An Empirical Evaluation of Disaster Data Interoperability—A Case of Uganda

Abstract

One of the grand challenges of disaster management is for stakeholders to be able to discover, access, integrate and analyze task-appropriate disaster data together with their associated algorithms and work-flows. Even with a growing number of initiatives to publish disaster data using open principles, integration and reuse are still difficult due to existing interoperability barriers within datasets. Several frameworks for assessing data interoperability exist but do not generate best practice solutions to existing barriers based on the assessment they use. In this study, we assess disaster data interoperability in Uganda and identify generic solutions to interoperability challenges in the context of disaster data. Semi-structured interviews and focus group discussions were used to collect qualitative data from stakeholders in the disaster sector in Uganda. Data interoperability was measured to provide an understanding of interoperability in the disaster sector. Interoperability maturity is measured using qualitative methods, while data compatibility metrics are computed from identifiers in the RDF-triple model. Results indicate high syntactic and technical interoperability maturity for disaster data. On the contrary, there exists considerable semantic and legal interoperability barriers that hinder disaster data integration and reuse. A mapping of the interoperability challenges in the disaster management sector to solutions reveals a potential to reuse established patterns for managing interoperability. These include; the federated pattern, linked data patterns, broadcast pattern, rights and policy harmonization patterns, dissemination and awareness pattern, ontology design patterns among others. Thus a systematic approach to combining patterns is critical to managing data interoperability barriers among actors in the disaster management ecosystem.