Effectively combining machine learning techniques and visual analytics to support inferred results is an area of great importance. In this paper, we use the audio collection data provided as part of the 2018 VAST Challenge, which takes place in a fictitious natural preserve where a bird species has been claimed to be endangered by a polluting company. The goal of the mini challenge is to find evidence to support or refute the company's claim that the RoseCrested Blue Pipit (RCBP) is thriving across the Preserve. This required to characterize the patterns of all the bird species in the Preserve and classify newly collected audio recordings into their corresponding species. Our solution implements multiple visual analysis for spatiotemporal pattern discovery and to support results obtained through a machine learning model. Index Terms: [Human-centered computing]: Visualization—Visualization systems and tools