{"title":"煤矿空间聚集的尺度特征及聚类分析","authors":"Fankai Sun, Jin Zhang","doi":"10.1109/GEOINFORMATICS.2018.8557184","DOIUrl":null,"url":null,"abstract":"The spatial distribution of 1300 coal mines in Shanxi Province are researched using the nearest neighbor index, L(d) function, nearest neighbor hierarchical spatial clustering, and kernel density estimation. The results show that the coal mines in Shanxi Province present the aggregated distribution, and with the increases of spatial scale, the degree of aggregation increases first and then decreases, and reaches maximum with a spatial scale of 35 km. There are three small-scale and high-density coal mines cluster areas in Xishan Mining Area, Liliu Mining Area and Huodong Mining Area respectively and four large-scale banded cluster areas in Datong-Pingshuo Mining Area, Yangquan Mining Area, Xiangning-Huozhou Mining Area, Jincheng-Lu'an Mining Area, and a large-scale planar cluster area in Fenxi-Huozhou Mining Area. The cluster areas present the spatial distribution features of “overall dispersion and partial agglomeration”, small-scale high-intensity aggregation areas and large-scale aggregation areas coexisting. It is basically consistent with the existing division of coal resources in Shanxi Province.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Scale Features of Spatial Aggregation and Cluster Analysis of Coal Mines\",\"authors\":\"Fankai Sun, Jin Zhang\",\"doi\":\"10.1109/GEOINFORMATICS.2018.8557184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The spatial distribution of 1300 coal mines in Shanxi Province are researched using the nearest neighbor index, L(d) function, nearest neighbor hierarchical spatial clustering, and kernel density estimation. The results show that the coal mines in Shanxi Province present the aggregated distribution, and with the increases of spatial scale, the degree of aggregation increases first and then decreases, and reaches maximum with a spatial scale of 35 km. There are three small-scale and high-density coal mines cluster areas in Xishan Mining Area, Liliu Mining Area and Huodong Mining Area respectively and four large-scale banded cluster areas in Datong-Pingshuo Mining Area, Yangquan Mining Area, Xiangning-Huozhou Mining Area, Jincheng-Lu'an Mining Area, and a large-scale planar cluster area in Fenxi-Huozhou Mining Area. The cluster areas present the spatial distribution features of “overall dispersion and partial agglomeration”, small-scale high-intensity aggregation areas and large-scale aggregation areas coexisting. It is basically consistent with the existing division of coal resources in Shanxi Province.\",\"PeriodicalId\":142380,\"journal\":{\"name\":\"2018 26th International Conference on Geoinformatics\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 26th International Conference on Geoinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GEOINFORMATICS.2018.8557184\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 26th International Conference on Geoinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Scale Features of Spatial Aggregation and Cluster Analysis of Coal Mines
The spatial distribution of 1300 coal mines in Shanxi Province are researched using the nearest neighbor index, L(d) function, nearest neighbor hierarchical spatial clustering, and kernel density estimation. The results show that the coal mines in Shanxi Province present the aggregated distribution, and with the increases of spatial scale, the degree of aggregation increases first and then decreases, and reaches maximum with a spatial scale of 35 km. There are three small-scale and high-density coal mines cluster areas in Xishan Mining Area, Liliu Mining Area and Huodong Mining Area respectively and four large-scale banded cluster areas in Datong-Pingshuo Mining Area, Yangquan Mining Area, Xiangning-Huozhou Mining Area, Jincheng-Lu'an Mining Area, and a large-scale planar cluster area in Fenxi-Huozhou Mining Area. The cluster areas present the spatial distribution features of “overall dispersion and partial agglomeration”, small-scale high-intensity aggregation areas and large-scale aggregation areas coexisting. It is basically consistent with the existing division of coal resources in Shanxi Province.