smart cities l4 13.3.2017 spring semester 2017, eth zürich ...€¦ · mooc exercise 1: qua-kit...
TRANSCRIPT
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SMAR
TCITIES
L413.3.2017
SpringSemester2
017,ETH
Zürich
GerhardSchm
iD
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SmartCiEes
1GSET:IntroducEon
ObjecEves,DefiniEon,MOOC
Exercise1:QUA-KIT
DefiniEonsContext
SmartObjects,SmartBuildings,SmartCiEes
3GS:UrbanBigData
StocksandFlowsinUrban
Systems
4GSET:Urban
Measurement
MeasurementandSimulaEon
Exercise2:Urban
Measurement
5GS:UrbanScience
CiEzenDesignScience
6GS:ComplexityScience
ComplexityScience
Exercise3:QUA-KIT
7GS:SmartGovernance
ParEcipatoryDesignandManagement
8GS:SmartLivability
CityLivabilityRankings
10GS:FromsmartciEestoresponsive
ciEes
FromsmartciEesto
responsiveciEes
FinalpresentaEonon
MOOCdiscussiontopics
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The story so far: • 13.3.2017 Can you improve what you do not measure? • 6.3.2017 Big Data as new urban raw material, made useful with
Information Architecture and with the Stocks and Flows concept • 27.2.2017 From smart houses to smart cities – emerging
criteria for smart cities as urban systems • 20.2.2017 Cities are complex systems. Ideally, they are
sustainable, resilient, livable, smart, and finally responsive – from production machines to human habitat
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Quote from „The Responsive City“ “I have a rule of thumb: if you can’t measure it, you can’t manage it” June 2014, Michael Bloomberg, Former Mayor of New York City
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Habitat Research Based on science à measurement and simulation Influenced by people à behaviour • Building Research: Understanding Buildings and their interaction with
people, cities, stocks and flows • Urban Research: Understanding Cities and their interaction with people,
territories, stocks and flows à Complex Systems • Territorial Research: Understanding regions, countries, and their
interaction with stocks and flows à Complex Systems
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Smart Cities Criteria India - Europe • IndianMinistryofUrbanDevelopment
1 adequatewatersupply2 assuredelectricitysupply3 sanitaEon,includingsolidwastemanagement4 efficienturbanmobilityandpublictransport5 affordablehousing,especiallyforthepoor6 robustITconnecEvityanddigitalizaEon7 goodgovernance,especiallye-Governanceand
ciEzenparEcipaEon8 sustainableenvironment9 safetyandsecurityofciEzens,parEcularly
women,childrenandtheelderly10 healthandeducaEon
• EuropeanInnovaEonPartnershiponSmartCiEesandCommuniEes
1 SustainableUrbanMobility2 DistrictsandBuiltEnvironment3 IntegratedInfrastructures4 CiEzenFocus5 PolicyandRegulaEon6 IntegratedPlanningandManagement7 KnowledgeSharing8 Baseline,PerformanceIndicatorsandMetrics9 OpenData10 Standards11 BusinessModels,FinanceandProcurement12 GeneralImplementaEonModes
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hDp://www.news18.com/news/india/know-the-criteria-for-selecEng-smart-ciEes-1195936.html
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Measurements for the Smart City: Danielle Griego
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Approach Data analysis
20-Feb-17| 14CreaEveDataMining|L01|DanielleGriego|
DatacollecEon
DataselecEon Processing TransformaEon MachineLearning VisualizaEon&InterpretaEon
TypicalKnowledgeDiscoveryDiagram(KDD)
Whatdowewanttoknow?
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Approach Data collection/Selection
20-Feb-17| 15CreaEveDataMining|L01|DanielleGriego|
DatacollecEon
DataselecEon Processing TransformaEon MachineLearning VisualizaEon&InterpretaEon
TypicalKnowledgeDiscoveryDiagram(KDD)
Domainspecificdatasource(s)
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Approach The time consuming, but essential part of data analysis
20-Feb-17| 16CreaEveDataMining|L01|DanielleGriego|
DatacollecEon
DataselecEon Processing TransformaEon MachineLearning VisualizaEon&InterpretaEon
TypicalKnowledgeDiscoveryDiagram(KDD)
Isthedatausable?
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20-Feb-17| 17CreaEveDataMining|L01|DanielleGriego|
LocaEonWiedikonZürich 14surveycheckpointsalongexperimentalpath
Case study ESUM- Analyzing trade-offs between Energy and Social performance of Urban Morphologies
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Case study
Datafrom37parEcipantsinZurichto:- InvesEgateimpactofconstant(urban
morphology)anddynamicfeatures(environmentalsensors)ofthebuiltenvironmentonpercepEon(usingsurveysandbiofeedbackdata)
ESUM- Analyzing trade-offs between Energy and Social performance of Urban Morphologies
20-Feb-17| 18CreaEveDataMining|L01|DanielleGriego|
antenna
GPS
USB
Hub
EthernetConnection
WIFI
Sniffer
PC
Ethernet
DOCK STATION
HDMI
USB
Power
GasBoard
Smart CitiesBoard
Battery
Mobile
App
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Mobile sensor equipment Sensor-backpack with environmental and position sensors
20-Feb-17| 19CreaEveDataMining|L01|DanielleGriego|
Dust
Temp
NoiseIlluminance
RelaEveHumidity
CO2
NO2
antenna
GPS
USB
Hub
EthernetConnection
WIFI
Sniffer
PC
Ethernet
DOCK STATION
HDMI
USB
Power
GasBoard
Smart CitiesBoard
Battery
Mobile
App
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Mobile Sensor equipment Biofeedback wristband
20-Feb-17| 20CreaEveDataMining|L01|DanielleGriego|
antenna
GPS
USB
Hub
EthernetConnection
WIFI
Sniffer
PC
Ethernet
DOCK STATION
HDMI
USB
Power
GasBoard
Smart CitiesBoard
Battery
Mobile
App
hDps://www.empaEca.com/e4-wristband
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Mobile Sensor equipment Biofeedback wristband
20-Feb-17| 21CreaEveDataMining|L01|DanielleGriego|
antenna
GPS
USB
Hub
EthernetConnection
WIFI
Sniffer
PC
Ethernet
DOCK STATION
HDMI
USB
Power
GasBoard
Smart CitiesBoard
Battery
Mobile
App
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Experimental data-set ESUM- Analyzing trade-offs between Energy and Social performance of Urban Morphologies
20-Feb-17| 22CreaEveDataMining|L01|DanielleGriego|
Device Sensor/Measurement units Measurementrange Measurementfrequency Accuracy Response9meWaspCity SoundPressure dB 50-100dB 0.4Hz ±2.5dB NotGiven
Luminosity % 0-100%(400-700nm) 0.4HzResisEvesensor20MOhm(Darkness)5-20kOhm(Light) NotGiven
Dust mg/m3 Typical0.5V/(0.1mg/m3) 0.4Hz OperaEngsupplyvoltage5±0.5V 10±1msWaspGas
Temperature C -40~125C 0.25Hz ±2C(0-70C),±4C(<0C,>70C) 1.65secondsAtmosphericPressure kPa 15-115kPa 0.25Hz <±1.5%V 20ms
Humidity %RH 0-100%RH 0.25Hz<±4%RH(a25C,range30-80%),±6%RH(range0-100) <15seconds
MeshliumScannerAP
WifiScanner MACaddressWifiScanner(50-200m)BluetoothScanner(20-30m) [email protected]
Measurementrangedependsonheantennaandlineofsighttothedevice 60seconds
WifiScanner AP [email protected]
WifiScannerRSSI(ReceivedSignalStrenghtIndicator)
-40dBm(nearestnode)to-90dBm(marthesnodes) [email protected]
distanceof10m~=(50dBm),50m~=(75dBm)
MobileDevice
GPS Lat/Long outdooronlyvariable,dependentondevicesatelliteconnecEon
Survey 12quesEons,scale-2to2 NA Atcheckpoint GPS
GPS Lat/Long outdooronly 1Hz BiofeedbackWristband
PPG(Photoplethysmography)Sensoroutput:BloodVolumePulse(BPV) 64Hz 0.9nW/Digit
EDA(ElectrodermalAcEvity) 0.01mSiements-100mSiemens 4Hz SkinTemperatureInfraredthermopile C -40-115C 4Hz ±0.2Cwithin36-39C 3Axisaccelerometer x,y,z 32Hz
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Data Processing: ESUM Experiment
13-Mar-17| 23CreaEveDataMining|L04|DanielleGriego|
D.Griego,V.Buff,E.Hayos,I.Moise,E.Pournaras(2017),SensingandminingurbanqualiDesinsmartciDes,proceedingsinAINAIEEE31stConference
Data cleaning: unified date/time, convert WGS84 spherical coordinates to CH1903 planar coordinates
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Data Processing: ESUM Experiment
13-Mar-17| 24CreaEveDataMining|L04|DanielleGriego|
D.Griego,V.Buff,E.Hayos,I.Moise,E.Pournaras(2017),SensingandminingurbanqualiDesinsmartciDes,proceedingsinAINAIEEE31stConference
Frequency reduction to integrate data from multiple sources
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Data processing: ESUM Experiment Geo-referencing data to specific locations
13-Mar-17| 25CreaEveDataMining|L04|DanielleGriego|
D.Griego,V.Buff,E.Hayos,I.Moise,E.Pournaras(2017),SensingandminingurbanqualiDesinsmartciDes,proceedingsinAINAIEEE31stConference
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Data analysis: ESUM Experiment Time-series sensor visualization: sound
13-Mar-17| 26CreaEveDataMining|L04|DanielleGriego|
D.Griego,V.Buff,E.Hayos,I.Moise,E.Pournaras(2017),SensingandminingurbanqualiDesinsmartciDes,proceedingsinAINAIEEE31stConference
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Data analysis: ESUM Experiment Comparing data sources: Measured and perceived noise
13-Mar-17| 27CreaEveDataMining|L04|DanielleGriego|
D.Griego,V.Buff,E.Hayos,I.Moise,E.Pournaras(2017),SensingandminingurbanqualiDesinsmartciDes,proceedingsinAINAIEEE31stConference
MeasuredAmbientNoise
PerceivedNoiseMeasuredAmbientNoise
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20-Feb-17| 28CreaEveDataMining|L01|DanielleGriego|
CHAOTIC | ORDERED CHAOTIC | ORDEREDENCLOSED | OPEN DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKED DISLIKED | LIKEDENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN DISLIKED | LIKED CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN CHAOTIC | ORDERED ENCLOSED | OPEN
MEAN8 7 +1+1ALL MEDIAN
SURVEY POINT ONE SURVEY POINT TWO SURVEY POINT THREE SURVEY POINT FOUR SURVEY POINT FIVE SURVEY POINT SIX SURVEY POINT SEVEN SURVEY POINT EIGHT SURVEY POINT NINE SURVEY POINT TEN SURVEY POINT ELEVEN SURVEY POINT TWELVE SURVEY POINT THIRTEEN SURVEY POINT FOURTEEN
SURVEY POINT ONE
1 2 3 4 5 6 7 8 9 10 11 12 13 14
SURVEY POINT TWO SURVEY POINT THREE SURVEY POINT FOUR SURVEY POINT FIVE SURVEY POINT SIX SURVEY POINT SEVEN SURVEY POINT EIGHT SURVEY POINT NINE SURVEY POINT TEN SURVEY POINT ELEVEN SURVEY POINT TWELVE SURVEY POINT THIRTEEN SURVEY POINT FOURTEEN
parti
cipa
nts
−2 −1 0 1 2
CHAOTICENCLOSEDDISLIKED
ORDERDOPENLIKED
−56 -28 0 28 56
MEAN0 0
MEDIAN MEAN6 +1
MEDIAN MEAN MEDIAN15 +1 18 +1
MEAN MEDIAN7 -1
MEAN MEDIAN8 -1
MEAN MEDIAN35 +1
MEAN MEDIAN45 +2
MEAN MEDIAN MEAN MEDIAN+1 | -1
MEAN MEDIAN6 0
MEAN MEDIAN
34 +1MEAN MEDIAN
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0 | +1MEDIAN
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-1MEAN MEDIAN
24 +1MEAN MEDIAN
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7 +1MEAN MEDIAN
3 0
MEAN MEDIAN
3 0
MEAN MEDIAN0 | -1
MEDIAN+1200
MEAN MEDIAN+1-9
MEAN MEDIAN
STRUCTURE | SPACIOUSNESS | PREFERENCE
SURVEY POINT TYPOLOGIES
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CreaDveDataMiningFS2016FinalprojectfromJochenAartsandStéphanedeWeck
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Measurements for the Smart City: Estefania Tapias
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Tapias, E. 2013. Shadow rage simulation and visibility analysis. Residential area in Altstetten, Zurich
The transformation from data to information
and knowledge is one of the most
important activities in every society and are
the elements that structure the Information
Architecture concept.
Data, Information and Knowledge
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Tapias, E. 2016. Weather data from mini portable weather stations.
“We refer to data as the smallest entities of
information, as values given to objects,
expressions, functions or properties. Data
becomes information by interpretation.” Gerhard Schmitt, Information City
Data
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Readings from a weather station console showing weather parameters.
Connections or relations of data results in
information.
Information
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Friedrich, E. 2013. An interactive tool for modelling Ethiopia’s energy future.
The basic assumption here is that we can
only improve the performance of a system,
such as a city, if we know its present
performance.
Data collection
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Climate-sensitive Urban Adaptation Measurement Network – Barranquilla Colombia
OTC
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Climate-sensitive Urban Adaptation Measurement Network – Barranquilla Colombia
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Climate-sensitive Urban Adaptation Measurement Network – Barranquilla Colombia
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Data collection
Climate-sensitive Urban Adaptation Measurement Network – Barranquilla Colombia
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Climate-sensitive Urban Growth Measurement Network – Barranquilla Colombia
Outdoor Indoor
Server
Data collection
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Climate-sensitive Urban Growth Measurement Network – Barranquilla Colombia
Data collection
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Climate-sensitive Urban Growth Measurement Network – Barranquilla Colombia
Data collection
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Climate-sensitive Urban Adaptation Measurement Network – Barranquilla Colombia
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Calculations & Correlations
Thermal sensation!
Location: UniNorte – stations
TS!
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Thermal sensation!
Calculations & Correlations
TS!
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Thermal sensation!
Calculations & Correlations
TS!
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Climate-sensitive Urban Adaptation Crowdsourcing – Barranquilla Colombia
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INFORMATION ARCHITECTURE OF CITIES
MOOC exercises Data collection
Information Architecture
Prof. Dr. Gerhard Schmitt
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MOOCÐiAcourse
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hDps://www.edx.org/
hDp://ia.arch.ethz.ch/datamap/
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hDp://ia.arch.ethz.ch/datamap/
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ResultsfromfirstrunofSmartCiEesMOOC
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Addi?onalpartforETHcourse:1. Selecttwoplaces,onethatyoulikeandanotheronethatyoudislike.MakethetwodataentriesforeachlocaEoninthewebmapandmakeascreenshotofboth.Describethetwoplacesyouselectedincludingtheaspectsyoulikeordislikeabouteachoftheplaces.
2. Usingthisworldmapwiththeresultsfromlastsemester,selecttwoplacesintheworldwhereparEcipantsaddeddata.Lookatthedatameasuredandtheperceivedata(qualitaEveandquanEtaEvedata)andtrytoanalyzehowpeopleperceivetheplaceinrelaEontothemeasureddata.Pleasemakescreenshotofthedatafrombothplacesandcreateaworddocumentwiththeanalysis.
Pleaseusethesamedocumentforbothpartsoftheexercise.Uploadthedocumenthereinmoodleby27.03.2017
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Summary• Urbanresearchrequiresmeasurements,resulEngindata.Ifdataare
combined,theyturnintoinformaEon.IfinformaEoniscombined,itturnsintoknowledge
• InformaEonandknowledge,combinedwithobservaEonandcompliance,areneededtoimproveacity
• Tounderstandurbansystems,measurementsareimportantonallscales:buildingsandneighbourhoods,districtsandciEes,regionsandterritories
• Measurementsareanecessary(butnotsufficient)acEvityforquanEtaEveandqualitaEveurbanimprovements
• IgnoringinformaEonandknowledge,ornothavingaccesstoit,canbedeadlyàtransportaEonàPompei