Multi-scale object matching is the key technology for upgrading feature cascade and integrating multi-source spatial data. Considering the distinctiveness of data at different scales, the present study selects residential areas in a multi-scale database as research objects and focuses on characteristic similarities. This study adopts the method of merging with no simplification, clarifies all the matching pairs that lack one-to-one relationships and places them into one-to-one matching pairs, and conducts similarity measurements on five characteristics (i.e., position, area, shape, orientation, and surroundings). The relevance vector machine (RVM) algorithm is introduced, and the method of RVM-based spatial entity matching is designed, thus avoiding the needs of weighing feature similarity and selecting matching thresholds. Moreover, the study utilizes the active learning approach to select the most effective sample for classification, which reduces the manual work of labeling samples. By means of 1:5000 and 1:25,000 residential areas matching experiments, it is shown that the RVM method could achieve high matching precision, which can be used to accurately recognize 1:1, 1:m, and m:n matching relations, thus improving automation and the intelligence level of geographical spatial data management.

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Abstract. Natural gas is the third largest energy pillar in the world, the best energy that all countries are scrambling to develop. Five main influencing factors of natural gas consumption are analyzed by collecting relevant information, including GDP, the gross industrial output value, the increased value of the third industrial production, the urban population, and the proportion of natural gas in primary energy. Then based on data from 2001 to 2011, factor analysis is taken by using the SPSS software. Then a linear regression model is obtained to predict the natural gas consumption. At last, the natural gas consumption in 2011-2013 is predicted by the proposed model, and the result is analyzed which shows that the model based on SPSS is reasonable and efficient.

The contour line is one of the basic elements of a topographic map. Existing contour line simplification methods are generally applied to maps without topological errors. However, contour lines acquired from a digital elevation model (DEM) may contain topological errors before simplification. Targeted at contour lines with topological errors, a progressive simplification method based on the two‐level Bellman–Ford algorithm is proposed in this study. Simplified contour lines and elevation error bands were extracted from the DEM. The contour lines of the elevation error bands were initially simplified with the Bellman–Ford (BF) algorithm. The contour lines were then segmented using the vertices of the initial simplification result and connected curves with the same bending direction were merged into a new curve. Subsequently, various directed graphs of the merged curves were constructed and a second simplification was made using the BF algorithm. Finally, the simplification result was selected based on the similarity between several simplification results and adjacent contour lines. The experimental results indicate that the main shapes of the contour groups can be maintained with this method and original topological errors are resolved.

Mining operation technology refers to the operating technical means selected for obtaining mine resources. Among them, the more commonly used types are empty-field mining mode, immersion mining mode, rock formation solidification and cave-in mining mode. With the continuous progress of today’s society, the influence of natural mineral resources on people’s daily life is constantly strengthened. The economic development of the entire society and the progress of industrial economy have continuously increased the scale of demand for mineral resources. It is important to analyze the green development index for its healthy and long-term development.