I was tasked with developing a street colouring algorithm using Python3. The algorithm should color lines in the same color when perceived as a single street.
Out of the sample files we are interested in the .shp file primarily (I could work with just it using shapefile, but gpd needs also .dbf and .shx).
The task immediately looks like an assignment to color a graph using dfs, but remains to be seen if there's a better solution.
For each multiline end point, if there exist another multiline, it is of the same color (street),
- If only one, then it's the same color
- If more than one, then one of them, specifically, the one with the smallest angle to the current one. Additionally, if the smallest angle (e.g., >45°?), it's a different color.
- I used
geopandasto read and manipulate the shape data. I've added a new rowstyleto color them and check visited shapes. - The biggest challenge was ensuring that the algorithm was selecting only one neighbor (done in
identify_street_continuation()), and that it chose specifically the one that has the smallest angle to the current one (usingangle_between()with dot product formula, and then returned abs deviation from the straight line). I then passed the best candidate for either ends of the current segment to be painted recursively. - Finally, I utilized
matplotlibto plot the streets.
Super-worst case for my dfs is O(n^2) if all segments are neighbors of each other. But real-life case should be closer to O(n). All other operations should work either for O(n) or constant time.
However, there are some places that are quite suboptimal (e.g., using GDF.at() and .intersects(), or checking the same neighboring segments more than once) and could be significantly improved. One idea I'd like to explore is using an R-tree for that.
