User experience designers have to put in tremendous effort to convey complex information such that it can be easily understood and be visually appealing to the users. In today's world of big data, visualization techniques are essential to analyse massive amounts of complex data and make data-driven decisions. To support better decision making, visualization technologies enable users to uncover hidden patterns. During the COVID-19 pandemic, these techniques have been used as user interface in order to communicate the impact of the pandemic on the public. Considerable effort has been devoted to monitor the spread of the disease across the world and understand the various aspects of the pandemic. This chapter emphasizes the role of the data visualization technique as an effective user interface. Visualization of COVID-19 data is performed in the form of interactive dashboards, which can be beneficial to healthcare users and policy makers to plan the resource allocation and implement strategies to mitigate the effects of the pandemic.
TopIntroduction
In today’s digitized world, everyday huge amount of data is generated over the internet. As per survey (“How Much Data Is Created Every Day? [27 Powerful Stats],” n.d.) per day 1.145 trillion MB data is created all over the world. With the growth of internet users there is a huge increase in the data being generated. History of internet started from 1960 with email facility and then expanded its use for file sharing in the year 1970. In the year 1989, revolution was created with the invention of world wide web (WWW). Tim Berners Lee was the scientistist who invented the WWW, which made us to share the information using network of computers. Since then, a plethora of sources have been available on the internet for people to share data. This massive amount data is called as big data. There are three major sources from which the data is generated day-by-day over the internet: 1. Transactional data, 2. Social media data and 3. Machine generated data. Apart from this public data, the private data is also generated from oganizations. This type of data resides behind the firewall.
Figure 1. Yearwise voume of data (“How Much Data Is Created Every Day? [27 Powerful Stats],” n.d.)
Figure 1 shows the volume of data being created every year. Due to easy access of internet, users digital footprint has resulted in rapid growth in the data and the data size is measured in megabytes, gigabytes, and terabytes, petabytes to zetabytes. As per source (“Total data volume worldwide 2010-2025 | Statista,” n.d.) it is expected that till 2025, 181 zetabytes of data will be genearted. Since 2019, data usage has increased due to COVID-19 pandemic. During the countrywide lockdown, people work from home and internet usage has increased. Huge number of users are joining various social network sites to share their views. From the graph, it is clear that there is a large amount of data generated from the year 2019. There is a sudden increase from 42 zetabytes to 64.2 zetabytes. It is very important to know whether, the data generated over the internet or within organization is structured or unstrcuted. Unstrstured data needs a lot of efforts to make sense of it.
Figure 2. Sources of data (“Volume of data generated in a single year for various sources. | Download Scientific Diagram,” n.d.)
The graph (“Volume of data generated in a single year for various sources. | Download Scientific Diagram,” n.d.) in Figure 2 shows the data generated from different types of sources that is used in data science for analysis. Out of all the data souces, 64% of the transactional data is used by many professional oganizations for analysis.
Eventhough huge amount of data is created everyday, if we don’t know how to use and represent huge amount data strategically, then it is of no use. We should represent the data in an understandable form to gain actionable insights of huge and complex data.This type of representation can be done with the help of data visualization. When huge data is in the form text or any other structured or unstructured form, it is very difficult to understand it. We can call this data as information. To convert this information into understandable and interpretable form, data visuazilation is required.
This book chapter provides an insight of data visualization elements, techniques, its application areas and some of the popular data visualization tools. The chapter explores the online COVID -19 datasets of confirmed covid cases, recovered and deceased cases, data of hospitals, beds available and COVID -19 test data i.e., the number of tests conducted, number of positive cases across different states in India.
The key contributions of this book chapter are:
- 1.
Emphasize the role of data visualization techniques as an effective user interface.
- 2.
Apply visualization techniques for COVID-19 data (both Quantitativeand Qualitative) using Python libraries, PowerBI and Tableu to gain a better understanding of the pandemic situation.
- 3.
c. Design appropriate visuals such as charts, word clouds and dashboards so that the users, policy makers and healthcare workers can easily compare data, detect relationships, predict future trend and use it for better decision making.