Abstract

In the emerging data era, demand for increasingly detailed information is steadily growing, hence providing the chance to mirror processes of the economy and society more adequately. Although such highly disaggregated datasets can usually be generated independently, their true added value lies in their integration with other sources. In response to exploit the advantages, the Hungarian Central Statistical Office has prioritized the multi-purpose use of data (the enhancing of interoperability), the benefits arising from structuring and linking datasets (creating a form of “data space”), as well as the interpretation and visualization of detailed data to ensure accessibility and usability. A new data ecosystem grounded in spatial granularity and interoperability is being developed to enhance both the relevance and the utility of official statistics. To address these challenges, the Hungarian Central Statistical Office has initiated the Virtual Hungary pilot project, which seeks to organize the most detailed datasets into a fully integrated system. This presentation aims to introduce the possibilities of small level detailed data integration within the framework of this project by choosing grid level aggregated analytical cases. Examples will be presented on how the spatial analytical challenges have been embraced by incorporating new data sources, such as administrative cash register data as well as spatially detailed wage-mass estimations, to enhance retail statistics. Grid level integrated datasets proved usefulness not only because they offered an opportunity to bridge the gap between detailed spatial information analysis and sensitive data disclosure, but also because of opening completely new avenues of spatial analytical possibilities. Examples will be introduced to underline that grid data can be interpreted as simplified raster data, and as such, pseudo-raster operations can be performed such as spatial moving average or convolution statistics.