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Activity Number: 403 - SPAAC Poster Competition
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #306823
Title: Using Black-Box Machine Learning Techniques to Identify Spatial Dependence in Occupancy Data
Author(s): Narmadha Mohankumar* and Trevor Hefley
Companies: Kansas State University and Kansas State University
Keywords: Ecological statistics; Bayesian hierarchical models; Spatial statistics

In ecology, occupancy data are a contaminated binary response that is used to map the presence or absence of a species. Models for occupancy data are used to estimate the occurrence of a species, where the true presence of a species is a function of a spatially varying process. In the standard spatial occupancy model, most researchers assume that the spatial component is a Gaussian process. This assumption leads to an inability to identify non-traditional spatial dependence such as discontinuities and abrupt transitions which are common in ecological data. Bayesian machine learning techniques have the potential to identify non-traditional spatial structure, but these technologies do not account for contamination in the binary response. We embed Bayesian machine learning methods into the hierarchical occupancy model to account for non-traditional spatial dependence and contamination in the binary response. We conduct a simulation experiment by selecting a few commonly encountered cases of traditional and nontraditional spatial dependencies in ecology and include an application of our method using data on Thomson's gazelle in Tanzania.

Authors who are presenting talks have a * after their name.

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