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

Transcript

1. σʔλ͔Β৽͍͠؍఺΍ Ձ஋ΛҾ͖ग़ͯ͠ ʮ఻͑ΔʯͨΊͷ σʔλϏδϡΞϥΠζ 2020.7.29 (Wed.) SCI-JAPAN΢ΣϏφʔγϦʔζɿ εϚʔτγςΟͱσʔλϰΟδϡΞ ϥΠθʔγϣϯ Ұൠࣾஂ๏ਓϦϯΫσʔλ ୅දཧࣄ Լࢁ ࣿ୅ࢠ
2. Enterprise Public Sector Civic Tech Sayoko Shimoyama 2020/7/29 Sayoko Shimoyama, LinkData 2
3. ඇৗۈߨࢣͱͯ͠σʔλϏδϡΞϥΠζߨ࠲Λ୲౰ɿ ؠֶ࡚Ԃ৘ใՊֶઐ໳ֶߍɾ෢ଂେֶ 2020/7/29 SAYOKO SHIMOYAMA, LINKDATA 3
4. σʔλϏδϡΞϥΠζߨ࠲ͷ໨ඪ 2020/7/29 Sayoko Shimoyama, LinkData 4 ͲͷۀքͰ΋׆༂Ͱ͖ΔΑ͏ʹͳΔͨΊͷ lσʔλ׆༻εΩϧz
   Λशಘ͢Δ • 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
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
6. ՄࢹԽ͢Δͱ ঢ়گ͕෼͔Γ΍͘͢ͳΔ ྫɿࢪઃͷ೥ؒͷར༻ঢ়گ   
     
     
     
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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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13. Sayoko Shimoyama and Misa Nishimura VISUALIZING DIVERSITY OF THE CITY ˞ൃදࢿྉͷൈਮ൛ ʢϑϧόʔδϣϯ͸ͪ͜Βɿ https://speakerdeck.com/shishamous/visualizing-diversity-of-the-cityʣ
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
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
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
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
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
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
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
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
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
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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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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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
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
51. Natural Disaster won’t go away That’s Why Disaster risk reduction is Important 2020/7/29 Sayoko Shimoyama, LinkData 51
52. Our area is …. 2020/7/29 Sayoko Shimoyama, LinkData For Disaster Risk Reduction 52
53. 2020/7/29 Sayoko Shimoyama, LinkData Hazard maps are hardly noticeable. Less than 20% people have read and understand hazard maps. 53
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
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
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
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
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
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
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
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
62. 3.Extract Renamed Place Names Covered With Hazard Maps Sayoko Shimoyama, LinkData 灘 米田 Bad image Good image Land Slide Hazard Map Keep Name Place Name Renamed Place Name 2020/7/29 62
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
64. Conclusion Change Name Sayoko Shimoyama, LinkData 2020/7/29 64
65. ਆށɾόϧηϩφ͔ΒͷࢀՃऀͷօ͞·ʹΑΔ ૉ੖Β͍͠࡞඼Λ͝ཡ͍ͩ͘͞ https://opendata-ajuntament.barcelona.cat/en/finalistes-data-viz-2019-kobe 2020/7/29 Sayoko Shimoyama, LinkData 65
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
67. bibliomaps ਆށ൛ http://demo.lab.sugimototatsuo.com/2016kobe/ 2020/7/29 Sayoko Shimoyama, LinkData 67
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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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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72. ౎ࢢΛ਍அ͢Δ  4BZPLP
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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
74. ױ ऀ ͷ ੜ ଘ ཰ 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
75. ݕূՄೳͳʮσʔλʯ͕ ڞ༗͞ΕΔඞཁ͕͋Δ nूܭ݁Ռͷ਺஋΍άϥϑͷը૾ͷܗͰ͸ɺΤϏσϯεͱͯ͠ ৴པͰ͖Δ΋ͷ͔Ͳ͏͔ݕূͰ͖ͳ͍ nʮσʔλʯͷܗͰڞ༗͞ΕΔඞཁ͕͋Δ  4BZPLP
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76. Ұൠࣾஂ๏ਓϦϯΫσʔλ ୅දཧࣄ Լࢁ ࣿ୅ࢠ Email: support@linkdata.org ຊࢿྉʹؔ͢Δ࣭͝໰ɾ͝ҙݟ΍ɺ σʔλ׆༻ݚमɾϫʔΫγϣοϓ։࠵ͷ͝૬ஊͳͲ͸ ͪ͜Β΁͓د͍ͤͩ͘͞ɻ  4BZPLP
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