Global Bicycle Cities Index 2019

Bicycle Cities Index 2019

At Coya, cycling is in our DNA. As digital insurance specialists and committed bikers ourselves, we firmly believe that cyclists should be free to get from A to B, without having to worry about road quality, safety or bicycle theft. To delve into the topic further, we decided to investigate which cities around the world are improving their overall cycling conditions, as well as encouraging bicycle-usage as a healthy, sustainable mode of transport. We then ranked each location's efforts to determine the best cycling cities, as, after all, we believe that the road to future mobility is on two wheels.

To conduct the study, we first selected cities from around the world including traditional cycling cities, as well as some lesser-known locations improving their bicycle infrastructure. Then, we analysed each location for several factors which determine if a city is good for cycling or not. To begin, we looked at the percentage of bicycle users, as this is a huge indication of the overall cycling conditions, but also basic requirements such as safety, bicycle-related crime and road infrastructure. This research included the number of stolen bicycles, cycling fatalities and accidents, length of specialised cycling roads, road quality investments and more.

Next, since many casual cyclists are impacted by weather conditions, we determined the average hours of sunshine, millimetres of rainfall and number of extreme weather days to paint an overall picture of the climate in each city. Considering that the sharing economy is a billion-dollar industry, we wanted to include information about the boom in bike-sharing by looking into the volume of shared bicycles, as well as the number of sharing and rental stations in each city. Finally, to determine which locations go the extra mile for cyclists and their community, we included the popularity of special cycling-related events such as No Car Day and Critical Mass. We then ranked all factors to determine the best cycling cities overall.

The table below reveals the best cities for cycling, where the higher the total score, the better the results. The legend below describes each of the factors within each category. You can filter each factor from highest to lowest and vice versa by clicking on the icon above each column. For a full explanation of how each factor was calculated, please see the methodology at the bottom of the page.

Index Scores

Weather Score
Bicycle Theft Score
Investment & Infrastructure Quality Score
Sharing Score
% Bicycle Usage
Safety Score
Infrastructure Score
No Car Day?
Fatalities / 100k Cyclists
Number of Bicycle Shops / 100k Cyclists
Number of Bicycle Sharing & Rental Stations / 100k Cyclists
Critical Mass Score
Accidents / 100k Cyclists
Specialised Roads & Road Quality Score
Number of Shared Bicycles / 100k Cyclists
Event Score
Crime & Safety
Infrastructure
Sharing
Events
#
City
Country
Size
Total Score
1
Utrecht
Netherlands
S
63.83
51.00%
1.94
279.88
78.99
82.46
37.18
60.78
98.87
57.51
21.77
12.07
17
n
7.32
279.88
77.84
2
Munster
Germany
s
58.74
39.10%
0.53
445.58
83.04
88.43
28.15
53.05
88.04
51.23
35.52
26
31
n
23.84
445.58
65.93
3
Antwerp
Belgium
s
62.67
28.90%
1.61
1165.81
84.61
77.05
73.57
53.37
75.31
34.28
78.87
100
89
y
14.12
1165.81
60.51
4
Copenhagen
Denmark
m
61.19
29.00%
1.04
218.44
66.62
83.01
62.47
61.6
86.54
55.23
12.17
17.33
15
y
74.05
218.44
60.46
5
Amsterdam
Netherlands
m
63.42
32.00%
1.77
1019.18
84.49
77.75
43.72
61.71
98.87
55.9
32.06
33.73
33
n
22.63
1019.18
60.24
6
Malmo
Sweden
s
58.98
30.00%
0.37
433.11
90.87
91.89
26.56
52.4
91.85
46.55
10.8
9.46
10
n
7
433.11
55.88
7
Hangzhou
China
l
54.20
30.00%
1.71
476.73
74.48
80.06
11.92
35.06
63.55
32.72
46.15
49.7
48
n
3.28
476.73
52.55
8
Bern
Switzerland
s
53.27
15.00%
0.69
482.36
75.78
84.99
46.95
65.46
98.73
65.1
71.6
96.52
84
y
4.09
482.36
48.76
9
Bremen
Germany
m
58.86
21.00%
1.38
334.85
87.34
87.15
18.64
55.96
94.64
59.78
19.32
19.74
20
n
5.92
334.85
47.81
10
Hannover
Germany
m
58.83
19.00%
2.99
773.00
91.23
76.96
15.33
51.51
89.63
53.68
27.33
19.74
24
y
8.83
773.00
46.7
11
Strasbourg
France
s
61.26
16.00%
0.63
267.57
61.80
82.73
40.81
58.82
91.61
56.44
12.77
8.78
11
y
9.56
267.57
45.45
12
Bordeaux
France
s
67.14
10.00%
0.72
305.14
83.16
89.01
37.76
59.4
91.61
60.23
100
53.85
77
y
56.03
305.14
45.42
13
Hamburg
Germany
l
58.26
15.00%
0.34
263.80
87.53
92.54
20.37
54.9
94.99
55.1
19.84
33.82
27
n
100
263.80
44.97
14
Leipzig
Germany
m
58.67
15.20%
0.71
323.29
75.11
86.21
13.93
51.05
93.4
49.49
25.2
28.34
27
y
19.13
323.29
44.25
15
Bristol
United Kingdom
m
69.85
14.00%
0.58
1054.36
92.11
85.23
72.57
56.17
78.04
40.61
3.64
8.88
6
y
5.7
1054.36
43.76
16
Montreal
Canada
l
49.60
18.20%
0.42
986.47
94.79
87.51
19.05
44.12
72.73
45.91
7.42
3.67
6
y
8.82
986.47
43.68
17
Nuremberg
Germany
m
60.73
13.00%
0.83
310.97
92.31
91.48
23.95
52.95
94.08
47.73
18.43
25.21
22
y
44.76
310.97
43.62
18
Innsbruck
Austria
s
55.14
17.00%
0.78
1329.96
86.78
79.87
29.32
56.18
94.08
53.81
28.79
23.37
26
n
3.12
1329.96
43.12
19
Berlin
Germany
l
57.69
15.00%
0.58
407.56
86.91
89.86
14.82
50.79
92.81
48.71
10.36
24.43
17
n
69.79
407.56
42.59
20
Melbourne
Australia
l
78.86
16.10%
0.32
174.60
97.05
96.68
13.3
43.19
72.4
47.33
4.37
2.05
3
n
2.42
174.60
42.54
#
City
Country
Size
Total Score
1
Utrecht
Netherlands
S
63.83
51.00%
1.94
279.88
78.99
82.46
37.18
60.78
98.87
57.51
21.77
12.07
17
n
7.32
279.88
77.84
2
Munster
Germany
s
58.74
39.10%
0.53
445.58
83.04
88.43
28.15
53.05
88.04
51.23
35.52
26
31
n
23.84
445.58
65.93
3
Antwerp
Belgium
s
62.67
28.90%
1.61
1165.81
84.61
77.05
73.57
53.37
75.31
34.28
78.87
100
89
y
14.12
1165.81
60.51
4
Copenhagen
Denmark
m
61.19
29.00%
1.04
218.44
66.62
83.01
62.47
61.6
86.54
55.23
12.17
17.33
15
y
74.05
218.44
60.46
5
Amsterdam
Netherlands
m
63.42
32.00%
1.77
1019.18
84.49
77.75
43.72
61.71
98.87
55.9
32.06
33.73
33
n
22.63
1019.18
60.24
6
Malmo
Sweden
s
58.98
30.00%
0.37
433.11
90.87
91.89
26.56
52.4
91.85
46.55
10.8
9.46
10
n
7
433.11
55.88
7
Hangzhou
China
l
54.20
30.00%
1.71
476.73
74.48
80.06
11.92
35.06
63.55
32.72
46.15
49.7
48
n
3.28
476.73
52.55
8
Bern
Switzerland
s
53.27
15.00%
0.69
482.36
75.78
84.99
46.95
65.46
98.73
65.1
71.6
96.52
84
y
4.09
482.36
48.76
9
Bremen
Germany
m
58.86
21.00%
1.38
334.85
87.34
87.15
18.64
55.96
94.64
59.78
19.32
19.74
20
n
5.92
334.85
47.81
10
Hannover
Germany
m
58.83
19.00%
2.99
773.00
91.23
76.96
15.33
51.51
89.63
53.68
27.33
19.74
24
y
8.83
773.00
46.7
11
Strasbourg
France
s
61.26
16.00%
0.63
267.57
61.80
82.73
40.81
58.82
91.61
56.44
12.77
8.78
11
y
9.56
267.57
45.45
12
Bordeaux
France
s
67.14
10.00%
0.72
305.14
83.16
89.01
37.76
59.4
91.61
60.23
100
53.85
77
y
56.03
305.14
45.42
13
Hamburg
Germany
l
58.26
15.00%
0.34
263.80
87.53
92.54
20.37
54.9
94.99
55.1
19.84
33.82
27
n
100
263.80
44.97
14
Leipzig
Germany
m
58.67
15.20%
0.71
323.29
75.11
86.21
13.93
51.05
93.4
49.49
25.2
28.34
27
y
19.13
323.29
44.25
15
Bristol
United Kingdom
m
69.85
14.00%
0.58
1054.36
92.11
85.23
72.57
56.17
78.04
40.61
3.64
8.88
6
y
5.7
1054.36
43.76
16
Montreal
Canada
l
49.60
18.20%
0.42
986.47
94.79
87.51
19.05
44.12
72.73
45.91
7.42
3.67
6
y
8.82
986.47
43.68
17
Nuremberg
Germany
m
60.73
13.00%
0.83
310.97
92.31
91.48
23.95
52.95
94.08
47.73
18.43
25.21
22
y
44.76
310.97
43.62
18
Innsbruck
Austria
s
55.14
17.00%
0.78
1329.96
86.78
79.87
29.32
56.18
94.08
53.81
28.79
23.37
26
n
3.12
1329.96
43.12
19
Berlin
Germany
l
57.69
15.00%
0.58
407.56
86.91
89.86
14.82
50.79
92.81
48.71
10.36
24.43
17
n
69.79
407.56
42.59
20
Melbourne
Australia
l
78.86
16.10%
0.32
174.60
97.05
96.68
13.3
43.19
72.4
47.33
4.37
2.05
3
n
2.42
174.60
42.54
21
Cologne
Germany
l
57.88
12.00%
0.40
372.23
87.73
91.28
18.11
50.04
87.43
49.6
13.84
29.13
21
y
57.75
372.23
42.07
22
Dresden
Germany
m
57.38
12.00%
0.39
403.51
90.70
91.99
25.76
54.27
94.13
50.42
19.89
26.78
23
y
12.13
403.51
41.89
23
Frankfurt
Germany
m
64.10
12.50%
0.86
232.84
90.31
91.45
15.75
51.89
91.32
52.84
41.47
28.34
35
n
12.04
232.84
40.61
24
Tokyo
Japan
l
60.83
15.00%
1.44
1220.14
97.14
81.43
10.5
53.85
100
53.61
1.97
3.37
3
n
2.85
1220.14
40.26
25
Dusseldorf
Germany
m
70.73
12.40%
0.69
279.60
89.96
91.63
16.65
50.87
90.32
50.19
13.56
25.21
19
n
9.64
279.60
40.16
26
Bonn
Germany
s
62.72
12.00%
1.32
461.83
87.43
86.2
24.45
51.72
88.6
49.2
16.04
14.26
15
n
16.39
461.83
38.55
27
Nice
France
s
83.56
8.00%
0.92
389.41
86.49
88.39
28.34
55.04
91.61
53.52
35.58
38.61
37
N
2.08
389.41
38.37
28
Nantes
France
S
67.76
6.00%
0.77
325.85
96.42
92.96
43.95
64.62
91.61
71.72
66.32
39.99
53
n
11.97
325.85
38.27
29
Geneva
Switzerland
s
56.48
7.00%
0.95
623.47
77.80
83.08
43.4
66.49
98.73
70.57
8.16
8.96
9
y
17.16
623.47
37.88
30
Wellington
New Zealand
S
72.48
10.00%
0.52
1188.31
93.01
84.46
49.02
55.82
74.28
59.23
14.08
6.07
10
N
4.71
1188.31
37.69
31
Seville
Spain
m
70.41
7.00%
0.89
1142.89
97.80
84.85
46.83
52.18
79.21
44.84
36.18
39.26
38
y
2.45
1142.89
37.68
32
Paris
France
l
66.31
3.00%
0.75
318.14
69.64
84.28
29.51
58.51
91.61
63.15
69.24
93.33
81
y
32.67
318.14
37.53
33
Vienna
Austria
l
69.29
7.00%
1.58
1015.93
92.63
81.31
13.99
53.44
94.08
55.94
15.85
9.73
13
y
32.61
1015.93
36.97
34
Munich
Germany
l
50.52
10.00%
0.60
371.62
92.95
92.12
17.96
53.95
95.1
53.75
24.13
29.13
27
n
14.01
371.62
36.94
35
Helsinki
Finland
m
47.16
10.00%
1.05
222.36
88.75
90.19
25.97
51.96
91.61
45.89
36.87
42.18
40
n
12.41
222.36
36.62
36
Tel Aviv
Israel
s
84.54
9.00%
0.48
1067.21
79.85
81.52
23.76
37.88
56.9
39.84
59.5
52.41
56
n
14.11
1067.21
36.57
37
Vancouver
Canada
m
65.09
7.00%
0.44
1455.97
86.97
80.2
43.96
51.09
72.73
50
42.72
29.79
36
y
8.66
1455.97
36.54
38
Beijing
China
l
58.74
14.00%
1.14
384.95
85.32
87.08
0.2
32.11
63.55
31.79
13.7
8.1
11
n
10.86
384.95
35.91
39
San Francisco
United States
m
88.58
3.90%
0.56
2586.71
85.15
67.97
38.22
56.12
90.4
51.29
37.95
22.23
30
y
21.54
2586.71
35.68
40
Sydney
Australia
l
75.56
10.00%
0.28
345.84
97.04
95.15
9.24
39.8
72.4
39.88
1.43
2.16
2
n
9.84
345.84
35.57
41
Portland
United States
m
71.32
6.30%
0.96
1692.47
79.37
73.07
48.43
61.96
90.4
61.91
45.14
18.76
32
n
9.47
1692.47
35.28
42
Casablanca
Morocco
l
89.45
12.00%
4.13
995.83
94.23
70.7
5.77
21.3
28.78
30.36
1.1
1.09
1
y
4.72
995.83
35.07
43
Ljubljana
Slovenia
s
46.05
10.00%
1.43
840.20
93.79
84.11
24.36
34.75
50.98
35.96
52.13
22.42
37
y
11.81
840.20
34.81
44
Madrid
Spain
l
68.34
6.00%
0.61
1104.19
92.21
84.65
20.32
46.6
79.21
46
12.34
7.33
10
y
23.48
1104.19
34.49
45
Dortmund
Germany
m
63.96
6.40%
0.39
147.85
92.46
95.09
12.87
48.71
87.46
49.13
19.44
28.34
24
n
19.88
147.85
34.05
46
Barcelona
Spain
l
83.04
2.50%
0.50
1796.71
59.17
67.42
54.69
54.15
79.21
45.46
28.66
35.22
32
y
33.16
1796.71
33.7
47
Stuttgart
Germany
m
56.34
5.00%
0.00
153.26
95.44
97.76
11.39
49.25
93.48
45.72
26.81
27.56
27
n
59.65
153.26
33.46
48
Reykjavik
Iceland
S
46.55
3.00%
0.50
593.25
91.69
89.98
110.58
61.99
62.11
48.35
15.95
6.79
11
Y
23.39
593.25
33.09
49
Auckland
New Zealand
l
74.64
8.00%
0.25
453.72
98.01
94.54
11.3
38.83
74.28
33.73
1.08
1.07
1
n
1.48
453.72
33.04
50
Seattle
United States
m
71.92
3.50%
0.67
1688.20
82.14
75.3
32.59
56.73
90.4
56.92
10.84
4.62
8
y
4.23
1688.20
32.93
51
Shanghai
China
l
59.01
10.00%
1.13
309.92
88.81
89
2.23
32.75
63.55
32.33
16.11
21.1
19
n
1.13
309.92
32.51
52
Singapore
Singapore
L
58.32
1.00%
2.35
339.25
100.00
86.98
23.57
56.79
97.24
56.36
27.69
34.02
31
Y
10.1
339.25
31.62
53
Washington
United States
L
60.97
4.60%
0.57
1699.01
85.34
76.7
8.79
48.32
90.4
47.77
6.43
7.16
7
Y
8.9
1699.01
31.43
54
Edinburgh
United Kingdom
S
62.56
3.00%
0.95
1175.78
84.37
79.84
53.07
52.85
78.04
43.82
11.57
10.59
11
Y
3.02
1175.78
31.32
55
Oslo
Norway
M
47.45
7.00%
0.53
226.40
83.65
90.79
20.07
38.58
71.03
30.31
42.86
46.15
45
N
10.13
226.40
31.31
56
Brussels
Belgium
L
63.34
3.00%
1.84
1060.01
90.47
79
19.59
40.75
75.31
32.85
40.86
37.88
39
Y
29.9
1060.01
30.98
57
Los Angeles
United States
L
89.82
1.10%
0.85
2148.35
85.29
71.02
16.37
50.65
90.4
49.64
7.41
3.67
6
Y
16.15
2148.35
30.93
58
Santiago
Chile
L
81.05
3.90%
2.51
362.72
90.79
82.97
16.07
34.34
50.88
40.44
5.96
2.35
4
Y
4.58
362.72
30.35
59
Warsaw
Poland
L
49.50
5.00%
2.39
345.40
95.94
85.37
24.58
29.5
44.32
26.71
24.13
16.88
21
Y
72.72
345.40
30.06
60
Dublin
Ireland
M
68.72
3.00%
0.63
517.05
81.95
86.92
66.19
43.56
49.21
35.91
47.07
37.14
42
N
7.44
517.05
29.97
61
Boston
United States
M
59.44
2.40%
0.49
3459.77
82.81
58.9
30.41
54.2
90.4
50.81
32
30.69
31
Y
10.64
3459.77
29.81
62
London
United Kingdom
L
64.69
2.00%
0.68
1299.08
90.06
81.69
13.52
42.61
78.04
39.8
6.58
14.53
11
Y
44.87
1299.08
29.72
63
Lisbon
Portugal
M
81.33
1.00%
1.76
1947.91
97.68
73.04
24.16
44.94
65.12
52.53
10.42
4.64
8
Y
30.52
1947.91
29.51
64
Stockholm
Sweden
M
51.40
1.00%
0.56
366.39
89.63
91.27
16.73
51.97
91.85
51.9
20.94
9.23
15
Y
3.99
366.39
29.46
65
Milan
Italy
L
53.92
6.00%
1.16
1542.70
85.34
75.63
15.69
35.26
63.76
30.56
29.24
33.1
31
N
54
1542.70
29.24
66
Athens
Greece
M
82.73
2.00%
0.75
571.98
83.85
86.48
32.27
32.47
44.5
30.26
2.82
1.53
2
Y
4.47
571.98
28.55
67
New York
United States
L
60.75
1.20%
0.66
2157.77
85.19
71.76
7.58
48.2
90.4
48.21
23.98
16.33
20
Y
2.52
2157.77
28.1
68
Krakow
Poland
M
50.24
1.20%
2.16
312.40
96.73
87
42.59
33.39
44.32
26.25
40.12
18.43
29
Y
77.32
312.40
27.93
69
Seoul
South Korea
L
57.64
1.50%
2.37
341.99
98.74
86.44
6.95
43.62
78.72
46.57
1.18
1.23
1
Y
1.9
341.99
27.67
70
Rome
Italy
L
77.83
1.00%
1.41
1003.01
88.38
80.8
11.41
34.17
63.76
30.18
2.04
1.34
2
Y
12.24
1003.01
27.03
71
Chicago
United States
L
56.68
1.70%
0.62
2197.60
85.30
71.54
12.05
49.65
90.4
49.55
16.25
21.29
19
N
35.43
2197.60
26.72
72
Detroit
United States
M
57.38
1.00%
0.91
858.76
59.15
74.79
38.48
55.79
90.4
50.13
4.31
6.82
6
N
23.8
858.76
26.71
73
Prague
Czech Republic
L
55.80
1.00%
1.74
838.27
96.51
83.63
44.79
37.34
56.65
24.28
7.04
2.83
5
Y
10.79
838.27
25.87
74
Istanbul
Turkey
L
75.00
0.50%
0.41
1837.11
88.95
77.23
2.88
31.71
54.81
37.52
3.11
1.61
2
Y
13.5
1837.11
25.48
75
Cairo
Egypt
L
77.58
5.00%
0.47
1929.06
93.94
77.72
3.2
18.35
30.65
21.38
1.06
1.05
1
N
27.08
1929.06
25.28
76
São Paulo
Brazil
L
66.93
5.10%
1.71
475.44
18.86
61.73
5.12
12.04
21.97
9.84
2.5
1.9
2
Y
60.17
475.44
24.81
77
Johannesburg
South Africa
M
85.48
0.20%
7.43
349.85
1.00
31.57
146.84
57.11
42.44
28.9
6.95
2.63
5
N
24.9
349.85
24.68
78
Nairobi
Kenya
L
78.59
2.00%
1.13
459.97
83.16
85.68
25.53
24.1
26.94
27.27
1.76
1.14
1
N
16.3
459.97
24.16
79
New Delhi
India
L
52.24
4.00%
1.08
441.42
88.96
87.98
1.48
23.38
41.07
27.22
1.25
1.04
1
N
32.87
441.42
23.96
80
Mexico City
Mexico
L
80.54
0.08%
0.65
411.56
47.19
76.42
11.73
23.45
32.81
28.76
4.46
8.5
6
Y
2.86
411.56
23.83
81
Bogotá
Colombia
L
70.16
4.00%
3.21
567.44
28.51
57.33
12.26
17.3
14.6
28.16
1
1
1
Y
20.27
567.44
23.62
82
Cali
Colombia
L
74.26
4.50%
3.69
622.11
29.75
55.05
22.84
13.07
14.6
8.32
1.14
1.19
1
Y
11.66
622.11
23.24
83
Buenos Aires
Argentina
L
72.86
1.20%
0.83
835.41
84.15
83.64
25.33
16.94
20.06
12.81
7.17
2.87
5
N
60.97
835.41
22.47
84
Hong Kong
SAR China
L
50.13
0.50%
1.19
1117.99
98.16
83.89
3.58
34.89
69.78
31.61
13.78
13.87
14
N
7.6
1117.99
22.24
85
Jakarta
Indonesia
L
31.92
2.00%
0.97
428.31
93.87
90.24
6.85
21.24
33.95
24.3
1.29
1.02
1
Y
9.09
428.31
21.66
86
Moscow
Russia
L
45.49
3.00%
1.26
693.27
69.64
78.35
6.4
13.84
29.52
6.83
4.15
3.91
4
Y
4.34
693.27
21.35
87
Tbilisi
Georgia
L
64.45
0.03%
0.76
2272.83
97.43
74.21
8.96
14.85
16.88
20.77
2.63
1.98
2
Y
18.23
2272.83
20.41
88
Bangkok
Thailand
L
23.62
0.25%
3.29
554.42
91.93
78.02
7.62
24.75
41.03
27.22
10.85
4.81
8
Y
7.05
554.42
18.9
89
Medellín
Colombia
L
76.22
0.50%
3.99
1098.53
29.56
48.96
5.91
9.27
14.6
8.36
5.54
3.71
5
Y
31.55
1098.53
18.85
90
Lagos
Nigeria
L
29.24
0.14%
2.97
525.48
73.72
73.71
2.13
1.43
1
1
1.01
1.02
1
N
21.76
525.48
11.81
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Methodology

The 2019 Bicycle Cities Index analyses the conditions for cycling in 90 cities across the globe to determine if they are good for cyclists.

City Selection

90 cities were selected for their willingness to invest and work on initiatives to improve cycling infrastructure and safety. The study does not reflect the best and worst cities for cycling, but rather evaluates the cycling climate for these 90 cities based on factors related to bike-users.

City Size: S stands for cities with less than 500,000 inhabitants, M for cities with between 500,000 and 999,999 inhabitants and L for cities with 1 million inhabitants and above.

The study focuses on six main categories with the following factors that make a city cycling-friendly:

  • Weather
  • Percentage Bicycle Usage.
  • Crime & Safety: Fatalities / 100,000 Cyclists, Accidents / 100,000 Cyclists, Bicycle Theft Score.
  • Infrastructure: Number of Bicycle Shops / 100,000 Cyclists, Specialised Roads & Road Quality Score, Investment & Infrastructure Quality Score.
  • Sharing: Number of Bicycle Sharing & Rental Stations / 100,000 Score, # Shared Bicycles / 100,000 Score.
  • Events: No Car Day, Critical Mass Score.

A weighted average was used for all of the factors in order to create the final scores for each category, for example the Weather Score was generated by analysing and aggregating the Hours of Sunshine, Rainfall and Extreme Weather Days of each city.

All of the information collected is based on the latest data available.

Scoring

Scores are normalized such that 1 represents the lowest and 100 the highest value in the dataset, meaning that the higher the score, the better. However, for the factors Fatalities and Accidents / 100,00 cyclists, a lower value is better as it represents a higher safety rating for each city.

The equation for normalization is as follows: score = 10 * [x - min(X)] / [max(X) - min(X)]; or score inverted = 10 -10 * [x - min(X)] / [max(X) - min(X)] for inverted scores

The formula used for the weighted average, where n is the number of categories, and i is the i-th factor, is as follows: weighted_average: sum_(i=1)^n w_i*x_i, there w_i is the weight of column i, n - total number of columns used for the weighted average, x_i : i-th column.

WEATHER

The Weather Score was calculated using an aggregated score, taking into account the total annual hours of sunshine, average annual precipitation in millimetres, and the number of weather days below 0 °C and over 30 °C in a city.
Sources: World Weather Online, Weather Base, Deutscher Wetter Dienst, other websites.

BICYCLE USAGE

Percentage of people using bicycles in everyday life in each city.
Sources: Local statistical departments, Greenpeace, UN, Eco Mobility, The League of American Cyclists, and others.

CRIME & SAFETY

Fatalities / 100,000 Cyclists

Deaths in bicycle accidents (includes deaths related to bicycle theft) per 100,000 cyclists in cities. Total number of cyclists estimated from bicycle usage rates as well as bicycle ownership rates.
Sources: OECD, local statistical departments. Bicycle ownership rate source: Oke, O., et al., Tracking global bicycle ownership patterns. Journal of Transport & Health (2015).

Accidents / 100,000 Cyclists

An estimate of bicycle-related accidents that resulted in at least light injuries, per 100,000 cyclists. Total number of cyclists estimated from bicycle usage rates as well as bicycle ownership rates.
Sources: OECD, local statistical departments. Bicycle ownership rate source: Oke, O., et al., Tracking global bicycle ownership patterns. Journal of Transport & Health (2015).

Bicycle Theft Score

A weighted average of the following subcategories collected in order to offset the low crime report rates in countries where crime is prevalent but underreported.

  • Stolen bicycles / 100,000 cyclists
  • An estimate of stolen bicycle rate per 100,000 cyclists. Total number of cyclists estimated from bicycle usage rates as well as bicycle ownership rates. Sources: local statistical departments. Bicycle ownership rate source: Oke, O., et al., Tracking global bicycle ownership patterns. Journal of Transport & Health (2015).
  • Homicide Rate
  • In order to account for low report rates of bicycle theft in some countries, the homicide rate was added as it is the most reported crime.
    Source: World Bank.

INFRASTRUCTURE

Number of bicycle shops / 100,000 cyclists

Total number of bicycle shops within the city. Total number of cyclists estimated from bicycle usage rates as well as bicycle ownership rates.
Sources: the yellow pages, Google search engine result pages, Open Street Maps Overpass API responses. Bicycle ownership rate source: Oke, O., et al., Tracking global bicycle ownership patterns. Journal of Transport & Health (2015).

Specialised Roads and Road Quality

Bicycle roads length per population.
Sources: Open Street Maps Overpass API responses: km of ways (highways) tagged for bicycle usage (allowed and specific). Road Quality score. Source: World Economic Forum.

Investment and Infrastructure Quality

An average of the scored subcategories (country-level for international; investment and infrastructure: city level for German cities):

  • Infrastructure Investment Score.
    Sources: World Bank LPI infrastructure score, German Institute of Economy (DIW): investment in infrastructure, Bertelsmann Stiftung.
  • Infrastructure Quality Score.
    Sources: World Economic Forum Global Competitiveness Index Quality of Overall Infrastructure.

SHARING

Number of bicycle sharing and rental stations / 100,000

An estimate of bicycle sharing and rental stations per 100,000 of population.
Sources: The yellow pages, google search engine result pages, Open Street Maps Overpass API responses.

Number of shared bicycles / 100,000

An estimate of shared bicycle fleet per 100,000 of population.
Sources: Local statistical departments, local bicycle sharing company websites, bicycle share map (http://bikes.oobrien.com).

EVENTS

No Car Day

Score dependant on the existence of a car-free day, where motorists are encouraged to give up their car for one day. 1 - Has No Car Day. 0 - Does not have a No Car Day.

Critical Mass Score

Average of the subcategories: Size of Critical Mass events attendees.

  • Sources: local statistical departments, local websites, social media group pages (e.g. Facebook). Latest available event.
  • Size of local Critical Mass communities.
    Sources: local Critical Mass event social media group size (e.g. Facebook).

Bicycle theft insurance as additional protection

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