Tracking Elephant Clusters

In today’s episode, we interview Gregory Glatzer,  an undergraduate student at the Pennsylvania University, College of Information Science and Technology, studying applied Data Science. 

He speaks about his work, especially in the intersection of wildlife conservation and data science. Gregory said more about his last research on understanding the settlement of wildlife animals, particularly elephants. He built a machine learning model that predicted the next settlement of elephants based on their historical track record. This would aid the protection of these animals from poachers. Gregory explained how they got the dataset for this study, and why it was critical to protect such data from the general public. 

In the interview, Gregory explained that they used a movement tracking sensor to capture the location and physical movement of elephants. However, he needed more than a migration dataset. Gregory used the temperature data as well for his study. He explained why the temperature data was equally critical.  Gregory mentioned that the historical weather data was obtained from an API called Meteostat.

Gregory went on to discuss the algorithms he used for this task. He explained that a combination of two machine learning algorithms were used: DBSCAN and K-Means Clustering.  

Gregory extensively discussed why he used both algorithms in one project and how they played out. He mentioned using other algorithms such as Optics to evaluate how it would measure up against the first two.

Going forward, Gregory touched on the anomaly he observed in the data due to environmental situations beyond his control. In the end however, the study results were impressive, even though he acknowledged that this was just the beginning. He virtually presented his results in the Towiri Conference organized by the Tanzania Wildlife Institute.

He rounded up by disclosing his passion for frontend development and how you can reach him at g1776.github.io