Exploring a Group of Variables

There are thousands of different variables avaialble via the US Census API. One way to navigate through them is to look through a hierarchy of web pages starting with a year, like 2020, on a page like https://api.census.gov/data/2020.html.

From here we can navigate down to a particular data source, by following the link in the Group List column of the first row, which is for the dataset acs/acs5. This takes us to https://api.census.gov/data/2020/acs/acs5/groups.html.

From there, we can choose the group named B03002, which takes us to https://api.census.gov/data/2020/acs/acs5/groups/B03002.html, where we can see all the variables in the group.

Some of these variables are estimates, and some of them are annotations. Normally, we are interested in the estimates.

Every variable has a label, which is a string of components seperated by !!. For example, B03002_007E has the label, “Estimate!!Total:!!Not Hispanic or Latino:!!Native Hawaiian and Other Pacific Islander alone”. The !! seperators imply a tree among the variables, from the root all the way down to leaves. Internal nodes in the tree represent variables that are aggregates of those lower than them in the tree. For example, B03002_002E is the size of the population that is not Hispanic or Latino, regardless of race. It is the sum of B03002_003E, B03002_004E, B03002_005E, B03002_006E, B03002_007E, B03002_008E, and B03002_009E, which count people of different races who are not Hispanic or Latino. All of these are leaves of the tree, except B03002_009E, which is further subdivided into B03002_010E and B03002_011E.

All of this can get really confusing when you look at it in the tabular form on the web page. In order to make it less confusing, the censusdis package includes code to fetch and present the variable hierarchy in a more understandable way. That’s what the remainder of this notebook is about.

Import and Configuration

import censusdis.data as ced
import censusdis.geography as cgeo
from censusdis.states import STATE_NJ

YEAR = 2020
DATASET = "acs/acs5"
GROUP = "B03002"

Programmatic Access to a Group Variable Tree

We can get the whole collection of variables in tree form and print it. This format makes it easier to see the relationships we saw in the table at https://api.census.gov/data/2020/acs/acs5/groups/B03002.html.

We can see clearly where variables exist at internal nodes of the tree and where they exist at the leaves. Not all internal nodes have variables but all leaves do.

tree = ced.variables.group_tree(DATASET, YEAR, GROUP)
print(tree)

+ Estimate
    + Total: (B03002_001E)
        + Not Hispanic or Latino: (B03002_002E)
            + White alone (B03002_003E)
            + Black or African American alone (B03002_004E)
            + American Indian and Alaska Native alone (B03002_005E)
            + Asian alone (B03002_006E)
            + Native Hawaiian and Other Pacific Islander alone (B03002_007E)
            + Some other race alone (B03002_008E)
            + Two or more races: (B03002_009E)
                + Two races including Some other race (B03002_010E)
                + Two races excluding Some other race, and three or more races (B03002_011E)
        + Hispanic or Latino: (B03002_012E)
            + White alone (B03002_013E)
            + Black or African American alone (B03002_014E)
            + American Indian and Alaska Native alone (B03002_015E)
            + Asian alone (B03002_016E)
            + Native Hawaiian and Other Pacific Islander alone (B03002_017E)
            + Some other race alone (B03002_018E)
            + Two or more races: (B03002_019E)
                + Two races including Some other race (B03002_020E)
                + Two races excluding Some other race, and three or more races (B03002_021E)

Accessing a Sub-Tree

Most of the time we are only interested in variables that are estimates, so we can look down in that part of the tree alone.

print(tree["Estimate"])

+ Total: (B03002_001E)
    + Not Hispanic or Latino: (B03002_002E)
        + White alone (B03002_003E)
        + Black or African American alone (B03002_004E)
        + American Indian and Alaska Native alone (B03002_005E)
        + Asian alone (B03002_006E)
        + Native Hawaiian and Other Pacific Islander alone (B03002_007E)
        + Some other race alone (B03002_008E)
        + Two or more races: (B03002_009E)
            + Two races including Some other race (B03002_010E)
            + Two races excluding Some other race, and three or more races (B03002_011E)
    + Hispanic or Latino: (B03002_012E)
        + White alone (B03002_013E)
        + Black or African American alone (B03002_014E)
        + American Indian and Alaska Native alone (B03002_015E)
        + Asian alone (B03002_016E)
        + Native Hawaiian and Other Pacific Islander alone (B03002_017E)
        + Some other race alone (B03002_018E)
        + Two or more races: (B03002_019E)
            + Two races including Some other race (B03002_020E)
            + Two races excluding Some other race, and three or more races (B03002_021E)

Leaves

In many cases, we are really just interested in the leaves, because the internal nodes of the tree contain variables that are aggregate sums of the subtrees below them.

leaves = ced.variables.group_leaves(DATASET, YEAR, GROUP)
leaves

['B03002_003E',
 'B03002_004E',
 'B03002_005E',
 'B03002_006E',
 'B03002_007E',
 'B03002_008E',
 'B03002_010E',
 'B03002_011E',
 'B03002_013E',
 'B03002_014E',
 'B03002_015E',
 'B03002_016E',
 'B03002_017E',
 'B03002_018E',
 'B03002_020E',
 'B03002_021E']

Notice that the set of leaves we got does not include those for annotations. If we really want to see those too, we can add an optional argument.

all_leaves = ced.variables.group_leaves(DATASET, YEAR, GROUP, skip_annotations=False)
all_leaves

['B03002_003E',
 'B03002_004E',
 'B03002_005E',
 'B03002_006E',
 'B03002_007E',
 'B03002_008E',
 'B03002_010E',
 'B03002_011E',
 'B03002_013E',
 'B03002_014E',
 'B03002_015E',
 'B03002_016E',
 'B03002_017E',
 'B03002_018E',
 'B03002_020E',
 'B03002_021E']

Programmatic Access to Geographic Hierarchies

It’s great to know the variables that are available, but in order to make full use of the US Census API and the censusdis API around it, we have to know something about the geography hierarchies that are avaialble for each dataset in each year it is available. These are available on web pages like https://api.census.gov/data/2020/acs/acs5/geography.html, which contains a table of the geographies supported by the ACS5 dataset we have been looking at for the year 2020.

But again, we’d prefer to have access to this information in a pythonic way. The most common way to get this is by calling censusdis.geography.geo_path_py_specs as shown below. This gives us the available hierarchies using Python-friendly snake-case names that we can use to download data.

Each item in the dictionary has a key that represents the geography hierarchy and a value that is the list of the components of the hierachy in snake-case as it can be passed as an argument to censusdis.data.download.

cgeo.geo_path_snake_specs(DATASET, YEAR)

{'010': ['us'],
 '020': ['region'],
 '030': ['division'],
 '040': ['state'],
 '050': ['state', 'county'],
 '060': ['state', 'county', 'county_subdivision'],
 '067': ['state', 'county', 'county_subdivision', 'subminor_civil_division'],
 '070': ['state', 'county', 'county_subdivision', 'place_remainder_or_part'],
 '140': ['state', 'county', 'tract'],
 '150': ['state', 'county', 'tract', 'block_group'],
 '155': ['state', 'place', 'county_or_part'],
 '160': ['state', 'place'],
 '170': ['state', 'consolidated_city'],
 '172': ['state', 'consolidated_city', 'place_or_part'],
 '230': ['state', 'alaska_native_regional_corporation'],
 '250': ['american_indian_area_alaska_native_area_hawaiian_home_land'],
 '251': ['american_indian_area_alaska_native_area_hawaiian_home_land',
  'tribal_subdivision_remainder'],
 '252': ['american_indian_area_alaska_native_area_reservation_or_statistical_entity_only'],
 '254': ['american_indian_area_off_reservation_trust_land_only_hawaiian_home_land'],
 '256': ['american_indian_area_alaska_native_area_hawaiian_home_land',
  'tribal_census_tract'],
 '258': ['american_indian_area_alaska_native_area_hawaiian_home_land',
  'tribal_census_tract',
  'tribal_block_group'],
 '260': ['american_indian_area_alaska_native_area_hawaiian_home_land',
  'state_or_part'],
 '269': ['american_indian_area_alaska_native_area_hawaiian_home_land',
  'state_or_part',
  'place_remainder_or_part'],
 '270': ['american_indian_area_alaska_native_area_hawaiian_home_land',
  'state_or_part',
  'county_or_part'],
 '280': ['state',
  'american_indian_area_alaska_native_area_hawaiian_home_land_or_part'],
 '283': ['state',
  'american_indian_area_alaska_native_area_reservation_or_statistical_entity_only_or_part'],
 '286': ['state',
  'american_indian_area_off_reservation_trust_land_only_hawaiian_home_land_or_part'],
 '290': ['american_indian_area_alaska_native_area_hawaiian_home_land',
  'tribal_subdivision_remainder',
  'state_or_part'],
 '291': ['american_indian_area_reservation_only',
  'tribal_census_tract_or_part'],
 '292': ['american_indian_area_off_reservation_trust_land_only_hawaiian_home_land',
  'tribal_census_tract_or_part'],
 '293': ['american_indian_area_reservation_only',
  'tribal_census_tract_or_part',
  'tribal_block_group_or_part'],
 '294': ['american_indian_area_off_reservation_trust_land_only_hawaiian_home_land',
  'tribal_census_tract_or_part',
  'tribal_block_group_or_part'],
 '310': ['metropolitan_statistical_area_micropolitan_statistical_area'],
 '311': ['metropolitan_statistical_area_micropolitan_statistical_area',
  'state_or_part'],
 '312': ['metropolitan_statistical_area_micropolitan_statistical_area',
  'state_or_part',
  'principal_city_or_part'],
 '313': ['metropolitan_statistical_area_micropolitan_statistical_area',
  'state_or_part',
  'county'],
 '314': ['metropolitan_statistical_area_micropolitan_statistical_area',
  'metropolitan_division'],
 '315': ['metropolitan_statistical_area_micropolitan_statistical_area',
  'metropolitan_division',
  'state_or_part'],
 '316': ['metropolitan_statistical_area_micropolitan_statistical_area',
  'metropolitan_division',
  'state_or_part',
  'county'],
 '320': ['state',
  'metropolitan_statistical_area_micropolitan_statistical_area_or_part'],
 '321': ['state',
  'metropolitan_statistical_area_micropolitan_statistical_area_or_part',
  'principal_city_or_part'],
 '322': ['state',
  'metropolitan_statistical_area_micropolitan_statistical_area_or_part',
  'county'],
 '323': ['state',
  'metropolitan_statistical_area_micropolitan_statistical_area_or_part',
  'metropolitan_division_or_part'],
 '324': ['state',
  'metropolitan_statistical_area_micropolitan_statistical_area_or_part',
  'metropolitan_division_or_part',
  'county'],
 '330': ['combined_statistical_area'],
 '331': ['combined_statistical_area', 'state_or_part'],
 '332': ['combined_statistical_area',
  'metropolitan_statistical_area_micropolitan_statistical_area'],
 '333': ['combined_statistical_area',
  'metropolitan_statistical_area_micropolitan_statistical_area',
  'state_or_part'],
 '335': ['combined_new_england_city_and_town_area'],
 '336': ['combined_new_england_city_and_town_area', 'state_or_part'],
 '337': ['combined_new_england_city_and_town_area',
  'new_england_city_and_town_area'],
 '338': ['combined_new_england_city_and_town_area',
  'new_england_city_and_town_area',
  'state_or_part'],
 '340': ['state', 'combined_statistical_area_or_part'],
 '341': ['state',
  'combined_statistical_area_or_part',
  'metropolitan_statistical_area_micropolitan_statistical_area_or_part'],
 '345': ['state', 'combined_new_england_city_and_town_area_or_part'],
 '346': ['state',
  'combined_new_england_city_and_town_area_or_part',
  'new_england_city_and_town_area_or_part'],
 '350': ['new_england_city_and_town_area'],
 '351': ['new_england_city_and_town_area', 'state_or_part'],
 '352': ['new_england_city_and_town_area', 'state_or_part', 'principal_city'],
 '353': ['new_england_city_and_town_area', 'state_or_part', 'county_or_part'],
 '354': ['new_england_city_and_town_area',
  'state_or_part',
  'county_or_part',
  'county_subdivision'],
 '355': ['new_england_city_and_town_area', 'necta_division'],
 '356': ['new_england_city_and_town_area', 'necta_division', 'state_or_part'],
 '357': ['new_england_city_and_town_area',
  'necta_division',
  'state_or_part',
  'county_or_part'],
 '358': ['new_england_city_and_town_area',
  'necta_division',
  'state_or_part',
  'county_or_part',
  'county_subdivision'],
 '360': ['state', 'new_england_city_and_town_area_or_part'],
 '361': ['state', 'new_england_city_and_town_area_or_part', 'principal_city'],
 '362': ['state', 'new_england_city_and_town_area_or_part', 'county_or_part'],
 '363': ['state',
  'new_england_city_and_town_area_or_part',
  'county_or_part',
  'county_subdivision'],
 '364': ['state',
  'new_england_city_and_town_area_or_part',
  'necta_division_or_part'],
 '365': ['state',
  'new_england_city_and_town_area_or_part',
  'necta_division_or_part',
  'county_or_part'],
 '366': ['state',
  'new_england_city_and_town_area_or_part',
  'necta_division_or_part',
  'county_or_part',
  'county_subdivision'],
 '400': ['urban_area'],
 '410': ['urban_area', 'state'],
 '430': ['urban_area', 'state', 'county'],
 '500': ['state', 'congressional_district'],
 '510': ['state', 'congressional_district', 'county_or_part'],
 '550': ['state',
  'congressional_district',
  'american_indian_area_alaska_native_area_hawaiian_home_land_or_part'],
 '610': ['state', 'state_legislative_district_upper_chamber'],
 '612': ['state',
  'state_legislative_district_upper_chamber',
  'county_or_part'],
 '620': ['state', 'state_legislative_district_lower_chamber'],
 '622': ['state',
  'state_legislative_district_lower_chamber',
  'county_or_part'],
 '795': ['state', 'public_use_microdata_area'],
 '860': ['zip_code_tabulation_area'],
 '950': ['state', 'school_district_elementary'],
 '960': ['state', 'school_district_secondary'],
 '970': ['state', 'school_district_unified']}

Loading Data for Leaves

Once we know the variables we want, for example the leaves of group B03002 that we found above, and we select one of the geogpraphy types and fill in the particular values we want at each level, we can load data for them. In our case, we will use the one with the key '610', which represents the districts of the upper house of the legeslature of a state. The value associated with that key is a list of the keyword arguments we can pass to censusdis.data.download to specifiy what state and what district or disctricts within the state we want data for.

This call will load data for all the variablles at the leaves of the dataset we examined earlier for all state senate districts in NJ by using the keyword arguments state=STATE_NJ and state_legislative_district_upper_chamber="*".

ced.download(
    DATASET, YEAR, leaves, state=STATE_NJ, state_legislative_district_upper_chamber="*"
)

	STATE	STATE_LEGISLATIVE_DISTRICT_UPPER_CHAMBER	B03002_003E	B03002_004E	B03002_005E	B03002_006E	B03002_007E	B03002_008E	B03002_010E	B03002_011E	B03002_013E	B03002_014E	B03002_015E	B03002_016E	B03002_017E	B03002_018E	B03002_020E	B03002_021E
0	34	001	142196	21618	925	1963	6	371	487	4965	24460	1820	191	107	25	9969	2943	1306
1	34	002	107867	31770	547	17962	98	650	694	5588	16532	1398	694	128	16	18301	4893	1337
2	34	003	149678	33696	452	4264	24	237	358	4327	15417	931	179	22	22	6880	3862	590
3	34	004	148292	39088	216	7032	17	507	411	5723	8885	2564	287	41	0	5886	1202	1466
4	34	005	114996	42660	206	7225	18	690	142	3783	16543	2455	239	64	24	24589	5088	963
5	34	006	144747	22647	138	21829	63	1447	570	4509	11936	1762	92	40	71	9901	2583	1188
6	34	007	134612	47217	52	12108	50	871	561	7288	9159	1511	332	207	4	4596	2102	948
7	34	008	160610	22823	35	7878	113	388	561	6264	8291	1528	170	49	134	4174	2510	984
8	34	009	194162	7586	117	5480	46	418	196	4949	11273	928	12	64	0	3328	1989	183
9	34	010	188982	7378	188	5765	136	986	209	4130	11925	653	114	34	0	4483	2396	689
10	34	011	139559	29607	133	7849	11	776	599	4654	18109	1774	297	46	15	10266	3385	1317
11	34	012	161981	14825	172	17919	0	321	543	3885	16380	1193	141	136	0	4129	3543	428
12	34	013	176797	5123	115	17925	71	433	762	3196	10866	207	154	66	9	2256	1645	505
13	34	014	129659	18482	222	37101	63	284	591	3335	20948	1050	28	167	7	4978	2207	849
14	34	015	88586	55027	407	23661	29	161	278	3645	23449	2288	525	35	164	9688	4578	468
15	34	016	138275	11402	512	48874	29	1049	1224	4449	12458	581	168	51	46	3262	2454	458
16	34	017	70856	45295	353	52596	24	1699	1144	3791	31629	2269	474	129	6	13613	2775	602
17	34	018	96906	16389	144	69965	198	1125	1769	4270	13519	716	107	139	9	7926	3235	1546
18	34	019	82172	21725	234	37332	91	1005	836	2655	52658	3034	493	348	42	15201	7470	2693
19	34	020	43416	64102	124	9020	32	6246	1034	2581	39353	3078	894	278	0	50647	8436	1632
20	34	021	157029	7157	130	23822	4	2092	178	4102	13664	764	27	252	9	6667	3113	745
21	34	022	85166	48110	302	10880	0	1933	526	3266	30431	2802	751	82	53	27643	6986	1867
22	34	023	158090	8589	137	17450	67	547	823	2975	14501	537	76	227	10	5269	3280	900
23	34	024	172261	5102	54	5774	0	366	550	3145	13744	509	33	38	18	2185	2191	778
24	34	025	149612	8463	95	12998	0	520	435	4235	23251	561	398	33	43	8778	3354	1009
25	34	026	158999	3980	312	29933	68	560	710	3779	15035	276	187	124	0	2051	1752	515
26	34	027	135411	27575	149	27637	47	1903	600	5410	13974	1106	117	71	10	4368	3807	949
27	34	028	48262	124943	418	9828	0	1361	1550	3354	19526	2941	144	143	6	11208	3433	2255
28	34	029	39357	69740	518	6621	133	3461	775	2477	51169	4322	444	140	78	35428	11447	2838
29	34	030	198248	5260	79	4598	0	361	560	1229	13560	498	163	19	0	7169	2567	499
30	34	031	61153	54849	533	48350	68	1749	1560	4958	26783	4000	426	575	0	23355	6615	2378
31	34	032	61149	6135	235	25696	0	1031	920	2492	73439	2620	1182	731	38	42161	14261	3065
32	34	033	79083	10164	264	36231	280	994	886	4272	53524	4358	744	366	13	21507	10898	2940
33	34	034	62307	86049	245	11297	22	1408	1534	3534	23737	2531	244	73	86	20076	4877	1035
34	34	035	48580	41180	60	9525	8	994	418	2014	45974	2416	256	116	35	54886	10741	2598
35	34	036	96442	8583	293	24153	90	779	591	2463	59863	1956	538	391	6	15771	14727	1889
36	34	037	83635	28162	285	55617	44	949	1060	3964	32260	2368	289	169	23	8331	4299	3252
37	34	038	125937	9772	342	33934	43	578	467	3175	29169	1151	261	254	12	5890	5918	1866
38	34	039	159377	4062	328	26230	0	550	971	3655	17047	730	100	278	0	2499	2966	954
39	34	040	164360	4799	219	17246	129	508	428	3114	16899	361	27	72	0	3039	3884	604

Error Handling

What happens if we get one of the keywords wrong? We can always look a few cells up at the output from cgeo.geo_path_snake_specs, but we also get a friendly exception message to tell us what the options are.

try:
    ced.download(DATASET, YEAR, leaves, state=STATE_NJ, unknown_geo="*")
except ced.CensusApiException as e:
    print(e)

Unable to match the geography specification {'state': '34', 'unknown_geo': '*'}.
Supported geographies for dataset='acs/acs5' in year=2020 are:
['us']
['region']
['division']
['state']
['state', 'county']
['state', 'county', 'county_subdivision']
['state', 'county', 'county_subdivision', 'subminor_civil_division']
['state', 'county', 'county_subdivision', 'place_remainder_or_part']
['state', 'county', 'tract']
['state', 'county', 'tract', 'block_group']
['state', 'place', 'county_or_part']
['state', 'place']
['state', 'consolidated_city']
['state', 'consolidated_city', 'place_or_part']
['state', 'alaska_native_regional_corporation']
['american_indian_area_alaska_native_area_hawaiian_home_land']
['american_indian_area_alaska_native_area_hawaiian_home_land', 'tribal_subdivision_remainder']
['american_indian_area_alaska_native_area_reservation_or_statistical_entity_only']
['american_indian_area_off_reservation_trust_land_only_hawaiian_home_land']
['american_indian_area_alaska_native_area_hawaiian_home_land', 'tribal_census_tract']
['american_indian_area_alaska_native_area_hawaiian_home_land', 'tribal_census_tract', 'tribal_block_group']
['american_indian_area_alaska_native_area_hawaiian_home_land', 'state_or_part']
['american_indian_area_alaska_native_area_hawaiian_home_land', 'state_or_part', 'place_remainder_or_part']
['american_indian_area_alaska_native_area_hawaiian_home_land', 'state_or_part', 'county_or_part']
['state', 'american_indian_area_alaska_native_area_hawaiian_home_land_or_part']
['state', 'american_indian_area_alaska_native_area_reservation_or_statistical_entity_only_or_part']
['state', 'american_indian_area_off_reservation_trust_land_only_hawaiian_home_land_or_part']
['american_indian_area_alaska_native_area_hawaiian_home_land', 'tribal_subdivision_remainder', 'state_or_part']
['american_indian_area_reservation_only', 'tribal_census_tract_or_part']
['american_indian_area_off_reservation_trust_land_only_hawaiian_home_land', 'tribal_census_tract_or_part']
['american_indian_area_reservation_only', 'tribal_census_tract_or_part', 'tribal_block_group_or_part']
['american_indian_area_off_reservation_trust_land_only_hawaiian_home_land', 'tribal_census_tract_or_part', 'tribal_block_group_or_part']
['metropolitan_statistical_area_micropolitan_statistical_area']
['metropolitan_statistical_area_micropolitan_statistical_area', 'state_or_part']
['metropolitan_statistical_area_micropolitan_statistical_area', 'state_or_part', 'principal_city_or_part']
['metropolitan_statistical_area_micropolitan_statistical_area', 'state_or_part', 'county']
['metropolitan_statistical_area_micropolitan_statistical_area', 'metropolitan_division']
['metropolitan_statistical_area_micropolitan_statistical_area', 'metropolitan_division', 'state_or_part']
['metropolitan_statistical_area_micropolitan_statistical_area', 'metropolitan_division', 'state_or_part', 'county']
['state', 'metropolitan_statistical_area_micropolitan_statistical_area_or_part']
['state', 'metropolitan_statistical_area_micropolitan_statistical_area_or_part', 'principal_city_or_part']
['state', 'metropolitan_statistical_area_micropolitan_statistical_area_or_part', 'county']
['state', 'metropolitan_statistical_area_micropolitan_statistical_area_or_part', 'metropolitan_division_or_part']
['state', 'metropolitan_statistical_area_micropolitan_statistical_area_or_part', 'metropolitan_division_or_part', 'county']
['combined_statistical_area']
['combined_statistical_area', 'state_or_part']
['combined_statistical_area', 'metropolitan_statistical_area_micropolitan_statistical_area']
['combined_statistical_area', 'metropolitan_statistical_area_micropolitan_statistical_area', 'state_or_part']
['combined_new_england_city_and_town_area']
['combined_new_england_city_and_town_area', 'state_or_part']
['combined_new_england_city_and_town_area', 'new_england_city_and_town_area']
['combined_new_england_city_and_town_area', 'new_england_city_and_town_area', 'state_or_part']
['state', 'combined_statistical_area_or_part']
['state', 'combined_statistical_area_or_part', 'metropolitan_statistical_area_micropolitan_statistical_area_or_part']
['state', 'combined_new_england_city_and_town_area_or_part']
['state', 'combined_new_england_city_and_town_area_or_part', 'new_england_city_and_town_area_or_part']
['new_england_city_and_town_area']
['new_england_city_and_town_area', 'state_or_part']
['new_england_city_and_town_area', 'state_or_part', 'principal_city']
['new_england_city_and_town_area', 'state_or_part', 'county_or_part']
['new_england_city_and_town_area', 'state_or_part', 'county_or_part', 'county_subdivision']
['new_england_city_and_town_area', 'necta_division']
['new_england_city_and_town_area', 'necta_division', 'state_or_part']
['new_england_city_and_town_area', 'necta_division', 'state_or_part', 'county_or_part']
['new_england_city_and_town_area', 'necta_division', 'state_or_part', 'county_or_part', 'county_subdivision']
['state', 'new_england_city_and_town_area_or_part']
['state', 'new_england_city_and_town_area_or_part', 'principal_city']
['state', 'new_england_city_and_town_area_or_part', 'county_or_part']
['state', 'new_england_city_and_town_area_or_part', 'county_or_part', 'county_subdivision']
['state', 'new_england_city_and_town_area_or_part', 'necta_division_or_part']
['state', 'new_england_city_and_town_area_or_part', 'necta_division_or_part', 'county_or_part']
['state', 'new_england_city_and_town_area_or_part', 'necta_division_or_part', 'county_or_part', 'county_subdivision']
['urban_area']
['urban_area', 'state']
['urban_area', 'state', 'county']
['state', 'congressional_district']
['state', 'congressional_district', 'county_or_part']
['state', 'congressional_district', 'american_indian_area_alaska_native_area_hawaiian_home_land_or_part']
['state', 'state_legislative_district_upper_chamber']
['state', 'state_legislative_district_upper_chamber', 'county_or_part']
['state', 'state_legislative_district_lower_chamber']
['state', 'state_legislative_district_lower_chamber', 'county_or_part']
['state', 'public_use_microdata_area']
['zip_code_tabulation_area']
['state', 'school_district_elementary']
['state', 'school_district_secondary']
['state', 'school_district_unified']