Use of Convex Hull For Detection of Outliers in Oceanographic Data Pertaining to Indian Ocean
This work discusses a new method of identifying erroneous surface meteorology data using ICOADS data. An
'n' sided polygon (convex hull) with least area encompassing all the points is constructed based on the Jarvis March
algorithm. The periphery points from the clusters formed while plotting the parameter (e.g.: Air temperature, humidity)
against longitude and latitudes is used for building the polygons. Subsequently, Point-In-Polygon (PIP) principle is used to
classify the data as in or out of the polygon. It is observed that all possible outlier associated with the data can be identified
using this method.
Keywords— Convex Hull, Polygon, Jarvis March, Point-In-Polygon (PIP), Outliers, ICOADS, AWS.