n the Y-axis measure; those below the trendline have primarily an X-axis error.
Given those criteria, we can draw the following conclusions about the outliers:
South Carolina: properly staffed, crime is too high. Primary problem: crime rate.
New York, New Jersey: over staffed, respectable crime rates. Primary problem: overstaffing.
Vermont: overstaffed, too high a crime rate. Primary problem: crime rate.
Louisiana: overstaffed, much too high a crime rate. Primary problem: crime rate.
D.C.: tremendously over-staffed, too high a crime rate. Primary problem: overstaffing.
The States with a primary problem as their crime rate need to look at the factors that are causing their crime rates. Are the police operating effectively? Is there corruption within the police force? Are the police over-policing and thus creating a reporting anomaly? Are there poverty problems caused by a lack of education or a high tax rate? Vermont and Louisiana should reduce their staffing levels to at least 2.9. South Carolina may want to increase its staffing levels towards 2.9 until the crime problem is solved.
The States (New York and New Jersey) and D.C., with staffing levels as their primary problem, should work to immediately reduce the cost to taxpayers of superfluous police forces. Returning that money to the productive economy will do more to decrease poverty than it’s doing with an inordinately high level of police staffing, which should have a positive impact (decrease) on crime rates. Target 2.9 as a first step, verify the crime rates remain consistent or decrease, then move towards the median level of 2.2 per thousand.
D.C. deserves special mention because it’s such a mess on both axes. It may face special challenges as being a city-district (future work should compare it with other cities specifically) but placing D.C’s crime rate on a straight line between South Carolina and Louisiana (which it lies between in terms of crime rate) excuses a staffing level of no more than 3.2.
Further work should look at factors such as population density and tax rates to see what impact they have on the crime rates and see if the outliers would be brought back into the ‘normal cloud’ if their numbers were adjusted for the Fifty-States trend for those two factors.