تحلیل ساختارهای پنهان هوشمندی فضایی و شکل‌گیری اجتماعات محلی؛ کاربرد روش چیدمان فضا در بافت مرکزی شهر بجنورد

Introduction: In contemporary urban design, the role of spatial configuration in shaping local interactions and fostering urban communities has gained increasing attention. The concept of spatial intelligence reflects the ability of urban space to guide behavior, movement, and social interaction—an area that still requires extensive investigation at both urban and neighborhood scales. This study aims to uncover the hidden structures of spatial intelligence and examine their relationship with the formation of local communities in the city of Bojnord.
Methodology: Street-network data for the central urban fabric of Bojnord were processed using the Space Syntax method and Depthmap software. Network indicators were analyzed across four main categories: (1) movement and accessibility, (2) routes and flow, (3) local and network properties, and (4) geometric characteristics, forming a comprehensive analytical framework for interpretation.
Results: The results reveal that segments with high integration, accessibility, and choice values constitute the core of the network, guiding major movement flows and shaping residents’ social interactions. Secondary segments with moderate levels of connectivity and control create localized nodes of sociability at the neighborhood scale. A comparative analysis with the city’s neighborhood map indicates that areas adjacent to the primary urban corridors benefit most from spatial integration and network connectivity, whereas peripheral neighborhoods play a more limited role. After generating the axial map and conducting the initial analysis of connectivity and line length, the next stage involved performing graph analysis in Depthmap. Through this process, the complete spatial graph of the street network was generated, and an analytical table containing configurational metrics and related variables was obtained (Table 1).  This table served as the basis for a more detailed examination of the hidden relational structures within the spatial network and for assessing the role of each line in shaping movement patterns and the formation of local communities in the central urban fabric of Bojnord.
Discussion: A These patterns reflect the embedded spatial intelligence of the urban fabric, indicating that the street network and built form can autonomously organize daily movement, social flows, and local interactions without relying solely on institutional decisions or advanced technologies.  The comparative analysis of the street network in the central fabric of Bojnord and the existing neighborhood map demonstrates that neighborhoods located adjacent to major urban corridors, such as Shahid Taleghani, Imam Khomeini, and Shahid Beheshti, contain a substantial portion of the core street network and exhibit the highest levels of spatial integration, accessibility, and network connectivity. Owing to their strategic location along these principal corridors, these neighborhoods play a central role in shaping movement flows and local social interactions. Their main streets predominantly appear within the red and orange ranges of the axial map, indicating a high degree of spatial significance (e.g., Shariati-e Shomali, Chaharshanbeh Bazaar, Ghiam, and Jajarmi neighborhoods). In contrast, neighborhoods located at a relative distance from these primary corridors, represented by the blue spectrum of the axial map, benefit less from spatial integration and network connectivity. Consequently, they play a more limited role in directing everyday movement flows and fostering local social interactions (e.g., Dehkhoda, Mirza Kuchak Khan, and Energy neighborhoods). This pattern demonstrates that the spatial configuration of the central street network not only identifies the principal urban corridors and the core movement structure but also highlights the functional differentiation among neighborhoods based on their relative positions within the network. These findings provide a valuable basis for identifying local sociability hotspots and guiding urban design interventions aimed at strengthening neighborhood-level connectivity and enhancing social cohesion.
Conclusion: The findings highlight the latent spatial intelligence embedded within the central urban fabric of Bojnord and demonstrate that the street-network structure, even in the absence of smart technologies, possesses an inherent capacity to facilitate social interaction and strengthen neighborhood cohesion. The study provides a practical analytical framework for identifying urban potentialities and supporting sustainable, context-sensitive urban regeneration strategies. The spatial analysis, when overlaid with the neighborhood map, indicates that neighborhoods adjacent to major corridors such as Taleghani, Imam Khomeini, and Shahid Beheshti exhibit the highest levels of accessibility and network connectivity, thereby playing a central role in local social flows and neighborhood activities. In contrast, peripheral neighborhoods with lower spatial indices benefit less from these advantages and possess a more limited capacity to foster social interaction. These findings suggest that the spatial structure of traditional and medium-sized Iranian cities, such as Bojnord, holds considerable potential for strengthening local communities and facilitating sustainable urban regeneration. Compared with previous studies conducted in historic Iranian cities and international contexts, the primary contribution of this research lies in the explicit linkage it establishes between quantitative spatial-configuration indicators, street-network structure, and the latent spatial intelligence embedded in social interactions and urban movement patterns. The findings demonstrate that even within traditional urban fabrics, and in the absence of advanced technologies, spatial configurations themselves can generate a form of urban intelligence. This provides a robust basis for targeted planning and urban design interventions aimed at enhancing social cohesion and improving overall urban performance.