Imputing features with NA values in classification task
Thank you for clarifying the question @Eisen. So the question looks at two main things:

To show which districts need more police presence.
To classify people as to whether they will commit 1+ traffic violations, regardless of the district, given their previous number of violations, by district.

For the first point, I think what would be a good idea is to yes show the break down of people visiting each of the districts and committing 1+ traffic violations. But, I think adding (95%) confidence intervals would be particularly helpful to see what is a reliable estimate of people who commit 1+ traffic violations in a given district.
In terms of the second point, maybe you can use a feedforward neural network which will take as input the traffic violation categories for each district and output whether the person has committed an accident in 2019 (Accident19).
The architecture is pretty much up to you, but the fail layer would need to be a 2-node softmax layer, which will create the probability distribution over the two classes (has had an accident in 2019 or not).
To represent this categorical data, I would suggest making the NA it's own category. Furthermore, best to represent the district traffic violation categories as one-hot encoded vectors. Then for a particular person, you concatenate these one-hot encoded vectors from all districts in your dataset.