latest_ny_times_counties_with_populations (view)

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⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.

Data license: LICENSE · Data source: The New York Times · About: simonw/covid-19-datasette

53 rows where state = "North Dakota" sorted by cases_per_million descending

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state 1

  • North Dakota · 53 ✖
date county state fips cases deaths population deaths_per_million cases_per_million ▲
2022-05-13 Rolette North Dakota 38079 6121 43 14176 3033 431786
2022-05-13 Stark North Dakota 38089 11683 86 31489 2731 371018
2022-05-13 Burleigh North Dakota 38015 35087 303 95626 3168 366919
2022-05-13 Morton North Dakota 38059 11274 142 31364 4527 359456
2022-05-13 Stutsman North Dakota 38093 7113 97 20704 4685 343556
2022-05-13 Eddy North Dakota 38027 761 6 2287 2623 332750
2022-05-13 Hettinger North Dakota 38041 828 8 2499 3201 331332
2022-05-13 Cass North Dakota 38017 59940 333 181923 1830 329480
2022-05-13 Grand Forks North Dakota 38035 22468 125 69451 1799 323508
2022-05-13 Sioux North Dakota 38085 1350 19 4230 4491 319148
2022-05-13 Walsh North Dakota 38099 3374 34 10641 3195 317075
2022-05-13 Ramsey North Dakota 38071 3638 57 11519 4948 315826
2022-05-13 Mercer North Dakota 38057 2577 24 8187 2931 314767
2022-05-13 Foster North Dakota 38031 992 23 3210 7165 309034
2022-05-13 Ward North Dakota 38101 20887 251 67641 3710 308792
2022-05-13 Mountrail North Dakota 38061 3254 37 10545 3508 308582
2022-05-13 Dickey North Dakota 38021 1496 39 4872 8004 307060
2022-05-13 Pembina North Dakota 38067 1996 16 6801 2352 293486
2022-05-13 Towner North Dakota 38095 641 12 2189 5481 292827
2022-05-13 Barnes North Dakota 38003 3024 47 10415 4512 290350
2022-05-13 Ransom North Dakota 38073 1515 28 5218 5366 290341
2022-05-13 Benson North Dakota 38005 1980 23 6832 3366 289812
2022-05-13 McLean North Dakota 38055 2737 49 9450 5185 289629
2022-05-13 Williams North Dakota 38105 10496 68 37589 1809 279230
2022-05-13 McIntosh North Dakota 38051 688 9 2497 3604 275530
2022-05-13 Traill North Dakota 38097 2205 26 8036 3235 274390
2022-05-13 Bowman North Dakota 38011 828 9 3024 2976 273809
2022-05-13 Golden Valley North Dakota 38033 476 4 1761 2271 270300
2022-05-13 Adams North Dakota 38001 598 10 2216 4512 269855
2022-05-13 Pierce North Dakota 38069 1065 32 3975 8050 267924
2022-05-13 Nelson North Dakota 38063 768 18 2879 6252 266759
2022-05-13 Wells North Dakota 38103 1018 12 3834 3129 265519
2022-05-13 Sargent North Dakota 38081 1031 9 3898 2308 264494
2022-05-13 Griggs North Dakota 38039 583 2 2231 896 261317
2022-05-13 Bottineau North Dakota 38009 1598 26 6282 4138 254377
2022-05-13 Richland North Dakota 38077 4108 23 16177 1421 253940
2022-05-13 LaMoure North Dakota 38045 1015 20 4046 4943 250865
2022-05-13 Renville North Dakota 38075 556 15 2327 6446 238934
2022-05-13 Dunn North Dakota 38025 1041 8 4424 1808 235307
2022-05-13 Logan North Dakota 38047 435 12 1850 6486 235135
2022-05-13 McHenry North Dakota 38049 1318 31 5745 5395 229416
2022-05-13 Sheridan North Dakota 38083 299 7 1315 5323 227376
2022-05-13 Emmons North Dakota 38029 728 18 3241 5553 224622
2022-05-13 Cavalier North Dakota 38019 822 7 3762 1860 218500
2022-05-13 McKenzie North Dakota 38053 3272 25 15024 1664 217784
2022-05-13 Kidder North Dakota 38043 510 13 2480 5241 205645
2022-05-13 Steele North Dakota 38091 388 2 1890 1058 205291
2022-05-13 Burke North Dakota 38013 433 3 2115 1418 204728
2022-05-13 Divide North Dakota 38023 459 4 2264 1766 202738
2022-05-13 Grant North Dakota 38037 451 11 2274 4837 198328
2022-05-13 Oliver North Dakota 38065 329 5 1959 2552 167942
2022-05-13 Billings North Dakota 38007 145 1 928 1077 156250
2022-05-13 Slope North Dakota 38087 63 0 750 0 84000

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CREATE VIEW latest_ny_times_counties_with_populations AS 
    select
      ny_times_us_counties.date,
      ny_times_us_counties.county,
      ny_times_us_counties.state,
      ny_times_us_counties.fips,
      ny_times_us_counties.cases,
      ny_times_us_counties.deaths,
      us_census_county_populations_2019.population,
      1000000 * ny_times_us_counties.deaths / us_census_county_populations_2019.population as deaths_per_million,
      1000000 * ny_times_us_counties.cases / us_census_county_populations_2019.population as cases_per_million
    from
      ny_times_us_counties
      join us_census_county_populations_2019 on ny_times_us_counties.fips = us_census_county_populations_2019.fips
    where
      "date" = (
        select
          max(date)
        from
          ny_times_us_counties
      )
    order by
      deaths_per_million desc;