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County Trends Dataset 2 (CTD2)
-- single year of age by race/ethnicity by gender estimates and projections
The County Trends Dataset 2 (CTD2) is a part of the broader
Situation & Outlook database.
County Trends Dataset 2 includes annual estimates and projections, 2000 to 2020, typically of interest to users analyzing more detailed characteristics
of the population by age-race/ethnicity-gender.
The County Trends Dataset 1 (CTD1) provides annual estimates and projections, 2000 to 2020, for total population,
components of change, housing and employment with monthly data, 2008 to 2010 for labor force attributes.
CountyTrends datasets are structured as dBase4 files but may be easily converted to other formats.
Use the CTD files with any version of CommunityViewer (including no fee version).
CTD files may be opened with Excel and directly integrated with shapefiles.
Subscribe to CTD
CountyTrends Datasets are available as current version or annual subscription with updates
as posted (CTD1 updated monthly, CTD2 updated periodically through year).
Get one county, a few counties or all counties.
Files are delivered/accessed electronically.
Annual subscribers download file replacements when updates are posted.
Both options are perpetual use licenses with no redistribution. Other options are available;
Please contact us for more information ...
use this form (note CTD, CTD1 and/or CTD2 as
applicable in text and add questions/describe geographic scope of interest) or call (888)364-7656.
Scope CTD2 Annual Items
Historical annual items are updated on a periodic basis as these official estimates are
released/updated by the Census Bureau and National Center for Health Statistics.
Projections are developed/updated monthly by Proximity and extend to 2020.
Population totals are consistent between CTD1 and CTD2.
Projections are based on the latest demographic-economic conditions, including but not limited to,
new preliminary and revised monthly estimates and the periodically updated annual estimates.
CTD2 Data Record Content and Organization
The CTD2 is a hierachically structured file.
CTD2 contains one record for each county iterated by gender and race/ethnicity.
The file structure is designed so that flat files may be easily generated for the total population or
a specific set of gender and race/ethnicity combinations.
Similarly, the single year of age detail in each record enables grouping of individual ages to any specification
such as 5-year ago cohorts.
Fields included in each record are listed in the table presented below.
The record is structured as follows:
• Geographic and code attributes (same for CTD1 and CTD2)
• Race/Gender and Ethnicity codes
• Total population and population by age
CTD2 Data Fields
| FieldName | FieldType | Description |
| VINTAGE | C6 | Vintage (DDMMYY) |
| NAME1 | C60 | County name |
| STAB | C2 | State USPS abbreviation |
| ST | C2 | State FIPS code |
| CTY | C3 | County FIPS code |
| NAME2 | C50 | County name (includes county type and state integrated) |
| CTYNAME | C30 | County name (if component of CBSA, MD or CSA, otherwise blank) |
| STATE | C20 | Spelled out state name |
| STCTY | C5 | state FIPS code + county FIPS code |
| CTYSTATUS | C12 | Central or Outlying (if in CBSA, else blank) |
| CBSA | C5 | Current metropolitan statistical area/micropolitan statistical area code |
| MD | C5 | Current metropolitan division code |
| CSA | C3 | Current combined statistical area code |
| CBSANAME | C60 | CBSA name |
| CBSATYPE | C4 | CBSA type (MSA or MISA) |
| MDNAME | C60 | MD name |
| CSANAME | C60 | CSA name |
| RACEGENDER | C1 | Race/Gender
1=White male
2=White female
3=Black or African American male
4=Black or African American female
5=American Indian or Alaska Native male
6=American Indian or Alaska Native female
7=Asian or Pacific Islander male
8=Asian or Pacific Islander female |
| ETHNICITY | C1 | Ethnicity
1=not Hispanic or Latino
2=Hispanic or Latino |
| YEAR | C4 | Subject Matter Reference Year
2000 (7/1/00)
2001 (7/1/01)
...
2020 (7/1/2020) |
| TOTPOP | N9.0 | Population, total |
| P00 | N9.0 | Population, age less than 1 |
| P01 | N9.0 | Population, age 1 |
| P02 | N9.0 | Population, age 2 |
| P03 | N9.0 | Population, age 3 |
| P04 | N9.0 | Population, age 4 |
| P05 | N9.0 | Population, age 5 |
| P06 | N9.0 | Population, age 6 |
| P07 | N9.0 | Population, age 7 |
| P08 | N9.0 | Population, age 8 |
| P09 | N9.0 | Population, age 9 |
| P10 | N9.0 | Population, age 10 |
| P11 | N9.0 | Population, age 11 |
| P12 | N9.0 | Population, age 12 |
| P13 | N9.0 | Population, age 13 |
| P14 | N9.0 | Population, age 14 |
| P15 | N9.0 | Population, age 15 |
| P16 | N9.0 | Population, age 16 |
| P17 | N9.0 | Population, age 17 |
| P18 | N9.0 | Population, age 18 |
| P19 | N9.0 | Population, age 19 |
| P20 | N9.0 | Population, age 20 |
| P21 | N9.0 | Population, age 21 |
| P22 | N9.0 | Population, age 22 |
| P23 | N9.0 | Population, age 23 |
| P24 | N9.0 | Population, age 24 |
| P25 | N9.0 | Population, age 25 |
| P26 | N9.0 | Population, age 26 |
| P27 | N9.0 | Population, age 27 |
| P28 | N9.0 | Population, age 28 |
| P29 | N9.0 | Population, age 29 |
| P30 | N9.0 | Population, age 30 |
| P31 | N9.0 | Population, age 31 |
| P32 | N9.0 | Population, age 32 |
| P33 | N9.0 | Population, age 33 |
| P34 | N9.0 | Population, age 34 |
| P35 | N9.0 | Population, age 35 |
| P36 | N9.0 | Population, age 36 |
| P37 | N9.0 | Population, age 37 |
| P38 | N9.0 | Population, age 38 |
| P39 | N9.0 | Population, age 39 |
| P40 | N9.0 | Population, age 40 |
| P41 | N9.0 | Population, age 41 |
| P42 | N9.0 | Population, age 42 |
| P43 | N9.0 | Population, age 43 |
| P44 | N9.0 | Population, age 44 |
| P45 | N9.0 | Population, age 45 |
| P46 | N9.0 | Population, age 46 |
| P47 | N9.0 | Population, age 47 |
| P48 | N9.0 | Population, age 48 |
| P49 | N9.0 | Population, age 49 |
| P50 | N9.0 | Population, age 50 |
| P51 | N9.0 | Population, age 51 |
| P52 | N9.0 | Population, age 52 |
| P53 | N9.0 | Population, age 53 |
| P54 | N9.0 | Population, age 54 |
| P55 | N9.0 | Population, age 55 |
| P56 | N9.0 | Population, age 56 |
| P57 | N9.0 | Population, age 57 |
| P58 | N9.0 | Population, age 58 |
| P59 | N9.0 | Population, age 59 |
| P60 | N9.0 | Population, age 60 |
| P61 | N9.0 | Population, age 61 |
| P62 | N9.0 | Population, age 62 |
| P63 | N9.0 | Population, age 63 |
| P64 | N9.0 | Population, age 64 |
| P65 | N9.0 | Population, age 65 |
| P66 | N9.0 | Population, age 66 |
| P67 | N9.0 | Population, age 67 |
| P68 | N9.0 | Population, age 68 |
| P69 | N9.0 | Population, age 69 |
| P70 | N9.0 | Population, age 70 |
| P71 | N9.0 | Population, age 71 |
| P72 | N9.0 | Population, age 72 |
| P73 | N9.0 | Population, age 73 |
| P74 | N9.0 | Population, age 74 |
| P75 | N9.0 | Population, age 75 |
| P76 | N9.0 | Population, age 76 |
| P77 | N9.0 | Population, age 77 |
| P78 | N9.0 | Population, age 78 |
| P79 | N9.0 | Population, age 79 |
| P80 | N9.0 | Population, age 80 |
| P81 | N9.0 | Population, age 81 |
| P82 | N9.0 | Population, age 82 |
| P83 | N9.0 | Population, age 83 |
| P84 | N9.0 | Population, age 84 |
| P85UP | N9.0 | Population, age 85 and over |
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