Spatial Search Strategies for Open Government Data: A Systematic Comparison

TL;DR

This paper systematically compares different spatial search strategies for open government data, evaluating their performance and user relevance, and finds that Hausdorff distance slightly improves relevance ratings over area overlap.

Contribution

It provides a comprehensive assessment of spatial search strategies for open government data, including theoretical and user-based evaluations, which was previously lacking.

Findings

Hausdorff distance slightly outperforms area overlap in user relevance.

Switching between overlap and Hausdorff distance has minimal impact on performance.

Provides a baseline for future spatial search strategy research.

Abstract

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.