The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis
Abstract
:1. Introduction
2. Smart City Benchmarking
3. Methodology and Database
3.1. SOM
3.2. Social Network Analysis
3.3. Database and Indicators
Ten smart cities in China | |
---|---|
Wuhan | Chengdu |
Shanghai | Hangzhou |
Beijing | Wuxi |
Dalian | Guangzhou |
Tianjin | Shenzhen |
- Communication: The validity of any city’s claim to be smart has to be based on information and communication technologies (ICTs) [39]. Smart city forerunners such as San Diego, San Francisco, Ottawa, Brisbane, Amsterdam, Kyoto, and Bangalore are famous for their communication infrastructure, such as mobile technologies, connected devices and network platforms [19]. In this research, two indicators—Internet users (households) and Mobile phone users (households)—are adopted. Some local governments in China started to apply e-government or e-democracy by discussing with the public through social network services. However, this communication function is not applied by all the select smart cities in this research. Besides, some online discussion platforms could not get sufficient public engagement due to the limitations on discussion topics. Therefore, the e-government or e-democracy indicator will not be considered in this research.
- Business: Smart cities do not seek for economic growth only. However, it is undeniable that sustainable economic growth and knowledge-based industries are two major components of smart cities [13,38]. We use the per capita GDP and the proportion of GDP tertiary industry account for to measure the business factor of the selected cities. The per capita GDP is selected in this research as the general GDP does not reflect the public benefit. The proportion of GDP tertiary industry accounts for was selected as the tertiary industry (service industry) could represent the business innovation capacity in China. The GDP of primary and secondary industry are not adopted in this research, as both of these industries are still in the extensive management stage in China. Besides, cities with a high proportion of primary or secondary industry, e.g., Anshan, could not be selected into smart city groups because of the unfriendly living environment. Many cities in China have been operating for mining resources or developing key industries since the 1950s. The power of a factory owner within these cities could be larger than the mayor. However, these cities may lose vitality once they run out of nature resources.
- Human capital: Human capital issue, such as information consumption, life-long learning, cultural facilities, etc., is also a key component for smart cities [13,38]. A smart city should attract young, well-educated professionals and the city should be a sign of cultural activity and diversity [56]. The integrated knowledge-intensive activities provided by smart cities should encourage knowledge sharing and innovation [57]. In this research, the proportion of tertiary industry employment indicator is adopted for representing “knowledge”. The Public Library Collection (per one hundred persons) indicator is also adopted to represent information consumption and life-long learning. The Public Library Collection (per one hundred persons) indicator is a common one adopted by previous scholars [4,13,35].
- Environment: Environmental issues are related to urban growth [28]. We are going to use the residential water consumption, the urban and rural residents electricity consumption and the per capita green area (square meters/person) to measure the environmental factor of smart cities in China, which are also adopted by former researchers [3,35,36]. The Pollution Index in China is not taken into account as its authority has been questioned.
- Public Service. The public service issues are selected in this research for two reasons: First, the e-government is a key component of smart cities. Second, a smarter city should manage natural resources, ICT infrastructures and other assets wisely by providing cross-agency visibility of planned interventions [26]. Three factors: Financial investment in science (ten thousand yuan); Per urban capita road area (square meters); and Financial investment in education (ten thousand yuan) are adopted in the research. Table 2 lists all the indicators of the five dimensions.
Dimensions | Indicators |
---|---|
Communication | Internet users (households) |
Mobile phone users (households) | |
Business | The proportion of GDP tertiary industry accounts for |
The per capita GDP (yuan) | |
Public service | Financial investment in science (ten thousand yuan) |
Per urban capita road area (square meters) | |
Financial investment in education (ten thousand yuan) | |
Human capital | The proportion of tertiary industry employment |
Public Library Collection (Every one hundred people) | |
Environment | Residential water consumption (ton) |
Urban and rural residents electricity consumption (10,000 KWH) | |
Per capita green area (square meters / person) |
4. Patterns and Dynamics of Smart Cities in China: 2005–2010
SOM parameters in this research | |||
---|---|---|---|
Sigma Max | 10 | Learning Rate Min | 0.01 |
Sigma Min | 2 | Iterations | 100 |
Sigma Decreasing Rate | 0.1 | Number of X Neurons | 20 |
Learning Rate Max | 0.1 | Number of Y Neurons | 20 |
Location | Cities and Year |
---|---|
15:8 | Wuhan 2005 |
17:8 | Shanghai 2007 |
17:9 | Beijing 2007 |
14:10 | Shanghai 2008 |
16:10 | Shanghai 2009 |
18:10 | Dalian 2005; Beijing 2009; Beijing 2010 |
15:11 | Shanghai 2010 |
19:11 | Beijing 2006; Wuhan 2007 |
15:12 | Beijing 2008 |
17:12 | Tianjin 2005; Wuhan 2006 |
19:12 | Dalian 2006; Chengdu 2006 |
18:13 | Hangzhou 2005; Tianjin 2007; Hangzhou 2007; Chengdu 2007; Hangzhou 2008; |
19:13 | Chengdu 2008; Tianjin 2009; Hangzhou 2009 |
17:14 | Tianjin 2006; Hangzhou 2006 |
18:14 | Wuxi 2006; Guangzhou 2007; Shenzhen 2007; Wuxi 2007; Dalian 2007; Guangzhou 2008; Shenzhen 2008; Tianjin 2008; |
19:14 | Chengdu 2005; Wuhan 2008; Tianjin 2010; Hangzhou 2010; Wuxi 2008; Dalian 2008; Guangzhou 2009; Shenzhen 2009; |
18:15 | Wuhan 2009; Wuxi 2009; Dalian 2009; Chengdu 2009; Guangzhou 2010; Shenzhen 2010; Wuhan 2010; Wuxi 2010; |
19:15 | Beijing 2005; Shanghai 2005; Guangzhou 2005; Shenzhen 2005; Wuxi 2005; Shanghai 2006; Guangzhou 2006; Shenzhen 2006; |
18:16 | Dalian 2010; Chengdu 2010 |
5. Conclusions and Recommendation
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Lu, D.; Tian, Y.; Liu, V.Y.; Zhang, Y. The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis. Sustainability 2015, 7, 7604-7621. https://doi.org/10.3390/su7067604
Lu D, Tian Y, Liu VY, Zhang Y. The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis. Sustainability. 2015; 7(6):7604-7621. https://doi.org/10.3390/su7067604
Chicago/Turabian StyleLu, Dong, Ye Tian, Vincent Y. Liu, and Yi Zhang. 2015. "The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis" Sustainability 7, no. 6: 7604-7621. https://doi.org/10.3390/su7067604