Chapter 1. Data Visualization
Humans began to record things long before writing systems were created. When the number and diversity of things to remember outgrew the capacity of human memory, we began to use external devices to register quantitative information. Clay tokens were used as early as 8000-7500 BC to represent commodities like measures of wheat, livestock, and even units of man labor. These objects were handy to perform operations that would have been difficult to do with the real-life counterparts of the tokens; distribution and allocation of goods became easier to perform. With time, the tokens became increasingly complex, and soon, the limitations of the complex token system were identified and the system began to be replaced with simpler yet more abstract representations of quantities, thereby originating the earlier systems of writing.
Keeping records has always had a strong economic and practical drive. Having precise accounts of grains and pastures for the livestock allowed people to plan rations for the winter, and knowing about seasons and climate cycles allowed people to determine when to plant and when to harvest. As we became better at counting and registering quantitative information, trading with other nations and managing larger administrative units became possible, thereby providing us with access to goods and knowledge from other latitudes. We keep records because we think it's useful. Knowing what we have allows us to better distribute our assets, and knowing the past allows us to prepare for the future.
Today, we register and store more data than ever. Imagine that you want to go out for a morning cup of coffee. If you pay in cash, the date, price of the coffee, and the kind of coffee will be recorded before your coffee was actually prepared. These records will feed the accounting and stock systems of the store, being aggregated and transformed to financial statements, staff performance reports, and taxes to be paid by the store. Paying with credit card will generate a cascade of records in the accounting system of your bank. We measure things hoping that having the information will help us to make better decisions and to improve in the future.
History demonstrates that gathering and understanding data can help to solve relevant problems. An example of this is the famous report of John Snow about the Broad Street cholera outbreak. On August 31, 1854, a major outbreak of cholera was declared in the Soho district of London. Three days later, 127 people died from the disease. At the time, the mechanism of transmission of the cholera was not understood. The germ theory was yet to exist, and the mainstream theory was that the disease spread by a form of bad air. The physician, John Snow, began to investigate the case, collecting and classifying facts, recording deaths and their circumstances as well as a great number of testimonials. Refer to the following screenshot:
Details of the original map made for Snow, displaying the deaths by cholera in the Soho district
He gave special attention to the exceptions in the map and noticed that neither the workhouse inmates nor the brewery workers had been affected. The exceptions became further proof as he discovered that about 70 employees who worked in the brewery drank only beer made with water from a pump inside the walls of the brewery. In the workhouse, which also had its own water pump, only 5 out of 500 died, and further investigation revealed that the deceased were admitted when the outbreak had already begun. Although the map is convincing enough, Snow's original report contains more than 150 pages filled with tables and testimonials that support or raise questions about his theory. The local council decided to disable the pump by removing its handle, when the outbreak had already began to decline.
The report from John Snow is a great triumph of detective work and data visualization. He gathered information about the deaths and their circumstances and displayed them as data points in their geographic context, which made the pattern behind the causalities visible. He didn't stop at studying the data points; he also investigated the absence of the disease in certain places, faced the exceptions instead of quietly dismissing them, and eventually formed stronger evidence to support his case.
In this chapter, we will discuss what makes visual information so effective and discuss what data visualization is. We will comment about the different kinds of data visualization works, which gives a list of references to learn more about it. We will also discuss D3 and its differences with other tools to create visualizations.
