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データから新しい観点や価値を引き出して「伝える」ためのデータビジュアライズ

 データから新しい観点や価値を引き出して「伝える」ためのデータビジュアライズ

2020年7月29日(水)に開催された「SCI-JAPANウェビナーシリーズ:スマートシティとデータヴィジュアライゼーション」( https://sci-jwebinar20200729.peatix.com/ )における発表資料です。
ウェビナー動画:https://www.youtube.com/watch?v=hA8mYGjPWho

関連資料:
・都市の多様性の可視化 (Visualizing Diversity of the City)
https://speakerdeck.com/shishamous/visualizing-diversity-of-the-city
・混雑状況を直感的に把握可能にするための人流センシング再現手法の開発 (Development of a Reproduction Method of a Stream of People for Intuitively Recognize a State of Congestion)
https://speakerdeck.com/shishamous/hun-za-zhuang-kuang-wozhi-gan-de-niba-wo-ke-neng-nisurutamefalseren-liu-sensinguzai-xian-shou-fa-falsekai-fa-development-of-a-reproduction-method-of-a-stream-of-people-for-intuitively-recognize-a-state-of-congestion
・「寿司詰めモード」デモ動画
https://youtu.be/HU8lyP7BZbg

Sayoko Shimoyama

July 29, 2020
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  1. σʔλ͔Β৽͍͠؍఺΍
    Ձ஋ΛҾ͖ग़ͯ͠
    ʮ఻͑ΔʯͨΊͷ
    σʔλϏδϡΞϥΠζ
    2020.7.29 (Wed.)
    SCI-JAPAN΢ΣϏφʔγϦʔζɿ
    εϚʔτγςΟͱσʔλϰΟδϡΞ
    ϥΠθʔγϣϯ
    Ұൠࣾஂ๏ਓϦϯΫσʔλ
    ୅දཧࣄ Լࢁ ࣿ୅ࢠ

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  2. Enterprise
    Public
    Sector
    Civic
    Tech
    Sayoko
    Shimoyama
    2020/7/29 Sayoko Shimoyama, LinkData 2

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  3. ඇৗۈߨࢣͱͯ͠σʔλϏδϡΞϥΠζߨ࠲Λ୲౰ɿ
    ؠֶ࡚Ԃ৘ใՊֶઐ໳ֶߍɾ෢ଂେֶ
    2020/7/29 SAYOKO SHIMOYAMA, LINKDATA 3

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  4. σʔλϏδϡΞϥΠζߨ࠲ͷ໨ඪ
    2020/7/29 Sayoko Shimoyama, LinkData 4
    ͲͷۀքͰ΋׆༂Ͱ͖ΔΑ͏ʹͳΔͨΊͷ
    lσʔλ׆༻εΩϧzΛशಘ͢Δ
    • σʔλΛ࢖͏ϝϦο
    τͱσϝϦοτΛ
    ཧղ͢Δ
    • ໨తʹԠͯ͡Ͳͷ
    σʔλΛ࢖͏΂͖͔
    ൑அͰ͖Δ
    • σʔλ͔Β৘ใΛ
    ਖ਼͘͠ಡΈऔΕΔ
    • ෳ਺ͷ؍఺Λ࣋ͬͯ
    ղऍͰ͖Δ
    • σʔλͷޡΓΛ
    ൃݟͰ͖Δ
    • σʔλͷޡΓΛൃݟ
    ͢ΔͨΊͷϧʔϧΛ
    ઃܭͰ͖Δ
    • σʔλΛ࢖ͬͯࣗ෼
    ͷߟ͑ΛදݱͰ͖Δ
    • ૬खʹͱͬͯཧղ͠
    ΍͘͢ɺҹ৅ʹ࢒Δ
    දݱ͕Ͱ͖Δ
    σʔλͷ
    ಛੑͷཧղ
    σʔλΛ
    ղऍ͢Δྗ
    σʔλΛ
    ݕূ͢Δྗ
    σʔλͰ
    දݱ͢Δྗ

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  5. ੜσʔλΛͦͷ··ݟͯ΋
    ྑ͘෼͔Βͳ͍
    ྫɿ͋ΔࢢͰӡӦ͍ͯ͠Δࢪઃͷ೥ؒͷར༻ঢ়گ
    ࢪઃ໊ ։ؗ೔਺ʢ೔ʣ ར༻ऀ਺ʢਓʣ ར༻ྉʢ؍ཡྉ౳ʣʢԁʣ ӡӦඅʢԁʣ
    ࢪઃ"
    ࢪઃ#
    ࢪઃ$
    ࢪઃ%
    2020/7/29 Sayoko Shimoyama, LinkData 5

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  6. ՄࢹԽ͢Δͱ
    ঢ়گ͕෼͔Γ΍͘͢ͳΔ
    ྫɿࢪઃͷ೥ؒͷར༻ঢ়گ










    ࢪઃ" ࢪઃ# ࢪઃ$ ࢪઃ%
    ӡӦඅʢ୯Ґɿԁʣ











    ࢪઃ" ࢪઃ# ࢪઃ$ ࢪઃ%
    ೥ؒͷར༻ਓ਺ʢ୯Ґɿਓʣ
    ࢪઃ"ͷӡӦʹଟ͘ͷίετ͕
    ׂ͔Ε͍ͯΔ
    ίετ͕͔͚ΒΕ͍ͯΔࢪઃ͸
    ར༻ऀ͕ଟ͍
    2020/7/29 Sayoko Shimoyama, LinkData 6

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  7. ෼ੳ→ՄࢹԽ͢Δͱ
    ͞Βʹৄ͍͠ঢ়گ͕ݟ͑ͯ͘Δ
    ྫɿࢪઃͷ೥ؒͷར༻ঢ়گ













    ࢪઃ" ࢪઃ# ࢪઃ$ ࢪઃ%
    ਓʹαʔϏε͢ΔͨΊʹ͔͔Δֹۚ
    ʢ୯Ґɿԁʣ
    ֤ࢪઃͷίετύϑΥʔϚϯε
    Λ஌Γ͍ͨ
    ར༻ऀਓ͋ͨΓʹαʔϏε͢Δ
    ͨΊʹ͔͔ΔֹۚΛܭࢉ
    ӡӦඅ ར༻ྉ

    ×
    ར༻ਓ਺ ࢪઃ"ͷӡӦʹ͸࠷΋ଟ͘ͷӡӦඅ͕
    ׂ͔Ε͍ͯΔ͕ɺίετύϑΥʔϚϯε
    ͸Ұ൪ߴ͍
    2020/7/29 Sayoko Shimoyama, LinkData 7

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  8. σʔλ෼ੳͷྲྀΕ
    4":0,04)*.0:"." -*/,%"5"
    ԾઆΛ
    ཱͯΔ
    σʔλΛ
    ४උ͢Δ
    ෼ੳɾ
    ධՁ
    ࢦඪઃܭ
    ɾϞχλ
    Ϧϯά
    ՝୊
    ৽͍͠՝୊ͷݕ౼
    αΠΫϧΛճͯ͠஌ݟΛ஝ੵ
    ग़యɿ$PEFGPS+BQBOࢢ઒ ത೭ࢯ࡞੒ͷσʔλΞΧσϛʔڭࡐΑΓ

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  9. σʔλ෼ੳͷྲྀΕͱ
    σʔλϏδϡΞϥΠζͷ࢖͍Ͳ͜Ζ
    4":0,04)*.0:"." -*/,%"5"
    ԾઆΛ
    ཱͯΔ
    σʔλΛ
    ४උ͢Δ
    ෼ੳɾ
    ධՁ
    ࢦඪઃܭ
    ɾϞχλ
    Ϧϯά
    ՝୊
    ৽͍͠՝୊ͷݕ౼
    αΠΫϧΛճͯ͠஌ݟΛ஝ੵ
    ᶃࣗ෼͕ঢ়گΛ
    ೺Ѳ͢ΔͨΊͷ
    ՄࢹԽ
    ʢ୳ࡧతՄࢹԽʣ
    ᶄୈࡾऀʹ
    ൑அࡐྉΛࣔ͢
    ͨΊͷՄࢹԽ
    ʢઆ໌తՄࢹԽʣ

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  10. ਓؒͷ೴͸
    ϏδϡΞϧΛ
    ςΩετͷ
    ສഒ
    ଎͘ೝ஌
    ਺ࣈ͚ͩݟͤΔΑΓ
    ΋ɺϏδϡΞϥΠζ
    ͢Δͱ఻ΘΓ΍͘͢
    ͳΔ
    4":0,04)*.0:"." -*/,%"5"
    https://www.slideshare.net/elsekramer/show-dont-tell-the-rise-of-visual-on-social-media/35-brand_identity

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  11. 8PSME%BUB7J[$IBMMFOHFͱͷؔΘΓ
    աڈճࢀՃʢόϧηϩφϥ΢ϯυ͸օۈ৆!ʣ
    ࢀՃͷ͖͔͚ͬɿ
    ◦ σʔλϏδϡΞϥΠζͷख๏Λ࢖ͬͯσʔλʹ৽͍͠؍఺΍Ձ஋Λ༩͑Δ͜ͱΛࢼ͔ͨͬͨ͠
    ◦ ਆށࢢͰσʔλར׆༻ਪਐҕһΛ͍ͯͨͭ͠ͳ͕Γ
    ◦ εϚʔτγςΟઌਐ౎ࢢͷόϧηϩφ΁ͷڵຯ
    ◦ খྛ͞Μʹ͸৭ʑ͓ੈ࿩ʹͳ͍ͬͯͯ಄্͕͕Βͳ͍ͷͰஅΕͳ͔ͬͨ
    4":0,04)*.0:"." -*/,%"5"
    WDVC2016 WDVC2018 WDVC2019

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  12. 8PSME%BUB7J[$IBMMFOHFͱͷؔΘΓ
    4":0,04)*.0:"." -*/,%"5"
    WDVC2016 WDVC2018 WDVC2019
    ςʔϚɿ౎ࢢͷଟ༷ੑͷՄࢹԽ

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  13. Sayoko
    Shimoyama
    and
    Misa
    Nishimura
    VISUALIZING
    DIVERSITY
    OF THE CITY
    ˞ൃදࢿྉͷൈਮ൛
    ʢϑϧόʔδϣϯ͸ͪ͜Βɿ
    https://speakerdeck.com/shishamous/visualizing-diversity-of-the-cityʣ

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  14. 1. Visualize and Numerize the DIVERSITY of the City
    § Using a biological formula to culculate “DIVERSITY INDEX”.
    § Statistics relate to human nature used to resemble the species.
    (e.g. nationality, industry types)
    2. Verify the Effect of Diversity on the City
    § Compare with the trends of economic indicators of the city. (T.B.D.)
    CHALLENGE
    2020/7/29 Sayoko Shimoyama, LinkData 14

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  15. ¡ Simpson’s Diversity Index
    § One of the most used index in Biological research
    § It measures the probability that two individuals randomly selected from a sample
    will belong to the same species.
    METHOD
    n = the total number of organisms of a particular species
    N = the total number of organisms of all species
    2020/7/29 Sayoko Shimoyama, LinkData 15

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  16. 0 ≤ 1-λ ≤ 1
    FEATURES OF
    THE SIMPSON’S DIVERSITY INDEX
    DIVERSITY
    High
    Low
    A
    B C
    D
    E
    A B
    C
    D
    E
    A community dominated
    by one or two species is
    considered to be less
    diverse than one in
    which several different
    species have a similar
    abundance.
    2020/7/29 Sayoko Shimoyama, LinkData 16

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  17. SUBJECT OF INVESTIGATION
    CITY POPULATION AREA (km2)
    Barcelona 1,604,555 101.4
    Kobe 1,537,418 557.02
    Yokohama 3,725,185 437.49
    2020/7/29 Sayoko Shimoyama, LinkData 17

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  18. POPULATION OF BARCELONA
    COUNTRY POPULATION
    Spain 1,371,436
    Italy 25,707
    Pakistan 19,414
    China 17,487
    France 13,281
    Morocco 12,601
    Bolivia 9,946
    Ecuador 8,647
    Philippines 8,491
    Peru 8,486
    0.296
    DIVERSITY
    INDEX
    2020/7/29 Sayoko Shimoyama, LinkData 18

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  19. POPULATION OF KOBE
    COUNTRY POPULATION
    Japan 1,498,991
    Korean 20,429
    China 14,285
    Vietnam 1,449
    U.S.A. 1,305
    India 1,071
    Philippines 1,045
    Brazil 558
    U.K. 372
    Thailand 307
    0.056
    DIVERSITY
    INDEX
    2020/7/29 Sayoko Shimoyama, LinkData 19

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  20. POPULATION OF YOKOHAMA
    COUNTRY POPULATION
    Japan 3,648,675
    China 34,433
    Korean 13,615
    Philippines 7,021
    Vietnam 4,204
    Taiwan 2,465
    Nepal 2,458
    Brazil 2,399
    U.S.A. 2,307
    India 1,984
    0.044
    DIVERSITY
    INDEX
    2020/7/29 Sayoko Shimoyama, LinkData 20

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  21. ¡ Barcelona has the highest diversity
    ¡ Diversity in KOBE is higher than YOKOHAMA
    ¡ Need to compare with more cities
    DIVERSITY IN NATIONALITY
    BARCELONA: 0.296 KOBE: 0.056 YOKOHAMA: 0.044
    2020/7/29 Sayoko Shimoyama, LinkData 21

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  22. WORKERS BY INDUSTRIAL CATEGORY IN KOBE
    CATEGORY POPULATION
    General eateries 49,025
    Public health 43,662
    Food and beverage
    retailing 42,300
    Other business services
    industry 38,412
    Social insurance, social
    welfare and nursing care
    business 32,806
    Other retailers 30,111
    School Education 26,349
    Entertainment eateries 20,357
    Road freight
    transportation industry 18,514
    Local public service 17,459
    0.971
    DIVERSITY
    INDEX
    2020/7/29 Sayoko Shimoyama, LinkData 22

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  23. WORKERS BY INDUSTRIAL CATEGORY IN
    YOKOHAMA
    CATEGORY POPULATION
    restaurant 13,892
    Real estate leasing
    and management
    industry 8,509
    Other retailers 7,983
    Laundry, barber,
    beauty and bath
    services 7,493
    Food and beverage
    retailing 6,919
    Public health 6,325
    Job by Contractors 4,458
    General Contractors 4,033
    Equipment
    Contractors 3,856
    Other education,
    learning support 3,734
    0.958
    DIVERSITY
    INDEX
    2020/7/29 Sayoko Shimoyama, LinkData 23

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  24. 8PSME%BUB7J[$IBMMFOHFͱͷؔΘΓ
    4":0,04)*.0:"." -*/,%"5"
    WDVC2016 WDVC2018 WDVC2019
    ςʔϚɿਓྲྀσʔλΛ࢖ͬͨ
    ࠞࡶঢ়گͷՄࢹԽ

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  25. Development of a Reproduction Method of a Stream of People
    for Intuitively Recognize a State of Congestion
    ࠞࡶঢ়گΛ௚ײతʹ೺ѲՄೳʹ͢ΔͨΊͷਓྲྀηϯγϯά࠶ݱख๏ͷ։ൃ
    Sayoko Shimoyama, Hiroki Uematsu
    WORLD DATA VIZ CHALLENGE 2018
    ˞ൃදࢿྉͷൈਮ൛
    ʢϑϧόʔδϣϯ͸ͪ͜Βɿ
    https://speakerdeck.com/shishamous/hun-za-zhuang-kuang-wozhi-gan-
    de-niba-wo-ke-neng-nisurutamefalseren-liu-sensinguzai-xian-shou-fa-
    falsekai-fa-development-of-a-reproduction-method-of-a-stream-of-
    people-for-intuitively-recognize-a-state-of-congestion)

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  26. 4":0,04)*.0:"." -*/,%"5"

    Ideal form of smart city must control
    a stream of people

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  27. 4":0,04)*.0:"." -*/,%"5"
    Japanese major cities are sometimes badly congested.
    This picture shows the train platform in the rush hours in Tokyo.

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  28. 4":0,04)*.0:"." -*/,%"5"
    寿司詰め
    Sushi-Zume
    (n) jam-packed; packed in like sushi (like sardines)

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  29. .FUIPET GPSDPVOUJOH QFEFTUSJBOT
    4":0,04)*.0:"." -*/,%"5"

    manual sensor camera
    • low cost
    • easy to install
    • data aggrigation
    • human error
    • low cost
    • real-time information
    • cannot get attribute
    • enable to get attribute
    • expensive
    • privacy issue

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  30. %FNPOTUSBUJPO 1SPKFDUJOUIF
    6OEFSHSPVOE1BTTBHF JO4BQQPSP$JUZ
    4":0,04)*.0:"." -*/,%"5"
    By 663highland, CC BY 2.5
    Sapporo,
    Hokkaido,
    Japan

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  31. 5IFNPTUMPOHFTU
    VOEFSHSPVOEQBTTFHF JOKBQBO
    4":0,04)*.0:"." -*/,%"5"

    520m
    Station Park

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  32. *OTUBMMFETFOTPSTBUQPJOU
    4":0,04)*.0:"." -*/,%"5"

    + + + +
    +
    Station Park

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  33. 4":0,04)*.0:"." -*/,%"5"
    sensors
    installed
    on a ceiling

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  34. 4":0,04)*.0:"." -*/,%"5"
    +
    Station Park
    pedestrians/1 minute at J1 on 2018.8.10

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  35. 4":0,04)*.0:"." -*/,%"5"
    from station to station sum
    pedestrians/1 minute at J1 on 2018.8.10

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  36. 8IJDIQPJOUIBTUIFMBSHFTUGFXFTU
    OVNCFSPGQFEFTUSJBOT
    4":0,04)*.0:"." -*/,%"5"

    + + + +
    +
    Station Park
    point road width
    J1 18m
    J2 21m
    J3 18m
    J4 21m
    J5 6m

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  37. 4":0,04)*.0:"." -*/,%"5"

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  38. 4":0,04)*.0:"." -*/,%"5"

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  39. 4":0,04)*.0:"." -*/,%"5"

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  40. 4":0,04)*.0:"." -*/,%"5"

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  41. 4":0,04)*.0:"." -*/,%"5"
    Earthquake
    in September 6, 2018
    出典:札幌市「2018 年度上期(2018年4月〜9月)の来札観光客数の状況」
    Many tourists
    canceled the
    reservations

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  42. 4":0,04)*.0:"." -*/,%"5"
    What time is rush hourʁ

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  43. 4":0,04)*.0:"." -*/,%"5"

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  44. 4":0,04)*.0:"." -*/,%"5"

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  45. 130#-&.ɿ*U`TEJGGJDVMUUPSFDPHOJ[F
    UIFTUBUFPGDPOHFTUJPOJOUVJUJWFMZ
    n7JTVBMJ[JOHOVNFSJDBMWBMVFBTBDIBSUIFMQTVTVOEFSTUBOEUIFTJUVBUJPO
    n#VUJU`TEJGGJDVMUUPLOPXIPXNVDIDPOHFTUJPOEFHSFFJTUIFSF
    4":0,04)*.0:"." -*/,%"5"
    ʁ
    Sushi-Zume Not Sushi-Zume

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  46. 40-65*0/ɿ3FQSPEVDFUIFTUSFBN
    PGQFPQMFVTJOH%NPEFM
    n3FQSPEVDFUIFTUSFBNPG
    QFPQMFGSPNUIFOVNFSJD
    WBMVFNFBTVSFECZTFOTPST
    VTJOH%NPEFM
    n*UFOBCMFTJOUVJUJWFMZ
    SFDPHOJ[FBTUBUFPG
    DPOHFTUJPO
    4":0,04)*.0:"." -*/,%"5"

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  47. 4":0,04)*.0:"." -*/,%"5"
    σϞϯετϨʔγϣϯ
    %FNPTUSBDJÓO
    IUUQTZPVUVCF)6MZ1#;CH

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  48. 8PSME%BUB7J[$IBMMFOHFͱͷؔΘΓ
    4":0,04)*.0:"." -*/,%"5"
    WDVC2016 WDVC2018 WDVC2019
    ςʔϚɿݮࡂͷͨΊͷ
    ࡂ֐༝དྷ஍໊ͷՄࢹԽ

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  49. Disaster risk reduction by use of place
    names stemming from past disasters
    減災のための災害由来地名の活用
    Mayuri Tanaka
    Hiroki Uematsu
    Sayoko Shimoyama

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  50. Increse Natural Disasters In The World
    2020/7/29 Sayoko Shimoyama, LinkData
    By Justin1569 at English Wikipedia
    By United States Geological Survey
    By Our World in Data
    50

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  51. Natural Disaster won’t go away
    That’s Why
    Disaster risk reduction is Important
    2020/7/29 Sayoko Shimoyama, LinkData
    51

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  52. Our area is ….
    2020/7/29 Sayoko Shimoyama, LinkData
    For Disaster Risk Reduction
    52

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  53. 2020/7/29 Sayoko Shimoyama, LinkData
    Hazard maps are hardly noticeable.
    Less than
    20%
    people have read and understand hazard maps.
    53

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  54. Disaster-Prone
    Country
    JAPAN
    54
    By (NASA)/ REUTERS
    By Geospatial Information Authority of Japan
    By U.S. Navy photo
    2020/7/29 Sayoko Shimoyama, LinkData
    54

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  55. Place Names Result From Disasters
    Ôfunazawa
    大船沢
    When tsunami struck this area,
    a large ship was beached.
    Place Name Origin ( Mean )
    big,
    large
    a boat,
    a ship
    a stream
    Sayoko Shimoyama, LinkData
    2020/7/29
    55

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  56. Changed Place Name
    After
    Before
    kami me guro
    上目黒
    jya kuzure
    蛇崩
    snake collapse,
    tumble, up, top,
    above
    Sayoko Shimoyama, LinkData
    a bland
    city name
    2020/7/29
    56

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  57. Disasters
    Place Names
    Result from Disasters
    Increase Disaster
    Risk Reduction
    Change
    Place Names
    Decrease a
    Sense of Crisis
    Decrease Effect
    from Disasters
    Long Ago
    Sayoko Shimoyama, LinkData
    Theory
    2020/7/29
    57

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  58. 1.Collect Data
    2.Extract renamed place names.
    3.Extract renamed place names
    covered with hazard maps
    4.Extract renamed place names
    have effected from disasters
    in addition to covered with hazard maps
    Method
    2020/7/29
    58

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  59. • Old Place Names Data
    by Human Culture Research Institute (人間文化研究機構)
    • Now Place Names Data
    by Ministry of Land, Infrastructure, Transport and Tourism (国土交通省)
    • Hazard Maps Data
    by Ministry of Land, Infrastructure, Transport and Tourism (国土交通省)
    1.Collect Data
    2020/7/29
    59

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  60. 2.Extract Renamed Place Names
    Renamed
    37%(18,970)
    Renamed Percentage,
    ,
    63%(32,992)
    Total number of place names : 51,962
    2020/7/29
    60

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  61. 3.Extract Renamed Place Names
    Covered With Hazard Maps
    Sayoko Shimoyama, LinkData
    Land Slide Hazard Map
    Keep Name Place Name
    Renamed Place Name
    2020/7/29
    61

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  62. 3.Extract Renamed Place Names
    Covered With Hazard Maps
    Sayoko Shimoyama, LinkData

    米田
    Bad image
    Good image
    Land Slide Hazard Map
    Keep Name Place Name
    Renamed Place Name
    2020/7/29
    62

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  63. Sayoko Shimoyama, LinkData
    By Tottori prefecture
    4. Extract renamed place names
    have effected from disasters
    in addition to covered with hazard maps
    2020/7/29
    63

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  64. Conclusion
    Change Name
    Sayoko Shimoyama, LinkData
    2020/7/29
    64

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  65. ਆށɾόϧηϩφ͔ΒͷࢀՃऀͷօ͞·ʹΑΔ
    ૉ੖Β͍͠࡞඼Λ͝ཡ͍ͩ͘͞
    https://opendata-ajuntament.barcelona.cat/en/finalistes-data-viz-2019-kobe
    2020/7/29 Sayoko Shimoyama, LinkData 65

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  66. Aging and disability:
    a challenge present and future
    https://drive.google.com/file/d/1OrvIJnn9dlsanN89xO5bk_aIxGzaVtE5/view
    2020/7/29 Sayoko Shimoyama, LinkData 66

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  67. bibliomaps ਆށ൛
    http://demo.lab.sugimototatsuo.com/2016kobe/
    2020/7/29 Sayoko Shimoyama, LinkData 67

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  68. Disaster Support Facilities in City of Kobe
    ~ΦʔϓϯσʔλͰͨ͘͞ΜͷਓͷΞΠσΟΞΛ
    ूΊɺΑΓΑ͍ະདྷΛܴ͑ΔͨΊʹ
    https://public.tableau.com/profile/kaori#!/vizhome/WorldDataVizChallenge2018FinalRou
    ndKobe/DisasterSupportFacilitiesinCityofKobe
    2020/7/29 Sayoko Shimoyama, LinkData 68

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  69. ը૾ग़యݩɿ7JTVBMJ[JOHFBDIPG#BSDFMPOB`TJOIBCJUBOUT $BSMPT$BSSBTDP'BSSÉ
    IUUQXXXDDGBSSFDPNWJTVBMJ[JOHFBDIPGCBSDFMPOBSTRVPTJOIBCJUBOUTIUNM
    4BZPLP4IJNPZBNB -JOL%BUB

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  70. σʔλͰࠜڌΛࣔ͢͜ͱͰ߹ҙܗ੒ΛਐΊͨྫɿ
    εʔύʔϒϩοΫߏ૝ʢόϧηϩφࢢʣ
    nόϧηϩφͰ͸ࣗಈंͷަ௨ྔ͕ଟ͘ɺԚછ෺࣭΍CO2ɺ
    ૽ԻͳͲʹΑΔެ֐͕՝୊
    nʮࢢຽத৺౎ࢢʯΛίϯηϓτʹ͔͔͛ɺஈ֊తʹं͕௨ΕΔ
    ಓΛ੍ݶ͢ΔܭըΛ࣮ࢪ
    nަ௨ྔ΍؀ڥʹؔ͢ΔηϯγϯάσʔλΛ༻͍ͯ෼ੳΛߦ͍ɺ
    ܭը͕ਐΜͩ৔߹ʹͲͷ͘Β͍໰୊͕վળ͢Δͷ͔ɺ
    ۩ମతͳ਺஋Ͱࣔ͢͜ͱͰ߹ҙܗ੒Λਪਐ
    • ަ௨ྔ͕21%ݮ
    • 94%ͷࢢຽ͕ةݥͳϨϕϧͷཻࢠঢ়෺࣭ʹࡽ͞ΕΔ͜ͱ͸ͳ͘ͳΔ
    • 73.5%ͷࢢຽ͸ʮ65σγϕϧҎ্ͷ૽ԻʯΛܦݧͤͣʹ͢Ή
    2020/7/29 Sayoko Shimoyama, LinkData 70
    https://citiesofthefuture.eu/superblocks-barcelona-answer-to-car-centric-city/

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  71. 4BZPLP4IJNPZBNB -JOL%BUB
    出典元: http://www.bcnecologia.net/en/statutes
    όϧηϩφ౎ࢢੜଶֶிɿ
    σʔλαΠΤϯεΛۦ࢖ͯ͠౎ࢢͷ
    ϚωδϝϯτΛߦ͏ࢢͷઐ໳ػؔ
    l౎ࢢΛ਍அ͢Δz
    ͱ͍͏දݱ͕Α͘ग़ͯ͘Δ

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  72. ౎ࢢΛ਍அ͢Δ
    4BZPLP4IJNPZBNB -JOL%BUB
    ਍அɿ
    ਍࡯΍ݕࠪΛߦ͍ɺಘΒΕͨॾ
    ৘ใΛ༻͍ͯɺױऀͷ݈߁ঢ়ଶ
    ΍පؾͷঢ়ଶΛ൑அ͢Δ͜ͱ
    ౎ࢢͷ਍அɿ
    ౎ࢢͷঢ়ଶΛௐࠪ͠؍ଌ͢Δ͜ͱͰɺ
    ౎ࢢػೳ͕ྑ޷ͳঢ়ଶ͔ɺ
    ՝୊͕͋Δঢ়ଶ͔Λ൑அ͢Δ͜ͱ
    画像出典元: http://www.bcnecologia.net/en/projects/urban-plan-llevant-sector-figueres

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  73. σʔλʹ
    ج͔ͮͳ͍ࢪࡦ͸
    ຽؒྍ๏ͱಉ͡
    σʔλΛ׆༻ͤͣʹɺ
    ܦݧ΍צ΍׳ྫͷΈͰࢪࡦΛਐΊΔͷ͸ɺ
    ຽؒྍ๏ͰපؾΛ࣏ͦ͏ͱ͢ΔΑ͏ͳ΋ͷ
    2020/7/29 Sayoko Shimoyama, LinkData 73

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  74. ױ

    ͷ



    ܦݧ΍Χϯ͚ͩͰ͸ͳ͘ɺ
    ͦΕΛཪ෇͚Δ٬؍తͳσʔλ͕ඞཁ
    nEBPMɿ
    Evidence Based Policy Making
    …ΤϏσϯεʢՊֶతࠜڌʣʹج
    ͍ͮͨ੓ࡦཱҊ
    nEBPM͸ҩֶ
    ʢEvidence Based Medicineʣ
    ͔Β೿ੜͨ͠ߟ͑ํ
    • 1989೥ɺ౰࣌Ұൠతʹ࢖༻͞Εͯ
    ͍ͨෆ੔຺ͷༀͷޮՌΛσʔλΛ
    औͬͯݕূͨ͠ͱ͜Ζɺ෰༻ʹ
    Αͬͯࢮ๢཰͕ߴ·Δ͜ͱ͕൑໌
    2020/7/29 Sayoko Shimoyama, LinkData 74
    ౰࣌Ұൠతͳෆ੔຺ͷༀ
    ϓϥηϘʢِༀʣ
    ܦݧత஌ࣝͷΈͰ
    ൑அ͢Δ͜ͱͷ
    ةݥੑ

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  75. ݕূՄೳͳʮσʔλʯ͕
    ڞ༗͞ΕΔඞཁ͕͋Δ
    nूܭ݁Ռͷ਺஋΍άϥϑͷը૾ͷܗͰ͸ɺΤϏσϯεͱͯ͠
    ৴པͰ͖Δ΋ͷ͔Ͳ͏͔ݕূͰ͖ͳ͍
    nʮσʔλʯͷܗͰڞ༗͞ΕΔඞཁ͕͋Δ
    4BZPLP4IJNPZBNB -JOL%BUB

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  76. Ұൠࣾஂ๏ਓϦϯΫσʔλ
    ୅දཧࣄ Լࢁ ࣿ୅ࢠ
    Email: [email protected]
    ຊࢿྉʹؔ͢Δ࣭͝໰ɾ͝ҙݟ΍ɺ
    σʔλ׆༻ݚमɾϫʔΫγϣοϓ։࠵ͷ͝૬ஊͳͲ͸
    ͪ͜Β΁͓د͍ͤͩ͘͞ɻ
    4BZPLP4IJNPZBNB -JOL%BUB

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