Easy Park

2017 Smart Cities Index


At EasyPark, our ambition is to build more livable cities for the future. It doesn’t matter if you are liberal or conservative, a student or a professional, a smarter city makes urban life easier. We undertook this study not only to highlight those metropolises which are on the forefront of smart urban growth, but also to learn from those cities which are showing impressive acceleration towards making life smoother for their citizens through digitalization.

To start the study, we first looked at the factors which define a smart city. We discovered that such a city should be digitalized first and foremost—with 4G, plentiful Wi-Fi hotspots and high smartphone usage. Transport and mobility should be knowledge-based, with smart parking, traffic sensors and car sharing apps. A smart city is sustainable, with a focus on clean energy and environmental projection. In addition, there is excellent online access to governmental services and a high level of citizen participation.

We then analysed over 500 cities worldwide for all of these factors, and ranked the top 100 to determine the ultimate Smart City Index. To round off the study, we asked over 20,000 technology and urban planning journalists for their expert opinion on how the cities where they’ve lived are moving with the curve of digitalization.

“Big Data has changed the face of the world as we know it, because it allows us to create better solutions to real world problems. Without better solutions, global urbanization would lead to problems such as traffic congestion, housing shortages and pollution—by using Big Data, we can help tackle these important global issues. In our case, we target mobility and help reduce the footprint of driving traffic while creating a much more welcoming experience for drivers.” commented Mauritz Börjeson, CBDO of EasyPark Group. “Every city in this index deserves to be applauded for their efforts, and while the results clearly indicate those cities which are leaps and bounds ahead, it also brings to attention the admirable efforts of many cities looking forward towards a smart future.”

  •  Smart Parking
  •  Car Sharing Services
  •  Traffic
  •  Public Transport
  •  Clean Energy
  •  Smart Building
  •  Waste Disposal
  •  Environment Protection
  •  Citizen Participation
  •  Digitalization of Goverment
  •  Urban Planning
  •  Education
  •  Business Ecosystem
  •  4G LTE
  •  Internet Speed
  •  Wifi Hotspots
  •  Smartphone Penetration
  •  Living Standard
  •  How the City is Becoming Smarter
      Transport and mobility Sustainability Governance Innovation
Economy
Digitalization Living
Standard
Expert
Perception
 
# CITY
COUNTRY
Smart Parking
 Car sharing services
 Traffic
Public Transport
Clean Energy
Smart Building
Waste Disposal
Environment Protection
Citizen Participation
Digitalization of Goverment
Urban Planning
Education
Business ecosystem
4G LTE
Internet Speed
Wifi Hotspots
Smartphone Penetration
Living Standard How The City Is Becoming Smarter
RANK/
SCORE
1 Copenhagen Denmark 9.81 8.62 8.18 6.82 7.92 9.83 8.24 6.11 9.38 8.53 7.09 5.85 9.13 8.63 7.66 4.12 9.74 8.70 9.12 8.24
2 Singapore Singapore 7.30 6.63 4.20 10.00 2.26 8.44 7.62 7.15 10.00 5.47 7.82 5.12 8.62 8.71 7.75 6.63 7.55 8.18 9.30 7.83
3 Stockholm Sweden 7.49 5.93 6.71 6.54 8.44 6.88 8.94 8.79 9.29 10.00 7.62 7.66 9.57 8.37 9.22 6.28 8.69 7.32 8.20 7.82
4 Zurich Switzerland 7.80 7.75 4.98 9.83 8.62 10.00 10.00 8.70 2.07 8.10 9.03 9.02 9.74 4.69 4.38 5.59 7.55 10.00 9.00 7.75
5 Boston United States 8.01 8.70 7.71 7.21 3.60 5.15 4.26 6.56 5.30 6.97 5.12 10.00 10.00 6.06 9.39 6.80 9.17 8.22 9.30 7.70
6 Tokyo Japan 9.57 7.13 7.66 8.79 3.86 8.36 8.24 4.25 6.60 6.28 3.59 7.71 7.19 6.37 6.50 9.57 8.61 7.21 8.60 7.59
7 San Francisco United States 9.05 9.05 5.08 3.43 3.60 5.15 4.26 6.38 6.23 6.59 5.44 5.67 9.91 7.91 10.00 9.05 9.17 9.01 9.10 7.55
8 Amsterdam Netherlands 7.95 7.06 8.36 7.06 2.47 7.32 7.79 3.86 9.02 9.83 5.94 7.84 8.82 8.40 6.63 5.33 6.85 9.01 8.20 7.54
9 Geneva Switzerland 8.06 4.98 6.11 6.97 8.62 10.00 10.00 9.13 1.80 8.36 8.59 9.14 8.96 8.11 8.79 3.94 7.55 9.80 8.10 7.53
10 Melbourne Australia 7.97 7.14 4.55 8.72 2.90 6.29 5.15 2.90 9.82 5.38 9.24 9.31 6.02 10.00 7.84 6.72 9.30 8.01 7.30 7.51
11 Vancouver Canada 9.74 10.00 3.08 8.96 9.05 2.64 3.29 2.99 4.83 7.66 9.38 8.44 8.44 9.23 8.27 7.49 7.89 8.15 7.10 7.47
12 Sydney Australia 7.23 8.79 3.08 7.48 2.90 4.29 5.15 2.50 9.55 4.12 9.03 9.05 6.63 9.83 7.58 7.23 9.30 8.10 8.20 7.43
13 Berlin Germany 6.92 9.74 4.38 7.84 6.88 7.31 9.82 3.34 7.33 5.85 6.12 7.99 8.88 2.71 3.16 8.88 5.02 8.93 7.90 7.39
14 Hamburg Germany 6.02 8.14 4.23 9.05 6.88 9.02 9.82 3.42 5.90 5.41 5.41 7.32 7.32 4.09 5.07 4.29 5.02 9.60 9.10 7.36
15 Gothenburg Sweden 6.88 6.18 8.79 1.95 8.44 6.88 8.94 9.39 8.57 9.74 8.24 7.58 7.49 7.69 8.53 2.64 8.69 7.58 8.50 7.23
16 Montreal Canada 9.83 7.84 6.11 8.96 9.05 5.64 3.29 2.30 3.67 7.32 4.44 8.62 7.84 7.86 6.71 6.11 7.89 8.41 7.90 7.22
17 London United Kingdom 8.48 9.48 2.73 7.66 5.24 8.10 6.12 6.07 4.65 3.00 5.94 7.91 9.05 4.34 5.24 9.83 6.68 7.63 8.20 7.18
18 Tel Aviv Israel 8.95 7.02 3.08 6.88 1.17 9.39 7.88 5.85 6.39 7.92 6.71 7.03 8.79 5.03 6.60 7.14 8.95 6.88 8.10 7.15
19 Paris France 7.20 9.01 3.34 5.76 4.46 7.14 6.38 6.45 4.92 8.90 4.44 7.27 8.18 7.26 8.18 9.50 8.10 7.80 7.30 7.14
20 Toronto Canada 9.39 6.97 5.41 9.65 9.05 2.64 3.29 1.87 5.54 3.08 6.47 8.88 8.01 9.49 8.70 8.53 5.89 7.24 7.20 7.14
21 Seoul South Korea 5.12 8.53 5.41 6.54 2.69 6.19 7.00 2.04 5.72 7.51 3.24 7.40 8.41 8.80 8.44 9.74 9.91 6.72 8.90 7.13
22 Luxembourg Luxembourg 4.20 3.77 8.01 6.88 3.68 7.40 6.56 2.73 9.73 6.11 9.74 3.60 7.40 9.57 9.48 3.16 10.00 9.05 8.90 7.10
23 Helsinki Finland 8.53 5.85 8.70 7.40 7.23 7.41 6.82 5.33 5.28 9.48 10.00 4.55 6.80 5.11 4.63 8.00 8.40 7.63 7.90 7.02
24 New York United States 7.13 9.65 3.86 3.61 3.60 5.15 4.26 6.26 2.78 7.58 2.68 9.48 9.31 6.74 9.83 9.91 8.17 7.79 8.00 6.99
25 München (Munich) Germany 4.35 8.88 5.41 8.62 6.88 9.31 9.82 3.51 6.88 5.15 5.41 9.39 8.36 2.80 3.42 4.12 5.02 8.11 8.01 6.99
26 Düsseldorf Germany 5.71 6.45 7.92 8.53 6.88 9.31 9.82 4.29 7.24 2.30 5.06 7.06 6.45 4.26 5.41 3.60 5.02 8.97 8.05 6.98
27 Västerås Sweden 7.73 3.08 9.83 5.41 8.44 6.88 8.94 9.83 8.84 8.79 7.62 5.76 7.66 7.77 8.88 1.69 8.69 6.37 7.40 6.95
28 Washington, DC United States 8.27 9.83 6.11 2.13 3.60 5.15 4.26 2.47 4.03 8.96 3.38 9.57 9.83 5.29 8.96 8.70 8.17 7.13 8.20 6.91
29 Bayreuth Germany 6.02 2.82 9.74 8.79 6.88 9.31 9.82 4.98 6.88 7.23 9.74 6.88 5.33 4.46 3.90 3.17 6.02 8.36 7.90 6.87
30 Hannover Germany 6.12 2.64 7.23 8.01 6.88 9.31 9.82 6.46 6.35 7.75 7.88 6.97 5.15 4.77 6.11 2.56 5.02 8.44 8.10 6.87
31 Köln (Cologne) Germany 6.15 7.66 4.20 7.92 6.88 9.31 9.82 3.77 7.24 4.46 5.24 8.01 7.14 3.66 4.46 2.99 5.02 7.89 7.50 6.84
32 Vienna Austria 7.06 8.01 4.98 9.57 9.31 9.48 7.18 5.24 8.04 4.89 7.62 5.07 4.98 5.97 4.20 7.32 2.83 7.40 6.10 6.84
33 Frankfurt am Main Germany 4.94 7.40 6.71 7.14 6.88 9.31 9.82 3.94 7.68 2.99 4.44 8.18 6.97 3.14 4.03 5.07 7.02 7.14 7.90 6.74
34 Oslo Norway 6.01 5.59 5.41 6.28 9.91 5.93 7.53 4.89 7.86 6.37 8.24 4.46 5.93 6.83 3.68 4.20 6.33 8.88 9.10 6.73
35 Philadelphia United States 8.79 7.32 8.18 4.63 3.60 5.15 4.26 1.78 3.41 6.45 4.71 9.13 9.22 6.31 9.57 5.15 8.17 7.74 7.50 6.72
36 Chicago United States 8.53 7.49 7.66 2.13 3.60 5.15 4.26 3.61 3.94 5.33 3.12 9.65 9.48 5.89 9.31 8.79 8.17 8.30 7.00 6.69
37 Dubai United Arab Emirates 5.24 5.76 8.11 6.82 2.33 8.78 5.76 9.31 1.09 5.98 8.35 6.35 5.55 6.86 7.87 6.59 9.48 7.80 8.20 6.65
38 Trondheim Norway 6.76 4.63 9.31 5.07 9.91 5.93 7.53 5.59 7.50 8.70 10.00 4.81 4.03 9.31 6.37 1.09 6.33 8.62 7.70 6.65
39 Helsingborg Sweden 5.42 3.42 10.00 5.41 8.44 6.88 8.94 9.91 8.66 9.91 7.88 5.93 7.92 6.40 6.54 1.87 8.69 6.37 7.40 6.64
40 Ottawa Canada 7.92 5.24 6.71 7.75 9.05 2.64 3.29 2.64 6.61 6.71 6.12 7.75 7.06 9.40 8.36 3.25 5.89 7.06 7.50 6.63
41 Perth Australia 5.59 1.52 7.14 9.39 2.90 4.29 5.15 4.35 9.11 2.13 8.59 8.10 3.86 9.74 7.32 3.42 9.30 8.53 7.80 6.61
42 Dublin Ireland 8.10 6.71 2.04 5.85 4.81 8.18 5.24 3.60 5.37 1.09 7.62 6.19 8.70 4.97 6.59 6.54 7.45 7.75 7.90 6.59
43 Stavanger Norway 7.93 4.72 8.96 5.07 9.91 5.93 7.53 5.76 6.97 8.44 9.74 3.86 3.68 7.43 4.98 1.52 6.33 9.65 7.50 6.58
44 Manama Bahrain 5.80 2.38 7.63 3.34 2.34 8.21 6.53 4.21 3.32 6.80 8.50 7.01 8.35 7.43 9.09 7.82 8.86 7.01 8.90 6.50
45 Aarhus Denmark 5.59 4.20 9.22 4.03 7.92 9.83 8.24 6.63 9.64 7.49 8.24 6.02 6.88 8.46 7.23 2.30 9.74 7.84 4.50 6.49
46 Los Angeles United States 9.31 6.80 1.78 1.00 3.60 5.15 4.26 1.43 3.23 5.07 2.59 9.74 9.48 6.49 9.65 9.48 8.17 7.48 7.90 6.47
47 Stuttgart Germany 4.70 7.92 4.20 3.25 6.88 9.31 9.82 4.20 7.59 3.68 10.00 6.63 6.54 2.89 3.77 3.08 5.02 8.71 8.05 6.40
48 Auckland New Zealand 6.45 3.16 3.34 8.44 9.22 2.13 4.00 2.82 5.10 5.24 7.88 5.50 3.94 7.34 6.19 8.10 7.03 7.68 8.90 6.36
49 Bergen Norway 6.28 4.55 9.48 1.09 9.91 5.93 7.53 5.50 7.50 8.18 9.38 4.29 3.34 9.74 8.62 1.35 6.33 9.31 6.70 6.35
50 Espoo Finland 5.40 2.67 7.66 4.70 7.23 5.41 6.82 4.81 5.46 8.88 8.24 4.38 8.53 5.80 5.85 5.85 6.40 7.76 9.21 6.35
51 Madrid Spain 6.71 6.19 7.84 5.15 5.93 6.88 3.65 6.88 6.70 5.85 5.94 5.59 4.89 6.57 6.80 6.02 7.29 4.46 8.10 6.32
52 Osaka Japan 9.65 9.22 4.72 9.31 3.86 8.36 8.24 3.68 2.60 6.63 1.71 4.98 2.38 1.86 3.34 3.34 1.61 7.03 8.50 6.24
53 Barcelona Spain 6.44 7.23 4.98 5.59 5.93 6.88 3.65 7.32 5.99 5.67 3.12 5.33 5.85 7.09 7.14 6.19 7.29 3.94 8.10 6.23
54 Abu Dhabi United Arab Emirates 2.47 1.69 8.70 5.82 1.52 8.78 6.76 9.74 1.45 6.80 8.18 5.61 5.69 5.03 6.04 8.86 9.48 8.54 8.00 6.07
55 Birmingham United Kingdom 3.60 6.37 7.66 9.74 5.24 8.10 6.12 6.02 3.05 2.38 8.85 8.70 7.23 3.23 3.86 4.46 6.68 6.34 6.90 6.06
56 Bochum Germany 3.08 2.99 8.44 7.23 6.88 9.31 9.82 4.63 7.24 1.61 9.38 8.79 5.07 3.83 4.81 1.26 5.02 8.98 5.78 6.00
57 Taipei Taiwan 8.18 3.51 2.73 9.91 2.04 9.57 8.41 4.03 5.81 8.62 2.06 2.82 4.63 4.51 3.08 7.06 7.12 3.77 8.10 5.96
58 Doha Qatar 4.43 2.13 6.50 3.60 2.95 8.26 4.41 3.17 1.00 5.04 7.35 5.70 5.80 6.49 6.13 7.66 8.86 8.96 8.90 5.87
59 Lyon France 6.19 5.15 6.11 5.93 4.46 7.14 6.38 7.84 4.74 7.06 4.71 7.49 5.59 6.66 7.40 2.90 5.10 5.50 5.80 5.85
60 Milan Italy 6.45 8.18 5.41 2.47 7.66 7.75 5.76 7.06 6.17 1.87 3.21 7.23 5.24 4.94 4.29 8.01 5.02 4.55 6.50 5.80
61 Adelaide Australia 2.99 3.34 7.14 2.90 2.90 4.29 5.15 1.52 9.91 3.25 8.59 8.53 2.90 7.69 3.51 4.81 9.30 8.27 7.50 5.74
62 Brussels Belgium 6.11 7.58 3.34 4.55 2.99 5.50 8.32 4.55 8.75 1.17 5.06 2.90 4.72 6.23 4.12 4.89 5.98 5.45 6.90 5.64
63 Daejeon South Korea 2.21 8.44 6.19 6.54 1.69 6.19 7.00 2.90 5.63 2.64 2.59 6.11 2.21 5.37 5.33 9.39 9.91 5.33 4.90 5.48
64 Lisbon Portugal 4.98 6.28 3.68 5.07 7.32 5.59 4.79 8.53 2.25 5.93 4.44 4.20 6.11 3.57 5.93 5.24 5.46 4.99 7.50 5.46
65 Leeds United Kingdom 2.90 4.03 7.66 3.08 5.24 8.10 6.12 6.28 3.58 6.88 7.09 8.36 6.71 5.46 6.45 1.95 6.68 4.12 6.90 5.32
66 Ljubljana Slovenia 5.41 5.07 8.88 4.38 5.67 5.15 6.47 7.14 3.76 3.34 9.74 3.68 3.77 4.43 3.60 5.93 3.36 7.73 5.50 5.32
67 Tampere Finland 3.64 2.30 9.39 7.40 7.23 5.41 6.82 5.41 5.01 9.57 7.62 4.63 6.37 5.20 4.89 2.13 2.40 5.67 5.90 5.30
68 Hong Kong China 8.62 1.50 3.68 4.81 5.50 6.02 4.62 8.01 1.18 4.63 6.47 6.37 6.28 7.17 6.97 7.92 3.71 3.60 6.20 5.29
69 Turin Italy 6.56 8.27 7.84 2.47 7.66 7.75 5.76 7.49 6.52 3.94 5.94 6.54 2.30 3.40 2.47 3.51 5.02 4.29 5.00 5.27
70 Reykjavik Iceland 4.89 2.47 8.53 1.35 10.00 3.42 4.71 4.38 7.77 6.02 3.47 3.16 5.67 8.29 8.01 1.61 7.03 5.59 6.50 5.23
71 Rome Italy 6.14 8.10 2.73 1.52 7.66 7.75 5.76 6.54 6.44 1.35 6.82 6.80 2.47 3.31 2.38 6.71 5.02 3.42 6.00 5.19
72 Prague Czech Republic 6.54 5.50 6.71 7.66 2.13 3.77 2.32 3.08 1.98 2.82 5.94 3.34 4.81 6.14 5.76 9.22 5.11 5.08 5.20 5.14
73 Vilnius Lithuania 4.03 5.33 6.88 6.28 5.76 3.77 3.38 8.36 2.43 8.27 9.12 2.30 2.99 7.94 9.05 3.77 3.18 2.30 6.50 5.13
74 Marseille France 4.46 2.04 2.73 4.12 4.46 7.14 6.38 7.23 4.48 3.60 2.59 7.92 3.51 7.00 8.10 2.04 3.10 6.02 5.50 5.04
75 Riga Latvia 4.55 3.25 6.11 7.49 9.13 2.21 2.06 9.57 3.85 9.05 6.82 1.69 3.25 7.51 7.92 6.45 2.57 2.13 4.90 4.90
76 Tallinn Estonia 3.16 3.60 6.88 4.20 2.56 2.30 7.09 3.16 4.56 9.39 7.62 2.47 5.76 5.63 6.02 5.76 5.46 2.90 6.50 4.75
77 Moscow Russia 5.67 8.36 1.87 9.31 4.20 1.43 5.41 1.69 1.62 7.84 2.06 2.38 1.52 2.20 3.25 7.58 2.14 1.35 6.40 4.50
78 Panama City Panama 1.95 1.95 1.52 6.88 8.70 4.38 1.00 10.00 7.95 2.30 2.24 1.00 1.17 1.94 2.56 4.38 3.70 6.20 6.50 4.49
79 Budapest Hungary 7.66 2.73 8.36 4.03 1.87 3.86 3.47 7.92 2.69 3.16 3.74 2.64 2.56 9.06 7.06 5.41 1.09 2.21 4.80 4.38
80 Sao Paulo Brazil 3.42 4.38 1.69 1.69 9.57 3.60 4.53 8.44 8.31 2.73 1.53 3.08 2.13 2.54 3.94 8.27 1.96 3.16 5.20 4.35
81 Beijing China 4.29 9.57 1.61 1.78 5.50 3.34 2.94 7.75 1.00 1.95 2.06 3.94 4.29 4.60 2.99 8.62 3.71 3.26 5.70 4.31
82 Bratislava Slovakia 3.25 2.90 9.13 3.77 4.89 3.16 2.59 7.58 3.50 5.50 6.47 2.04 2.73 5.54 6.28 4.63 2.49 2.56 5.40 4.21
83 Naples Italy 3.47 1.87 4.55 2.47 7.66 7.75 5.76 7.40 6.08 4.03 3.29 6.28 1.78 3.74 2.64 2.21 5.02 3.70 4.70 4.21
84 Kuala Lumpur Malaysia 3.77 2.56 4.20 4.55 1.09 2.90 1.26 5.93 8.49 3.77 4.44 2.56 4.12 1.26 1.43 9.65 5.46 2.38 5.50 4.17
85 Shanghai China 4.46 6.54 2.38 3.51 5.50 3.34 2.94 7.66 1.00 1.78 1.44 3.51 4.29 4.00 2.21 6.88 3.71 2.43 6.80 4.12
86 Rio de Janeiro Brazil 1.61 4.46 1.35 2.99 9.57 3.60 4.53 8.62 8.22 4.81 5.94 2.99 1.43 1.69 1.69 8.96 1.96 1.78 4.60 4.07
87 Bucharest Romania 5.33 1.35 1.26 3.42 5.59 1.09 1.18 8.18 1.36 3.86 3.74 1.26 2.64 8.20 9.91 8.18 3.36 1.87 4.90 4.00
88 St Petersburg Russia 4.63 4.89 2.38 5.59 4.20 1.43 5.41 1.95 1.53 4.55 3.82 1.87 1.87 2.63 4.55 3.68 2.14 2.52 7.10 3.98
89 Warsaw Poland 5.07 1.43 3.51 5.67 2.21 2.82 4.35 4.72 2.34 1.52 5.06 2.73 4.46 3.06 5.15 6.37 1.96 2.47 5.90 3.97
90 New Delhi India 1.52 9.39 1.09 8.18 4.72 2.13 2.24 9.05 4.12 1.43 1.18 2.13 3.42 1.00 1.61 9.31 2.30 1.09 4.15 3.93
91 Athens Greece 1.87 4.81 3.51 3.77 4.55 3.94 1.97 5.67 5.19 1.26 3.12 4.89 1.95 3.91 1.35 8.36 3.80 3.51 4.50 3.90
92 Cape Town South Africa 1.09 4.12 3.86 8.10 1.35 1.17 1.71 6.19 6.26 8.01 4.44 2.21 4.38 1.77 1.00 2.73 3.44 2.50 5.60 3.82
93 Mumbai India 1.17 9.31 2.38 6.28 4.72 2.13 2.24 8.96 4.21 3.42 5.06 1.09 3.60 1.51 1.95 8.44 2.45 1.17 3.50 3.80
94 Sofia Bulgaria 3.68 1.61 6.11 4.38 2.30 1.61 2.50 6.37 2.87 7.40 7.09 1.17 3.16 5.71 5.67 6.97 2.66 1.69 4.50 3.78
95 Santiago Chile 1.69 3.86 2.04 3.16 6.97 2.73 1.00 8.27 1.71 1.69 2.76 3.77 3.08 1.34 1.78 5.67 5.46 4.82 5.50 3.65
96 Buenos Aires Argentina 1.26 1.17 2.13 6.28 6.02 1.87 1.71 6.97 8.13 6.19 1.71 4.72 1.09 1.26 1.26 7.75 2.83 1.61 4.30 3.63
97 Medellin Colombia 2.35 1.26 4.67 4.87 9.39 4.46 1.21 9.22 2.89 4.72 3.74 2.78 1.61 1.60 2.17 2.47 3.35 3.04 5.50 3.62
98 Monterrey Mexico 3.86 1.78 4.29 1.26 4.03 3.08 1.44 8.88 2.16 4.29 1.00 1.52 2.04 2.29 2.73 7.84 3.26 3.95 5.70 3.54
99 Riyadh Saudi Arabia 1.33 1.09 7.14 2.82 1.78 1.52 1.79 2.13 1.00 2.99 1.00 3.42 1.00 1.09 1.52 9.13 5.54 7.49 5.50 3.47
100 Mexico City Mexico 4.84 1.94 1.17 1.26 4.03 3.08 1.44 8.10 1.27 6.54 1.00 3.15 2.82 2.11 2.30 7.40 3.90 2.33 4.20 3.19

METHODOLOGY

To create the index, we first studied over 500 cities worldwide, looking at countries with very high and medium development, as defined by the Human Development Index. We also looked at those cities which appear on the UN prosperity list and the European Commission’s Digital City Index. We then analysed these 500 cities for 19 factors relating to smart cities to determine the final list of 100, aiming to cover a wide range of regions, and prioritising capitals, financial centres and other points of interest wherever possible. We did not analyse really new cities that are planned to be the smartest in the world but are not yet fully finished or not widely known (e.g. Masdar City) due to lack of data.

Every city in the final index should be praised for their efforts as it showcases not only those cities which are at the forefront of smart urban growth, but also those which are making impressive strides towards a digitalized infrastructure. Therefore, cities at the bottom of the ranking should not be interpreted as ‘not smart’, but rather that they are newly emerging smart cities with room to grow and improve.

We analysed each city according to the following categories to create the final score; Transport and Mobility, Sustainability, Governance, Innovation Economy, Digitalization, Living Standard and Expert Perception.

To create the final score we ranked the raw data from highest to lowest value and then we awarded a standard score based on their ranking in the following manner:

Each factor is scored from 1 – 10, the higher the score, the better. Below you can find a description of how each factor was researched.

Smart Parking

  • Percentage of people owning cars (city). Source: local census reports, Eurostat NUTS 2 statistical level data
  • Number of parking spaces in city center per klm2
  • Smartphone penetration. Sources: local reports, online databases
  • Availability of parking apps and usage penetration


Car Sharing Services

  • Estimation of the car sharing industry fleet (number of cars) in the city with respect to the city’s population. Sources: local reports, official sites of car2Go, GoGet, Zipcar, DriveNow, Communauto, Car4away, Autonapůl, LetsGo, GreenMobility, Autolib’, GoCar, Enjoy, XXImo, Bluemove, Sunfleet, Mobility Carsharing and Flinkster.
  • Population data from Google


Traffic

  • Levels of congestion. Sources:TomTom Traffic index, INRIX traffic scorecard (adjusted to TomTom), Google traffic (adjusted to TomTom).


Public Transport

  • Public transport satisfaction percentage. Sources: local reports, European Commission report


Clean Energy

  • Percentage of electricity production from renewable sources. Source: International Energy Statistics report


Smart Building

  • Research centers: Investment to research and development (percentage of GDP). Source: Global Innovation Index 2017 (report)
  • Efficiency of buildings: GDP per unit of energy use. Source: Global Innovation Index 2017 (report)


Waste Disposal

  • Percentage of waste landfilled. Sources: local reports, United Nations


Environment Protection

  • Green House Gases emission per capita. Source: United Nations
  • CO2 Emissions per capita. Source: United Nations.
  • Adjusted to population. Source: Population data from Google.


Citizen Participation

  • Election turnout for parliament (for Hong Kong latest local elections), percentage. Source: International Institute for Democracy and Electoral Assistance. Where no parliament exists, local elections participation rate was used.


Digitalization of Government

  • Digital Infrastructure Rank. Source: Digital City Index (supported by the European Commision)
  • Traffic of local government sites as a percentage of the population.


Urban Planning

  • Rank according to percentage of green public areas in the city. Source: Data from city records and satellite data (Google)


Education

  • PCs per 1000 population. Source: Online databases and local reports.
  • Information technologies development index (Measuring the Information Society Report). Source: International Telecommunications Union
  • Number of universities the country has in the top university list, country level. Source: World University Rankings 2016
  • Number of universities in the top 10 list, city level. Source: World University Rankings 2016 
  • Number of students in top 3 universities from the list, city level. Source: World University Rankings 2016
  • Adjusted to city population, country population (data from Google)


Business Ecosystem

  • Source: Global Innovation Index
  • Number of startups as registered on Angel.co
  • Adjusted to population (Google)


4G LTE

  • Mbs, Speed Test Global Index (mobile). Source: Online Speed Test


Internet Speed

  • Download Mbs, Speed Test Global Index (fixed broadband). Source: Online Speed Test Global Index
  • Download Mbs, Source: Ookla
  • Download Mbs, Source: Publicly available data from the Digital City Index


Wi-Fi Hotspots

  • Free Wi-Fi hotspots (estimate) Sources: Online Wi-Fi databases
  • Adjusted to the city area (from Google).


Smartphone Penetration

  • Smartphone penetration (country). Source: local reports, online databases


Living Standard

  • Average sum spent on (Fast food, Restaurant, Clothes, Rent, Transportation). Source: Expanistan
  • Average Net Salary. Source: Average salary survey data
  • Adjusted to the GDP per capita levels. Source: World Bank Data


Expert Perception

  • 20,000 technology and urban planning journalists were asked to rate how smart each city was. Source: poll, only on top 100 cities.