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