A Systematic Review of the Role of GIS-Based Cellular Automata Models in Analyzing Urban Sprawl and Population Distribution; A Comparative Study with Emphasis on the Tehran Metropolitan Are

Urban sprawl, as one of the fundamental challenges of sustainable development in metropolitan areas, is associated with the horizontal and decentralized growth of cities, leading to extensive environmental, economic, and social consequences. This systematic review aims to comprehensively analyze the research conducted on urban sprawl and the application of GIS-based Cellular Automata (CA) models. With a special emphasis on the Tehran metropolitan area, this study comparatively reviews 20 key articles published between 2019 and 2025 from various cities in Iran and around the world. The findings indicate that the Tehran metropolitan area faces a compound annual growth rate of approximately 2.5% (based on the increase in built-up area from 60,123 hectares in 1985 to 142,701 hectares in 2020). According to the Holdren model, the peak of urban sprawl occurred in the 1980s (Solar Hijri calendar), with 75% of urban growth attributed to land area expansion rather than population growth. The integration of GIS with CA-Markov models, achieving a Kappa coefficient of 0.90 in Tehran, is identified as the most suitable option for practical planning applications. Diverse spatial patterns, including continuous low-density growth, marginal, infill, corridor-coastal, polycentric, and hybrid (leapfrog, infill, strip) developments, have been identified in various studies. Based on the content analysis of 80 domestic articles, migration to cities (19 occurrences), government policies (16 occurrences), natural population growth (15 occurrences), and land price increases (14 occurrences) are the most significant driving factors. Advanced models such as GSA-CA (accuracy 98.42–98.52%), UESP (accuracy 92.5%), and ABM-learning demonstrate a paradigm shift towards artificial intelligence and machine learning approaches. The consequences of this phenomenon include the loss of agricultural land, increased infrastructure costs, and spatial inequality in access to urban services. Future research should focus on developing integrated population-land use models, utilizing big data and artificial intelligence, evaluating policy effectiveness, conducting international comparative analyses, and developing 3D models.