Real-world geometry is a mess and an endless headache. You can write brute-force tooling to grind through it and auto-repair issues it can classify across the m

Real-world geometry is a mess and an endless headache. You can write brute-force tooling to grind through it and auto-repair issues it can classify across the myriad formats but it isn’t computationally cheap and you’ll still find “wtf” cases that you have to investigate manually. I’ve worked with official government GIS data sets where 1-5% of all geometry was defective in some way. You have to check it, the percentage of data sets with no defects is much smaller than you’d hope. 8,000 rows is small but the typical processing isn’t fast. Optimizing it has limited ROI. I use a custom Python library I wrote for this kind of work, which makes it a bit slow, but you constantly run across new types of inexplicable geometry issues so the ability to rapidly write custom routines is paramount, which Python excels at. GIS data is computationally expensive to process even beyond its obvious properties.