Identifying Components Effective on Happy City based on Citizens’ Perceived Happiness (Case study: Minoodar Neighborhood, Qazvin)

Introduction: Happiness is increasingly recognized as a key dimension of urban quality of life; however, its translation from policy discourse into practical implementation remains unclear. The decline in social capital and satisfaction indices in Iranian cities suggests that relying solely on the “Happy City” model at the planning level -without assessing citizen perceptions- is insufficient for enhancing quality of life. Therefore, the present study was conducted with the objective of identifying and measuring the effective components of achieving a Happy City in the Minoodar neighborhood of Qazvin. The necessity of this research stems from the lack of indigenous, data-driven models to measure this link.
Materials and Methods: The research is applied in its objective and descriptive–analytical in nature, adopting a correlational and causal approach. The analysis was performed using Structural Equation Modeling (SEM) in SmartPLS. To determine the total sample size, the Cochran formula was initially used, yielding a sample size of 324. Subsequently, to enhance the precision and reliability of the research, the sample size was increased to 383 participants. Data were collected using a researcher-made 51-item questionnaire based on 11 “Happy City” criteria, extracted from theoretical foundations, employing a random cluster sampling method. In this method, the Minoodar neighborhood was first divided into various clusters based on its city blocks, and then samples were randomly selected from each block to complete the questionnaire, proportionally weighted according to each block’s population ratio to the neighborhood’s total population. This method is particularly suitable when complete and accurate information about all members of the population is unavailable, as it facilitates the collection of representative data from all parts of the community. Subsequently, path analysis was performed between the latent and observed variables in the conceptual model, with the reliability and validity of the data confirmed through Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE).
Results and Discussion: The findings indicate that the Security component played a key and pivotal role in neighborhood happiness. This component exerted its influence both directly (coefficient of 0.618) and indirectly through channels such as social interactions (coefficient of 0.02). Furthermore, the The Climatic Comfort component had a significant direct effect on happiness and quality of life directly, with a coefficient of 0.281, suggesting that favorable environmental conditions, access to green space, and appropriate activity density can enhance general well-being and satisfaction with urban life. The Safety component also had a direct effect on neighborhood happiness with a coefficient of 0.149, and the Hygiene component showed a negative effect with a coefficient of -0.255 on residents’ perception of neighborhood happiness. In contrast, components such as Spatial Justice, Visual Attractiveness, and Social Interactions, despite their theoretical importance, did not have a direct impact in the study area.
Conclusion: The results demonstrate that citizens’ perception of security is closely linked to their economic and livelihood conditions, and ensuring livelihood can lead to the formation of a sense of security and subsequently, urban happiness. This pattern shows that in the specific context of the Minoodar neighborhood, the model places the greatest emphasis on fundamental variables, namely Livelihood, Security, and Climatic Comfort. In other words, the components directly tied to people’s basic satisfaction are prerequisites for achieving a Happy City. Accordingly, attaining a Happy City at the neighborhood scale necessitates prioritizing security, livelihood, and climatic comfort before implementing symbolic interventions, followed by focusing on completing supporting infrastructure and socio-physical dimensions.