Yes as you describe. It may be useful to use existing classifications if they are available in the census categories, or you could use means and std devs to produce your classes or just subdivide the % data based upon some criteria of your choosing.
for example
high > =1 std dev
norm -1 std - 1 std dev
low < = -1 std
as an example should the distribution appear normal, this will give you some supportable criteria. Should the %age data not be normal, then you might want to examine the distribution to assess break points.
The key point is .... your categories, when derived from interval/ratio data, need to have some kind of justification! you could get the "how did you produce those classes" or "why did you choose those classes" questions (we examiners are not a cruel lot...just looking for thoughtful consideration and not a "because?!" person)
in any event, plot you data first to see if there is any clustering/pattern/ etc in the data, then do your descriptive statistics...then and only then, choose your inferential test. Parametric statistics has its requirements and if the data don't conform, then your non-parametric options (eg. chi) can step in.
Good luck and post more questions and/or graphs if needed.
PS I hope your advisor and committee are giving you similar advise.... take theirs over mine should there be a disagreement. If none is forthcoming, pose these issues with them...there are individuals that really don't care about appropriateness of the test and would say use pearson's but do it versus transformed data... eg log(%) versus sqrt(distance)+0.137 ... just so the distribution becomes 'normal'-ish but that is another isssue