HaystaqDNA Block Level P2 population projections joined to 2020 Decennial Census Boundary Data ##Redistricting Data Hub (RDH) Retrieval Date 05/03/2021 Dataset updated: 05/15/2021 ##Sources Projections courtesy of HaystaqDNA Boundary shapefiles retrieved from the Census Redistricting Data (P.L. 94-171) Shapefiles website: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html ##Fields Note: P2 Table - Census Block Level Population Projections corresponding to variable names from the P2 Table of the Census Summary Files (https://api.census.gov/data/2010/dec/sf1/variables.html) Field Name Description geoid_2020 15-character 2020 Census Block GEOID COUNTYFP20 2020 County FIPS Code TRACTCE20 2020 Census Tract Code BLOCKCE20 2020 Census Block Code state_fips 2020 Census State FIPS Code p20_nh_tot 2020 projected population corresponding to P005002 p20_nh_wh 2020 projected population corresponding to P005003 p20_nh_aa 2020 projected population corresponding to P005004 p20_nh_ai 2020 projected population corresponding to P005005 p20_nh_asi 2020 projected population corresponding to P005006 p20_nh_pac 2020 projected population corresponding to P005007 p20_nh_oth 2020 projected population corresponding to P005008 p20_nh_tom 2020 projected population corresponding to P005009 p20_h_tot 2020 projected population corresponding to P005010 p20_h_wh 2020 projected population corresponding to P005011 p20_h_aa 2020 projected population corresponding to P005012 p20_h_ai 2020 projected population corresponding to P005013 p20_h_asi 2020 projected population corresponding to P005014 p20_h_pac 2020 projected population corresponding to P005015 p20_h_oth 2020 projected population corresponding to P005016 p20_h_tom 2020 projected population corresponding to P005017 ...The same sequence of columns for 2021-2029 p30_nh_tot 2030 projected population corresponding to P005002 p30_nh_wh 2030 projected population corresponding to P005003 p30_nh_aa 2030 projected population corresponding to P005004 p30_nh_ai 2030 projected population corresponding to P005005 p30_nh_asi 2030 projected population corresponding to P005006 p30_nh_pac 2030 projected population corresponding to P005007 p30_nh_oth 2030 projected population corresponding to P005008 p30_nh_tom 2030 projected population corresponding to P005009 p30_h_tot 2030 projected population corresponding to P005010 p30_h_wh 2030 projected population corresponding to P005011 p30_h_aa 2030 projected population corresponding to P005012 p30_h_ai 2030 projected population corresponding to P005013 p30_h_asi 2030 projected population corresponding to P005014 p30_h_pac 2030 projected population corresponding to P005015 p30_h_oth 2030 projected population corresponding to P005016 p30_h_tom 2030 projected population corresponding to P005017 ##Processing The population projections were joined with geospatial data from Census Redistricting Data (P.L. 94-171) Shapefiles using the pandas and geopandas libraries in Python on the unique identifier field ('GEOID20' converted to 'geoid_2020' to match the HaystaqDNA csvs) and extracted as a shapefile. ##Additional Notes **Overview from Haystaq's metadata on all of their projections: ** These projections are at the 2020 Census Block Group level and the 2020 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 Transform 2010 Census Block data and 2019 ACS 5-year estimates at the 2010 Block Group level to the new 2020 boundaries. We used the Census Relationship Files to do this transformation. The Relationship Files give the 2010 Block area, 2020 Block area, and the intersection area of the 2010 and 2020 Blocks. We apportion population to the 2020 Blocks proportional to the intersection area of the 2010 to 2020 Blocks. For example, if 60% of Block 1000 in 2010 intersects with Block 1001 in 2020, we add 60% of the population of Block 1000 in 2010 to the population of Block 1001 in 2020. For the Block Group level transformation, we first create a Block Group level version of the Relationship Files, before applying the same methodology. STEP 2 Aggregate 2010 Census Block data on the 2020 Blocks to the 2020 Block Group level. Use the 2019 ACS 5-year estimates to calculate a rate of change at the 2020 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. Apply an adjustment factor to the calculated Apportionment Population state totals. STEP 3 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 4 Update 2010 Census Block Group data on 2020 Block Groups 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 5 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 2020 Census Block to Block Group proportions to disaggregate the Block Group level projections. STEP 6 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 the numbers will not match any Census/ACS data. Please contact info@redistrictingdatahub.org for more information.