1. DATAL A B M E TR O
Gulnaz Aksenova & Artur Shakhbazyan
UR B A N D ATA G OE S PER S O N AL
2. Data as a means of understanding the interaction
between citizens and the city
Image credits: Fox; Abigail Daker
3. Image credits: Fox; Abigail Daker
Data as a means of understanding the interaction
between citizens and the city
4. U R B A N D A T A
M a c r o d a t a
U s e r - g e n e r a t e d d a t a
S t a t i s t i c s
B i o d a t a
E n v i r o n m e n t a l d a t a
Image credits: Brian Black
17. C O L L E C T E D O N C E A M O N T H
N O E X A C T T I M E
N O S I G N I F I C A N T R E A S O N
Temperature measures at
the stations
18. C O L L E C T E D O N C E A M O N T H
N O E X A C T T I M E
N O S I G N I F I C A N T R E A S O N
Imagecredits:MattGroening
Temperature measures at
the stations
19. Sources: Moscow metro official website (www.mosmetro.ru),
Brand Media marketing company (http://www.brand-met-
ro.ru/)
Hourly distribution
of ridership in one day
Tagansko-Krasnopresnenskaya
Serpuhovsko-Timiryazevskaya
Zamoskvoreckaya
Kalugsko-Rigskaya
60000
30000
120000
150000
90000
6:00
7:00
11:00
12:00
00:00
01:00
23:00
00:00
7:00
8:00
8:00
9:00
Kahovskaya
Butovskaya
Arbatsko-Pokrovskaya
Sokolnicheskaya
Lublinskaya
Kalininskaya
Kolcevaya
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
25. 5 T W E E T S / H O U R
8 0 % R E T W E E T F O U R S Q U A R E
A P I L I M I T A T I O N S
Twitter as a source of data
related to metro
26. 5 T W E E T S / H O U R
8 0 % R E T W E E T F O U R S Q U A R E
A P I L I M I T A T I O N S
Imagecredits:MattGroening
Twitter as a source of data
related to metro
32. City context influence how
people behave in the metro.
These changes can be traced through
their online activity.
Hence, user-generated data collected in
the metro reflects larger-scale urban
environment
33. B I O - A N D E N V I R O N M E N T A L D A T A
35. BIODATA
BRAIN WAIVES LIGHT INTENSITY
NOISE LEVEL
AIR TEMPERATURE
HUMIDITY
BODY TEMPERATURE
HEART BEAT
GALVANIC SKIN
RESPONSE
ENVIRONMENTAL
DATA
t
t
Body as a module
36. 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
37. S O K O L N I C H E S K A Y A L I N E :
1 9 S T A T I O N S
3 M I N U T E S / S T A T I O N
T O T A L T I M E : 1 H 3 6
Experiment 1:
Bio- and environmental data collection
50. 1:36:2641:00 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
32
30
34
36
38
-40
-20
0
24
26
28
26
24 24 24 24 24 2423 23 2323
25 25 25 2525
32
1 31
30
31
30
31
30
31
30 30
28
29
0.51
2222 33334
5
6
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 2.251:05
32 32 32 32
31 31
34 34 34 34
300
600
900
-60
Going home to
eat!! Yess!!
No feelings to uknown stations
I am hungry
and tired
N O S T R E S S W HEN N OT HIN G H A PPEN S
51. MIDDAY TRIP
24/05/2013 12:05 PM
NIGHT TRIP
22/05/2013 00:30 AM
GALVANIC SKIN RESPONSE
BODY TEMPERATURE
AIR TEMPERATURE
LIGHT INTENSITY
NOISE LEVEL
0
50
100
150
200
250
300
350
envSound
0
100
200
300
400
500
0
200
400
600
800
31
32
33
34
35
0
200
400
600
800
1000
1200
0
100
200
300
400
500
S
31
30
29
28
27
26
20
10
30
0
50
100
150
200
250
300
350
10
20
30
MIDDAY TRIP
24/05/2013 12:05 PM
NIGHT TRIP
22/05/2013 00:30 AM
GALVANIC SKIN RESPONSE
BODY TEMPERATURE
AIR TEMPERATURE
LIGHT INTENSITY
NOISE LEVEL
0
50
100
150
200
250
300
350
envSound
0
100
200
300
400
500
0
200
400
600
800
31
32
33
34
35
0
200
400
600
800
1000
1200
0
100
200
300
400
500
S
31
30
29
28
27
26
20
10
30
0
50
100
150
200
250
300
350
10
20
30
S TAT IO N S TAT IO NS TAT IO N S TAT IO N
T U N N EL T U N N ELO U T SID E O U T SID EO U T SID E O U T SID E
Experiment 2:
Regular trip
D AY NIG H T
52. MIDDAY TRIP
24/05/2013 12:05 PM
NIGHT TRIP
22/05/2013 00:30 AM
GALVANIC SKIN RESPONSE
BODY TEMPERATURE
AIR TEMPERATURE
LIGHT INTENSITY
NOISE LEVEL
0
50
100
150
200
250
300
350
envSound
0
100
200
300
400
500
0
200
400
600
800
31
32
33
34
35
0
200
400
600
800
1000
1200
0
100
200
300
400
500
S
31
30
29
28
27
26
20
10
30
0
50
100
150
200
250
300
350
10
20
30
MIDDAY TRIP
24/05/2013 12:05 PM
NIGHT TRIP
22/05/2013 00:30 AM
GALVANIC SKIN RESPONSE
BODY TEMPERATURE
AIR TEMPERATURE
LIGHT INTENSITY
NOISE LEVEL
0
50
100
150
200
250
300
350
envSound
0
100
200
300
400
500
0
200
400
600
800
31
32
33
34
35
0
200
400
600
800
1000
1200
0
100
200
300
400
500
S
31
30
29
28
27
26
20
10
30
0
50
100
150
200
250
300
350
10
20
30
S TAT IO N S TAT IO NS TAT IO N S TAT IO N
T U N N EL T U N N ELO U T SID E O U T SID EO U T SID E O U T SID E
D AY NIG H TAnalysis of
regular trip
S T R E S S L E V E L
A N D T E M P E R A T U R E
I N C R E A S E
I N T H E M E T R O
54. 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.
55. 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
56. 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
63. t_39
colour_red
gender: male
height: 175 cm
hair: dark
Coughing: 75 level
Health:Health: at risk
ATTENTION!
Particles: Intensive
Sound analysis: Strong cough
5 PEOPLE IN ONE TRAIN
Population infection: at risk
ATTENTION!
64. W E I G H T M E A S U R I N G T U R N S T I L E
66. m = 76.6Kg
C O L L E C T I N G A N A L Y S I N G V I S U A L I Z I N G
67. P H A S E 1 :
C O L L E C T I N G
P H A S E 2 :
M O N I T O R I N G
68.
69.
70. 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 WE
LOOKING FOR
DATA SOURCES
& PRODUCERS
TYPES & PARAMETERS OF DATA
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 ALT H C A R E
71. HE ALT H C A R E
EC O N O M Y
D E M O G R A P HIC S
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
WHAT ARE WE
LOOKING FOR
DATA SOURCES
& PRODUCERS
TYPES & PARAMETERS OF DATA
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