urban social networks - mse - mywebpages · slide 20 may 31, 2010 monday, 31 may 2010. slide 20 may...
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Madeira Interactive Technologies InstituteUniversity of Madeira
Vassilis Kostakos
Urban Social NetworksSensing, Modelling and Visualising Urban Mobility
and Copresence Networks
Monday, 31 May 2010
May 31, 2010Slide
Motivation
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
– System = Computer
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
– System = Computer– System = Computer + Human
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
– System = Computer– System = Computer + Human– Formal descriptions of this system
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
– System = Computer– System = Computer + Human– Formal descriptions of this system
• Models (Human processor model)
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
– System = Computer– System = Computer + Human– Formal descriptions of this system
• Models (Human processor model)• Fitt’s law
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
– System = Computer– System = Computer + Human– Formal descriptions of this system
• Models (Human processor model)• Fitt’s law
• System = Computer + Human + Location
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
– System = Computer– System = Computer + Human– Formal descriptions of this system
• Models (Human processor model)• Fitt’s law
• System = Computer + Human + Location– Technology is increasingly mobile
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
– System = Computer– System = Computer + Human– Formal descriptions of this system
• Models (Human processor model)• Fitt’s law
• System = Computer + Human + Location– Technology is increasingly mobile
• Seek tools, techniques and models to describe and predict “the system”
2
Monday, 31 May 2010
May 31, 2010Slide
Motivation• Traditional understanding of computer systems
– System = Computer– System = Computer + Human– Formal descriptions of this system
• Models (Human processor model)• Fitt’s law
• System = Computer + Human + Location– Technology is increasingly mobile
• Seek tools, techniques and models to describe and predict “the system”
• THE CITY IS THE SYSTEM
2
Monday, 31 May 2010
May 31, 2010Slide
Data collection
3
Monday, 31 May 2010
May 31, 2010Slide 4
Scanner
4
1
6
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5
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6
5
time
Monday, 31 May 2010
May 31, 2010Slide 5
Monday, 31 May 2010
May 31, 2010Slide 6
Collected data• 12 months• 70 000 unique devices• 3 000 000 unique data points
Monday, 31 May 2010
May 31, 2010Slide 7
Analysis & Visualisation
Monday, 31 May 2010
May 31, 2010Slide 8
Main concepts• Sessions• Trails• Encounters
Monday, 31 May 2010
May 31, 2010Slide 9
Scanner
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6
5
time
Monday, 31 May 2010
May 31, 2010Slide 9
Scanner
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5
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5
time
Monday, 31 May 2010
May 31, 2010Slide 9
Scanner
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time
Session
Monday, 31 May 2010
May 31, 2010Slide 9
Scanner
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5
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time
SessionEncounter
Monday, 31 May 2010
May 31, 2010Slide 9
Scanner
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time
Monday, 31 May 2010
May 31, 2010Slide 9
Location 1Scanner
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time
Monday, 31 May 2010
May 31, 2010Slide 9
Location 1
Location 2 Location 3
Scanner
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6
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time
Monday, 31 May 2010
May 31, 2010Slide 9
Location 1
Location 2 Location 3
Scanner
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1
6
3
2
5
1
2
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6
5
time
Monday, 31 May 2010
May 31, 2010Slide 10
Monday, 31 May 2010
May 31, 2010Slide 10
Monday, 31 May 2010
May 31, 2010Slide 11
Monday, 31 May 2010
May 31, 2010Slide 12
length
slope
Monday, 31 May 2010
May 31, 2009Slide 13
Transient Bluetooth Activity(session < 90 sec.)
0
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour of day
Devi
ces
City GateUniversity Gate
Flow of people
Monday, 31 May 2010
May 31, 2010Slide 14
A
B C
D
E
Monday, 31 May 2010
May 31, 2010Slide 15
Monday, 31 May 2010
May 31, 2010Slide 16
Monday, 31 May 2010
May 31, 2010Slide 17
Monday, 31 May 2010
May 31, 2010Slide 18
Monday, 31 May 2010
May 31, 2010Slide 19
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Average
Thu, Feb 18, 2010Fri, Feb 19, 2010Sat, Feb 20, 2010Sun, Feb 21, 2010Mon, Feb 22, 2010Tue, Feb 23, 2010Wed, Feb 24, 2010Thu, Feb 25, 2010Fri, Feb 26, 2010Sat, Feb 27, 2010Sun, Feb 28, 2010Mon, Mar 1, 2010Tue, Mar 2, 2010Wed, Mar 3, 2010Thu, Mar 4, 2010Fri, Mar 5, 2010Sat, Mar 6, 2010Sun, Mar 7, 2010Mon, Mar 8, 2010Tue, Mar 9, 2010Wed, Mar 10, 2010Thu, Mar 11, 2010Fri, Mar 12, 2010Sat, Mar 13, 2010Sun, Mar 14, 2010Mon, Mar 15, 2010Tue, Mar 16, 2010Wed, Mar 17, 2010Thu, Mar 18, 2010Fri, Mar 19, 2010
0 3 12 3 0 1 1 1 0 0 0 0 1 2 1 1 3 7 1 4 1 0 2 11 6 3 0 2 2 60 0 7 2 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 2 5 0 1 0 1 20 1 5 1 0 0 0 0 0 0 0 0 0 0 0 0 2 7 0 0 0 0 0 1 6 0 0 0 1 00 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 00 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 2 3 0 0 0 0 00 4 1 0 0 0 0 0 0 0 0 0 0 0 0 1 2 7 1 2 2 1 3 1 6 2 3 5 2 00 15 2 5 0 0 1 0 1 0 2 2 4 2 3 4 1 2 5 6 5 6 6 0 4 6 9 15 10 130 17 2 6 2 1 2 3 0 2 0 4 6 7 7 6 7 4 5 9 8 8 10 3 1 11 18 16 14 170 15 4 8 1 2 0 3 0 0 3 9 6 8 5 7 5 13 12 5 14 12 6 7 4 15 18 10 17 100 14 4 19 8 1 4 4 1 3 2 2 7 22 9 9 7 15 20 14 31 18 19 24 9 33 17 13 23 130 15 8 37 1 2 3 7 2 6 3 8 13 19 20 10 16 19 25 19 18 12 16 15 8 29 28 18 14 170 26 3 4 4 3 4 4 3 4 4 10 12 21 24 11 13 15 14 20 15 14 12 17 8 29 23 26 18 220 31 4 3 1 3 3 7 7 0 5 23 18 26 16 17 4 19 18 26 18 16 14 16 13 36 29 19 23 240 39 2 1 3 3 1 3 0 1 2 11 13 13 12 10 17 15 18 17 14 11 24 19 20 27 32 29 23 1814 31 0 2 4 1 2 4 1 1 1 10 12 15 14 9 6 20 18 11 5 19 12 9 19 37 21 33 19 837 30 0 3 0 1 3 0 6 0 3 12 21 20 6 8 11 18 18 18 4 14 12 8 21 28 31 23 21 1629 27 0 1 3 2 1 2 2 1 2 10 10 12 7 10 13 14 14 10 4 19 11 11 17 22 26 25 18 1136 20 0 0 5 2 3 3 3 3 2 4 13 9 5 7 13 13 11 10 5 9 21 15 13 30 23 18 25 1531 27 0 2 0 1 2 1 1 1 1 2 7 7 1 6 10 8 17 8 4 7 11 7 13 19 26 28 17 1514 11 0 1 0 0 0 1 0 0 1 1 0 4 1 2 6 5 3 11 2 7 13 8 8 12 13 16 15 813 10 0 0 1 0 0 1 0 0 0 0 1 1 2 4 7 6 3 4 1 2 6 3 12 6 4 7 11 712 14 1 0 0 0 0 1 0 1 0 1 0 3 0 3 6 3 4 4 5 1 7 9 4 5 9 9 10 810 13 5 0 0 1 0 1 1 1 0 2 1 4 1 3 4 3 2 4 3 4 7 5 4 4 10 5 3 98.1667 15.125 2.63 4.125 1.4 1 1.3 1.9 1.17 1 1.3 4.6 6.1 8.1 5.6 5.4 6.6 9 8.8 8.5 6.6 7.5 8.8 8 8.6 15 14 13 12 10
0
10
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30
40
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Chart 2
Thu, Feb 18, 2010 Fri, Feb 19, 2010 Sat, Feb 20, 2010 Sun, Feb 21, 2010 Mon, Feb 22, 2010Tue, Feb 23, 2010 Wed, Feb 24, 2010 Thu, Feb 25, 2010 Fri, Feb 26, 2010 Sat, Feb 27, 2010Sun, Feb 28, 2010 Mon, Mar 1, 2010 Tue, Mar 2, 2010 Wed, Mar 3, 2010 Thu, Mar 4, 2010Fri, Mar 5, 2010 Sat, Mar 6, 2010 Sun, Mar 7, 2010 Mon, Mar 8, 2010 Tue, Mar 9, 2010Wed, Mar 10, 2010 Thu, Mar 11, 2010 Fri, Mar 12, 2010 Sat, Mar 13, 2010 Sun, Mar 14, 2010Mon, Mar 15, 2010 Tue, Mar 16, 2010 Wed, Mar 17, 2010 Thu, Mar 18, 2010 Fri, Mar 19, 2010
0
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, 2
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, 2
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, M
ar
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, 2
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, 2
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ar
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, 2
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, 2
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Avenida - Recovery
Devi
ces
seen
Floods
Monday, 31 May 2010
May 31, 2010Slide 20
Monday, 31 May 2010
May 31, 2010Slide 20
Monday, 31 May 2010
May 31, 2010Slide 21
Monday, 31 May 2010
May 31, 2010Slide 22
Urban Networks of Encounter
Monday, 31 May 2010
May 31, 2010Slide
Scanner
4
1
6
3
25
1
2
3
4
6
5
time
23
Monday, 31 May 2010
May 31, 2010Slide
Scanner
4
1
6
3
25
1
2
3
4
6
5
time
24
Monday, 31 May 2010
May 31, 2010Slide
1
2
3
4
6
5
time
25
Monday, 31 May 2010
May 31, 2010Slide 26
Monday, 31 May 2010
May 31, 2010Slide 27
Monday, 31 May 2010
May 31, 2010Slide
28
Monday, 31 May 2010
May 31, 2010Slide 29
Monday, 31 May 2010
May 31, 2010Slide
30
Considering time
Monday, 31 May 2010
May 31, 2010Slide
Results
31
Monday, 31 May 2010
May 31, 2010Slide 32
Monday, 31 May 2010
May 31, 2010Slide 32
Monday, 31 May 2010
May 31, 2010Slide 32
a = 2.5
Monday, 31 May 2010
May 31, 2010Slide 32
Probability of encountering more than 10 people is 10-2.5
a = 2.5
Monday, 31 May 2010
May 31, 2010Slide 33
Presence Frequency
Nodes
Links
a=1.9 a=2.6
a=2.3 a=2.7
Ses
sion
sE
ncou
nter
s
Duration Frequency
Monday, 31 May 2010
May 31, 2010Slide 33
Presence Frequency
Nodes
Links
a=1.9 a=2.6
a=2.3 a=2.7
How much time people spend at
a location
Ses
sion
sE
ncou
nter
s
Duration Frequency
Monday, 31 May 2010
May 31, 2010Slide 33
Presence Frequency
Nodes
Links
a=1.9 a=2.6
a=2.3 a=2.7
How much time people spend at
a location
How many times people visited a
location
Ses
sion
sE
ncou
nter
s
Duration Frequency
Monday, 31 May 2010
May 31, 2010Slide 33
Presence Frequency
Nodes
Links
a=1.9 a=2.6
a=2.3 a=2.7
How much time people spend at
a location
How many times people visited a
location
How much time any two people spend together
Ses
sion
sE
ncou
nter
s
Duration Frequency
Monday, 31 May 2010
May 31, 2010Slide 33
Presence Frequency
Nodes
Links
a=1.9 a=2.6
a=2.3 a=2.7
How much time people spend at
a location
How many times people visited a
location
How much time any two people spend together
How often any two people meet
each other
Ses
sion
sE
ncou
nter
s
Duration Frequency
Monday, 31 May 2010
May 31, 2010Slide 33
Presence Frequency
Nodes
Links
a=1.9 a=2.6
a=2.3 a=2.7
How much time people spend at
a location
How many times people visited a
location
How much time any two people spend together
How often any two people meet
each other
Ses
sion
sE
ncou
nter
s
Duration Frequency
Monday, 31 May 2010
May 31, 2010Slide 33
Presence Frequency
Nodes
Links
a=1.9 a=2.6
a=2.3 a=2.7
How much time people spend at
a location
How many times people visited a
location
How much time any two people spend together
How often any two people meet
each other
Ses
sion
sE
ncou
nter
s
Duration Frequency
Monday, 31 May 2010
May 31, 2010Slide 33
Presence Frequency
Nodes
Links
a=1.9 a=2.6
a=2.3 a=2.7
How much time people spend at
a location
How many times people visited a
location
How much time any two people spend together
How often any two people meet
each other
Ses
sion
sE
ncou
nter
s
Duration Frequency
Monday, 31 May 2010
May 31, 2010Slide 34
0 1 0 1
Monday, 31 May 2010
May 31, 2010Slide 35
Presence Frequency
Nodes
Links
Ses
sion
sE
ncou
nter
s
Duration Frequency
Monday, 31 May 2010
May 31, 2010Slide 36
Monday, 31 May 2010
May 31, 2010Slide 37
Monday, 31 May 2010
May 31, 2010Slide
People with Bluetooth devices bumping into each other(shopping, school, work)
Cityware servers analyse data
Cityware
Facebook applicationpresents data
Cityware nodesrecord & upload data Users' social network
grows
38
Monday, 31 May 2010
May 31, 2010Slide 39
Monday, 31 May 2010
May 31, 2010Slide
40
Monday, 31 May 2010
May 31, 2010SlideFacebook Bluetooth
41
Monday, 31 May 2010
May 31, 2010Slide 42
Monday, 31 May 2010
May 31, 2010Slide 43
Monday, 31 May 2010
May 31, 2010Slide 44
Monday, 31 May 2010
May 31, 2010Slide 45
Monday, 31 May 2010
May 31, 2010Slide 46
It’s a small world!
Monday, 31 May 2010
May 31, 2010Slide
Develop theory to...
47
Monday, 31 May 2010
May 31, 2010Slide
Develop theory to...• Model the “system”
47
Monday, 31 May 2010
May 31, 2010Slide
Develop theory to...• Model the “system”
–frequency of people’s visits
47
Monday, 31 May 2010
May 31, 2010Slide
Develop theory to...• Model the “system”
–frequency of people’s visits–duration of their visits
47
Monday, 31 May 2010
May 31, 2010Slide
Develop theory to...• Model the “system”
–frequency of people’s visits–duration of their visits
• Quantify the differences between two locations
47
Monday, 31 May 2010
May 31, 2010Slide
Develop theory to...• Model the “system”
–frequency of people’s visits–duration of their visits
• Quantify the differences between two locations–Funchal vs. Oulu?
47
Monday, 31 May 2010
May 31, 2010Slide
Develop theory to...• Model the “system”
–frequency of people’s visits–duration of their visits
• Quantify the differences between two locations–Funchal vs. Oulu?
• Measure the impact of our technology
47
Monday, 31 May 2010
May 31, 2010Slide
Develop theory to...• Model the “system”
–frequency of people’s visits–duration of their visits
• Quantify the differences between two locations–Funchal vs. Oulu?
• Measure the impact of our technology–Metrics
47
Monday, 31 May 2010
May 31, 2010Slide
Develop tools to...
48
Monday, 31 May 2010
May 31, 2010Slide
Develop tools to...• Automate observations in the wild
48
Monday, 31 May 2010
May 31, 2010Slide
Develop tools to...• Automate observations in the wild• Explore people’s trails in the city
48
Monday, 31 May 2010
May 31, 2010Slide
Develop tools to...• Automate observations in the wild• Explore people’s trails in the city
–What trails do couples “leave” on a Friday night?
48
Monday, 31 May 2010
May 31, 2010Slide
Develop tools to...• Automate observations in the wild• Explore people’s trails in the city
–What trails do couples “leave” on a Friday night?–or groups of friends?
48
Monday, 31 May 2010
May 31, 2010Slide
Develop tools to...• Automate observations in the wild• Explore people’s trails in the city
–What trails do couples “leave” on a Friday night?–or groups of friends?
• Develop “attention span interfaces”
48
Monday, 31 May 2010
May 31, 2010Slide
Develop tools to...• Automate observations in the wild• Explore people’s trails in the city
–What trails do couples “leave” on a Friday night?–or groups of friends?
• Develop “attention span interfaces”–posters, displays, speakers, and mobile devices
48
Monday, 31 May 2010
May 31, 2010Slide
Develop tools to...• Automate observations in the wild• Explore people’s trails in the city
–What trails do couples “leave” on a Friday night?–or groups of friends?
• Develop “attention span interfaces”–posters, displays, speakers, and mobile devices–adapt presentation based on visitors’ patterns
48
Monday, 31 May 2010
May 31, 2010Slide 50
Monday, 31 May 2010
May 31, 2010Slide
Privacy
51
Monday, 31 May 2010
May 31, 2010Slide
Privacy• Bluetooth can be turned off
51
Monday, 31 May 2010
May 31, 2010Slide
Privacy• Bluetooth can be turned off
–Should be turned on if there exist potential benefits
51
Monday, 31 May 2010
May 31, 2010Slide
Privacy• Bluetooth can be turned off
–Should be turned on if there exist potential benefits–Build systems that provide such benefits
51
Monday, 31 May 2010
May 31, 2010Slide
Privacy• Bluetooth can be turned off
–Should be turned on if there exist potential benefits–Build systems that provide such benefits
• Each Bluetooth device has a unique hexadecimal ID
51
Monday, 31 May 2010
May 31, 2010Slide
Privacy• Bluetooth can be turned off
–Should be turned on if there exist potential benefits–Build systems that provide such benefits
• Each Bluetooth device has a unique hexadecimal ID–Associate this ID with a person’s identity
51
Monday, 31 May 2010
May 31, 2010Slide
Privacy• Bluetooth can be turned off
–Should be turned on if there exist potential benefits–Build systems that provide such benefits
• Each Bluetooth device has a unique hexadecimal ID–Associate this ID with a person’s identity –VISA, RFID ticket, IMEI : stored in a database
51
Monday, 31 May 2010
May 31, 2010Slide
Privacy• Bluetooth can be turned off
–Should be turned on if there exist potential benefits–Build systems that provide such benefits
• Each Bluetooth device has a unique hexadecimal ID–Associate this ID with a person’s identity –VISA, RFID ticket, IMEI : stored in a database–Bluetooth: not stored centrally
51
Monday, 31 May 2010
May 31, 2010Slide 52
Monday, 31 May 2010