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