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