I suggest you ...

algorithmically clean some outliers

Some 1km gridcells in the 2015 UN-adjusted dataset have population values as high as 183,000 persons per km2. These hyperpopulated cells account for ~30% of global population!

Hong Kong and Brazzavile are two examples: in real life according to wikipedia Hong Kong's densest suburb has density of 57,000 people/km2. Similarly you have values as low as 1e-12. Why not set a ceiling and floor, and redistribute surplus population to the whole country.

As it stands we users have to do a lot of data cleaning to be able to use dataset but automatically cleaning such outliers should be rather easy step to add to your processing pipeline.

3 votes
Vote
Sign in
(thinking…)
Sign in with: Facebook Google
Signed in as (Sign out)
You have left! (?) (thinking…)
dan shared this idea  ·   ·  Flag idea as inappropriate…  ·  Admin →

0 comments

Sign in
(thinking…)
Sign in with: Facebook Google
Signed in as (Sign out)
Submitting...

Feedback and Knowledge Base