Datasets:

openenvironments
/

blockgroupvoting

License:

Dataset card Viewer Files Files and versions Community

Dataset Preview

View in Dataset Viewer

Viewer

Split (1)

train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 missing columns ({'Unnamed: 0'})

This happened while the csv dataset builder was generating data using

zip://2020blockgroupvoting.csv::/tmp/hf-datasets-cache/medium/datasets/34069626094448-config-parquet-and-info-openenvironments-blockgro-22ec08c0/downloads/74a411eddf9314f50d4a60108d97ceefde3538575c5a0308c24a4bd74d8bae61

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              REP: double
              DEM: double
              LIB: double
              OTH: double
              area: double
              gap: double
              precincts: int64
              BLOCKGROUP_GEOID: int64
              STATE: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1261
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), 'REP': Value(dtype='float64', id=None), 'DEM': Value(dtype='float64', id=None), 'LIB': Value(dtype='float64', id=None), 'OTH': Value(dtype='float64', id=None), 'area': Value(dtype='float64', id=None), 'gap': Value(dtype='float64', id=None), 'precincts': Value(dtype='int64', id=None), 'BLOCKGROUP_GEOID': Value(dtype='int64', id=None), 'STATE': Value(dtype='string', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 missing columns ({'Unnamed: 0'})
              
              This happened while the csv dataset builder was generating data using
              
              zip://2020blockgroupvoting.csv::/tmp/hf-datasets-cache/medium/datasets/34069626094448-config-parquet-and-info-openenvironments-blockgro-22ec08c0/downloads/74a411eddf9314f50d4a60108d97ceefde3538575c5a0308c24a4bd74d8bae61
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Open a discussion for direct support.

Unnamed: 0 int64	REP float64	DEM float64	LIB float64	OTH float64	area float64	gap float64	precincts int64	BLOCKGROUP_GEOID int64	STATE string
0	112.127739	534.97283	4.331114	2.085351	2,919,245.140912	-0	3	10,730,059,033	AL
1	238.139112	1,128.574145	9.350864	4.420572	6,173,555.610505	0	5	10,730,059,031	AL
2	87.400801	750.192068	4.658955	0.838575	3,483,037.530101	0	3	10,730,059,053	AL
3	64.588253	469.813181	2.064465	1.179694	2,076,797.852439	-0	3	10,730,059,051	AL
4	8.078341	376.039237	1.616617	0.646077	1,054,731.55096	-0	4	10,730,057,023	AL
5	51.200234	383.604457	2.65651	1.693264	2,403,020.441501	0	4	10,730,124,021	AL
6	79.793164	195.605818	2.216485	0.831187	3,637,223.466304	-0	2	10,730,124,022	AL
7	10.522264	201.831467	2.630322	2.045301	756,895.123049	-0.000001	4	10,730,106,023	AL
8	238.654759	580.659124	6.694034	2.638914	12,971,331.017016	0	5	10,730,124,023	AL
9	143.760124	326.829538	3.991349	1.831325	11,430,118.87901	-0	6	10,730,124,031	AL
10	222.973959	687.00585	5.172531	1.959651	19,074,758.680019	-33.984228	7	10,730,125,003	AL
11	22.314268	221.587827	2.134387	0.582107	1,476,230.555898	-0	3	10,730,001,005	AL
12	31.897073	337.739607	3.224635	0.856471	2,398,507.948856	-0	3	10,730,001,003	AL
13	39.414593	329.271154	6.186368	2.544282	1,270,075.982838	-0	3	10,730,003,001	AL
14	8.562229	131.423417	0.81545	0.271817	876,007.086049	-0	3	10,730,003,002	AL
15	8.331875	128.171993	0.796508	0.264843	856,890.172022	0	4	10,730,003,003	AL
16	4.712907	148.639373	1.284048	0.231841	1,762,284.842348	-0	2	10,730,004,001	AL
17	1.657523	58.519776	0.506465	0.092085	687,385.044923	0	1	10,730,004,002	AL
18	2.175824	75.694579	0.654957	0.11898	890,158.202254	0	2	10,730,004,003	AL
19	1.266542	44.71596	0.386999	0.070363	525,242.652044	-0	2	10,730,004,004	AL
20	71.428123	525.217772	2.363311	1.275591	2,333,474.831494	0	3	10,730,059,052	AL
21	123.728673	566.291265	4.743223	2.103171	3,124,692.64144	-0	2	10,730,059,071	AL
22	76.302532	373.365414	2.920402	1.334316	1,976,893.656698	-0	4	10,730,059,082	AL
23	46.779283	285.927011	1.623188	0.296123	1,150,937.730231	-0	5	10,730,059,083	AL
24	34.67965	146.007519	1.028368	0.325253	3,899,167.7263	0	2	10,730,125,001	AL
25	262.052674	160.134131	3.630936	0.736917	9,696,495.220525	0	5	10,730,125,004	AL
26	96.248866	112.990821	4.498529	1.837399	2,380,591.605085	-0	3	10,730,126,021	AL
27	230.098855	944.731922	6.148195	2.676441	509,916,033.278729	-0.00001	11	11,056,871,001	AL
28	82.780822	253.648929	1.48581	0.424517	75,251,141.848854	0	3	11,056,870,002	AL
29	18.589596	649.338516	5.618697	1.021009	7,633,297.071855	-0	5	10,730,004,005	AL
30	3.113403	86.699972	0.716425	0.343786	592,124.824336	0	2	10,730,005,001	AL
31	6.655781	148.569236	1.134693	0.503433	1,007,256.644511	0	3	10,730,005,003	AL
32	2.961518	163.306567	1.128197	0.282049	856,317.518598	-0	1	10,730,007,003	AL
33	4.280411	236.598217	1.623828	0.406029	1,257,436.864299	-0	4	10,730,007,002	AL
34	4.913426	173.707828	1.409904	0.128141	1,454,993.233037	-0	4	10,730,008,001	AL
35	44.863437	248.009699	1.633872	0.524201	1,132,214.731407	-0	3	10,730,059,081	AL
36	116.728042	144.230231	2.968986	0.62505	2,096,678.506808	0	1	10,730,100,012	AL
37	182.150702	232.263157	4.60479	1.04699	3,300,009.898488	0	3	10,730,100,014	AL
38	268.238166	213.165433	5.142299	1.972938	2,792,380.996555	-0	3	10,730,100,022	AL
39	643.460315	498.84932	12.289231	4.721534	6,366,240.290638	-0	3	10,730,100,023	AL
40	78.486003	92.477937	3.716607	1.530368	1,797,949.443111	-0	1	10,730,126,022	AL
41	245.753954	353.954107	10.527954	3.673898	8,102,028.933388	0	3	10,730,127,014	AL
42	85.038708	84.135944	2.096353	0.409198	7,015,331.836037	0	3	10,730,127,013	AL
43	231.003065	143.172364	8.361857	2.823229	1,152,674.91118	-0	2	10,730,128,022	AL
44	247.729729	21.678269	1.259203	0.82999	66,733,249.780757	0	3	10,730,141,024	AL
45	14.708646	673.154725	4.328648	1.082162	7,665,600.928238	-0	6	10,730,008,002	AL
46	1.992918	118.0154	0.693189	0.173297	815,818.800836	0	1	10,730,008,005	AL
47	7.456836	434.547973	2.53261	0.637728	2,982,590.384361	0	8	10,730,008,004	AL
48	4.624681	181.671249	1.087813	0.297324	977,311.326172	-0	2	10,730,011,001	AL
49	14.869891	419.652202	2.101221	0.37388	2,514,746.185033	-0	4	10,730,011,002	AL
50	8.782565	299.888479	1.46376	0.182975	1,812,672.737106	0	5	10,730,011,004	AL
51	18.869396	607.973357	2.985736	0.38107	3,712,726.140944	-0	4	10,730,011,005	AL
52	101.092225	78.305655	1.929808	0.742234	997,999.62978	-0	2	10,730,100,024	AL
53	3.040316	117.077236	0.196098	0.000055	1,038,114.191843	0	3	10,730,101,001	AL
54	63.676923	286.902517	1.132386	0.687844	2,429,193.955394	-0	5	10,730,102,002	AL
55	13.227154	173.161878	1.097683	0.049066	710,924.421344	0	3	10,730,102,003	AL
56	22.577728	265.333865	1.04045	1.723924	854,396.64133	-0	4	10,730,103,011	AL
57	44.997293	385.741079	2.330832	0.239091	1,566,008.843193	-0	5	10,730,103,012	AL
58	91.31183	403.547151	2.167651	0.423988	1,974,531.148061	0	3	10,730,104,015	AL
59	540.785429	355.725777	12.631056	8.224873	5,475,468.002428	-0.00038	3	10,730,129,101	AL
60	859.023641	367.483849	16.339953	17.340745	4,670,074.967507	0	3	10,730,129,052	AL
61	28.716502	1,173.228924	7.418931	2.281424	6,150,682.309525	-0	6	10,730,011,003	AL
62	11.093241	389.218115	1.271345	1.243118	4,827,121.301964	-0	6	10,730,012,001	AL
63	13.402467	546.917596	1.555896	2.14817	5,616,016.094489	-0	7	10,730,012,002	AL
64	2.928544	114.088844	0.46027	0.214026	776,231.623972	-0	3	10,730,012,004	AL
65	6.32437	255.865563	1.07079	0.535395	889,356.938538	0	2	10,730,014,001	AL
66	6.818855	302.385175	1.380412	0.666205	1,043,594.216462	0	4	10,730,014,003	AL
67	8.387228	372.94729	1.68017	0.831099	1,285,614.98063	-0	6	10,730,014,002	AL
68	5.030381	81.378628	0.700972	0.247312	431,144.191279	0	3	10,730,015,004	AL
69	10.529383	197.126546	1.108315	0.45917	795,231.565313	0	3	10,730,015,001	AL
70	29.019069	131.210621	0.694236	0.138847	631,467.43929	0	2	10,730,104,011	AL
71	33.425146	250.830765	1.507214	0.118307	1,055,452.645417	-0	2	10,730,104,012	AL
72	40.218332	174.126135	0.948209	0.181737	864,851.848432	-0	3	10,730,104,014	AL
73	19.672117	311.187381	2.048051	0.002087	1,165,920.555124	0	4	10,730,104,013	AL
74	52.411267	222.88894	2.049056	0.300741	3,458,740.241727	-0	5	10,730,104,021	AL
75	11.270556	219.697792	1.07423	0.433377	1,147,416.863256	-0	5	10,730,105,001	AL
76	10.578799	149.539819	0.130602	0.52241	1,013,222.566377	-0	2	10,730,105,002	AL
77	29.642758	447.738815	0.848965	1.394774	2,857,568.687248	0	4	10,730,105,003	AL
78	36.618117	873.337584	7.703414	4.90151	3,262,935.502212	0	7	10,730,106,021	AL
79	3.8735	184.033089	0.843186	0.419343	519,830.332411	0	2	10,730,106,024	AL
80	4.852319	270.294817	1.010922	0.40442	738,509.885025	0	2	10,730,106,025	AL
81	10.470446	148.625947	0.543919	1.087839	654,071.227396	-0	3	10,730,020,003	AL
82	415.776648	466.815954	12.538091	9.069023	3,981,000.653593	-0	5	10,730,129,071	AL
83	7.55413	353.155587	1.510826	0.755413	1,248,122.231103	-0	5	10,730,130,021	AL
84	4.653134	147.373517	0.504563	0.209323	629,539.104569	-0	2	10,730,015,003	AL
85	11.949846	188.383089	1.813718	0.617302	848,134.328713	-0	3	10,730,016,003	AL
86	12.012266	255.499	1.144025	0.762684	712,782.398261	-0	3	10,730,019,022	AL
87	11.195874	211.798455	1.067235	0.623975	680,177.599746	0	2	10,730,019,023	AL
88	66.469817	733.159631	3.395519	5.523254	3,519,231.081865	0.000001	5	10,730,020,001	AL
89	12.383073	152.121304	0.844885	0.141717	682,128.601839	-0	2	10,730,132,002	AL
90	24.343507	73.743014	0.831242	0.118749	680,030.929225	-0	1	11,130,302,001	AL
91	4.258624	237.240867	0.887213	0.354885	648,189.312736	-0	3	10,730,106,026	AL
92	24.199043	925.846062	4.343644	3.649875	2,603,009.495522	-0	4	10,730,106,022	AL
93	22.626466	746.324178	2.846042	0.00884	2,508,394.986619	0	5	10,730,106,031	AL
94	298.485018	452.05561	14.502848	11.843076	2,027,744.49341	0	5	10,730,107,021	AL
95	367.008258	611.405461	19.291645	19.427796	1,754,335.912828	-0	6	10,730,107,022	AL
96	122.284203	132.296179	4.76432	3.65955	744,835.719572	0	2	10,730,107,023	AL
97	587.015186	541.013634	18.384504	21.773969	2,103,294.753388	0	4	10,730,107,031	AL
98	12.449045	581.996939	2.489817	1.24486	2,056,799.486498	0	5	10,730,130,022	AL
99	13.624456	630.797778	4.168172	0.313709	2,186,217.690051	0	5	10,730,131,002	AL

End of preview.

Problem and Opportunity

In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected.

For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups:

are exclusive, and do not overlap
are adjacent, fully covering their corresponding state and potentially county
have roughly the same size in area, population and voter presence

Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure.

The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates.

Downloads last month: 1

Edit dataset card