Optimized spatial data conflation with topological conditions

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This project addresses the study of geographic data conflation, which is the process of merging two separate datasets of the same geographical region. In particular, the goal of the project is to develop algorithms that can automate the process of conflation. Conventionally, conflation has been achieved by time-consuming manual processes, which can be unreliable and expensive to implement. By developing methods that can be integrated into geographic information systems, this project increases the potential for diverse organizations to conduct conflation at larger scales in reliable and reproducible ways. The project also contributes to the training and education of multiple graduate students.The overarching objective of this project is to advance computational methods for matching and merging map objects reliably and better than trained human practitioners. To accomplish this goal, the researchers developed automated methods that are based on strategies purportedly underlying the logic used by humans. The first strategy is error minimization, in which humans tend to match individual spatial objects based on their similarity or distance. This is known as optimized conflation, in which object matching is treated as a natural problem of minimizing discrepancy. The second strategy is to use spatial context to deal with the more complex, ambiguous matching cases. This latter perspective has been largely absent from previous implementations. By allowing computers to incorporate the same contextual conditions in neighboring objects, the new models will generate spatially consistent matches, thus allowing human practitioners to focus on resolving a small percentage of anomalous cases. Due to the increased reliability, the new models and algorithms of this project can reduce the monetary and time cost of conflation, making it a tool that can be used by students, planners, researchers in geography and other spatial disciplines as well as the average user of Geographic Information Systems (GIS).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.