  The idea of representation has been used in various fields of study from data
analysis to political science. In this paper, we define representativeness and
describe a method to isolate data points that can represent the entire data
set. Also, we show how the minimum set of representative data points can be
generated. We use data from GLOBE (a project to study the effects on Land
Change based on a set of parameters that include temperature, forest cover,
human population, atmospheric parameters and many other variables) to test &
validate the algorithm. Principal Component Analysis (PCA) is used to reduce
the dimensions of the multivariate data set, so that the representative points
can be generated efficiently and its Representativeness has been compared
against Random Sampling of points from the data set.
