Urban Social Networks Sensing, Modelling and Visualising Urban Mobility and Copresence Networks Vassilis Kostakos Madeira Interactive Technologies Institute University of Madeira
Motivation Slide 2
Motivation Traditional understanding of computer systems Slide 2
Motivation Traditional understanding of computer systems System = Computer Slide 2
Motivation Traditional understanding of computer systems System = Computer System = Computer + Human Slide 2
Motivation Traditional understanding of computer systems System = Computer System = Computer + Human Formal descriptions of this system Slide 2
Motivation Traditional understanding of computer systems System = Computer System = Computer + Human Formal descriptions of this system Models (Human processor model) Slide 2
Motivation Traditional understanding of computer systems System = Computer System = Computer + Human Formal descriptions of this system Models (Human processor model) Fitt s law Slide 2
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 Slide 2
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 Slide 2
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 Slide 2
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 Slide 2
Data collection Slide 3
2 5 1 2 1 3 4 Scanner 4 5 3 6 6 time Slide 4
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Collected data 12 months 70 000 unique devices 3 000 000 unique data points Slide 6
Analysis & Visualisation Slide 7
Main concepts Sessions Trails Encounters Slide 8
2 5 1 2 1 3 4 Scanner 4 5 3 6 6 time Slide 9
2 5 1 2 1 3 4 Scanner 4 5 3 6 6 time Slide 9
2 5 1 2 1 3 4 Scanner 4 5 3 6 6 time Session Slide 9
2 5 1 2 1 3 4 Scanner 4 5 3 6 6 time Session Encounter Slide 9
2 5 1 2 1 3 4 Scanner 4 5 3 6 6 time Slide 9
2 1 5 1 2 3 Location 1 4 Scanner 4 5 3 6 6 time Slide 9
2 1 5 1 2 3 Location 1 4 Scanner 4 5 3 6 6 time Location 2 Location 3 Slide 9
2 1 5 1 2 3 Location 1 4 Scanner 4 5 3 6 6 time Location 2 Location 3 Slide 9
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length slope Slide 12
Flow of people Transient Bluetooth Activity (session < 90 sec.) City Gate University Gate 16 14 12 Devices 10 8 6 4 2 0 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 Slide 13 May 31, 2009
B C E D A Slide 14
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20 Avenida - Recovery 15 Floods Devices seen 10 5 0 Thu, Feb 18, 2010 Fri, Feb 19, 2010 Sat, Feb 20, 2010 Sun, Feb 21, 2010 Mon, Feb 22, 2010 Tue, Feb 23, 2010 Wed, Feb 24, 2010 Thu, Feb 25, 2010 Fri, Feb 26, 2010 Sat, Feb 27, 2010 Sun, Feb 28, 2010 Mon, Mar 1, 2010 Tue, Mar 2, 2010 Wed, Mar 3, 2010 Thu, Mar 4, 2010 Fri, Mar 5, 2010 Sat, Mar 6, 2010 Sun, Mar 7, 2010 Mon, Mar 8, 2010 Tue, Mar 9, 2010 Wed, Mar 10, 2010 Thu, Mar 11, 2010 Fri, Mar 12, 2010 Sat, Mar 13, 2010 Sun, Mar 14, 2010 Mon, Mar 15, 2010 Tue, Mar 16, 2010 Wed, Mar 17, 2010 Thu, Mar 18, 2010 Fri, Mar 19, 2010 Slide 19
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Urban Networks of Encounter Slide 22
2 5 1 2 1 3 4 Scanner 4 5 3 6 6 time Slide 23
2 5 1 2 1 3 4 Scanner 4 5 3 6 6 time Slide 24
1 2 3 4 5 6 time Slide 25
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Considering time Slide 30
Results Slide 31
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a = 2.5 Slide 32
a = 2.5 Probability of encountering more than 10 people is 10-2.5 Slide 32
Presence Duration Frequency Frequency Sessions Nodes a=1.9 a=2.6 Encounters Links a=2.3 a=2.7 Slide 33
Presence Duration Frequency Frequency Sessions Nodes How much time people spend at a location a=1.9 a=2.6 Encounters Links a=2.3 a=2.7 Slide 33
Presence Duration Frequency Frequency Sessions Nodes How much time people spend at a location a=1.9 How many times people visited a location a=2.6 Encounters Links a=2.3 a=2.7 Slide 33
Presence Duration Frequency Frequency Sessions Nodes How much time people spend at a location a=1.9 How many times people visited a location a=2.6 Encounters Links How much time any two people spend together a=2.3 a=2.7 Slide 33
Presence Duration Frequency Frequency Sessions Nodes How much time people spend at a location a=1.9 How many times people visited a location a=2.6 Encounters Links How much time any two people spend together a=2.3 How often any two people meet each other a=2.7 Slide 33
Presence Duration Frequency Frequency Sessions Nodes How much time people spend at a location a=1.9 How many times people visited a location a=2.6 Encounters Links How much time any two people spend together a=2.3 How often any two people meet each other a=2.7 Slide 33
Presence Duration Frequency Frequency Sessions Nodes How much time people spend at a location a=1.9 How many times people visited a location a=2.6 Encounters Links How much time any two people spend together a=2.3 How often any two people meet each other a=2.7 Slide 33
Presence Duration Frequency Frequency Sessions Nodes How much time people spend at a location a=1.9 How many times people visited a location a=2.6 Encounters Links How much time any two people spend together a=2.3 How often any two people meet each other a=2.7 Slide 33
0 1 0 1 Slide 34
Presence Duration Frequency Frequency Encounters Links Sessions Nodes Slide 35
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Cityware facebook Cityware servers analyse data Cityware nodes record & upload data People with Bluetooth devices bumping into each other (shopping, school, work) Facebook application presents data Users' social network grows Slide 38
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Slide 41 Facebook Bluetooth
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It s a small world! Slide 46
Develop theory to... Slide 47
Develop theory to... Model the system Slide 47
Develop theory to... Model the system frequency of people s visits Slide 47
Develop theory to... Model the system frequency of people s visits duration of their visits Slide 47
Develop theory to... Model the system frequency of people s visits duration of their visits Quantify the differences between two locations Slide 47
Develop theory to... Model the system frequency of people s visits duration of their visits Quantify the differences between two locations Funchal vs. Oulu? Slide 47
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 Slide 47
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 Slide 47
Develop tools to... Slide 48
Develop tools to... Automate observations in the wild Slide 48
Develop tools to... Automate observations in the wild Explore people s trails in the city Slide 48
Develop tools to... Automate observations in the wild Explore people s trails in the city What trails do couples leave on a Friday night? Slide 48
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? Slide 48
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 Slide 48
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 Slide 48
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 Slide 48
The end Vassilis Kostakos vk@m-iti.org Slide 49
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Privacy Slide 51
Privacy Bluetooth can be turned off Slide 51
Privacy Bluetooth can be turned off Should be turned on if there exist potential benefits Slide 51
Privacy Bluetooth can be turned off Should be turned on if there exist potential benefits Build systems that provide such benefits Slide 51
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 Slide 51
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 Slide 51
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 Slide 51
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 Slide 51
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