The Living Thing / Notebooks : Spatial processes and statistics thereof

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

Especially, processe on a continuous index set with continuous state and undirected interaction. 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 also curious about how spatial statistics generalise to high-dimensional fields such as fitness landscapes, loss functions, and embedding of network processes in space, and other stuff that doesn’t spring to mind.

This is not about Geographic Information Systems, although 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

  1. Classic general-purpose spatial data anlaysis.
Python. Library of statistical functions for continuous-state spatial processes.
Python. GUI full of statistical analyses.


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Anselin, L., Cohen, J., Cook, D., Gorr, W., & Tita, G. (2000) Spatial analyses of crime.
Baddeley, A., Turner, R., Møller, J., & Hazelton, M. (2005) Residual analysis for spatial point processes (with discussion). Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(5), 617–666. DOI.
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