I updated the code to make a KDTree using scipy and numpy for the 100,000 set. Building the tree only took seconds, and then counting the neighbors took around a minute.
1 - Who are you kidding that there is any economic benefit of tourism?
2 - Clearly such development will bring in more jobs to local individuals than tourism, even in the best case tourism scenarios
Seriously, how about showing how much tourism $$$'s there actually are before saying that something will decrease them.
So the idea is, race is not entirely discrete, and that darker skinned individuals suffer more racial animus than lighter skinned individuals. Ok, pretty straight forward and no argument there.
The second part though, looking at the correlation between skin tone and particular outcomes, and then saying it is evidence of racial bias, is misleading. This is problematic, because people are the accumulation of their entire experiences. The outcomes of adults are likely the product of a bunch of stuff going on in there prior history.
For example, the same theory would potentially lead to worse educational experiences, as teachers are likely to be racially prejudiced against darker skinned students. Thus employment outcomes in adults may occur even without racial discrimination due to the earlier education disparities.
Being a social scientist and mostly doing observation research I am sympathetic to "it is the best we can do, and there will always be confounds". I'm ok living with the uncertainty - I'm not ok with people making interpretations that require cognitive dissonance though to be true.
Part of my issue with this design is that it hides behind complexity. On its face it appears like a neat little identification strategy, but it isn't, and is subject to the same critiques of inferring racial discrimination from more traditional discrete measures.
- John Finn Institute of Public SafetyAnalyst, 2009 - present
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