Mi proyecto del curso: Curso de visualización de datos: convertir datos en arte

Mi proyecto del curso: Curso de visualización de datos: convertir datos en arte

Hello!! I am going to share my process for the final project.

Firstly, choosing the data was a bit difficult because many of the databases for the SDG goals were in percentages rather than numbers. I kept searching until I found one with numbers in thousands and millions. The database I chose is Goal 2 - Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES).

Reading the metadata document, it indicated that there were two units of measure: Percentage (representing the prevalence of food insecurity) and Millions of people (Number of people with food insecurity). However, in the Excel document, the unit of measure used was thousands, so I worked with this. Therefore, the data presents a lot of information divided by location (rural, urban, total), age (all or over 15 years old), and gender (male and female). I chose to work with the total location because there were not enough data for each country in the rural and urban categories. Additionally, I worked with the age group over 15 years old to reduce the enormous amount of available data a bit.

Thus, I started by cleaning the data. Using Excel, I filtered only the years 2016 and 2021 as these years were among the few with complete information. Additionally, with these two years, we can compare how the problem has evolved over time. This left me with a total of 54 countries with numbers that varied greatly (I want to clarify that I did not include data that combined two countries or counted by continents). From all the variables, I chose to keep the gender variable (male and female) as the other location data was incomplete.

Next, to start with the sketches, I hand-drew a chart where I put the data for two countries: one of them was Pakistan, which had the highest number, while the second country, Cambodia, presented average numbers. This way, I could start making graphs with their metrics. I liked the idea of using circles for the visualization.

Having experimented with the shape, I started applying various colors or gradients to see which was most suitable. I had an idea to use circles or irregular circles to somehow reference the topic of food insecurity through the representation of crumbs, but visually, it didn't convince me much.

For the distribution, I was inclined to use a circular visualization with the data in curved lines, as I thought it could look aesthetically pleasing.

After the sketches and trials, I created my document in Adobe Illustrator with my initial idea of distributing the data in curved lines that follow a circumference. However, I later realized it wasn't the most ideal approach, as it was difficult to quickly understand or relate which data points belonged to which country. The larger the number, the farther it would be from the country's name. So, I set out to find another way to visualize the data.

Regarding the colors I was going to use, I chose the mustard tone associated with the SDG goal and red to create an interesting contrast of warm colors. This way, women would be represented by red and men by mustard. For the years, I used different saturations of the colors: desaturated, almost pastel colors for 2016, and more vibrant and saturated colors for 2021. Initially, in my sketches, I had planned it the other way around, but as I worked, I realized that using the stronger or darker color for the latest year gives a sense of the passage of time, while using the lighter color for the current year gives the impression of going back in time (maybe it's just me who perceives this, hehe).

I came up with the idea of creating a visualization resembling a wheat plant (since wheat is the main component of bread, which is the most basic food) and thought of distributing the countries in 2 columns, with 27 on each side. I did some small tests of this idea and liked it, so I brought the data into Illustrator and started working. However, after almost finishing the first column, I didn't like it because the data I was working with varied greatly—some countries had less than one million, while others had over 30 or 40 million. So, I decided to go back to Excel and remove the outliers to make the visualization look better. With ChatGPT's help (as I don't know much about formulas or data science), I eliminated the outliers for each geographic region (for more coherence), ending up with 43 countries. I also sorted them in ascending order of the number of women suffering from food insecurity in 2021.

With the cleaned database, I started experimenting with different ways to represent the information. Then I came up with the idea of creating a circular chart similar to the initial one but incorporating these "leaf" or "plant" elements. So, I followed this path.

For the format, I did several tests, but the one I liked the most was the horizontal one. I added a small introduction to the visualization topic and also included interesting data I could analyze from the countries, along with the legend.

And this was the final result! I am very happy to have finished it, as it was very challenging, but I enjoyed it a lot and would like to keep improving in this field.

I also want to thank you, SuperDot Studio, for creating this dataviz course and sharing your knowledge, tips, and experience with us!!!

*As I have shared all my process in the forums, here I just added the video that summarizes the creative process :D