MetroData Lab. Urban data goes personal
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DATAL AB METRO
Gulnaz Aksenova & Artur Shakhbazyan
UR BAN DATA GOES PERSONAL
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Data as a means of understanding the interaction between citizens and the city
Image credits: Fox; Abigail Daker
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Image credits: Fox; Abigail Daker
Data as a means of understanding the interaction between citizens and the city
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URBAN DATA
Macrodata
User-generated data
Statistics
Biodata
Environmental data
Image credits: Brian Black
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Image credits: Oliver O’Brien, London City Dashboard
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WHAT IS THE POTENTIAL?
Christian Nold, Bartlett, UCLMichael Holland, NYU Mara Balestrini, UCL, ICRI
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TRANSPORT + INFRASTUCTURE + PEOPLE =
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Image credits: Yuri Degtyarev; KVenz
U N I QUE / REGULATED
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Image credits: appaIoosa
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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.”
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MACRODATA / STATISTICS
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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
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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
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Temperature measures atthe stations
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COLLECTED ONCE A MONTH
NO EXACT TIME
NO SIGNIFICANT REASON
Temperature measures atthe stations
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COLLECTED ONCE A MONTH
NO EXACT TIME
NO SIGNIFICANT REASON
Image credits: Matt Groening
Temperature measures atthe stations
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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
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Macrodata averages the image and does not exactly reflects the reality
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Macrodata averages the image and does not exactly reflects the reality
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finer resolution
personal data
social networks
individual scale
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USER-GENERATED DATA
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5 TWEETS / HOUR
80% RETWEET FOURSQUARE
API LIMITATIONS
Twitter as a source of datarelated to metro
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5 TWEETS / HOUR
80% RETWEET FOURSQUARE
API LIMITATIONS
Image credits: Matt Groening
Twitter as a source of datarelated to metro
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350 000 users checked-in 1,35 million times
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source: foursquare.com
Yugo-Zapadnaya 21 689Kitay-gorod 20 313Vykhino 19 512Kiyevskaya 18 257VDNH 17 766
Top-5 stations by numberof check-ins
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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
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“Place attachment”
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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
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BIO- AND ENVIRONMENTAL DATA
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source: mk.ru
Sensory experience in crowded metro
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BIODATA
BRAIN WAIVES LIGHT INTENSITY
NOISE LEVEL
AIR TEMPERATURE
HUMIDITY
BODY TEMPERATURE
HEART BEAT
GALVANIC SKINRESPONSE
ENVIRONMENTAL
DATA
t
t
Body as a module
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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
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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
0
100
200
300
400
500
242628
0
100
200
300
400
500
3230
343638
-40
-20
0
242628
30
2624 24 24 24 24 24 24 24 24 24 24 2423 23 2323
25 25 25 25 25 25 252523 23 23
31 31
BODY TEMPERATURE (C): MY BODY FEELS
32 3231 31 30 31 30 31 30
3130 30
2829
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
4444 5555 66
17/05/2013
2:30 min 2:00 3:00 2:30 4:00 2:00 2:30 2:00 3:30 1:30 4:30 2:30 3:10 2:00 4:00 2:00 3:30 1:30 1:30 1:30 1:35 3:35 1:10 2:45 2:15 5:35 2:15 2:15 1:20 3:00 2:10 4:00 2:35 3:15 1:35 2.25 1:05
11:36 am 14:01 pm
t
t
LUMINOCITY INTENSITY (%): I SEE NOISE LEVEL (dB): I HEAR
3230
343638
ENVIRONMENTAL HUMIDITY (%): MY BODY FEELS 34 34
32 32 32 32 32 31 3134 34 34 34 34 34
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
300
600
900
-60
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
-40
-20
0
300
600
900
-60
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
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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
242628
0
100
200
300
400
500
3230
343638
-40
-20
0
242628
30
2624 24 24 24 24 24 24 24 24 24 24 2423 23 2323
25 25 25 25 25 25 252523 23 23
31 3132 32
31 31 30 31 30 31 3031
30 3028
29
0.512222 33333333 22
4444 5555 66
2:30 min 2:00 3:00 2:30 4:00 2:00 2:30 2:00 3:30 1:30 4:30 2:30 3:10 2:00 4:00 2:00 3:30 1:30 1:30 1:30 1:35 3:35 1:10 2:45 2:15 5:35 2:15 2:15 1:20 3:00 2:10 4:00 2:35 3:15 1:35 2.25 1:05
11:36 am
t
t
3230
343638
34 3432 32 32 32 32 31 31
34 34 34 34 34 34
300
600
900
-60
-40
-20
0
300
600
900
-60
STRESS LEVEL - G ALVANIC SKIN RESPONSE (kOh m): I A M EM OTIONAL
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
242628
0
100
200
300
400
500
3230
343638
-40
-20
0
242628
30
2624 24 24 24 24 24 24 24 24 24 24 2423 23 2323
25 25 25 25 25 25 252523 23 23
31 3132 32
31 31 30 31 30 31 3031
30 3028
29
0.512222 33333333 22
4444 5555 66
2:30 min 2:00 3:00 2:30 4:00 2:00 2:30 2:00 3:30 1:30 4:30 2:30 3:10 2:00 4:00 2:00 3:30 1:30 1:30 1:30 1:35 3:35 1:10 2:45 2:15 5:35 2:15 2:15 1:20 3:00 2:10 4:00 2:35 3:15 1:35 2.25 1:05
11:36 am
t
t
3230
343638
34 3432 32 32 32 32 31 31
34 34 34 34 34 34
300
600
900
-60
-40
-20
0
300
600
900
-60
STRESS LEVEL
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4444 5555 66
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BODY TEMPER ATURE (C): M Y BODY FEELS
B ODY TEMPER ATURE
![Page 42: MetroData Lab. Urban data goes personal](https://reader035.fdocuments.us/reader035/viewer/2022062710/559a6df81a28ab9a028b46f1/html5/thumbnails/42.jpg)
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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LU MINOCIT Y INTENSIT Y (%): I SEE
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4444 5555 66
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LUMINOCIT Y INTENSIT Y
![Page 43: MetroData Lab. Urban data goes personal](https://reader035.fdocuments.us/reader035/viewer/2022062710/559a6df81a28ab9a028b46f1/html5/thumbnails/43.jpg)
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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4444 5555 66
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NOISE LEVEL
![Page 44: MetroData Lab. Urban data goes personal](https://reader035.fdocuments.us/reader035/viewer/2022062710/559a6df81a28ab9a028b46f1/html5/thumbnails/44.jpg)
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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4444 5555 66
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ENVIRON MENTAL HU MIDIT Y (%): M Y BODY FEELS
ENVIRONMENTAL HUMIDIT Y
![Page 45: MetroData Lab. Urban data goes personal](https://reader035.fdocuments.us/reader035/viewer/2022062710/559a6df81a28ab9a028b46f1/html5/thumbnails/45.jpg)
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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ENVIRON MENTAL TEMPER ATURE (C): M Y BODY FEELS
ENVIRONMENTAL TEMPER ATURE
![Page 46: MetroData Lab. Urban data goes personal](https://reader035.fdocuments.us/reader035/viewer/2022062710/559a6df81a28ab9a028b46f1/html5/thumbnails/46.jpg)
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CROWD INTENSIT YCROWD INTENSIT Y
![Page 47: MetroData Lab. Urban data goes personal](https://reader035.fdocuments.us/reader035/viewer/2022062710/559a6df81a28ab9a028b46f1/html5/thumbnails/47.jpg)
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VISUAL SENSORY (CA MER A IM AGE): I SEE
VISUAL SENSORY
![Page 48: MetroData Lab. Urban data goes personal](https://reader035.fdocuments.us/reader035/viewer/2022062710/559a6df81a28ab9a028b46f1/html5/thumbnails/48.jpg)
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WE ARING THE DEVICE DOES NOT CAUSE STRESS
![Page 49: MetroData Lab. Urban data goes personal](https://reader035.fdocuments.us/reader035/viewer/2022062710/559a6df81a28ab9a028b46f1/html5/thumbnails/49.jpg)
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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
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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
242628
0
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3230
343638
-40
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0
242628
30
2624 24 24 24 24 24 24 24 24 24 24 2423 23 2323
25 25 25 25 25 25 252523 23 23
31 3132 32
31 31 30 31 30 31 3031
30 3028
29
0.512222 33333333 22
4444 5555 66
2:30 min 2:00 3:00 2:30 4:00 2:00 2:30 2:00 3:30 1:30 4:30 2:30 3:10 2:00 4:00 2:00 3:30 1:30 1:30 1:30 1:35 3:35 1:10 2:45 2:15 5:35 2:15 2:15 1:20 3:00 2:10 4:00 2:35 3:15 1:35 2.25 1:05
11:36 am
t
t
3230
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34 3432 32 32 32 32 31 31
34 34 34 34 34 34
300
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Going home to eat!! Yess!!
No feelings to uknown stations
I am hungryand tired
NO STRESS WHEN NOTHING HAPPENS
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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
050
100150200250300350
envSound
0
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300
400500
0
200
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3132333435
0200
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0
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S
313029282726
20
10
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050
100150200250300350
10
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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
050
100150200250300350
envSound
0
100
200
300
400500
0
200
400
600
800
3132333435
0200
400
600
800
1000
1200
0
100
200
300
400
500
S
313029282726
20
10
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100150200250300350
10
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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
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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
050
100150200250300350
envSound
0
100
200
300
400500
0
200
400
600
800
3132333435
0200
400
600
800
1000
1200
0
100
200
300
400
500
S
313029282726
20
10
30
050
100150200250300350
10
20
30
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
050
100150200250300350
envSound
0
100
200
300
400500
0
200
400
600
800
3132333435
0200
400
600
800
1000
1200
0
100
200
300
400
500
S
313029282726
20
10
30
050
100150200250300350
10
20
30
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
DAY NIGHTAnalysis of regular trip
STRESS LEVEL
AND TEMPERATURE
INCREASE
IN THE METRO
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BMW health car
source: TECHNISCHE UNIVERSITAET MUENCHEN
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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.
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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
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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
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Project: Datalab_metro
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MONITORING EPIDEMIC DISEASES
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SHE IS COUGHING BEHIND ME
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ANCHOO!
ANCHOO!
ANCHOO!
ANCHOO!
ANCHOO!
ANCHOO!
ANCHOO!
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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!
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WEIGHT MEASURING TURNSTILE
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source: Maplecroft
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m = 76.6Kg
COLLECTING ANALYSING VISUALIZING
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PHASE 1:
COLLECTING
PHASE 2:
MONITORING
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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
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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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