Monitoring Spatial-Temporal Transition Dynamics of Transport Infrastructure Space in Urban Growth Phenomena: A Case Study of Lagos—Nigeria

Abstract

Lagos is one of the fastest growing world mega-cities with a huge urban mobility crisis, the traditional aggregate city’s development model could not provide reliable scientific solutions to monitor the competing demands of various land-use components and the urbanization’s effects on transport infrastructure space. This study uses a disaggregated predictive spatial modeling approach to investigate the evolutionary dynamics of transportation infrastructure space to address the fragmented urban chain process. The methodology involves analysis and modeling of the land-use spatial transition changes that have occurred over the past three decades using three Landsat imagery epochs (1984, 2013, and 2019) in remote sensing ARC-GIS 10.7. Furthermore, the prediction of the two-temporal milestones (2030 and 2050) using hybrid cellular automata-Markov (CA-Markov) implemented in IDIRISI SELVA 17.0 software when the tides of social-demographic factors were expected to bring about significant urban spatial transformation. The forecast results are expected to increase the area for transport infrastructure spaces by 93 km2 (7.3%) in 2030 and 157 km2 (12.4%) in 2050. The model’s kappa reliability coefficient estimates for the three temporal scales (k1984 = 85%; k2013 = 88% and k2019 = 89%) are higher than the 80% minimum adjudged strong agreement between the ground truth and prediction classified images in literature. The model provides efficient tool in urban development planning and sustainable transport decisions.

Construction and optimization of ecological security pattern based on landscape ecological risk assessment in the affected area of the Lower Yellow River

“…For example, Budaeva et al (2021) planned the nature park reserve using a multi-criteria decision analysis method, which provided a reference for the nature conservation and tourism development of the park through sensitive zoning results. Otuoze et al (2021) used cellular automata theory to construct a dynamic model to simulate and quantitatively predict urban growth, strengthened scientific urban planning and control of green space patterns. Zhelonkina et al (2021) promoted regional conservation planning by identifying key spatial components of ecological conservation and evolutionary processes from the perspective of biodiversity conservation.…”