BirdVis: Visualizing and Understanding Bird Populations

Abstract

Fig. 1. Tag cloud lenses in BirdVis are used as a tool to explore and understand relative habitat preferences suggested by a species distribution model over space, time, and across species. Here, we show occurrence maps for the Indigo Bunting. The lenses cover three different regions on three different dates of the breeding season of 2009: May 4, June 22, and August 24, 2009. They show important differences in habitat preferences across these regions as well as how the preferences change over time within a region. The ability to interact with these visualizations, by changing regions and dates, and comparing different bird species, provides an unprecedented tool for scientists to understand bird distribution and consequently obtain insights about the environment and how it changes over time. The supplemental video gives an overview of different features and visualizations provided by BirdVis.Abstract-Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatialtemporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.

Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species

“…According to the goals of the analysis system stated in Section 2, we need to offer the user an overview first before interactively digging deeper into several spatial areas according to the domain knowledge of the user who could be either a farmer, vintner or researcher. Existing related systems such as BirdVis [18] offer heat map overview visualizations. However, as we want to investigate the distribution of a species over time, we designed a map overlay consisting of several glyphs.…”

Towards Understanding Real-Estate Ownership in New York City

“…To support the visual specification of spatial queries, we have adapted tag-cloud lenses (TCLs) [2]. These lenses provide an easy-to-use, interactive mechanism for researchers to explore multiple variables in a geographical context.…”

TRI-Direct: Interactive Visual Analysis of TRI Data

“…Recent visual analytics approaches for geo-spatial datasets often contain a choropleth map and additional information visualization tools suited for special application requirements. BirdVis [10] was developed to analyze bird distribution models to identify relevant correlations and patterns. Kim et al [17] developed Bristle Maps for aggregation, abstraction, and stylization of spatiotemporal data that enabled multi-attribute visualization, exploration, and analysis.…”