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countyendangeredspecies

Bayesian implementation inspired in attribution models to disentangle the effect of anthropogenic factors (agriculture, road construction, etc) in the endangered status of rare plants across counties. Analogous to attribution models, conversion (whether a species is endangered or not) cannot be unambiguously attributed to a specific touching-point (county). If a species is endangered, this should be the case in at least one of the counties (but potentially more). The credit that each county receives (probability of a species to be endangered there) depends on the combination of all species, and the variation of distributions of the species make it possible to determine which counties have higher or lower credits towards endangerment on a specific factor.

ml_celloccupancyfrompresencebackground

This project aims to estimate occupancy in cells in a continent based on presences and sampling effort not collected systemalically

sfguide-data-engineering-with-snowpark-python