Statistics on fields with index sets of more than one dimension of support and, frequently, an implicit 2-norm.

Especially, for processes on a continuous index set with continuous state and undirected interaction. Usually not calculated on fancy manifolds, but plain old euclidean space. Lattice models are frequently considered spatial statistics, but more arbitrary graph structures usually get filed under undirected graphical models/random fields. For spatial point processes I will make a new notebook. There are many other random fields we might also wish to infer that also relate to spatial index sets, and these can be taxonomised as I notice their existence.

I’m curious about how spatial statistics generalise to high-dimensional fields such as fitness landscapes, loss functions, and embedding of network processes in space…

This is not about Geographic Information Systems, or other spatial data viz although presumably some of those do use spatial statistics.

## Spatial point processes

A particular sub-case combining point processes with spatial statics, now with its own notebook

## Implementations of methods

### spatstat

Spatstat is the reference general-purpose spatial data analysis. based on R.

### Pysal

PySAL Python. Library of statistical functions for continuous-state spatial processes.

### PASSaGE

Passage is also Python. GUI full of statistical analyses.

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