I have 5 different polygon vectors:
- All have over 20.000 features;
- All have multiple attributes;
- All overlap each other at least more than 80%, but are shaped differently;
- All display information of large areas, about 1/3 of the North Sea;
- All have an attribute called "benefit" which ranges between 0-1 (I normalized the data);
My mission: Using the attribute
"benefit" I want to find optimal locations for a specific activity. The sum of attributes for 5 different vector files, would indicate where I can find such optimal sites. The higher the better.
For example, let's say I choose two different geographic locations close to each other somewhere off the eastern coast of England. For each location I would retrieve the relevant value of the attribute "benefit".
For the attribute "benefit" I would get:
Location 1 layer 1: 0.5 layer 2: 0.3 layer 3: 0.6 layer 4: 0.8 layer 5: 0.9 Location 2 layer 1: 0.1 layer 2: 0.3 layer 3: 0.2 layer 4: 0.1 layer 5: 0.4 Location 1 would be better compared to location 2. Sum layer 1: 3.1 vs Sum layer 2: 1.1
THE QUESTION: What would be the best methdos to do this for all layers across the all extends?
At my disposal I have: QGIS, Python and pgAdmin
1) QGIS would be the least technical but I'm not even sure it's possible. With such large vector files I think it would crash. I could use the python console maybe...?
2) pgAdmin might be quicker, but for each feature I would need to check if it overlaps any of the other features...and with 5 vector files each containing more than 20,000 features...(some even 100,000)...I'm not sure if this is possible.
3) Python might also allow for a quick analyses of the data. But I'm not sure which packages to use.