Age, Race, Sex and Ethnicity, by tract for San Diego county
sandiegodata.org-demographics-tract-1.1.1
. Modified 2020-11-26T02:47:27
Resources | Packages | Documentation| Contacts| References| Data Dictionary
Resources
- tracts. San Diego county tract boundaries
- age_sex. Age and sex per San Diego tract
- total_populaltion. Total population of San Diego tracts
- raceeth. Race and Ethnicity for SaN Diego tracts
Documentation
The dataset includes three files all from the Census, detailing age and sex and race ethnicity. All of the files are built from Census data; see the References section for the specific dates and releases.
Age and Sex
The age_sex
data comes from B01001 and the associated race iterations. The file combines all of the race iteration tables and adds columns for sex, race/ethnicity codes and age groups.
THe race ethnicity codes are:
- white
- nhwhite
- black
- aian
- asian
- nhopi
- other
- many
- hisp
For each of the race/ethnicity values except nhwhite and hisp, the category sums the associated ‘nh_‘ and ‘hisp_‘ categories from the raceeth table.
Race And Ethnicity
The raceeth
is based on the census table C03002: Hispanic or Latino Origin by Race, but recormats the structure, use a code for race/ethnicity in the raceeth
column to indicate the oricinal clumn name in the Census dataset. These codes are:
- total: B03002_001 Total
- nonhisp: B03002_002 Not Hispanic or Latino
- nh_white: B03002_003 Not Hispanic or Latino – White alone
- nh_black: B03002_004 Not Hispanic or Latino – Black or African American alone
- nh_aian: B03002_005 Not Hispanic or Latino – American Indian and Alaska Native alone
- nh_asian: B03002_006 Not Hispanic or Latino – Asian alone
- nh_nhopi: B03002_007 Not Hispanic or Latino – Native Hawaiian and Other Pacific Islander alone
- nh_other: B03002_008 Not Hispanic or Latino – Some other race alone
- nh_multiple: B03002_009 Not Hispanic or Latino – Two or more races
- nh_multiple_other: B03002_010 Not Hispanic or Latino – Two or more races – Two races including Some other race
- nh_multiple_more: B03002_011 Not Hispanic or Latino – Two or more races – Two races excluding Some other race and three or more races
- hisp: B03002_012 Hispanic or Latino
- hisp_white: B03002_013 Hispanic or Latino – White alone
- hisp_black: B03002_014 Hispanic or Latino – Black or African American alone
- hisp_aian: B03002_015 Hispanic or Latino – American Indian and Alaska Native alone
- hisp_asian: B03002_016 Hispanic or Latino – Asian alone
- hisp_nhopi: B03002_017 Hispanic or Latino – Native Hawaiian and Other Pacific Islander alone
- hisp_other: B03002_018 Hispanic or Latino – Some other race alone
- hisp_multiple: B03002_019 Hispanic or Latino – Two or more races
- hisp_multiple_other: B03002_020 Hispanic or Latino – Two or more races – Two races including Some other race
- hisp_multiple_more: B03002_021 Hispanic or Latino – Two or more races – Two races excluding Some other race and three or more races
For the most part you will use the nh_*
for all races, hisp_white
for hispanics, and nh_white
for whites.
Documentation Links
Contacts
- Wrangler
Data Dictionary
tracts | age_sex | total_populaltion | raceethtracts
Column Name | Data Type | Description |
---|---|---|
statefp | integer | State FIPS code |
countyfp | integer | County FIPS code |
tractce | integer | Census tract id |
geoid | string | Tract geoid |
name | number | Name of tract. Mostly useless. |
namelsad | string | Longer name for trace. More useless. |
mtfcc | string | Feature class code. https://www.census.gov/library/reference/code-lists/mt-feature-class-codes.html |
funcstat | string | Functional status. https://www.census.gov/library/reference/code-lists/functional-status-codes.html |
aland | integer | Area of land in square meters |
awater | integer | Area of water in square meters |
intptlat | number | Lattitude of an internal point, usually the centroid |
intptlon | number | Longitude of the internal point, usually the centroid |
geometry | string | Geometry of boundary, in WKT format. |
age_sex
Column Name | Data Type | Description |
---|---|---|
geoid | string | Tract geoid |
column | string | ID of original census table column |
sex | string | Sex category |
age | string | Age group |
min_age | integer | Minimum age in age group |
max_age | integer | Maximum age in age group |
raceeth | string | Race/ethnicity. |
estimate | integer | Population estimate |
margin | integer | 90% margin of error |
total_populaltion
Column Name | Data Type | Description |
---|---|---|
geoid | string | Tract geoid |
total_population | integer | Total population of tract |
total_popualtion_m90 | integer | 90% margin of error of tract |
raceeth
Column Name | Data Type | Description |
---|---|---|
geoid | string | Tract geoid |
raceeth | string | Race/ethnicity code |
estimate | integer | Population estimate |
margin | integer | 90% margin of error |
References
Urls used in the creation of this data package.
- census://2018/5/CA/tract. Census Tract boundaries for tracts in California
- census://2018/5/CA/tract/B01003. Total tract population
- census://2018/5/CA/tract/b03002. Hispanic or Latino Origin by Race
Packages
- s3 s3://library.metatab.org/sandiegodata.org-demographics-tract-1.1.1.csv
- csv http://library.metatab.org/sandiegodata.org-demographics-tract-1.1.1.csv
Accessing Data in Vanilla Pandas
import pandas as pd
tracts_df = pd.read_csv('http://library.metatab.org/sandiegodata.org-demographics-tract-1.1.1/data/tracts.csv')
age_sex_df = pd.read_csv('http://library.metatab.org/sandiegodata.org-demographics-tract-1.1.1/data/age_sex.csv')
total_populaltion_df = pd.read_csv('http://library.metatab.org/sandiegodata.org-demographics-tract-1.1.1/data/total_populaltion.csv')
raceeth_df = pd.read_csv('http://library.metatab.org/sandiegodata.org-demographics-tract-1.1.1/data/raceeth.csv')
Accessing Package in Metapack
import metapack as mp
pkg = mp.open_package('http://library.metatab.org/sandiegodata.org-demographics-tract-1.1.1.csv')
# Create Dataframes
tracts_gdf = pkg.resource('tracts').geoframe()
age_sex_df = pkg.resource('age_sex').dataframe()
total_populaltion_df = pkg.resource('total_populaltion').dataframe()
raceeth_df = pkg.resource('raceeth').dataframe()
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