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DGGSTools is a Python package and command-line application to process geospatial datasets within the spatial reference provided by the rHEALPix Discrete Global Grid System (DGGS). Its main objective is to provide the tools necessary to transform geospatial datasets to equivalent ones following the rHEALPix DGGS, and to allow its users to work with both raster and vector versions of those datasets as preferred. Once these equivalent datasets have been produced with DGGSTools, the users can obtain the benefits of working with a DGGS, intended to facilitate the efficient integration and retrieval of heterogenous global data, while using their preferred GIS tools and workflows for the analysis of those datasets. This short paper describes the main characteristics of the first public version of DGGSTools. Although a small part of its code had already been used in some geospatial research experiments, this is the first version which is publicly available. It includes a good,number of automatic tests, it is properly packaged as a Python wheel to facilitate its installation, and offers a command-line application so the package can be used without writing a single line of code. [ABSTRACT FROM AUTHOR]

Spain is the third-largest producer of pork meat in the world, and many farms in several regions depend on the evolution of this market. However, the current pricing system is unfair, as some actors have better market information than others. In this context, historical pricing is an easy-to-find and affordable data source that can help all agents to be better informed. However, the time lag in data acquisition can affect their pricing decisions. In this paper, we study the effect that data acquisition delay has on a price prediction system using multiple prediction algorithms. We describe the integration of the best proposal into a decision support system prototype and test it in a real-case scenario. Specifically, we use public data from the most important regional pork meat markets in Spain published by the Ministry of Agriculture with a two-week delay and subscription-based data of the same markets obtained on the same day. The results show that the error difference between the best public and data subscription models is 0.6 Euro cents in favour of the data without delay. The market dimension makes these differences significant in the supply chain, giving pricing agents a better tool to negotiate market prices. [ABSTRACT FROM AUTHOR]