I'm looking at ACS 5YR 2019 vintage and its B01001e1 population estimates.
Population looks nicely distributed between few hundred and a few thousand people.
Except, there are several block groups with more than 20,000 people and a few with more than 50,000.
Does anyone know a rule or definition that explains these extremely large block groups?
For example: 380170405002 Cass County, North Dakota - outside Fargo 481576729001 Fort Bend County, Texas - outside Houston 121199112001 Sumter County, Florida - includes The Villages 060730187001 San Diego County, California - includes San Diego
just from experience in Florida in 2000 you could have a small portion of a block in the Everglades turn into multiple tracts in the next decade if it were developed. So that part’s not surprising to me…
Fort Bend county is one of the fastest growing counties in the USA. I haven't checked all the others you mentioned, but at a glance they are all urban areas. Why not population growth since 2010, when…
Population growth over the previous decade?
The estimate itself rather than growth ( using ACS_2019_5YR_BG.gdb )
A histogram of population (B01001e1) has a major right skew
because there are 3.9 million people in the 13 largest blockgroups:
blockgroup B01001e1060770035003 20430060730133141 22349060730187001 38754060770052063 20030121199112001 39928120310144121 20123120950167301 34230120950168071 20252120950167041 24985120950171032 22381130510107001 25166380170405002 42175481576729001 59947
How large were these block groups in 2010? That's what I'm asking. Certainly a development of 20,000 or more is conceivable in a 10-year period
Fort Bend county is one of the fastest growing counties in the USA. I haven't checked all the others you mentioned, but at a glance they are all urban areas. Why not population growth since 2010, when the block groups were last delineated?
I'll look into it.
And yep, the 2019 would represent a decade change from each BGs original delineation. These would then be growth hotspots. I'll use a log transform rather than treat them as outliers.
As always thanks Glenn!