Urban Social Networks

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1 Urban Social Networks Sensing, Modelling and Visualising Urban Mobility and Copresence Networks Vassilis Kostakos Madeira Interactive Technologies Institute University of Madeira

2 Motivation Slide 2

3 Motivation Traditional understanding of computer systems Slide 2

4 Motivation Traditional understanding of computer systems System = Computer Slide 2

5 Motivation Traditional understanding of computer systems System = Computer System = Computer + Human Slide 2

6 Motivation Traditional understanding of computer systems System = Computer System = Computer + Human Formal descriptions of this system Slide 2

7 Motivation Traditional understanding of computer systems System = Computer System = Computer + Human Formal descriptions of this system Models (Human processor model) Slide 2

8 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

9 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

10 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

11 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

12 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

13 Data collection Slide 3

14 Scanner time Slide 4

15 Slide 5

16 Collected data 12 months unique devices unique data points Slide 6

17 Analysis & Visualisation Slide 7

18 Main concepts Sessions Trails Encounters Slide 8

19 Scanner time Slide 9

20 Scanner time Slide 9

21 Scanner time Session Slide 9

22 Scanner time Session Encounter Slide 9

23 Scanner time Slide 9

24 Location 1 4 Scanner time Slide 9

25 Location 1 4 Scanner time Location 2 Location 3 Slide 9

26 Location 1 4 Scanner time Location 2 Location 3 Slide 9

27 Slide 10

28 Slide 10

29 Slide 11

30 length slope Slide 12

31 Flow of people Transient Bluetooth Activity (session < 90 sec.) City Gate University Gate Devices Hour of day Slide 13 May 31, 2009

32 B C E D A Slide 14

33 Slide 15

34 Slide 16

35 Slide 17

36 Slide 18

37 20 Avenida - Recovery 15 Floods Devices seen 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

38 Slide 20

39 Slide 20

40 Slide 21

41 Urban Networks of Encounter Slide 22

42 Scanner time Slide 23

43 Scanner time Slide 24

44 time Slide 25

45 Slide 26

46 Slide 27

47 Slide 28

48 Slide 29

49 Considering time Slide 30

50 Results Slide 31

51 Slide 32

52 Slide 32

53 a = 2.5 Slide 32

54 a = 2.5 Probability of encountering more than 10 people is Slide 32

55 Presence Duration Frequency Frequency Sessions Nodes a=1.9 a=2.6 Encounters Links a=2.3 a=2.7 Slide 33

56 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

57 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

58 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

59 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

60 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

61 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

62 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

63 Slide 34

64 Presence Duration Frequency Frequency Encounters Links Sessions Nodes Slide 35

65 Slide 36

66 Slide 37

67 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

68 Slide 39

69 Slide 40

70 Slide 41 Facebook Bluetooth

71 Slide 42

72 Slide 43

73 Slide 44

74 Slide 45

75 It s a small world! Slide 46

76 Develop theory to... Slide 47

77 Develop theory to... Model the system Slide 47

78 Develop theory to... Model the system frequency of people s visits Slide 47

79 Develop theory to... Model the system frequency of people s visits duration of their visits Slide 47

80 Develop theory to... Model the system frequency of people s visits duration of their visits Quantify the differences between two locations Slide 47

81 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

82 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

83 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

84 Develop tools to... Slide 48

85 Develop tools to... Automate observations in the wild Slide 48

86 Develop tools to... Automate observations in the wild Explore people s trails in the city Slide 48

87 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

88 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

89 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

90 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

91 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

92 The end Vassilis Kostakos Slide 49

93 Slide 50

94 Privacy Slide 51

95 Privacy Bluetooth can be turned off Slide 51

96 Privacy Bluetooth can be turned off Should be turned on if there exist potential benefits Slide 51

97 Privacy Bluetooth can be turned off Should be turned on if there exist potential benefits Build systems that provide such benefits Slide 51

98 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

99 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

100 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

101 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

102 Slide 52

Urban deployment of Bluetooth. Vassilis Kostakos

Urban deployment of Bluetooth. Vassilis Kostakos Urban deployment of Bluetooth Vassilis Kostakos CMU, Pennsylvania, 12 December 2007 Context Collaboration between CMU HCII & University of Madeira Joint Master s in HCI 4 new faculty to drive the collaboration