Sharafeldeen, DinaBakli, MohamedAlgergawy, AlsayedKönig-Ries, Birgitta2021-12-142021-12-142021978-3-88579-708-1https://dl.gi.de/handle/20.500.12116/37708In recent years, the exponential growth of spatiotemporal data has led to an increasing need for new interactive methods for accessing and analyzing this data. In the biodiversity domain, species co-occurrence models are critical to gain a mechanistic understanding of the processes underlying biodiversity and supporting its maintenance. This paper introduces a new framework that allows users to explore species occurrences datasets at different spatial and temporal periods to extract co-occurrence patterns. As a real-world case study, we conducted several experiments on a subset of the Global Biodiversity Information Facility (GBIF) occurrences dataset to extract species co-occurrence patterns interactively. For better understanding, these co-occurrence patterns are visualized in a map view and as a graph. Also, the user can export these patterns in CSV format for further use. For many queries, runtimes are in a range that allows for interaction already. Further optimizations are on our research agenda.enSpatiotemporal data miningCo-occurrence patternsBiodiversity data mining10.18420/informatik2021-0431617-5468