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sigam2019 presentation slide
バースト検知手法を用いた桜の見頃推定
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オープンデータ・パーソナルデータビジネス最前線
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川口勇作 (2016, August). 「外国語教育研究における尺度の構成と妥当性検証」 草薙邦広・徳岡大・川口勇作 (2016). 外国語教育メディア学会 第56回全国研究大会 公募シンポジウム「外国語教育研究における多変量解析の諸問題:よりよい質問紙研究を目指して」. 早稲田大学. で使用したスライドです。
外国語教育研究における尺度の構成と妥当性検証
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みんなビックデータビックデータって言ってるけど 名寄せとかどうしてんの?
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抗がん剤は効かないの罪
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sigam2019 presentation slide
バースト検知手法を用いた桜の見頃推定
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Takashi Inui
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オープンデータ・パーソナルデータビジネス最前線
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外国語教育研究における尺度の構成と妥当性検証
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みんなビックデータビックデータって言ってるけど 名寄せとかどうしてんの?
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抗がん剤は効かないの罪
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【プレゼン】見やすいプレゼン資料の作り方【初心者用】
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書籍化し、12万部突破しました。 【SlideShare広告回避用】 https://www.docswell.com/s/morishige/K3MXPZ-howtodesignslides ・PDFは無料でダウンロードできます ・自己学習や勉強会などの目的でしたらご自由にお使いいただけます ・授業・研修への利用はフォーム( https://forms.gle/WwgXTT974xFW78mFA )にご報告ください ・記事への参考資料にする際は適切な出典明記をお願いいたします 【使っているフォントについて】 M+フォント「MigMix1P」です。こちらもメイリオ同様おすすめです。 フリーで使えます。 【個人HP】 > https://mocks.jp > 仕事のご依頼はこちらから 【書籍情報】 デザイン入門:https://amzn.asia/d/4WDsTI6 デザイン図鑑:https://amzn.asia/d/gOBCWKc 【前作】 > http://www.slideshare.net/yutamorishige50/how-to-present-better
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Gis学術研究発表web大会_河地薫子
1.
詳細な地理情報やWEBから収集した データを用いた商店街の特性分類 河地 薫子 東京大学大学院 新領域創成科学研究科 社会文化環境学専攻
柴崎研究室 1
2.
目次 1. 2. 3. 4. 5. 6. 7. 8. Contents 研究背景 研究目的 研究方法 使用データ① 使用データ② 結果 事例 結論と考察 2
3.
Contents 1. 2. 3. 4. 5. 6. 7. 8. 研究背景 研究目的 研究方法 使用データ① 使用データ② 結果 事例 結論と考察 3
4.
1. 背景 商店街は地域の魅力や地域らしさが表出する場である。 http://itot5.jp/hikifune/wp-content/uploads/2013/07/116113_hikifune-web2504.jpg http://www.pref.oita.jp/uploaded/image/201096.jpg 4
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1. 背景 日本の商店街の現状 郊外の大型商業施設との競争に敗れ、多くが衰退傾向にある。 (シャッター街化、空き店舗増加) 5
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Contents 1. 2. 3. 4. 5. 6. 7. 8. 研究背景 研究目的 研究方法 使用データ① 使用データ② 結果 事例 結論と考察 6
7.
1.研究目的 これまでの主な商店街分類 近隣型 最寄品(食料品、日 用雑貨等)中心で徒 歩又は自転車等によ り買い物を行う商店 街 地域型 最寄品及び買回品の 店舗が混在し、近隣 型商店街よりもやや 広い範囲から、徒歩、 自転車、バス等で来 街する商店街 広域型 百貨店、量販店等を 含む大型店舗があり、 最寄品より買回品が 多い商店街 引用:中小企業庁委託「全国商店街調査」㈱三菱総合研究所 しかし近年、商店街は多様化し、これまでの分類では捉えきれない事例も存在 7
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2.研究目的 • 多様化する商店街を、客観的データを用いて分類する • 対策を施すべき商店街を発見できる環境を整備する 8
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研究方法 1. 2. 3. 4. 5. 6. 7. 8. Contents 研究背景 研究目的 研究方法 使用データ① 使用データ② 結果 事例 結論と考察 9
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3.研究方法 使用データの収集 画像データ 商業集積統計 • • • 店舗数 チェーン店率 業種(有効と考えられるものを選択) • • 重心の画像取得 重心以外の画像取得 それぞれのデータを変化量が大きいとこ ろで区切り分類 (既存研究を参考としながら) 神奈川県の商業集積を対象 に実験的に分類 人流データを用いて来客の 行動範囲を分析 全国を対象に分類 10
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Contents 1. 2. 3. 4. 5. 6. 7. 8. 研究背景 研究目的 研究方法 使用データ① 使用データ② 結果 事例 結論と考察 11
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4. 使用データ① 1.商業集積統計 (ポリゴンデータ) デジタル電話帳(テレポイントPack!株式会社ゼンリン)から得られ たデータをポイントデータ化し,さらに独自の空間処理によって商業集 積をポリゴンデータ化((テレポイントパック!)株式会社ゼンリン) 取得できる情報の例
商業集積の形状 商業集積の分布 チェーン店率 業種構成 12
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Contents 1. 2. 3. 4. 5. 6. 7. 8. 研究背景 研究目的 研究方法 使用データ① 使用データ② 結果 事例 結論と考察 13
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5.使用データ ② 2.Googleストリートビューから取得した画像データ 商店街の特徴や雰囲気を掴むためのデータとして、画像データを用い た。 GoogleストリートビューAPIで表示された画像データを自動で取得する システムを開発した。 ※Google ストリートビュー
API • 画像データのサイズ • 緯度経度座標 をGoogleストリートビューAPI • カメラの角度 のhttpに入力 • カメラの方位 ストリートビューの画像データが表示される 14
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5.使用データ ② <画像自動取得システムの概要> STEP1. 商業集積の重心地点の画像取得 商業集積ポリゴンデータには属性として集積の重心地点の座標(緯度 経度)が含まれている。 重心の座標 重心地点の座標を取得し、APIを用いて重心地点から東西南北を撮影し た画像データを取得した。 15
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5.使用データ ② STEP2. 重心以外の地点の画像取得 商店街の雰囲気を把握するためには重心地点の画像だけでは不十分で ある。 画像を取得する地点の数は、商店街の店舗数によって異なる。 Nn
= Sn / 100 (1)商業集積 Nn: 商業集積nの画像を取得する地点数[n] Sn: 商業集積nの店舗数[n] このうち、 した。 Nn>10となる場合は, Nn=10、Nn<2となる場合は, Nn=2と 16
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Contents 1. 2. 3. 4. 5. 6. 7. 8. 研究背景 研究目的 研究方法 使用データ① 使用データ② 結果 事例 結論と考察 17
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6. 結果 店舗数 タイプA:地域密着型 チェーン店率 10~122 0~12%、12%~ 62% 業種の特徴 その他 事例 食料品多い 昔ながらの雰囲気が残るタイプと 生麦駅周辺商店 チェーン店が進出しているタイプに 街 わかれる チェーン店率低く、地域の人々だ けでなく、広い範囲からの集客、 六角橋商店街 タイプAより規模大きい タイプB:地域広域複 合型 122~529 0~12% 食料品多い タイプC:ショッピング ストリート型 122~529 0~12% 専門品、高級感、地域の人々が 衣服など、特定の 利用するというよりも外からショッ
元町商店街 業種に特化 ピングに来る人々がターゲット タイプD:観光重視型 30~1000 0~12% 宿泊施設多い、 観光業有 遠方からの顧客、景観に特徴を 持つ(例:伝統的街並み) 箱根湯本駅前周 辺 タイプE:都心型 500~1000以上 3.8%~60%以上 娯楽施設有、食 料品少ない 遠方からの顧客、専門品 横浜駅西口パル ナード商店街 18
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Contents 1. 2. 3. 4. 5. 6. 7. 8. 研究背景 研究目的 研究方法 使用データ① 使用データ② 結果 事例 結論と考察 19
20.
タイプA -生麦駅周辺商店街- 店舗数 : 103 チェーン店率
: 4.9% 20
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タイプB -六角橋商店街- 店舗数 :
509 チェーン店率 : 3.3% 21
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タイプC -横浜元町商店街- 店舗数: 410 チェーン店率
: 0.9% 22
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タイプD -箱根湯本駅周辺商店街- 店舗数 :
62 チェーン店率 : 1.6% 23
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タイプE -横浜駅西口パルナード商店街- 店舗数 :
430 チェーン店率 : 6.3% 24
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Contents 1. 2. 3. 4. 5. 6. 7. 8. 研究背景 研究目的 研究方法 使用データ① 使用データ② 結果 事例 結論と考察 25
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8. 結論と考察 本研究では商店街の新たな分類のための手法を構築し,商店街分類の環境 を整備した上で,神奈川県の商店街を対象として実験的に分類を行った. <課題と今後の展開> • 重心の画像は,商店街の特徴を捉えることができない地点の画像が取 得されてしまうケースが多く見られた •
今後、画像からある程度自動的に栄えている,衰退している等の判断 が出来るような仕組みを考える必要がある • ひとつの商業集積ポリゴンの中に複数の商店街が入ってしまってい る場合や、商店街とその周辺の店舗がまとまって商業集積ポリゴン を形成している場合があり、店舗数やチェーン店率がその商店街の ものと言えないケースがある • 分類をより根拠あるものにするためにGPSデータなど、今後さら に様々なデータを用いて広域を対象に研究する必要がある。 26
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参考文献・データ • • • • • • 経済産業省(2008)「平成19年商業統計確報 概況」 経済産業省関東経済産業局(2011)「平成22年度中心市街地商業等活性 化支援業務等委託事業(ターゲティング分類等による中心市街地・商 店街の活性化成功事例調査事業及び中心市街地・商店街の競争力強化 のための指針提供事業)事業実施報告書」 秋山祐樹・ 仙石裕明・田村賢哉・柴崎亮介(2011)「日本全土の商業統 計ポリゴンデータの開発と商業集積地域ポリゴンデータの信頼性検 証」第20回地理情報システム学会講演論文集(CD-ROM,
F-2-3) 谷津憲司,吉川志保美,山田晴義(1997)「地方商店街における景観の構成 に関する研究」日本建築学会学術講演梗概集,1997,pp483-484 小林憲一郎(1986)「消費者のライフスタイルを中心とした商店街分類 とその診断の方向について:勤務者対象の商店街診断の可能性」流通 問題研究7,pp1-13 商業集積統計, デジタル電話帳(テレポイントPack!株式会社ゼンリ ン) ※スライド内の商店街の写真:Google Street View APIを用いて筆者が取得したもの 27
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ご清聴ありがとうございました。 28
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