Population distribution modelling can benefit many different domains, for example, transportation, urban planning, ecology or emergency management. Information about the location and number of people in an affected area is crucial for decision-makers during emergencies and crises. Mobile phone data represents relatively reliable and time accurate information on real-time population distribution, movement and behaviour. In this study, we evaluate the spatio-temporal distribution of population derived from phone data of the selected pilot area (City of Brno, Czech Republic). Analysis is based on the dataset describing the estimated human presence (EHP) with two values – visitors and transiting persons. The temporal change of data is first analysed and further processed using two methodological approaches. First, the dasymetric method is used where the building geometry and technical attributes served as a target layer. Second, the results of building level analysis are transformed into a regular grid zone of both visitors and the general EHP. Resulting spatio-temporal patterns are compared to the census data. Results demonstrate how the proposed building level dasymetric approach can improve the spatial granularity of EHP. Potential use of proposed methodology within selected emergency situations is further discussed.

The Support Centre for Students with Special Needs at Masaryk University (the Teiresias Centre) in Brno, Czech Republic asked the team at the Department of Geography at the Faculty of Science to develop a technological procedure for the preparation of tactile maps from available sources of data. Most users would be blind and visually impaired students of Masaryk University. A set of scripts in Python language was prepared for the ArcGIS environment. The scripts prepare the source data, modify the layers, generate the layout of map sheets and export map sheets. The grid of map sheets covers the whole area of Brno City. Printed map sheets are combinable into larger areas. The scale of each map sheet is 1:2500.

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostěnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.

This article reports on an empirical study investigating cultural differences in the visuospatial perception and cognition of qualitative point symbols shown on reference maps. We developed two informationally equivalent symbol sets depicted on identical reference maps that were shown to Czech and Chinese map readers. The symbols varied in visual contrast with respect to the base map. Our empirical results suggest the existence of cultural influences on map reading, but not in the predicted direction based on the previous cross-cultural studies. Our findings stress the importance of considering the cultural background of map readers, especially when designing reference maps aimed for global online use.

An amount of data measured with sensors is increasing year to year. Every sensor has a location and sensor data are mostly measured for long time period, so visualization of location and regular updating of visualized value is necessary. Various characteristics (e.g. meteorological conditions) can be automatically read at frequent intervals and those readings can be aggregated into the interactive map visualization. This map must be not only legible but also understandable also for readers that are experts in their specialisation, however, not in cartography. This paper presents possibilities of using and implementation of adaptive cartography and visual seeking principles for interactive visualization and analysis of sensor based data measured in real time. Our solution is described on experimental application for precise farming that we developed during research project Agrisensor