GeoDesign Future Green Smart City using BigData...Geodesign Future Green Smart City using BigData...
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Geodesign Future Green Smart City using BigData Yoshiki Yamagata, Head of Global Carbon Project Office & Principal Researcher of National Institute for Environmental Studies Co-Investigators Georgia Institute of Technology, United States National Institute for Environmental Studies, Japan The Institute of Statistical Mathematics, Japan The University of Tokyo, Japan Tokyo Denki University, Japan
Transcript of GeoDesign Future Green Smart City using BigData...Geodesign Future Green Smart City using BigData...
Geodesign Future Green Smart City using BigData
Yoshiki Yamagata, Head of Global Carbon Project Office & Principal Researcher of National Institute for Environmental Studies
Co-InvestigatorsGeorgia Institute of Technology, United StatesNational Institute for Environmental Studies, JapanThe Institute of Statistical Mathematics, JapanThe University of Tokyo, JapanTokyo Denki University, Japan
How can we disseminate the usefulness of bigdata and AI techniques to the society by establishing
smart and sustainable communities?
Motivation: Why, What, HowSource: https://edition.cnn.com/2018/07/23/asia/japan-heatwave-deadly-
intl/index.html; https://abcnews.go.com/International/dramatic-photos-raging-floods-japan/story?id=33657273; http://novavitamedspa.com/healthy-cities/; https://www.fingent.com/blog/role-of-data-analytics-in-internet-of-things-iot;
AP Photo
Ibaraki prefecture, northeast of Tokyo, Sept. 10, 2015
Kumagaya city, north of Tokyo, Monday, July 23, 2018
CNN Photo
Climate Change
Climate Resilience and Urban Decarbonization
Technical Advancement
December 28, 2018 by Tony Joseph, Fingent company
Smart and Sustainable Community
41.0 ˚C105.8 ̊ F
Heat waves
Flooding
Smart Society
Smart Energy & Water Management Smart
Mobility
Smart Commerce
Smart Security & Emergencies
Smart Health
Smart space
Smart Infrastructure
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Walker’s location
Heat risk
Heat vulnerabilityAge,
past disease etc.
Heat exposureLocation,
actions history etc.
Heat hazardSurface Temp.,
indoor Temp. etc.
Humancomfort
Evaluation: Heat Risk & Human Comfort
Comfort indices(wind speed, illumination intensity, etc.)
Heatwave measurement of in/out-door
Age structure of walking street
Spatio-temporal interporation method
Developing aNavigation system
Heat hazard information
Wearable devices
Extreme heatwave
Usual heatwave
Heatwave mapping - anytime & global range-
• Pros：Accurate location, global range
• Cons： Only one time
• Pros： any time (24 hours)• Cons：Non-accurate location,
local range
Aircraft ObservationTower Observation
Surface Temperature
Exposure estimation result
• Large exposure nearby railway stations• Weekday and holiday have significant difference
14:00, Aug 25 (Sat)14:00, Aug 22 (Wed)
South Ginza
Tokyostaion
Mobile GPS data• It records locations of users of a smartphone application
by 30 minutes.
Tokyo st. Asakusa st.
People locations in 9:00-10:00 (2015/8/19; weekday)
Study: Transportation mode detection on mobile GPS data
• GPS points recorded by 30 min or by 500m moves. • No tag on transportation mode.
-> We use machine learning techniques todetect transportation mode.
Yamagata et al. (2018) Big-data analysis for carbon emission reduction from cars: Towards walkable green smart community. Energy Procedia, in press.
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A popular walking street
Marathoncourse ofthe 2020 Olympics
Mobile GPS data
Measuring: Surf. Temp.
Collecting: Mobile GPS data
Surf. Temp. High Low
Overlapping
Vulnerability estimation result
Aging ratio of pedestrians (Aug, 25, 2012)- Greater vulnerability in suburban residential areas.
Old downtown
Heatwave risk estimation result• Heatwave risk = Hazard x Exposure x Vulnerability
⎼ Hotspots detected by the G*statistic suggest that the central area is especially risky.
12:00 16:00Estimated heatwave risk Estimated hot spots
Tokyostation
Ginza
Asakusa
Uenostation
Statistical significance
1%5%10%
Heat-related tweetsHeat-tweets : Tweets including any of synonyms of “heat,”
which are listed belowJapanese English Japanese Englishあつい Hot 蒸し Muggy熱い Hot 水分補給 Rehydration暑 Heat 体調管理 Health management猛暑 Heat wave 猛烈に Furious炎天下 Blazing sun だるい Dull真夏日 Hot day 死ぬ Dying残暑 Lingering summer heat 異常 Abnormal熱中症 Heat illness 不快感 Discomfortバテ Faint 不快 Discomfort寝苦しい Cannot sleep well イヤ Unpleasant夏本番 Midsummer 嫌 Unpleasant日差し Sunlight クソ Expletive照り Reflected heat Orz Expletive湿度 Humid きつい Hard湿気 Moisture 辛い Hard汗 Sweat 大変 Hardジメジメ Damp しんどい Tiredムシムシ Humid 厳しい Severeベタベタ Sticky 苦手 Weak
0AM 0PM 8PM
0.5
0.0
-0.5
Hourly variation
Aug.1 Aug.31
0.4
0.0
-0.4
Temperature Change of temp.
Aug.1 Aug.31 0AM 0PM 11PM0AM 0PM 11PM
Dailyvariation
Heat-tweet
Existing study
Murakami, D., Peters, G. W., Yamagata, Y., & Matsui, T. (2016). Participatory sensing data tweets for micro-urban real-time resiliency monitoring and risk management. IEEE Access, 4, 347-372.
Murakami et al. (2016) suggested that heat-tweets explain temperatures (hazard). But, they did not analyze to what degree heat-tweets explain heatwave risk.
Daily variation Hourly variation Hourly variation
Spatial distribution of heat-tweets (Aug, 2012)
→Do the heat-tweets explain heatwave risk?
Heat-tweets Other tweets
Concluding remarks• Findings
⎼ Spatial BigData is useful to monitor heatwave risk.⎼ Heat-tweet can be a useful indicator of heatwave risk of
individual people.
• Remining issues‒ Integration with more observations (e.g., Remote sensing, human
censoring,…).‒ Dynamic spatiotemporal modeling.‒ Social implementation, e.g., though a smartphone application.
Heatwave risk (hotspot)
Heat-tweet
