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Easy Analytic Software, Inc. (EASI)
- Methodology
Demographics, Related Data, and Life
Stage Clusters
Thank
you for your interest in Easy Analytic Software, Inc. (EASI) and The Right
Site®. We have included a description of our company, and a description
of our basic methodology.
The EASI Research Methodology – Our
Philosophy
Our
intent is to establish a proper benchmark or starting point for a data series,
which ensures a reliable and reasonable source for updating. We then find and
develop a logical and consistent set of information, from reliable sources,
which we then use to develop procedures, models, and algorithms to update and
forecast the data elements in a manner that allows for accountability and
accuracy.
Definition Link
http://www.easidemographics.com/mdbhelp/mdbhelp.htm
Introduction
Easy
Analytic Software, Inc. (EASI) is a New York-based independent developer and
marketer of desktop and internet demographic data and software solutions that
provide demographic reports with unique search and analysis tools. EASI
provides targeted site analysis software and updated demographics and related
data for standard and customized geographies (Block Groups, ZIP Codes, Cities,
Counties, CBSAs, etc.). Included with all software is a simple-to-learn mapping
tool that does street lookups, point maps, ring studies, create quintile
analysis, and much more. EASI has been in business since 1995 with over 1,500
clients who use our databases, software, and online services.
Take
a moment to read the Testimonials
on our website! While there you can also test our software – for
free. EASI offers key reports from the 2010 Census. Thousands of corporate,
magazines, colleges and other users go to our site for their Census
demographics.
All of our estimates are based upon
the official 2010 Census.
We
have several versions of our software, The Right Site ®. They all have
different data but the same software. The software has simple to interpret
standard demographic reports, sales potential analysis, site analysis
(three-ring reports), Trend reports (Census, current, and five-year
projection), and user defined demographic profiles (clusters). Our software
also has unique features such as the EASI ® Significant Variable Report. This
EASI-created report instantly shows what makes each study area special.
The results of that can then be used to find other similar areas anywhere in
the US!
EASI
provides targeted demographic data, site analysis, and general reference
software that is really easy to use – we guarantee it.
At
our web site www.easidemographics.com you can
compare the reports and data contained in The Right Site – Executive,
Professional, or Advanced – and determine which one is right for you. At our
site, you can determine all the variables found in this and other versions.
EASI Master Database and The Right Site ® Methodology for Updates
The
following is a general description of the methodology used by EASI to update
the demographic and economic characteristics for the United States, States,
Counties, ZIP Codes, Cities, Census Tracts, Block Groups, Carrier Routes, and
ZIP Plus 4’s (and custom geographies which are derived from other geographies).
The
purpose of this explanation is not to divulge any proprietary methods but to illustrate
the efforts made on your behalf to create accurate updates. EASI statistician’s
and programmers have over 30 years of experience updating these types of data.
By industry standard EASI estimates would be considered of the highest quality.
Quick Annual Summary
While
the 2010 Census doesn’t change for the released data (population by age sex
race and ethnicity and certain limited household data) EASI has to use a
five-year analysis of The American Community Survey (ACS) to estimate Census
data (income, educational attainment etc.). And in some instances our Census
data can change (4/1/2010) as we have new information.
Each
year we have a new ACS and a new five years’ worth to analyze
over time at the Block Group level or Census Tract (the actual level of
geography we use from ACS varies by data element and is based on sample size
reported in each geography). These data form the basis of how we forecast these
data elements forward.
Separately
the Census release annual estimates (previous March) for age by sex by race and
for household income by race. We use these data as the basis for our current
year and five year forecasts for US controls totals. Each year the Census
releases county population estimates for July 1 of the previous years. We analyze these data by birth, death, and migration and they
form the basis of current and forecasted county control totals.
To
get a handle on recent local change (Block Groups) we analyze
annually a USPS file of postal deliveries by type. This is a ZIP4 file which we
use to assign annual changes to the Block Group level. These data relate well
to households (change in mailable households compared
to actual households). We also use this file to annually determine how ZIP
Codes change annually.
a)
With the current release EASI will
benchmark at the Block Group and higher levels all of the details supplied with
the 2010 Census (all related releases at the Block Group level). All data are
now derived from BG data from American Community Survey (ACS) and Public Use
Microdata Survey (PUMS) combined with the released 2010 Census data for April
1, 2010. Cities are now based on the results of the 2010 (population 100 or
more as of 4/1/2010).
Note: Details of 2010 Census are at
the end of this methodology
b)
In all previous estimates EASI
racial data including Black Population, Asian Population, White Population, and
Other Population. However, these are now based on different questions
with the 2010 Census. The 2010 data are not compatible with the 2000 Census
(multiple race categories are now possible and are part of the other group).
The groups now are:
White Population, Alone;
Black Population, Alone;
Asian Population, Alone;
American Indian and Alaska Native
Population, Alone;
Other Race Population, Alone, and
Two or More Races Population.
EASI
uses the 2010 Census Block Group data as our benchmark and makes adjustments
for consistency for age and sex and for all household counts.
c)
EASI has collected from the Census
Bureau all current local (counties etc.) and national updates and estimates for
all the key demographic information. All these official estimates have been analyzed and then incorporated into our estimates and
projections using a variety of EASI models. However, starting with the 2010
Census benchmark the largest impact of our estimates will now be coming from
the American Community Survey and Public Use Microdata Sample (details later).
d)
EASI has summarized from the United
States Postal Service (USPS) mailable Households at a
County, ZIP Code, Census Tract, and Block Group level. These data have been
used as the primary input to estimate local current change within a small area
such as a Block Group. Mailable households are not
the same as Census Households but are used to indicate recent annual change in
household formations. These changes are combined with an EASI proprietary model
for updating and forecasting at the Block Groups.
e)
The Mailable
Household data match starts by identifying, for every ZIP Plus 4, (ZIP+4),
which Block Group it belongs to. EASI develops a split file and a plurality
file of these matches using the latest TIGER (Topologically Integrated
Geographic Encoding and Referencing system) file, to determine which Block
Group (primary) they should be assigned to. One of the key goals is to identify
all correct current ZIP Codes and ZIP+4’s and then assign them to the correct
Block Group that these Mailable Households should be
assigned to. EASI has also reconfigured the 2000 Block Groups into the 2010
Block Group configuration to estimate the 2000 population for comparative
purposes. An analysis of this decade change is also included in our model.
f)
EASI has also analyzed
the 2010 Census Block files in order to create a population Centroid for
each Block Group. The results of that analysis are used for all ring
study analysis.
g) Specific
other sources include:
1.
Bureau of the Census – 2010 Census
PL 94 – 171; American Community Survey (ACS) and Public Use Microdata Samples
(PUMS). Other related sources are: Annual Demographic Survey, Current
Population Reports (P20; P25; P60; and numerous special Census reports.
2.
ZIP and County Business Patterns (US
Department of Commerce – Economics and Statistics Administration- Bureau of the
Census.)
3. US
Department of Justice – Federal Bureau of Investigation.
4. National Center for Education Statistics – Common Core of Data (CCD)
5.
National Oceanic and Atmospheric Administration
– National Environmental Satellite, Data and Information Service - National
Climatic Data Center.
6.
United States Department of the
Interior – Geological Survey – Office of Earthquakes, Volcanoes, and
Engineering.
7.
Bureau of Labor Statistics –
Department of Labor.
8.
Geography Changes – EASI has
captured more ZIP Code demographics this year by including Point ZIPs such as
PO Boxes. In the case of these areas, the physical area served by these
demographics will be the area defined by the Block Groups in which these Point
ZIPs are located.
9.
Geography Changes (2010 Census) –
EASI has added a CITYTYPE identifier field to help users determine how the EASI
City relates to Census Designations. All City Records except CITYTYPE C3 and T1
are Census Places. The seven C3 records are consolidated cities such as
Indianapolis and Nashville. The remaining T1
Records are County Subdivisions. Please note that where both Places and County
Subdivisions exist, some of the areas may be coextensive (not mutually
exclusive).
2. Data Preparation
The
steps in creating the ZIP Plus 4 (ZIP+4) and Block Group mailable
Households include:
a)
Start with a USPS ZIP+4 file for end
of December (year prior to the estimating year) which includes all valid
residential ZIP+4’s in the country whether they are residential mail or not.
b)
For each ZIP+4’s, we add Census
Blocks Groups based upon a current TIGER file distance formula. Over 20 million
records (of the approximate 38 million or so) are processed by this direct
match.
c)
For each remaining ZIP+4, we match
against our internal geocode file (latitude and longitude). This file is based
on running through address matching/geocoding software. Approximately 18% of
total are matched to their Block Group this way.
d)
For each remaining ZIP+4 that cannot
be geocoded by b) or c), we use a calculated carrier route or Block Group
centroid. We weight the geographies to a larger area and calculate a latitude
and longitude. We then determine which is the closest (by
distance) Block Group. This approach is used for approximately 5% of
total.
e)
If a ZIP4 is still unassigned then,
we use nearest neighbor ZIP+4. There are
approximately 2% or total are done through this approach (recent, 6 months old
ZIP+4s are often in this category).
f)
Block Groups assignments are from the
most recent Census TIGER file. TIGER errors, where identified (such as wrong
FIPS Codes) have been corrected.
g)
ZIP Plus 4’s are assigned data based
upon the data of the Block Group that it has been assigned to. (Note: There are
no official Census Bureau data for ZIP+4.)
h)
These mailable
household data analysis are for residential ZIP+4’s (no business-exclusive
ZIP+4’s are included).
EASI
has developed a series of models which use the relationship between the count
of the current mailable households at the Block Group
(BG) level to develop estimates of the change in the household size
relationships at the BG compared to the county and to the ZIP Code. In
addition, EASI analyzes the change in relationships
between these mailable households over time and
compares them to the county and to the ZIP Code households using a proprietary
formula. Care is taken in this approach since there can be ZIP4 definitional
changes. The analysis relates the current estimate of mailable
households to the number of mailable households at
the time of the 2010 Census (4/1/00) and as time progresses.
One
key component of the analysis is a proximity site review of all ZIP+4’s based
upon their Block Group assignment (217,740 Block Groups in Census 2010 versus
208,790 Block Groups in Census 2000). EASI analysis includes the many new Block
Groups that didn’t exist and the many Block Groups with the same codes
but different shapes. This analysis prepares our input data before use
in EASI demographic models.
EASI
uses newly released Census county estimate information which are analyzed and compared to prior releases to develop current
and forecasted county control totals through an analysis of population
component changes (births, deaths, migration, etc.). In addition EASI uses
information from the current American Community Survey and Census Population
Estimates Program (PEP) to generate annual adjustments for population changes
by Race and Ethnicity, Gender, and Age.
Annually,
EASI also incorporates relevant national and state data as control totals. This
is done for a variety of demographic factors. EASI derives this from analysis
of national data, over time, from the Annual Demographic Survey, the Current
Population Survey, American Community Survey, Public Use Microdata Surveys, and
the Annual Housing Survey. There are also from a variety of sources at the
Census Bureau web site (www.census.gov).
ZIP
Code results are independently compared to the USPS current ZIP Code file of
residential deliveries including residential post office boxes. Additional
updating sources include: USPS AMS files and Postal bulletins (the ZIP Alert);
these record any annual changes that take place to ZIP codes including
name changes, delivery or branch changes as they become official. Other sources
include: U.S. Postal Service City-State File (monthly) and Delivery Statistics
File. These CD ROM’s incorporate main inventory of ZIP Codes and the post
office and other names associated with them. Each year EASI conducts a complete
review of these files to maintain a current ZIP Code roster. EASI inventories
the old ZIP Codes as well. Note: Starting with the 2011 release EASI is
updating Post Office Boxes to include their demographics.
Updates to the current year and a five-year projections are
first done at the United States level and for key variables at the county level
as well. Block Group (BG) level estimates are all controlled to the county
control totals. That is, the Block Group data will add to the separately generated county data for all data elements. In a similar manner,
other geographies are summarized from the Block Group level. However, parts of
BGs are added to get ZIP Codes and to get cities.
4. Consistency – year-to-year
changes
Each year
EASI uses all available sources to maintain the highest quality of our estimates. Sometimes
the new information will makes year to year changes less meaningfu e.g. a current ZIP Code may have a different definition of BG’s because of postal changes in
the last year. However, the changes from our 5 year
forecast, within an EASI calendar year, are consistent from the current
estimate but changes from last’s years estimates are not necessarily so. EASI
geography estimates are all based on the same geography, which
is all ZIP Code estimates for April 1, 2010, 1/1/current year; and a five-year
forecast are all based on the same geographic definition.
Starting
in 2007 the Census Bureau has begun releasing The American Community Survey
(ACS - www.census.gov) to
supplement its Census 2010 data. EASI has incorporated all of these estimates
into our current updates and forecasts. These ACS adjusted estimates will be
carried forward and will annually use the latest released data as part of the
updating process. Note: Use of these new ACS estimates offer an immediate
improvement and a vast improvement over time. If you have questions or concerns
about the impact of ACS, please call EASI at 800-HOW-EASI (469-3274) for a
thorough and complete discussion.
Users
must be use caution when comparing data from prior censuses or even releases of
new Census data. As mentioned before, the 2010 Census has a new Race question
(White Population, Alone; Black Population, Alone; Asian Population, Alone;
American Indian and Alaska Native Population, Alone; Other Race Population,
Alone, and Two or More Races Population.
Another
factor in consistency is that with some data sources information becomes
available annually but with others data elements may not be released but once
every two or even three years.
Starting
with 2015 we’ve added BLS data to our methodology, prior to this year we relied
on the American Community Survey (ACS) data for changes in unemployment.
However since the ACS is a rolling 5 year average, the data for 2013 was based
approximately on mid-year 2010 data. In the last few years there has been a
period of rapid changes in employment which wasn’t reflected in our estimates.
So now with the new use of current BLS numbers to make the
employment/unemployment it will make our estimates more consistent with outside
sources.
Occasionally
a post Census estimates can be subject to revision for several reasons. In one
instance a data series may be deemed more important by Congress and as result a
sample size can be expanded to allow for more detailed results. Another change
could be that the sample is framed against any new data such as the 2010
Census. EASI with decades of experience analyzes all
information and then EASI incorporates the results into our estimates.
ZIP
Code Details - As mentioned above, ZIP Codes even
if they seem to be the same (same 5 digits) are especially difficult for
consistency from year to year (they are always consistent within the EASI data
and software.) Since each ZIP Code area may change from year to year EASI
spends considerable time and effort to develop new ZIP Code data for each and
every year. That is, EASI assigns a portion of each Block Group to a ZIP Code
based on the latest information for each year (2010, current and five year
forecast). Note: Annually EASI’s creates a proprietary ZIP to Block Group
(partial) analysis and we also allocate all land area to create each ZIP Code.
Income
There
are many different definitions of income that are available for analysis. With
the release of the 2010 Census EASI has been using the ACS 5 year data to
develop a Census Income estimate (for the year 2009) as our starting point.
These estimates are then modeled using the P60 Money
Income in the United States (Current Population Reports – Consumer Income) as
well as other data. EASI income models are based on race and by family
characteristics to obtain a current estimate. All Income estimates use the 2010
Census household definition of income as a benchmark.
EASI
income estimates are controlled to analysis from the Money income data after analyzing the differences in that sample compared to the
actual ACS data.
EASI
estimates inflation (current dollars) in all of our estimates and forecasts.
EASI also maintains Income distributions based on gross income (includes
all taxes).
EASI Crime Models
EASI analyzed
actual county level FBI data for the various types of crimes as follows:
A. Split the counties with reported
crime data into two random groups of 200 counties each.
B. We then
developed a series of regression models which could then be applied to EASI
updated demographics.
C. Finally we then tested the models
on the 200 counties that were used to develop the regressions.
D.
Statistical analysis showed that the models did an excellent job of predicating
crime rates at the county level.
EASI
then used this model to develop a similar methodology for estimating crime at
the Block Group and other levels of geography.
Consumer
Expenditure Survey (CEX)
The
results of the CEX are analyzed annually by EASI and
then combined with EASI estimates at the Block group level. The Bureau of Labor Statistics and the Bureau of the Census conduct the
CEX. There are two parts to the survey. The first part is a diary, which is
completed by respondents for two consecutive one-week periods. The second part
is an interview survey, which are conducted quarterly (three months) for five
quarters. The interview survey includes about 95% of all expenditures and
includes large expenditures such as property, automobiles, major appliances,
rent, utility payments insurance premiums, and many others.
EASI annually models these results of about 600+ categories
of expenditures against our updated demographic estimates. EASI’s models use
our own BG demographic estimates to update these potential sales.
An example:
EASI
models the age of respondent, income of respondent, and tenure (own home versus
rent). Then for each demographic characteristic we have an average expenditure
for the previous calendar year (e.g. a respondent earning $50,000 to $75,000
spent $210 (for example only) and we might then see that a respondent with
income of $35,000 to $50,000 spent $150 (for example only).
We
take all the values for the demographics and then develop a model for this CEX
characteristic that combines the factors to get one BG level estimate.
EASI is estimating CEX categories by
race and ethnicity.
Standard Occupational Classification
Codes (SOC)
EASI
has developed estimates for both major and minor occupations (over 800). These
are based on estimates of occupations within NAICS employment groups (4
digits). EASI adds up each SOC category estimate within a 4 digit NAICS
code (employment) and then adds up these parts to get a total for each level of
geography.
A.
ZIP and County Business Patterns (US
Department of Commerce - Economics and Statistics Administration- Bureau of the
Census.) County Business Patterns is an annual series that provides subnational
economic data by industry. This series includes the number of establishments,
employment during the week of March 12, first quarter payroll, and annual
payroll. This data is useful for studying the economic activity of small areas;
analyzing economic changes over time; and as a
benchmark for other statistical series, surveys, and databases between economic
censuses. Businesses use the data for analyzing
market potential, measuring the effectiveness of sales and advertising
programs, setting sales quotas, and developing budgets. Government agencies use
the data for administration and planning.
ZIP
Code Business Patterns data are available shortly after the release of County
Business Patterns. It provides the number of establishments by employment-size
classes by detailed industry in the U.S. Here is a link for the 2018 ZIP
Business Patterns and County Business Patterns: https://www.census.gov/data/datasets/2018/econ/cbp/2018-cbp.html .
For
2017, EASI tested a new allocation method using a Carrier Route model instead of
the previous Block Group Model. This is a more detailed method of allocation
businesses from ZIP Codes (which are where the Census business data is based
on). This new approach should yield similar results for ring studies it does
result in a change in business counts for Block Groups, specifically the number
of “zero business” counts. This method results in a better presentation of
where businesses are actually located.
B.
Occupational Employment Statistics -
The Occupational Employment Statistics (OES) program produces employment and
wage estimates for over 800 occupations. These are estimates of the number of
people employed in certain occupations, and estimates of the wages paid to
them. Self-employed persons are not included in the estimates. http://www.bls.gov/oes/
Note: See NAICS Business Data
further down for that review.
Retail
Sales and Store Groups, Minor Stores and Major Merchandise Lines
EASI’s
Retail Sales Estimates include Food Service – Total Retail Sales includes the
standard 12 major stores plus Food Service; 55+ Minor Stores, and 45 Major
Merchandise Lines. All data are based on an extensive review of County and ZIP
Code Retail Trade data for 2012. EASI created a file of benchmark data from the
released Census data which is used for our annual update.
Each
year, EASI creates a new consistent file of benchmark and updated for 2002,
current, and a five-year forecast. EASI re-benchmarks estimates for each update
to a new set of Block Group estimates for all retail categories based on new
information so our data over time is consistent. These estimates are based on
our current analysis of the latest NAICS employment data for each retail store
and food service. Note: EASI resolves any inconsistencies between sources as
part of this annual process.
The 13 store groups that comprise
Total Retail Sales are:
1.
Motor Vehicle and Parts Dealers
2.
Furniture and Home Furnishings Stores
3. Electronics
and Appliance Stores
4. Building
Material and Garden Equipment and Supplies Dealers
5. Food and
Beverage Stores
6. Health and
Personal Care Stores
7. Gasoline
Stations
8. Clothing and
Clothing Accessories Stores
9. Sporting
Goods, Hobby, Book, and Music Stores
10. General
Merchandise Stores
11. Miscellaneous
Store Retailers
12. Non-store
Retailers
13. Food Services
Call EASI for Minor stores and Major
Merchandise Line information.
NAICS Business Data
ZIP
Business Patterns - Each year EASI models the Business
Counts and Employment from the ZIP Code level to the Block Group level. EASI
has identified 2 kinds of ZIP codes.
1.
Business to Business only ZIP
codes – EASI assigns all the business counts to the Block Group that is
the shortest distance from the Centroid of the ZIP Code).
2.
All other ZIP codes – EASI assigns portions
of the ZIP Code business data to each of the Block Groups that comprise the ZIP
Code (this includes portions of Block Groups). We do not know if there are or
are not business present but when added up they maintain the ZIP Code business
data.
Note:
ZIP Codes even if they seem to be the same (same five digits) are especially
difficult for consistency from year to year (they are always consistent within
the EASI data and software.) Since each ZIP Code area may change from year to
year EASI spends considerable time and effort to develop new ZIP Code data for
each and every year. That is, EASI assigns a portion of each Block Group to a
ZIP Code based on the latest information for each year (2010 current and
five-year forecast). Annually EASI’s creates a proprietary ZIP to Block Group
(partial) analysis and we also allocate all land area to create each ZIP Code.
Benchmark Methodology and
Assumptions
These
retail data are benchmarked at the county level from the 2012 Census. Then EASI
develops a ZIP code version of this file. EASI models these actual store
locations at the Block Group level using a business employment relationship
developed from the latest ZIP Business Patterns. This is done in order to allow
the retail sales estimates to be used as part of standard database summaries.
Note: EASI does not know the actual locations of stores at the Block Group.
Other geographies are estimated by adding up the Block Group estimates.
The
updates are modeled against estimated changes based
upon the ZIP Business Patterns. Therefore, the sum of the BG’s retail sales
estimates within a ZIP Code is consistent to the ZIP Code Business employment
data. Any inconsistencies between sources are reviewed and made consistent to
the most current data from ZIP Business Patterns.
EASI
models the retail trade data to a Block Group based on a proximity model. The
model assigns exclusive Business or Retail ZIP Codes to the closest Block
Group. For example, from ZIP Business Patterns EASI can identify point business
locations and the retail configuration within each.
EASI ® Key Demographic Profiles
(Nickname = Complete Name)
EASI
has developed a series of independent profiles for each level of geography. The
purpose is to give a single picture or image of the most significant segment
found in this particular geography. EASI developed a series of independent
demographic and business variables that illustrate a wide range of lifestyles
and behavior-related variables.
The
individual Profiles are used to estimate the Dominant Profile (Cluster) for
each standard geography. All profiles are calculated using EASI ® software.
Profiles are based upon the relative rankings of each of the components of the
profile compared to the national average. These profiles are all independent of
each other.
EASI Profiles
|
1 |
Above Average Education |
AB_AV_EDU |
|
|
2 |
Apartments
(20 or more units) |
APT20 |
|
|
3 |
Available Renting Units |
RENTAL |
|
|
4 |
Pre-School |
|
PRESCHL |
|
5 |
Below Average Education |
BEL_EDU |
|
|
6 |
Blue Collar Employment |
BLUE_EMPL |
|
|
7 |
Born in America |
|
BORN_USA |
|
8 |
Expensive Homes |
|
EXP_HOMES |
|
9 |
Few Teens |
|
NO_TEENS |
|
10 |
House for Sale |
|
FOR_SALE |
|
11 |
In the Air Forces |
|
ARMFORCE |
|
12 |
Large Families |
|
LARGE_FAM |
|
13 |
Long Time Residents |
NO_MOVE |
|
|
Lots of Cars |
MANY_CARS |
|
|
15 |
Median Age |
MED_AGE |
|
16 |
Median Income |
MED_INC |
|
17 |
No Cars |
NO_CAR |
|
18 |
Not in Labor
Force |
NO_LABFOR |
|
19 |
Old and Rich Households |
RICH_OLD |
|
20 |
Old Homes |
OLD_HOMES |
|
21 |
New Homes |
NEW_HOMES |
|
22 |
Recent Movers |
RECENT_MOV |
|
23 |
Retired |
RETIRED |
|
24 |
Service Employment |
SERV_EMPL |
|
25 |
Subway or Bus to Work |
SUB_BUS |
|
26 |
Trailer Park City |
TRAILER |
|
27 |
Unattached |
UNATTACH |
|
28 |
Unemployed |
UNEMPL |
|
29 |
Very Asian |
ASIAN_LANG |
|
30 |
Very Rich Asians |
RICH_ASIAN |
|
31 |
Very Rich Blacks |
RICH_BLK |
|
32 |
Very Rich Families |
RICH_FAM |
|
33 |
Very Rich Hispanics |
RICH_HISP |
|
34 |
Very Rich Households |
VERY_RICH |
|
35 |
Very Rich Non Families |
RICH_NFAM |
|
36 |
Very Rich Whites |
RICH_WHT |
|
37 |
Very Spanish |
SPAN_LANG |
|
38 |
Work at Home |
WORK_HOME |
|
39 |
Young and Rich Households
RICH_YOUNG |
|
Note:
Dominant Profiles are selected from these except for items 30 to 36, which can
based on very small sample sizes.
EASI Sales and Other Potentials
|
1 |
Amusement Index |
AMUS_INDX |
|
2 |
Bargain Seekers Market |
BARGINS |
|
3 |
Culture Index |
CULT_INDX |
|
4 |
Education Index |
EDU_INDX |
|
5 |
Higher Priced Product Market |
EXP_PROD |
|
6 |
Luxury Priced Product Market |
LUX_PROD |
|
7 |
Medical Index |
MEDI_INDX |
|
8 |
Mortality Index (All Causes) |
MORT_INDX |
|
9 |
Religion Index |
RELIG_INDX |
|
10 |
Restaurant Index |
REST_INDX |
Potential Analyses –What are they?
Why the need?
Demographics
have many applications in sales, marketing and advertising. “Potential Analyses”
are unique and powerful evaluation tools based upon the demographics of the
residents of a target area and a special type of data called Potential
Variable. EASI’s extensive data library includes a variety of Potential
Variables including Consumer Expenditures, Health Care Database, and the
American Time Use Survey.
A
potential analysis creates an estimate that can be compared directly to what
you would normally expect to find in an area – where all things
(demographics) are equal. For example, a metropolitan area may comprise
1% of the population in the US. Many sales, marketing, and advertising expenses
are based on this value. But suppose that the key market population
target of the user is actually 1.2% in this market. The aim of an EASI Potential
is to estimate what that key market potential is (expressed as a US %).
So, if advertising costs are related to total population for the metro then
this market has a 20% bump automatically in the number of users that might hear
the ad compared to the normal population (and the cost). This is a desirable
situation for the advertiser.
The
chart below illustrates how to interpret the relationship between Potential and
Actual. Areas with High Actual counts and Low Potential are top performers.
Areas with Low Actual and High Potential are problem areas.
Actual
is a real count or figure number derived without
using any estimation techniques. The best example is a company’s sales in a
known geography (sales territory) for a specific time period.
Potential
is percentage of the US based on a model. For example, an EASI
demographic regression type model. Potential can also be based a similar statistical
procedure which has attempted to estimates what sales would be in
an area based on who lives there, their ages, their incomes, types of
businesses, and other relevant demographics using weights.
High
or low is based on an index number that is created by dividing the Actual by
the Potential for all of the areas you intend to evaluate.
Remember:
Potential variables are not actual variables, they are derived
estimates. Their purpose is to help you evaluate Actual data, not
to replace Actual data.
5. Accuracy
With
all estimates and with ours as well, the higher the level of data (national is
the highest) the more accurate the estimate. Our data follows standard
demographic techniques, all developed with over 35 years of experience in this
industry. It is considered a highly accurate technique.
EASI data has also been “field
tested”. That is, portions of our updated data are available at our web site
and have been used by hundreds of thousands of users. These
users raise questions about our
updates, which we investigate. This input does help us to review and check
results and makes our estimates better.
Here are some common questions:
Why are the Post Office mailable households different than EASI’s?
a)
One reason is that the differences
between the counts of ZIP households in the Census and the mailable
households from the post office is that there are differences in definitions
between mailable households and Census households.
There can be two mailable households in a residence
but only one household. The Census will call it a single household if there is
a relationship and the post office does not keep track of relationships.
How close is EASI updated data to
other sources?
b)
EASI has made an extensive effort to
obtain all relevant information and to incorporate it in a logical statistical
manner. Other companies who use similar sources and statistical approach should
give similar results. One method of comparison is a circle or ring study. An
analysis of comparable ring studies has shown a current population difference
of less than 2%. In denser population areas the results of the ring analysis
are within .005 percent analysis. With the release of the 2010 Census an
analysis showed that EASI ZIP Code estimates were in over 98% of the cases
within .005 percent.
EASI
has made numerous checks for internal and external consistency in all our
estimates. There are three types of checks that are rigorously reviewed. These
include; Census internal consistency, controlling updates to definitions of
estimates, and correcting for, or preventing, rounding errors, especially in
small geographies.
ACS
validity check is an analysis and comparison of the results of ACS estimates at
the Block Group level. Due to sample sizes and Census procedures for disseminating
the ACS results there frequently are ACS results which are inconsistent. These
results are analyzed and EASI has developed a series
of algorithms to adjust these estimates to make them consistent. (Examples are
mostly in small BGs where there might be a single household, by total or by
race, found in ACS but no population in ACS. Or a value for a single cell in a
detailed by race age distribution won’t re-add to the SF1 distribution for the
same results.) EASI strives to correct all of these problems with the Census
data and remove these as issues that could affect EASI updates.
EASI
updating validity checks involve controlling all Census 2010 distributions and
updates that require a controlling definition. EASI then makes the same checks on
the EASI updates in order to prevent inconsistencies from coming into the
updates.
The
next issue is the controlling of distribution to the correct sum. A basic
example of that is population by age and sex must add to population. This same
issue is where the sum of the male age 0 to 5 for White, Black, Asian, and
American Indian and Alaska Native Population, Other Race Population, and Two or
More Races. The sum of these population estimates by race must add to total 0
to 5. Another example is that education attainment is defined as for the
population 25. Note: Each distribution has a requirement like this. Many must
add to population 16+ or population 3+, or households, or population, etc.
Other key ones are that Hispanic must be less than or equal to Total Population
less White Non-Hispanic Population. Also key is that White Non-Hispanic
Population must be less than or equal to White Population. These conditions for
updates apply across all estimates including individual age groups (0 to 5, 6
to 11, etc.) and individual income groups ($0 to $15k, 15k to 25k, etc.).
The
last part of the validity check is to find and fix rounding errors. Rounding
errors are introduced in all estimates since results for the sum of a
distribution will frequently not exactly add to the require estimate. To
accommodate the rounding error EASI has developed various ways of adjusting the
error into the most likely cell (in EASI rounding errors are calculated
simultaneously as the distribution is being estimated, so when a group or cell
sum is off by 1 (high or low) EASI immediately makes the adjustment in that
actual group or cell.
These
checks are performed at the BG, City, and ZIP Code levels. This is required
since EASI splits BGs to create cities and ZIP Codes. Since splitting of BGs
can introduce these validity issues the EASI methodology require the BG checks
described above to be repeated at both cities and ZIP Codes as well.
Life Stage Clusters – The Basics
l Step
1: Begin with a collection of neighborhood (Census
Block Groups) demographic data series to learn about what comprises a “neighborhood”.
l Step 2:
Through thousands of multivariate analyses, EASI synthesized and identified the
independent variables, and their relationship to each other, that form the foundation
of the clusters. This statistical foundation of neighborhoods
form the basis of “Life Stages”.
l Step 3:
Based on the unique variables characterized by the Life Stages concept of
independent clusters, EASI was able to replicate and verify the accuracy and
utility of their neighborhood prediction model.
l Step
4: Create EASI Life Stages, an understandable, explainable, and statistically
relevant group of clusters which comprise a highly predictive neighborhood model of location.
For
a further discussion of these methodologies please call 800-HOW-EASI (469-3274)
or email info@easidemographics.com.
Summary Review
2010 Census Data: PL 94 file. 2010 Census data for April 1,
2010. Cities are now based on the results of the 2010 (population 100 or more
as of 4/1/2010). EASI reviews Census estimates for internal consistencies and
makes adjustments where required.
Current
Estimates and Five-Year Projections: EASI has collected from the Census Bureau
all current local (counties etc.) and national updates and estimates for all
the key demographic information. All these official estimates have been analyzed and then incorporated into our estimates and
projections using a variety of EASI models. However, starting with the 2010
Census benchmark the largest impact of our estimates will now be coming from
the American Community Survey and Public Use Microdata Sample.
EASI
has summarized from the United States Postal Service (USPS) mailable
Households at a County, ZIP Code, Census Tract, and Block Group level. These
data have been used as the primary input to estimate local current change
within a small area such as a Block Group. Mailable
households are not the same as Census Households but are used to indicate
recent annual change in household formations. These changes are combined with
an EASI proprietary model for updating and forecasting at the Block Groups.
The
Mailable Household data match starts by identifying,
for every ZIP Plus 4, (ZIP+4), which Block Group it belongs to. EASI develops a
split file and a plurality file of these matches using the latest TIGER
(Topologically Integrated Geographic Encoding and Referencing system) file, to
determine which Block Group (primary) they should be assigned to. One of the
key goals is to identify all correct current ZIP Codes and ZIP+4’s and then assign them to the
correct Block Group that these Mailable Households
should be assigned to. EASI has also reconfigured the 2000 Block Groups into the
2010 Block Group configuration to estimate the 2000 population for comparative
purposes. An analysis of this decade change is also included in our model.
EASI
has also analyzed the 2010 Census Block files in
order to create a population Centroid for each Block Group. The results of that
analysis are used for all ring study analysis.
Specific other sources include:
Bureau
of the Census – 2010 Census PL 94 – 171; American Community Survey (ACS) and Public
Use Microdata Samples (PUMS). Other related sources are: Annual Demographic
Survey, Current Population Reports (P20; P25; P60; and numerous special Census
reports.
EASI
has developed a series of EASI-based proprietary models (based on almost 40 years
of experience) for updating and forecasting age sex race income and all related
variables. EASI controls estimates to Census control totals and uses the most
recent ACS and PUMs as part of their modeling.
2010 Census is also developed from
the PL 94 combined with ACS and PUMS data.
Current
and 5-year forecasts CEX Data: An EASI proprietary model using the latest CEX
(Bureau of Labor Statistics; Consumer Expenditure
Study) data modeled against EASI current and
forecasted demographics. The results of the CEX are analyzed
annually by EASI and then combined with EASI estimates at the Block group
level. The Bureau of Labor Statistics and the Bureau
of the Census conduct the CEX. There are two parts to the survey. The first
part is a diary, which is completed by respondents for two consecutive one-week
periods. The second part is an interview survey, which are conducted quarterly
(3 months) for five quarters. The interview survey includes about 95% of all
expenditures and includes large expenditures such as property, automobiles,
major appliances, rent, utility payments insurance premiums, and many others.
EASI annually models these results of about 600+ categories
of expenditures against our updated demographic estimates. EASI’s models use
our own BG demographic estimates to update these potential sales.
Current
Business Counts: EASI uses the latest versions of ZIP Business Patterns and
County Business Patterns (US Department of Commerce - Economics and Statistics
Administration- Bureau of the Census). EASI has a proprietary method for
estimating any non-disclosures.
Current
Crime Data: EASI analyzed actual county level FBI
data (US Department of Justice - Federal Bureau of Investigation) for the
various types of crimes. EASI split the list of these counties (the only level
where actual crime data exists) into two random groups of 200 or so each.
EASI
then developed a series of regression models which could then be applied to
EASI updated demographics. EASI then tested the models on the 200 counties that
were not part of the actual regressions.
The
results proved that the models do an excellent job of predicating crime rates
at the county level.
EASI
then used the results to develop a similar methodology for estimating crime at
the Block Group and other levels of geography.
Current
Weather Data: EASI has analyzed National Oceanic and
Atmospheric Administration - National Environmental Satellite, Data and
Information Service - National Climatic Data Center.
Current
CPI Data: EASI has analyzed data from the Consumer
Price Index (Bureau of Labor Statistics - Department
of Labor - CPI Detailed Report December 2012).
Current
and five-year forecasts of Retail Sales Data: EASI’s Proprietary Retail Sales
Estimates include Food Service – Total Retail Sales includes the standard 12 major
stores plus Food Service; 55+ Minor Stores, and 45 Major Merchandise Lines. All
data are based on an extensive review of County and ZIP Code Retail Trade data
for 2012. EASI created a file of benchmark data from the released Census data
which is used for our annual update.
Each
year, EASI creates a new consistent file of benchmark and updated for 2012,
current, and a five-year forecast. EASI re-benchmarks estimates for each update
to a new set of Block Group estimates for all retail categories based on new
information so our data over time is consistent. These estimates are based on
our current analysis of the latest NAICS employment data for each retail store
and food service. Note: EASI resolves any inconsistencies between sources as
part of this annual process.
Current
and five-year forecast of Health Data: EASI has developed a series of
demographic models using the latest reports from:
Vital
and Health Statistics; Centers for Disease Control
and Prevention; Summary Health Statistics for US Adults: National Health
Interview Survey – Series 10, #235
Vital
and Health Statistics; Centers for Disease Control
and Prevention; Summary Health Statistics for US Children: National Health
Interview Survey – Series 10, #234
National
Vital Statistics Reports United States Life Tables; Centers
for Disease Control and Prevention – Volume 56, #9
Current
MRI Data: EASI has developed a proprietary model based on the results of the 26
MRI reports – These are for the most current year.
Current and
five-year forecast of Life Stage Clusters: EASI has developed a simplified
clustering system. A primary goal of the EASI development effort has been the
creation of a cluster system based on a Life Stage model. Life Stage Clusters
are a neighborhood classification system based on the
crucial factors (84 Possible Life Stages based upon:
Age
of Head of Households; Marital Status; and Household Income that determine
life's key decisions). It is a community-oriented scheme that identifies and
quantifies the factors that are involved in moving to a specific location. To
accomplish this goal EASI's statisticians have spent hundreds of hours analyzing the vast EASI demographic database, and
organizing the results into a simplified system designed for non-statisticians.
EASI
Master Database and The Right Site ® Methodology for
ZIP4 and Carrier Route Updates
The
following is a general description of the methodology used by EASI to update
the demographic and economic characteristics for the United States for the ZIP
Plus 4’s (Z4) geography.
Since
there is no official Census estimates for detailed demographics below the Block
Group level EASI has developed a methodology that estimates, for the over 37
million residential Z4s, an approximate value for a variety of key demographics
based upon likelihood.
EASI
first estimates the number of Households within a Z4 based upon a relation of
Z4 mailable households (developed from postal files)
to mailable households at the actual Block
Group that the Z4 is located in. Once that Z4 Household estimate is created
EASI then estimates the Z4 key demographics. EASI has develop a unique approach
that uses an inverse weighting formula based upon the distance that the Z4 is
from its nearest Block Groups.
For example, if the diagram below illustrates 2 Block Groups
(Block Group Population Centroid are the tops of the mountains) and the Z4s
that surround each, then the Z4s that are bordering (orange) another Block
Group are affected by their proximity to that other Block Group.
The
purpose of this explanation is not to divulge any proprietary methods but to
illustrate the efforts made on your behalf to create accurate updates. EASI
statistician’s and programmers have over 40 years of experience updating these
types of data. By industry standard EASI estimates would be considered of the
highest quality.
Note:
EASI has improved the consistency of its ZIP4 and Carrier Route files by
eliminating Business (only business deliveries) ZIP4s from our roster. The consequence
of this change will be fewer ZIP4 records in our ZIP4 Conversion File and ZIP4s
with our demographic files, as well as considerably fewer records in our
Carrier Route. There were almost 200,000 Business Carrier Routes that have been
dropped. This change helps make the demographics files (which are all
residentially based) more consistent in their allocations to ZIP4s and Carrier
Routes.
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