Analysis of Urban Population Flow and Spatial Distribution Patterns: A Study Based on Cluster Analysis Algorithm

Abstract

The research offers insightful information about how China's industrial structure and socioeconomic variables affect population flows between cities. It is not without restrictions, though. These include the possibility of biases and incompleteness in the data utilized, difficulties in understanding the intricate models that are used, and the restricted applicability of the results outside of China. In this research Analysis of urban population flow and spatial distribution patterns: a study based on cluster analysis algorithm (UPF-SDP-CAA) is suggested. At first, the Landsat satellite information is used to gather the input data. The image is provided to preprocessing phase. During the pre-processing phase, the NIOF  is used to remove noise for image. Then the preprocessed data are fed to feature extraction phase. Self-Supervised Nonlinear Transform (SSNT) is used to choose urban characteristics likeWard boundaries, geographic features, population density, socioeconomic characteristics, and environmental features. Extracted features were sent to the Progressive Graph Convolutional Network (PGCN) and the urban population flow and spatial distribution prediction used to classify like Low density and fragmented built-up land, High density built-up land greenery, Water bodies, crop land, and grassland wetlands, bare territory. In order to accurately classify, the PGCN classifier is optimized using the HSWOA. The proposed technique is implemented to Python. The effectiveness of the suggested UPF-SDP-CAA approach is evaluated using a number of performance criteria, including Accuracy, Recall, Precision, RMSE and AUC. The proposed UPF-SDP-CAA method covers 28.36%, 23.42% and 33.27%higher precision, 17.42%, 25.36%and 17.27%  higher recall and of 19.36%, 26.42% and 23.27%  higher accuracy compared with existing Simulating inter-city population flows based on graph neural networks (SIC-PF-GNN), An integrated simulation approach to the assessment of urban growth pattern and loss in UGS in Kolkata, India: A GIS-based analysis (UGP-UGS-ANN) and The effects of sample size and sample prevalence on cellular automata simulation of urban growth (CAS-UG-SVM) respectively.