Using maximum topology matching to explore differences in species distribution models

Jorge Poco, Harish Doraiswamy, Marian Talbert, Jeffrey Morisette, Cláudio T. Silva

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Species distribution models (SDM) are used to help understand what drives the distribution of various plant and animal species. These models are typically high dimensional scalar functions, where the dimensions of the domain correspond to predictor variables of the model algorithm. Understanding and exploring the differences between models help ecologists understand areas where their data or understanding of the system is incomplete and will help guide further investigation in these regions. These differences can also indicate an important source of model to model uncertainty. However, it is cumbersome and often impractical to perform this analysis using existing tools, which allows for manual exploration of the models usually as 1-dimensional curves. In this paper, we propose a topology-based framework to help ecologists explore the differences in various SDMs directly in the high dimensional domain. In order to accomplish this, we introduce the concept of maximum topology matching that computes a locality-aware correspondence between similar extrema of two scalar functions. The matching is then used to compute the similarity between two functions. We also design a visualization interface that allows ecologists to explore SDMs using their topological features and to study the differences between pairs of models found using maximum topological matching. We demonstrate the utility of the proposed framework through several use cases using different data sets and report the feedback obtained from ecologists.

Original languageEnglish (US)
Title of host publication2015 IEEE Scientific Visualization Conference, SciVis 2015 - Proceedings
EditorsJames Ahrens, Huamin Qu, Jos Roerdink
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9-16
Number of pages8
ISBN (Electronic)9781467397858
DOIs
StatePublished - Mar 8 2016
EventIEEE Scientific Visualization Conference, SciVis 2015 - Chicago, United States
Duration: Oct 25 2015Oct 30 2015

Publication series

Name2015 IEEE Scientific Visualization Conference, SciVis 2015 - Proceedings

Other

OtherIEEE Scientific Visualization Conference, SciVis 2015
CountryUnited States
CityChicago
Period10/25/1510/30/15

Keywords

  • Function similarity
  • computational topology
  • high dimensional visualization
  • persistence
  • species distribution models

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Software

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