Skip to content
#small language model Open access

火葬場3,511のうち、動いているのは1,349 ── 福井県は557施設のうち14 [I017]

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)
Urban and spatial planning

Abstract

衛生行政報告例(令和6年度)の第5表から、墓地・火葬場・納骨堂の数を経営主体別に数え、I010 で数えた火葬件数と突き合わせた。 墓地は876,042区域、火葬場は3,511施設、納骨堂は13,853施設。47都道府県の合計は4つとも全国の値とぴたり一致する。墓地は火葬場の250倍、恒常的に使用している火葬場の649倍ある。 火葬場3,511のうち、過去1年以内に稼働実績があるのは1,349施設(38.4%)で、2,162施設(61.6%)は使われていない。ただしこれは全国平均で、実態は二分されている。17都道府県では100%が動いており中央値は94.7%だが、福井県は557施設のうち14(2.5%)、石川県は437のうち13(3.0%)、三重県は640のうち28(4.4%)。平均が下がっているのは、極端に多くの火葬場を登録している県が少数あるためである。古くからの小さな火葬場が廃止の手続きをされないまま残っていると考えられるが、この表からは確かめられない。稼働率は人口(対数)とも高齢化率とも相関しない(+0.135、+0.146)。 墓地の82.4%(721,647区域)は個人が経営し、地方公共団体は3.4%、宗教法人は6.7%にすぎない。逆に、恒常的に使用している火葬場の97.5%は地方公共団体である(火葬場全体では52.2%)。納骨堂の73.9%は宗教法人。墓地は個人の手に、火葬は行政の手にある。 火葬1,674,137件を恒常火葬場1,349施設で割ると1施設あたり1,241件/年、1日あたり3.4件。東京都は5,576件(1日15.3件)、島根県は415件(1日1.1件)で13.4倍の開きがあり、人口(対数)との相関は+0.765。 人口10万人あたりの墓地は島根県15,270から大阪府36.0まで424倍、納骨堂は佐賀県77.4から神奈川県1.6まで48倍。墓地のうち個人経営の割合は沖縄県99.2%から北海道1.5%まで開く。 北海道は墓地2,002区域に対して納骨堂1,846施設でほぼ同数。納骨堂÷墓地は0.922で全国0.0158の58.3倍である。墓地の75.1%は地方公共団体、個人はわずか1.5%で47都道府県中最下位。納骨堂1,846は全国の13.3%(6位)。火葬は1施設あたり505件/年(45位)。理由として開拓の歴史・積雪・寺院の配置が考えられるが、この表では確かめられない。 経済センサスの795 火葬・墓地管理業は1,580事業所で、火葬場と納骨堂の合計17,364の10.99分の1、墓地876,042の0.18%にあたる。墓地は事業所として数えられず、火葬場の52.2%は地方公共団体で事業所にならない。釧路総合振興局管内は795が3事業所(釧路市・浜中町・鶴居村)、796 冠婚葬祭業が32事業所である。 この論文の位置づけ:新しい理論を主張するものではない。衛生行政報告例の第5表を経営主体別に数え、I010 の火葬件数と突き合わせただけである。墓地の単位は「区域」で1区域の大きさは分からない。火葬件数は都道府県の合計で火葬場ごとではない。衛生行政報告例に市区町村別の表は無い。相関は示したが、原因と結果の向きは示していない。 作成にあたって:本稿の着想と内容は、著者自身の考察に基づくものです。文章の構成整理や英訳、集計に用いたスクリプトの作成には AI(大規模言語モデル)の助力を得ました。最終的な内容の解釈や誤りがあれば、それらはすべて著者の責に帰します。お気づきの点があれば、ご教示いただければ幸いです。 ----- From Table 5 of the Report on Public Health Administration (FY2024) I counted cemeteries, crematoria and ossuaries by type of operator, and matched them against the cremations counted in I010. There are 876,042 cemetery areas, 3,511 crematoria and 13,853 ossuaries; all four counts reconcile exactly with the national row. Cemeteries outnumber crematoria 250 to 1 and crematoria in regular use 649 to 1. Of the 3,511 crematoria, 1,349 (38.4%) were operated within the past year and 2,162 (61.6%) were not used at all. But that is the national average and the reality is split in two: seventeen prefectures are at 100% and the median is 94.7%, while Fukui uses 14 of 557 (2.5%), Ishikawa 13 of 437 (3.0%) and Mie 28 of 640 (4.4%). The average is pulled down by a handful of prefectures registering extraordinary numbers of crematoria. Small old crematoria left on the register without formal closure is a plausible explanation, but this table cannot confirm it. The share in use correlates with neither population in logs nor the ageing rate (+0.135, +0.146). 82.4% of cemeteries (721,647 areas) are operated by individuals, against 3.4% for local governments and 6.7% for religious corporations. Conversely, 97.5% of the crematoria in regular use are run by local governments (52.2% of all crematoria), and 73.9% of ossuaries by religious corporations. Cemeteries are in private hands and cremation in the state's. Dividing 1,674,137 cremations by 1,349 crematoria in regular use gives 1,241 a year per facility, or 3.4 a day. Tokyo runs 5,576 a year (15.3 a day) and Shimane 415 (1.1 a day), a 13.4-fold spread correlating with population in logs at +0.765. Cemeteries per 100,000 population run from 15,270 in Shimane to 36.0 in Osaka, a spread of 424; ossuaries from 77.4 in Saga to 1.6 in Kanagawa, a spread of 48. The individual share of cemeteries runs from 99.2% in Okinawa to 1.5% in Hokkaido. Hokkaido has 2,002 cemetery areas and 1,846 ossuaries, almost the same number: ossuaries divided by cemeteries is 0.922 against a national 0.0158, a factor of 58.3. Local government operates 75.1% of its cemeteries and individuals only 1.5%, the lowest of the 47 prefectures; its 1,846 ossuaries are 13.3% of the national total (6th). Cremations per facility are 505 a year (45th). Settlement history, snow and the distribution of temples are candidate explanations, but this table cannot test them. Economic Census category 795, crematory and cemetery management, holds 1,580 establishments - one eleventh of the 17,364 crematoria and ossuaries, and 0.18% of the cemetery count. Cemeteries are not counted as establishments, and 52.2% of crematoria are municipal and so are not establishments either. The Kushiro Subprefecture has three establishments in 795 (Kushiro City, Hamanaka, Tsurui) and 32 in 796, ceremonial occasion services. Scope of this paper: no new theory is claimed. Table 5 is counted by operator and matched against the cremations of I010. Cemeteries are counted in "areas" of unknown size; cremation counts are prefectural totals, not per facility; and the health register has no municipal table. Correlations are reported; the direction of causation is not. Acknowledgement: The ideas and content of this paper stem from the author's own considerations. Assistance from an AI (a large language model) was used for structuring, English translation, and writing the scripts used for the tabulation. Any remaining errors or misinterpretations are solely the author's. Feedback and corrections are sincerely appreciated.

View source

Similar papers

#small language model Dataset Open access Oct 2026

Socratic guiding questions in synthetic arithmetic data: matched LoRA runs (revision v2)

Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...

O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al. · 465 citations
#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.