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The study and protection for traditional villages are very important for us to protect Chinese historical and cultural heritage. Data show that under the condition of rapid urbanization. The number and coverage of traditional villages in western China are decreasing. It is impossible to effectively protect a large number of rural settlements at the bottom of China's traditional settlement system. Therefore, it is necessary to explore the spatial survival status of traditional villages and protect them comprehensively and extensively. Using the digital elevation model (DEM) data of traditional villages in Gansu Province, China, published by the Ministry of Housing and Urban Development and the attribute data obtained by the Statistics Bureau of Gansu Province, China, the nuclear density, the Mulan indices, the correlations between the heights and the centers are calculated and used to study the spatial different characteristics of the villages, and a number of results have been achieved: 1) In spatial differentiation, the spatial agglomeration of the villages is obvious and different, which can be seen by the distribution of the villages from along the upper reaches of the Yellow River to the southeast, and the distribution of prefecture-level cities is related to the landforms. 2) In vertical spatial distribution, the span of the altitude data is large. Among the villages, the Zagana Village in Diebu County of Gannan City is the highest and the Zhengjiashe Village in Bingkou Town of Longnan City is the lowest. With the increase of altitude, the number of traditional villages generally shows a tendency of decrease. 3) The spatial differentiation of the traditional villages has a clear normal distribution with the elevation, and the spatial differentiation is low, showing a distinct gourd-like structure; the eastern and southern regions are more concentrated, while the northern and western regions are less concen

Economic growth has always been one of the hottest topics in economic research. Behind the rapid economic growth, the economic gap between regions is gradually widening, and the internal gap will have an impact on the overall coordination of economic growth. Research on the convergence of economic development and its causes has great strategic significance for narrowing the differences in raising economy among regions. In recent years, the impact of big data on economic analysis has become more and more obvious, and this fact has attracted the attention of the academic community. Big data are a new strategic resource and a tool for assessing economic trends. Adding big data technology to the research on the convergence raise of economics can predict the law of data changes, reduce data errors, optimize research results, and provide a more scientific basis for the coordinated development of regional economies. Based on big data theory and technology, this paper uses a spatial econometric model to empirically analyze the convergence of regional economic growth and its influencing factors. The experimental results show that the research on the convergence mechanism and spatial relationship of economic growth in the context of big data can improve the accuracy of the convergence analysis of economic growth to a certain extent. Through modeling analysis, the accuracy of economic convergence is improved by 4.1%. The utilization of big data in a trend of economic development makes the analysis results more reasonable and has greater reference value.

Based on the method of the Jenks natural break point, this paper constructs the floor area ratio level of urban habitat environmental quality, and proposes a method of analyzing spatial difference characteristics of the human living environment in urban neighborhoods using the Theil-index model and spatial difference measurement index. The proposed method can be used not only to characterize the spatial difference measurement value of urban neighborhoods, but also to calculate the inner spatial difference in city blocks. The effectiveness of the method is verified by the remote sensing image of the case point and the actual data.

An evolutionary algorithm-based optimal allocation method of wind resources under the background of carbon neutralization is proposed in order to better achieve the goal of energy conservation and emission reduction under the background of carbon neutralization, aiming at the current unreasonable allocation of wind resources. The evaluation model of balanced wind resource allocation is designed, and the evaluation index of optimal wind resource allocation is constructed using the evolutionary algorithm. The optimal allocation path of wind energy resources is chosen to achieve the goal of reasonable wind energy resource allocation. Finally, simulation experiments show that using an evolutionary algorithm to solve the problem of poor energy allocation and achieve the research goal, the optimal allocation method of wind energy resources under the background of carbon neutralization can effectively solve the problem of poor energy allocation.