DOI: 10.3390/ijgi15080375 ISSN: 2220-9964

Persistence-Based Analysis of Urban Growth Regimes, Spatial Drivers, and Growth-Pressure Screening: A Case Study of Tehran 2016–2030

SeyedMasoud Hamed Seyedbeiglou, Andreas Rienow, Ata Ghaffari Gilandeh

Urban expansion is often tracked with annual land-cover data, yet year-to-year classification noise can masquerade as persistent urban growth. Previous work has usually treated detection, morphology, spatial structure, driver analysis, and forward-looking modelling separately. Here we follow urban growth in Tehran County from 2016 to 2025 within one linked workflow and then extend the analysis to a 2025–2030 growth-pressure screening under static covariates. Annual 10 m built-up composites from Dynamic World were passed through a temporal stability filter to retain persistent change. Stable growth was classified into four regimes, tested for clustering and interaction scale, and examined using a Spatial Durbin Model and multiscale geographically weighted regression. A two-stage machine-learning branch then produced a ranked growth-pressure surface. In this study, growth-pressure screening means identifying where recent spatial conditions are most compatible with continued growth under unchanged covariates; it is intended for relative spatial ranking under stated assumptions rather than deterministic estimation of future urbanization. Stable new built-up area totaled 107.64 km2, most of it ribbon growth (57.60%) and edge expansion (32.77%). A stratified local validation of 300 samples returned a weighted overall accuracy of 97.30%, and a 27-scenario threshold test retained ribbon growth as the largest regime and edge expansion as the second largest in every case. Clustering was significant (Global Moran’s I = 0.326), with a dominant interaction range of about 8–10 km. Historical backtesting showed strong discrimination and ranking (ROC AUC = 0.979; PR AUC = 0.984; Spearman ρ = 0.902), while exact growth-magnitude performance was more moderate (R2 = 0.341). The growth-pressure surface is therefore more useful for hotspot identification and relative ranking than for estimating exact future growth magnitude.

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