To accommodate network allocation, population polygons are frequently transformed into singular, weighted centroids which are then integrated into the network either by snapping each centroid to the nearest network segment or by generating an artificial connector that becomes part of the network. In this article, an investigation of the connection method of network allocation is undertaken with two primary foci: (1) how the density of centroid connectors effects travel cost along the network; and (2) how the algorithms utilized to determine the placement of connectors are affected by the density of connectors. We hypothesize that both issues have an effect on network travel cost and, therefore, on network‐based modeling. These hypotheses are tested on three nested spatial networks in the New England region of the United States. Two fundamental facility location models, the p‐median problem, and p‐center problem, are solved at each spatial extent while varying the density of connectors from one to four. When generating more than one connector thought must be given to the method of connection, the angle of dispersion, the acceptable tolerance of connector length, segment crossing, and saturated connectivity. A novel and thorough framework is proposed to address these concerns.

中文翻译：

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将点连接到空间网络：对离散优化模型的影响

为了适应网络分配，通常将人口多边形转换为奇异的加权质心，然后通过将每个质心捕捉到最近的网络段或通过生成成为网络一部分的人工连接器，将其集成到网络中。在本文中，对网络分配的连接方法进行了研究，主要有两个重点：（1）质心连接器的密度如何影响沿网络的旅行成本；（2）用于确定连接器位置的算法如何受到连接器密度的影响。我们假设这两个问题都会影响网络旅行成本，因此也会影响基于网络的建模。这些假设在美国新英格兰地区的三个嵌套空间网络上进行了检验。在连接器的密度从一到四变化的同时，在每个空间范围内都解决了p中值问题和p中心问题。当生成多个连接器时，必须考虑连接方法，分散角度，可接受的连接器长度公差，线段交叉和饱和连接性。提出了一个新颖而彻底的框架来解决这些问题。