Measuring the effectiveness of the city from "smart city" indicators. Case Study: Zanjan

Document Type : Research Paper


Department of Geography and Urban Planning, Faculty of Humanities, University of Mohaghegh Ardabili, Ardabil, Iran


Cities play a vital role in the lives of the vast majority of people, yet face great challenges beyond ‎regions, nations and continents, and most cities are the main drivers of change. Recent ‎technological developments have renewed belief in the positive impacts of ICT and other ‎innovative technologies in the city. The combination of smart solutions (active technology) to ‎address major social challenges and focus on the city as key drivers of change has led to the ‎concept of smart city. Finally, scientific studies on smart city development readiness are largely ‎limited to developed countries. And the literature on smart city readiness in developing countries ‎is at an early stage and needs more empirical support. The transformation of a city into a smart ‎city requires considerable efforts by political representatives, managers, residents, entrepreneurs, ‎as well as its various communities. The concept of smart city is evolving rapidly, and the ‎attention of the world as a promising response to the challenge of urban sustainability in large ‎and small cities. Given the different structure of Iranian cities, this study seeks to localize the ‎indicators and analyze their interactions for any planning and management.‎

‎ Methodology
The study of the components of smart city in Zanjan deals with 6 components, 30 benchmarks ‎and 100 standard indices designed in scientific societies around the world. By method, the ‎present study is a descriptive field type. In order to carry out the research, the research ‎components, criteria and indices were extracted from the theoretical foundations of the research ‎and then prioritized by in-depth experts. Library and field methods have been used to collect the ‎required information. Due to the lack of familiarity of most experts with the concepts of smart ‎city, only experts in the statistical population who were familiar with the concept of smart city ‎were included. The sampling method was theoretical saturation and by this method in 21 samples ‎we achieved our goals. In this study, first, the importance and impact of indicators on the ‎intelligence of cities were measured and analyzed by SPSS. Then, the extent of impact as well as ‎the interactions between the components, criteria and indices were measured and analyzed with ‎Micmac software. Because interviews and questionnaires have been used, the research is a ‎composite (sequential exploratory model). And the composition is in the concluding phase. Both ‎questionnaires were used by experts in this field.‎

Results and discussion
In the first part, after determining the components, criteria and indicators affecting the ‎intelligence of cities and in line with the main objectives of the research, determining the ‎importance and weight of each component, criteria and indicators. Finally, 30 criteria with 100 ‎indicators were finalized and these indicators were prioritized by experts. The data were ‎analyzed by SPSS software using Friedman test and the significance of each criterion and index ‎was determined and prioritized. The three components of smart mobility, smart governance, and ‎smart economy with weights of 0.01577,‎‏ ‏‎0.01394 and 0.01381 are the first to third priority, ‎respectively, and have the highest weight and importance in smart cities. The criteria for smart ‎mobility component include technology infrastructure, national, local access and sustainable ‎mobility, which are prioritized with weights 0.01657, 0.01619, 0.01609 and 0.01424, ‎respectively. Two criteria of smart governance components including public-social services and ‎transparent governance with weights of 0.01636 and 0.01153 were significant. Also the criteria ‎of smart economy component were international interactions, productivity, innovation, ‎entrepreneurship, economic image of the city and labor market flexibility with weights 0.01601, ‎‎0.01597, 0.01544, 0.01455, 0.01161 and 0.00928 respectively. In the analytical analysis of the ‎findings, the indicators of Internet penetration, municipality planning strategy and e-government ‎access have the weights of 0.01694, 0.01687 and 0.01684, respectively.‎
In the second part, we have used Micmac software to analyze the interaction of criteria. ‎In order to make the results more realistic and realistic, the analysis software was created in the ‎Micmac Matrix software and adjusted in 30 different criteria to 30 * 30 dimensions. Based on ‎the matrix output, the 10 criteria that had the most direct impact on the system in order of rating ‎are: 1- Technology infrastructure (677) 2- Citizens empowerment (655) 3- Transparent ‎governance (610) 4- Citizen participation (519) 5- Cultural facilities (440) 6- Sustainable and safe ‎transportation (440) 7- Lifelong learning (429) 8- Tourism attraction (406) 9- Educational ‎facilities (395) 10- National access (395). The 10 criteria that have a direct impact on city ‎intelligence are, respectively, priority and priority; 1- Citizen competence and competence (542) ‎‎2- City pollution level (508) 3- Urban productivity (497) 4- Entrepreneurship in the city (497) 5- ‎Transparent Governance (463) 6- City Tourism Attraction (440) 7- International Interaction (440) ‎‎8- Preservation of Environment (418) 9- Educational Facilities (384) 10- Citizen Participation ‎‎(384).‎

‎ Conclusion
The analysis of the results of the first section showed that the relevant components, criteria and ‎indices in the three prioritization of the rankings were almost identical. Thus, the component and ‎criterion that is ranked higher in the ranking also have more important indicators, while the ‎component and the criterion that is in the lower priorities have the least important ones. In the ‎second stage, the analysis of the interaction of criteria on the intelligence of the city is examined. ‎The results of the MicMac method in impact analysis indicate the irregular distribution of criteria ‎in the impact plan. The analysis of the plan and related diagrams show that the variables in the ‎medium of influence are high density and the studied system is unstable. Based on the results of ‎Structural Analysis and Micmac software, the direct and indirect effective criteria were ‎determined. Key variables and strategic indicators were also identified. What is important is that ‎improvement and improvement in each of the dimensions and components of the smart city will ‎be very influential in city intelligence because systematic reviews and evaluations of smart cities ‎do not consider growth in all dimensions. Therefore, any benchmark and component that can be ‎upgraded should be a priority.‎


Main Subjects

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Volume 53, Issue 2
July 2021
Pages 521-543
  • Receive Date: 28 August 2019
  • Revise Date: 01 March 2020
  • Accept Date: 01 March 2020
  • First Publish Date: 22 June 2021