Spatial Search Strategies for Open Government Data: A Systematic Comparison
This work addresses the need for efficient access to open government datasets, though it is incremental as it compares existing methods without introducing new paradigms.
The study tackled the problem of identifying effective spatial search strategies for open government data by comparing area of overlap and Hausdorff distance, finding that Hausdorff distance slightly improved user relevance ratings while performance impact was minimal.
The increasing availability of open government datasets on the Web calls for ways to enable their efficient access and searching. There is however an overall lack of understanding regarding spatial search strategies which would perform best in this context. To address this gap, this work has assessed the impact of different spatial search strategies on performance and user relevance judgment. We harvested machine-readable spatial datasets and their metadata from three English-based open government data portals, performed metadata enhancement, developed a prototype and performed both a theoretical and user-based evaluation. The results highlight that (i) switching between area of overlap and Hausdorff distance for spatial similarity computation does not have any substantial impact on performance; and (ii) the use of Hausdorff distance induces slightly better user relevance ratings than the use of area of overlap. The data collected and the insights gleaned may serve as a baseline against which future work can compare.