Example Charts of Countries and Dependencies by Population Density

List of countries and dependencies showing population densities, populations, and areas

Location	Density	Population	Land area (km2)
World	55	8,231,613,070	148,940,000
Macau (China)	21,826	720,262	33
Monaco	19,428	38,857	2
Singapore	8,290	6,110,200	737
Hong Kong (China)	7,062	7,414,910	1,050
Gibraltar (UK)	5,800	39,329	6.8
Vatican City	2,205	882	0.44
Bahrain	2,034	1,607,049	790
Maldives	1,759	527,799	298
Malta	1,686	539,607	320
Bangladesh	1,333	173,562,364	130,170
Sint Maarten (NL)	1,275	43,351	34
Bermuda (UK)	1,197	64,637	54
Palestine	892	5,371,230	6,020
Jersey (UK)	891	103,387	116
Guernsey (UK)	867	67,642	78
Barbados	657	282,468	430
Taiwan	656	23,213,962	35,410
Mauritius	637	1,271,169	1,997
Aruba (NL)	600	108,066	180
Nauru	597	11,947	20
Rwanda	578	14,256,567	24,670
Lebanon	568	5,805,962	10,230
Saint Barthélemy (France)	563	11,258	20
San Marino	560	33,581	60
Burundi	547	14,047,786	25,680
Netherlands	541	18,080,943	33,670
South Korea	530	51,717,590	97,600
Saint Martin (France)	523	26,129	50
Israel	468	10,148,000	21,640
Comoros	466	866,628	1,861
India	441	1,450,935,791	3,287,263
Haiti	427	11,772,557	27,560
Curaçao (NL)	418	185,482	444
Philippines	389	115,843,670	298,170
Belgium	385	11,738,764	30,494
Sri Lanka	373	23,103,565	61,860
Puerto Rico (US)	366	3,242,204	8,870
Grenada	345	117,208	340
Japan	340	123,753,041	364,500
Tuvalu	322	9,646	30
Guam (US)	311	167,777	540
Cayman Islands (UK)	310	74,458	240
Vietnam	309	102,300,000	331,212
El Salvador	306	6,338,193	20,720
Saint Lucia	295	179,744	610
Trinidad and Tobago	294	1,507,782	5,130
Pakistan	289	255,300,000	882,363
United Kingdom	287	69,483,939	242,494
Seychelles	284	130,419	460
Saint Vincent and the Grenadines	281	109,296	389
Kuwait	277	4,934,508	17,820
Gambia	273	2,759,988	10,120
Qatar	265	3,048,423	11,490
British Virgin Islands (UK)	263	39,471	150
Jamaica	262	2,839,175	10,830
Luxembourg	261	673,036	2,575
Nigeria	255	232,679,478	910,770
Tokelau (New Zealand)	251	2,507	10
Uganda	249	50,015,093	200,520
Liechtenstein	249	39,870	160
São Tomé and Príncipe	245	235,537	960
U.S. Virgin Islands (US)	243	84,905	350
Germany	242	84,552,242	349,360
Dominican Republic	237	11,427,557	48,198
American Samoa (US)	234	46,765	200
Malawi	230	21,655,286	94,280
Switzerland	226	8,921,981	39,510
North Korea	220	26,498,823	120,410
Antigua and Barbuda	213	93,772	440
Marshall Islands	209	37,548	180
Nepal	207	29,651,055	143,350
Italy	201	59,342,867	295,720
Saint Kitts and Nevis	180	46,843	260
Togo	175	9,515,236	54,390
Andorra	174	81,938	470
Guatemala	172	18,406,359	107,160
Kiribati	170	138,000	810
Bonaire (NL)	169	26,552	288
Saba (NL)	166	2,158	13
Anguilla (UK)	162	14,599	90
Federated States of Micronesia	162	113,161	700
Sint Eustatius (NL)	156	3,270	21
United Arab Emirates	155	11,027,129	71,020
Kosovo	154	1,674,125	10,887
Ghana	151	34,427,414	227,533
China	151	1,419,321,278	9,388,210
Indonesia	150	283,487,931	1,892,555
Denmark	149	5,977,413	40,000
Isle of Man (UK)	148	84,161	570
Cyprus	147	1,358,282	9,240
Northern Cyprus	146	489,308	3,355
Tonga	145	104,176	720
Thailand	140	71,668,011	510,890
Czech Republic	139	10,735,860	77,171
Syria	133	24,672,760	185,178
Cape Verde	130	524,877	4,030
Jordan	130	11,552,876	88,794
Benin	128	14,462,724	112,760
Poland	126	37,600,280	306,090
Azerbaijan	125	10,336,578	82,650
France	122	66,548,531	547,557
Sierra Leone	120	8,642,023	72,180
Egypt	117	116,538,258	995,450
Ethiopia	117	132,059,768	1,128,571
Slovakia	115	5,506,760	48,080
Portugal	114	10,425,293	91,606
Turkey	114	87,473,805	769,630
Austria	111	9,120,813	82,520
Malaysia	108	35,557,674	328,550
Iraq	106	46,042,015	434,128
Hungary	106	9,676,135	91,260
Slovenia	105	2,118,697	20,136
Armenia	104	2,973,841	28,470
Albania	102	2,791,765	27,400
Costa Rica	100	5,129,910	51,060
Ivory Coast	100	31,934,230	318,000
Cambodia	100	17,638,801	176,520
Kenya	99	56,432,945	569,140
Honduras	97	10,825,704	111,890
Northern Mariana Islands (US)	96	44,278	460
Senegal	96	18,501,985	192,530
Spain	98	49,801,559	499,714
Timor-Leste	94	1,400,638	14,870
Moldova	92	3,034,961	32,890
Cuba	89	9,748,532	109,820
Dominica	88	66,205	750
Transnistria	88	367,000	4,163
Brunei	88	462,722	5,270
Burkina Faso	86	23,548,781	273,600
Serbia	85	6,567,783	88,499
Myanmar	84	54,500,091	652,670
Romania	83	19,015,088	230,080
Uzbekistan	83	36,361,859	440,652
French Polynesia (France)	81	281,807	3,471
Wallis and Futuna (France)	81	11,278	140
Tunisia	79	12,277,109	155,360
Samoa	78	218,020	2,780
Guinea-Bissau	78	2,201,352	28,120
Greece	78	10,047,817	128,900
Tanzania	77	68,560,157	885,800
Lesotho	77	2,337,423	30,360
Yemen	77	40,583,165	527,970
Tajikistan	76	10,590,928	138,790
Ireland	76	5,255,018	68,890
Ecuador	73	18,135,478	248,360
North Macedonia	72	1,823,009	25,220
Eswatini	72	1,242,822	17,200
Akrotiri and Dhekelia (UK)	72	18,195	254
Croatia	69	3,875,325	55,960
Equatorial Guinea	67	1,892,517	28,050
Mexico	67	130,861,007	1,943,950
Afghanistan	65	42,647,492	652,230
Ukraine	65	37,860,222	579,400
Bulgaria	62	6,757,689	108,560
Bosnia and Herzegovina	62	3,164,253	51,200
Cameroon	62	29,123,744	472,710
Panama	61	4,515,577	74,180
Norfolk Island (Australia)	61	2,188	36
Guinea	60	14,754,786	245,720
Liberia	58	5,612,817	96,320
Nicaragua	57	6,916,140	120,340
Cook Islands (New Zealand)	57	13,729	240
Iran	56	91,567,738	1,648,195
Madagascar	55	31,964,956	581,800
Georgia	55	3,807,670	69,490
South Africa	53	64,007,187	1,213,090
Morocco	53	38,081,173	710,850
Fiji	51	928,784	18,270
Djibouti	50	1,168,722	23,180
Turks and Caicos Islands (UK)	49	46,535	950
DR Congo	48	109,276,265	2,267,050
Colombia	48	52,886,364	1,109,500
Montenegro	47	638,479	13,450
Lithuania	46	2,859,110	62,604
Belarus	45	9,056,696	202,990
Mozambique	44	34,631,766	786,380
Montserrat (UK)	44	4,389	100
Cocos (Keeling) Islands (Australia)	43	602	14
Zimbabwe	43	16,634,373	386,850
Faroe Islands (Denmark)	40	55,400	1,370
Bahamas	40	401,283	10,010
Palau	38	17,696	460
United States	37	341,730,701	9,147,590
Kyrgyzstan	37	7,186,009	191,800
Somaliland	35	4,914,100	167,283
Laos	34	7,769,819	230,800
Venezuela	32	28,405,543	882,050
Estonia	32	1,360,546	42,730
Angola	30	37,885,850	1,246,700
Somalia	30	19,009,151	627,340
Latvia	30	1,871,872	62,230
Solomon Islands	29	819,198	27,990
Eritrea	29	3,535,603	121,041
Zambia	29	21,314,956	743,390
Sudan	27	50,448,963	1,868,000
Vanuatu	27	327,778	12,190
Peru	27	34,217,848	1,280,000
Chile	27	19,764,772	743,532
Sweden	26	10,606,999	407,280
Brazil	25	211,998,574	8,358,140
Saint Pierre and Miquelon (France)	24	5,628	230
Papua New Guinea	23	10,576,502	452,860
Niger	21	27,032,413	1,266,700
Bhutan	21	791,525	38,140
Mali	20	24,478,596	1,220,190
New Zealand	20	5,213,944	263,310
Åland (Finland)	20	30,237	1,583
Algeria	20	47,400,000	2,381,741
Uruguay	19	3,386,588	175,020
South Sudan	19	11,943,409	631,930
Congo	19	6,332,961	341,500
Finland	18	5,617,311	303,948
Belize	18	417,072	22,810
Oman	17	5,281,538	309,500
Argentina	17	45,696,159	2,736,690
Chad	16	20,299,123	1,259,200
New Caledonia (France)	16	292,640	18,280
Turkmenistan	16	7,494,499	469,930
Saudi Arabia	16	33,962,757	2,149,690
Paraguay	15	6,109,903	406,752
Norway	15	5,576,660	364,270
South Ossetia	14	56,520	3,900
Saint Helena, Ascension and Tristan da Cunha (UK)	13	5,238	390
Christmas Island (Australia)	13	1,692	136
Bolivia	11	12,413,315	1,083,300
Gabon	9.9	2,538,952	257,670
Russia	8.6	147,182,123	17,125,191
Central African Republic	8.6	5,330,690	622,980
Kazakhstan	7.6	20,592,571	2,699,700
Niue (New Zealand)	7.0	1,819	260
Mauritania	5.0	5,169,396	1,030,700
Canada	4.5	39,742,430	8,788,700
Botswana	4.4	2,521,139	566,730
Guyana	4.2	831,087	196,850
Libya	4.2	7,381,023	1,759,540
Suriname	4.0	634,431	160,508
Iceland	3.9	393,396	100,830
Namibia	3.7	3,030,131	823,290
Australia	3.5	26,713,205	7,692,020
Mongolia	2.2	3,475,540	1,557,507
Western Sahara	2.2	590,506	266,000
Pitcairn Islands (UK)	0.85	40	47
Falkland Islands (UK)	0.29	3,471	12,170
Greenland (Denmark)	0.026	55,840	2,166,086
Antarctica	0.009	1,300 (winter)5,100 (summer)	14,200,000
South Georgia and the South Sandwich Islands (UK)	0.008	30	3,756

^table

// 1. Read and parse data dynamically from the table
const table = Array.from(document.querySelectorAll("table")).find(t =>
  t.innerText.includes("Land area") && t.innerText.includes("Population")
);

const data = Array.from(table ? table.querySelectorAll("tbody tr") : []).map(tr => {
  const cells = Array.from(tr.querySelectorAll("td")).map(td => td.innerText.trim());
  return {
    location: cells[0],
    density: parseFloat(cells[1].replace(/,/g, "")),
    population: parseFloat(cells[2].replace(/,/g, "")),
    area: parseFloat(cells[3].replace(/,/g, ""))
  };
}).filter(d => d.location && d.location !== "World" && !isNaN(d.population) && !isNaN(d.area) && d.area > 0 && d.population > 0);

// 2. Standard Thresholds (10M Population, 100k km² Area)
const POP_THRESHOLD = 10_000_000;
const AREA_THRESHOLD = 100_000;

// 3. Log-Log Linear Regression (Trend Line)
const n = data.length;
const lx = data.map(d => Math.log10(d.area));
const ly = data.map(d => Math.log10(d.population));
const meanX = lx.reduce((a, b) => a + b, 0) / n;
const meanY = ly.reduce((a, b) => a + b, 0) / n;
const num = lx.reduce((acc, x, i) => acc + (x - meanX) * (ly[i] - meanY), 0);
const den = lx.reduce((acc, x) => acc + Math.pow(x - meanX, 2), 0);
const slope = num / den;
const intercept = meanY - slope * meanX;

const trend = [
  { area: 0.3, population: Math.pow(10, intercept + slope * Math.log10(0.3)) },
  { area: 20_000_000, population: Math.pow(10, intercept + slope * Math.log10(20_000_000)) }
];

// 4. Render
Plot.plot({
  title: "Population vs. Land Area",
  subtitle: "X = Land area (km²), Y = Population, Color = Density | Blue line: Regression trend | Yellow cross: Large state thresholds (10M pop / 100k km²)",
  width: 860,
  height: 580,
  grid: true,
  inset: 24,
  x: {
    type: "log",
    label: "Land Area (km²) →",
    tickFormat: "~s"
  },
  y: {
    type: "log",
    label: "↑ Population",
    tickFormat: "~s"
  },
  color: {
    type: "log",
    scheme: "turbo",
    legend: true,
    label: "Density (/km²)"
  },
  marks: [
    // --- Regression Trend Line (Blue) ---
    Plot.line(trend, {
      x: "area",
      y: "population",
      stroke: "#1d63b8",
      strokeWidth: 3.5
    }),

    // --- Threshold Crosshair (Yellow) ---
    Plot.ruleX([AREA_THRESHOLD], {
      stroke: "#f59e0b",
      strokeWidth: 2
    }),
    Plot.ruleY([POP_THRESHOLD], {
      stroke: "#f59e0b",
      strokeWidth: 2
    }),

    // --- Quadrant Corner Badges (Placed out of the way) ---
    Plot.text([
      { text: "↖ High Density / Small Land", frameAnchor: "top-left", dx: 10, dy: 10 },
      { text: "↗ Large States (High Pop & Land)", frameAnchor: "top-right", dx: -10, dy: 10 },
      { text: "↙ Microstates & Territories", frameAnchor: "bottom-left", dx: 10, dy: -10 },
      { text: "↘ Large Land / Low Pop", frameAnchor: "bottom-right", dx: -10, dy: -10 }
    ], {
      frameAnchor: "frameAnchor",
      text: "text",
      fill: "#888",
      fontSize: 10,
      fontStyle: "italic",
      dx: "dx",
      dy: "dy"
    }),

    // --- Country Dots ---
    Plot.dot(data, {
      x: "area",
      y: "population",
      fill: "density",
      r: 5.5,
      stroke: "#222",
      strokeWidth: 0.5,
      tip: true,
      title: d => `${d.location}\nArea: ${d.area.toLocaleString()} km²\nPopulation: ${d.population.toLocaleString()}\nDensity: ${d.density.toLocaleString()} /km²`
    }),

    // --- Key Country Labels (Always rendered on top with clear text) ---
    Plot.text(
      data.filter(d => ["China", "India", "Bangladesh", "Taiwan", "Singapore", "Monaco", "Vatican City"].includes(d.location)),
      {
        x: "area",
        y: "population",
        text: "location",
        dy: -12,
        fontSize: 11,
        fontWeight: "bold",
        fill: "#111",
        stroke: "#fff",
        strokeWidth: 2,
        paintOrder: "stroke" // Ensures the white halo stays behind the text
      }
    )
  ]
})

Or in the native way.

const table = Array.from(document.querySelectorAll("table")).find(t =>
  t.innerText.includes("Land area") && t.innerText.includes("Population")
);

const data = Array.from(table ? table.querySelectorAll("tbody tr") : []).map(tr => {
  const cells = Array.from(tr.querySelectorAll("td")).map(td => td.innerText.trim());
  return {
    location: cells[0],
    density: parseFloat(cells[1].replace(/,/g, "")),
    population: parseFloat(cells[2].replace(/,/g, "")),
    area: parseFloat(cells[3].replace(/,/g, ""))
  };
}).filter(d => d.location && d.location !== "World" && !isNaN(d.population) && !isNaN(d.area) && d.area > 0 && d.population > 0);

const POP_THRESHOLD = 10_000_000;
const AREA_THRESHOLD = 100_000;

const lx = data.map(d => Math.log10(d.area));
const ly = data.map(d => Math.log10(d.population));
const meanX = d3.mean(lx), meanY = d3.mean(ly);
const slope = d3.sum(lx, (x, i) => (x - meanX) * (ly[i] - meanY)) / d3.sum(lx, x => (x - meanX) ** 2);
const intercept = meanY - slope * meanX;

const trend = [0.3, 20_000_000].map(area => ({
  area,
  population: 10 ** (intercept + slope * Math.log10(area))
}));

const cornerStyle = {fill: "#888", fontSize: 10, fontStyle: "italic"};

const plot = Plot.plot({
  title: "Population vs. Land Area",
  subtitle: "Color = density (/km²); blue line = log-log regression; orange lines = 10M population / 100k km² thresholds",
  width: 860,
  height: 580,
  grid: true,
  inset: 24,
  x: {type: "log", label: "Land area (km²) →", tickFormat: "~s"},
  y: {type: "log", label: "↑ Population", tickFormat: "~s"},
  color: {type: "log", scheme: "turbo", legend: true, label: "Density (/km²)"},
  marks: [
    Plot.line(trend, {x: "area", y: "population", stroke: "#1d63b8", strokeWidth: 3.5}),
    Plot.ruleX([AREA_THRESHOLD], {stroke: "#f59e0b", strokeWidth: 2}),
    Plot.ruleY([POP_THRESHOLD], {stroke: "#f59e0b", strokeWidth: 2}),

    // Quadrant Corner Labels
    Plot.text(["↖ High Density / Small Land"], {frameAnchor: "top-left", dx: 10, dy: 10, ...cornerStyle}),
    Plot.text(["↗ Large States (High Pop & Land)"], {frameAnchor: "top-right", dx: -10, dy: 10, ...cornerStyle}),
    Plot.text(["↙ Microstates & Territories"], {frameAnchor: "bottom-left", dx: 10, dy: -10, ...cornerStyle}),
    Plot.text(["↘ Large Land / Low Pop"], {frameAnchor: "bottom-right", dx: -10, dy: -10, ...cornerStyle}),

    Plot.dot(data, {
      x: "area", y: "population", fill: "density", r: 5.5,
      stroke: "#222", strokeWidth: 0.5, tip: true,
      title: d => `${d.location}\nArea: ${d.area.toLocaleString()} km²\nPopulation: ${d.population.toLocaleString()}\nDensity: ${d.density.toLocaleString()} /km²`
    }),

    Plot.text(
      data.filter(d => ["China", "India", "Bangladesh", "Taiwan", "Singapore", "Monaco", "Vatican City"].includes(d.location)),
      {x: "area", y: "population", text: "location", dy: -12, fontSize: 11, fontWeight: "bold", fill: "#111", stroke: "#fff", strokeWidth: 2, paintOrder: "stroke"}
    )
  ]
});

Or like in the observableHQ notebook, with a display.

const table = Array.from(document.querySelectorAll("table")).find(t =>
  t.innerText.includes("Land area") && t.innerText.includes("Population")
);

const data = Array.from(table ? table.querySelectorAll("tbody tr") : []).map(tr => {
  const cells = Array.from(tr.querySelectorAll("td")).map(td => td.innerText.trim());
  return {
    location: cells[0],
    density: parseFloat(cells[1].replace(/,/g, "")),
    population: parseFloat(cells[2].replace(/,/g, "")),
    area: parseFloat(cells[3].replace(/,/g, ""))
  };
}).filter(d => d.location && d.location !== "World" && !isNaN(d.population) && !isNaN(d.area) && d.area > 0 && d.population > 0);

const POP_THRESHOLD = 10_000_000;
const AREA_THRESHOLD = 100_000;

const lx = data.map(d => Math.log10(d.area));
const ly = data.map(d => Math.log10(d.population));
const meanX = d3.mean(lx), meanY = d3.mean(ly);
const slope = d3.sum(lx, (x, i) => (x - meanX) * (ly[i] - meanY)) / d3.sum(lx, x => (x - meanX) ** 2);
const intercept = meanY - slope * meanX;

const trend = [0.3, 20_000_000].map(area => ({
  area,
  population: 10 ** (intercept + slope * Math.log10(area))
}));

const cornerStyle = {fill: "#888", fontSize: 10, fontStyle: "italic"};

const plot = display(Plot.plot({
  title: "Population vs. Land Area",
  subtitle: "Color = density (/km²); blue line = log-log regression; orange lines = 10M population / 100k km² thresholds",
  width: 900,
  height: 600,
  grid: true,
  inset: 24,
  x: {type: "log", label: "Land area (km²) →", tickFormat: "~s"},
  y: {type: "log", label: "↑ Population", tickFormat: "~s"},
  color: {type: "log", scheme: "turbo", legend: true, label: "Density (/km²)"},
  marks: [
    Plot.line(trend, {x: "area", y: "population", stroke: "#1d63b8", strokeWidth: 3.5}),
    Plot.ruleX([AREA_THRESHOLD], {stroke: "#f59e0b", strokeWidth: 2}),
    Plot.ruleY([POP_THRESHOLD], {stroke: "#f59e0b", strokeWidth: 2}),
    // Quadrant corner labels: one mark per corner (frameAnchor is a constant option)
    Plot.text(["↖ High Density / Small Land"], {frameAnchor: "top-left", dx: 10, dy: 10, ...cornerStyle}),
    Plot.text(["↗ Large States (High Pop & Land)"], {frameAnchor: "top-right", dx: -10, dy: 10, ...cornerStyle}),
    Plot.text(["↙ Microstates & Territories"], {frameAnchor: "bottom-left", dx: 10, dy: -10, ...cornerStyle}),
    Plot.text(["↘ Large Land / Low Pop"], {frameAnchor: "bottom-right", dx: -10, dy: -10, ...cornerStyle}),
    Plot.dot(data, {
      x: "area", y: "population", fill: "density", r: 5.5,
      stroke: "#222", strokeWidth: 0.5, tip: true,
      title: d => `${d.location}\nArea: ${d.area.toLocaleString()} km²\nPopulation: ${d.population.toLocaleString()}\nDensity: ${d.density.toLocaleString()} /km²`
    }),
    Plot.text(
      data.filter(d => ["China", "India", "Bangladesh", "Taiwan", "Singapore", "Monaco", "Vatican City"].includes(d.location)),
      {x: "area", y: "population", text: "location", dy: -12, fontSize: 11, fontWeight: "bold", fill: "#111", stroke: "#fff", strokeWidth: 2, paintOrder: "stroke"}
    )
  ]
}));