# Dealing with skewed mean

I have a data set of districts, farmland area and fertilizer subsidies issued for those areas. I.e, using made up numbers,

district | area | subsidy | subsidy per area (computed)
abc      |   20 |   500   |         25
cde      |   30 |   750   |         25
fgh      | 0.02 |    15   |        750    <--- looks off


I'm trying to visualise the subsidy per area but in districts that have very small amounts of farming the subsidy per area seems abnormal. The nationwide average is pretty much around 25. So, I can safely say that the subsidy amount is directly related to the area being subsidised, which is to be expected as fertiliser usage is dependent on the area being farmed. My theory is that the exception on small areas is due to there being a minimum subsidy amount irrespective of the land area.

Are there any techniques to deal with the above scenario when visualising data?

If districts are visualized in a scatterplot which subsidy is labeled as y-axis and area as x-axis, subsidy per area should be shown as the slope of the scatterplot. If subsidy per area is around the nationwide average of around 25, the slope of the scatterplot should be pretty much around 25.