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