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DATALAB METRO Gulnaz Aksenova & Artur Shakhbazyan URBAN DATA GOES PERSONAL

MetroData Lab. Urban data goes personal

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Presentation was made for the final review of ReCharge Information Studio at the Strelka Institute for Media, Architecture and Design.

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Page 1: MetroData Lab. Urban data goes personal

DATAL AB METRO

Gulnaz Aksenova & Artur Shakhbazyan

UR BAN DATA GOES PERSONAL

Page 2: MetroData Lab. Urban data goes personal

Data as a means of understanding the interaction between citizens and the city

Image credits: Fox; Abigail Daker

Page 3: MetroData Lab. Urban data goes personal

Image credits: Fox; Abigail Daker

Data as a means of understanding the interaction between citizens and the city

Page 4: MetroData Lab. Urban data goes personal

URBAN DATA

Macrodata

User-generated data

Statistics

Biodata

Environmental data

Image credits: Brian Black

Page 5: MetroData Lab. Urban data goes personal

Image credits: Oliver O’Brien, London City Dashboard

Page 6: MetroData Lab. Urban data goes personal

WHAT IS THE POTENTIAL?

Christian Nold, Bartlett, UCLMichael Holland, NYU Mara Balestrini, UCL, ICRI

Page 7: MetroData Lab. Urban data goes personal
Page 8: MetroData Lab. Urban data goes personal

TRANSPORT + INFRASTUCTURE + PEOPLE =

Page 9: MetroData Lab. Urban data goes personal
Page 10: MetroData Lab. Urban data goes personal

Image credits: Yuri Degtyarev; KVenz

U N I QUE / REGULATED

Page 11: MetroData Lab. Urban data goes personal

Image credits: appaIoosa

Page 12: MetroData Lab. Urban data goes personal

AUGE

— Marc Augé

“...the subway plays the

role of a magnifying mirror

that invites us to take

account of a phenomenon

that, without it, we

might risk or perhaps

try to be unaware of.”

Page 13: MetroData Lab. Urban data goes personal

MACRODATA / STATISTICS

Page 14: MetroData Lab. Urban data goes personal

6-7 7-8 8-9 10-11 11-129-10

Metro tra�c6:00 am - 12:00 am

Morning passenger traffic6:00 - 12:00

Page 15: MetroData Lab. Urban data goes personal

6-7 7-8 8-9 10-11 11-129-10

Metro tra�c6:00 am - 12:00 am

Image credits: Matt Groening

Morning passenger traffic6:00 - 12:00

Page 16: MetroData Lab. Urban data goes personal

Temperature measures atthe stations

Page 17: MetroData Lab. Urban data goes personal

COLLECTED ONCE A MONTH

NO EXACT TIME

NO SIGNIFICANT REASON

Temperature measures atthe stations

Page 18: MetroData Lab. Urban data goes personal

COLLECTED ONCE A MONTH

NO EXACT TIME

NO SIGNIFICANT REASON

Image credits: Matt Groening

Temperature measures atthe stations

Page 19: MetroData Lab. Urban data goes personal

Sources: Moscow metro official website (www.mosmetro.ru), Brand Media marketing company (http://www.brand-met-ro.ru/)

Hourly distributionof ridership in one day

Tagansko-KrasnopresnenskayaSerpuhovsko-TimiryazevskayaZamoskvoreckayaKalugsko-Rigskaya

60000

30000

120000

150000

90000

6:007:00

11:0012:00

00:0001:00

23:0000:00

7:008:00

8:009:00

KahovskayaButovskayaArbatsko-PokrovskayaSokolnicheskaya

LublinskayaKalininskayaKolcevaya

MEN 55.1%ABOVE 46 38.5%MARRIED 53.8%HIGHER EDUCATION 72.2%WORKER, BUILDER 14.4%

WOMAN 53.3%ABOVE 46 26,7%MARRIED 48%HIGHER EDUCATION 72.2%SERVICE 22,7%

WOMAN 54.3%19-22 YEARS OLD 24,7%NOT MARRIED 63%HIGHER EDUCATION 72.2%STUDENTS UNKNOWN %

WOMAN 54.4%19-22 YEARS OLD 30.4%NOT MARRIED 51.9%HIGHER EDUCATION 72.2%STUDENTS 30.8%

WOMAN 60.8%23-27 YEARS OLD 34.2%NOT MARRIED 57%HIGHER EDUCATION 62%STUDENTS 24.7%

MAN 69.7%19-22 YEARS OLD 37.2%NOT MARRIED 64.9%HIGHER EDUCATION 76.9%SHOPPING MALL WORKERS24.7%

Source: Moscow metro; Brand Media

Page 20: MetroData Lab. Urban data goes personal
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Macrodata averages the image and does not exactly reflects the reality

Page 22: MetroData Lab. Urban data goes personal

Macrodata averages the image and does not exactly reflects the reality

Page 23: MetroData Lab. Urban data goes personal

finer resolution

personal data

social networks

individual scale

Page 24: MetroData Lab. Urban data goes personal

USER-GENERATED DATA

Page 25: MetroData Lab. Urban data goes personal

5 TWEETS / HOUR

80% RETWEET FOURSQUARE

API LIMITATIONS

Twitter as a source of datarelated to metro

Page 26: MetroData Lab. Urban data goes personal

5 TWEETS / HOUR

80% RETWEET FOURSQUARE

API LIMITATIONS

Image credits: Matt Groening

Twitter as a source of datarelated to metro

Page 27: MetroData Lab. Urban data goes personal

350 000 users checked-in 1,35 million times

Page 28: MetroData Lab. Urban data goes personal

source: foursquare.com

Yugo-Zapadnaya 21 689Kitay-gorod 20 313Vykhino 19 512Kiyevskaya 18 257VDNH 17 766

Top-5 stations by numberof check-ins

Page 29: MetroData Lab. Urban data goes personal

source: foursquare.com / Moscow metro

Check-ins / official statisticscomparison

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source: foursquare.com / stroi.mos.ru

check-ins regularity

functional zones

maximum

residential

minimum

public

Regularity of check-ins

Page 31: MetroData Lab. Urban data goes personal

“Place attachment”

Page 32: MetroData Lab. Urban data goes personal

City context influence how people behave in the metro.

These changes can be traced throughtheir online activity.

Hence, user-generated data collected in the metro reflects larger-scale urban

environment

Page 33: MetroData Lab. Urban data goes personal

BIO- AND ENVIRONMENTAL DATA

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source: mk.ru

Sensory experience in crowded metro

Page 35: MetroData Lab. Urban data goes personal

BIODATA

BRAIN WAIVES LIGHT INTENSITY

NOISE LEVEL

AIR TEMPERATURE

HUMIDITY

BODY TEMPERATURE

HEART BEAT

GALVANIC SKINRESPONSE

ENVIRONMENTAL

DATA

t

t

Body as a module

Page 36: MetroData Lab. Urban data goes personal

GALVANIC SKIN RESPONSE SENSOR

MICROPHONE - NOISE SENSOR

HUMIDITY AND TEMPERATURE (AIR) SENSOR

BODY TEMPERATURE SENSOR

LIQUID-CRYSTAL DISPLAY

USB POWER PACK

ELECTRONIC PROTOTYPING PLATFORM TEENSY++

BUTTON 1 - BACKLIGHT SWITCH ON/OFF

BUTTON 2 - RECORD TYPE SWITCH STATION/TUNNEL

BUTTON 3 - RECORDING START/STOP

LIGHT INTENSITY SENSOR

Device for data collection

Page 37: MetroData Lab. Urban data goes personal

SOKOLNICHESKAYA LINE:

19 STATIONS

3 MINUTES / STATION

TOTAL TIME: 1H36

Experiment 1:Bio- and environmental data collection

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Experiment 1:Bio- and environmental data collection

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0 1:36:2630 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:00 23:30 24:00 24:30 25:00 25:30 26:00 26:30 27:00 27:30 28:00 28:30 29:00 29:30 30:00 30:30 31:00 31:30 32:00 32:30 33:00 33:30 34:00 34:30 35:00 35:30 36:00 36:30 37:00 37:30 38:00 38:30 39:00 39:30 40:00 40:30 41:00 41:30 42:00 42:30 43:00 43:30 44:00 44:30 45:00 45:30 46:00 46:30 47:00 47:30 48:00 48:30 49:00 49:30 50:00 50:30 51:00 51:30 52:00 52:30 53:00 53:30 54:00 54:30 55:00 55:30 56:00 56:30 57:00 57:30 58:00 58:30 59:00 59:30 1:00:00 1:00:30 1:01:00 1:01:30 1:02:00 1:02:30 1:03:00 1:03:30 1:04:00 1:04:30 1:05:00 1:05:30 1:06:00 1:06:30 1:07:00 1:07:30 1:08:00 1:08:30 1:09:00 1:09:30 1:10:00 1:10:30 1:11:00 1:11:30 1:12:00 1:12:30 1:13:00 1:13:30 1:14:00 1:14:30 1:15:00 1:15:30 1:16:00 1:16:30 1:17:00 1:17:30 1:18:00 1:18:30 1:19:00 1:19:30 1:20:00 1:20:30 1:21:00 1:21:30 1:22:00 1:22:30 1:23:00 1:23:30 1:24:00 1:24:30 1:25:00 1:25:30 1:26:00 1:26:30 1:27:00 1:27:30 1:28:00 1:28:30 1:29:00 1:29:30 1:30:00 1:30:30 1:31:00 1:31:30 1:32:00 1:32:30 1:33:00 1:33:30 1:34:00 1:34:30 1:35:00 1:35:30

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STRESS LEVEL - GALVANIC SKIN RESPONSE (kOhm): I AM EMOTIONAL

TRIP ACROSS 19 STATIONS OF SOKOLNICHESKAYA (RED) LINE OF MOSCOW METRO

ENVIRONMENTAL TEMPERATURE (C): MY BODY FEELS

CROWD (%): MY BODY EXPERIENCES

0.512222 33333333 22

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17/05/2013

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YUGO-ZAPADNAYA

PROSPEKTVERNADSKOGO

UNIVERSITET VOROBYOVY GORY

SPORTIVNAYA FRUNZENSKAYA PARK KULTURY

KROPOTKINSKAYA BIBLIOTEKA IMENI LENINA

OHOTNY RYAD

LUBYANKA CHISTYE PRUDY

KRASNYE VOROTA

SOKOLNIKI PREOBRAZHENSKAY PLOSHAD

CHERKIZOVSKAYA ULITSA PODBELSKOGO

KOMSOMOLSKAYA KRASNOSELSKAYA

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Coming home, Strelka is my home ha-ha =) Old lady carying

heavy bag upstairs makes me sad.... :(

Policeman islooking at me :0I am excited :)

I have never been here

someone smiling called me ‘Terrorist’ =Oand took a picture of me for instagram

Going home to eat!! Yess!!

I am hungryand tired

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Emotions are from experiencesPersonal attachments are connected to memory or special feelings

No stress for wearing device

No feelings to uknown stations

VISUAL SENSORY (CAMERA IMAGE): I SEE

Visualizationof collected data

Page 40: MetroData Lab. Urban data goes personal

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0 1:36:2630 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:00 23:30 24:00 24:30 25:00 25:30 26:00 26:30 27:00 27:30 28:00 28:30 29:00 29:30 30:00 30:30 31:00 31:30 32:00 32:30 33:00 33:30 34:00 34:30 35:00 35:30 36:00 36:30 37:00 37:30 38:00 38:30 39:00 39:30 40:00 40:30 41:00 41:30 42:00 42:30 43:00 43:30 44:00 44:30 45:00 45:30 46:00 46:30 47:00 47:30 48:00 48:30 49:00 49:30 50:00 50:30 51:00 51:30 52:00 52:30 53:00 53:30 54:00 54:30 55:00 55:30 56:00 56:30 57:00 57:30 58:00 58:30 59:00 59:30 1:00:00 1:00:30 1:01:00 1:01:30 1:02:00 1:02:30 1:03:00 1:03:30 1:04:00 1:04:30 1:05:00 1:05:30 1:06:00 1:06:30 1:07:00 1:07:30 1:08:00 1:08:30 1:09:00 1:09:30 1:10:00 1:10:30 1:11:00 1:11:30 1:12:00 1:12:30 1:13:00 1:13:30 1:14:00 1:14:30 1:15:00 1:15:30 1:16:00 1:16:30 1:17:00 1:17:30 1:18:00 1:18:30 1:19:00 1:19:30 1:20:00 1:20:30 1:21:00 1:21:30 1:22:00 1:22:30 1:23:00 1:23:30 1:24:00 1:24:30 1:25:00 1:25:30 1:26:00 1:26:30 1:27:00 1:27:30 1:28:00 1:28:30 1:29:00 1:29:30 1:30:00 1:30:30 1:31:00 1:31:30 1:32:00 1:32:30 1:33:00 1:33:30 1:34:00 1:34:30 1:35:00 1:35:30

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Page 41: MetroData Lab. Urban data goes personal

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B ODY TEMPER ATURE

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LU MINOCIT Y INTENSIT Y (%): I SEE

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CROWD INTENSIT YCROWD INTENSIT Y

Page 47: MetroData Lab. Urban data goes personal

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VISUAL SENSORY (CA MER A IM AGE): I SEE

VISUAL SENSORY

Page 48: MetroData Lab. Urban data goes personal

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WE ARING THE DEVICE DOES NOT CAUSE STRESS

Page 49: MetroData Lab. Urban data goes personal

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Coming home, Strelka is my home ha-ha =)

someone smiling called me ‘Terrorist’ =Oand took a picture of me for instagram

Emotions are from experiencesPersonal attachments are connected to memory or special feelings

Page 50: MetroData Lab. Urban data goes personal

0 1:36:2630 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:00 23:30 24:00 24:30 25:00 25:30 26:00 26:30 27:00 27:30 28:00 28:30 29:00 29:30 30:00 30:30 31:00 31:30 32:00 32:30 33:00 33:30 34:00 34:30 35:00 35:30 36:00 36:30 37:00 37:30 38:00 38:30 39:00 39:30 40:00 40:30 41:00 41:30 42:00 42:30 43:00 43:30 44:00 44:30 45:00 45:30 46:00 46:30 47:00 47:30 48:00 48:30 49:00 49:30 50:00 50:30 51:00 51:30 52:00 52:30 53:00 53:30 54:00 54:30 55:00 55:30 56:00 56:30 57:00 57:30 58:00 58:30 59:00 59:30 1:00:00 1:00:30 1:01:00 1:01:30 1:02:00 1:02:30 1:03:00 1:03:30 1:04:00 1:04:30 1:05:00 1:05:30 1:06:00 1:06:30 1:07:00 1:07:30 1:08:00 1:08:30 1:09:00 1:09:30 1:10:00 1:10:30 1:11:00 1:11:30 1:12:00 1:12:30 1:13:00 1:13:30 1:14:00 1:14:30 1:15:00 1:15:30 1:16:00 1:16:30 1:17:00 1:17:30 1:18:00 1:18:30 1:19:00 1:19:30 1:20:00 1:20:30 1:21:00 1:21:30 1:22:00 1:22:30 1:23:00 1:23:30 1:24:00 1:24:30 1:25:00 1:25:30 1:26:00 1:26:30 1:27:00 1:27:30 1:28:00 1:28:30 1:29:00 1:29:30 1:30:00 1:30:30 1:31:00 1:31:30 1:32:00 1:32:30 1:33:00 1:33:30 1:34:00 1:34:30 1:35:00 1:35:30

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Going home to eat!! Yess!!

No feelings to uknown stations

I am hungryand tired

NO STRESS WHEN NOTHING HAPPENS

Page 51: MetroData Lab. Urban data goes personal

MIDDAY TRIP24/05/2013 12:05 PM

NIGHT TRIP22/05/2013 00:30 AM

GALVANIC SKIN RESPONSE

BODY TEMPERATURE

AIR TEMPERATURE

LIGHT INTENSITY

NOISE LEVEL

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S TATIO N S TATIO NS TATIO N S TATIO N

T UN NEL T UN NELO U TSIDE O U TSIDEO U TSIDE O U TSIDE

Experiment 2:Regular trip

DAY NIGHT

Page 52: MetroData Lab. Urban data goes personal

MIDDAY TRIP24/05/2013 12:05 PM

NIGHT TRIP22/05/2013 00:30 AM

GALVANIC SKIN RESPONSE

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NIGHT TRIP22/05/2013 00:30 AM

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AIR TEMPERATURE

LIGHT INTENSITY

NOISE LEVEL

050

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T UN NEL T UN NELO U TSIDE O U TSIDEO U TSIDE O U TSIDE

DAY NIGHTAnalysis of regular trip

STRESS LEVEL

AND TEMPERATURE

INCREASE

IN THE METRO

Page 53: MetroData Lab. Urban data goes personal

BMW health car

source: TECHNISCHE UNIVERSITAET MUENCHEN

Page 54: MetroData Lab. Urban data goes personal

Unconcious bodily reactions reflect not only the immediate environment, but are also connected to

broader context through mental ties.

Contextual changes trigger emotional response, therefore can be traced through it.

Page 55: MetroData Lab. Urban data goes personal

Non-conventional data provides valuable and distinctive information about the

city.

Context of metro allows efficient and hassle free collection of this data

In combination with conventional data it can give much deeper understanding

of processes happening in Moscow

Page 56: MetroData Lab. Urban data goes personal

Non-conventional data provides valuable and distinctive information about the

city.

Context of metro allows efficient and hassle free collection of this data

In combination with conventional data it can give much deeper understanding

of processes happening in Moscow

Page 57: MetroData Lab. Urban data goes personal

Project: Datalab_metro

Page 58: MetroData Lab. Urban data goes personal
Page 59: MetroData Lab. Urban data goes personal

MONITORING EPIDEMIC DISEASES

Page 60: MetroData Lab. Urban data goes personal

SHE IS COUGHING BEHIND ME

Page 61: MetroData Lab. Urban data goes personal

ANCHOO!

ANCHOO!

ANCHOO!

ANCHOO!

ANCHOO!

ANCHOO!

ANCHOO!

Page 62: MetroData Lab. Urban data goes personal
Page 63: MetroData Lab. Urban data goes personal

t_39colour_red

gender: maleheight: 175 cm

hair: darkCoughing: 75 level

Health: Health: at riskATTENTION!

Particles: IntensiveSound analysis: Strong cough5 PEOPLE IN ONE TRAINPopulation infection: at riskATTENTION!

Page 64: MetroData Lab. Urban data goes personal

WEIGHT MEASURING TURNSTILE

Page 65: MetroData Lab. Urban data goes personal

source: Maplecroft

Page 66: MetroData Lab. Urban data goes personal

m = 76.6Kg

COLLECTING ANALYSING VISUALIZING

Page 67: MetroData Lab. Urban data goes personal

PHASE 1:

COLLECTING

PHASE 2:

MONITORING

Page 68: MetroData Lab. Urban data goes personal
Page 69: MetroData Lab. Urban data goes personal
Page 70: MetroData Lab. Urban data goes personal

FACE:

IRIS/RETINA OF EYE

BODY: SPEED OF WALKING

POSTURE

GAIT

WEIGHT

HEIGHT

BEHAVIORAL TRAITS

BIO PROCESSES

HANDS: PULSE

SWEAT

VOICE: TONE

PITCH

SKIN: TEMPERATURE

ENVIRONMENT

WHAT ARE WELOOKING FOR

DATA SOURCES & PRODUCERS

TYPES & PARAMETERS OF DATA

As it was mentioned previously, the data, which can be collected is very diverse, and is not really limited to numbers. Apart from quantitative data, we also propose to collect qualitative data about passengers. Metrics such as emotional state and behaviour patterns can give us valuable insights about quality of urban environment and it’s impact on citizens’ well-being.

BIO:PULSE

OXYGENATION

ELECTROCARDIOGRAM

BRAIN ACTIVITY

NERVE SYSTEM ACTIVITY

GALVANIC SKIN RESPONSE

GLUCOMETER

SCALES

VOICE TONE

VOCAL TRACT PROPERTIES

POSITION:VELOCITY

ACCELERATION

MOVEMENT

GLOBAL POSITIONING SYSTEM

ELECTROMAGNETIC ENVIRONMENT:CAPACITY

CONDUCTION

MAGNETIC FIELD CHANGE

CURRENT SENSING

ELECTROMAGNETIC WAVES MEASUREMENT

HUMAN-SENSIBLE ENVIRONMENTAL ATTRIBUTES:SOUND WAVE AND SOUND PRESSURE

BAROMETRIC PRESSURE

HUMIDITY

TEMPERATURE

AIR MIXTURE

PARTICLES IN THE AIR

LUMINANCE

LIQUID PRESSURE

LIQUID PRESENCE

INVISIBLE ENVIRONMENTAL CHARACTERISTICS:GAS MIXTURE

GAS FLOW

LIQUID MIXTURE

CAMERA

SMART PHONES DETECTOR

SMART-CARD

SCANNER

STRESS

OBESITY

INFLUENZA

FACIAL EXPRESSION

HE ALTH CARE

Page 71: MetroData Lab. Urban data goes personal

HE ALTH CARE

ECONO M Y

DEM OGR APHICS

FACE:

IRIS/RETINA OF EYE

FACIAL

CHARACTERISTICS

BODY: SPEED OF WALKING

POSTURE

GAIT

PROXIMITY

WEIGHT

HEIGHT

EXTREMITIES

BEHAVIORAL TRAITS

BIO PROCESSES

HANDS: PULSE

SWEAT

FINGERPRINTS

VOICE: TONE

PITCH

LANGUAGE

SMELL

SKIN: COLOUR

TEMPERATURE

ENVIRONMENT

OTHER

FRAMEWORK FOR DATA COLLECTION

WHAT ARE WELOOKING FOR

DATA SOURCES & PRODUCERS

TYPES & PARAMETERS OF DATA

Data, which can be collected is very diverse, and is not really limited to numbers. Apart from quantitative data, we also propose to collect qualitative data about passengers. Metrics such as emotional state and behaviour patterns can give us valuable insights about quality of urban environment and it’s impact on citizens’ well-being.

BIO:PULSE

OXYGENATION

ELECTROCARDIOGRAM

BRAIN ACTIVITY

NERVE SYSTEM ACTIVITY

GALVANIC SKIN RESPONSE

GLUCOMETER

SCALES

VOICE TONE

VOCAL TRACT PROPERTIES

POSITION:VELOCITY

ACCELERATION

MOVEMENT

GLOBAL POSITIONING SYSTEM

ELECTROMAGNETIC ENVIRONMENT:CAPACITY

CONDUCTION

MAGNETIC FIELD CHANGE

CURRENT SENSING

ELECTROMAGNETIC WAVES MEASUREMENT

HUMAN-SENSIBLE ENVIRONMENTAL ATTRIBUTES:SOUND WAVE AND SOUND PRESSURE

BAROMETRIC PRESSURE

HUMIDITY

TEMPERATURE

AIR MIXTURE

PARTICLES IN THE AIR

LUMINANCE

LIQUID PRESSURE

LIQUID PRESENCE

INVISIBLE ENVIRONMENTAL CHARACTERISTICS:GAS MIXTURE

GAS FLOW

LIQUID MIXTURE

INVISIBLE LIGHT WAVES MEASUREMENT

CAMERA

SMART PHONES DETECTOR

SMART-CARD

SCANNER

SMARTPHONE

WIFI

STRESS

OBESITY

INFLUENZA

CLOTHES

SMARTPHONE MODEL

TRAVEL PATTERNS

AGE

GENDER

LANGUAGE

RACE

FRAGRANCE

SMOKING

ALCOHOLISM

DRUG ADDICTION

SHIZOPHRENIA

FACIAL EXPRESSION

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