  |
はてなキーワード: Mongoliaとは
ソ連式のちゃんとした戦争ってのはな、機甲戦力（つまり戦車隊）に敵の前線陣地を食い破らせ余勢が止まるまで走らせて、その傷口にさらに新鮮な第２弾の機甲戦力（第二梯団、記事中では第二梯隊と表記）をぶち込んで敵が死ぬまでそれを繰り返し続けるんだよ。
生身の歩兵を陣地にぶち当ててちまちま抜いてくことじゃないんだよ。
やれやれ「機甲戦力」ではなく「諸兵科連合」であることがГлубокая операцияの肝です
下記を読んでください、本当の Глубокая операция を食べさせてあげますよ
U.S. Army Combined Arms Center
Chapter 5 A Look at Soviet Deep Operations
Maj. Elvis E. Blumenstock, US Marine Corps
Of these theories, one of the more significant is the theory of deep battle (a tactical measure) and deep operations (a more complex operational measure).
Tukhachevsky continued to refine his concept of successive operations.
By 1926, he wrote:
Modern operations involve the concentration of forces necessary to strike a blow, and the infliction of continual and uninterrupted blows of these forces against the enemy throughout an extremely deep area. . . .
Battle in a modern operation stretches out into a series of battles not only along the front but also in depth until that time when either the enemy has been struck by a final annihilating blow or when the offensive forces are exhausted.
The Field Regulation of 1936 defined deep operations as:
Simultaneous assault on enemy defenses by aviation and artillery to the depths of the defense, penetration of the tactical zone of the defense by attacking units with widespread use of tank forces, and violent development of tactical success into operational success with the aim of the complete encirclement and destruction of the enemy.
According to Tukhachevsky, deep operations objectives included operational reserves, army headquarters, major communication sites, airfields,
The Soviet concept expected both fronts and armies to conduct deep operations.
It expected a front to attack to depths of 150–250 kilometers and an army to attack to depths of 70 to 100 kilometers.
Operational formations consisted of an attack echelon, an exploitation echelon (mobile group), reserves, aviation, and airborne forces.
By 1936, the Soviets had created new mobile units to spearhead deep operations and fielded airborne units to cooperate with exploitation forces.
Mobile groups of tank, mechanized, and cavalry corps composed these exploitation forces.
The concept also placed a strong emphasis on air defense by aviation and air defense artillery.
The general pattern for the conduct of deep operations changed little after 1943.
Offensive operations depended on maneuver.
Each front had a mobile group of one to three tank armies; each army had a mobile group of one to two tank or mechanized corps. Fronts routinely attacked to depths of 150 to 300 kilometers, armies to depths of 100 to 150 kilometers.
Operational pursuit was important and occurred both day and night at high tempo.
Typically, first echelon forces penetrated the tactical defenses.
The mobile group then attacked into the enemy rear to “perform the mission of creating conditions for developing tactical success into operational, and sometimes into operational-strategic.”
Mobile groups were not simply second echelon forces.
Mobile groups had specific missions that included the following:
1) defeating enemy operational reserves (one of the main missions),
2) encircling enemy forces,
3) fixing enemy reserves in place,
4) occupying important objectives for follow-on forces,
5) pursuing a retreating enemy,
6) disrupting command and control,
7) disorganizing the enemy rear
The campaign plan the General Staff designed to fulfill the operational strategy was simple, yet bold.
The Transbaikal Front was to make the main attack, driving from Mongolia through Manchuria and preventing Japanese reinforcement from northern China.
This attack would maneuver into the Japanese rear.
The 1st Far Eastern Front was the primary supporting attack.
It was to outflank the Japanese in the east, prevent reinforcement
from Japan, and attack the major command and control centers and transportation nodes located at Harbin and Kirin.
After these two Fronts had converged in the Mukden, Changchun, Harbin, and Kirin areas of south central Manchuria, they would advance together to crush the final Japanese resistance and capture Port Arthur, an important naval base in the south.
The 2nd Far Eastern Front was to fix Japanese forces in the north.
The use of maneuver by the Soviets enhanced the surprise their deception caused.
Over 41 percent of the Soviet forces conducted the main attack along the Transbaikal Front, which faced the weakest Japanese forces.
Designed to envelop the entire Kwantung Army, the Soviets desired this Front to maneuver into the Japanese rear, attack key command centers and transportation nodes, and prevent reinforcement from northern China.
Retreating Japanese units found themselves facing the Soviet main attack.
The Soviets designed the main supporting attack, the 1st Far
Eastern Front, to envelop Japanese forces from the East and to attack key command centers and transportation nodes.
All levels down to divisions relied on maneuver, particularly in the Transbaikal Front.
Powerful, fast-moving, combined-arms advanced detachments outflanked Japanese defensive positions and operated deep in the Japanese rear seeking command and control sites.
They bypassed, isolated, and later reduced Japanese strongpoints. This enabled the main fighting forces to continue to move and not get bogged down into set-piece battles.
Soviet units achieved rapid momentum, which made Japanese efforts to move into defensive positions futile.
Synchronization was a critical aspect of this campaign. A few important examples are as follows:
1) The main feature of this campaign is the employment of integrated combined-arms. Ground, sea, and air forces were mutually supporting.
Requirements determined specific force adjustments.
The net effect was an integrated, responsive, all-purpose military. This close coordination helped ensure success.
2) Soviet forces attacked on every possible axis simultaneously on all fronts.
They synchronized these movements with aerial reconnaissance, deep interdiction strikes, and airborne assaults and amphibious landings on key objectives in the enemy center, rear, and flanks.
This pinned down Japanese forces along the entire length of the front. Japanese commanders were unable to determine which effort was the main attack.
The use of high speed advances and maneuver to bypass and isolate Japanese defenses left Japanese commanders confused and off-balance.
Moreover, Japanese commanders were unable to regroup, retaliate, or counterattack effectively because of the physical separation.
The Manchurian Campaign remains a subject of intensive study by Soviet military professionals.
They view this campaign as the successful application of Tukhachevsky’s deep operations theory.
In particular, the success of the 6th Guards Tank Army, the primary operational level mobile group, has been promoted as a useful example for training commanders and staffs today.
The 6th Guards Tank Army is clearly the predecessor of the operational maneuver group of the 1980s.
Much of modern Soviet military art can be attributed to this campaign.
Soviet military leaders have characterized the Manchurian Campaign as an instructive model for modern offensive operations.
It is considered the main precedent for strategically decisive, offensive operations.
It is a campaign worthy of study by American military professionals as well.
異次元の少子化対策が求められている、岸田令和日本である。しかし、具体的には、どのような対策が有効なのか。
対策の効果を測定するためにも、出生率と結びつきが強く、しかも、分かりやすい指標が求められている。
そこで、今回は、世界経済フォーラムが発表する、ジェンダー・ギャップ・ランキングに注目したい。
ジェンダー開発学の分野では、ジェンダー・ギャップ・ランキングの順位が高い国で、ジェンダー平等が達成され、
女性が子育てと社会進出を両立しやすく、結果的に、少子化も改善されていることが知られている。
日本の少子化対策についての記事の中で、ジェンダー・ギャップ・ランキングの順位の低迷と、
例えば、次の記事では、題名の中にジェンダー・ギャップ・ランキングの順位と出生数が盛り込まれている。
ジェンダーギャップ121位、出生数90万人以下の日本で、女性たちの未来への備えとは
https://woman.nikkei.com/atcltrc/blog/shirakawatouko/post/dddad1acb2e14d2a9ad1acb2e1cd2a4b/
更に、次の記事では、ジェンダー分野の専門家の対談の中で、GDPや労働生産性と共に出生率、そして、
ジェンダー・ギャップ・ランキングの順位の恥ずかしさについての問題が指摘されている。
ジェンダー指数から、いわゆる、ジェンダー・ギャップ・ランキングの順位が計算される。
上野千鶴子×酒井順子「単身世帯は38％、最も多い家族の姿に。1985年から86年は『女・女格差』元年。女性が3分割された結果、中高年女性単身者の貧困が生まれた」
酒井 2022年の日本のジェンダー指数は、世界146ヵ国中116位。それを永田町のおじさんたちは、恥ずかしいとは思っていないんでしょうね。国際会議に出席する日本代表が男性だけでも平気でいられる。
酒井 出生率も落ちる一方。出生率が高いのは、共働きでケアの公共化がされている場合だと海外ではデータがはっきり出ているのに、なぜ変えようとしないのでしょう。
上野 おじさんたちが合理的選択をしないのは、ホモソーシャルな組織文化を守りたいからだとしか私には思えません。ホモソーシャルな集団のなかで、男として認められたい。そのためには自己犠牲もいとわない。
上で紹介したような関連性の指摘のみならず、実際に、ジェンダー・ギャップ・ランキングや、
類似する、ジェンダー・ギャップを示す指標と、少子化の関係性の分析もなされている。
https://news.yahoo.co.jp/byline/shirakawatoko/20211029-00265552
日本のジェンダーギャップと少子化。この二つはリンクしているとずっと言い続けてきた。日本は世界経済フォーラムが算出するジェンダーギャップ指数では156カ国中120位と先進国では最下位。下から数えた方が早い。先進国に限ってはジェンダーギャップ指数と出生率がリンクしていることがOECD（経済協力開発機構）の分析でわかっている。
2020年4月の内閣府政策統括官（経済社会システム担当）の資料には、「ジェンダーギャップ指数が高い（男女格差が少ない）ほど、出生率は高まる傾向」を示すグラフが掲載されている【図１】。女性が社会進出をすると一旦は少子化になるが、その後回復するかどうかは、ジェンダーギャップをいかに埋めるかにかかっている。
ところが、冒頭のツイートの図にあった通り、他の先進国では事情が違います。女性の社会進出と出生率が相関関係にあるのです。なぜかというと、女性の社会進出と子育てが、トレードオフの関係になっていないからです！
子どもが生まれたら、パートナーたる男性も、当事者としてしっかり家事育児にコミットします。これだけでも、女性の負担はケタ違いでしょう。みての通り、男性の家事育児の負担割合が高い国ほど、出生率が高いのがわかります（我が国は定位置の左下）。
しかし、ジェンダー・ギャップ・ランキングの話をすると、クソリプと呼ばれる意見が寄せられたり、
指標のことに異論を挟む声も、少なくない。そこで今回は、改めて白黒はっきりつけ、
ジェンダー・ギャップ・ランキングが少子化を説明できる、卓越した指標であることを示す。
Fertility rate, total (births per woman) - World Bank Data
ジェンダー・ギャップ・ランキングは、次の、同じ2020年のデータを使う。
https://www.weforum.org/reports/gender-gap-2020-report-100-years-pay-equality/
どちらにも掲載されている、153か国のデータを使って、ジェンダー・ギャップ・ランキングと、
Country Name | GGIRank | GGIScore | Fertility rate, total (births per woman)2020 |
| Iceland | 1 | 0.877 | 1.72 |
| Norway | 2 | 0.842 | 1.48 |
| Finland | 3 | 0.832 | 1.37 |
| Sweden | 4 | 0.82 | 1.66 |
| Nicaragua | 5 | 0.804 | 2.349 |
| New Zealand | 6 | 0.799 | 1.61 |
| Ireland | 7 | 0.798 | 1.63 |
| Spain | 8 | 0.795 | 1.23 |
| Rwanda | 9 | 0.791 | 3.873 |
| Germany | 10 | 0.787 | 1.53 |
| Latvia | 11 | 0.785 | 1.55 |
| Namibia | 12 | 0.784 | 3.349 |
| Costa Rica | 13 | 0.782 | 1.555 |
| Denmark | 14 | 0.782 | 1.67 |
| France | 15 | 0.781 | 1.83 |
| Philippines | 16 | 0.781 | 2.777 |
| South Africa | 17 | 0.78 | 2.401 |
| Switzerland | 18 | 0.779 | 1.46 |
| Canada | 19 | 0.772 | 1.4 |
| Albania | 20 | 0.769 | 1.4 |
| United Kingdom | 21 | 0.767 | 1.56 |
| Colombia | 22 | 0.758 | 1.737 |
| Moldova | 23 | 0.757 | 1.77 |
| Trinidad and Tobago | 24 | 0.756 | 1.631 |
| Mexico | 25 | 0.754 | 1.905 |
| Estonia | 26 | 0.751 | 1.58 |
| Belgium | 27 | 0.75 | 1.55 |
| Barbados | 28 | 0.749 | 1.628 |
| Belarus | 29 | 0.746 | 1.382 |
| Argentina | 30 | 0.746 | 1.911 |
| Cuba | 31 | 0.746 | 1.5 |
| Burundi | 32 | 0.745 | 5.177 |
| Lithuania | 33 | 0.745 | 1.48 |
| Austria | 34 | 0.744 | 1.44 |
| Portugal | 35 | 0.744 | 1.4 |
| Slovenia | 36 | 0.743 | 1.6 |
| Uruguay | 37 | 0.737 | 1.477 |
| Netherlands | 38 | 0.736 | 1.55 |
| Serbia | 39 | 0.736 | 1.48 |
| Poland | 40 | 0.736 | 1.38 |
| Jamaica | 41 | 0.735 | 1.358 |
| Bolivia | 42 | 0.734 | 2.651 |
| Lao PDR | 43 | 0.731 | 2.541 |
| Australia | 44 | 0.731 | 1.581 |
| Zambia | 45 | 0.731 | 4.379 |
| Panama | 46 | 0.73 | 2.344 |
| Zimbabwe | 47 | 0.73 | 3.545 |
| Ecuador | 48 | 0.729 | 2.051 |
| Bulgaria | 49 | 0.727 | 1.56 |
| Bangladesh | 50 | 0.726 | 2.003 |
| Luxembourg | 51 | 0.725 | 1.37 |
| Cabo Verde | 52 | 0.725 | 1.908 |
| United States | 53 | 0.724 | 1.6375 |
| Singapore | 54 | 0.724 | 1.1 |
| Romania | 55 | 0.724 | 1.6 |
| Mozambique | 56 | 0.723 | 4.713 |
| Chile | 57 | 0.723 | 1.537 |
| Honduras | 58 | 0.722 | 2.394 |
| Ukraine | 59 | 0.721 | 1.217 |
| Croatia | 60 | 0.72 | 1.48 |
| Bahamas, The | 61 | 0.72 | 1.394 |
| Madagascar | 62 | 0.719 | 3.918 |
| Slovak Republic | 63 | 0.718 | 1.57 |
| Israel | 64 | 0.718 | 2.9 |
| Uganda | 65 | 0.717 | 4.693 |
| Peru | 66 | 0.714 | 2.216 |
| Venezuela, RB | 67 | 0.713 | 2.23 |
| Tanzania | 68 | 0.713 | 4.795 |
| Bosnia and Herzegovina | 69 | 0.712 | 1.359 |
| North Macedonia | 70 | 0.711 | 1.3 |
| Montenegro | 71 | 0.71 | 1.75 |
| Kazakhstan | 72 | 0.71 | 3.13 |
| Botswana | 73 | 0.709 | 2.836 |
| Georgia | 74 | 0.708 | 1.971 |
| Thailand | 75 | 0.708 | 1.341 |
| Italy | 76 | 0.707 | 1.24 |
| Suriname | 77 | 0.707 | 2.371 |
| Czechia | 78 | 0.706 | 1.71 |
| Mongolia | 79 | 0.706 | 2.9 |
| El Salvador | 80 | 0.706 | 1.819 |
| Russian Federation | 81 | 0.706 | 1.505 |
| Ethiopia | 82 | 0.705 | 4.243 |
| Eswatini | 83 | 0.703 | 2.89 |
| Greece | 84 | 0.701 | 1.34 |
| Indonesia | 85 | 0.7 | 2.194 |
| Dominican Republic | 86 | 0.7 | 2.303 |
| Vietnam | 87 | 0.7 | 1.955 |
| Lesotho | 88 | 0.695 | 3.049 |
| Cambodia | 89 | 0.694 | 2.381 |
| Malta | 90 | 0.693 | 1.13 |
| Cyprus | 91 | 0.692 | 1.328 |
| Brazil | 92 | 0.691 | 1.649 |
| Kyrgyz Republic | 93 | 0.689 | 3 |
| Azerbaijan | 94 | 0.687 | 1.7 |
| Brunei Darussalam | 95 | 0.686 | 1.796 |
| Cameroon | 96 | 0.686 | 4.543 |
| Liberia | 97 | 0.685 | 4.174 |
| Armenia | 98 | 0.684 | 1.575 |
| Senegal | 99 | 0.684 | 4.454 |
| Paraguay | 100 | 0.683 | 2.497 |
| Nepal | 101 | 0.68 | 2.055 |
| Sri Lanka | 102 | 0.68 | 2 |
| Fiji | 103 | 0.678 | 2.495 |
| Malaysia | 104 | 0.677 | 1.818 |
| Hungary | 105 | 0.677 | 1.56 |
| China | 106 | 0.676 | 1.281 |
| Ghana | 107 | 0.673 | 3.623 |
| Korea, Rep. | 108 | 0.672 | 0.837 |
| Kenya | 109 | 0.671 | 3.397 |
| Belize | 110 | 0.671 | 1.999 |
| Sierra Leone | 111 | 0.668 | 4.08 |
| India | 112 | 0.668 | 2.051 |
| Guatemala | 113 | 0.666 | 2.484 |
| Myanmar | 114 | 0.665 | 2.174 |
| Mauritius | 115 | 0.665 | 1.44 |
| Malawi | 116 | 0.664 | 3.995 |
| Timor-Leste | 117 | 0.662 | 3.247 |
| Angola | 118 | 0.66 | 5.371 |
| Benin | 119 | 0.658 | 5.048 |
| United Arab Emirates | 120 | 0.655 | 1.46 |
| Japan | 121 | 0.652 | 1.34 |
| Kuwait | 122 | 0.65 | 2.14 |
| Maldives | 123 | 0.646 | 1.712 |
| Tunisia | 124 | 0.644 | 2.114 |
| Guinea | 125 | 0.642 | 4.489 |
| Vanuatu | 126 | 0.638 | 3.778 |
| Papua New Guinea | 127 | 0.635 | 3.274 |
| Nigeria | 128 | 0.635 | 5.309 |
| Burkina Faso | 129 | 0.635 | 4.869 |
| Turkiye | 130 | 0.635 | 1.917 |
| Bhutan | 131 | 0.635 | 1.433 |
| Algeria | 132 | 0.634 | 2.942 |
| Bahrain | 133 | 0.629 | 1.832 |
| Egypt, Arab Rep. | 134 | 0.629 | 2.96 |
| Qatar | 135 | 0.629 | 1.816 |
| Gambia, The | 136 | 0.628 | 4.777 |
| Tajikistan | 137 | 0.626 | 3.237 |
| Jordan | 138 | 0.623 | 2.873 |
| Mali | 139 | 0.621 | 6.035 |
| Togo | 140 | 0.615 | 4.323 |
| Mauritania | 141 | 0.614 | 4.455 |
| Cote d'Ivoire | 142 | 0.606 | 4.472 |
| Morocco | 143 | 0.605 | 2.353 |
| Oman | 144 | 0.602 | 2.687 |
| Lebanon | 145 | 0.599 | 2.103 |
| Saudi Arabia | 146 | 0.599 | 2.465 |
| Chad | 147 | 0.596 | 6.346 |
| Iran, Islamic Rep. | 148 | 0.584 | 1.708 |
| Congo, Dem. Rep. | 149 | 0.578 | 6.206 |
| Syrian Arab Republic | 150 | 0.567 | 2.798 |
| Pakistan | 151 | 0.564 | 3.555 |
| Iraq | 152 | 0.53 | 3.551 |
| Yemen, Rep. | 153 | 0.494 | 3.886 |
核を落とす目的としては
1. 敵対者への直接的な攻撃を行い、敵の戦争継続能力を奪う。
2. 示威行為として
の二つがあると思う。
WW2の際の広島・長崎への原爆の投下は、日本へのダメージを与えるだけではなく、戦後体制へ向けた他の主要国へのアピールでもあり、上記の二つを満たしていた。
さて、追加で考えるべきことは、可能な限り自国が核報復を受けないようにすることだろう。
攻撃対象として核を持つ国を選択した場合、余すところなく相手を蒸発させるのでなければ高確率で報復が想定される。
ただ、非核保有国を核攻撃した場合、その同盟国や核保有国による報復を招くことももちろん可能性としてある。
そこで「誰が撃ったのかわからない核攻撃」を演出することで、報復の可能性をさらに下げることを考える。
もちろん、世界情勢から誰しもが「撃ったのはあの国だ」と思うとしても、「落としたのは別のあの国だ」と言い張れればよい。
そうして多少なりともその意見に同調する意見を生み出せれば、他国のために核を使おうという意見をかなり減らすことができるだろう。
以上より次の条件で国を抽出してみる。
・2か国以上の核保有国と(陸上・海上)国境を接する国（本当は"近い国"で抽出したかったのだが、面倒なので簡略化。問題もある。後述）
・核保有国か核戦力共有を行っている国でない。
すると下記の21ヶ国が該当する。（それぞれGDP順）
3ヶ国と接する
2ヶ国と接する
ノルウェーと同じく三ヶ国と接していそうな韓国が入っていないのは、中国ともロシアとも国境は接していなかったから。
近い国で抽出しなかったので同じような抜けが他にもあるかもしれない。
（国境が接していない国の間でも領有権問題があるのは寡聞にして知らなかった）
・人口や経済の都市部集中度合い（一度に可能な限り大きい打撃を与える）
などの要素を加味してランキングを決めたいが、面倒なのでこれも略。
(-位 韓国: 3ヶ国)
1位 ノルウェー: 3ヶ国
2位 日本: 2ヶ国
3位 スペイン: 2ヶ国
おわり。
https://www.worldometers.info/coronavirus/
| Country | Deaths/1M pop |
|---|---|
| Peru | 5,963 |
| Brazil | 2,833 |
| Argentina | 2,535 |
| Colombia | 2,466 |
| USA | 2,297 |
| Belgium | 2,230 |
| Mexico | 2,206 |
| Italy | 2,189 |
| UK | 2,057 |
| Spain | 1,868 |
| France | 1,798 |
| Russia | 1,634 |
| Sweden | 1,472 |
| Germany | 1,144 |
| Netherlands | 1,071 |
| Malaysia | 878 |
| Israel | 869 |
| Turkey | 825 |
| Canada | 759 |
| 世界平均 | 643.4 |
| Sri Lanka | 638 |
| Mongolia | 518 |
| Indonesia | 517 |
| Philippines | 387 |
| Nepal | 382 |
| Myammar | 341 |
| India | 328 |
| Thailand | 275 |
| Vietnam | 224 |
| Egypt | 178 |
| Bangladesh | 167 |
| Norway | 164 |
| Cambodia | 164 |
| Japan | 145 |
| Pakistan | 126 |
| Singapore | 69 |
| Australia | 67 |
| S. Korea | 56 |
| Taiwan | 35 |
| Hong Kong | 28 |
| Laos | 9 |
| New Zealand | 6 |
| Bhutan | 4 |
| China | 3 |
Allowed countries
AE - United Arab Emirates
AL - Albania
BE - Belgium
BG - Bulgaria
BI - Burundi
BM - Bermuda
BN - Brunei Darussalam
BO - Bolivia (Plurinational State of)
BZ - Belize
CD - Congo (Democratic Republic of the)
CH - Switzerland
CK - Cook Islands
CN - China
CO - Colombia
CR - Costa Rica
CY - Cyprus
CZ - Czech Republic
DE - Germany
DO - Dominican Republic
EE - Estonia
EG - Egypt
FI - Finland
FK - Falkland Islands (Malvinas)
FM - Micronesia (Federated States of)
GB - United Kingdom of Great Britain and Northern Ireland
GG - Guernsey
GH - Ghana
GN - Guinea
GS - South Georgia and the South Sandwich Islands
HK - Hong Kong
HM - Heard Island and McDonald Islands
HT - Haiti
HU - Hungary
IN - India
IO - British Indian Ocean Territory
IR - Iran (Islamic Republic of)
JE - Jersey
JO - Jordan
KE - Kenya
KI - Kiribati
KW - Kuwait
KZ - Kazakhstan
LA - Lao People's Democratic Republic
LB - Lebanon
LI - Liechtenstein
LK - Sri Lanka
LR - Liberia
LU - Luxembourg
LY - Libya
ME - Montenegro
NO - Norway
NU - Niue
OM - Oman
PE - Peru
PL - Poland
PM - Saint Pierre and Miquelon
QA - Qatar
RE - Réunion
RS - Serbia
RU - Russian Federation
RW - Rwanda
SC - Seychelles
SH - Saint Helena, Ascension and Tristan da Cunha
SK - Slovakia
SO - Somalia
SZ - Swaziland
TF - French Southern Territories
TJ - Tajikistan
TL - Timor-Leste
TN - Tunisia
TO - Tonga
TR - Turkey
TZ - Tanzania, United Republic of
UG - Uganda
UM - United States Minor Outlying Islands
US - United States of America
UY - Uruguay
UZ - Uzbekistan
VC - Saint Vincent and the Grenadines
VE - Venezuela (Bolivarian Republic of)
VI - United States Virgin Islands
VU - Vanuatu
YE - Yemen
ZA - South Africa
ZM - Zambia
ZW - Zimbabwe
なんかやってしまった
wget https://raw.githubusercontent.com/lukes/ISO-3166-Countries-with-Regional-Codes/master/all/all.csv wget https://github.com/owid/covid-19-data/raw/master/public/data/owid-covid-data.csv SELECT country.name as '国名', covid.population AS '人口', covid.population_density as '密度', covid.gdp_per_capita as '一人あたりのGDP?', MAX(covid.total_cases) as '報告件数', ROUND(1.0 * MAX(covid.total_cases) / MAX(covid.population), 7) as '人口あたりの報告件数', MAX(covid.total_deaths) as '死者', ROUND(1.0 * MAX(covid.total_deaths) / MAX(covid.population), 9) as '人口あたりの死者件数', ROUND(1.0 * MAX(covid.total_deaths) / MAX(covid.total_cases) , 3) as '感染者死亡率' from all.csv as country inner join owid-covid-data.csv as covid on covid.iso_code = country.\"alpha-3\" WHERE country.\"sub-region\" = 'Eastern Asia' GROUP BY covid.iso_code ORDER BY 6 DESC"
| 国名 | 人口 | 密度 | 一人あたりのGDP? | 報告件数 | 人口あたりの報告件数 | 死者 | 人口あたりの死者件数 | 感染者死亡率 |
| Korea, Republic of | 51269183.0 | 527.967 | 35938.374 | 11165 | 0.0002178 | 266 | 5.188e-06 | 0.024 |
| Japan | 126476458.0 | 347.778 | 39002.223 | 16536 | 0.0001307 | 808 | 6.389e-06 | 0.049 |
| China | 1439323774.0 | 147.674 | 15308.712 | 84081 | 5.84e-05 | 4638 | 3.222e-06 | 0.055 |
| Mongolia | 3278292.0 | 1.98 | 11840.846 | 141 | 4.3e-05 | 0 | 0.0 | 0.0 |
| Taiwan, Province of China | 23816775.0 | 441 | 1.85e-05 | 7 | 2.94e-07 | 0.016 | ||
| Hong Kong | 7496988.0 | 7039.714 | 56054.92 | 0 | 0.0 | 00.0 |
| 国名 | 人口 | 密度 | 一人あたりのGDP? | 報告件数 | 人口あたりの報告件数 | 死者 | 人口あたりの死者件数 | 感染者死亡率 |
| Turkey | 84339067.0 | 104.914 | 25129.341 | 154500 | 0.0018319 | 4276 | 5.07e-05 | 0.028 |
| Iran (Islamic Republic of) | 83992953.0 | 49.831 | 19082.62 | 131652 | 0.0015674 | 7300 | 8.6912e-05 | 0.055 |
| India | 1380004385.0 | 450.419 | 6426.674 | 125101 | 9.07e-05 | 3720 | 2.696e-06 | 0.03 |
| China | 1439323774.0 | 147.674 | 15308.712 | 84081 | 5.84e-05 | 4638 | 3.222e-06 | 0.055 |
| Saudi Arabia | 34813867.0 | 15.322 | 49045.411 | 67719 | 0.0019452 | 364 | 1.0456e-05 | 0.005 |
| Pakistan | 220892331.0 | 255.573 | 5034.708 | 52437 | 0.0002374 | 1101 | 4.984e-06 | 0.021 |
| Qatar | 2881060.0 | 227.322 | 116935.6 | 40481 | 0.0140507 | 19 | 6.595e-06 | 0.0 |
| Singapore | 5850343.0 | 7915.731 | 85535.383 | 30426 | 0.0052007 | 23 | 3.931e-06 | 0.001 |
| Bangladesh | 164689383.0 | 1265.036 | 3523.984 | 30205 | 0.0001834 | 432 | 2.623e-06 | 0.014 |
| United Arab Emirates | 9890400.0 | 112.442 | 67293.483 | 27892 | 0.0028201 | 241 | 2.4367e-05 | 0.009 |
| Indonesia | 273523621.0 | 145.725 | 11188.744 | 20796 | 7.6e-05 | 1326 | 4.848e-06 | 0.064 |
| Kuwait | 4270563.0 | 232.128 | 65530.537 | 19564 | 0.0045811 | 138 | 3.2314e-05 | 0.007 |
| Israel | 8655541.0 | 402.606 | 33132.32 | 16690 | 0.0019282 | 279 | 3.2234e-05 | 0.017 |
| Japan | 126476458.0 | 347.778 | 39002.223 | 16536 | 0.0001307 | 808 | 6.389e-06 | 0.049 |
| Philippines | 109581085.0 | 351.873 | 7599.188 | 13597 | 0.0001241 | 857 | 7.821e-06 | 0.063 |
| Korea, Republic of | 51269183.0 | 527.967 | 35938.374 | 11165 | 0.0002178 | 266 | 5.188e-06 | 0.024 |
| Afghanistan | 38928341.0 | 54.422 | 1803.987 | 9216 | 0.0002367 | 205 | 5.266e-06 | 0.022 |
| Bahrain | 1701583.0 | 1935.907 | 43290.705 | 8414 | 0.0049448 | 12 | 7.052e-06 | 0.001 |
| Kazakhstan | 18776707.0 | 6.681 | 24055.588 | 7919 | 0.0004217 | 35 | 1.864e-06 | 0.004 |
| Malaysia | 32365998.0 | 96.254 | 26808.164 | 7137 | 0.0002205 | 115 | 3.553e-06 | 0.016 |
| Oman | 5106622.0 | 14.98 | 37960.709 | 6794 | 0.0013304 | 32 | 6.266e-06 | 0.005 |
| Armenia | 2963234.0 | 102.931 | 8787.58 | 5928 | 0.0020005 | 74 | 2.4973e-05 | 0.012 |
| Iraq | 40222503.0 | 88.125 | 15663.986 | 3964 | 9.86e-05 | 147 | 3.655e-06 | 0.037 |
| Azerbaijan | 10139175.0 | 119.309 | 15847.419 | 3855 | 0.0003802 | 46 | 4.537e-06 | 0.012 |
| Uzbekistan | 33469199.0 | 76.134 | 6253.104 | 3078 | 9.2e-05 | 13 | 3.88e-07 | 0.004 |
| Thailand | 69799978.0 | 135.132 | 16277.671 | 3040 | 4.36e-05 | 56 | 8.02e-07 | 0.018 |
| Tajikistan | 9537642.0 | 64.281 | 2896.913 | 2350 | 0.0002464 | 44 | 4.613e-06 | 0.019 |
| Kyrgyzstan | 6524191.0 | 32.333 | 3393.474 | 1364 | 0.0002091 | 14 | 2.146e-06 | 0.01 |
| Maldives | 540542.0 | 1454.433 | 15183.616 | 1274 | 0.0023569 | 4 | 7.4e-06 | 0.003 |
| Lebanon | 6825442.0 | 594.561 | 13367.565 | 1086 | 0.0001591 | 26 | 3.809e-06 | 0.024 |
| Sri Lanka | 21413250.0 | 341.955 | 11669.077 | 1068 | 4.99e-05 | 9 | 4.2e-07 | 0.008 |
| Cyprus | 875899.0 | 127.657 | 32415.132 | 927 | 0.0010583 | 17 | 1.9409e-05 | 0.018 |
| Georgia | 3989175.0 | 65.032 | 9745.079 | 723 | 0.0001812 | 12 | 3.008e-06 | 0.017 |
| Jordan | 10203140.0 | 109.285 | 8337.49 | 700 | 6.86e-05 | 9 | 8.82e-07 | 0.013 |
| Palestine, State of | 5101416.0 | 778.202 | 4449.898 | 608 | 0.0001192 | 4 | 7.84e-07 | 0.007 |
| Nepal | 29136808.0 | 204.43 | 2442.804 | 548 | 1.88e-05 | 3 | 1.03e-07 | 0.005 |
| Taiwan, Province of China | 23816775.0 | 441 | 1.85e-05 | 7 | 2.94e-07 | 0.016 | ||
| Viet Nam | 97338583.0 | 308.127 | 6171.884 | 324 | 3.3e-06 | 0 | 0.0 | 0.0 |
| Yemen | 29825968.0 | 53.508 | 1479.147 | 205 | 6.9e-06 | 33 | 1.106e-06 | 0.161 |
| Myanmar | 54409794.0 | 81.721 | 5591.597 | 201 | 3.7e-06 | 6 | 1.1e-07 | 0.03 |
| Brunei Darussalam | 437483.0 | 81.347 | 71809.251 | 141 | 0.0003223 | 1 | 2.286e-06 | 0.007 |
| Mongolia | 3278292.0 | 1.98 | 11840.846 | 141 | 4.3e-05 | 0 | 0.0 | 0.0 |
| Cambodia | 16718971.0 | 90.672 | 3645.07 | 124 | 7.4e-06 | 0 | 0.0 | 0.0 |
| Syrian Arab Republic | 17500657.0 | 59 | 3.4e-06 | 4 | 2.29e-07 | 0.068 | ||
| Bhutan | 771612.0 | 21.188 | 8708.597 | 24 | 3.11e-05 | 0 | 0.0 | 0.0 |
| Timor-Leste | 1318442.0 | 87.176 | 6570.102 | 24 | 1.82e-05 | 0 | 0.0 | 0.0 |
| Lao People's Democratic Republic | 7275556.0 | 29.715 | 6397.36 | 19 | 2.6e-06 | 0 | 0.0 | 0.0 |
| Hong Kong | 7496988.0 | 7039.714 | 56054.92 | 0 | 0.0 | 0 | 0.0 |
あってんのかなあ？
https://www.youtube.com/watch?v=ghZpyHP7B_g
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Allowed countries
AD - Andorra AE - United Arab Emirates AF - Afghanistan AG - Antigua and Barbuda AI - Anguilla AL - Albania AM - Armenia AO - Angola AQ - Antarctica AR - Argentina AS - American Samoa AT - Austria AU - Australia AW - Aruba AX - Åland Islands AZ - Azerbaijan BA - Bosnia and Herzegovina BB - Barbados BD - Bangladesh BE - Belgium BF - Burkina Faso BG - Bulgaria BH - Bahrain BI - Burundi BJ - Benin BL - Saint Barthélemy BM - Bermuda BN - Brunei Darussalam BO - Bolivia (Plurinational State of) BR - Brazil BS - Bahamas BT - Bhutan BV - Bouvet sland BW - Botswana BY - Belarus BZ - Belize CA - Canada CC - Cocos (Keeling) Islands CD - Congo (Democratic Republic of the) CF - Central African Republic CG - Republic of the Congo CH - Switzerland CI - Côte d'Ivoire CK - Cook Islands CL - Chile CM - Cameroon CN - China CO - Colombia CR - Costa Rica CU - Cuba CV - Cabo Verde CX - Christmas Island CY - Cyprus CZ - Czech Republic DE - Germany DJ - Djibouti DK - Denmark DM - Dominica DO - Dominican Republic DZ - Algeria EC - Ecuador EE - Estonia EG - Egypt EH - Western Sahara ER - Eritrea ES - Spain ET - Ethiopia FI - Finland FJ - Fiji FK - Falkland Islands (Malvinas) FM - Micronesia (Federated States of) FO - Faroe Islands FR - France GA - Gabon GB - United Kingdom of Great Britain and Northern Ireland GD - Grenada GE - Georgia (country) GF - French Guiana GG - Guernsey GH - Ghana GI - Gibraltar GL - Greenland GM - Gambia GN - Guinea GP - Guadeloupe GQ - Equatorial Guinea GR - Greece GS - South Georgia and the South Sandwich Islands GT - Guatemala GU - Guam GW - Guinea-Bissau GY - Guyana HK - Hong Kong HM - Heard Island and McDonald Islands HN - Honduras HR - Croatia HT - Haiti HU - Hungary ID - Indonesia IE - Republic of Ireland IL - Israel IM - Isle of Man IN - India IO - British Indian Ocean Territory IQ - Iraq IR - Iran (Islamic Republic of) IS - Iceland IT - Italy JE - Jersey JM - Jamaica JO - Jordan KE - Kenya KG - Kyrgyzstan KH - Cambodia KI - Kiribati KM - Comoros KN - Saint Kitts and Nevis KP - North Korea KR - Korea (Republic of) KW - Kuwait KY - Cayman Islands KZ - Kazakhstan LA - Lao People's Democratic Republic LB - Lebanon LC - Saint Lucia LI - Liechtenstein LK - Sri Lanka LR - Liberia LS - Lesotho LT - Lithuania LU - Luxembourg LV - Latvia LY - Libya MA - Morocco MC - Monaco MD - Moldova (Republic of) ME - Montenegro MG - Madagascar MH - Marshall Islands MK - Republic of Macedonia ML - Mali MM - Myanmar MN - Mongolia MO - Macao MP - Northern Mariana Islands MQ - Martinique MR - Mauritania MS - Montserrat MT - Malta MU - Mauritius MV - Maldives MW - Malawi MX - Mexico MY - Malaysia MZ - Mozambique NA - Namibia NC - New Caledonia NE - Niger NF - Norfolk Island NG - Nigeria NI - Nicaragua NL - Netherlands NO - Norway NP - Nepal NR - Nauru NU - Niue NZ - New Zealand OM - Oman PA - Panama PE - Peru PF - French Polynesia PG - Papua New Guinea PH - Philippines PK - Pakistan PL - Poland PM - Saint Pierre and Miquelon PN - Pitcairn PR - Puerto Rico PS - State of Palestine PT - Portugal PW - Palau PY - Paraguay QA - Qatar RE - Réunion RO - Romania RS - Serbia RU - Russian Federation RW - Rwanda SA - Saudi Arabia SB - Solomon Islands SC - Seychelles SD - Sudan SE - Sweden SG - Singapore SH - Saint Helena, Ascension and Tristan da Cunha SI - Slovenia SJ - Svalbard and Jan Mayen SK - Slovakia SL - Sierra Leone SM - San Marino SN - Senegal SO - Somalia SR - Suriname ST - Sao Tome and Principe SV - El Salvador SY - Syrian Arab Republic SZ - Swaziland TC - Turks and Caicos Islands TD - Chad TF - French Southern Territories TG - Togo TH - Thailand TJ - Tajikistan TK - Tokelau TL - Timor-Leste TM - Turkmenistan TN - Tunisia TO - Tonga TR - Turkey TT - Trinidad and Tobago TV - Tuvalu TW - Taiwan TZ - Tanzania, United Republic of UA - Ukraine UG - Uganda UM - United States Minor Outlying Islands US - United States of America UY - Uruguay UZ - Uzbekistan VA - Vatican City State VC - Saint Vincent and the Grenadines VE - Venezuela (Bolivarian Republic of) VG - British Virgin Islands VI - United States Virgin Islands VN - Viet Nam VU - Vanuatu WF - Wallis and Futuna WS - Samoa YE - Yemen YT - Mayotte ZA - South Africa ZM - Zambia ZW - Zimbabwe
A=Australia(オーストラリア),Austria(オーストリア)
B=Brunei(ブルネイ),Bangladesh(バングラデシュ),Brazil(ブラジル)
E=Egypt(エジプト)
G=Germany(ドイツ),Georgia(ジョージア),Gunma(グンマ)
I=Indonesia(インドネシア),India(インド),Italy(イタリア),Iceland(アイスランド),Israel(イスラエル)
J=Jordan(ヨルダン)
K=Kazakhstan(カザフスタン),Kuwait(クウェート),Kirghiz(キルギス)
L=Laos(ラオス),Luxembourg(ルクセンブルグ)
M=Mongolia(モンゴル),Myanmar(ミャンマー),Malaysia(マレーシア),Maldives(モルディブ)
N=Nepal(ネパール),New zealand(ニュージーランド),Netherlands(オランダ),Norway(ノルウェー)
P=Philippines(フィリピン),Pakistan(パキスタン),Portugal(ポルトガル)
Q=Qatar(カタール)
S=Singapore(シンガポール),Sri Lanka(スリランカ),Saudi Arabia(サウジアラビア),South Korea(韓国),Switzerland(スイス),Spain(スペイン),Sweden(スウェーデン)
T=Thailand(タイ),Tajikistan(タジキスタン),Turkey(トルコ),Taiwan(台湾)
U=Uzbekistan(ウズベキスタン),United kingdom(イギリス)
俺たち日本はとんでもない奴らを相手にしようとしてる。。。。。
