I am aiming to use K-means to cluster lat-lon points, but I want to apply a weight to each point's distance based on two attributes of the point.
Attribute 1 is population and attribute 2 is percent of low income households. My goal is to find optimal locations for resource distribution (center of clusters) which decreases distance from cluster center to points but is weighted closer to points of high population and low income.
I have seen this method used before, but I am not sure if this is the best way to add the weight:
weight = |attr1|^a *|attr2|^b
where a,b ∈ [0,1)
Parameters a and b are chosen depending on how much I want to weight either attribute. For example, if I wanted to favor attr1 over att2, I would choose a higher value for a.