Analysis of the Driving Force of Land Use Change Based on Geographic Detection and Simulation of Future Land Use Scenarios

Abstract

Land use and land cover changes (LULCC) are the result of the combined action of many influencing factors such as nature, society, economy and politics. Taking Chongqing as an example, the driving factors of urban land expansion in Chongqing from 1999 to 2019 are analyzed using a geographic detection (GD) method. Based on this analysis, a land use scenario of Chongqing in 2029 is simulated by an Artificial Neural Network-Cellular Automata model. The results of the analysis of factors affecting land use change show that five factors have a significance >0.05: population, distance from central city, school density, GDP and the distance from railway, showing that these factors have a high impact on LULCC in Chongqing. In addition, the results of risk detection analysis show that areas with a population >50/km2; the areas with a distance <200 km from the city center; areas with a school density >5/km2; areas with a high GDP; and areas with a distance <25 km from the railway have a greater impact on urban land use change than other areas. The land use scenario in 2029 also is simulated based on the land use situation in 2019. The predicted results clearly reflect a land use change trend of increasing urban land and decreasing agricultural land in the region. These land use changes are especially related to the expansion of the population, economy, roads, and schools in the process of urbanization. This analysis also shows that the GD-ANN-CA model developed in this paper is well suited to urban land use simulation.

Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA- artificial neural network model

“…To simulate future LULC, it is recommended that a number of predictor variables, which play a major role in LULC change and transition should be considered [ 77 ]. Based on LULC change drivers that were reported in previous studies [ 78 – 80 ], and the availability of such factor datasets, we selected 8 predictor variables ( Fig 3 ) to describe the LULC change processes that occurred in Gadaref state between 1988–2018. These predictors include topographic variables such as slope, aspect and elevation; and human disturbance variables like distance from Gedared state center, towns, highways, roads and the railway line ( Fig 3 ).…”

Spatiotemporal differences of land use pattern between mountainous areas and basin areas at township scale: A case study of Yuxi City

“…As internal factors, the natural factors determine the basic pattern of the large-scale land use, with a cumulative effect in time. In the short term, social and economic factors are the main external driving force for land use change (Wu et al, 2022). As the secondary core of the urban agglomeration in central Yunnan, Yuxi City has obvious advantages in location.…”

Determinants of land-use and cover change: role of natural resources and human activities in spatial-temporal evolution

“…50 Drivers form the cornerstone of a detailed study of land, which is essential for the sustainable utilization of land resources. 51,52 The methods used to analyze the factors influencing drivers include principal component analysis, 53,54 logistic regression modeling, 55,56 and geographically weighted regression. 57,58 These methods of analysis have played important guiding roles in the study of land use change factors.…”