A data warehouse that integrates mobility data from various sources with corresponding context data such as weather conditions, economics, etc. The main subjects are trajectories from Beijing and Hannover. The visual layer allows analyzing trajectories on the interactive world map with various filters. It's also possible to analyze data with PostgreSQL queries.
- Postgresql (with PostGIS and MobilityDB)
- Python (mainly GeoPandas)
The data warehouse follows Kimball's star schema with the main table trajectory as fact connected to dimension tables.
Fact constellation schema would allow more in-depth analysis, but conducted tests have shown that the star schema performs much better and provides sufficient analytic possibilities.
- Geolife GPS trajectory dataset: https://www.microsoft.com/en-us/download/details.aspx?id=52367
- Hannover trajectories: https://data.uni-hannover.de/dataset/single-user-trajectory-collection-for-the-region-of-hannover
- weather: https://www.ncei.noaa.gov/cdo-web/datasets
- economics: https://databank.worldbank.org/source/world-development-indicators
- fuel prices for China: https://info.ceicdata.com/
- fuel prices for Hannover: https://www.destatis.de/EN/Home/_node.html
(after clicking on the link don't close 'Add data to map' panel but wait a bit for data to load)
