The use of information granules to detect anomalies in spatial behavior of animals. (March 2022)

• Record Type: Journal Article
• Authors: Kiersztyn, Adam; Karczmarek, Paweł; Łopucki, Rafał; Kiersztyn, Krystyna; Nowicki, Tomasz; Perzanowski, Kajetan; Olech, Wanda
• Is Part Of: Ecological indicators. Volume 136(2022)
• Publication Date: 2022-03
• Subjects: Fuzzy numbers -- Information granules -- Outlier detection -- Data transformation -- European bison -- Telemetry data -- Ecological data; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.108583
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Detecting anomalies in the space use patterns of the European bison. A new approach to detecting anomalies in spatial behavior using granular computing. Using fuzzy statistical semantics to describe the space use patterns of the European bison Abstract: Nowadays, in the era of automation of ecological measurements, more and more often we are dealing with large data sets in which various unexpected anomalies may occur. Their detection is often crucial for a proper assessment of ecological trends and processes. Therefore, methods allowing for identification of anomalous data are especially important for a deep understanding of ecological phenomena and their relationships in practical domains. In this study, we present an innovative application of information granules to the in-depth study of spatial behavior of the European bison ( Bison bonasus ), based on GPS data. As evidenced by a series of numerical experiments, this granular computing-based approach allows to detect both anomalies and regularities in the atypical behavior of the European bison, a species important for local ecosystems. The transformation of the original data space into a new semantic multidimensional space, defining the degree of membership in the anomaly class represented by new descriptors allows for more unambiguous analysis of non-standard animal behavior.