Introduction & Inspiration
Although most of us live without starvation, the food crisis has always been an unresolved global problem. The United Nations reported that more than 820 million people worldwide did not have enough food in 2018. This is the third consecutive year that the number of hungry people in the world has increased. The global pandemic will further increase the number of people living in hunger. In contrast, 1.3 billion tons of edible food is wasted globally every year. So I became interested in the data of food waste and wanted to further study the status and causes of food waste in different regions.
Material and Methodology
I use the food loss and waste database from Food and Agriculture Organization of the United Nations, which is an agency of the United Nations that leads international efforts to defeat hunger and improve nutrition and food security. The data includes the global food waste situation from 1945 to 2017. This report mainly focuses on the data since 2010. The values may differ from the current situation, but it can reflect a lot of global food waste problem from a macro-scope.
The visualizations were created in Tableau Public and it has practical tutorials to teach you to make beautiful charts dashboard. Before importing the data, I simplified the table and removed some columns that are not relevant to the chart, such as data source links. Finally, switch between different chart formats and categorical dimensions to get clear and valuable charts.
Results & Analysis
Visualization-1 Severity of worldwide food waste
Treemaps are a good way to show the overall food waste situation because it can clearly show which countries are the most problematic countries and using the block’s color, size, and layout to separate. Overall, food waste is widely existing in developed and developing countries on all continents. The value is the average of 2010-2016. But it is worth noting that this chart reflects the food waste percentage rather than the total food waste. Therefore, although the food waste percentage in the United States is not the highest, the total food waste amount is the first in the world.
Visualization 2- Food waste from different stages
This chart shows the average value of food waste from 2010 to 2016 on the map. The filter can select different stages of food waste. It can be seen that the relatively large proportion of food waste in retail and consumers happened in relatively developed countries. In contrast, the majority of food waste in farm, processing, and storage stages happened in developing countries. Besides the low annual output value, developing countries also lose a lot of food due to low production efficiency, poor storage conditions, and slow transportation.
Visualization 3- Food waste differences
This chart selects two typical developed countries in North American and developing countries in the African. It can be seen from the bar chart that the percentage of food waste in developing countries is higher than that in developed countries. The waste of fruits and vegetables is a common problem because these products are very difficult to keep fresh and transport. But the difference is that the higher proportion of waste in developed countries is meat, and the waste rate of non-perishable basic crops is very low. In comparison, the waste percentage of basic crops in developing countries is pretty high, and foods with high nutritional value such as meat, eggs and milk are relatively scarce.
This lab research is practical and Tableau is a very powerful tool. It can easily visualize the database in various forms, which help researchers to obtain more useful insights, and can also modify the visual design such as color, font, and labels.
In this lab research, I use the average value between 2010 and 2017. When I put the annual food waste percentage on the horizontal axis of time to generate a line chart, many African countries have appeared strange trends. I think it may be because the data collected in these regions is not accurate enough every year. Therefore, this research uses average values to get insights from macro-scope, and the accuracy of specific data that changes over the years in some regions is limited.