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Smart City and Spatial Big Data -Studies and cases in Japan-

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FOSS4G Korea
Smart City with Open Geospatial Technologies in Emerging Cities
August 31, 2016

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Smart City and Spatial Big Data -Studies and cases in Japan-

  1. 1. Yuki Akiyama(aki@csis.u-tokyo.ac.jp) Assistant professor Micro Geodata lab Center for Spatial Information Science, University of Tokyo Smart City and Spatial Big Data -Studies and cases in Japan- August 31, 2016 FOSS4G Korea Smart City with Open Geospatial Technologies in Emerging Cities
  2. 2. Self introduction Name:Yuki Akiyama (秋山祐樹) Birth place:Okayama city, Okayama pref. Japan Affiliation:Assistant Professor Micro Geodata laboratory Center for Spatial Information Science, UT Visiting Research Officer Policy Research Institute for Land, Infrastructure, Transport and Tourism (PRLIT) Visiting Research Fellow Korea Research Institute for Human Settlements Main fields: Spatial information Science, Urban engineering Geography 2 Urban heat island GIS Micro Geodata Smart city Tokyo Seoul Okayama
  3. 3. Background Many problems in urban area Reactivation of city center Aging Traffic management Disaster prevention Facility placement planning Design urban planning to realize smart city Many kinds of spatial data and census data can support to solve them. 3
  4. 4. Recently, various geo data are available. 4
  5. 5. Building scale Building data:Zmap-TOWNⅡ ©Zenrin CO. Ltd. Detailed geo data are available. Building scale → household scale → person scale… 5
  6. 6. Building scale Household scale Micro Population Census ©Yuki Akiyama Detailed geo data are available. Building scale → household scale → person scale… 6
  7. 7. People flow data ©CSIS Person scale Building scale Household scale Detailed geo data are available. Building scale → household scale → person scale… 7
  8. 8. Person scale People flow data ©CSIS Big data with location and time or geo data =Micro Geo Data(MGD) Building scale Household scale Detailed geo data are available. Building scale → household scale → person scale… 8
  9. 9. Many conferences related with MGD!! http://sigspatial2016.sigspatial.org/ http://giscience.geog.mcgill.ca/https://cupum2015.mit.edu/ http://www.isprs2016-prague.com/ 9 Me
  10. 10. Japanese government is looking to the utilization of MGD. Japanese government begins to introduce legislation toward promotion of utilization of big data. NHK News Web(2014/06/09) http://www3.nhk.or.jp/news/html/20140609/k10015066701000.html Regional Economy Society Analyzing System (RESAS) https://resas.go.jp/ 10 Japanese government is developing legislation to use big data enciphered personal information without owner’s consent. Japanese Cabinet Office is developing the “RESAS” which visualize various census data and big data for Japanese local governments.
  11. 11. The widespread use of detailed digital map creates demands for more detailed and reliable spatial data than before. Not only researches but also private and government sectors are also interested in how to utilize these data. Micro Geodata (MGD) is gathering attention now how to develop, share and apply for realization of smart city. Coming of age to utilize MGD Recently, computers and smart phones are becoming widespread and internet environment are being developed rapidly. Anyone can access detailed digital map. 11
  12. 12. 1) Available Japanese MGD for urban monitoring 2) Examples of studies & cases to utilize MGD 2.1 Utilize person MGD 2.2 Utilize disaster big data 2.3 Utilize public big data 3) Conclusions and future works Today’s contents 12
  13. 13. Commercial accumulation Statistics Eric Fischer, “Eric Fischer’s photostream”, http://www.flickr.com/photos/walkingsf/ Continuation Change Emergence Demise LegendTime-series changes 2003-2008 Time-series tenant data Inter-enterprise transaction big data Residential map Telephone directory Web information (SNS and search results) MGD about Buildings, shops and enterprises 1. Available Japanese MGD for urban monitoring
  14. 14. Person flow data Various mobile census Pseudo person flow data based on SNS tweets with geo tags Micro population census Person flow project http://pflow.csis.u-tokyo.ac.jp/index-j.html ©Nightley Inc. ©Shibasaki & Sekimoto lab. Univ. of Tokyo ©Micro Geodata forum ©Person flow project ©Center for Spatial Information Science, The Univ. of Tokyo Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of Micropopulation Census through Disaggregation of National Population Census", CUPUM2013 conference papers, 110. 1. Available Japanese MGD for urban monitoring MGD about person distributions and flows
  15. 15. 1) Available Japanese MGD for urban monitoring 2) Examples of studies & cases to utilize MGD 2.1 Utilize person MGD 2.2 Develop disaster big data 2.3 Utilize public big data 3) Conclusions and future works Today’s contents 15
  16. 16. Recently, various census data are being digitalized and we can get them easily from websites of national and local governments. Especially, the population census is used as base data to understand distribution and movement of population for following urban problems. 2.1 Utilize person MGD Urban planning Disaster damage estimation and prevention Traffic planning Marketing support Epidemic prevention More detailed and reliable population data are required than before to resolve them. How to monitor detailed population distribution ? 16
  17. 17. 2.1 Utilize person MGD How to monitor detailed population distribution ? 1. Residential population > Micro population census (MPC) 2. Dynamic population > Mobile phone data (CDR, GPS log etc.) 17
  18. 18. 18 Micro population census (MPC) (Population census + residential map + housing statistics) MPC can monitor detailed residential population. It was realized to disaggregate population census. (Disaggregate: data processing to reallocate data statistically based on other statistics and spatial features.) 東松原駅 LEGEND Households 1 person 3 persons 4 persons 5 persons ≧6 persons 2 persons Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of Micropopulation Census through Disaggregation of National Population Census", CUPUM2013 conference papers, 110. 18
  19. 19. 19 東松原駅 Attribute table Building type house Longitute 139.65633 Latitude 35.663664 Area[m2 ] 105.34 Family type 8 Household size 5 Householder [age-gender] 45 - 1 Spouse [age - gender] 40 - 2 Number of child 2 Information of children 5-1 | 10 - 2 Number of parent 1 Information of parent 75-2 Number of others 0 Information of others None LEGEND Households 1 person 3 persons 4 persons 5 persons ≧6 persons 2 persons Micro population census (Population census + residential map + housing statistics) Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of Micropopulation Census through Disaggregation of National Population Census", CUPUM2013 conference papers, 110. MPC can monitor detailed residential population. It was realized to disaggregate population census. (Disaggregate: data processing to reallocate data statistically based on other statistics and spatial features.) 19
  20. 20. 20 LEGEND Households 1 person 3 persons 4 persons 5 persons ≧6 persons 2 persons Micro population census (Population census + residential map + housing statistics) Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of Micropopulation Census through Disaggregation of National Population Census", CUPUM2013 conference papers, 110. 20
  21. 21. 21 LEGEND Households 1 person 3 persons 4 persons 5 persons ≧6 persons 2 persons Micro population census (Population census + residential map + housing statistics) Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of Micropopulation Census through Disaggregation of National Population Census", CUPUM2013 conference papers, 110. 21
  22. 22. LEGEND Households 1 person 3 persons 4 persons 5 persons ≧6 persons 2 persons Micro population census (Population census + residential map + housing statistics) Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of Micropopulation Census through Disaggregation of National Population Census", CUPUM2013 conference papers, 110. 22
  23. 23. LEGEND Households 1 person 3 persons 4 persons 5 persons ≧6 persons 2 persons The Important thing to understand this data This data is estimated data. Values of each point are necessarily match as actual states. Micro population census (Population census + residential map + housing statistics) Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of Micropopulation Census through Disaggregation of National Population Census", CUPUM2013 conference papers, 110. 23
  24. 24. Aggregation by grid (250m square meter) Micro population census (Population census + residential map + housing statistics) 24
  25. 25. Aggregation by grid (250m square meter) LEGEND Population 0 - 250 501 - 1000 1001 - 2000 2001 - 251 - 500 Aim of the Micropopulation census =Development the new population census which can be aggregated into arbitrary spatial unit. Micro population census (Population census + residential map + housing statistics) 25
  26. 26. Aggregation by city blocks LEGEND Population 0 - 50 101 - 250 251 - 500 501 - 51 - 100 Micro population census (Population census + residential map + housing statistics) 26
  27. 27. LEGEND Aging rate (Over 65 yrs. old)[%] 0 - 10 20 - 30 30 - 50 50 - 10 - 20 27 Monitoring of aging rate (rate of population over 65years old in 250m square grid) 27
  28. 28. 凡例 高齢化率 (65歳以上割合)[%] 0 - 10 20 - 30 30 - 50 50 - 10 - 20 28 Monitoring of aging rate (rate of population over 65years old in 250m square grid) LEGEND Aging rate (Over 65 yrs. old)[%] 0 - 10 20 - 30 30 - 50 50 - 10 - 20 28
  29. 29. 29 Monitoring of aging rate (rate of population over 65years old in 250m square grid) 29
  30. 30. 4.3 billion people use mobile phones in 2014 *1. Many mobile phones have GPS function. Studies to monitor dynamic population using mass mobile phone GPS data attract considerable attention for resolve various urban problems *2. *1: DESIGNBAM, “5 billion people will use mobile phones by 2017”, http://designbam.com/2013/10/04/5-billion-people-will-use-mobile-phones-by-2017/ *2: Sekimoto, Y., Horanont T. and Shibasaki, R., 2011. Trend of People Flow Analysis Technology Using Mobile Phone. IPSJ Magazine,. 52(12): 1522-1530. (In Japanese)
  31. 31. 4 Application example of mobile phone GPS data Example in USA This map shows characteristics of each road based on dynamic population calculated from mass mobile phone GPS data in San Francisco. This result is actually used for traffic planning in the city. PHYS.ORG, “Cellphone, GPS data suggest new strategy for alleviating traffic tie-ups”, http://phys.org/news/2012-12-cellphone-gps- strategy-alleviating-traffic.html bc: How popular they are as connectors between other roads Kroad : Number of geographic areas that contribute to traffic on a particular road 31
  32. 32. 5 Example in Kenya Three quarters of Kenyan use mobile phones. Mobile phone GPS data are used for animal quarantine. When livestock disease occurs, Kenyan farmer deliver and share information of them with location information. http://www.un.org/apps/news/sto ry.asp?NewsID=44259&Cr=livestoc k&Cr1=#.VFLsW_l_u51 Application example of mobile phone GPS data 32
  33. 33. Japanese mobile phone GPS data We can monitor estimate populations of every hours by 250m square grids (updated everyday). ・Real time data based on GPS logs by mobile phones ・It covers throughout Japan. Congestion analysis (Zenrin Data Com Co., Ltd.) http://lab.its-mo.com/densitymap/ 33
  34. 34. Estimated numbers of visitor in each commercial area Shinjuku Shibuya Legend Estimated number of visitors Application 1: Estimation of Dynamic population in commercial areas 34 秋山祐樹 ・Teerayut Horanont・柴崎亮介,2013年,「大規模 人流データを用いた商業地域における来訪者数の時系列分析」, 第22回地理情報システム学会講演論文集(CD-ROM, C-5-4)
  35. 35. Time-series numbers of visitor in each commercial area Shinjuku Shibuya Legend Estimated number of visitors Application 1: Estimation of Dynamic population in commercial areas Number of Shops:198 Average number of daily visitors:3,039 Average Hourly number of visitors in weekdays 0 500 1,000 1,500 2,000 2,500 3,000 3,500 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 35 秋山祐樹 ・Teerayut Horanont・柴崎亮介,2013年,「大規模 人流データを用いた商業地域における来訪者数の時系列分析」, 第22回地理情報システム学会講演論文集(CD-ROM, C-5-4)
  36. 36. Time-series numbers of visitor in each commercial area Shinjuku Legend Estimated number of visitors Application 1: Estimation of Dynamic population in commercial areas Number of Shops:198 Average number of daily visitors:3,039 Average Hourly number of visitors in weekdays 0 500 1,000 1,500 2,000 2,500 3,000 3,500 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 36 秋山祐樹 ・Teerayut Horanont・柴崎亮介,2013年,「大規模 人流データを用いた商業地域における来訪者数の時系列分析」, 第22回地理情報システム学会講演論文集(CD-ROM, C-5-4) Shibuya渋谷 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Number of Shops:1,580 Average number of daily visitors:92,619 Average Hourly number of visitors in weekdays
  37. 37. 37 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Averagenumberofvisitorsinweekday O’clock 浅草 歌舞伎町 原宿 八重洲1丁目 下北沢南口 Asakusa Kabuki-cho Harajuku Yaesu-1 Shimo-kitazawa It is realized to integrate the Commercial Accumulation Statistics with the mobile census data by ZDC in the city center of Tokyo. (365days in 2012) Application 1: Estimation of Dynamic population in commercial areas ! 37
  38. 38. 25 Regional characteristics in each grids were calculated as follows. Numbers of each kind of stay points can be calculated in all grids. Regional characteristics are defined by this method. Akiyama, Y. and Shibasaki, R., 2014, "Time-series Monitoring of Area Characteristics Using Mass Person Flow Data by Mobile Phone GPS Data - Case study in Greater Tokyo Region-",The International Symposium on City Planning 2014,SS03,S03-12. Application 2: Estimation of regional characteristics 38
  39. 39. Time-series estimation of regional characteristics in Tokyo using mobile census data in 2012 39 Akiyama, Y. and Shibasaki, R., 2014, “Time-series Monitoring of Area Characteristics Using Mass Person Flow Data by Mobile Phone GPS Data“, The International Symposium on City Planning – Vietnam 2014, SS03,S03-12. Application 2: Estimation of regional characteristics 39
  40. 40. Akiyama, Y. and Shibasaki, R., 2014, “Time-series Monitoring of Area Characteristics Using Mass Person Flow Data by Mobile Phone GPS Data“, The International Symposium on City Planning – Vietnam 2014, SS03,S03-12. Number of grids throughout Kanto region by 500m square grid Population of each types throughout Kanto region by 500m square grid 0 10,000 20,000 30,000 40,000 50,000 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Numberofgrid Time Commercial/Sightseeing Residential Business Transport Mixed 0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Population Time Commercial/Sightseeing Residential Business Transport Mixed Application 2: Estimation of regional characteristics 40
  41. 41. Application 3: Event detection Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification by MDSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84. We defined sudden population concentration spatio-temporally as the “Event”. Events contains positive events (festivals, concerts, ceremonies etc.) and negatives (traffic accidents, disasters etc.). 41
  42. 42. 15 4. Result Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification by DMSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84. 42
  43. 43. 15 4. Result A: Open event on the Iwakuni base of US air force(May 5) C: Omagari firework display (August 26) Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification by DMSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84. 43
  44. 44. 4. Result Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification by MDSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84. 15 D: New Year events in Saijo Inari shrine holidays) Many events were held in New Year holidays intensively F: Typhoon effect (September 30 ~ October 1) Event Meshes with a small number of Event visitors. Many tourists were stuck due to the effect of a severe typhoon. 44
  45. 45. 1) Available Japanese MGD for urban monitoring 2) Examples of studies & cases to utilize MGD 2.1 Utilize person MGD 2.2 Develop disaster big data 2.3 Utilize public big data 3) Conclusions and future works Today’s contents 45
  46. 46.  Japanese government estimates that victims by Tokai- Tonankai earthquakes are over 300 thousands.  Calculation and estimation of micro-scale regional disaster risks throughout Japan is very important for planning of earthquake disaster prevention in Japan. 2.2 Develop disaster big data Y, Akiyama., Y, Ogawa., H, Sengoku., R, Shibasaki. And T, Kato., “Development of Micro Geo Data for Evaluation of Disaster Risk and Readiness by Large-scale Earthquakes Throughout Japan”, Proceeding of Annual conference on Infrastructure and Management, 2013, 392. (in Japanese) Y, Ogawa., Y, Akiyama. and R, Shibasaki, “The Development of Method to Evaluate the damage of Earthquake Disaster Considering Community-based Emergency Response Throughout Japan”, GI4DM2013, 2013, TS03-1. Using various MGD and census data… Estimation data about earthquake damage risk and death rate of each building = Disaster big data 46
  47. 47. Estimation of disaster risk and first responder power of all buildings Building collapse risk ①Est. structure ②Est. building age Existing census data Micro Geo Data (MGD) Earthquake motion Human damage risk ⑤Resident(age, gender etc.) first responder power ⑥Expected number of rescuee ⑦Distance from fire stations Realization of environment for estimation of damage situation in an arbitrary spatial unit scale-seamlessly. →Development micro scale base data throughout Japan Building fire risk ③Est. fire resistance performance ④Est. fire occurrence ratio Housing and land census Population census etc. Residential map Telephone directory Commercial accumulation etc. Seismic activity map 2.2 Develop disaster big data 47
  48. 48. Seismic input Estimation of damage and first responder power in each building Bldg. structure Bldg. age fire occurrence ratio Fire spread cluster Residents Rescue power by fire authorities Mutual power by neighborhood Calculation of collapse risk Calculation of fire risk Calculation of mutual power Calculation of rescue power Number of recsuee from collapsed buildings rescued by neighborhoods. Fire extinguished possibility by fire authorities 2.2 Develop disaster big data 48
  49. 49. Building unit →Aggregation data =Scale seamless damage estimation Municipality District Grid City block Collapse risk Fire risk Mutual power Rescue power Fire extinguished possibility by fire authorities - - Disaster risks First responder powers Damage estimation Estimated number of casualty Number of recsuee from collapsed buildings rescued by neighborhoods. 2.2 Develop disaster big data 49
  50. 50. Earthquake Disaster damage estimation by MGDEarthquake Disaster damage estimation by MGD (250m square grid) Estimated death rate [%] In case of earthquake which will occur 2% in 50years
  51. 51. Estimated death rate [%] In case of earthquake which will occur 2% in 50years Earthquake Disaster damage estimation by MGD (250m square grid)
  52. 52. Estimated death rate [%] In case of earthquake which will occur 2% in 50years Earthquake Disaster damage estimation by MGD (250m square grid)
  53. 53. Estimated death rate [%] In case of earthquake which will occur 2% in 50years Earthquake Disaster damage estimation by MGD (250m square grid)
  54. 54. Estimated death rate [%] In case of earthquake which will occur 2% in 50years Earthquake Disaster damage estimation by MGD (250m square grid)
  55. 55. Estimated death rate [%] In case of earthquake which will occur 2% in 50years Earthquake Disaster damage estimation by MGD (250m square grid)
  56. 56. Estimated death rate [%] In case of earthquake which will occur 2% in 50years Earthquake Disaster damage estimation by MGD (250m square grid)
  57. 57. Earthquake Disaster damage estimation by MGD Estimated death rate [%] In case of earthquake which will occur 2% in 50years
  58. 58. This result received a lot of attention in Japan. Medias (NHK) Magazines and books Get research funds (Ministry of Land, Infrastructure, Transport and Tourism etc.) 2.2 Develop disaster big data Utilized disaster drill for citizens 58
  59. 59. 1) Available Japanese MGD for urban monitoring 2) Examples of studies & cases to utilize MGD 2.1 Utilize person MGD 2.2 Develop disaster big data 2.3 Utilize public big data 3) Conclusions and future works Today’s contents 59
  60. 60. 2.3 Utilize public data 60 Local governments have many precious public data. Now we resolve a problem of local government using various public data. The problem of local government is increasing Vacant house. Local government want to monitor locations of vacant houses.
  61. 61. 2.3 Utilize public data 61 Collect some public data without private information (citizen’s names) Vacant house? 1) Resident Register information of each house 2) Consumption data of city water of each house 3) Property tax data of each house > Building age and structure We developed estimating model of vacant house to integrate public data with results of field surveys in some sample areas. e.g. No residents, no water consumption and no taxation on the house A, > The house is estimated a vacant house.
  62. 62. 2.3 Utilize public data 62 City block unit Result: Estimate Number of vacant house (Kagoshima city) This research project is ongoing in some cities. Tokyo Seoul Kagoshima This result was provided for housing and city planning sector of Kagoshima city. Our result shows there are about 1,700 vacant houses out of about 32,000 houses (5.36%) in city center of Kagoshima city.
  63. 63. 1) Available Japanese MGD for urban monitoring 2) Examples of studies & cases to utilize MGD 2.1 Utilize person MGD 2.2 Develop disaster big data 2.3 Utilize public big data 3) Conclusions and future works Today’s contents 63
  64. 64. Applicative studies which were difficult before are being realized. ・Today is data “abundant” and “accumulating” era. ・Unrealizable studies (only ideas) can be realized by MGD. Anyone can develop an environment to handle and analyze MGD. ・We can acquire high spec PC and high-capacity HDD at low prices. Our approaches expect to support policy development based on quantitative bases. ・It is expected that data-driven policies will have persuasive power for citizens. ・Conduct of policies based on data will support to realize the smart city. Conclusions 3 Conclusions and future works 64
  65. 65. Challenges Development of human resources who can handle and analyze MGD is needed. ・We need to acquire skills to handle and understand MGD. > Programming skills to handle big data > Skills to handle GIS software for visualization > Knowledge of statistics to analyze results Integration of MGD with field data ・We need to understand MGD is not universal data. ・To integrate MGD with field data, MGD will be used more than now. > Skill and sense to integrate field data with MGD Collaboration with government sectors for smart cities ・To realize smart cities, we need to collaborate with national and local governments and to share our researches and their tasks. ・It is difficult to collect useful public data. However it is possible if our suggestion is helpful for local government and citizens. 3 Conclusions and future works 65
  66. 66. Thank you for your kind attention <Contact> Yuki Akiyama(aki@csis.u-tokyo.ac.jp) Assistant professor Center for Spatial Information Science (CSIS) The University of Tokyo URL: http://akiyama-lab.jp/yuki/ (You can download some my papers and slides) Web page of Micro Geo Data Forum http://microgeodata.jp/ Search「秋山祐樹」or” akiyama.yuuki”!

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