Research On The Extraction Method Of The Geographical Names Cultural Landscape Cluster Area In Historical Offices

The government office was the seat of the ancient government,Its name carries cultural information such as the geographical and human environment of the region.The study of the geographical name culture of the place of governance can not only discover the naming pattern and historical changes of the place of governance,but also provide reference for the protection and revision of ancient geographical names.However,the study of the spatial distribution of the geographical name culture of the region suffers from the shortage of quantitative spatial analysis methods and the imprecision of research results,thus the goal of this study is to provide methodological support for the accurate description of the spatial distribution of the geographical name culture of the region,based on the above research objectives,the following research results were achieved in the thesis.1.The concept of "cultural agglomeration of geographical names" is proposed.Current research on the spatial distribution of geographical names mainly adopts the kernel density analysis method,which shows that certain types of geographical names are clustered in a certain area,but the area may not contain one type of geographical name culture,but a certain geographical name culture occupies a numerical advantage in the area.It is an accurate description of the spatial distribution of different types of cultural place names.2.The algorithm for extracting the cultural clustering area of the geographical names is designed and encapsulated into a corresponding tool.The first one is the dual space clustering algorithm,in order to make the existing dual space clustering algorithm have better applicability to this study,this paper optimizes the parameter and initial value settings and extends the attribute dimension on the basis of the original dual space clustering algorithm,the second one is the region extraction algorithm,in order to meet the demand of this study to obtain the outermost boundary of the point cluster The second is the region extraction algorithm.By comparing the results of the optimized algorithm with those of the original algorithm,the algorithm of this study shows good performance,which is reflected in:(1)the initial central data are more spatially discrete after sorting,(2)the K-Means algorithm fits faster and has higher clustering accuracy,and(3)the region extraction algorithm based on the curve of the number of spatial points within the kernel density contour(kernel-point relationship curve)can accurately extract the outer boundary of the spatial point population.3.Using the chronological data of county offices in CHGIS V6 as the data source,the data of county offices in the middle and lower reaches of the Yellow River(five provinces: Hebei,Henan,Shandong,Shanxi,and Shaanxi;two cities: Beijing and Tianjin)from 221 B.C.to 1911 were selected as the research objects.The data were pre-processed and then extracted by using the cultural cluster extraction tool to extract the natural and human cultural clusters in each historical period,and correlation analysis was performed.The results show that:(1)the algorithm and tools for extracting the cultural agglomerations of geographical names can effectively extract the cultural agglomerations with the same cultural attributes and spatial connectivity,and the agglomerations can accurately describe the spatial distribution of the geographical names of county offices.(2)The natural cultural place-name clusters of the counties in the study area are mainly located along the rivers,the Fen River valley and the Wei River valley,which have a high incidence and spatial continuity along the rivers.(3)The humanistic cultural geographic name agglomerations of the counties in the study area are mainly located in the North China Plain,which have topographic terrain dependence and economic and cultural dependence.(4)The spatial distribution of the cultural agglomerations of the counties in the study area has gradually stabilized,and the spatial distribution of the cultural agglomerations of the counties has changed less and less with the evolution of dynasties.