Introduction
The ever-growing volume of data and its importance for business make data visualization an essential part of business strategy for many companies. No matter newbies, programmers, or professionals dealing with data all day, there’s a need for them to distinguish various technologies and to decide which one to go for. This report reviews the development of technologies involved in data visualization and major data visualization tools.
In this timeline, I use two ways to demonstrate the development of technology and software used in data visualization. By listing events of forms to record data from print technique, steam engine to computational machine, we have an overview of how the print age, industrial age and electronical age cast an effect to the evolvement of data visualization technology. Secondly, by comparing different tools to generate data charts, maps, etc, we are able to see what techniques and tools are most suitable for people under different situations.
Method
The Knight Lab’s interactive TimelineJS tool provided an excellent medium to explore and visualize the development of technologies and software used in data visualization chronologically. TimelineJS is open-source and allows users to build custom timelines with a Google Sheets template.
By entering and formatting data for each planned TimelineJS slide in the Sheet template’s pre-labeled cells (year/s, headlines, event, and item description) with the option of uploading multimedia by URL and tweaking background image and color, users generate their timeline by pasting the Sheet URL into a portal on the Knight Lab website. Then they can return to the Google Sheet, add or edit data, make adjustments, and play around with the live timeline.
With each row in the Google Sheet representing the data for a slide on the timeline, I made sure to carefully enter the information I’d compiled for each slide into its pre-labeled cell. This included year, headline, item description, and URLs for images and thumbnails. I edited text for item descriptions and headlines and accompanied each description in the Google Sheet with citations and media credit.
Visualization
Inspired and collected datasets by Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization. This website is dedicated to compiling digitized materials about the histories of thematic cartography, statistical graphics, and data visualization which are intertwined with each other. Among their offerings, I intentionally focus on events of technology and there are certain pages to images of technology, many of which were scanned and contributed by scientists and researchers who share the passion in the history of data visualization. They start in pre-1600, and they are going till most updated.
After deciding which media/image can mostly present the event, I went to research for clear and authorized pictures online. I accessed most of the images from the digitized collections at their home repositories or from research articles on the history of data visualization. For the tools events, I went to their official website and GitHub repositories collecting related media.
Speaking of the selection of the events, the first thing I considered is to tell the technology developing story by highlighting several essential events chronologically, such as the printing and electronic machine. Another thing that also comes to my mind is to cross-compare software to present the traits and advantages of each popular visualization tool in order to better suit professionals’ needs. For the first several events, I choose them because they as milestones both present the turning points of data visualization. Since the first astronomical pictures “Starry Messenger” inprinting technique enables the recording of data as a reading form. For example as another milestone for visualizing and making sense of large tables, Table lens Focus+Context techniques support visualizing an entire information structure at once as well as zooming in on specific items, This interplay between focus and context supports
searching for patterns in the big picture and fluidly investigating interesting details without losing framing context.
Apart from chronologically presenting the related events, the focus on spreadingly selecting events of tools of data visualization indicating the evolvement and iteration of software in data visualization. R, as a free programming language utilized in statistical computations, data analysis, and visualization. Its contributions have made it a go-to for many users, attracting scholars and researchers as they strive to supercharge their data analysis quests. R provides a range of tools that makes the process a lot more manageable. Its several packages can be utilized in various instances. You can use the ggplot2 package if your primary focus is visualizing data and ggedit for plotting. The ggedit package enhances the aesthetic appeal of your visual representation.
Compared and contrast to using a lot of coding in R, another tool that speaking a lot in data visualization is Tableau. The user-friendly feature is the major strength of Tableau. This feature demonstrates the ability of an individual to work without any technical or coding knowledge. Since Tableau offers most of its features in a drag-and-drop form and each visualization is built-in and self-depicting, any newbie can work without any prior set of skills. Tableau is used more frequently as the tool allows to analyze the data more quickly with great visualizations and detailed insights. By work with different data sources, Tableau enables us to make dashboards that give actionable insights and spreads the business faster.
Reflection
There are more events in history that highlight the development of data visualization, and a lot more powerful tools that achieve the goal of visualize and analysis data into sensiful graphs and charts. From this technology history one may also see that most of the innovations in data visualization arose
from concrete, often practical goals: the need or desire to see phenomena and relationships
in new or different ways. It is also clear that the development of graphic methods depended
fundamentally and indispensably on parallel advances in technology, data collection and statistical theory. After generating and analysing this timeline, I believe that the application of technology of data visualization to its own history offers some interesting views of the past, thoughts and further questions and challenges for the future:
- What are the future techiniques and how the evolement of technology will affect the form of presenting data visualization? What can data scientists earn from recent motion data visualization?
- How to efficiently and effectively using tools for data visualization especially when it comes to combine data visualizations into effective dashboards? Is there other creative storytelling ways to make data more meaningful?
- For programmers and for people doesn’t code very much, what’s the intersection for them to use data visualization tools? And how to choose the most suitable tools for them?
Reference
The Evolution of Data Visualization
TUNDRA: A multilingual corpus of found data for TTS research created with light supervision
Ramana Rao and Stuart K. Card. (1994) The Table Lens: Merging Graphical and Symbolic Representations in an Interactive Focus+ Context Visualization for Tabular Information. https://hci.stanford.edu/courses/cs448b/papers/Rao-TableLens.pdf
Importance of Using R in Data Visualization
Why Tableau is The Most Important Tool For Data Visualization?
Michael Friendly. (2006) A Brief History of Data Visualization. https://www.datavis.ca/papers/hbook.pdf
Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization