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Based on the significant hotspots analysis method (Getis-Ord Gi* significance statistics), space-time cube model (STC) and the Mann–Kendall trend test method, this paper proposes a G-STC-M spatio-temporal analysis method based on Archaeological Sites. This method can integrate spatio-temporal data variable analysis and the space-time cube model to explore the spatio-temporal distribution of Archaeological Sites. The G-STC-M method was used to conduct time slice analysis on the data of Archaeological Sites in the study area, and the spatio-temporal variation characteristics of Archaeological Sites in East China from the Tang Dynasty to the Qing Dynasty were discussed. The distribution of Archaeological Sites has temporal hotspots and spatial hotspots. Temporally, the distribution of Archaeological Sites showed a gradual increasing trend, and the number of Archaeological Sites reached the maximum in the Qing Dynasty. Spatially, the hotspots of Archaeological Sites are mainly distributed in Jiangsu (30°~33° N, 118°~121° E) and Anhui (29°~31° N, 117°~119° E) and the central region of Zhejiang (28°~31° N, 118°~121° E). Temporally and spatially, the distribution of Archaeological Sites is mainly centered in Shanghai (30°~32° N, 121°~122° E), spreading to the southern region. [ABSTRACT FROM AUTHOR]

Based on geographic information system (GIS) technology in conjunction with two methods for assessing landslide susceptibility (LS)—namely, a method using experts' knowledge and experience, and a mathematical/statistical method—the LS of southern Anhui, China is assessed using an analytic hierarchy process (AHP) via an AHP-weighted information content method. Landslide-affecting factors are categorized into three main types and 10 subtypes. The values of spatial characteristics of the landslide-affecting factors are obtained using GIS technology. The AHP method is then employed to compare the importance and weights of landslide-affecting factors. The information content method is used to convert the measured values of the landslide-affecting factors in the study area to data reflecting regional stability. The closeness of the relationships between the classification levels of each landslide-affecting factor and landslide occurrence are calculated. The LS of the study area is assessed using the proposed method. The LS assessment shows that high LS, relatively high LS, moderate LS, relatively low LS and low LS regions account for 21.3%, 20.6%, 20.1%, 11.7% and 26.3% of the study area, respectively. Finally, the accuracy of the LS assessment results is analyzed using two methods: the assessment, including an analysis of random landslide sites for the validating models; and the area below a receiver operating characteristic (ROC) curve of area under curve (AUC) value. The results show that the proportion of landslide sites in the regions of each LS level determined using the AHP-weighted information content method increases as the LS level increases, and that the accuracies of the AHP-weighted information content method were 8.1% and 5.7% higher than those of the AHP method and information content method, respectively. [ABSTRACT FROM AUTHOR]