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With recent advances in computer sciences (including geosciences) it is possible to combine various methods for geodata processing. There are many methods established for geodata scale restriction, but none of these take into account the concept of information entropy. Our research focused on using genetic algorithm that calculates information entropy in order to set an optimal number of intervals from original non-restricted geodata. We used fitness function by minimizing information entropy loss and we compared the results with commonly used classification method in geosciences. We propose an experimental method that provides promising approach for geodata scale restriction and consequent proper visualization, which is very important for geographical phenomena interpretation.

Background 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. Methods We combined the complex joint spatio-temporal analysis with comprehensive visualisation. We analysed the spatio-temporal distribution of the campylobacteriosis in the Czech Republic between 2008 and 2012. We applied three main approaches in the study: (1) the geovisual analytics of the surveillance data that were visualised in the form of bubble chart; (2) the geovisual analytics of the disease’s weekly incidence surfaces computed by spatio-temporal kriging and (3) the spatio-temporal scan statistics that was employed in order to identify high or low rates clusters of affected municipalities. The final data are stored in Keyhole Markup Language files and visualised in Google Earth™ in order to apply geovisual analytics. Results Using geovisual analytics we were able to display and retrieve information from complex dataset efficiently. Instead of searching for patterns in a series of static maps or using numerical statistics, we created the set of interactive visualisations in order to explore and communicate results of analyses to the wider audience. The results of the geovisual analytics identified periodical patterns in the behaviour of the disease as well as fourteen spatio-temporal clusters of increased relative risk. Conclusions We prove that Google Earth™ software is a usable tool for the geovisual analysis of the disease distribution. Google Earth™ has many indisputable advantages (widespread, freely available, intuitive interface, space-time visualisation capabilities and animations, communication of results), nevertheless it is still needed to combine it with pre-processing tools that prepare the data into a form suitable for the geovisual analytics itself. Electronic supplementary material The online version of this article (doi:10.1186/1476-072X-14-7) contains supplementary material, which is available to authorized users.

Due to the ongoing changes in settlement, it is becoming increasingly difficult to properly define rural and urban areas in Czechia. This delimitation problem has been intensively studied in Europe, including Czechia, for decades, but only the “crisp” rules have been set for this purpose. This is no longer sufficient because of substantial population movements. Our research focused on applying the fuzzy set theory to the delimitation of rural and urban areas and on a subsequent visualization and statistical comparison. This paper focuses on an introduction of such approaches and aims to present the chances of using geocomputational methods in geographical research. The main result is a new methodology that applies the principles of fuzzy approach (fuzzy sets and numbers operations, fuzzy regulation) on socio-economic data to show the transitional character of municipalities. Two ways of fuzzy approach, the easy-to-obtain Łukasiewicz T-norm and the more complex fuzzy regulation, are used in order to demonstrate its advantage over the Boolean logic procedure in the delimitation of rural and urban areas.