Harmonizing Full and Partial Matching in Geospatial Conflation: A Unified Optimization Model
Harmonizing Full and Partial Matching in Geospatial Conflation: A Unified Optimization Model
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DOI：
10.3390/ijgi11070375
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Ting L. Lei;Zhen Lei
中科院分区：
文献类型：
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作者：
Ting L. Lei;Zhen Lei
空间数据合并的目的是将两个数据集中的对象匹配并合并成一个更全面的数据集。从20世纪80年代的“地图分配问题”开始，优化合并模型将特征匹配视为最小化某些度量（例如总差异）的自然优化问题。优化合并的一个复杂性是异构数据集可以以不同的方式表示地理特征。特征可以在一对一的基础上（形成完全匹配）或在多对一的基础上（形成部分匹配）对应于其他数据集中的目标特征。传统模型只考虑完全匹配或部分匹配。这种二分法有几个问题。首先，完全匹配模型是有限的，不能捕获任何部分匹配。其次，部分匹配模型将完全匹配视为部分匹配，并且它们更容易承认错误匹配。第三，现有的合并模型可能会引入冲突的方向匹配。本文提出了一种新的模型，同时捕获完整和部分匹配。这允许我们对完全/部分匹配施加不同的结构约束，并强制执行定向匹配之间的一致性。实验结果表明，新模型在准确率（89.2%）方面优于传统的优化合并模型，同时达到类似的召回率（93.2%）。
Spatial data conflation is aimed at matching and merging objects in two datasets into a more comprehensive one. Starting from the “map assignment problem” in the 1980s, optimized conflation models treat feature matching as a natural optimization problem of minimizing certain metrics, such as the total discrepancy. One complication in optimized conflation is that heterogeneous datasets can represent geographic features differently. Features can correspond to target features in the other dataset either on a one-to-one basis (forming full matches) or on a many-to-one basis (forming partial matches). Traditional models consider either full matching or partial matches exclusively. This dichotomy has several issues. Firstly, full matching models are limited and cannot capture any partial match. Secondly, partial matching models treat full matches just as partial matches, and they are more prone to admit false matches. Thirdly, existing conflation models may introduce conflicting directional matches. This paper presents a new model that captures both full and partial matches simultaneously. This allows us to impose structural constraints differently on full/partial matches and enforce the consistency between directional matches. Experimental results show that the new model outperforms conventional optimized conflation models in terms of precision (89.2%), while achieving a similar recall (93.2%).