An accurate information on the amount and location of Land use and land cover (LULC) changes is necessary to develop and implement a sustainable-urban planning.This research investigates the potential of an integrated Multi-Layer Perceptron and Markov Chain Analysis (MLP-MCA) to map and accurately predict the future LULC change scenarios in Lagos Metropolitan Region of Nigeria. Multi-temporal LULC datasets derived from remotely sensed Landsat images from 1984, 2000 and 2015 were used for modeling, validation and prediction. Predicted LULC changes for 2030 and 2050 were performed based on the LULC map of 2015 using MLP-MCA method. The result reveals a significant expansion of built-up areas during the whole study period. Analysis of LULC distribution in Lagos metropolitan region shows that about 50% of urban land expansion happened beyond the administrative boundary of Lagos State during the period of 2000-2015. It is predicted that more than 75% of future urban growth will occur across the border of Lagos State, in the neighbouring Ogun State by 2050. These results imply that a strong and consistent collaboration between different states is crucial to establish an effective regional planning framework and ensure a proper planned growth of the metropolitan region.ARTICLE HISTORY
Evolutionary graph analytics have attracted attention from many research communities with the main purpose of understanding the changing pattern of real-world networks through evolutionary analysis of graph metrics and dynamic interactions between entities. Graphs of real-world networks evolve as new nodes and edges continually appear and disappear in the structure but, more importantly, their metrics such as density, average path length and network diameter also evolve. Uncovering and understanding hidden patterns in an evolving network requires evolutionary analysis of the network over different temporal resolutions. Evolutionary graph analytics have been explored for use in different types of networks including web citation and co-authorship networks [1-4], online social networks [5-10], biology and disease networks [11-14], as well as in communication networks [15-20]. All networks do not evolve at the same rate; some
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