Palm oil is used in a variety of household products, from food products such as packaged pastries and chips, to cosmetics and soaps, and even biofuels. However, most palm oil is produced in single-crop plantations grown on vast tracts of land that were once rainforests and other biodiverse ecosystems.
Mapping the connections between palm oil mills, multinational corporations, and future deforestation risks is a difficult problem to solve with data science, but the University of Chicago Data Science Institute and Inclusive Development International (IDI) has created a new tool to help fill in the gaps in understanding. problem.
DSI and IDI, with support from the 11th Hour Project, launched a new tool called PalmWatch on February 22nd. PalmWatch uses rigorous data science and advanced, low-cost data visualization techniques to track palm oil supply from the ground. The environmental and social impacts of palm oil cultivation also accrue to consumer brands that use the oil in their products.
“This announcement of the PalmWatch tool has been a long time coming,” said David Uminski, executive director of the University of Chicago Data Science Institute. “This has all the hallmarks of a good data science problem.”
“We are very excited that this dashboard will be owned by local communities and nonprofit organizations working in this space,” said Launa Greer, software engineer at DSI. “Previously, investigating the impact of the palm oil supply chain was a painstaking process. Now the group can do the analysis instantly.”
Connecting data sources
In an effort to increase transparency, multinational brands now report on the palm oil mills from which they source their raw materials. However, this information must be collected and standardized to create a repository that categorizes and organizes factories around the world. And even with this information, additional computational techniques are needed to understand how each factory impacts local deforestation risk.
The PalmWatch project began as part of the Data Science Clinic, an experiential project-based course in which students work as data scientists under the supervision of DSI staff and faculty.
To build the tool, DSI’s 11th Hour project, led by Dylan Halpern, technical lead at Open Spatial Lab, first collected public information from 13 multinational consumer brands indicating which factories they sourced from. It was necessary to collect.
This information needed to be standardized by locating palm oil mills on a searchable map. Data scientists also collect information about factories, such as which companies own and operate them, which consumer brands they work with, and their RSPO certification status (a measure of the sustainability of palm oil production). It was also necessary.
