Skip to content

Foundry

I-Global Risks Index

NXS-GRX-ARG · South America · Regions

Buenos Aires

First-level unit, Argentina: people, area, the units inside and neighbours, with risk values when released.

Map All 23 first-level units
Page GRX-ARG-REdition Baseline Edition 2026Evidence close 2026-09-30ISO ARG / ARStatus UN member stateValues not yet publishedMethod v1.0.0-draft
12,320,230
people (GHSL 2025)
382,902 km²
area
28.8%
of Argentina
140
units inside

01Map

Inside Buenos Aires

The 140 ADM2 units of Buenos Aires.

FIG 1 · 140 units inside Buenos Aires
Loading the map

Units rise with people per km² · GHSL 2025 raster · terrain · ADM1 ADM2Risk values when released
Lowest fifthLow fifthMiddle fifthHigh fifthHighest fifthNo data
Column height and colour show people per km² by unit, in quantile fifths among the units shown (the key on the map gives the breaks); units with no people on the grid are grey, not the lowest class; the raster beneath is the GHSL 2025 population grid. Source observations, not index values. Choose a humanitarian figure from the list to colour and raise the units by it; each figure shows its source on the unit page. Select a unit to drill into the units inside it; the path above the map climbs back.
02Figures

People, area and density

Source observations from open grids summed inside the boundary; not index values.

People · GHSL 2025

12,320,230

28.8% of Argentina.

People · WorldPop 2025

12,304,337

0% against GHSL; the two grids differ in method.

Indawo

382,902 km²

32 people per km².

Ikhowudi

AR-B

Subdivision code as carried by the boundary source.

Low coastal zone

1,776,133

people on land at or below 10 m, 14.4% of the unit (GEBCO 2024 and GHSL 2025).

03Humanitarian data

Buenos Aires in humanitarian data

Source observations from the Humanitarian Data Exchange, matched to this unit by name, each with its reference date, provider and licence. They are not index values.

Population · 2021-12-31

17,709,598

people. UNFPA · Creative Commons Attribution for Intergovernmental Organisations (CC BY-IGO).

04Units

140 units inside Buenos Aires

Each unit has its own page.

Boundary licences: Creative Commons Attribution 2.5 Generic · Creative Commons Attribution 3.0 Intergovernmental Organisations (CC BY 3.0 IGO)

Unit Ikhowudi People (GHSL) People (WorldPop) Area km² Per km² Share Coast ≤ 10 m
25 de Mayo – 41,604 34,095 4,781.11 9 0.3% 0
9 de Julio – 53,779 50,656 4,288.2 13 0.4% 0
Adolfo Alsina – 20,257 16,392 5,858.07 3 0.2% 0
Adolfo Gonzáles Chaves – 13,871 12,771 3,786.84 4 0.1% 0
Alberti – 12,265 12,419 1,115.63 11 0.1% 0
Almirante Brown – 685,645 669,687 129.53 5,293 5.4% 70,014
Arrecifes – 34,136 31,515 1,240.5 28 0.3% 0
Ayacucho – 23,454 21,803 6,749.81 3 0.2% 0
Azul – 76,253 74,873 6,531.98 12 0.6% 0
Bahía Blanca – 326,632 327,070 2,249.01 145 2.6% 69,337
Balcarce – 52,140 51,334 4,119.8 13 0.4% 0
Baradero – 33,612 35,574 1,530.16 22 0.3% 3,003
Benito Juárez – 24,024 21,468 5,347.87 4 0.2% 0
Berazategui – 382,719 380,997 220.12 1,739 3% 73,197
Berisso – 93,692 89,402 144.57 648 0.7% 89,823
Bolívar – 40,381 36,571 4,928.59 8 0.3% 0
Bragado – 48,985 44,516 2,201.08 22 0.4% 0
Brandsen – 30,696 32,453 1,117.81 27 0.2% 270
Campana – 101,320 100,453 988.81 102 0.8% 54,527
Cañuelas – 69,669 88,161 1,190.29 59 0.6% 0
Capitán Sarmiento – 17,862 15,878 549.54 33 0.1% 0
Carlos Casares – 26,551 22,870 2,522.18 11 0.2% 0
Carlos Tejedor – 13,436 12,908 3,910.06 3 0.1% 0
Carmen de Areco – 17,250 17,264 1,061.4 16 0.1% 0
Castelli – 9,615 10,854 2,114.7 5 0.1% 4,770
Chacabuco – 56,650 51,394 2,296.12 25 0.4% 0
Chascomús – 42,664 42,455 3,164.69 13 0.3% 19,475
Chivilcoy – 76,180 70,733 2,055.18 37 0.6% 0
Colón – 29,101 26,725 998.65 29 0.2% 0
Colón – 58,313 62,358 2,762.7 21 0.5% 11,994
Concordia – 167,116 177,142 3,279.66 51 1.3% 6,306
Coronel de Marina L. Rosales – 68,396 62,845 1,291.34 53 0.5% 34,206
Coronel Dorrego – 18,104 14,891 5,836.15 3 0.1% 349
Coronel Pringles – 26,149 23,761 5,258.33 5 0.2% 0
Coronel Suárez – 46,216 43,748 5,969.01 8 0.4% 0
Daireaux – 20,157 18,983 3,820.17 5 0.2% 0
Diamante – 50,850 51,958 2,680.27 19 0.4% 351
Dolores – 31,445 30,512 1,971.5 16 0.2% 24,780
Ensenada – 66,771 63,117 113.4 589 0.5% 60,519
Escobar – 265,480 277,838 302 879 2.1% 71,734
Esteban Echeverría – 310,288 296,785 121.76 2,548 2.5% 65,629
Exaltación de la Cruz – 38,858 47,215 635.6 61 0.3% 262
Ezeiza – 222,881 222,240 237.45 939 1.8% 6,156
Federación – 77,118 78,847 3,775.52 20 0.6% 0
Federal – 28,106 29,497 5,217.53 5 0.2% 0
Feliciano – 16,502 16,423 3,174.98 5 0.1% 0
Florencio Varela – 510,975 547,759 190.33 2,685 4.1% 26,458
Florentino Ameghino – 10,097 9,062 1,808.32 6 0.1% 0
General Alvarado – 21,321 21,844 1,629.41 13 0.2% 1,885
General Alvear – 13,189 9,178 3,367.23 4 0.1% 0
General Arenales – 17,110 15,624 1,485.09 12 0.1% 0
General Belgrano – 20,667 21,306 1,849.28 11 0.2% 0
General Guido – 3,416 3,400 2,325.28 1 0% 1,422
General Juan Madariaga – 31,177 31,446 2,983.6 10 0.2% 29,036
General La Madrid – 12,360 11,498 4,811.53 3 0.1% 0
General Las Heras – 16,118 16,612 752.23 21 0.1% 0
General Lavalle – 18,440 24,921 2,692.31 7 0.1% 17,964
General Paz – 13,398 14,662 1,197.65 11 0.1% 0
General Pinto – 13,161 12,085 2,553.63 5 0.1% 0
General Pueyrredón – 586,216 562,398 1,462.05 401 4.7% 3,018
General Rodríguez – 75,002 116,730 364.55 206 0.6% 0
General Viamonte – 19,018 18,100 2,144.52 9 0.2% 0
General Villegas – 37,298 34,136 7,250.84 5 0.3% 0
Gualeguay – 67,604 65,872 6,613.06 10 0.5% 17,821
Gualeguaychú – 121,470 124,481 7,536.75 16 1% 53,552
Guaminí – 13,304 10,250 4,822.64 3 0.1% 0
Hipólito Yrigoyen – 11,488 10,408 1,647.38 7 0.1% 0
Islas del Ibicuy – 12,881 13,948 4,931.37 3 0.1% 12,137
José C. Paz – 177,838 194,646 50.34 3,533 1.4% 0
Junín – 107,040 99,975 2,253.23 48 0.8% 0
La Costa – 28,974 35,984 247.86 117 0.2% 10,641
La Paz – 69,171 71,626 6,642.85 10 0.5% 0
La Plata – 778,159 773,325 890.41 874 6.2% 164,567
Laprida – 12,063 11,570 3,446.17 4 0.1% 0
Las Flores – 27,812 26,539 3,340.32 8 0.2% 0
Leandro N. Alem – 19,281 17,029 1,604.68 12 0.2% 0
Lezama – 8,030 6,180 1,042.86 8 0.1% 1,599
Lincoln – 51,140 46,196 5,784.31 9 0.4% 0
Lobería – 20,413 17,065 4,722.94 4 0.2% 14
Lobos – 42,862 41,576 1,725.85 25 0.3% 0
Luján – 118,333 105,281 772.83 153 0.9% 37
Magdalena – 23,796 23,140 1,850.14 13 0.2% 15,710
Maipú – 11,711 11,274 2,602.82 4 0.1% 895
Malvinas Argentinas – 231,120 226,099 63.22 3,656 1.8% 0
Mar Chiquita – 25,241 32,977 3,090.77 8 0.2% 6,913
Marcos Paz – 54,107 58,580 424.49 127 0.4% 0
Mercedes – 68,480 71,491 1,050.28 65 0.5% 0
Merlo – 520,968 515,109 174.11 2,992 4.1% 0
Monte – 25,977 23,892 1,887.31 14 0.2% 0
Monte Hermoso – 7,934 9,029 205.55 39 0.1% 2,085
Moreno – 605,918 682,678 186.27 3,253 4.8% 775
Navarro – 20,379 20,438 1,619.16 13 0.2% 0
Necochea – 106,030 97,565 4,559.15 23 0.8% 20,099
Nogoya – 42,198 41,877 4,381.74 10 0.3% 0
Olavarría – 131,678 121,587 7,655.82 17 1% 0
Paraná – 375,151 389,559 5,111.28 73 3% 8,401
Patagones – 32,404 33,116 13,548.35 2 0.3% 2,431
Pehuajó – 46,589 41,906 4,524.62 10 0.4% 0
Pellegrini – 6,980 7,149 1,812.2 4 0.1% 0
Pergamino – 124,461 112,584 3,005.92 41 1% 0
Pila – 4,368 4,723 3,452.65 1 0% 173
Pilar – 351,828 402,521 384.06 916 2.8% 28,995
Pinamar – 26,584 35,156 66.3 401 0.2% 5,012
Presidente Perón – 92,123 103,196 120.98 761 0.7% 0
Puán – 17,930 15,421 6,348.15 3 0.1% 10
Punta Indio – 11,155 11,927 1,608.73 7 0.1% 2,377
Quilmes – 541,099 511,196 91.46 5,916 4.3% 284,509
Ramallo – 34,894 37,275 1,047.99 33 0.3% 5,648
Rauch – 17,713 16,976 4,307.85 4 0.1% 0
Rivadavia – 20,707 20,446 3,948.34 5 0.2% 0
Rojas – 27,675 25,902 2,061.68 13 0.2% 0
Roque Pérez – 15,429 13,966 1,547.72 10 0.1% 0
Saavedra – 25,036 21,875 3,533.61 7 0.2% 0
Saladillo – 38,831 35,647 2,710.19 14 0.3% 0
Salliqueló – 9,868 9,825 790.06 12 0.1% 0
Salto – 37,972 39,955 1,613.46 24 0.3% 0
San Andrés de Giles – 25,282 26,069 1,127.5 22 0.2% 0
San Antonio de Areco – 24,938 25,907 864.97 29 0.2% 9
San Cayetano – 9,862 9,525 3,004.3 3 0.1% 9
San Fernando – 1,923 2,752 990.31 2 0% 92,630
San Nicolás – 165,580 155,359 681.55 243 1.3% 29,843
San Pedro – 70,050 68,734 1,356.59 52 0.6% 19,235
San Salvador – 18,760 20,839 1,332.84 14 0.1% 0
San Vicente – 71,416 106,427 655.25 109 0.6% 0
Suipacha – 12,491 12,303 942.09 13 0.1% 0
Tala – 27,580 30,952 2,645.21 10 0.2% 0
Tandil – 151,459 150,670 4,844.33 31 1.2% 0
Tapalqué – 11,049 10,828 4,168.83 3 0.1% 0
Tigre – 552,615 537,279 394.1 1,402 4.4% 326,594
Tordillo – 1,959 2,728 1,332.85 1 0% 1,944
Tornquist – 13,935 12,633 4,175.08 3 0.1% 0
Trenque Lauquen – 50,643 47,339 5,501.59 9 0.4% 0
Tres Arroyos – 60,374 56,247 5,924.03 10 0.5% 393
Tres Lomas – 11,326 8,743 1,255.25 9 0.1% 0
Uruguay – 110,746 114,400 5,150.37 22 0.9% 22,026
Victoria – 157,049 112,701 6,643.1 24 1.2% 99,319
Villa Gesell – 7,202 9,219 165 44 0.1% 3,092
Villaguay – 52,622 55,019 6,582.53 8 0.4% 0
Villarino – 38,440 33,012 10,392.4 4 0.3% 1,036
Zárate – 143,095 133,657 1,182.89 121 1.1% 46,658
05Ukuchaphazeleka

Open exposure layers

Count of H3 resolution-3 cells whose centre falls in the unit and that carry each Natural Earth layer.

Urban areas
3,252 H3 cells
Ports
6 H3 cells
Airports
4 H3 cells
Major roads
9,025.6 H3 cells
Railways
10,139.5 H3 cells
Rivers and lakes
1,672.5 H3 cells
06Umngcipheko

Risk values

Values when released. Subnational risk values for Buenos Aires publish with the edition from The Global Risks Index. Until then, run a scenario on open data for Argentina.
Run a scenario Hazards Domain indices
07Neighbours

Neighbouring units

08Imithombo

Sources, credits and licences

ADM1 boundaries
geoBoundaries (William & Mary geoLab), gbOpen ARG ADM1, source: geoBoundaries, Wikimedia, licence: Creative Commons Attribution 2.5 Generic · source · represents 2006
ADM2 boundaries
geoBoundaries (William & Mary geoLab), gbOpen ARG ADM2, source: Instituto Geografico Nacional and UNHCR, OCHA ROLAC, licence: Creative Commons Attribution 3.0 Intergovernmental Organisations (CC BY 3.0 IGO) · source · represents 2020
Basemap
OpenFreeMap © OpenMapTiles (CC BY 4.0) · data © OpenStreetMap contributors (ODbL 1.0). Vector tiles requested by the reader’s browser, unmodified; the map falls back to plain land and sea when the host does not answer.
Terrain
Terrain Tiles (Mapzen, AWS Open Data), compiled from SRTM and other public elevation sources (NASA, USGS, ETOPO1, GMTED, EU-DEM under the Copernicus programme, Geoscience Australia, Kartverket, INEGI, LINZ, data.gov.uk). Display relief, not survey data.
People
GHSL 2025 population grid, European Commission Joint Research Centre (CC BY 4.0), shown at 0.05° and summed per unit; WorldPop 2025 (CC BY 4.0) as a second estimate. Units smaller than a grid cell take the density of the cell under them.
Amazwe
Natural Earth (public domain).
Elevation
GEBCO Compilation Group (2024) GEBCO 2024 Grid (public domain), reduced to 0.05°: the share of each cell’s land at or below 10 m, weighted by GHSL 2025 people for the low coastal zone.
Position
Boundaries are shown for location only and take no position on any boundary, name or status.

Imeko

The methodology, structure and definitions of The Global Risks Index are a preparatory working draft (2026), circulated for review and comment and not for implementation. No value appears until an edition is released from a governed evidence cut; values then carry intervals, classes, sources and their correction pathway. Simulators run the published methods on numbers you choose and never show a NXS-GRX value.

A country profile reads public evidence about a place. It is not a statement by or about the policy of any government, takes no position on the status of any territory, and does not imply a national programme or participation. Names follow UN M49.

What NXS-GRX is not

NXS-GRX is research and risk-analytics information. It is not a credit or ESG rating and uses no rating scale, not a benchmark for financial instruments, not official statistics, not an emergency warning or official classification, and not advice. It must not be used to evaluate natural persons. Risk classes are bands on a published scale, never rankings of countries or people.

Pulse signals and outlook probabilities are information for review by competent authorities, never public warnings; official warnings come only from mandated authorities.

Institutions and sources

The Nexus Consortium is stewarded by three independent not-for-profit institutions: The Global Centre for Risk and Innovation (GCRI), through GCRI US in Washington, DC and GCRI Canada in Ontario, with GCRI Canada as Lead Institution, Consortium Secretariat and lead contracting organisation; The Global Risks Forum (GRF); and The Global Risks Alliance (GRA). Technology, infrastructure and service providers work with the Consortium at arm’s length under written agreements, like every other external party in the public-private-planet model, and hold no role in its stewardship.

Every value carries the attribution of its sources, which keep their own licences. External names are cited for interoperability only. Nexus-authored text is intended for CC BY 4.0 on release.

Avis en français

La méthodologie et les définitions du NXS-GRX sont un projet de travail préparatoire (2026). Le NXS-GRX est une information de recherche et d’analyse des risques : ni notation, ni indice de référence financier, ni statistique officielle, ni alerte, ni conseil. Les classes de risque sont des bandes sur une échelle publiée, non des classements.