Landslides pose a serious threat to human lives and property. Accurate landslide susceptibility mapping (LSM) is crucial for sustainable development. Machine learning has recently become an important means of LSM. However, the accuracy of machine learning models is limited by the heterogeneity of environmental factors and the imbalance of samples, especially for large-scale LSM. To address these problems, we created an improved random forest (RF)-based LSM model and applied it to Guangdong Province, China. First, the RF-based LSM model was constructed using rainfall-induced landslide samples and 13 environmental factors and by exploring the optimal positive-to-negative and training-to-test sample ratios. Second, the performance of the RF-based LSM model was evaluated and compared with three other machine learning models. The results indicate that: (1) the proposed RF-based model has the best performance with the highest area under curve (AUC) of 0.9145, based on optimal positive-to-negative and training-to-test sample ratios of 1:1 and 8:2, respectively; (2) the introduction of rainfall and global human modification (GHM) can increase the AUC from 0.8808 to 0.9145; and (3) rainfall and topography are two dominant factors in Guangdong landslides. These findings can facilitate landslide risk prevention and serve as a technical reference for large-scale accurate LSM.

Accurately measuring industrial spatial agglomeration patterns is crucial for promoting regional economic development. However, few studies have considered both agglomeration degrees and cluster locations of industries. Moreover, the traditional multi-scale cluster location mining (MCLM) method still has limitations in terms of accuracy, parameter setting, calculation efficiency, etc. This study proposes a new framework for analyzing industrial spatial agglomeration patterns, which uses the Duranton and Overman (DO) index for estimating agglomeration degrees and a newly developed local DO (LDO) index for mining cluster locations. The MCLM-LDO method was proposed by incorporating the LDO index into the MCLM method, and it was validated via comparisons with three baseline methods based on two synthetic datasets. The results proved that the MCLM-LDO method can achieve accuracies of 0.945 and 1 with computational times of 0.15 s and 0.11 s on two datasets, which are superior to existing MCLM methods. The proposed framework was further applied to analyze the spatial agglomeration patterns of the industry of computer, communication, and other electronic equipment manufacturing in Guangdong Province, China. The results showed that the framework gives a more holistic perspective of spatial agglomeration patterns, which can serve as more meaningful references for industrial sustainable development.