Visualising superdiversity through social networks
migrant
time in city
visa category
job
ethnic background
pan-ethnic background
gender
age
marital status
parent
106 108 102 115 128 118 319 120 127 101 129 131 112 315 126 117 116 316 303 314 130 132 123 113 119 104 312 109 310 317 105 134 107 307 125 114 313 309 311 121 133 308 306 304 321 124 103 301 111 110 318 305 320 302
migrant
1.0
1.0
1.0
1.0
0.95
0.94
0.92
0.92
0.9
0.9
0.9
0.89
0.88
0.88
0.88
0.86
0.86
0.84
0.83
0.83
0.81
0.8
0.8
0.79
0.75
0.73
0.71
0.7
0.67
0.67
0.67
0.67
0.65
0.64
0.6
0.6
0.57
0.56
0.56
0.56
0.56
0.5
0.5
0.43
0.4
0.4
0.33
0.27
0.25
0.25
0.18
0.0
0.0
0.0
time in city
1.0
0.73
0.5
0.0
0.52
0.29
0.54
0.58
0.9
0.3
0.3
0.68
0.88
0.75
0.0
0.14
0.1
0.89
1.0
0.33
0.69
1.0
0.6
0.92
0.5
0.18
0.94
0.6
1.0
1.0
0.33
0.27
0.29
0.82
0.8
0.1
0.14
1.0
0.67
0.44
0.33
1.0
0.5
0.43
1.0
0.05
1.0
0.0
0.13
0.08
0.09
0.86
0.42
0.0
visa category
1.0
0.73
0.5
0.56
0.62
0.65
0.54
0.75
0.8
0.7
0.1
0.58
1.0
0.75
0.75
0.05
0.29
0.89
0.33
0.17
0.56
1.0
0.4
0.25
0.38
0.55
0.94
0.5
1.0
0.89
0.08
0.27
0.53
0.0
0.1
0.5
0.71
1.0
0.94
0.33
0.56
1.0
0.88
0.86
0.9
0.0
0.11
0.0
0.25
0.75
0.0
0.0
0.0
1.0
job
0.75
0.27
0.25
0.44
0.29
0.71
0.62
0.83
0.5
0.3
0.5
0.47
0.63
0.13
0.63
0.43
0.14
0.63
0.17
0.33
0.44
0.2
0.4
0.54
0.38
0.27
0.59
0.1
0.67
0.44
0.42
0.73
0.0
0.27
0.0
0.6
0.14
0.22
0.22
0.22
0.89
0.13
0.25
0.29
0.3
0.3
0.0
0.27
0.63
0.58
0.23
0.43
0.37
0.0
ethnic background
0.0
0.36
0.17
0.44
0.9
0.53
0.15
0.0
0.6
0.5
0.9
0.84
0.25
0.5
0.75
0.29
0.38
0.58
0.67
0.0
0.56
0.3
1.0
0.5
0.13
0.55
0.53
0.1
0.5
0.89
0.5
0.6
0.35
0.55
0.6
0.2
0.0
0.78
0.33
0.33
0.56
0.69
0.5
0.0
0.6
0.5
0.0
0.27
0.0
0.17
0.36
0.57
0.0
0.0
pan-ethnic background
0.5
0.64
0.17
0.56
0.9
0.88
0.15
0.0
0.6
0.5
0.9
0.84
0.25
0.5
0.75
0.29
0.43
0.58
0.67
0.0
0.63
0.3
1.0
0.63
0.5
0.64
0.53
0.4
0.5
0.89
0.5
0.6
0.47
0.55
0.7
0.5
0.0
0.78
0.33
0.33
0.56
0.69
0.5
0.0
0.6
0.5
0.0
0.27
0.0
0.17
0.36
0.57
0.0
0.0
gender
0.25
0.82
0.67
0.67
0.62
0.88
0.62
0.58
0.7
0.9
0.5
0.58
0.63
0.88
1.0
0.48
0.81
0.89
0.67
1.0
0.75
0.9
0.8
0.71
0.38
0.64
0.71
0.6
0.83
0.89
0.5
0.53
0.82
0.45
0.7
0.8
1.0
0.89
0.72
0.78
0.89
0.81
1.0
0.71
0.7
0.4
0.33
0.82
0.75
0.75
0.59
0.57
0.68
0.8
age
1.0
0.55
0.83
1.0
0.38
0.47
0.0
0.58
1.0
0.4
0.2
0.58
0.75
0.13
0.75
0.95
0.62
0.32
0.17
0.67
0.94
0.4
0.4
0.54
0.25
0.55
0.24
0.2
0.67
0.11
0.33
0.6
0.71
0.36
0.4
0.7
0.57
0.11
0.22
0.44
0.56
0.31
0.5
0.14
0.2
0.25
0.44
0.36
0.63
0.25
0.41
0.14
0.79
0.6
marital status
0.25
0.91
0.58
0.56
0.05
0.24
0.46
0.83
0.2
0.4
0.7
0.32
0.38
0.63
0.13
0.52
0.38
0.74
0.5
0.33
0.38
0.8
1.0
0.08
0.13
0.64
0.59
0.4
0.0
0.67
0.42
0.67
0.24
0.73
0.6
0.6
1.0
0.56
0.78
0.72
0.44
0.31
0.75
0.0
0.7
0.45
0.56
0.64
0.88
0.42
0.91
0.0
0.26
1.0
parent
1.0
0.91
0.83
0.67
0.38
0.41
0.31
0.67
0.6
0.7
0.5
0.79
0.5
0.75
0.75
1.0
0.62
0.89
1.0
0.33
0.63
0.7
1.0
0.54
0.5
0.82
0.82
0.8
0.67
0.56
0.83
0.73
0.59
1.0
0.4
0.8
1.0
1.0
0.67
0.67
0.56
0.69
0.88
0.71
0.9
0.45
0.67
0.36
0.88
0.75
0.64
0.86
0.53
0.4
 
Legend
           
← more homophilous

How to read and use this graphic:

This heatmap shows the multidimensional relational-diversity evident in the networks of my respondents. Each column shows homophily scores by respondent. Each row shows those scores by the superdiversity aspect listed in the first row on the left side of the visualisation. Homophily measures proportionally how similar an individual is to the people in their network.

To explore different patterns simply click on one of the superdiversity aspects (e.g. “time in city”). The heatmap will be re-ordered by the clicked aspect. More homophilous networks on the chosen aspect will be moved to the left side and the more heterophilous ones to the right side of the screen.

The numbers at the top of the page refer to the unique identifies for specific respondents whose networks are part of the analysis. Unique identifiers are used to protect individual privacy. Those starting with a one (1) are networks of London respondents. Those starting with a three (3) are the networks of respondents who lived in Toronto.