Two toponymical maps presented in this paper show 2332 inhabited places of Lithuania that have names (oikonyms) associated with vegetation and animals. The maps and the dataset are the outcomes of a project that is aimed at combining the outcomes of professional onomastic research with an environment that fosters exploration. The oikonyms were extracted from the reference base dataset of Lithuania by means of an automated algorithm. Original cartographic symbols have been designed for depicting categories and species. The multiscale map application, with its exploratory tools, makes it easy to see spatial distribution of geographic names related with particular groups of plants and animals. Analysis of map data enables the assertion that local toponymy reflects a distribution of species that were characteristic to the territory over past centuries. The maps are supplemented by comparative density maps and statistical charts. The reference scale of the main maps is 1: 500,000. ARTICLE HISTORY

The data of the cartographic base at a smaller scale are updated by using spatial objects at a larger scale. However, this process in Lithuania is still mostly manual, only partially automated and following generalisation parameters that are usually selected subjectively. The success of several projects on the automated update of the spatial data at smaller scales implemented by national mapping agencies in other countries opened the way for the pilot project in Lithuania in 2013. The main goal of the project was to investigate the possibilities for the process of updating data of the cartographic base at a scale of 1:50,000 in Lithuania with the help of the automated generalisation of spatial data at a scale of 1:10,000. The above mentioned spatial data usually had only abstract specifications regardless of the interoperability of the data or possibilities for the optimisation of update processes between the data at different scales. Therefore the differences of semantics, attributes and representations were detected during the analysis of the spatial data at different scales. That made the full automation of the update process more complicated. However it is important to make the compatibility of the spatial data at different scales as soon as possible to simplify the process and reduce the usage of resources. The authors analyse the possibilities of the update of cartographic base at a scale of 1:50,000 and provide recommendations for the automation of this process. The proposed solutions of the detected problems are dedicated for the spatial data of the cartographic base in Lithuania due to the fact that the mentioned data may carry the specific character. The solutions were implemented using of ArcGIS software and tested on real spatial data sets. Therefore the presented ideas how to solve the problems may be applicable for generalisation of others spatial data sets.

The multi-scale base map compiled from the official 1:10 000 framework data is served as the background in the national geoportal map browser. High expectations of the users of this map – both up-to-datedness and comfort of use – are pressing to search for more efficient methods to generate it preserving highest cartographic quality. There are two ways towards that: (a) automated generalization of the georeference base dataset into smaller scale datasets that are then used as sources for the multi-scale web map and (b) automated cartographic generalization of the single source dataset into multi-scale map layers (used in Lithuanian geoportal). As it is commonly believed that generation of Web map layers from separately generalised data sources is more appropriate, the authors performed a research in order to compare the two methods in terms of precision of representations, efficiency of update and communicative quality of the resulting maps. Some procedures that allow for improvement of visualization quality when the second method is used are discussed in the paper. The main conclusion drawn from the research is that a multi-scale map generated by means of cartographic generalization can for many applications successfully replace multi-scale map generated from separately generalized data sources.