نوع مقاله : مقاله مستخرج از طرح پژوهشی
نویسندگان
1 گروه برنامه ریزی شهری و منطقه ای، دانشکده برنامه ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران
2 گروه برنامه ریزی شهری و منطقه ای، دانشکده برنامه ریزی و علوم محیطی، دانشگاه تبریز، ایران
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Extended Abstract
1. 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 quality of life. Well-designed urban furniture contributes to place identity, tourism attractiveness, and sustainable urban development. Many 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. Previous Iranian studies focus on distributional inequality (Ghaffari Gilandeh et al., 2015), aesthetics (Asli Fallah & Dehghan Talebi, 2016), or vandalism (Ghanbari et al., 2016); few address cold climates; none combine quantitative assessment, expert weighting, and spatial autocorrelation. International studies (Grabiec et al., 2022; Nadour & Dahou, 2022) examine materials/sustainability but lack Ardabil's context. 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.
2. Methodology
Applied descriptive-analytical mixed-methods (Creswell, 2014). 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.
3. Results
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.
4. Discussion
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). Methodologically, this is Iran's first integration of MQI, SWOT-AHP, and Moran 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.
5. 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) show 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.
کلیدواژهها [English]
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