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The Sanhe region in the middle reaches of the Yellow River is an important area for the origin and development of early civilization in China. Many early sites, from the Paleolithic to the Xia, Shang and Zhou Dynasties, remain in the region, all of which are important material carriers to record the historical process from the emergence of human beings to the formation of early civilization. In this study, all of the early archaeological sites in the research area were collected and loaded into the GIS platform. With the help of kernel density estimation, adjacent index analysis, standard deviation ellipse and other tools, the spatial and temporal distribution characteristics of these sites were explored, and the correlation between the distribution of early sites and geographical factors was explored through coupling analysis with the geographical environment. The results show that: (1) the evolution of the spatial distribution characteristics of early sites in the time dimension can reflect the development process of early civilization; (2) elevation, slope, aspect, topographic relief, hydrology and other factors are closely related to the distribution characteristics of early sites in the Sanhe region, and the correlation between site distribution and geographical factors is also different in different periods; (3) under the combined effects of elevation, slope, aspect, topographic relief and hydrological factors, the early sites show the existing spatial–temporal distribution characteristics. It is hoped that this study can provide reference ideas for the origin and development of early civilization in the future, as well as the discovery, protection and utilization of early sites.

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Uniformity testing, or testing whether independent observations are uniformly distributed, is the prototypical question in distribution testing. Over the past years, a line of work has been focusing on uniformity testing under privacy constraints on the data, and obtained private and data-efficient algorithms under various privacy models such as central differential privacy (DP), local privacy (LDP), pan-privacy, and, very recently, the shuffle model of differential privacy.In this work, we considerably simplify the analysis of the known uniformity testing algorithm in the shuffle model, and, using a recent result on "privacy amplification via shuffling," provide an alternative algorithm attaining the same guarantees with an elementary and streamlined argument.

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