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秋山祐樹・柴崎亮介,2016年,「震災ビッグデータの開発と今後の展開~様々なマイクロジオデータ解析による大規模地震被害推定の精緻化と地域柔靭化に向けた応用~」,第1回防災推進国民大会 スマートシュリンク・地域柔靭化研究会,講演.
第1回防災推進国民大会発表
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Date: March 14, 2015(Sat) 9:30- Venue: TKP Garden city Sendai Kotodai, Hall 2 "The 3 rd World Conference on Disaster Risk Reduction in Sendai ~Redesigning for resilient national land using big data of environment and disasters~" 講演資料
Earthquake Damage Risk Evaluation by Micro Geo Data マイクロジオデータベースによる 地震災害リス...
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The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
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Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits. To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups. Technology For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did. While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis. Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad. Age and Gender When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same. Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers. Race Affects Attitudes As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
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秋山祐樹・柴崎亮介,2016年,「震災ビッグデータの開発と今後の展開~様々なマイクロジオデータ解析による大規模地震被害推定の精緻化と地域柔靭化に向けた応用~」,第1回防災推進国民大会 スマートシュリンク・地域柔靭化研究会,講演.
第1回防災推進国民大会発表
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地域課題の解決に向けた公共データと 民間ビッグデータの活用 ~空き家分布推定・企業間取引分析など~
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Date: March 14, 2015(Sat) 9:30- Venue: TKP Garden city Sendai Kotodai, Hall 2 "The 3 rd World Conference on Disaster Risk Reduction in Sendai ~Redesigning for resilient national land using big data of environment and disasters~" 講演資料
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Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits. To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups. Technology For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did. While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis. Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad. Age and Gender When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same. Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers. Race Affects Attitudes As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
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Gisa学術研究発表web大会 小川芳樹
1.
GISA学術研究発表Web大会 広域地震災害の被害想定のための 日本全土における建物単体データの開発 東京大学大学院新領域創成科学研究科 柴崎研究室 ○小川芳樹 秋山祐樹 仙谷裕明 柴崎亮介 ogawa@csis.u-tokyo.ac.jp 1
2.
研究背景 2 現状の被害想定における技術的課題 利用データの問題:集計データ(建物構造,築年数など) 市区町村単位のデータを建物棟数でメッシュに按分. ⇒・集計データの分布には,メッシュにより偏りがある. 個別建物データ(マイクロジオデータ)の全国整備 木造 非木造 木造 非木造 木造 非木造 木造 非木造 ・集計データの利用 ・個別建物データの利用
3.
個別データでの被害評価 3 個別建物データの利用 被害評価方法の改善: 例:火災 ⇒個別建物データの業種, 地震動を考慮 空間分布を考慮した初期対応力 (公助・共助)の導入 リスクコミュニケーションの促進:
全国統一基準でのミクロな わかりやすい評価 自治体を超えた具体的な防災計画
4.
研究目的 個別建物データの整備 建物構造,耐火性能,築年数,居住者 統合的なリスク評価 具体的な意思決定支援データ 4 既存の被害評価手法を改良し 全国統一基準での被害推定 業種・地震動を考慮した出火率 空間分布を考慮した初期対応力の 改善 住民分布、ネットワーク距離,消防力・消防団数
5.
本研究のフローチャート 5 個別建物データ(全国6000万棟) 確率的地震動入力 (PGV) 耐火性能 出火率 (延焼考慮) 消火期待 棟数 統合的な リスク評価 地震リスク 建物構造 倒壊率 築年数 人口分布 救助期待 人数
6.
データ整備(耐火性能) 6 個別建物データ(全国6000万棟) 確率的地震動入力 (PGV) 耐火性能 出火率 (延焼考慮) 消火期待 棟数 統合的な リスク評価 地震リスク 建物構造 倒壊率 築年数 人口分布 救助期待 人数
7.
耐火性能(耐火・準耐火・防火造)の推定 住宅地図 電話帳 商業集積統計 7 住宅土地統計 木造率 面積 用途 教師データ 商業集積 パラメータ の調整 階数 個別建物の 調査結果 (世田谷区) 耐火造 強い 準耐火造 防火造 弱い
8.
耐火性能(耐火・準耐火・防火)の推定結果(例) 8 静岡県菊川市 建築面積の大きい建物や幅員の広い道路周辺では耐火が進んでいることが分かる.一方中 央の川を挟んで東側の住宅地では多くが防火造になっている.
9.
耐火・準耐火・防火構造の推定結果の信頼性検証 9 検証データ 世田谷区の公開データの一部を利用して信頼性検証. 検証データ(真値) 耐火 準耐火 防火 耐火 推定データ 1191 158 82 準耐火 284 483 379 防火 341 694 4807 合計 1816 1335 5268 合計 1431 1146 5842 8419 信頼性 83.2% 42.1% 82.3% 77.0%
10.
データ整備(建物構造) 10 個別建物データ(全国6000万棟) 確率的地震動入力 (PGV) 耐火性能 出火率 (延焼考慮) 消火期待 棟数 統合的な リスク評価 地震リスク 建物構造 倒壊率 築年数 人口分布 救助期待 人数
11.
建物構造(木造・非木造)の推定 住宅地図 商業集積統計 電話帳 11 耐火性能 耐火造 面積 商業集積 用途 階数 準耐火造 防火造 統計値に合うように配分 住宅土地統計 木造率 木造 非木造
12.
建物毎の構造(木造・非木造)の推定結果(例) 12 耐火性能の高い地域で非木造化が進んでいることが分かる.また建築面積が大きい建物や 高層の建物で非木造化が進んでいる.
13.
木造・非木造の推定結果の信頼性検証 13 検証データ 首都圏の不動産情報データ(㈱アットホーム)を利用して信頼性検証 木造 木造 推定棟数 非木造 合計 検証データ(真値) 非木造 合計 信頼性 71746 8244 79990 89.69% 63369 157130
220499 71.26% 135115 165374 300489 76.17%
14.
データ整備(築年数) 14 個別建物データ(全国6000万棟) 確率的地震動入力 (PGV) 耐火性能 出火率 (延焼考慮) 消火期待 棟数 統合的な リスク評価 地震リスク 建物構造 救助期待 人数 倒壊率 築年数 人口分布 (秋山ら2013)
15.
築年代の推定 住宅地図 電話帳 面積 用途 階数 住宅土地統計 15 DID 建物構造 1970 1980 1990 2000 2005 世帯主年齢 10代 20-24 木造 25-29 ・・・ 非木造 ・・・ 65- 統計値に合うよう各年代を建物に配分 築年代×階数×用途 ×構造×世帯年齢 教師データ 築年代付き 個別建物データ* ~1969年 1970年代 1990年代 *約4万棟の築年付きの不動産データ㈱at home
による提供 1980年代 2000年代
16.
築年代の推定結果(例) 16 静岡県菊川市 古くから都市化されている地域(DID(1970))には1970年より以前に建った建物が多数分布する. 一方近年都市化した地域(DID(2005))には新しい建物が多いことが分かる.
17.
建物毎の築年代の検証 17 不動産情報データ(株式会社アットホーム)を利用して信頼性検証 築年代 ~1969 ~1969 693 1970 132 1980 46 推定データ 1990 246 2001~ 13 合計 1130 1970 386 921 2351 465 1836 5959 検証データ(真値) 1980 1990 2001~ 合計 信頼性 418 214 31 1742 39.78% 684 1083 374 3194 28.84% 4141 2829 1524 10891 38.02% 925 6454 327 8417 76.68% 4961 1643 6481 14934 43.40% 11129 12223 8737 39178 47.71% パラメータの設定により信頼性の低い年代があるため, 今後詳細な属性を考慮するなど改良が必要である.
18.
データ整備(地震リスク) 18 個別建物データ(全国6000万棟) 確率的地震動入力 (PGV) 耐火性能 出火率 (延焼考慮) 消火期待 棟数 統合的な リスク評価 地震リスク 建物構造 倒壊率 築年数 人口分布 救助期待 人数
19.
既存の評価手法を用いた, 各建物における出火率・倒壊率の推定 19 本研究では50年超過確率2%の地震が冬の夕方発生すると想定 建物の属性の把握 個別建物データ 空間 結合 (構造や築年など) 建物用途データ 百貨店 共同住宅 住宅 既存の評価手法を利用し一部改良 地震動予測地図 (PGV) 結合 建物火災延焼モデル (加藤2006) 建物被害関数 (東京都2013) 50年超過確率2%, 5%, 10%,
39% 30年超過確率3%, 6% 共同住宅 百貨店 0.0050% 0.034% 住宅 10.0% 1.0% 0.0043% 20.0% PGV PGV PGV 出火率,倒壊率付き 建物データ(用途別)
20.
大規模地震発生時における倒壊・火災によるリスク 20 静岡県菊川市 凡例 % (× ) 死 火
2.00 者 災 1.00 率に よ 0.50 -3る 10 0.25 0 0.5 1.0 2.0 4.0 % 倒壊による死者率(×10-2)
21.
大規模地震発生時における倒壊・火災によるリスク (建物単位のものを500mメッシュ集計) 凡例 21 % (× ) 死 火
2.00 者 災 1.00 率に よ 0.50 -3る 10 0.25 0 0.5 1.0 2.0 4.0 % 倒壊による死者率(×10-2) 50年超過確率2%の地震動入力の場合 東京~大阪の平野部で広く倒壊による死者率が高いことが分かる.また主要都市では中心部 で火災による死者率が高く,その周辺では倒壊・火災の死者率共に高くなる.
22.
データ整備(消火期待棟数,救助期待棟数) 22 個別建物データ(全国6000万棟) 確率的地震動入力 (PGV) 耐火性能 出火率 (延焼考慮) 消火期待 棟数 統合的な リスク評価 地震リスク 建物構造 倒壊率 築年数 人口分布 救助期待 人数
23.
既往研究における初期対応力評価 (中村・加藤(2006)) 23 『地震災害に関する自治体間の相対的な災害対応力評価』 公開されている市区町村単位のデータを利用して,被害 リスク評価,初期対応力評価を行なっている. 課題 評価スケールが市区町村単位. 消防からの距離など空間分布を 考慮していない. →住民への利活用が難しい故にリスクコミニュケーション が促進されない
24.
消防組織による消火期待棟数(公助力)の算出 24 消防組織による消火期待棟数(公助力) ※阪神大震災時の調査より,火災建物1棟を消火するために,平均して消防ポンプ車2台, 消防職員・団員数20人が必要(消防ポンプ車には消防職員5人). ・消火期待棟数= 消防ポンプ車台数×𝑅 消防団員数 + 2 20 (消防職員数-5×ポンプ車台数)×𝑅 + 20 ・消防ポンプ車台数, 消防職員数,消防団員数 ・消防施設からの道路ネットワーク距離 ・消防署からの到達時間で重み付け 1 aaa(t
11.7) log( t 10.7) 1 R a1aaa(t 11.7) Ra ・各消防署が管轄する建物の棟数の把握
25.
周辺住民による救助期待人数(共助力)の算出 25 周辺住民による倒壊建物からの救助期待人数(共助力) ※阪神大震災時の調査より周辺住民5人で1人救助可能. 建物半径100m圏内の居住者数 ・救助期待人数= ×dw×E 5 ・対象となる建物からの距離に応じて重み付け. (100mで期待人数は半減(0.5で重み付け).) 1.502 dw j log(1
d j ) 1 ・屋内人口から救助活動実施率及び,性別・年齢による体力を考慮(阪神淡路大震災時) 年代 10 20 30 40 50 60 70~ 男子体力 女子体力 1 0.85 1 0.76 0.96 0.76 0.93 0.73 0.9 0.72 0.84 0.7 0.78 0.65 実施率 男子活動率 女子活動率 男子期待値 女子期待値 0.228 0.76 0.24 0.1733 0.0465 0.228 0.76 0.24 0.1733 0.0416 0.229 0.72 0.28 0.1583 0.0487 0.298 0.72 0.28 0.1995 0.0609 0.228 0.63 0.37 0.1293 0.0607 0.191 0.74 0.26 0.1187 0.0348 0.129 0.75 0.25 0.0755 0.0210
26.
大規模地震発生時における初期対応力 (建物単位のものを500mメッシュ集計) 凡例 26 % (× ) 公 10.0 助
5.0 力 10-3 2.5 1.0 0 0.5 1.0 2.5 5.0 人 共助力(×10-1) 都市部では共助力が,山間部では公助力が大きくなる.東京や大阪などの大都市の中心部で は共助力・公助力共に高い地域もある.大都市周辺で何れの値も小さくなっている.
27.
データ整備(統合的なリスク評価) 27 各建物データ(全国6000万棟) 確率的地震動入力 (PGV) 耐火性能 出火率 (延焼考慮) 消火期待 棟数 統合的な リスク評価 地震リスク 建物構造 倒壊率 築年数 人口分布 救助期待 人数
28.
倒壊・火災リスクと初期対応力を組み合わせた 統合的なリスク評価 28 ここまでに求めてきた値を組み合わせることで推定死者率を算出する. 災害リスク 初期対応力 倒壊による 推定死者率 共助力(倒壊建物からの救助) - 火災による 推定死者率 - 被害推定 推定死者率 = 公助力(火災建物の消火) = 消防車+消防職員+消防団員 共助力・公助力が 作用した後のその 地域の死者率を推 定.
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今後に向けて~被害を軽減させるために~ 46 データ ・建物データの信頼性向上 :他の統計データ及び細かい属性を考慮、住民によるデータ入力 ・評価結果の信頼性 :過去の地震被害と比較することで検証 ・可視化の方法 :集計単位により信頼性が変わるため、それを考慮した可視化 ・冬期夕方以外の時間帯で発生する地震 :モバイル統計,PTデータ等を活用して時間帯別人口のデータの 整備 ◇住民 ・住民の声:実際に住民との対話を通して,適切な共有方法を模索 ・提供する最小集計単位 :集計単位によって信頼性がどの程度変わるのか検討 →データ整備からデータの活用方法の提案へ
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