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Geospatial data conflation is the process of identifying and merging the corresponding features in two datasets that represent the same objects in reality. Conflation is needed in a wide range of geospatial analyses, yet it is a difficult task, often considered too unreliable and costly due to various discrepancies between GIS data sources. This study addresses the reliability issue of computerized conflation by developing stronger optimization-based conflation models for matching two network datasets with minimum discrepancy. Conventional models match roads on a feature-by-feature basis. By comparison, we propose a new node-arc conflation model that simultaneously matches road-center lines and junctions in a topologically consistent manner. Enforcing this topological consistency increases the reliability of conflation and reduces false matches. Similar to the well-known rubber-sheeting method, our model allows for the use of network junctions as "control" points for matching network edges. Unlike rubber sheeting, the new model is automatic and matches all junctions (and edges) in one pass. To the best of our knowledge, this is the first optimized conflation model that can match nodes and edges in one model. Computational experiments using six road networks in Santa Barbara, CA, showed that the new model is selective and reduces false matches more than existing optimized conflation models. On average, it achieves a precision of 94.7% with over 81% recall and achieves a 99.4% precision when enhanced with string distances. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Geospatial data conflation is aimed at matching counterpart features from two or more data sources in order to combine and better utilize information in the data. Due to the importance of conflation in spatial analysis, different approaches to the conflation problem have been proposed ranging from simple buffer-based methods to probability and optimization based models. In this paper, I propose a formal framework for conflation that integrates two powerful tools of geospatial computation: optimization and relational databases. I discuss the connection between the relational database theory and conflation, and demonstrate how the conflation process can be formulated and carried out in standard relational databases. I also propose a set of new optimization models that can be used inside relational databases to solve the conflation problem. The optimization models are based on the minimum cost circulation problem in operations research (also known as the network flow problem), which generalizes existing optimal conflation models that are primarily based on the assignment problem. Using comparable datasets, computational experiments show that the proposed conflation method is effective and outperforms existing optimal conflation models by a large margin. Given its generality, the new method may be applicable to other data types and conflation problems. [ABSTRACT FROM AUTHOR]

Geospatial data conflation is the process of combining two datasets to create a better one. It has received increased research attention due to the emergence of new data sources and the need to combine information from these sources in spatial analyses. Many conflation methods exist to date, ranging from simple ones based on spatial join, to sophisticated methods based on statistics and optimization models. This paper focuses on the optimization-based conflation approach. It treats feature-matching in conflation as an optimization problem of finding a plan to match features in two datasets that minimizes the total discrepancy. Optimization based conflation methods may overcome some limitations of conventional methods, such as sub-optimality and greediness. However, they have often been deemed impractical in day-to-day analysis because they induce high computational costs (especially in combining large geospatial data). In this paper, we demonstrate the feasibility of performing optimization-based conflation for large geographic data in Geographic Information Systems. This is accomplished by utilizing efficient network flow-based conflation models and a divide-and-conquer strategy that allows the conflation models to scale to large data. Experiments show that the network-flow based model achieves average recall and precision rates of 97.7% and 90.8%, respectively in small test areas, and outperforms the traditional assignment problem by about 9% each. For larger data, it took the original network-flow model (without divide-and-conquer) nearly two days to conflate the road network in a portion of Los Angeles area near the LAX international airport. By contrast, the same model can be used to conflate the road networks of the entire Los Angeles County, CA in under 3 h with the divide and conquer strategy. • Utilized optimization models for geospatial conflation, which overcome sub-optimality in conventional methods. • Compared the new network-flow based conflation model with traditional models based on the assignment problem. • Proposed and tested divide-and-conquer strategies for geospatial data conflation. • Discussed and tested the impact of buffering data tiles on divide-and-conquer. • Developed an "equalized" tiling method based on quad-tree, which produces more balanced workload for data tiles. [ABSTRACT FROM AUTHOR]