I have to analyse data referred to different spatial partition of a city: some data belong to ZIP code areas, others to neighborhoods, others to an administrative division of the city, others are in a sort of grid.. They overlap in certain points but have different shapes.
How do you recomend me to procede?
My first idea is to chose a partition (the one that has bigger areas) and "convert" some partitions into it (es. spatial join or through centroids relocation). Another idea is to convert everything in a grid and then analyze not considering areas but more buffer zones.
Any suggestion is highly appreciated, thank you!
Hi Dan, thanks for answering.
I am analysing socio-demographic patterns in the city. Different layers, with different territorial partitions, have different attributes like: presence of foreign students, hotels, price of houses, ecc.
What do you suggest? Maybe put all in grids is the best?
Thank you for your answer 🙂
I would pay some attention to how you are going to have to represent some of the data since some spatial patterns may need to be aggregated while others may need to be potentially over-represented.