Poverty Dashboard - any feedback is appreciated

The issue of "county based" poverty numbers or even any numbers from all of the counties is that there are very many small counties in the United States.  In fact, only about 1,300 are large than 20,000.  So when you look at the Poverty population, which is say 20% in some counties, you are looking a universe size of  260, and a sample size of possibly as small as 20 to 40,  SAIPE since it is model based is more likely to be more accurate for this estimate, and Other estimates may be better for other purposes rather than the ACs Sample.  Which is around 10% at best for the five year.

The issue of "county based" poverty numbers or even any numbers from all of the counties is that there are very many small counties in the United States.  In fact, only about 1,300 are large than 20,000.  So when you look at the Poverty population, which is say 20% in some counties, you are looking a universe size of  260, and a sample size of possibly as small as 20 to 40,  SAIPE since it is model based is more likely to be more accurate for this estimate, and Other estimates may be better for other purposes rather than the ACs Sample.  Which is around 10% at best for the five year.

Yes, you are correct, and I might add a population filter to help. SAIPE is better in a number of ways and might be something I do after bfrss and cdc wonder mortality. There is just something my mind likes about data for every single country from ACS. Everything is an estimate and I want to hit a home run in ACS data (maybe 10 dashboards) before I fight the next windmill.

I'm working on a R package that will give the Supplemental Poverty Measure (SPM) at the tract level.  Eventually I hope to post the package on GItHub. The method uses PUMS data including the SPM PUMS data https://www.census.gov/data/datasets/time-series/demo/supplemental-poverty-measure/acs-research-files.html.  The SPM is a research measure that, unlike the Census poverty threshold measure, includes income/support  from government programs such as SNAP as well as expenses based on variables that vary across geographies (read "local cost of living.")  https://crsreports.congress.gov/product/pdf/R/R45031  The statistical method is small area estimation using IPF to adjust a PUMA level cross tabulation (13 variables) to ACS 5-year marginal tract level tables. I've done all the census tracts in Virginia (about 2000 tracts), which takes several days on my PC (20gb memory). You can then aggregate the tract level synthetic data to higher level geographies.  All 13 covariates (including SPM) are carried along. It is possible to add replicate weights to the synthetic (output) data file.  Any comments welcome.

Dave