Linear feature conflation: An optimization‐based matching model with connectivity constraints
Linear feature conflation: An optimization‐based matching model with connectivity constraints
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DOI：
10.1111/tgis.13062
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发表时间：
2023-05
影响因子：
2.4
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
地理空间数据合并是将关于地理现象的多个数据集合并以产生单个更丰富的数据集的过程。由于其在地图制作，交通，规划和时间地理空间分析等方面的许多应用，它已受到越来越多的研究关注。一种合并方法，从一开始就在文献中尝试，是使用基于优化的合并方法。合并被视为一个自然的优化问题，最大限度地减少差异的总数，同时从两个数据集找到相应的功能。与传统方法相比，基于优化的合并具有几个优点，包括简洁性，能够找到最佳解决方案以及易于实现。然而，目前基于优化的合并方法也是有限的。当前优化合并模型（以及其他传统方法）的主要缺点是它们通常太弱，并且在匹配相应特征时无法利用每个数据集中的空间上下文。特别是，目前的最佳合并模型匹配功能的目标独立于其他功能，因此将每个GIS数据集作为一个集合的不相关的元素，让人想起意大利面GIS数据模型。重要的上下文信息，如相邻元素（如道路）之间的连接在匹配过程中被忽略。因此，这样的模型可能会产生拓扑不一致的结果。在这篇文章中，我们通过引入新的基于优化的合并模型来解决这个问题，该模型具有结构约束，以保持特征之间的连接性和邻接关系。该模型使用整数线性规划实现，并在多个测试数据集上与传统的意大利面条式模型进行比较。实验结果表明，新的元素连接（ec‐bimatching）模型减少了错误匹配，并始终优于传统模型。
Geospatial data conflation is the process of combining multiple datasets about a geographic phenomenon to produce a single, richer dataset. It has received increased research attention due to its many applications in map making, transportation, planning, and temporal geospatial analyses, among many others. One approach to conflation, attempted from the outset in the literature, is the use of optimization‐based conflation methods. Conflation is treated as a natural optimization problem of minimizing the total number of discrepancies while finding corresponding features from two datasets. Optimization‐based conflation has several advantages over traditional methods including conciseness, being able to find an optimal solution, and ease of implementation. However, current optimization‐based conflation methods are also limited. A main shortcoming with current optimized conflation models (and other traditional methods as well) is that they are often too weak and cannot utilize the spatial context in each dataset while matching corresponding features. In particular, current optimal conflation models match a feature to targets independently from other features and therefore treat each GIS dataset as a collection of unrelated elements, reminiscent of the spaghetti GIS data model. Important contextual information such as the connectivity between adjacent elements (such as roads) is neglected during the matching. Consequently, such models may produce topologically inconsistent results. In this article, we address this issue by introducing new optimization‐based conflation models with structural constraints to preserve the connectivity and contiguity relation among features. The model is implemented using integer linear programming and compared with traditional spaghetti‐style models on multiple test datasets. Experimental results show that the new element connectivity (ec‐bimatching) model reduces false matches and consistently outperforms traditional models.