DOI: 10.5281/zenodo.7766561
title: UML Sourcing Domain Predictions

description: <strong>WHAT: </strong>This data accompanies Glick, H.B., Ament, J.M., Dallinga, J.S., Torres-Batlló, J., Verma, M., Clinton, N., and Wilcox, A. (2023). Model-based prediction and ascription of deforestation risk within commodity sourcing domains: Improving traceability in the palm oil supply chain.<br> <br> The data represents a collection of geographically referenced raster images (GeoTIFF) capturing the predicted sourcing domains of 1,570 palm oil processing facilities (mills) in Indonesia and Malaysia. There is one image per facility, delivered in WGS84 (EPSG:4326) with a nominal equatorial spatial resolution of 250 m. With respect to the models discussed in Glick et al (2023), each image file captures the results of our most accurate model, which was an ensemble of predictions from MaxEnt, random forest, and gradient boosted regression tree-based machine learning models, trained on passive geolocational traceability data (n = 3,355,437 cellular pings). The individual pixel values in each image are the probability of containing aggregated cellular ping data from individuals that have been spatio-temporally linked to the given processing facility. Functionally, these values represent the probability that a given pixelated location is part of a facility's sourcing domain, where a sourcing domain encompasses both harvesting locations and the intermediary transportation and sub-processing space. Please refer to the parent manuscript for details. Please note that our predictions were derived from a processing chain that used a modified World Mollweide projected coordinate system (essentially ESRI:54009), where the central meridian (longitude of origin) was set to 109.5 degrees. The images are delivered here in WGS84 (EPSG:4326). Users can access the data in its original coordinate reference system using a Google Earth Engine ImageCollection: ee.ImageCollection('projects/ul-gs-d-901791-09-prj/assets/users/hglick/Glick_et_al_2023/Ensemble). <strong>WHEN:</strong> Passive geolocational training data was gathered in 2020 and 2021. Palm oil processing facilities were derived from the Universal Mill List in November 2021. <strong>WHERE:</strong> All images contain predictions for palm oil processing facilities located in Indonesia and Malaysia, with predictions made to a maximum Euclidean distance of 100 km from each facility. <strong>WHY:</strong> Palm oil accounts for approximately 50% of global vegetable oil production, and trends in consumption have driven large-scale expansion of oil palm (<em>Elaeis guineensis</em>) plantations in Southeast Asia. This expansion has led to deforestation and other socio-environmental concerns that challenge consumer goods companies to meet no deforestation and sustainability commitments. In support of these commitments and supply chain traceability, we seek to improve on the current industry standard sourcing model for ascribing social and environmental risks to particular actors. Among other uses, this data can support the ascription or allocation of deforestation, carbon loss, and biodiversity risk to relevant actors, permitting targeted outreach, contract negotiation, and mitigation of large-scale resource degradation. <strong>HOW:</strong> Passive geolocational training data was gathered by Orbital Insights. All modeling was conducted in Python; Google Earth Engine served as the primary distributed computing platform. Details are presented in Glick et al (2023).