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As a symbol language, toponyms have inherited the unique local historical culture in the long process of historical development. As the birthplace of Manchu, there are many toponyms originated from multi-ethnic groups (e.g., Manchu, Mongol, Korean, Hui, and Xibe) in Northeast China which possess unique cultural connotations. This study aimed to (1) establish a spatial-temporal database of toponyms in Northeast China using a multi-source data set, and identify their ethnic types and origin times; and (2) explore the geographical distribution characteristics of ethnic toponyms and the evolution of rural settlements by comparing the spatial analysis and spatial information entropy methods. The results found that toponyms reflect not only the spatial distribution characteristics of the density and direction of ethnic groups, but also the migration law of rural settlements. Results also confirm that toponyms contain unique cultural connotations and provide a theoretical basis for the protection and promotion of the cultural connotations of toponyms. This research provides an entropic perspective and method for exploring the spatial-temporal evolutionary characteristics of ethnic groups and toponym mapping.

Aiming at the problem that the estimation of electric power consumption (EPC) by using night-time light (NTL) data is mostly concentrated in large areas, a method for estimating EPC in rural areas is proposed. Rural electric power consumption (REPC) is a key indicator of the national socio-economic development. Despite an improved quality of life in rural areas, there is still a big gap between electricity consumption between rural residents and urban residents in China. The experiment takes REPC as the research target, selects Dehong (DH) Dai Jingpo Autonomous Prefecture of Yunnan Province as an example, and uses the NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) carried by the Suomi National Polar-orbiting Partnership (NPP) Satellite from 2012 to 2017, toponym and points-of-interest (POI) data as the main data source. By performing kernel density estimation to extract the urban center and rural area boundaries in the prefecture, and combining the county-level boundary data and electric power data, a linear regression model of the total rural NTL intensity and REPC is estimated. Finally, according to the model, the EPC in ethnic minority rural areas is estimated at a 1-km spatial resolution. The results show that the NPP-REPC model can simulate REPC within a small average error (17.8%). Additionally, there are distinct spatial differences of REPC in ethnic minority areas.

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The problem of algal bloom caused by eutrophication has attracted global attention. Many scholars have studied the problem associated with algae bloom, but few have carried out dynamic monitoring, instead focusing on the formation mechanism of cyanobacteria. For our study of the Taihu Lake in China, we used Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat remote sensing image data from 2017 to establish a prediction model. First, we used MODIS data to retrieve the concentration of N, P, and chlorophyll a in water. Then, we applied the analytic hierarchy process (AHP) model to the inversion results to construct the diffusion potential index. Finally, we used C# to compile the cellular automata (CA) model. We found that the distribution of cyanobacteria predicted by our method was consistent with the algal bloom situation of Taihu Lake in 2017. The results showed that the method effectively predicts the dynamic transfer of cyanobacteria from outbreak to diffusion in a short period of time, which can help decision-makers monitor lake health.