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Rural–urban fringe areas serve as crucial transitional zones within urban structures, and their spatiotemporal evolution holds significant reference value for scientifically planning urban configurations. The existing research predominantly focuses on large cities, overlooking the spatiotemporal evolution mechanisms of small- to medium-sized cities. This study employs nighttime light data as the data source to ensure continuous and consistent data, overcoming administrative boundaries. Taking Taizhou City as a case study, a combination of the threshold method and an improved Mann–Kendall algorithm is employed to reveal the evolution process of fringe areas. And a gravity model is utilized to unearth the interaction relationships among regions. The results indicate that from 2010 to 2020, the urban area expanded from 1097 km2 to 2791 km2, with fringe areas experiencing initial contraction followed by gradual expansion. The central urban areas of Jiaojiang, Luqiao, and Huangyan gradually merged, forming a concentrated region. Linhai maintained a high level of attraction, while spatial gravity weakened in other areas. This study quantitatively analyzes the expansion trends of fringe areas in small- to medium-sized cities, elucidating the attractive effects of time–cost distance and land resources on development and providing valuable support for subsequent spatial planning and governance. [ABSTRACT FROM AUTHOR]

Landslides are critical natural disasters characterized by a downward movement of land masses. As one of the deadliest types of disasters worldwide, they have a high death toll every year and cause a large amount of economic damage. The transition between urban and rural areas is characterized by highways, which, in rugged Himalayan terrain, have to be constructed by cutting into the mountains, thereby destabilizing them and making them prone to landslides. This study was conducted landslide-prone regions of the entire Himalayan belt, i.e., National Highway NH-44 (the Jammu–Srinagar stretch). The main objectives of this study are to understand the causes behind the regular recurrence of the landslides in this region and propose a landslide early warning system (LEWS) based on the most suitable machine learning algorithms among the four selected, i.e., multiple linear regression, adaptive neuro-fuzzy inference system (ANFIS), random forest, and decision tree. It was found that ANFIS and random forest outperformed the other proposed methods with a substantial increase in overall accuracy. The LEWS model was developed using the land system parameters that govern landslide occurrence, such as rainfall, soil moisture, distance to the road and river, slope, land surface temperature (LST), and the built-up area (BUA) near the landslide site. The developed LEWS was validated using various statistical error assessment tools such as the root mean square error (RMSE), mean square error (MSE), confusion matrix, out-of-bag (OOB) error estimation, and area under the receiver operating characteristic (ROC) curve (AUC). The outcomes of this study can help to manage landslide hazards in the Himalayan urban–rural transition zones and serve as a sample study for similar mountainous regions of the world. [ABSTRACT FROM AUTHOR]