Spatiotemporal urban growth dynamics and its interactions with transportation networks in an unplanned urban conglomeration using satellite imagery: A case study of Mumbai Metropolitan Region, India

The present study analyses the spatiotemporal dynamics of urban growth in India's rapidly urbanising Mumbai Metropolitan Region (MMR). A maximum likelihood supervised classification algorithm is used to classify land use/land cover from 1999 to 2019 using the Landsat dataset. The spatial indices, landscape metrics, growth type index, Shannon Entropy, and transportation proximity analysis help quantify urban growth, its morphology, complexity, typologies, and transportation-urban growth interactions. The results show that urban areas in MMR expanded by 89 % over two decades. Growth indices, calculated at the Traffic Analysis Zone (TAZ) level, reveal that urban cores across municipal corporations and councils generally exhibit slower growth rates. However, peripheral zones near the cores experience high growth rates. Additionally, rapid expansion is observed in rural and special notified areas, reflecting diverse growth dynamics across the region. Landscape metrics highlight the slowdown in urban growth of Greater Mumbai and the emergence of new urban clusters in other parts of MMR, pointing towards a polycentric development. The growth typologies and compactness of TAZs suggest that different cities in MMR are simultaneously associated with varying growth phases, and these growth phases oscillate between diffusion-coalescence patterns. Proximity analysis to transportation networks suggests a higher degree of dispersion from suburban stations (Hn = 0.84–0.89) compared to highways (Hn = 0.56–0.57), with regression indicating a strong affinity of urban growth to the transportation networks. The study provides valuable insights to the decision-makers for devising proactive policies and strategies to ensure sustainable development.