Hi William,
According to the documentation, the "partitionDetections" function is primarily designed to work with Cartesian coordinates. It partitions detections based on distance using either the Euclidean or Mahalanobis distance metric, both of which are more straightforward to compute in Cartesian space. In spherical coordinates, calculating distance becomes complex due to the curvature of the space. This might be why you are seeing less sensible results.
However, the function has been updated to also support the "DBSCAN" algorithm which maybe better for handling data in spherical coordinates as it is a density-based clustering algorithm that can adapt to the data's shape. Experimenting with this algorithm or adjusting the distance metrics might yield better outcomes.
If you continue to have issues, converting your spherical data back to Cartesian coordinates before applying "partitionDetections" could be a workaround. You can use the "sph2cart" function for this conversion, and "cart2sph" for converting back if needed.
You can read more about these functions here:
- sph2cart: https://www.mathworks.com/help/releases/R2020b/matlab/ref/sph2cart.html
- cart2sph: https://www.mathworks.com/help/releases/R2020b/matlab/ref/cart2sph.html
Hope this helps!
