In class today, we were asked to make three visualisations from a set of data, which were death rates from ambient particulate air pollution; it showed trends in emissions from 1990 to 2017. One of them had to be a choropleth, a map that uses differences in shading, colouring, or the placing of symbols to indicate the average values of a particular quantity in those areas. The data presented a global-level overview of air pollution.
Why visualise?
To help us gain an insight and an understanding into complex issues
The Functional Art:
An introduction to information graphics and visualisation by Alberto Cairo.
Cairo reads an article on the population of the world, he then reads of the conflicting ideas on the fertility rates; the average number of children born in each country.
Rising fertility in poor regions is the reason the earth has to support 7 billion people now and a forecast of 9 billion in the next 2 decades. Other doomsayers focus on the aging populations in developed countries where fertility rates are below 2.1 children per woman. This number is known as the replacement rate. If the replacement rate in a country is significantly below 2.1 that population will shrink over time, if it’s much higher than 2.1 you’ll have a much younger population in the future which can cause problems. Predominantly younger populations show greater rates of violence and crime.
The author of this article contradicted both of these apocalyptic thinking by discussing two interesting trends. On average fertilely in rich countries is very low but in the past few years, there has been a slight increase in the trend. On the other hand, poor countries are showing a decrease in average fertility. The author suggests that due to these trends, fertility rates everywhere will converge around 2.1 in a few decades and the world population will stabilise at 9 billion people.
The graph, however, does have some insufficient information, the graph is an aggregate of the data of all countries in the world it doesn’t show the multiple patterns the author discussed, it doesn’t show the rich countries with recovering fertilities and poor countries stabilizing their populations.
It’s hard to extract meaning from a table. Data from a table is difficult to look at and find a number and then memorise additional numbers and then make a comparison. It’s much clearer when looking at a graph and then easier to make compassions.
All the examples of graphs require an active engaged reader.
Part Two to the history lecture pod continued to expand my knowledge and understanding of why we visualise data, how useful they are their primary functions.
Jordan's Data Visualisation Blog 