Automation of Processing GNSS Track Records for Designing Intensity Maps
The diploma thesis compiles the solid foundation of the state-of-the-art and the growing importance of GNSS, data mining of track records and the geovisualization in Jupyter Notebook. Based on the previous research done in the field, the methodology is described to accomplish the first two sub-goals: (1) automation of spatial analyses of GNSS trajectory data on street network, (2) implementation of possible appropriate quantitative geovisualization. The tool development discusses the design, debugging and exceptions, and documentation and distribution of the product. Lastly, the third sub-goal has been addressed in the chapters of Testing and Assessment, Results and Discussion. The aim is to assess the results and outline the possibilities for further use.
The thesis overall provides a scientific approach to solving challenges for geovisualization of positioning data. The work profits from the community publishing reproducible, free and open-source research. New ideas for processing big geographic data, coming from satellites, sensors, wearable devices etc., have a vast potential to hold the development processes sustainably. Such approaches lead to an increased quality of life and prosperity in the form of economic assets. The geoprocessing tool is distributed in cloud platforms of two types. The published work anticipates contributing to the GI community with a new method to process GNSS track records and being the ground for future developments either in the infrastructure or geovisualization perspective.
Poster