ارزیابی کیفیت کالبدی و اولویت‌بندی راهبردی مبلمان شهری با تأکید بر رویکرد تحلیل فضایی مطالعه موردی: منطقه ۲ اردبیل

A B S T R A C T
Urban furniture shapes public space and quality of life. Ardabil's District 2, despite good roads and tourism potential, suffers from decay, visual discord, and no integrated furniture pattern.This study aims to assess the current situation and propose an optimal urban furniture model with an emphasis on the physical dimension. This applied research adopted a descriptive-analytical methodology. Data were collected from field surveys conducted across 44 streets and 22 parks using a checklist of 12 physical indicators, as well as an AHP questionnaire administered to 15 experts. Data analysis was performed by integrating the SWOT matrix, AHP, and the Furniture Quality Index (MQI). Global Moran's I tested spatial autocorrelation of furniture quality across land-use and street layers. Findings indicate that the ‘extensive road network’ (weight: 0/375) represents the most significant strength, while ‘inadequate paving’ (weight: 0/296) constitutes the most critical internal weakness. Among threats, ‘lack of integrated management’ (coefficient: 0/400) has the greatest negative impact. According to the MQI, only 2/2% of streets and 4/5% of parks are in ‘excellent’ condition, whereas 52/3% of streets and 50% of parks are rated as ‘poor’ or ‘very poor.’ The Moran analysis revealed that the land-use pattern of the district exhibits significant positive spatial autocorrelation (Moran's I = 0/384, z-score: 1613/39, p-value < 0/01), with High-High clusters concentrated mainly in the central and northern parts. Street quality overlay shows 'excellent/good' streets near HH clusters; 'poor/very poor' streets in LL clusters or low-high outliers. With no significant internal–external difference, the precautionary principle favors a conservative strategy.
Extended Abstract
Introduction
Urban furniture – including benches, lighting, waste bins, signage, paving, railings, public transport stations, green spaces, sports equipment, shading, and identity/artistic elements – directly influences physical comfort, psychological well-being, social interactions, and life quality. Well-designed urban furniture contributes to place identity, tourism attractiveness, and sustainable urban development. Numerous Iranian cities suffer deteriorated, visually incoherent, and climatically inappropriate furniture. District 2 of Ardabil – a historic northwestern city with >130 freezing days annually, home to Sheikh Safi al-Din World Heritage Site, with extensive road networks, parks (Shurabil, Safaviyeh, Nastaran, Saba, Qods, Banovan), and cool summer climate – exhibits dilapidation, disharmony, poor accessibility, frost-degraded materials. Preceding national studies focused on distributional inequality (Ghaffari Gilandeh et al., 2015), aesthetics (Asli Fallah & Dehghan Talebi, 2016), or vandalism (Ghanbari et al., 2016); few addressed cold climates; none combine quantitative assessment, expert weighting, and spatial autocorrelation. International studies (Grabiec et al., 2022; Nadour & Dahou, 2022) examined materials/sustainability but did not account for the contextual specificities of Ardabil. This research evaluates physical quality via MQI, identifies/weights factors via SWOT-AHP, examines spatial patterns via Moran's I, and proposes an optimal context-sensitive model.
 
Methodology
This applied research adopted a descriptive-analytical methodology. Study area: District 2 of Ardabil (~7°C, >130 freezing days). Data collection: field surveys across 44 streets and 22 parks using structured checklist evaluating 12 indicators (paving, lighting, benches, waste bins, drinking fountains, signage, railings, public transport stations, green space, sports furniture, shading, identity elements) scored 0-3 Likert; AHP questionnaires from 15 experts (municipal specialists, university faculty, consultants) with pairwise SWOT comparisons (CR<0.1 all matrices). Analysis: MQI = Σ(indicator score × AHP weight), classified into excellent (0.80-1.00), good (0.60-0.79), moderate (0.40-0.59), poor (0.20-0.39), very poor (0.00-0.19); SWOT-AHP total weighted scores, strategic positioning (x: strengths-weaknesses; y: opportunities-threats); Global Moran's I (inverse distance) and Anselin Local Moran's I (cluster/outlier) with street quality overlay.
 
Results and Discussion

A) Weighted SWOT: Strengths: road network/accessibility (0.375), cool summer climate (0.270); Weaknesses: inappropriate paving (0.296), deterioration (0.197), insufficient lighting (0.182); Opportunities: municipal improvement schemes (0.421), climate-resilient technologies (0.263); Threats: lack of integrated management (0.400), limited finance (0.261). Total scores: strengths=3.425, weaknesses=3.385, opportunities=2.979, threats=2.950; differences 0.04 and 0.029. Strategic position: central SWOT; conservative (WT) strategy preferred, hybrid possible.
B) MQI: Streets (44): Excellent 1 (2.2%, 0.85), Good 7 (15.9%), Moderate 13 (29.5%), Poor 16 (36.4%), Very Poor 7 (15.9%, <0.20). Worst: Arsalī (0.16), Bāqerpūr (0.17), Payām (0.18), 27 metri Rāzī (0.19). Parks (22): Excellent 1 (4.5%, 0.85), Good 2 (9.1%), Moderate 8 (36.4%), Poor 4 (18.2%), Very Poor 7 (31.8%). Qods=0.00, Bānovān=0.08, Nastaran=0.19. Gaps: paving (1.2/4), freeze-thaw resistance, lighting (1.8/4), maintenance/management, ergonomic/cultural design.
C) Spatial: Global Moran's I=0.384 (z=1613.39, p<0.01) > expected (-0.000027) → positive autocorrelation, clustering. HH clusters central/north (dense residential/commercial); LL clusters southwest (low-density); HL/LH outliers. Overlay: excellent/good streets adjacent HH; poor/very poor within LL or LH outliers → strong spatial correlation, inequitable distribution.

Despite favorable strengths, >52% streets and 50% parks are poor/very poor. High weights for paving (0.296) and management (0.400) indicate systemic managerial failures, not merely cosmetic. No unified maintenance, inspection, or citizen feedback; Ardabil lacks integrated management and institutionalized participation (vs. Barcelona Superilles, Paris participatory budgeting). This research is the first in Iran to methodologically integrate the MQI, SWOT-AHP, and Moran's I spatial analysis. Near-equilibrium (0.04, 0.029) demands cautious strategy: conservative (WT) with low-cost, high-impact interventions (durable paving, basic lighting) on worst streets (Arsalī, Bāqerpūr) and LL clusters, plus integrated management system. Spatial injustice: southwest fringe (LL) requires priority resource reallocation.
 
Conclusion
Main findings: (1) Conditions largely unsatisfactory – only 2.2% streets and 4.5% parks excellent; 57% streets and 50% parks poor/very poor; paving, lighting, climatic incompatibility worst deficits. (2) Strategic position: central/near-equilibrium SWOT; conservative (WT) preferred over aggressive (SO). (3) Root problem: managerial – T5 (0.400) and W4 (0.296) indicate piecemeal interventions futile without institutional reform. (4) Spatial: Moran's I=0.384 confirms clustering and clear quality-land use association (good near HH central/north, poor in LL southwest), demanding equity-oriented interventions.
Optimal Three-Axis Model:
- Physical-Infrastructural: Immediate paving/lighting rehabilitation with frost-resistant materials (wood, wood-plastic composites, frost-resistant concrete, insulated seating), prioritize Arsalī/Bāqerpūr and LL clusters; leverage O1/O3 opportunities, start low-cost pilots.
- Managerial-Institutional: Integrated management system: digital inventory, scheduled maintenance, citizen reporting platform, clear accountability – counters T5; phased implementation due T2 constraints.
- Design-Aesthetic: ‘Local urban furniture design guide’ with Safavid/Sabalan cultural symbols, climate-appropriate colors/forms, universal accessibility; engage citizens (S3) and leverage O5 via participatory workshops.
Future Research: Pilot on Arsalī or Bāqerpūr with 6-month MQI reassessment; longitudinal studies on vandalism, pedestrian activity, tourism satisfaction; comparative studies with other cold-climate Iranian cities (Tabriz, Hamedan); geographically weighted regression (GWR) for spatially varying relationships.
 
Funding
This article is derived from a research project entitled "Comprehensive Plan for Organizing Urban Furniture in Ardabil with Design Patterns (Case Study: District 2)" under contract number 16/792342, dated 2025/05/06, which was conducted with the financial and spiritual support of Ardabil Municipality.
 
Authors’ Contribution
Fereydoun Babaie Aghdam: Conceptualization, methodology design, theoretical framework development, final writing and editing; Iraj Teimouri: Quantitative analyses (MQI, AHP, SWOT), spatial analyses (Moran's I and Getis-Ord Gi), and preparation of tables and maps; Ali Oskouee Aras: Field data collection, questionnaire administration, fieldwork (checklist and photography); Parinaz Badamchizadeh: Literature review, source extraction, initial drafting of the introduction and theoretical foundations, and language editing.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
The authors would like to express their sincere gratitude and appreciation to Ardabil Municipality for their invaluable support.