The tool gpx2intensity is Attachment 1 to Master thesis Automation of Processing GNSS Track Records for Designing Intesity Maps. The notebook aims automate the processing of GNSS track data into a linear georeferenced layer suitable for methods of quantitative visualization. The author explains the approach and its settings for matching GNSS track records to a road network for visualizing passage frequency. The tool outputs geodata ready-to-use in GIS and the graduating color web map (see below). The main notebook is suppoerted by the following tools for other use cases.
Dark mode of output preview with a map layout description.
Voilà is user-friendly way to present Jupyer Notebooks workflow. No coding skills required. The launch of the virtual environment may take a while depending on the server status. If the launching fails, refresh the page again.
The python script allows a user to compress the GPX data with a simplification option1 available. The final output consists of generalised geographic coordinates only. Voilà tool allows the user upload max 10 MB, therefore, the tool may be used as an additional data preprocessing step.
🔗 Sample GPX Data Repository
The tool has been tested in three case studies of different environments. Firstly, it is an urbanised area of Olomouc – a city in the eastern Czech Republic. The second case study area is situated in the mountain range Malá Fatra, Slovakia. The third case study area is National Park Slovak Paradise. All three study areas are known for their potential to attract the visitors to explore the region, therefore, the data-backed planning is vital for their sustainable infrastructure development. The data has been preprocessed in the GPX compression tool.
The parametrization and data upload parts are the only parts expecting a direct user interaction of the workflow. The user sets 11 parameters that have a direct impact on the final product. The parameters consist of (2) directories for data input and output, (1) parameter for street network download, (9) map matching parameters such as level of generalization, GNSS measurement noise, thresholds etc. The detailed explanation of parametrization is explained in script comentaries or explanatory table. The data upload refers to the link of GPX data either to the local file or to the given sample data web repository. The access to the set of GPX files is mandatory for the automation process to execute.
Map Matching follows the structure of a Python package responsible for matching the GNSS measurements to the street network2. The matching is enriched with spatial filtering and generalization. The core part starts with the download of the street network from OSM3, the extent is defined by the input data and the parameter for buffering.
The MM works on the probabilistic calculation of possible paths, therefore, the parametrization is crucial for the result. The output of the analysis is a sequence of passed nodes on the street network. The nodes construct matched routes to the network. In the same step, the traces of mobility are concatenated into so-called tracks that serve as a reference to the matched result.
Post-processing has two main goals for automation. Firstly, the matched routes are overlayed with a blank street network on which are calculated frequencies i.e., the number of matched passages through the street. Secondly, the variables are transformed into data structures ready for data geospatial visualization and storage.
The result visualization and storage part serves the user as a preview of the result. The asset supports the user with a simple visualization in form of an interactive web map4. Based on the visualization one can readjust the parametrization that would fit the characteristics of the study area or the input data. The results can be stored as the web map (HTML format) or as geodata (GeoJSON, GeoPackage formats). More sophisticated and accurate maps should be made in GIS integrating the output geodata.
The work is part of the Master Thesis
"Automation of Processing GNSS Track Records for Designing Intesity Maps"
submitted in Palacký University in Olomouc and University of Salzburg
by Benjamín Šramo and supervised by Radek Barvíř in 2023.
Footnotes
-
Douglas, David; Peucker, Thomas (1973). "Algorithms for the reduction of the number of points required to represent a digitized line or its caricature". The Canadian Cartographer. 10 (2): 112–122. doi:10.3138/FM57-6770-U75U-7727 ↩
-
Meert, W., & Verbeke, M. (2018). HMM with non-emitting states for Map Matching. In European Conference on Data Analysis (ECDA), Date: 2018/07/04-2018/07/06, Location: Paderborn, Germany. Python package documentation. ↩
-
Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126-139. ↩
-
Wu, Q. (2021). Leafmap: A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. Journal of Open Source Software, 6(63), 3414. https://doi.org/10.21105/joss.03414 ↩