smart cities l4 13.3.2017 spring semester 2017, eth zürich ...€¦ · mooc exercise 1: qua-kit...

SMAR

TCITIES

L413.3.2017

SpringSemester2

017,ETH

Zürich

GerhardSchm

iD

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

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

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

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

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

hDp://www.news18.com/news/india/know-the-criteria-for-selecEng-smart-ciEes-1195936.html

Measurements for the Smart City: Danielle Griego

Approach Data analysis

20-Feb-17| 14CreaEveDataMining|L01|DanielleGriego|

DatacollecEon

DataselecEon Processing TransformaEon MachineLearning VisualizaEon&InterpretaEon

TypicalKnowledgeDiscoveryDiagram(KDD)

Whatdowewanttoknow?

Approach Data collection/Selection


DatacollecEon



Domainspecificdatasource(s)

Approach The time consuming, but essential part of data analysis


DatacollecEon



Isthedatausable?


LocaEonWiedikonZürich 14surveycheckpointsalongexperimentalpath

Case study ESUM- Analyzing trade-offs between Energy and Social performance of Urban Morphologies

Case study

Datafrom37parEcipantsinZurichto:-  InvesEgateimpactofconstant(urban

morphology)anddynamicfeatures(environmentalsensors)ofthebuiltenvironmentonpercepEon(usingsurveysandbiofeedbackdata)

ESUM- Analyzing trade-offs between Energy and Social performance of Urban Morphologies


antenna

GPS

USB

Hub

EthernetConnection

WIFI

Sniffer

PC

Ethernet

DOCK STATION

HDMI

USB

Power

GasBoard

Smart CitiesBoard

Battery

Mobile

App

Mobile sensor equipment Sensor-backpack with environmental and position sensors


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

Mobile Sensor equipment Biofeedback wristband


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

Mobile Sensor equipment Biofeedback wristband


antenna

GPS

USB

Hub

EthernetConnection

WIFI

Sniffer

PC

Ethernet

DOCK STATION

HDMI

USB

Power

GasBoard

Smart CitiesBoard

Battery

Mobile

App

Experimental data-set ESUM- Analyzing trade-offs between Energy and Social performance of Urban Morphologies


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

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

Data Processing: ESUM Experiment



Frequency reduction to integrate data from multiple sources

Data processing: ESUM Experiment Geo-referencing data to specific locations



Data analysis: ESUM Experiment Time-series sensor visualization: sound



Data analysis: ESUM Experiment Comparing data sources: Measured and perceived noise



MeasuredAmbientNoise

PerceivedNoiseMeasuredAmbientNoise


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

6 0MEAN MEDIAN

22 0 | +1MEAN MEDIAN

0 | +1MEDIAN

1 0MEAN MEDIAN

0MEAN

-10 -1MEAN MEDIAN

34 +1MEAN MEDIAN

-16 -1MEAN MEDIAN

29 +1MEAN MEDIAN

-8 +1MEAN MEDIAN

-3 13 +1MEAN MEDIAN

-1 0MEAN MEDIAN

-1MEAN

+1MEAN MEDIAN

8 +1MEAN MEDIAN

3 0MEAN MEDIAN

37 +1MEAN MEDIAN

15 +1MEAN MEDIAN

-13 -1MEAN MEDIAN

+7 -17 +1MEAN MEDIAN

11 +1MEAN MEDIAN

7 -1MEAN MEDIAN

-1MEAN MEDIAN

24 +1MEAN MEDIAN

-8 -1MEAN MEDIAN

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

0

1000

2000

3000

4000

5000

6000

7000

8000

50

60

70

80

0

50000

100000

150000

200000

CreaDveDataMiningFS2016FinalprojectfromJochenAartsandStéphanedeWeck

Measurements for the Smart City: Estefania Tapias

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

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

Readings from a weather station console showing weather parameters.

Connections or relations of data results in

information.

Information

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

Climate-sensitive Urban Adaptation Measurement Network – Barranquilla Colombia

OTC

Data collection


Climate-sensitive Urban Growth Measurement Network – Barranquilla Colombia

Outdoor Indoor

Server

Data collection

Climate-sensitive Urban Growth Measurement Network – Barranquilla Colombia

Data collection

Calculations & Correlations

Thermal sensation!

Location: UniNorte – stations

TS!

Thermal sensation!

Calculations & Correlations

TS!

Climate-sensitive Urban Adaptation Crowdsourcing – Barranquilla Colombia

INFORMATION ARCHITECTURE OF CITIES

MOOC exercises Data collection

Information Architecture

Prof. Dr. Gerhard Schmitt

MOOC&ETHiAcourse

hDps://www.edx.org/

hDp://ia.arch.ethz.ch/datamap/

hDp://ia.arch.ethz.ch/datamap/

ResultsfromfirstrunofSmartCiEesMOOC

Addi?onalpartforETHcourse:1. Selecttwoplaces,onethatyoulikeandanotheronethatyoudislike.MakethetwodataentriesforeachlocaEoninthewebmapandmakeascreenshotofboth.Describethetwoplacesyouselectedincludingtheaspectsyoulikeordislikeabouteachoftheplaces.

2. Usingthisworldmapwiththeresultsfromlastsemester,selecttwoplacesintheworldwhereparEcipantsaddeddata.Lookatthedatameasuredandtheperceivedata(qualitaEveandquanEtaEvedata)andtrytoanalyzehowpeopleperceivetheplaceinrelaEontothemeasureddata.Pleasemakescreenshotofthedatafrombothplacesandcreateaworddocumentwiththeanalysis.

Pleaseusethesamedocumentforbothpartsoftheexercise.Uploadthedocumenthereinmoodleby27.03.2017

Summary•  Urbanresearchrequiresmeasurements,resulEngindata.Ifdataare

combined,theyturnintoinformaEon.IfinformaEoniscombined,itturnsintoknowledge

•  InformaEonandknowledge,combinedwithobservaEonandcompliance,areneededtoimproveacity

•  Tounderstandurbansystems,measurementsareimportantonallscales:buildingsandneighbourhoods,districtsandciEes,regionsandterritories

•  Measurementsareanecessary(butnotsufficient)acEvityforquanEtaEveandqualitaEveurbanimprovements

•  IgnoringinformaEonandknowledge,ornothavingaccesstoit,canbedeadlyàtransportaEonàPompei

smart cities l4 13.3.2017 spring semester 2017, eth zürich ...€¦ · mooc exercise 1: qua-kit...

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