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