Best practices for spatial language data harmonization, sharing and map creation-A case study of Uralic.

Despite remarkable progress in digital linguistics, extensive databases of geographical language distributions are missing. This hampers both studies on language spatiality and public outreach of language diversity. We present best practices for creating and sharing digital spatial language data by collecting and harmonizing Uralic language distributions as case study. Language distribution studies have utilized various methodologies, and the results are often available as printed maps or written descriptions. In order to analyze language spatiality, the information must be digitized into geospatial data, which contains location, time and other parameters. When compiled and harmonized, this data can be used to study changes in languages' distribution, and combined with, for example, population and environmental data. We also utilized the knowledge of language experts to adjust previous and new information of language distributions into state-of-the-art maps. The extensive database, including the distribution datasets and detailed map visualizations of the Uralic languages are introduced alongside this article, and they are freely available.

A methodological Markov-CA projection of the greening agricultural landscape-a case study from 2005 to 2017 in southwestern Finland.

The aim of the reformed EU Common Agricultural Policy (CAP) 2014-2020 is to enhance greening via an ecological focus area, arable crop diversification, and the maintenance of permanent grasslands. This study tests the greening process in the case of agricultural landscape in southwestern Finland by projecting land use between 2005 and 2017. The study method integrates the quantitative results of Markov chains and spatial features of a cellular automata model. Initially, land use change was recognized by appropriate metrics. The trend of greening following the CAP policy indicated that permanent grassland patches were more persistent with forest patches than agricultural land that lost its vegetated strips to neighboring land use patches. The modeling approach was demonstrated to provide acceptable performance when used as a spatial assessment tool for observing critical patch level changes reflecting the greening agricultural policy.