Methods and systems to create clusters in an area

A system to create clusters in an area, the system comprising:

• a memory;
• and
• a processor coupled to the memory, wherein the processor is configured to execute program instructions stored in the memory for:
• obtaining a plurality of location data points associated with a plurality of entities in an area, wherein each location data point includes geographic coordinates;
• computing a range of location data points required in each cluster based on metadata related to an organization;
• forming a farthest point cluster comprising:
• determining a centroid of the plurality of location data points present in the area;
• calculating an angular distance of the plurality of each of the plurality of location data points from the centroid;
• determining a farthest location data point from the centroid based on the angular distance;
• identifying a centroid distance between each location data point and the centroid, and a farthest distance between each location data point and the farthest location data point;
• identifying a first set of location data points having the centroid distance lesser than the farthest distance;
• creating a centroid cluster comprising the first set of location data points;
• identifying a second set of location data points having the farthest distance lesser than the centroid distance;
• creating a farthest cluster comprising the second set of location data points, wherein the number of second set of location data points in the farthest cluster meets the range of location data points;
• and
• iteratively forming a new farthest point cluster by excluding the second set of location data points from the plurality of location data points.

A non-transitory computer program product having embodied thereon a computer program for creating clusters in an area, the computer program product storing instructions for:

• obtaining a plurality of location data points associated with a plurality of entities in an area, wherein each location data point includes geographic coordinates;
• computing a number of location data points required in each cluster based on metadata related to an organization;
• forming a farthest point cluster comprising:
• determining a centroid of the plurality of location data points present in the area;
• calculating an angular distance of each of the plurality of location data points from the centroid;
• determining a farthest location data point from the centroid based on the angular distance;
• identifying a centroid distance between each location data point and the centroid, and a farthest distance between each location data point and the farthest location data point;
• identifying a first set of location data points having the centroid distance lesser than the farthest distance;
• creating a centroid cluster comprising the first set of location data points;
• identifying a second set of location data points having the farthest distance lesser than the centroid distance;
• creating a farthest cluster comprising the second set of location data points, wherein the number of second set of location data points in the farthest cluster is limited to the computed number of location data points;
• and
• iteratively forming a new farthest point cluster by excluding the second set of location data points from the plurality of location data points.