تحلیل فضایی شاخص‌های روستای هوشمند در سکونتگاه‌های پیراشهری (مطالعه موردی: روستاهای پیرامون کلان‌شهر تهران)

1. Introduction
Smart villages are rural communities that actively address challenges and create opportunities through diverse initiatives, often incorporating digital technologies, but emphasizing social innovation and sustainable practices. They aim to be self-sufficient, empowering residents (especially youth) using local resources and appropriate technologies to enhance decentralized governance and employment. Smart villages prioritize infrastructure improvements, healthcare, education, and environmental sustainability, and technology acts as a facilitator of development and improved well-being. The smart village approach recognizes the heterogeneity of rural areas, calls for tailored solutions based on specific local contexts and problem-solving strategies, using information and communication technologies to bridge the urban-rural digital divide. Periurban villages in Iran face opportunities and challenges due to their unique geographical location near large cities. Uncontrolled population growth, physical sprawl, and unsustainable resource use have led to issues such as land use conflicts, social and psychological insecurity, demographic imbalances, agricultural hazards, natural hazards, and inconsistent policymaking, all of which hinder sustainable development. The high rate of rural-urban migration highlights the consequences of neglecting rural areas and poses significant challenges for policymakers. Therefore, achieving sustainable rural development requires serious and systematic research. The smart village approach, within the framework of sustainable development theory, offers a potential path to overcome instability and achieve sustainable development in these areas. Islamshahr’s favorable location has made it a major migration destination in recent decades, which has led to significant economic, social, and environmental challenges, including increasing rural poverty in some villages. However, its proximity to Tehran and its location along the Tehran-Saveh Road have significant potential for rural development. This research aims to identify and spatially analyze the factors affecting the formation of smart villages in Islamshahr County, and to examine their importance and interrelationships. This research will address the following questions: What are the factors affecting the formation of the smart village approach in rural settlements in Islamshahr County? And then, what is the spatial analysis of smart village indicators in peri-urban rural settlements of Tehran in Islamshahr County?
 

Methodology

The present study is applied in terms of purpose and descriptive and analytical in terms of method. In the present study, the method of data collection was library and field (questionnaire), the validity of the research tool was confirmed by obtaining the opinions of university professors and after making the necessary amendments in several stages. The reliability level of the research questionnaire was above 7.0, which indicates a high reliability coefficient. Data analysis was performed with SPS software, and SPSS software and AHP decision-making models were used for their analysis, and GIS was used for drawing maps. Also, AHP was used to weight the components of smart village formation in rural settlements of Islamshahr county, and the ARAS model was used to rank the studied villages. After examining and normalizing the data using the Kolmogorov-Smirnov test, descriptive statistics (mean and standard deviation) and inferential statistics (independent t-test, diagnostic analysis, and logistic regression) were used. A wide range of indicators in the ten components of the smart village, including smart education, smart connectivity, smart economy, smart energy, smart health, smart environment, smart governance, smart infrastructure and mobility, smart agriculture, and smart tourism, were examined in the form of questionnaires from villagers and experts and field studies. It is worth noting that the opinions of experts, specialists, university professors, and identified executive officials in the rural area familiar with the peri-urban rural settlements of the Tehran metropolis within the limits of Islamshahr county were used to weight the research criteria.
 

Findings

The results of the study of individual characteristics of rural households show that 56.5% of the respondents are male and 43.5% are female. In terms of literacy status, secondary education is the most prevalent with 35% and primary education is the least prevalent with 1.2%. The study of the type of employment in the studied villages shows that the highest frequency of employment is 35% of farmers, 28.5% of freelancers, 20% of livestock farmers, 7.3% of workers and 5.4% of employees. Using the Analytic Hierarchy Process (AHP), this study determined the relative importance of ten smart village development indicators: smart education, connectivity, economy, energy, health, environment, governance, infrastructure/mobility, agriculture and tourism. Smart education (26.7%), smart connectivity (20.4%) and smart economy (15.3%) were identified as the most influential factors. Smart environment, agriculture and tourism had lower weights (2.3, 2.3 and 1.8% respectively). The Kolmogorov-Smirnov test confirmed the normality of the data distribution for all independent and dependent variables. Then, a one-sample t-test was used to analyze the ten smart village indicators. According to the results obtained, all ten smart village indicators are significant. The upper and lower bounds of all variables are positive, indicating that the mean is proportional to the value under test. Among the indicators, according to the villagers, smart tourism has had the greatest impact on the development of the smart village. Spatial analysis of the ten smart village indicators in the study area using the ARAS method shows that the most important indicator from the respondents' point of view is the smart connectivity index. According to the results obtained in terms of having smart village indicators, Nezamabad and Firouz Bahram villages are ranked first and second respectively, and Irin village is ranked last among the villages in the study area. Finally, it can be said that proximity to the metropolis of Tehran and access to extensive facilities and communication infrastructure have had a greater impact on the formation of the smart village approach in rural settlements close to the city.
 

Discussion and Conclusion

Ignoring technological advances hinders rural development and increases migration, especially among educated residents. However, smart villages promote long-term social, economic, and environmental improvements, and foster participation and entrepreneurship. The effectiveness of smart village indicators directly impacts sustainable development outcomes. By combining endogenous resource-based development with the leverage of innovation and external connectivity, smart villages offer a promising approach to sustainable rural development. Peri-urban areas, such as those near Tehran, are better placed to implement smart village initiatives due to better access to digital technologies, networks, etc. In examining the role of smart village indicators in sustainable rural development, this study identified ten key indicators: smart education, connectivity, economy, energy, health, environment, governance, infrastructure/mobility, agriculture, and tourism. The one-sample t-test showed that smart tourism and smart energy indicators were the most important smart village indicators in the studied villages with a statistical value of 9.265 and 8.996, respectively. According to the respondents, the smart energy component with an average of 3.33 ranked first and the smart tourism component with an average of 3.31 ranked second in terms of their influence in shaping the smart village approach. The results of the spatial analysis of smart village indicators using the ARAS weighting method show that Nezamabad and Firouz Bahram villages are ranked first and second, respectively, in terms of the ten smart village indicators among the villages in the studied region. This study, which is the first of its kind to analyze the factors affecting the development of smart villages in this specific region, cannot be directly compared with previous studies. However, its findings are consistent with those of Rob Kitchin (2013), Ella and Andre (2019), Aziza and Susanto (2020), Vizio and Di Laitra (2019) are in line. In general, smart villages are made up of rural people who take the initiative to discover practical solutions to solve fundamental challenges and seize new opportunities.