Annual Estimates 2001-2009

from $995.00
Product Description

We have Estimates for each of the years 2001 through the current year. We have packaged together the years 2001-2009 because they are all in the same 2000 boundaries. If you want the 2011 to our current year please click here. We also sell Estimates with 5-year Projections (for example 2015/2020) instead of multiple single years. For researchers who want to look at the annual trends we have special pricing for multi-year packages.

Like our current estimates and 5-year projections Annual Estimates are offered as three different versions giving you more and more data.

New geographies are available in the latest edition of the Annual Estimates Premium - now we also include Towns (County Subdivisions), Cities (Places), and MSAs.

Each product has GeoLytics' built-in map viewer that lets you create thematic maps of your variables, as well as quickly and easily export data and geographic boundaries. All three products have their data available in the following geographies: nation, states, counties, tracts, block groups, and zip codes. You can also run a radius around a latitude/longitude point.

Comparison Table

  Basic Professional Premium
State Single User $995.00 $1,695.00 $2,895.00
National Single User $1,995.00 $3,195.00 $5,795.00
       
Geographic Identifiers 23 variables 23 variables 23 variables
Summaries 81 variables 81 variables 81 variables
Population   313 variables 313 variables
Households   36 variables 36 variables
Housing   34 variables 34 variables
Income   41 variables 41 variables
Consumer Expenditures   99 variables 99 variables
Profiles   45 variables 45 variables
School Enrollment Public vs. Private for population 3+, broken out by sex and education   47 variables 47 variables
Educational Attainment for population 25+, broken out by sex and by education level   35 variables 35 variables
Family Income     23 variables
Poverty     81 variables
Labor     15 variables
Occupation     45 variables

Methodology

Methodology - Population, Housing, and Income Estimates

First a quick overview:

In building population estimates there are several pieces needed to begin. The changes that occur in an area will be the addition of births, subtraction of deaths and the addition/subtraction of those who moved. The starting point is the 2000 Short Form (SF1) BLOCK level data set. This has the most detailed and comprehensive numbers about where the entire population of the US lives, their age and their race. To progress from the 2000 data to current year estimates, we use the US Census Bureau's (USCB) County and State level annual estimates to roll the numbers forward to the current year. But the USCB data is only available at the County and State level, so the next challenge is distributing the data down to the smaller geographies. The next step is to work with actuarial tables for births and deaths by age and race, and use them to create a model of "likelihood" of dying or likelihood of having a child. This then is what creates the engine driving the increase and decrease in population growth.

The third step is to look at immigration and emigration. Where are people moving "to" and where are they moving "from". The US Postal Service keeps track of all moves as a "to" and "from" location.

Now the more detailed explanation:

  1. Working with the Census Bureau "estimation base" county level numbers.

    This data is processed to obtain "race distribution" coefficients. However, the Census Bureau estimation base data do not include "other" race category. Also, "two or more races" category is much smaller than it is in SF1/SF3 Census data. By comparing the estimation base to SF1 county level data, it is possible to obtain some numeric ratios as to how "other race" and "two or more races" populations were distributed among the remaining races in the USCB's estimation base. These coefficients allow us to re-map the SF1 block level data and redistribute the "other race" and part of the "two or more races" population among the 6 remaining mutually exclusive races.

  2. The SF1 block level data are processed with these new racial distribution coefficients. The resulting dataset is our estimation base. It includes 8 race/origin groups:

    WA White alone

    BA Black alone

    NA Native American alone

    AA Asian alone

    PA Pacific alone

    R2 Two or more races

    HS Hispanic

    WN White, not Hispanic

    A few words on Census analogs: The Total Population count corresponds to the Census table P001, count P0010001. The rest correspond to Census age-race-sex tables from P012A to P012I, with the P012F (Other Race table) dropped. We do not have the "Other Race" category in the estimates even though Census 2000 does, because the USCB dropped the "Other Race" data from its estimates. They switched to 8 races in 2001 and we had to follow. It is worth mentioning that the USCB redistributed the racial counts of Other Race completely and the counts for "2 or more Races" were partially redistributed between the rest of the races in their estimates. We did the same and therefore the racial breakdown differs from the Census 2000 but fits the 2001 USCB estimates. We believe that the USCB made these changes because there are no actuarial tables for "other" or "2 or more" races so they needed to redistribute those people into one of the race categories by which they could create estimates

  3. Having dealt with Race we then turn to Age. The USCB groups the population into 18 age groups. These range from age 0 (under 1) to age 108. The age groups are each 5 year intervals (0-4, 5-9, etc) except the ages 85 and up (85-108) are treated as a single group.

  4. Now that we have the entire population broken down into age and race categories we begin building the death-birth model. With the use of Actuarial tables we calculate the statistical likelihood for any given age/race group to die or to give birth. We then apply these coefficients to the 2000 data to create an estimation base for 2001, the coefficients are reapplied to create 2002, and so on until we get to the current year.

    The model includes:

    • transformation of age group distribution to "exact age" distribution. The resulting data set has population groups for each single year of age from 0 to 108.

      application of death probabilities for a specific age, sex and race group.

    • application of birth rates for a specific age, sex and race group. The white population is treated as a mix of white not Hispanic and Hispanic population. The mix ratio is determined from the block data.
    • 1 year shift.
    • collecting the annual data into 5-year buckets.
    • comparison of the results with Census Bureau estimates for this year.
    • the results of comparison are used to tweak birth rates and death probabilities to make the numbers of both newborn and deceased in the model to be exactly equal to Census Bureau numbers for each county. The racial distribution is also tweaked to reflect that of Census Bureau data. It puts the annual estimates in sync with USCB data as much as possibe.