Demographics Reference Guide

This guide is a compilation of information from multiple sources. All census variable definitions are from the Census Bureau's Factfinder Glossary. MOSAIC definitions are from Experian, and several variables are from Synergos Technologies, Inc

This information is easily accessible on either the Census Bureau, Synergos Technologies, or Experian websites. ACS Technologies Group, Inc. does not claim original authorship of this information.

Introduction and Overview

About Demographics

Demographics is the study of physical characteristics about people. The word comes from the Greek word for people, demos. As an academic discipline, it is generally considered to be part of sociology.

Technically demographics refer to geo-demographics because of the relationship of people to place. At the most basic level, demographics counts people in geographic places. Once the total population is counted, then attributes about those people can be collected and quantified. Attributes such as age, sex, education level, marital status, occupation are all characteristics of people.

Households are also part of demographic study. In this case, it is characteristics of particular households that are captured and reported.

Demographic data is always related to some kind of geographic space.

Decennial Census

Article 1 of the US Constitution requires that a census (a count) of all citizens be taken every 10 years (thus decennial census) to reapportion seats in the U.S. House of Representatives. A census form is provided (in theory) to every U.S. household where one person representative of the household completes a set of questions about the occupants of the household.

The Census Bureau historically has fielded two kinds of census counts referred to as the "short form" and the "long form." The short form is to go to every household. The long form which is much more extensive, is a sample.

Standard Census Geographies

The Census Bureau developed and maintains a hierarchical system of geographic areas. Each level aggregates to the next level up in a fairly consistent manner. (There are exceptions, but they are rare.) The Census Bureau graphic illustrates the core hierarchy (running down the middle). It also shows the relationship of other geographic areas such as legislative districts, zip codes and places.

In this Guide, not all Census Bureau geographies are discussed. We only focus on those built into MissionInsite. For more information on other geographies, visit the Census Bureau's website.

Census Bureau Blocks

The United States National Boundary
This boundary includes all geographic areas within the national boundaries of the United States of America.
The first major division is into states.
States are divided into counties or parishes, depending upon the particular state.

In Louisiana, these subdivisions are known as parishes. Alaska has no counties however the county equivalents are boroughs In four states (Maryland, Missouri, Nevada and Virginia), there are one or more cities that are independent of any county and thus constitute primary subdivisions of their states. The District of Columbia has no primary divisions, and the entire area is considered equivalent to a county for statistical purposes.

Census Tracts
Each county is subdivided into census tracts.

Census tracts usually contain between 1,500 and 8,000 people with a target size of 4,000.Census tracts are set at each decennial census. Many never change because they exist in established population areas.

On urban edges and country areas, census tracts can be quite large since they are drawn in such a way as to come as close as possible to the targeted population threshold. This can create problems in period between a decennial census if new residential development occurs. While the problem will be corrected at the next census when a census tract is subdivided to reflect the new population reality, spatial queries in the interim period can fail to accurately capture the real profile of a particular census tract.

The following illustration demonstrates the problem.

Census Tracts
Census Blockgroups are standard Census Bureau geographies. Typically 4 to 6 blockgroups aggregate to form a census tract.

A blockgroup is the lowest level of geography for which census data is released—for privacy purposes. Blockgroups generally contain between 600 and 3,000 people with a targeted optimum size of 1,500. Blockgroups never cross the boundaries of states, counties, or statistically equivalent entities, except for a blockgroup delineated by American Indian tribal authorities. They never cross the boundaries of a census tract.

The Blockgroup is the lowest level that the Census Bureau tabulates sample (long form, for example) data.

A block is the smallest geographic unit for which the Census Bureau tabulates 100-percent data. In urban areas, blocks usually follow city blocks, bounded by streets. This rule does not follow in rural areas where a block may encompass multiple square miles and my not be bounded by streets.

Over 11 million blocks are identified for Census 2010. The Census Bureau does not release data at the block level to protect privacy

Other Geographies

Zip codes
Technically, zip codes are not geographic areas. Created by the Postal Service, they represent carrier delivery routes. The Census Bureau developed Zip Code Tabulation Areas (ZCTAs) as a way to create geographic areas that approximate the carrier routes reflected in the Postal Service zip codes. This allows geographic information systems to conduct spatial queries on an area that approximates a zip code delivery area. These boundaries are updated a least once per year to reflect the changes of zip codes.

Sizes of standard census geographies
Cities are incorporated places with municipal boundaries. These are public domain boundaries and may or may not reflect current reality for municipalities. You can query on cities in MissionInsite.
School Districts
The Census Bureau maintains a set of boundaries for school districts. These also are public domain boundaries and are not always accurate or up-to-date. The boundaries are available for integration into GIS Systems.

There are three different school district boundary files reflecting the diversity of school district configurations in US. They are: Unified School Districts, Elementary School Districts and High School Districts. These layers can be found in MissionInsite.

Data Sources

MissionInsite's demographic provider, Synergos Technologies Inc. (STI), derives information from multiple sources and is updated twice a year.

Data Sources

MissionInsite's demographic provider, Synergos Technologies Inc. (STI), derives information from multiple sources and is updated twice a year.
Bureau of Economic Analysis (BEA)
The BEA promotes a better understanding of the U.S. economy by providing timely, relevant, and accurate economic accounts data. The BEA is an agency of the Dept. of Commerce. Along with the Census Bureau and STAT-USA, BEA is part of the Department's Economics and Statistics Administration
Bureau of Labor Statistics (BLS)
The BLS is an independent national statistical agency that collects, processes, analyzes, and disseminates labor economics and statistics data to the public, the U.S. Congress, other federal agencies, state and local governments, business, and labor entities. Among the data used for PopStats is data from the BLS's Local Area Unemployment Statistics (LAUS) program, which produces monthly and annual employment, unemployment, and labor force data for Census regions and divisions, states, counties, metropolitan areas, and many cities, by place of residence.
Bureau of Transportation Statistics (BTS)
The BTS was established as a statistical agency to administer transportation data collection, analysis, and reporting, and to ensure the most cost-effective use of transportation-monitoring resources. Among the data used for PopStats is the BTS's American Travel Survey, which obtains information about long distance travel of people living in the U.S.
Centers for Disease Control (CDC)
The CDC's mission is to collaborate to create the expertise, information, and tools that people and communities need to protect their health – through health promotion, prevention of disease, injury and disability, and preparedness for new health threats. Among the data used for PopStats are the CDC's natality and mortality files.
Department of Defense (DOD)
The DOD is the federal department charged with coordinating and supervising all agencies and functions of the government relating directly to national security and the military. The DOD has three major components – the Army, the Navy, and the Air Force. Among the data used for PopStats are the data files on military personnel maintained by the Defense Manpower Data Center (DMDC)
Federal Aviation Administration (FAA)
The FAA is an agency of the U.S. Dept. of Transportation with authority to regulate and oversee all aspects of civil aviation in the U.S. Among its major roles are regulating U.S. commercial space transportation, and regulating air navigation facilities' geometry and flight inspection standards. Among the data used for PopStats are the FAA's flight statistics.
Federal Financial Institutions Examination Council (FFIEC)
The FFIEC is a formal body of the U.S. government empowered to prescribe uniform principles, standards, and report forms for the federal examination of financial institutions by the Board of Governors of the Federal Reserve System (FRB), the Federal Deposit Insurance Corporation (FDIC), the National Credit Union Administration (NCUA), the Office of the Comptroller of the Currency (OCC), and the Office of Thrift Supervision (OTS); and to make recommendations to promote uniformity in the supervision of financial institutions.
Federal Housing Finance Agency (FHFA)
The FHFA is an independent federal agency formed by a legislative merger of the Office of Federal Housing Enterprise Oversight (OFHEO), the Federal Housing Finance Board (FHFB), and the U.S. Dept. of Housing and Urban Development (HUD). The FHFA regulates Fannie Mae, Freddie Mac, and the 12 Federal Home Loan Banks.
Integrated Postsecondary Data Education System (IPEDS)
The IPEDS collects standardized data from all institutions of higher education that receive federal student financial assistance. It has core oversight of the post-secondary education data collection program for the National Center for Education Statistics (NCES) (see below).
Internal Revenue Service (IRS)
The IRS is the U.S. government agency responsible for tax collection and tax law enforcement. Among the data used for PopStats is the IRS's Survey of Income (SOI).
National Center for Education Statistics (NCES)
The NCES is the primary federal entity for collecting, analyzing, and reporting data related to education in the U.S. and other nations. NCES is located within the U.S. Dept. of Education and the Institute of Education Sciences. Among the data used for PopStats are the NCES's public and private records.
National Center for Health Statistics (NCHS)
The NCHS provides U.S. public health statistics, including diseases, pregnancies, births, aging, and mortality. It is a division of the CDC.
National Parks Service (NPS)
The NPS is the U.S. federal agency that manages all national parks, many national monuments, and other conservation and historical properties. It is an agency of the U.S. Dept. of the Interior, a federal executive department. Among the data used for PopStats are the NPS's park attendance records.
Social Security
U.S. Social Security is a social insurance program funded through dedicated payroll taxes called the Federal Insurance Contributions Act (FICA). Tax deposits are formally entrusted to several funds, including primarily the Federal Old-Age and Survivors Insurance Trust Fund.
U.S. Census Bureau
As part of the U.S. Dept. of Commerce, the Census Bureau serves as a leading source of data about America's people and economy. The most visible role of the Census Bureau is to perform the official decennial count of people living in the U.S. Public resources from the Census Bureau include population, economic, industry, and geography studies. Along with population data, several reports from the U.S. Census Bureau are used for PopStats, including the American Community Survey (ACS) and the Current Population Survey (CPS).
U.S. Postal Service (USPS)
The USPS is an independent agency of the U.S. government responsible for providing postal service. It is one of the few government agencies authorized by the U.S. Constitution.

Demographic Variables and Descriptions

Population is at the most basic level of demographics. This includes total population counts as well as population counts by a multitude of population characteristics and attributes such as racial/ethnicity, age, educational attainment etc.

Population Based Variables

Population based variables are all demographic data that are based upon population counts. Data is organized according to the following sub-categories and includes:

  • Population

  • Age

  • Education

  • Marital Status

  • Housing

  • Race and Ethnicity

  • Employment

Household Based Variables

Household based variables are all demographic data that are based upon household counts. Households are further classified into family households and non-family households.

Family households consist of a householder and one or more other persons living in the same household who are related to the householder by birth, marriage, or adoption.

Non-family households can consist of one person living alone or unrelated individuals living together.

Householder: The person, or one of the people, in whose name the home is owned, being bought, or rented. If there is no such person present, any household member 15 years old and over can serve as the householder for the purposes of the census.

Two types of householders are distinguished: a family householder and a nonfamily householder. A family householder is a householder living with one or more people related to him or her by birth, marriage, or adoption. The householder and all people in the household related to him are family members. A nonfamily householder is a householder living alone or with non-relatives only.

These data are organized according to the following sub-categories and include:

  • Households

  • Size and Type

  • Income

  • Families

  • Non-families

  • Vehicles


A household includes all people who occupy a housing unit. A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room occupied (or if vacant, intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other people in the building and that have direct access from the outside of the building or through a common hall.

The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living quarters.

Five data points are provided including

  • 2000

  • 2010

  • Current year estimate

  • Five-year projection

  • Ten-year forecast

People not living in households are classified as living in group quarters. Group quarters are addressed under population.

Housing Related Variables

Housing variables present characteristics of housing stock in a community.

Housing includes a house, an apartment, a mobile home or trailer, a group of rooms, or a single room occupied as separate living quarters, or if vacant, intended for occupancy as separate living quarters. Separate living quarters are those in which the occupants live separately from any other individuals in the building and which have direct access from outside the building or through a common hall.

For vacant units, the criteria of separateness and direct access are applied to the intended occupants whenever possible.


Housing is the baseline variable establishing the total number of housing units of any type within a geographic area. Three data points are reported.

Housing Availability Score

Housing Availability Score (HAS) or Demand Saturation. The HAS Score is similar to an occupancy rate in that it measures the percent of existing housing that is currently occupied. It differs from a true occupancy rate in that it does not distinguish between permanently vacant housing and housing available but currently vacant.

The Housing Availability Score is presented as a percentage. A score of 100% means there is saturation—no open housing available.

  • 97% to 100% No open housing available

  • 90% to 96% Limited open housing available

  • 70% to 89% Some open housing available

  • Less than 70% Open housing available

This score may be used as an indicator of potential population growth without new construction taking place. For a school district, this score could indicate an area where there is potential for growth in student enrollment. If there is open housing and families with children move in, enrollment may increase. Conversely, if the percentage begins to drop in a neighborhood, this indicates the loss of households and can contribute to a decline in enrollment in local schools.

The Housing Availability Score can act as an indicator of housing saturation (all existing housing is occupied). A score of 97% or greater should be considered a fully saturated neighborhood, this is due to the time elapse between one household moving out and the other moving in.

The indicator is purposely set to zero in very rural areas, due to insufficient information to make a determination

Housing Units by Occupancy

The Housing Units by Occupancy presents both the number of units occupied and vacant. A total of all housing units is the sum of these two.

Three data points provided and the following categories.

  • Occupied

  • Vacant

Housing Trends: By Dwelling Type

Housing units are segmented into one of three types; single unit attached, single unit detached and two or more units (or multiple unit dwellings).

Single Unit attached: A structure that has one or more walls extending from ground to roof separating it from adjoining structures. In row houses (sometimes called townhouses), double houses, or houses attached to nonresidential structures, each house is a separate, attached structure if the dividing or common wall goes from ground to roof.

Single-Unit, detached: A structure detached from any other house; that is, with open space on all four sides. Such structures are considered detached even if they have an adjoining shed or garage. Mobile homes or trailers to which one or more permanent rooms have been added or built also are included.

Two or more units (Multi-unit dwellings): Units in structures containing 2 or more housing units, further categorized as units in structures with 2, 3 or 4, 5 to 9, 10 to 19, 20 to 49, and 50 or more units.

Categories reported:

  • 1 Unit Attached

  • 1 Unit Detached

  • 2 Units

  • 3 to 19 Units

  • 20 to 49 Units

  • 50 or More Units

  • Mobile Home or Trailer

  • Other

Housing Trends: Units by Year Built

Units by Year Built refers to when the building was first constructed, not when it was remodeled, added to, or converted. A single data point is provided.

Categories include:

  • 1939 or Earlier

  • 1940 to 1949

  • 1950 to 1959

  • 1960 to 1969

  • 1970 to 1979

  • Built 1980 to 1989

  • Built 1990 to 1994

  • Built 1995 to 1998

  • Built 1999 to March 2003

Housing by Occupancy Type

This variable segments the housing stock by owner versus renter occupied.

Owner occupied: A housing unit is owner occupied if the owner or co-owner lives in the unit even if it is mortgaged or not fully paid for.

Renter occupied: All occupied housing units that are not owner occupied, whether they are rented for cash rent or occupied without payment of cash rent, are classified as renter occupied.

Three data points are provided. Categories include:

  • Owner Occupied

  • Renter Occupied

Housing Trends: Value Owner-Occupied

Value reflects an estimate of how much a property (house and lot, mobile home and lot, or condominium unit) would sell for if it were for sale. It includes only owner-occupied dwellings. A single data point is provided.

  • Less than $20,000

  • $20,000 to $39,999

  • $40,000 to $59,999

  • $60,000 to $79,999

  • $80,000 to $99,999

  • $100,000 to $149,999

  • $150,000 to $199,999

  • $200,000 to $299,999

  • $300,000 to $399,999

  • $400,000 to $499,999

  • $500,000 to $749,999

  • $750,000 to $999,999

  • $1,000,000 or more

Mortgage Risk: Filings by Risk Ratio

Mortgage Risk Exposure expresses the number of mortgage filings over a three year moving average and categorizes them by the risk exposure ratio given to each one. The total number of mortgages within the period are noted and then spread across 7 aggregated ratio ranges.

  • 1.2 or less - Very high debt to income/very high risk

  • 1.3 to 1.8 - High debt to income/high risk

  • 1.9 to 2.4 - Somewhat high debt to income/ somewhat high risk

  • 2.5 to 3.0 - Acceptable debt to income/Reasonable risk

  • 3.1 to 3.6 - Low debt to income/Low risk

  • 3.7 to 4.2 - Very low debt to income/Very low risk

  • 4.3 or greater - Extremely low debt to income/Extremely low risk

Traditionally, a debt to income ratio considered acceptable to banks to make mortgages was 2.5 to 1. This means a person's income should be two and a half times their monthly debt load.

When the ratios are greater than 2.5 it suggests the person has disposable income. When the ratio drops below 2.5 it suggests that the person's debt load is getting to heavy and thus the risk for a mortgage is greater.

A separate score that is an average of the level of risk for a defined geographic area is also available.


MOSAIC is a demographic segmentation system created by Experian. It seeks to provide a multidimensional view of a community taking into account multiple socioeconomic and life stage factors.

MOSAIC is truly unique as a demographic segmentation system. It classifies US consumers into one of 71 types and 19 groups.

Mosaic Communication Channel Preferences

Channel Preferences and Descriptions

Channel preferences describe how likely an individual is to respond to an advertising method.

An index greater than 100 means that individuals are more likely to respond to that channel's advertisements, and an index less than 100 means that individuals are less likely to respond.

Broadcast/Streaming TVBroadcast/Streaming TV
Direct MailDirect Mail
Mobile, SMS, Text MessageMobile, SMS, Text Message
Social MediaSocial Media

Mosaic Technology Adoption

Technology Adoption Groups and Descriptions

An individual's technology group/type shows how likely they are to engage with technology.
IconGroup NameGroup Description
WizardsWizardsTechnology plays an important and broad role in a Wizard's life. These individuals cannot live without the internet or imagine life without new electronic gadgets.

Wizards are enthusiastic and adventurous users of new technology. It helps them keep track of their social calendars and makes leisure time more interesting.

This group still wants more from technology, and their desire helps move the industry forward.

JourneymenJourneymenJourneymen have become skilled users of new technology.

They are very confident with and knowledgeable about technology and are willing to experiment with a few features, benefits, and devices. Adopting new technology lets Journeymen continually improve upon the way they use it.

While cutting-edge electronic devices may help Journeymen organize their busy lifestyles, new technology is not always their top priority.

ApprenticesApprenticesApprentices take advantage of technology, using the internet often to get needed information.

New gadgets enhance their lives to a large extent, but there is still room for them to expand their usage.

Apprentices are willing to learn and incorporate new technology into their lives, but they are sensitive to price and often make their purchase decisions based on this rather than desire alone.

NovicesNovicesNovices are disconnected from emerging technology and resistant to adopting a new technology-focused life.

They may have a desire for new digital produces and services if they are simple and easy to use.

This group does not understand what kinds of opportunities and experiences technology can provide them, and they have very limited engagement with new technology.


About Demographic Retrieval and Spatial Queries

Researchers, planners and marketers all use demographic information to understand the characteristics of a targeted geographic area. Their targeted geographic area may be a particular zip code(s), counties, cities, school districts, neighborhoods or some other defined area. To obtain this data, it must be retrieved from a demographic database. The process of gathering these data out of a demographic database is called 'demographic retrieval' and it is accomplished through a 'spatial query'.

For example, suppose a researcher wanted to know the characteristics of zip code 92714. She would need to "ask" a demographic database for the characteristics of that zip code. Such characteristics might include its current and projected future population, racial-ethnic composition, average household income, and education level, etc. The process of accomplishing this "ask" requires querying a demographic database and retrieving from it the requested information. Because the "ask" is about a geographic area—the zip code—it is called a 'spatial query' because the researcher is looking for data about that piece of "space".

In summary, demographic retrieval is the process of retrieving data via a spatial query resulting in a demographic data report of the population characteristics of the target geographic area.

About Census Geographic Areas

There are two kinds of geographies to query in sophisticated demographic research systems; a) predefined geographies and b) custom or user defined geographies. Predefined geographies are geographic areas such as zip codes, counties, states, census tracts, school district boundaries, school attendance area boundaries, city boundaries, etc. These geographies already exist.

Custom defined geographies are geographic areas defined by a user at the time of doing the demographic query. Such geographies can be custom polygons or radius studies. For custom defined geographies, the user must use a tool to define the area of inquiry prior to requesting the data.

About Census Demographic Data

How does demographic retrieval work? To understand this question, one must first understand two concepts about demographic data; the census bureau's hierarchical geographic system and how census data is provided to the public.

Census Bureau Hierarchical System: The Census Bureau maintains a fairly consistent hierarchical system of geographies. At the bottom is the individual house address. These are clustered into census blocks. Census blocks cluster into census blockgroups, blockgroups into census tracts, census tracts into counties, counties into states and finally, all states together, the nation. Each geography lower on the hierarchy, consistently aggregates with others of the same unit into the geography above it. This is important to keep in mind when we get to the question of how demographic retrieval works.

Census Bureau Hierarchical System

Storage of Demographic Data: The wealth of demographic data released to the public is stored at a census blockgroup level. This is to protect privacy. While a blockgroup in populated areas is a fairly small geographic area, the population within it is large enough to blur out any ability to access private household data. The census bureau's optimal target for blockgroups is roughly 1500 people at the point of the decennial census. Over the course of 10 years that population may grow or decline. Since this is the smallest census geography for which the census bureau releases data, it is at this level that all retrieval engines must work. It is also the level at which most demographic updating vendors will supply data. Retrieval engines then aggregate up whole or partial blockgroups to provide demographic reports.

Three Approaches to Demographic Retrieval

Unless the area a person wants data on perfectly matches one or more blockgroup boundaries, obtaining demographic data requires a calculation that allocates some portion of a blockgroup to the spatial query to get demographic totals. There are three approaches to demographic retrieval through spatial queries to consider within the purview of this paper and which are the approaches used in most demographic retrieval systems.
Proration of Percentage of Blockgroup
The first model of retrieval requires the system spatial query to identify all of the blockgroups totally or partially included in a defined geographic area, such as a polygon. For those blockgroups fully enclosed by the geographic area, 100% of its data is included in the query results. For blockgroups partially included, the system calculates what percentage of the blockgroup is included in the geographic area and includes only that percentage of the data for the blockgroup in the query totals. For example, suppose a polygon includes 25% of the geographic area of a blockgroup. The current year population estimate for the block group is 100 people. The query would return 25% of that or 25 people for that blockgroup and add it to the total of the query. This model works fairly well in well established areas. But for areas not fully developed or that will only be partially developed into housing, this model potentially creates a problem. This is easy to illustrate. Suppose again our blockgroup with an estimate of 100 people and suppose that the population is all clustered in one corner so that 90% of the population for entire blockgroup is really perhaps in 20% of the total geographic area of the blockgroup. The resulting query will under project population for that blockgroup. Instead of returning 90 out of 100 representing the 90%, the query would only return 20 out of the 100. Now when multiple blockgroups are aggregated together, undercounts and over counts tend to average out. But in queries that are for smaller areas, the returning projection can be a problem.
Allocation of Blockgroup Population by Census Block Percentage
A second method for retrieval is based upon census blocks. A census block is roughly between 0 and 600 people, though most would be significantly less than 100. While specific data at the block level is not released, the total population and households by block are released. Since all blocks are a part of a blockgroup, adding up the population of all blocks in a blockgroup will equal the population for the blockgroup. Because of this, each block represents a percentage of the total population of the blockgroup. In the illustration below, you will see that the four blocks added together equal the total population of the blockgroup of 1,000, or 100%. But each block represents a different percentage of the whole because the population of each at the last census varies.

How is this used for retrieval? First, the current year estimate and five year projection for the blockgroup (where the estimates and projections are built) is applied back to the blocks based upon the most recent decennial census percentages for each block. This is done by multiplying the percentage of each block times the population of the current year estimate (or 5 year projection) for the blockgroup.

So in the next example, we see the same blockgroup only the total population for the blockgroup has grown from 1,000 in census year 2000ce to 1,500 for the the current year estimate (which in the example is 2010ce). Using the formula above, the estimated population for the blockgroup is allocated based upon the percentage of the blockgroup each block included in the last census. So now, whereas Block 1's population was 600 in census year 2000, it is now 900 for the current year estimate (2010).

The results of the same calculation are presented for all of the blocks in the blockgroup in this next illustration.

Having calculated the estimated current year population for each block, the second step is for the system to determine which blocks are included in the user defined geographic area and only includes the data for those blocks captured (by the zip code, radius or polygon). This is the fundamental difference with the first approach. Recapping again the first approach; it calculates what percentage of a blockgroup is captured in the user defined geographic area. If 20% of the blockgroup is included, then 20% of the population of the blockgroup gets added to the total.

However, in this second approach instead of calculating a percentage to include, the system determines which blocks have been included. It then aggregates the population data for every block captured. In the next example, assume the geographic area defined by the user includes only Blocks 1 and 2. Since Block 1 was 60% of the total population in the last census, it receives 60% of the current year estimate and Block 2 was 5% in the last census so it receives 5% of the current year estimate. Together, the system retrieves 65% or 975 of the total blockgroup's current year estimate and includes that in the total spatial query output.

The following example illustrates the process.

This allocation method is better for the most part than proration approach of the first method in that it is more sensitive to where population actually resided as of the last census. So if a large portion of a blockgroup had no population in the last census and still doesn't, the method of taking a percentage of the total area of the blockgroup and multiplying it times the current year estimate and five year projection will not provide satisfactory results. This second method, tied to where population resided at the last census is more likely to allocate the block group estimate consistent with what is the case. The proration method, for example might conclude that only 10% of the blockgroup falls with geographic area to be queried. On that basis, instead estimating the population at 975, it would estimate it at 150.

Allocation of Blockgroup Population by Census Block Percentage after Adjustments for Change
While the second method is a clear improvement over the first, neither takes into account significant changes in the residential information that may have occurred.

The third method of conducting the query, like the second is based upon the allocation of a block's percentage of the population of the blockgroup. But it is different in that it will adjust the block percentages based upon a constant monitoring of two factors: 1) the addition of new zip+4s and 2) indication that first class mail is now being delivered to addresses within the new zip+4 zones.

Let us explain. New zip+4s are created when the postal service anticipates new residences to which they must deliver mail. These are being created constantly and the data source we use monitors these changes. But in addition, to further confirm that people actually live within the new zip+4s, evidence that first class mail is actually being delivered within the new zip+4s is monitored. A zip+4 usually includes between 10 and 20 houses so the level of detail is pretty fine grained. With these data, since the demographers know in which block the zip+ 4 is located, they can adjust the percentages each block reflects of the total estimated and projected population of the blockgroup.

This sensitivity is reflected in the following illustration. Notice again that Block 1 in the last census reflected 60% of the total population in the blockgroup. It is now estimated that it only represents 20%. Most likely this shift among the blocks reflects changes in housing development.

This method allows for a greater level of precision when studying small geographic areas and is especially helpful in areas on the edge of residential development.