🔗 Links
Github: https://github.com/curiositydataanalytics/Shark-Tracker-w-MovingPandas-Python-Streamlit
In this video, we explore the powerful MovingPandas library and its wide range of features for spatiotemporal data analysis. Using real-world shark tracking data from Guadalupe Island, Mexico, we showcase how MovingPandas can be applied to analyze movements, calculate metrics, and visualize patterns.
📝 Topics covered in this video:
- Introduction to MovingPandas and spatiotemporal data
- Loading and preprocessing shark tracking data
- Creating trajectory collections and calculating movement metrics
- Visualizing trajectories, speed distributions, and path simplifications
- Applying smoothing techniques and calculating coverage areas
- Aggregating data to generate heatmaps of shark activity
Don't forget to like, share, and subscribe for more videos.
#sharktracking #movingpandas #python #geospatialanalysis #datavisualization #marinedata #geopandas #pythonprogramming #folium #leafmap #spatiotemporaldata #trajectoryanalysis #sharks #oceanresearch #sharkconservation #streamlit #plotly #datainsights #heatmaps #mapvisualization #trajectorysmoothing #kalmanfilter #openocean #searesearch #guadalupeisland #sharkdata #datascience #marinebiology #spatialanalysis #oceanmaps #trackingtechnology #dataengineering #conservationtechnology #sharkresearch #geojson #spatialdata #datastorytelling #timelinedata #environmentalconservation #sharkbehavior #marinescience
