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Ranking

Densest barangays in Northern Samar

Ranks 501 to 569 of 569 barangays in Northern Samar. The first page has the leader.

People are counted as of July 1, 2024 (the 2024 Census of Population) and May 1, 2020 (the 2020 Census of Population and Housing), both by the Philippine Statistics Authority.

Open Northern Samar
Densest barangays in Northern Samar, ranks 501 to 569
RankNameCity or municipalityPeople, 2024People, 2020Change since 2020People per km²
501San JuanSan Isidro, Northern Samar1,9772,365−16.4%about 50
502Alejandro VillageAllen, Northern Samar297251+18.3%about 49
503SantanderBobon, Northern Samar1,2191,214+0.4%about 49
504San JoseLas Navas, Northern Samar198256−22.7%about 49
505InanahawanPambujan, Northern Samar390616−36.7%about 49
506H. JolejoleLas Navas, Northern Samar424406+4.4%about 48
507CaputoanLas Navas, Northern Samar346332+4.2%about 46
508Cag-aguingayLope De Vega, Northern Samar442342+29.2%about 46
509BagacayPalapag, Northern Samar1,1401,248−8.7%about 46
510MabarasPalapag, Northern Samar231270−14.4%about 46
511LunetaGamay, Northern Samar222295−24.8%about 45
512Upper CaynagaLope De Vega, Northern Samar227250−9.2%about 45
513LawaanSan Roque, Northern Samar632628+0.6%about 45
514Deit De TuragSilvino Lobos, Northern Samar592604−2.0%about 45
515MagongonCatubig, Northern Samar506446+13.5%about 43
516IgotPambujan, Northern Samar469819−42.7%about 43
517San IsidroSilvino Lobos, Northern Samar374405−7.7%about 43
518RoxasCatubig, Northern Samar1,1331,143−0.9%about 42
519Vienna MariaCatubig, Northern Samar417308+35.4%about 41
520Santa CatalinaMondragon, Northern Samar509508+0.2%about 41
521PalmeraLaoang, Northern Samar336319+5.3%about 39
522GebonawanSilvino Lobos, Northern Samar267248+7.7%about 38
523San PascualCatarman, Northern Samar4064060.0%about 37
524Cagda-oSilvino Lobos, Northern Samar267270−1.1%about 37
525HenaronaganLope De Vega, Northern Samar233220+5.9%about 36
526MaraganoPalapag, Northern Samar411330+24.6%about 36
527LonoyGamay, Northern Samar107144−25.7%about 34
528Catoto-oganLas Navas, Northern Samar253334−24.3%about 34
529RufinoLas Navas, Northern Samar244224+8.9%about 34
530Camaya-anSilvino Lobos, Northern Samar195194+0.5%about 34
531E. DuranBobon, Northern Samar934916+2.0%about 33
532CabayhanCatarman, Northern Samar258242+6.6%about 31
533Tagan-ayanLas Navas, Northern Samar326338−3.6%about 31
534YnaguingayanPambujan, Northern Samar672908−26.0%about 31
535KailinganRosario, Northern Samar455338+34.6%about 31
536GebalagnanCatarman, Northern Samar365423−13.7%about 30
537CuencoLas Navas, Northern Samar266311−14.5%about 30
538Gen. LunaLope De Vega, Northern Samar319365−12.6%about 29
539CagmanabaMondragon, Northern Samar364332+9.6%about 29
540Gecbo-anSilvino Lobos, Northern Samar425467−9.0%about 29
541San AntonioSilvino Lobos, Northern Samar157156+0.6%about 29
542San FranciscoLope De Vega, Northern Samar181136+33.1%about 27
543MaghipidLope De Vega, Northern Samar392453−13.5%about 26
544MagsaysayLope De Vega, Northern Samar992570+74.0%about 26
545San IsidroMondragon, Northern Samar574619−7.3%about 26
546SenonoganPambujan, Northern Samar249741−66.4%about 26
547P. RebadullaCatubig, Northern Samar418459−8.9%about 25
548Geparayan De TuragSilvino Lobos, Northern Samar716858−16.6%about 24
549PaguiteLope De Vega, Northern Samar319430−25.8%about 22
550MagsaysayMapanas, Northern Samar550624−11.9%about 22
551MandugangSan Jose, Northern Samar632576+9.7%about 22
552LuisitaVictoria, Northern Samar682600+13.7%about 22
553GinagdananSan Roque, Northern Samar1,2421,266−1.9%about 20
554GebonawanLope De Vega, Northern Samar542525+3.2%about 19
555SomoroyLope De Vega, Northern Samar158164−3.7%about 19
556San JoseMapanas, Northern Samar619630−1.8%about 19
557ManeringCatubig, Northern Samar319325−1.9%about 18
558SampaguitaLope De Vega, Northern Samar150152−1.3%about 18
559BuenasuerteVictoria, Northern Samar484498−2.8%about 18
560PasabuenaVictoria, Northern Samar521418+24.6%about 16
561ChansvillaLavezares, Northern Samar13850+176.0%about 15
562TobgonSilvino Lobos, Northern Samar123125−1.6%about 14
563Sixto T. Balanguit, Sr.Pambujan, Northern Samar261369−29.3%about 12
564RoxasLope De Vega, Northern Samar179191−6.3%about 10
565ImeldaSilvino Lobos, Northern Samar264210+25.7%about 9
566Sag-odLas Navas, Northern Samar163195−16.4%about 8
567MontalbanSilvino Lobos, Northern Samar132158−16.5%about 6
568San JoseMondragon, Northern Samar5811,068−45.6%about 5
569Senonogan de TubangSilvino Lobos, Northern Samar128324−60.5%about 5

Density is a place's 2024 people divided by its land area. A barangay has no official area: Scene measures the area inside its border on UN OCHA's map, so its density is approximate. A barangay with no mapped border is not ranked.

Sources

Every count here is the PSA's own, for the place as the PSA names it today. The change and the order are Scene's arithmetic on those counts. How Scene works out the rest.

  1. 1.Philippine Statistics Authority. Total Population, Household Population, and Number of Households by Province, City, Municipality, and Barangay as of 01 July 2024: Eastern Visayas (OpenSTAT table 0081A6DTPH7). Published August 5, 2026. Read by Scene October 2, 2026. CC BY (PSA Terms of Use). Open the source
  2. 2.Philippine Statistics Authority. Philippine Standard Geographic Code (PSGC), 4th Quarter 2022: Publication Datafile, with the 2015 and 2020 census counts (the PSA's file, as the Internet Archive keeps it). Published December 31, 2022. Read by Scene October 2, 2026. CC BY (PSA Terms of Use). Open the source
  3. 3.Philippine Statistics Authority, from the Land Management Bureau (DENR). Population, Land Area, Population Density, and Percent Change in Population Density by Region, Province/Highly Urbanized City, and City/Municipality: 2015, 2020, and 2024 (OpenSTAT table 0221A6DLPD0). Published August 7, 2026. Read by Scene October 2, 2026. CC BY (PSA Terms of Use). Open the source
  4. 4.UN OCHA, from the PSA and NAMRIA. Philippines: Subnational Administrative Boundaries (COD-AB), barangay layer. Published February 13, 2025. Read by Scene September 25, 2026. CC BY-IGO 3.0. Open the source

Scene works out the rest from these sources: growth rates, shares, areas, densities, coordinates, neighbours and distances. How.

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