Population Projection: Block Level P1 Based on 2010 Census Data
HaystaqDNA population projections
##Redistricting Data Hub (RDH) Retrieval Date
12/17/2020
Dataset updated: 03/01/2021
##Sources
Projections courtesy of HaystaqDNA
##Fields
P1 Table - Census Block Level Population Projections corresponding to variable names from the P1 Table of the PL 94-171 Redistricting Summary Files (https://api.census.gov/data/2010/dec/sf1/variables.html)
Field Name Description
geoid 15-character Census Block GEOID
state_fips State FIPS Code
p19_total 2019 projected population corresponding to P001001
p19_white 2019 projected population corresponding to P003002
p19_afam 2019 projected population corresponding to P003003
p19_ai 2019 projected population corresponding to P003004
p19_asian 2019 projected population corresponding to P003005
p19_pac 2019 projected population corresponding to P003006
p19_other 2019 projected population corresponding to P003007
p19_tom 2019 projected population corresponding to P003008
p19_n_hlo 2019 projected population corresponding to P004002
p19_hlo 2019 projected population corresponding to P004003
p20_total 2020 projected population corresponding to P001001
p20_white 2020 projected population corresponding to P003002
p20_afam 2020 projected population corresponding to P003003
p20_ai 2020 projected population corresponding to P003004
p20_asian 2020 projected population corresponding to P003005
p20_pac 2020 projected population corresponding to P003006
p20_other 2020 projected population corresponding to P003007
p20_tom 2020 projected population corresponding to P003008
p20_n_hlo 2020 projected population corresponding to P004002
p20_hlo 2020 projected population corresponding to P004003
... integer The same sequence of columns for 2021-2029
p30_total 2030 projected population corresponding to P001001
p30_white 2030 projected population corresponding to P003002
p30_afam 2030 projected population corresponding to P003003
p30_ai 2030 projected population corresponding to P003004
p30_asian 2030 projected population corresponding to P003005
p30_pacific 2030 projected population corresponding to P003006
p30_other 2030 projected population corresponding to P003007
p30_tom 2030 projected population corresponding to P003008
p30_n_hlo 2030 projected population corresponding to P004002
p30_hlo 2030 projected population corresponding to P004003
##Processing
The RDH did not do any additional processing to the files from HaystaqDNA.
##Additional Notes
**Overview from Haystaq's metadata on their projections: **
These projections are at the 2010 Census Block Group level and the 2010 Census Block level, which is the most granular level of geography currently available. The projections update the current population data to match Census projections for overall statewide totals from 2020 and 2030. They project population nationally, from 2020 to 2030, with total population, as well as Census race groups and Hispanic origin categories.
Although the US Census has generated population projections for 2020 and 2030, these projections are at the state level. While it would be simple to apply the statewide growth rate to each Census Block in a given state, we know that population growth is not uniform statewide. In order to generate projections at the Block and Block Group level, we need to estimate where in each state the population growth is occurring. A large share of population growth comes from new housing developments built in Census Blocks that were previously undeveloped and had zero population in 2010.
We addressed the question of population growth in new housing developments using a combination of Census American Community Survey (ACS) Block Group level population estimates, and geocoded commercial data files from which we can find individuals currently living in Census Blocks that had zero population in 2010.
**Haystaq Projection Methodology **
STEP 1 Aggregate 2010 Census Block data to the Block Group level. Update the 2018 ACS 5-year estimates at the state level to match the Census Population Estimates for 2018. Use the updated 2018 ACS 5-year estimates to calculate a rate of change at the Block Group level. Calculate this rate of change for total population, as well as for every race category and hispanic origin category, so as not to assume a standard rate of change across race categories.
STEP 2 Use geocoded voter file and commercial file data to identify areas of new development – areas that are now inhabited that were previously unpopulated in the 2010 Census. Calculate the rate of change for total population, and race and hispanic origin categories, at the county level for the counties that have areas of new development, excluding the Block Groups with new development. We found that by using the county rate of change for these areas, we can estimate a rate of change that is more stable and geographically accurate than a Block Group level average for the area, for example.
STEP 3 Update 2010 Census Block Group data using the calculated rates of change from STEP 1, for areas that are not new developments. For new developments, update the 2010 Census Block Group data using the county-level rates of change calculated in STEP 2.
STEP 4 Disaggregate population projections at the Block Group level to the Block level. Use the commercial file data to find the Block to Block Group population proportions, as this data is from 2020. For Block Groups that contain Blocks that do not have population on the 2020 commercial file, use the 2010 Census Block to Block Group proportions to disaggregate the Block Group level projections.
STEP 5 Use the Largest Remainder Method to round the population projections for the race and hispanic origin categories to sum to the total projected population at the Block and Block Group levels.
##Disclaimer
The HaystaqDNA population projections are intended to help users approximate populations for the coming decade. Because the data is a statistical estimate, the Redistricting Data Hub cannot guarantee the accuracy of any of the numbers within, and will not match any Census/ACS data.
Please contact info@redistrictingdatahub.org for more information.