The aim of this study was to conduct a geospatial analysis of natural disasters occurring worldwide between 1960 and 2018. A total of 9962 disasters were considered, covering 39,953 locations. These phenomena were analysed in terms of frequency and number to determine changes over time and predict future trends. The data included information such as location and year of occurrence. The following natural disasters were included in the analyses: droughts, earthquakes, extreme temperatures, floods, landslides, earth mass movements, storms, and volcanic activity. The analyses were carried out across three dimensions: by continent, by number of disasters in relation to population, and in relation to the area of the continents concerned. An additional objective was to visualise the distribution of disasters by affected countries. This aspect shows the incidence of disasters by year during the period under study, with a 20-year forecast for the coming years.

The article examines the quality of reprocessed GNSS data for determination of seasonal effect in a coordinates’ time series. The authors are looking for long-term changes of NEU components on the basis of weekly EPN solutions from the years 1996–2016. This is one of the first such in-depth studies using such a long time period and a large number of stations. Amplitude and its RMS values of fitting function are analysed. The analysis shows that there are significant repeatable coordinate variations: 4.7 mm, 2.9 mm and 8.3 mm for the dN, dE and dU components, respectively. The research also shows a group of coordinates’ time series with very small RMS values below the coordinates’ accuracy (1 mm for horizontal, 3 mm for vertical components), leading to a very good function fitting with relatively small amplitude magnitudes. The authors did not remark any connection with the above described phenomena and geographical location or type of setting up of a base station.

The main objective of this research work was to study the accuracy of GNSS code receivers under poor sky visibility conditions based on measurements on three different objects (point, line, and surface) and additionally to test results on point positioning with good sky visibility conditions. The measurement was based on 3 smartphones (in the same mode to check repeatability) and 2 handheld receivers (working in GPS+GLONASS modes). The methodology was based on the RTK technique, whose coordinates were assumed as a reference. Based on the results, the significant influence of measuring in the vicinity of high trees on the obtained accuracy was observed for both the precise geodetic equipment and the tested code receivers. More favorable results of point positioning were observed when using mobile phones. On the other hand, in the case of measurement in motion, the handheld receivers guaranteed higher accuracy. Moreover, the study showed that handheld receivers might achieve a better accuracy than smartphones, and that position might be determined with a greater accuracy and reliability. Furthermore, handheld receivers were characterized by a smaller number of outliers.

This research is focused on searching for frequency and noise characteristics for available GNSS (Global Navigation Satellite Systems). The authors illustrated frequency stability and noise characteristics for a selected set of data from four different GNSS systems. For this purpose, 30-s-interval clock corrections were used for the GPS weeks 1982–2034 (the entirety of 2018). Firstly, phase data (raw clock corrections) were preprocessed for shifts and removal of outliers; GLONASS and GPS satellites characterize a smaller number of outliers than BeiDou and Galileo clock products. Secondly, frequency and Hadamard deviation were calculated. This study concludes that the stability of GPS and Galileo is better than that of BDS (BeiDou Navigation Satellite System) and GLONASS. Regarding noise, the GPS, Galileo, and BDS clocks are affected by the random walk modulation noise (RWFM), flashing frequency modulation noise (FFM), and white frequency modulation noise (WFM), whereas the GLONASS clocks are mainly affected only by WFM.