Using Estimated Missing Spatial Data with the 2-Median Model
Using Estimated Missing Spatial Data with the 2-Median Model
复制标题
将估计缺失空间数据与 2 中位数模型结合使用
DOI：
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发表时间：
2003
影响因子：
4.8
通讯作者：
D. Griffith
中科院分区：
文献类型：
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作者：
D. Griffith
空间运筹学问题寻求“最佳”位置，通常是总加权距离最小的点，需要地理参考数据作为输入。这些数据的地图往往是不完整的，在地理分布上有漏洞。空间统计程序可用于完成这些数据集，并对缺失值进行最佳估计。本文探讨了这种估算对2中位数设施选址分配方案的影响。研究了空间均值的抽样分布和这些中值的标准距离。由于人口密度可以用一个相对简单的空间统计模型准确地描述，因此在确定位置分配方案时使用人口密度作为权重属性。
Spatial operations research problems seek “best” locations, often points of minimum aggregate weighted distance, requiring georeferenced data as input. Frequently maps of such data are incomplete, with holes in their geographic distributions. Spatial statistical procedures are available to complete these data sets with best estimates of the missing values. Impacts such imputations have on 2-median facility location–allocation solutions are explored. The sampling distribution of the spatial mean and standard distance of these medians are studied. Population density is used as the weight attribute in determining location-allocation solutions because it can be accurately described with a relatively simple spatial statistical model.