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