Multiscale Exploration of Spatial Statistical Datasets: A Linked Data Mashup Approach

Many national and international organizations today leverage semantic web technologies to make statistical datasets available as Linked Open Data (LOD). A key advantages of this approach is that the data not only becomes publicly available, but also machine-readable and hence suitable for automated discovery and exploration. Whereas this has great potential to support interesting use cases, it remains difficult for end users today to utilize and combine these statistical Linked Data. Three challenges are: (i) directing users to relevant data sources based on a specified location; (ii) facilitating data integration despite a lack of outgoing links between datasets; and (iii) offering flexible means to integrate and aggregate data from various sources. As time and location are highly relevant dimensions in most statistical data, we address the identified challenges by first constructing geographical metadata for statistical sources. Following a mashup approach, we introduce mechanisms to recommend interesting datasets to end users and automatically enable data integration, visualization, and comparisons based on userdefined criteria.