Curious about abbreviations? Here’s (a new) one: Linked Open Government Statistical Data LOGSD.
LOGSD are statistical data official statistics agencies provide in a LOD format for reuse. And such reuse may combine (mash up) statistical LOD with other sources in the LOD Cloud.
For example: ONS
The Office of National Statistics ONS and others in UK are very active in this field. So for better accessing geographical metadata which are essential in presenting statistics:
‘The solution is to use data.gov.uk as a single access point for discovery of geographic data, and to link from there to a geoportal (that is currently in development) where users could download the geographic products online. This goes most of the way to delivering the tools that users need to work with statistical data but there is also an opportunity to go further and provide geographic data as linked data, using the GSS codes that uniquely identify each geography to link the attributes from the different geographic products. Now, instead of a 9 character GSS identifier, each geography is given a URI that allows it to not only be uniquely identified but also makes it available online. We therefore end up with identifiers such as http://statistics.data.gov.uk/id/statistical-geography/E05008305 that only require users to change the GSS code at the end to get to the geographic information that they need.’ http://data.gov.uk/blog/update-from-ons-on-data-interoperability-0
And here an example how LOD and statistical (not yet LOGSD) data could work together. It’s an experimental proof of concept using data from Mercer quality of living survey and Transparency International, enriching these data with more information from DBpedia and calculating correlations that lead to hypotheses about the data.
Heiko Paulheim from Technische Universität Darmstadt made this interesting experiment which illustrates how linking data works. Abstract of Paulheim’s study “Generating Possible Interpretations for Statistics from Linked Open Data’ :
Statistics are very present in our daily lives. Every day, new statistics are published, showing the perceived quality of living in different cities, the corruption index of different countries, and so on. Interpreting those statistics, on the other hand, is a difficult task. Often, statistics collect only very few attributes, and it is difficult to come up with hypotheses that explain, e.g., why the perceived quality of living in one city is higher than in another. In this paper, we introduce Explain-a-LOD, an approach which uses data from Linked Open Data for generating hypotheses that explain statistics. We show an implemented prototype and compare different approaches for generating hypotheses by analyzing the perceived quality of those hypotheses in a user study.’