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This paper focuses on ancient settlements in the Luzhong region (the centre of Shandong Province) of China and analyses the spatial–temporal distribution and morphological characteristics of ancient settlements with the help of GIS technology and the perspectives of archaeology and cultural geography. Specifically, the 1972 settlements collected were used to establish a database of settlement site attributes. Then, the DEM data were superimposed with the settlement sites, and calculations of the kernel density, elevation, slope, aspect, and buffer zone were further carried out. The distribution and characteristics were refined based on quantitative and qualitative analyses. The study found that the Neolithic period, the Shang–Zhou period, and the Qin–Northern and Southern Dynasties were the three high points of settlement development. In these three periods, the centres of the large-scale distribution of settlements experienced changes from a "single centre" to a "continuous belt" to a "double centre'. In general, the spatial and temporal characteristics of the settlement distribution were continuously developed through time, while the spatial characteristics show that the main body continued to change locally. In different periods, settlements tended to be in the alluvial plains located between 20 and 60 m and with a slope of less than 6°. At the same time, they showed the obvious characteristic of living close to water. The past, present, and future are in the same chain of time; meanwhile, these settlements are the predecessors of today's cities, towns, and villages. So, this study provides a basis for protecting their heritage value and provides a reference for the coordination of human–land relations, which can help achieve global sustainable development. [ABSTRACT FROM AUTHOR]

• Chinese soil loss equation model was used to estimate long-term soil erosion. • Soil erosion rates increased in the basin from 2010 to 2017. • Interactions between main driving factors analyzed using boosted regression trees. • The B factor was the primary driver of soil erosion. Soil erosion reduces soil fertility and land productivity while enhancing desertification, which seriously threatens ecological security, carbon sequestration, and sustainable economic and social development. Therefore, it is fundamental to precisely assess the long-term dynamics of soil erosion and explore its drivers to control the risk of soil erosion. Currently, machine learning-based analyses of the factors driving soil erosion are lacking in the Beiluo River Basin (BRB), China. This paper aimed to investigate the spatiotemporal dynamics of soil erosion by the Chinese soil loss equation coupled with the gully erosion factor, and then reveal its driving factors using the Boosting Regression Tree method (BRT) and estimate the influence of drivers' interactions on soil erosion in the BRB between 1990 and 2017. The findings demonstrated that the expanded Chinese soil loss equation accurately explained the variations in soil erosion in the BRB. The range of average soil erosion rates was 2,852.83–800.35 t km−2 a−1. Steep gully farmland, particularly areas with relatively rough terrain, was more vulnerable to intense erosion. Soil erosion rates for different geomorphologies decreased as follows: hilly–gully areas > gully–plateau areas > alluvial river plains > Rocky Mountains. The major drivers affecting the soil erosion rate were the biomass control factor (B) and slope, followed by slope length (L) and slope steepness (S). B–soil erodibility (1990), B–LS (2000), LS–rainfall erosivity (2010), and slope–rainfall erosivity (2017) interactions had the greatest influence on soil erosion, with interaction intensities of 32.3%, 17.75%, 46.05%, and 35.69%, respectively. The average soil erosion rates according to land classification types decreased as follows: farmland > grassland > forest. The results indicate that water and soil conservation in the study area benefited greatly from implementation of the Grain for Green Program, with forests reducing erosion more effectively than grassland. [ABSTRACT FROM AUTHOR]

This study investigated the spatial pattern evolution of the rural settlement system in the town of Menggang, China, based on settlement patches extracted from remote sensing data for four time points between 1990 and 2018. Five typical villages were selected to study their spatial textures from point to surface. The reasons for the evolution of the rural settlements were examined as well. The results indicated that the number of settlement patches in Menggang decreased, the area of a single patch expanded, and the scale of the rural settlements continued to expand. Settlements were distributed randomly, but an agglomeration trend began to appear. The plain terrain had little restriction on settlement site selection. Cofferdams and embankments were the limiting factors for settlement expansion, which led to the compact development of the settlements. Economic development, population growth, and family miniaturization were positive factors for the expansion of the rural settlements. In the typical villages, cluster settlements and cluster settlements with a banded tendency had higher spatial utilization efficiency. Several high-density building centers were formed in the settlements, and the trend of residential buildings was mainly northeast–southwest. The plain terrain had little effect on the orientation of buildings, single-building areas, or the public spaces of the settlements. Traditional culture and traffic routes affected the orientation of the main buildings of folk houses. This study has theoretical and reference value for the structure optimization and sustainable development of rural settlements in the lower Yellow River plain. [ABSTRACT FROM AUTHOR]