1. Choose movement data of your own or from a package.

  2. Build a 100% MCP, 100% KDE_UD, 100% LoCoH_UD

  3. Plot home range area vs. percentage isopleth to see the relationship in your spatial data and how the separate home range estiamtors differ. (Similar to the output of mcp.area or kernel.area)

  4. Try comparing outputs of k-LoCoH and a-LoCoH

  5. Try comparing across smoothing parameters and/or other kernel and hull methods found in the adehabitatHR library

  6. Adapt your code to run across multiple individuals or datasets, see what you can infer about animal life history behavior from home range size and shape.