Due to the suburbanisation process, it is becoming more difficult to properly define rural and urban areas in the Czech Republic. This delimitation problem has been intensively studied in Europe, including the Czech Republic, for decades, but only so-called ‘crisp’ rules have been set for the categorisation of urban and rural. This is no longer satisfactory because of substantial population movements. Our research focuses on applying fuzzy set theory to the delimitation of rural and urban areas and on the subsequent advanced cartographic visualisation. We used the principles of fuzzy regulation, or fuzzy inference systems, on socio-economic data to show the transitional character of municipalities. The generated Main map is at scale of 1:500,000, whereas secondary maps are at scale of 1:2,500,000. Map visualisation of municipalities in the Czech Republic provides a very unique combination of geographical information science, cartography and modern geo-computational methods. Information perception via a map is an adequate way to analyse geographic information, and the problem of delimiting rural and urban areas can be suitably visualised using these methods.

Quality of life and life satisfaction are topics that currently receive a great deal of attention across the globe. Many approaches exist, which use both qualitative and quantitative methods, to capture these phenomena. Historically, quality of life was measured exclusively by economic indicators. However, it is indisputable that other factors influence people’s life satisfaction, which is captured by subjective survey-based data. By contrast, objective data can easily be obtained and cover a wider range, in terms of population and area. In this research, the multiple fuzzy linear regression model is applied in order to explain the relationship between subjective life satisfaction and selected objective indicators used to evaluate quality of life. The great advantage of the fuzzy model lies in its ability to capture uncertainty, which is undoubtedly associated with the vague concept of subjective life satisfaction. The main outcome of the paper is the detection of indicators that have a statistically significant relationship with life satisfaction. Subsequently, a pan-European sub-national prediction of life satisfaction after the consideration of the most relevant input indicators was proposed, including the uncertainty associated with the prediction of such a phenomenon. The study revealed significant regional differences and similarities between the originally reported satisfaction of life and the predicted one. With the help of spatial and non-spatial statistics supported by visual analysis, it is possible to assess life satisfaction more precisely, while taking into account the ambiguity of the perception of life satisfaction. Additionally, predicted values supplemented with the uncertainty measure (fuzzy approach) and the synthesis of results in the form of European typology help to compare and contrast the results in a more useful manner than in existing studies.

Visual analytics aims to connect the processing power of information technologies and the user’s ability of logical thinking and reasoning through the complex visual interaction. Moreover, the most of the data contain the spatial component. Therefore, the need for geovisual tools and methods arises. Either one can develop own system but the dissemination of findings and its usability might be problematic or the widespread and well-known platform can be utilized. The aim of this paper is to prove the applicability of Google Earth™ software as a tool for geovisual analytics that helps to understand the spatio-temporal patterns of the disease distribution.

The differences in welfare amongst European countries are especially evident in border regions, and this affects cross-border cooperation and relationships. Due to the historical development of Central and Eastern European countries over the last century, the affected countries are unique “laboratories” for geographical research. This study assesses disparities in socio-economic indicators representing socio-economic phenomena in the Czech-Polish border region, through the analysis of cross-border (spatial) continuity, using quantitative methods (multivariate statistics and socio-economic profiling), GIS analysis and cartographic visualisation. It is demonstrated how such a combination of methods is useful for the comparison and evaluation of the complex socio-economic situations in neighbouring countries. This research project identifies the most suitable common indicators for a proper evaluation of cross-border (spatial) continuity, and it reveals the spatial patterns as reflected by a cluster analysis. The greatest cross-border (spatial) continuity is apparent in the easternmost part of the borderlands, while significant differences on both sides of the border are evident in the very central part of the areas under study. The paper also describes methodological aspects of the research in order to provide a quantitative approach to borderland studies.