This is work in progress towards developing tiled geospatial data layers for symbolisation of complex multi-attribute choropleths.
The original proof-of-concept code is in R and Rmd files in the r-stuff folder. Needed datasets to run the code are in the data folder.
There is now a working version in python using geopandas and shapely. It will be more extensible in the longer run and will be the basis of any further work at this stage. The python implementation also appears less prone to topological glitches when tiles are dissolved to form tilings and does not require that we use any additional libraries to get reasonable(ish) performance when tiling large maps. The python source is in the weavingspace folder.
There are several jupyter notebooks in this repo that show examples of how to use the codebase, and the API is documented here.
The kind of things we can make are:
An overview of the concepts assembled from the proof-of-concept R code is on this webpage. A similar follow-up talk is available here
Slides from a more recent talk explaining the work, extended to tiled maps (of which woven maps are a special case) is available here.
Any of the notebooks in the main file list above may be of interest. Some background material and thinking about tiling is in this notebook.
Some earlier abortive work in python based on generating geometries directly is in the old-python-stuff folder.
Some sketches figuring things out are in sketches.
The follow documents describe aspects of the project (mostly referencing R code rather than the python refactoring):
- Notes on biaxial weave implementation as at 25 Nov 2021
- Notes on triaxial weave implementation as at 25 Nov 2021
- Towards triaxial weaves using matrices as at 25 Nov 2021
This work-in-progress summarises conceptual progress on this topic before it was completed around 28 Nov 2021.