Easy Park

Smart Cities Index 2018


Chez EasyPark, nous misons sur le progrès technologique pour créer des villes intelligentes, plus efficaces et intuitives. Nous pensons qu'un réseau de transport et de communication intégré dans le paysage urbain conduit à des économies de temps, d'énergie et d'argent. La digitalisation ne fait pas que moderniser les villes : elle en améliore la qualité de vie.

Les villes sont confrontées à de nombreux défis en constante évolution. Ces défis, en plus d´être davantage complexes, sont spécifiques à chaque ville. C’est pourquoi, chez EasyPark, nous avons créé le Smart Cities Index 2018 (Indice des villes intelligentes 2018). Avec de nouveaux critères et des résultats actualisés, l’indice fournit des données détaillées sur le fonctionnement et les investissements publics de villes dans le monde entier. Du nombre d'emplacements pour véhicules électriques à la fréquence de transactions des Bitcoin, EasyPark regroupe les données les plus détaillées et précises obtenues à ce jour.

La définition d'une ville intelligente évolue en fonction de l'avancement technologique et des découvertes qui améliorent notre niveau de vie. Dans cette perspective, l´indice de cette année comprend les points suivants dans notre définition de villes intelligentes : affectation de places de « Smart Parking », taux de recyclage, investissement destiné aux infrastructures, emplacements pour véhicules électriques, écosystème d'entreprise et de Blockchain, cybersécurité et indice de développement durable. Pour la deuxième édition de cette étude, nous avons évalué, sur 24 critères, 500 villes dans le monde et nous avons déterminé le classement de 100 villes qui gèrent efficacement leurs biens et de ressources.

"De plus en plus de personnes migrent vers les villes - 3 millions de personnes par semaine – et cela entraîne l'apparition de nouveaux défis que seule la technologie peut relever. L’un d'eux, la congestion du trafic est principalement causée par les automobilistes à la recherche de places de stationnement. Chez EasyPark, nos solutions intelligentes de stationnement facilitent la vie urbaine", a déclaré Johan Birgersson, PDG du groupe EasyPark.

Le Smart Cities Index 2018 (Indice des villes intelligentes 2018) est une analyse avancée des villes les plus sophistiquées au monde en matière de technologie. Il offre une perspective ville par ville des principaux facteurs clés définissant une ville intelligente, y compris le nombre de points d'accès Wi-Fi, de bâtiments intelligents et, bien sûr, de stationnements intelligents. Tandis que certains pays sont déjà très avancés dans ce domaine, nous observons de nombreuses initiatives pour créer des lieux de vie plus sains, plus propres et plus efficaces à l´échelle mondiale.

  •  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 Cyber
Security
Living
Standard
Expert
Perception
 
# CITY
COUNTRY
Smart Parking
 Car sharing services
 Traffic
Public Transport
Clean Energy
Smart Building
Waste Disposal
Environment Protection
Sustainability Index
Citizen Participation
Digitalization of Goverment
Urban Planning
Education
Business ecosystem
Blockchain Ecosystem
4G LTE
Internet Speed
Wifi Hotspots
Smartphone Penetration
Cyber security
Living Standard How The City Is Becoming Smarter
RANK/
SCORE
1 Odense Denmark 4.75 2.80 9.48 2.88 8.47 6.78 8.18 6.55 8.58 9.38 8.35 4.53 9.33 6.48 1.31 7.90 7.23 1.60 9.70 7.15 8.50 6.46 6.49
2 Aalborg Denmark 2.58 2.05 8.73 2.88 8.47 6.78 8.18 6.33 8.58 9.23 5.95 7.08 8.58 7.38 1.31 8.20 7.83 1.83 9.70 7.15 7.98 6.46 6.25
3 Oulu Finland 7.23 2.13 8.73 6.70 7.86 3.63 5.24 3.33 9.33 4.31 5.05 7.08 8.50 8.13 7.05 7.45 6.40 2.80 8.87 8.73 7.00 3.00 6.20
4 Koge Denmark 3.10 2.95 10.00 2.88 8.47 6.78 8.18 6.85 8.58 9.69 7.08 2.05 9.70 7.23 1.31 6.70 5.73 1.45 9.70 7.15 7.45 3.00 6.18
5 Strasbourg France 3.25 6.10 6.33 5.65 3.82 8.35 6.29 9.25 6.55 4.62 7.68 7.08 8.05 4.98 7.05 8.28 9.10 2.58 3.65 6.70 5.20 6.46 6.15
6 Bordeaux France 3.70 6.03 4.60 5.65 3.82 8.35 6.29 9.33 6.55 4.46 8.80 9.55 8.88 5.73 7.05 4.53 4.38 1.08 3.65 6.70 5.28 3.00 5.93
7 Turku Finland 3.33 2.20 9.48 6.70 7.86 3.63 5.24 3.40 9.33 5.15 4.15 4.53 8.80 7.68 7.05 8.35 7.68 2.65 8.87 8.73 6.78 7.15 5.90
8 Nice France 4.15 3.63 5.73 5.65 3.82 8.35 6.29 9.03 6.55 4.38 5.65 2.28 6.78 8.50 7.05 7.98 8.88 3.18 3.65 6.70 4.53 3.00 5.80
9 Vantaa Finland 4.00 5.88 9.85 6.70 7.86 3.63 5.24 3.25 9.33 3.85 8.88 4.53 7.75 5.58 7.05 6.03 4.30 2.13 8.87 8.73 5.88 3.00 5.79
10 Joensuu Finland 4.98 2.73 8.73 6.70 7.86 3.63 5.24 4.00 9.33 3.92 6.93 4.53 9.63 6.25 7.05 4.98 3.48 1.68 8.87 8.73 6.70 6.46 5.79
11 Nantes France 2.35 4.23 7.53 5.65 3.82 8.35 6.29 9.10 6.55 4.77 8.43 5.50 7.45 6.63 7.05 6.33 6.55 1.00 3.65 6.70 5.05 3.00 5.78
12 Jyväskylä Finland 1.68 2.43 7.98 6.70 7.86 3.63 5.24 3.55 9.33 4.15 8.20 4.53 9.10 5.95 7.05 8.05 7.15 1.30 8.87 8.73 6.63 3.00 5.71
13 Lahti Finland 7.08 2.58 4.23 6.70 7.86 3.63 5.24 3.63 9.33 6.46 5.80 4.53 9.03 6.10 7.05 4.30 2.58 1.75 8.87 8.73 6.93 3.00 5.70
14 Hämeenlinna Finland 2.13 2.88 10.00 6.70 7.86 3.63 5.24 4.08 9.33 4.77 9.40 2.05 9.48 6.48 7.05 4.68 2.88 1.15 8.87 8.73 7.60 6.46 5.62
15 Verona Italy 3.78 1.90 9.48 2.28 6.87 7.38 7.20 8.13 4.30 7.23 7.30 7.08 7.38 3.25 7.05 7.68 7.30 3.03 4.63 3.93 4.23 3.38 5.58
16 Perugia Italy 3.93 2.28 9.48 2.28 6.87 7.38 7.20 8.28 4.30 7.38 3.10 4.53 8.65 1.98 7.05 5.58 4.75 2.43 4.63 3.93 4.75 1.00 5.20
17 Florence Italy 2.43 6.40 7.23 2.28 6.87 7.38 7.20 7.83 4.30 6.62 8.28 4.98 6.63 3.40 7.05 4.00 3.03 4.08 4.63 3.93 3.63 6.46 5.12
18 Bari Italy 2.28 1.83 6.63 2.28 6.87 7.38 7.20 7.98 4.30 6.85 5.28 2.13 7.60 4.08 7.05 8.43 8.58 1.53 4.63 3.93 4.45 3.00 5.11
19 Catania Italy 2.20 4.68 5.73 2.28 6.87 7.38 7.20 8.05 4.30 6.92 4.83 1.23 7.00 2.80 7.05 7.15 6.78 2.50 4.63 3.93 4.83 6.46 4.97
20 Ancona Italy 1.45 2.65 8.73 2.28 6.87 7.38 7.20 8.80 4.30 6.46 5.50 4.53 9.18 2.13 7.05 2.95 2.05 1.38 4.63 3.93 4.60 3.00 4.86
21 Genova Italy 3.48 3.40 7.98 2.28 6.87 7.38 7.20 7.68 4.30 5.77 5.43 2.50 4.83 1.53 7.05 4.23 3.40 2.88 4.63 3.93 4.30 1.08 4.64
22 Copenhagen Denmark 6.03 7.23 8.73 2.88 8.47 6.78 8.18 5.88 8.58 9.46 9.25 8.80 6.40 9.18 3.48 5.58 4.53 5.43 9.70 7.15 6.10 9.23 6.75
23 Singapore Singapore 7.00 6.85 3.78 9.93 1.46 6.40 9.09 3.93 1.98 10.00 3.25 3.33 1.15 8.88 7.67 9.93 10.00 9.85 9.77 10.00 8.35 10.00 6.13
24 Stockholm Sweden 6.85 7.90 6.33 4.60 8.93 5.05 8.71 9.70 10.00 9.23 10.00 7.45 5.05 5.80 3.33 6.78 7.00 6.18 8.03 7.75 7.75 10.00 6.95
25 Zurich Switzerland 3.18 7.53 4.60 8.43 8.93 7.53 8.94 9.55 8.13 2.00 8.58 4.83 7.68 8.95 4.10 8.88 8.95 5.13 7.73 7.98 9.63 6.46 6.67
26 Boston United States 6.93 8.43 6.33 6.85 2.45 9.03 5.76 2.58 4.83 3.77 4.98 4.68 7.90 10.00 9.69 5.35 8.35 6.18 6.97 6.18 8.88 9.23 6.81
27 Tokyo Japan 9.78 8.80 2.35 9.25 3.97 8.50 3.42 3.18 6.55 2.46 6.40 6.25 2.28 6.78 1.47 1.83 3.33 9.55 1.76 9.33 3.93 10.00 5.40
28 San Francisco United States 6.10 9.03 2.58 7.53 2.45 9.03 5.76 2.35 4.83 2.85 5.73 1.68 6.93 9.78 10.00 3.18 5.35 8.58 6.97 6.18 8.43 6.46 6.30
29 Amsterdam Netherlands 8.35 8.88 8.88 3.93 1.92 6.85 8.94 3.48 4.90 9.08 9.48 8.43 5.43 9.55 9.22 8.13 6.70 8.13 6.45 7.23 7.38 10.00 6.92
30 Geneva Switzerland 2.80 5.05 3.25 8.58 8.93 7.53 8.94 9.40 8.13 1.69 6.03 5.88 6.70 9.33 3.72 6.55 5.88 4.38 7.73 7.98 9.48 6.46 6.09
31 Melbourne Australia 8.58 6.55 4.23 9.70 2.75 6.33 4.18 1.08 3.18 9.85 3.93 6.40 2.73 2.65 3.64 6.03 2.13 5.95 4.93 9.63 6.40 6.46 5.21
32 Vancouver Canada 5.88 10.00 2.58 7.30 9.24 4.38 3.80 2.20 5.43 4.92 7.90 6.70 7.15 8.80 7.59 9.55 9.18 6.78 7.28 9.93 5.35 7.15 6.65
33 Sydney Australia 7.53 7.83 2.58 9.70 2.75 6.33 4.18 1.00 3.18 9.92 3.70 7.30 2.35 7.08 7.28 9.18 5.80 7.90 4.93 9.63 6.03 6.46 5.77
34 Berlin Germany 9.63 9.63 5.73 3.70 5.73 10.00 9.92 3.70 7.53 8.15 2.65 8.20 3.03 3.48 9.61 3.55 5.28 8.95 6.45 5.65 7.53 7.23 6.71
35 Hamburg Germany 8.80 8.73 4.23 3.70 5.73 10.00 9.92 4.23 7.53 6.77 3.25 8.58 4.23 8.20 9.30 2.20 3.10 5.28 6.45 5.65 7.08 7.15 6.52
36 Gothenburg Sweden 7.90 7.60 8.73 4.60 8.93 5.05 8.71 9.85 10.00 8.77 9.85 5.58 6.10 7.45 2.55 8.58 9.25 4.15 8.03 7.75 8.13 9.23 7.24
37 Montreal Canada 10.00 9.55 5.73 7.30 9.24 4.38 3.80 1.75 5.43 3.31 6.70 9.85 4.53 8.28 7.52 9.33 8.80 6.93 7.28 9.93 5.80 9.23 6.84
38 London United Kingdom 9.85 9.70 2.35 6.70 4.74 9.25 7.50 5.73 5.73 4.08 1.08 8.95 1.98 5.05 9.77 3.85 6.25 9.78 5.54 4.53 4.00 9.23 5.99
39 Tel Aviv Israel 2.05 6.70 3.48 4.83 1.53 5.88 2.74 4.30 2.35 3.23 7.83 8.88 6.85 8.73 3.95 2.05 3.70 7.30 6.45 2.35 2.65 10.00 4.43
40 Paris France 7.15 9.93 2.88 5.65 3.82 8.35 6.29 7.53 6.55 5.31 9.33 9.48 3.55 8.58 9.61 7.90 8.43 8.73 3.65 6.70 3.70 6.46 6.61
41 Toronto Canada 9.25 9.25 4.98 7.30 9.24 4.38 3.80 1.45 5.43 5.77 2.95 9.93 3.40 7.90 8.22 7.53 5.50 8.05 7.28 9.93 5.43 9.23 6.42
42 Seoul South Korea 6.33 9.78 2.35 9.25 1.38 5.28 8.71 2.43 6.78 6.23 3.85 5.28 1.60 6.33 1.00 7.60 7.45 9.03 7.58 4.68 3.25 9.23 5.30
43 Luxembourg Luxembourg 1.83 5.13 4.23 8.43 5.81 5.13 8.26 2.73 7.68 9.85 7.23 7.98 9.78 7.75 7.05 4.53 2.43 3.48 9.92 6.78 9.78 6.46 5.98
44 Helsinki Finland 6.48 5.50 7.23 6.70 7.86 3.63 5.24 3.85 9.33 5.38 9.55 2.80 5.20 7.15 8.14 8.65 7.90 5.73 8.87 8.73 5.65 9.23 6.21
45 New York United States 9.70 7.45 3.48 7.53 2.45 9.03 5.76 1.60 4.83 2.54 8.13 9.40 3.10 9.03 9.92 3.70 6.33 10.00 6.97 6.18 8.65 7.15 6.71
46 München (Munich) Germany 6.63 9.33 4.98 3.70 5.73 10.00 9.92 4.38 7.53 7.69 2.13 8.50 4.90 3.03 8.91 4.08 5.95 4.68 6.45 5.65 6.33 9.23 6.36
47 Düsseldorf Germany 5.35 7.75 7.98 3.70 5.73 10.00 9.92 5.05 7.53 8.00 1.75 1.38 7.30 7.00 7.05 7.00 9.33 4.30 6.45 5.65 7.83 6.46 6.63
48 Västerås Sweden 1.23 3.18 7.98 4.60 8.93 5.05 8.71 9.93 10.00 9.00 9.03 4.53 9.93 7.98 2.55 9.25 9.70 1.98 8.03 7.75 9.18 6.46 6.78
49 Washington, DC United States 6.25 8.58 5.73 1.00 2.45 9.03 5.76 2.58 4.83 3.62 9.70 9.78 8.28 9.85 9.38 3.55 6.03 9.10 6.97 6.18 8.80 6.46 6.86
50 Bayreuth Germany 1.08 1.68 7.23 3.70 5.73 10.00 9.92 4.83 7.53 7.69 7.75 2.58 5.95 7.30 7.05 2.13 2.65 1.23 6.45 5.65 9.25 3.38 5.67
51 Hannover Germany 2.95 7.15 5.73 3.70 5.73 10.00 9.92 5.20 7.53 7.15 7.53 6.10 8.35 4.83 7.05 4.53 6.85 2.95 6.45 5.65 8.20 3.00 6.48
52 Köln (Cologne) Germany 6.78 8.65 3.78 3.70 5.73 10.00 9.92 4.60 7.53 8.00 1.98 1.60 5.65 9.10 8.14 6.48 9.03 4.53 6.45 5.65 7.90 3.00 6.60
53 Vienna Austria 8.50 8.50 4.60 7.30 9.54 5.95 10.00 5.65 7.98 8.54 1.98 7.83 3.85 5.50 7.05 8.95 8.13 7.08 10.00 7.45 8.58 9.23 6.69
54 Frankfurt am Main Germany 5.20 8.05 6.33 3.70 5.73 10.00 9.92 4.75 7.53 8.38 1.60 5.35 6.55 8.43 8.99 3.18 4.60 5.50 6.45 5.65 6.85 9.23 6.46
55 Oslo Norway 9.10 7.08 4.98 7.90 9.92 1.75 8.18 5.80 9.70 8.15 7.38 7.38 5.58 2.43 3.87 9.85 9.48 5.05 9.17 9.10 9.55 9.23 6.84
56 Philadelphia United States 4.60 5.65 8.73 8.13 2.45 9.03 5.76 2.13 4.83 2.92 6.25 9.33 5.28 9.70 8.45 5.43 8.50 6.25 6.97 6.18 9.40 3.00 6.61
57 Chicago United States 9.48 4.98 7.23 9.03 2.45 9.03 5.76 1.90 4.83 3.46 5.20 10.00 4.15 9.40 9.46 4.83 7.60 9.63 6.97 6.18 9.33 3.00 6.95
58 Dubai United Arab Emirates 9.18 5.73 5.73 8.05 1.15 2.35 2.59 1.15 1.38 1.38 4.30 3.25 1.53 1.38 3.09 7.08 2.50 8.65 9.32 2.13 6.25 9.54 4.32
59 Trondheim Norway 6.18 4.30 9.48 7.90 9.92 1.75 8.18 6.25 9.70 7.54 8.73 4.53 9.25 3.78 1.78 9.40 8.05 2.35 9.17 9.10 9.70 3.00 6.70
60 Helsingborg Sweden 1.38 3.48 7.98 4.60 8.93 5.05 8.71 10.00 10.00 8.85 9.10 2.05 10.00 9.93 2.55 4.75 4.45 2.05 8.03 7.75 8.73 7.15 6.39
61 Ottawa Canada 9.55 5.20 6.33 7.30 9.24 4.38 3.80 1.98 5.43 7.00 6.18 9.70 5.13 6.55 3.17 7.00 4.90 4.98 7.28 9.93 5.58 9.54 6.25
62 Perth Australia 7.38 1.23 6.63 9.70 2.75 6.33 4.18 1.30 3.18 9.31 3.33 3.25 3.48 6.93 2.71 4.98 1.53 5.58 4.93 9.63 7.15 6.46 4.98
63 Dublin Ireland 5.50 5.95 1.60 5.05 4.81 6.03 7.20 3.85 8.20 5.23 1.15 4.60 6.48 8.65 8.14 2.80 4.68 6.33 9.92 7.38 8.28 6.69 5.45
64 Stavanger Norway 1.53 4.45 7.98 7.90 9.92 1.75 8.18 6.63 9.70 7.31 7.98 4.53 9.85 4.45 1.78 10.00 9.78 2.20 9.17 9.10 10.00 3.00 6.42
65 Manama Bahrain 1.00 2.35 9.48 2.28 1.00 1.00 2.13 2.05 1.08 6.31 7.60 4.53 8.73 1.75 1.00 1.30 1.38 3.85 7.58 1.00 6.55 0.00 3.47
66 Aarhus Denmark 2.65 3.33 8.73 2.88 8.47 6.78 8.18 6.10 8.58 9.62 6.70 2.28 7.83 7.60 1.31 6.10 5.05 3.25 9.70 7.15 7.23 6.46 5.93
67 Los Angeles United States 9.93 7.38 1.38 9.40 2.45 9.03 5.76 1.83 4.83 2.85 4.53 3.48 4.08 9.48 9.84 5.28 7.98 9.93 6.97 6.18 8.95 9.23 6.59
68 Stuttgart Germany 7.45 8.28 3.78 3.70 5.73 10.00 9.92 5.13 7.53 8.31 1.75 4.90 7.23 5.65 8.91 3.78 5.65 4.00 6.45 5.65 7.68 6.46 6.53
69 Auckland New Zealand 7.68 4.15 2.88 9.85 9.62 2.88 3.87 2.80 7.68 5.46 5.35 9.33 2.95 4.23 2.01 9.03 8.20 8.35 5.31 9.18 3.33 6.46 5.52
70 Bergen Norway 8.13 6.18 9.63 7.90 9.92 1.75 8.18 6.10 9.70 7.54 8.65 5.50 7.98 6.85 1.78 9.70 8.73 3.10 9.17 9.10 9.93 6.46 7.06
71 Espoo Finland 2.88 5.80 9.63 6.70 7.86 3.63 5.24 4.53 9.33 5.00 8.95 2.05 8.13 8.05 7.05 7.38 6.18 2.28 8.87 8.73 5.95 6.46 5.96
72 Madrid Spain 7.98 8.20 7.53 5.05 6.03 5.73 5.24 7.23 2.88 7.08 5.13 6.48 1.90 5.43 7.05 7.90 9.63 8.95 5.08 2.73 2.50 6.46 5.74
73 Osaka Japan 8.28 8.95 7.23 9.03 3.97 8.50 3.42 4.15 6.55 2.46 7.00 6.18 3.78 2.05 1.47 1.90 3.63 7.45 1.76 9.33 4.98 9.23 5.44
74 Barcelona Spain 5.65 6.93 4.60 5.05 6.03 5.73 5.24 7.30 2.88 6.62 4.53 1.15 2.13 5.35 8.91 2.35 3.18 6.03 5.08 2.73 2.05 10.00 4.60
75 Abu Dhabi United Arab Emirates 6.40 4.90 9.03 8.58 1.15 2.35 2.59 1.53 1.38 1.08 7.15 7.68 3.63 4.90 2.16 5.65 1.98 4.60 9.32 2.13 6.18 9.54 4.55
76 Birmingham United Kingdom 3.03 6.33 7.23 6.70 4.74 9.25 7.50 6.18 5.73 2.62 1.53 5.80 5.73 9.63 8.29 3.25 5.20 3.55 5.54 4.53 4.08 3.00 5.49
77 Bochum Germany 1.75 1.75 8.95 3.70 5.73 10.00 9.92 5.43 7.53 8.00 5.58 1.00 9.55 8.35 7.05 5.20 7.38 1.90 6.45 5.65 9.10 6.46 6.34
78 Taipei Taiwan 8.05 1.60 2.35 10.00 1.61 4.08 9.17 2.95 6.55 5.92 7.45 9.63 2.50 5.13 3.56 9.18 8.28 6.70 7.35 7.30 9.03 9.23 5.78
79 Doha Qatar 1.15 1.53 6.33 4.60 1.00 2.58 1.53 1.23 1.45 1.00 2.20 6.55 3.70 1.15 1.00 5.73 1.90 4.75 8.11 2.95 9.85 0.00 3.32
80 Lyon France 8.95 6.48 5.73 5.65 3.82 8.35 6.29 8.50 6.55 5.08 6.33 9.03 8.43 6.70 8.14 8.80 9.40 4.23 3.65 6.70 4.38 3.00 6.81
81 Milan Italy 6.55 8.13 4.98 2.28 6.87 7.83 7.20 7.15 4.30 5.62 1.83 6.63 4.68 2.35 7.05 7.30 6.93 7.53 4.63 3.93 3.48 9.23 5.41
82 Adelaide Australia 5.73 3.25 6.63 9.70 2.75 6.33 4.18 1.38 3.18 9.54 3.48 2.43 4.60 4.00 2.71 6.40 2.35 2.73 4.93 9.63 7.30 9.23 4.87
83 Brussels Belgium 4.68 7.00 2.88 4.68 4.20 5.88 9.02 4.90 5.05 8.92 1.00 9.18 4.30 4.75 7.05 8.58 7.08 7.68 5.16 4.23 5.50 6.69 5.34
84 Daejeon South Korea 4.30 7.98 8.73 9.85 1.38 5.28 8.71 2.88 6.78 6.08 3.03 1.08 4.38 2.50 1.00 9.48 9.85 7.75 7.58 4.68 3.85 6.46 5.38
85 Lisbon Portugal 5.80 5.58 3.25 4.00 7.94 1.45 2.97 9.78 3.48 2.31 6.55 3.40 7.53 6.18 7.05 3.18 4.15 4.90 3.72 4.83 2.43 6.46 4.76
86 Leeds United Kingdom 4.90 4.00 7.23 6.70 4.74 9.25 7.50 6.40 5.73 3.08 6.85 2.35 6.03 7.83 7.13 6.03 8.65 3.78 5.54 4.53 5.13 3.00 5.85
87 Ljubljana Slovenia 3.40 4.83 9.85 2.35 4.97 1.83 4.25 6.70 7.83 3.46 1.30 7.75 8.20 4.60 7.05 7.23 7.75 5.80 2.36 4.15 3.40 6.46 4.92
88 Tampere Finland 4.83 1.98 9.85 6.70 7.86 3.63 5.24 4.68 9.33 4.54 9.78 7.08 8.95 9.25 7.05 4.60 2.80 3.33 8.87 8.73 6.48 3.00 6.13
89 Hong Kong China 9.40 1.00 3.25 7.38 4.51 1.23 7.28 5.50 3.48 1.15 4.23 7.90 2.43 4.30 7.75 2.58 3.33 8.80 2.21 1.98 3.78 9.23 4.69
90 Turin Italy 1.90 5.35 7.53 2.28 6.87 7.83 7.20 7.45 4.30 6.08 2.05 7.53 5.50 4.68 7.05 5.13 4.00 4.45 4.63 3.93 4.68 7.15 4.99
91 Reykjavik Iceland 3.85 2.50 4.23 8.43 10.00 1.08 3.50 7.00 9.40 8.00 6.78 4.53 9.40 7.53 8.91 9.78 9.93 3.40 5.31 4.75 8.05 9.23 6.06
92 Rome Italy 8.73 8.35 2.35 2.28 6.87 7.83 7.20 6.93 4.30 5.92 1.38 9.10 2.65 5.20 7.05 2.58 1.68 7.60 4.63 3.93 3.55 1.00 5.13
93 Prague Czech Republic 7.83 6.78 6.33 5.65 1.84 2.80 2.82 3.10 5.80 1.85 3.85 7.68 4.45 5.28 3.25 6.18 3.85 8.50 3.12 4.90 2.80 6.46 4.56
94 Vilnius Lithuania 4.38 7.68 6.33 2.95 6.95 2.65 1.91 9.03 5.50 2.15 6.48 2.65 5.88 3.18 7.05 6.85 5.58 4.83 2.36 2.20 3.18 3.00 4.52
95 Marseille France 7.60 4.60 2.35 5.65 3.82 8.35 6.29 7.90 6.55 4.31 2.73 1.45 6.18 3.63 8.91 5.05 4.83 3.93 3.65 6.70 4.90 6.46 5.39
96 Riga Latvia 4.08 3.10 8.73 6.78 8.02 1.98 2.29 9.18 7.90 3.08 8.50 2.80 4.75 4.38 9.22 2.88 2.95 6.55 2.44 4.30 2.88 3.00 4.72
97 Tallinn Estonia 2.50 3.85 4.30 8.43 2.91 1.38 3.12 3.03 7.83 4.08 9.93 8.13 6.25 6.03 8.14 6.25 6.48 5.65 2.66 7.83 3.03 9.54 4.92
98 Moscow Russia 9.03 9.48 1.45 1.15 4.13 4.00 2.06 2.28 2.50 1.54 9.25 6.33 1.08 1.23 9.07 1.68 2.28 9.70 2.59 4.08 1.68 6.54 4.16
99 Panama City Panama 1.98 1.30 5.73 7.30 8.09 4.45 5.24 9.63 1.60 8.23 2.50 8.73 3.33 1.08 1.00 2.28 3.93 5.20 1.08 1.60 1.60 6.46 3.78
100 Budapest Hungary 8.20 7.30 8.88 3.93 1.76 2.13 3.42 8.43 2.73 3.77 4.08 8.35 2.80 2.88 1.54 9.63 9.55 7.98 1.98 3.10 2.58 9.23 4.82
101 Sao Paulo Brazil 8.43 5.43 4.98 1.15 9.39 5.58 1.53 8.20 2.65 8.62 3.63 6.03 1.23 2.20 7.36 1.38 1.30 8.43 1.38 1.53 1.38 3.38 4.47
102 Beijing China 8.65 9.40 1.30 9.03 4.51 5.43 1.83 5.35 3.48 1.00 2.80 5.28 1.75 3.93 8.37 3.63 4.98 9.25 2.21 1.98 1.00 3.00 4.69
103 Bratislava Slovakia 4.23 3.03 7.53 2.43 4.28 1.90 2.29 7.08 6.63 2.85 5.88 7.23 7.08 2.73 7.05 6.70 7.53 5.35 1.91 2.43 2.73 9.23 4.55
104 Naples Italy 1.30 1.45 4.23 2.28 6.87 7.83 7.20 7.38 4.30 5.54 4.75 1.30 5.35 1.90 7.05 5.80 5.13 3.70 4.63 3.93 4.15 3.38 4.38
105 Kuala Lumpur Malaysia 5.43 4.53 3.78 7.60 1.69 2.20 1.00 4.45 2.13 8.77 3.55 7.15 3.18 3.55 2.24 1.45 1.60 8.28 3.04 8.80 2.95 6.46 4.03
106 Shanghai China 8.88 9.85 1.90 9.10 4.51 5.43 1.61 5.28 3.48 1.00 2.43 5.28 1.38 5.88 7.21 2.65 3.55 8.20 2.21 1.98 1.08 9.23 4.60
107 Rio de Janeiro Brazil 5.28 4.38 1.15 2.28 9.39 5.58 1.53 8.58 2.65 6.69 4.90 5.95 1.68 1.30 3.41 1.53 1.75 9.18 1.38 1.53 1.30 6.46 3.94
108 Bucharest Romania 4.45 3.78 1.08 4.60 7.10 2.50 1.53 8.35 4.98 1.23 4.08 8.28 2.88 2.28 1.85 2.43 3.78 7.23 2.82 2.88 2.13 3.00 3.71
109 St Petersburg Russia 5.58 6.25 1.90 4.60 4.13 4.00 2.06 2.65 2.50 1.46 4.60 2.88 2.20 1.83 7.44 4.15 6.63 9.33 2.59 4.08 2.35 6.46 3.94
110 Warsaw Poland 7.30 6.63 3.03 3.78 2.83 3.85 3.42 4.98 5.13 2.31 1.53 8.65 3.93 3.85 3.79 3.93 6.10 6.85 3.04 3.03 2.20 3.38 4.26
111 New Delhi India 4.53 9.10 1.90 9.40 3.21 4.60 1.53 8.73 1.23 3.54 3.40 1.53 1.00 3.10 2.94 2.73 4.08 6.48 1.00 2.58 1.45 9.23 3.76
112 Athens Greece 6.70 4.75 3.03 2.88 4.89 2.50 2.89 6.78 2.20 4.92 1.23 2.95 5.80 1.60 7.05 3.40 1.23 9.48 2.89 1.30 3.10 6.46 3.95
113 Cape Town South Africa 5.95 5.28 3.48 4.83 1.38 1.30 2.44 5.58 1.00 6.23 4.38 3.10 6.33 3.33 2.32 1.00 1.08 6.40 1.23 1.08 1.15 9.23 3.46
114 Mumbai India 3.55 9.18 1.30 9.03 3.21 4.60 1.53 8.88 1.23 3.23 2.28 3.03 1.45 3.70 2.94 1.23 1.45 5.95 1.00 2.58 1.23 9.23 3.52
115 Sofia Bulgaria 5.05 3.70 5.73 2.28 3.29 1.15 1.00 6.48 2.35 2.08 8.13 8.05 4.00 4.53 8.91 8.80 5.43 7.00 1.45 2.80 1.75 3.00 4.24
116 Santiago Chile 3.63 3.93 1.60 4.60 7.03 2.05 1.08 7.75 1.83 1.62 2.35 5.65 1.83 4.15 4.03 1.83 2.73 6.63 2.74 2.28 1.98 6.46 3.27
117 Buenos Aires Argentina 1.60 3.55 1.68 8.05 5.88 3.78 2.44 5.95 1.90 8.46 9.63 5.05 2.58 2.58 3.02 1.15 1.15 7.15 1.83 1.38 2.28 6.46 3.60
118 Medellin Colombia 2.73 1.15 7.23 8.73 9.47 3.70 3.12 9.55 2.05 1.77 4.68 3.55 3.25 1.45 2.78 1.08 1.00 3.63 1.15 1.15 1.53 6.46 3.45
119 Monterrey Mexico 7.75 1.38 2.88 8.65 3.06 4.75 1.76 8.65 1.75 1.31 2.88 4.75 4.98 1.68 2.09 1.98 2.20 7.83 1.61 1.75 1.83 6.46 3.77
120 Riyadh Saudi Arabia 5.13 1.08 6.63 2.28 1.00 2.73 2.66 1.68 1.53 1.00 2.58 1.75 1.30 1.00 1.93 1.60 1.83 7.38 3.80 1.23 5.73 9.23 2.76
121 Mexico City Mexico 9.33 4.08 1.00 6.70 3.06 4.75 1.76 7.60 1.75 2.00 6.10 5.73 2.05 2.95 4.18 3.40 4.23 9.40 1.61 1.75 1.90 6.46 4.11

METHODOLOGY

We researched 500 cities worldwide with medium to high positions in the UN Human Development Index. The cities also rank on the UN prosperity list and the European Commission’s Digital City Index. We aimed to cover a wide range of regions, and prioritised capitals and financial centres.

We analysed the cities for 24 factors that determine a smart city, and then ranked the top 100. While the cities at the top of the index deserve praise, those at the bottom should also be given credit as emerging urban spaces making impressive strides towards integrated information networks.

We collected granular level data across a range of criteria: Transport and Mobility, Sustainability, Governance, Innovation Economy, Digitalisation, Cyber Security, Living Standard and Expert Perception.

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.