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