We envision a way to quickly combine/join movement and context data (vector based). In contrast to raster data, vector data is not available at each point in space (rather it consists of discrete geographical features), thus exhibiting a different set of possible operations (such as k-NN joins).
In a preliminary meeting we discussed some possible inspirations:
- Jonietz, David, Dominik Bucher, and Stefano-Franscini Platz. "Towards an Analytical Framework for Enriching Movement Trajectories with Spatio-Temporal Context Data." Proceedings of the 20th AGILE Conference on Geographic Information Science (AGILE2017), Wageningen, The Netherlands. 2017. (https://pdfs.semanticscholar.org/33e8/0a0ed6cbaf4764b34ed76822de4f54c9d7d4.pdf)
- https://colorbrewer2.org
- https://en.wikipedia.org/wiki/DE-9IM
- The spatial operations defined in https://www.beck-shop.de/worboys-duckham-gis/product/11837170 (intersection, within, etc.)
- Time Geography https://onlinelibrary.wiley.com/doi/full/10.1111/j.1538-4632.2005.00575.x
- Andrienko, Gennady, Natalia Andrienko, and Stefan Wrobel. "Visual analytics tools for analysis of movement data." ACM SIGKDD Explorations Newsletter 9.2 (2007): 38-46. (https://dl.acm.org/doi/pdf/10.1145/1345448.1345455)
We envision a way to quickly combine/join movement and context data (vector based). In contrast to raster data, vector data is not available at each point in space (rather it consists of discrete geographical features), thus exhibiting a different set of possible operations (such as k-NN joins).
In a preliminary meeting we discussed some possible inspirations: