In this section, a brief description of the Web-App is provided analyzing and describing all its components. The screen is divided in some areas: the central part is reserved for displaying graphs, on which, data is represented. On the left part of the screen a brief description of the selected graph is provided. On the right part of the screen there is a side bar with data from other nations and finally on the bottom part of the screen a slider allows the user to change the year which data is referred to.


The Web-App provides two different modes as depicted in the image below.

Daily Diet

In this mode a donut chart is used to show data about food consumption of each nation or of the world as a whole. Six colors are used for the six main categories:

  • Meat
  • Dairy & Eggs
  • Produce
  • Grain
  • Sugar & Fat
  • Other

Each of these categories is composed of some subcategories which use different gradations of the main color. By clicking on one of these categories or subcategories, the one which has been selected is highlighted in the chart itself and in all the charts of the sidebar. In this way it is very simple to compare data of different countries at different levels of granularity (whole distribution, category, sub-category).

It is also possible to visualize data in terms of "Calories per Person" or "Grams per Person" using the buttons depicted below.

Meat Consumption

In this mode data is represented in a pretty static way by a multi-layer Line Chart. It is possible to highlight the different kind of meat and this selection is reflected in the Side Bar as well.

Also in this mode it is possible to select different attributes to visualize as it is depicted in the picture below. The user, in fact, can select between "Calories per Person", "Gram per Person" and "Total Tons" consumed.

Since this representation is not so exciting, from now on the functionalities will be explained basing on the other mode. However, all that will be said about "Daily Diet" mode, if not explicitly indicated, can be applied as well for "Meat Consumption" mode.


Below the graph a slider allows to the user to select which year to visualize data for. This slider also has a play button that can be used to start an automatic animation with data from 1961 to 2011.

The play function is not available for the "Meat Consumption" mode since in that mode, data of all years is presented at the same time. The slider acts as a "marker" for the interesting years.


The sidebar offer to the user a great way to compare data of the selected country to data of other countries. The graphs of other countries and of the entire world are displayed in this area and updates according to the filter selections, in the same way as the main chart does.


To the left of the main chart a brief description of the selected chart is provided. It contains very useful information about the trends in the data for the selected country. It also contains interesting descriptions of the historic events which have led to changes in the data along the years.


Data was sourced from FAOSTAT. Values reflect domestic utilization for food consumption in each country or region from 1961 to 2011.
Data about China refers to FAO's "China, mainland".
Values of Russia preceding 1992 are represented with U.S.S.R. data.


The Web-App is presented with global data about the world as default selection. At first glance, what strike the user is the extreme simplicity of the app and how easy is to interact with it as soon as it is loaded. Although this simplicity can be considered a big pro for these kind of interactive visualizations, it reflects, on the other hand, a very limited and skinny representation which could have been extended a lot. By the way the application in its entirety is very well designed and some key features have been studied carefully.


One of the first things that the user perceive once used a bit, is that just two modes of representing the data are proposed and one of them (Meat Consumption) is not so effective. This is a huge limitation since different representation methods are crucial to analyze data in an appropriate way. Different graphs have different peculiarities and thus are able to highlight different kind of pattern or properties of the data.


The sidebar is the key feature of this application. In fact it allows the user to compare data of different countries immediately by simply looking how graphs change in accordance with the main graph.
Also the values of the graphs change with the selected filters. This is very important in order to efficiently provide actual numbers on data.

All of this is highlighted in the video on the right side as well as a bug that has been discovered on subcategories selections. As it is possible to notice, when a sub-category is selected the values of the graphs in the sidebar are not update anymore.

In the "Meat Consumption" mode, the main Line Chart scale is automatically adjusted in order to always have a perfect visualization of the chart itself. This doesn't happen on the sidebar and, in fact, all the graphs are represented on the same scale, in order to have truthful comparisons.


The donut chart is a very particular chart which can easily lead to some errors or to misleading representations. This visualization seems to have handled it in the proper way. Data has been represented in a truthful way by choosing to represent the data proportionally to the area of the annulus rather than the area of the whole disk or even worse the radius of the circle.

As it can be noticed in the previous image, the value of the daily calories in China has been doubled from 1961 to 2002. Even if humans are more acquainted with comparing length rather than areas, for the 2002 chart, choosing a radius which has the double of the length of the one of 1961 would lead to an area that is four times bigger. This is a common error that can be encountered with Pie or Donut Charts. In this visualization instead it is the area of the annulus that has doubled from 1961 to 2002.

Here below are the values of the two annulus for the 1961 and 2002 graphs. As it can be noticed, the area of 2002 is more or less the double of the one of 1961. The little error is due to the measurement approximations.


As previously pointed out, this application is very easy to use and has allowed to make some interesting discoveries very soon. Below there is a brief overview of interesting findings.


It's a Fat World

As expected the use of this application has revealed a big increasing of fat consumption which has started at first in the occidental countries (especially USA) until spreading all over the world. As a result of the globalization, this has lead also to the increasing of the daily calories consumption in all the countries of the world. This value, that should be around 2000 calories per day, has arranged to 3000 calories per day in all the industrialized country of the world.

China Boom

As pointed out in one of the previous sections, China had a big explosion in the last 50 years and it is reflected in the food consumption which has increased of more than 100%.


Somalia's tragedy

Somalia has the poorest caloric consumption of the world. Milk products make up more than half of their daily food intake. Nevertheless a shy increasing during the 80's, the food consumption has dramatically gone down again.

Hong Kong Meat Fever

Hong Kong's diet has changed drastically in the last 50 years: a very poor diet based on grain has been substituted by a new one almost totally based on meat. Hong Kong has the biggest meat consumption of the world and it has increased of about 200% from 60's.

North Korea vs South Korea

North Korea and South Korea had, at the beginning of the 60's a very similar and poor nutrition. North Korea has maintained its poverty along the years probably because of the strict dictatorial regime and isolation. Its daily consumption of grain cover almost the 75% of the food intake. South Korea, on the other hand, has had a great grow of its economy and, influenced by the occidental world, has constructed a nutrition based on meat and fats.


I am Andrea Piscitello. A computer science and engineer student at UIC and PoliMi.

This project has been developed in the context of the Visualization and Visual Analytics class (CS 424), taught by Professor Andrew Johnson at UIC during the Fall Semester of 2014.