Sustainability and Data science

current work

    • Development of a near-present, global human footprint index. Using existing data on global HFI, cloud-free satellite data from Landsat, and deep learning (with a convolutional neural network), we have developed the most up-to-date map of the human footprint index. The manuscript is Accepted at Environmental Research Letters, and be sure to checkout the preprint publication here at bioRxiv.

    • Revealing (potential) blindspots in the thematic content of scenarios research. Using latent Dirichlet analysis (LDA) to identify the latent themes and keywords across a corpus of texts, we aim to augment the human ability to distill thematic content from natural language. We explore this idea in the context of the pan-Arctic region, using a corpus of >1,500 web-based news articles about the future of the region.