Does beauty need microplastics?
This data visualization is related to microplastics and how they contribute to the pollution of the oceans, this situation does not only affect marine species and their environment, it also affects human health. This datavis has four objectives: 1) Reflect the immense amount of beauty products that contain microplastics, 2) Show the producers and brands that create most products with microplastics, 3) That the user knows which of the products he uses daily contributes to the pollution of the oceans. 4) Influence the user's consumption habits.
The database that we occupy has more than 3,000 beauty products that contain microplastics. Each figure represents a product that has microplastics. The symbol codifies the type of registered microplastic and the color, the type of use of the product in question.
Why do we choose this design?
We chose to show all the elements together to represent the vastness of cosmetic products containing microplastics. The random composition and movement of each element try to symbolize the microplastic particles moving in the ocean.
Colour
The uses were categorized by color: green - “scrubs and peeling”, blue - “shower and bath”, purple - “facial cleanse”, orange - “skin care”, pink - “makeup”, white - “toothpaste” and yellow - “diverse products”.
Symbols
We use 28 symbols to code each of the microplastics found in the database. The symbols were generated randomly using "Mathematics" software, then they were selected by the team and finally optimized in Illustrator.
The main typeface is related to the idea of microplastic particles.
We decided to do a scrolly telling to contextualize the problem, break down the coding of the data visualization and show some patterns found in the analysis of the database.
What dataset was used for visualization?
We used a database of more than 3000 products belonging to the cosmetics industry that contain microplastics, these data was raised by a joint effort of "The Plastic Soap Foundation" and NGOs from "The international Beat the Microbead coalition" (integrated by 92 NGOs in 38 countries) within the framework of the" Beat the Microbead "campaign, which seeks to raise awareness about the contamination of the oceans by microplastics, as well as modifying the production methods of the big producers of cosmetics.
Original Source: https://www.beatthemicrobead.org/
Database for this visualization
Why do we choose this dataset?
We chose this database due to the proximity of the mentioned products to our daily life. We believe in order to change the habits of consumers, people must realize the role their daily actions play in the pollution of the oceans, such as using makeup or brushing their teeth.
Why the data set is accepted by the scientific and civil society community?
The database we used contains information of the products sold in 24 countries, the NGOs in each country were responsible for the collection of the data of each product. The size of this joint effort and the participation of dozens of organizations at the international level is what makes us believe in the rigor of the data collected.
For what audience is data visualization intended?
Our data visualization was made for an adult population that purchases its cosmetic products and cares about the environment and pollution of the oceans.
Why the data visualization is designed for the intended audience?
We transform the data base in a visualization that presents in a simple way those products that contain microplastics. The search engine that we developed. offer the user a way to identify the products, brands and producers that he uses every day and pollute our oceans.
What the data visualization audience should learn or do?
We want that, through the exploration of this visualization, the public realizes that the products they buy and consume contribute to the crisis of ocean pollution and, at the same time, become aware of the role they play in solving the problem to finally change the cosmetics consumed by products that do not contain microplastics.
Datavis improvements
For future developments, we consider performing an acuter data cleansing and a thorough analysis to obtain more accurate results, as well as a deeper analysis of the different edges of the problem, through narrative, visualization and data analysis.
Credits
Information design : Mariana López
Analysis and writing: Erandi Flores Romero
Development and interactivity: Daniel Gómez
Editing and coordination: Oliver Morales
