116(6), 1857-1864.
Dmowska, A., & Stepinski, T. F. (2019). Racial Dot Maps Based on Dasymetrically Modeled Gridded Population Data. Social Sciences, 8(5), 157.
Wood, J., Kachkaev, A., & Dykes, J. (2018). Design exposition with literate visualization. IEEE transactions on visualization and computer graphics, 25(1), 759-768.