Comments (4)
what's off here?
from db-acs.
social_mismatches.csv.zip
all the numbers are wrong, and they are off by quite a bit, still investigating ..
from db-acs.
e.g.
C65plDVsn
==> S1810_C01_036
info = df.loc[df.geoid == '1024900',
S1810_C01_036].to_dict('records')
info = [{'S1810_C01_036E': 137.0}]
for most of these cases, we are not doing any calculation here
but in population's calculation, we get C65plDVsnE = 5
from db-acs.
The example you gave above should be an estimate of 5. It looks like the metadata sheet was incorrectly labeled so these were pulling totals rather than pop with a disability. Should be okay now.
meta_2.xlsx
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Related Issues (20)
- Find total variables and make p 100 percent HOT 2
- Estimation that involves subtraction
- CvEm16pl2 --> percentage estimate set to 100 for all NTAs
- calculation errors on population's end? HOT 1
- percentage estimate set to 100 HOT 2
- demo - AsnEast : for a lot of demographic variables, we are seeing lots of 11.0 for MOE fields. Should we exclude them??? HOT 3
- demo - c,e,m calculated correctly, but not p and z (wrong total?) HOT 2
- New total lookup function HOT 1
- 2018 update HOT 1
- 2018 update feedback HOT 1
- MOEs for zero-estimate data set to null HOT 1
- Change intermediate table naming -- low priority
- Check that all instances of collapsing variables is summing independent variables
- Y2006-2010 row count doesn't match Y2014-2018 HOT 5
- Don't inflate 2006-2010 ACS values because consuming app should do this
- 2014-2018 new issues HOT 9
- Catch controlled variables
- Adding CD level Aggregation for Community Profiles purpose
- python package for calculating pff
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