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Landslide susceptibility mapping (LSM) studies provide essential information that helps various authorities in managing landslide-susceptible areas. This study aimed at applying and comparing the performance of DIvisive ANAlysis (DIANA) and RObust Clustering using linKs (ROCK) algorithms for LSM in the Baota District, China. These methods can be applied when the data has no labels and when there is insufficient inventory data. First, based on historical records, survey reports, and previous studies, 293 landslides were mapped in the study area and 7 landslide-influencing attributes were selected for modeling. Second, the methods were clustered in the study area mapping units into 469 and 476 subsets, respectively; for mapping landslide susceptibility, the subsets were classified into 5 susceptibility levels through the K-means method using landslide densities and attribute values. Then, their performances were assessed and compared using statistical metrics and the receiver operating curve (ROC). The outcomes indicated that similarity measures influenced the accuracy and the predictive power of these clustering models. In particular, when using a link-based similarity measure, the ROCK performed better with overall performance accuracy of 0.8933 and an area under the curve (AUC) of 0.875. The maps constructed from the models can be useful in landslide assessment, prevention, and mitigation strategies in the study area, especially for areas classified with higher susceptibility levels. Moreover, this comparison provides a new perspective in the selection of a considerable model for LSM in the Baota District.

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Landslide susceptibility mapping (LSM) is a crucial step during landslide assessment and environmental management. Clustering algorithms can construct effective models for LSM. However, a random selection of important parameters, inconsideration of uncertain data, noise data, and large datasets can limit the implementation of clustering in LSM, resulting in low and unreliable performance results. Thus, to address these problems, this study proposed an optimized clustering algorithm named O-CURE, which combines: the traditional Clustering Using REpresentatives algorithm (CURE), that is, efficient for large datasets and noise data, the partition influence weight (PIW)-based method to enhance the selection of sample sets and the city block distance (CIBD) for processing of the uncertain data in CURE clustering during LSM modeling. A database containing 293 landslide location samples, 213 non-landslide samples, and 7 landslide conditioning factors was prepared for the implementation and evaluation of the method. Also, a Multicollinearity analysis was conducted to select the most appropriate factors, and all the factors were acceptable for modeling. Based on O-CURE, landslide density, and the partitioning around medoids (PAM) algorithm a susceptibility map was constructed and classified into very high (33%), high (18%), moderate (24%), low (13%), and very low (12%) landslide susceptible levels. To evaluate the performance of the O-CURE model, five statistic metrics including accuracy, sensitivity, specificity, kappa, and AUC were applied. The analysis shows that O-CURE obtained accuracy = .9368, sensitivity = .9215, specificity = .9577, kappa = .8496, and AUC = .896 is an indication of high-performance capability. Also, the proposed method was compared with the CURE algorithm, three existing clustering methods, and popular supervised learning methods. From this assessment, O-CURE outperformed the other clustering methods while showing significant and more consistent performance than the supervised learning methods. Therefore, we recommend that the O-CURE model and the constructed map can be useful in assessing landslides and contribute to sustainable land-use planning and environmental management in light of future disasters.

This paper discusses the placement of Chinese annotation from point of view of graphics. Area Feature is classified as simple polygon, complex polygon and special polygon. For simple ones, annotations are placed along the longest edge. For complex ones, firstly the polygon are simplified according to close points, then the longest diagonal is gotten by comparing length, lastly, annotations are placed along long-diagonal.For special ones, the polygon are partitioned into several parts by a certain rule for getting their sub-diagonals, then their annotation are placed by means of the second.

GML specification provides a set of core schemas. Users can define their own GML application schemas flexibly by using GML core schemas and schema components. However, due to this flexibility, schema structure becomes more complicated. Furthermore, due to the complexity and immensity of the spatial data, new requirements for the GML data processing software should be proposed. Based on the study of the core schemas of GML3.21 and the relationship among the types of elements, this paper presents a method to distinguish the elements of GML and its related properties. In addition, based on the characteristics of VTD-XML, a GML parser is developed. Finally, the GML parser is tested and its performance is analyzed.

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Landslide susceptibility mapping (LSM) is one of the crucial steps in managing and mitigating landslides. This study targets at developing a new LSM model for Baota District in China, using a new clustering method namely CIBD-CURE, which combines the city block distance (CIBD) and traditional clustering using representatives (CURE) algorithm. It aims at addressing limitations of inability to identify clusters (subclasses) with arbitrary shapes and varying sizes, sensitivity to noise, inability to perform well in large study areas with large dataset and inability to process rainfall (uncertain) data, which affect the results of traditional clustering algorithms in LSM. The CIBD was introduced into the CURE algorithm for processing uncertain data, then CIBD-CURE partitioned the mapping units into arbitrary shaped and sized subclasses with respect to their underlying geology and topography characteristics, and handled noise successfully. Furthermore, LEPAM method was proposed to sort the subclasses into five landslide susceptibility levels. Finally, standard statistical measures were applied to evaluate the model’s performance and compared it against CURE, AHC-OLID, HC and KPSO clustering models, along with DTU and NBU classification models. The result analysis showed data the proposed model attained higher performance. This LSM study will be useful for landslides management strategies not only for this study area but also other affected areas around the world.