Identifying driving factors of urban land expansion using Google Earth Engine and machine-learning approaches in Mentougou District, China

Abstract

The research on driving mechanisms of urban land expansion is hot topic of land science. However, the relative importance of anthropogenic-natural factors and how they affect urban land expansion change are still unclear. Based on the Google Earth Engine platform, this study used the support vector machine classifier to extract land-use datasets of Mentougou district of Beijing, China from 1990 to 2016. Supported by machine-learning approaches, multiple linear regression (MLR) and random forests (RF) were applied and compared to identify the influential factors and their relative importance on urban land expansion. The results show: There was a continuous growth in urban land expansion from 1990 to 2016, the increased area reached 6097.42 ha with an average annual rate of 8.01% and average annual intensity rate of 2.57%, respectively. Factors such as elevation, risk of goaf collapse, accessibility, local fiscal expenditure, industrial restructuring, per capita income in rural area, GDP were important drivers of urban land expansion change. The model comparison indicated that RF had greater ability than MLR to identify the non-linear relationships between urban land expansion and explanatory variables. The influencing factors of urban land expansion should be comprehensively considered to regulate new land policy actions in Mentougou.

Monitoring and Modeling of Spatio-Temporal Urban Expansion and Land Use/Land-Cover Change in Mountain Landscape: A Case Study of Dalat City, Vietnam

“…As an empirical estimation model, logistics regression is a data-driven rather than knowledgebased approach to the choice of predictor variables (Hu and Lo, 2007). Previous studies identified the significant factors that determine the potential for urban expansion and land-use changes (Park et al, 2011;Arsanjani et al, 2013;Musa et al, 2017;Aburas et al, 2017;Shafizadeh-Moghadam et al, 2017;Hassan and Elhassan, 2020;Cheng et al, 2022). This study used 12 factors driving the land-use change process and the data generated as Euclidean Distance maps in the ArcGIS environment (Figure 4).…”

Spatial Modelling Analysis for Potential Expansion of Cipanas City Candidate Area as Autonomous City

“…This expansion requires careful consideration of various factors, including the influence of urban heat islands on the environment, the introduction of environmental indices based on remote sensing data, and spatial patterns (Shi et al, 2011;Fan et al, 2022). In addition, it is crucial to consider accessibility factors, such as distance to major roads and city centres (Cheng et al, 2022).…”

Investigating metropolitan change through mathematical morphology and a dynamic factor analysis of structural and functional land-use indicators

“…Urban growth in advanced countries was demonstrated to cause subtle landscape transformations [1][2][3][4] . Patch fragmentation, spatial polarisation in urban and non-urban land, simplification and homologation of natural landscapes are transformative processes characteristic of suburban and rural districts experiencing economic growth, population increase, and settlement expansion [5][6][7][8] . The resulting landscape matrix became particularly complex and spatially entropic, with a massive increase in the fractal dimension of individual patches as one of the most evident attributes of change [9][10][11][12] .…”