[R-sig-Geo] Learning Resources Spatial Regression Models from the ground up

[Banerjee, Rudy]

https://a.co/d/1gF4NTE Bayesian Analysis for the Social Sciences by Simon Jackman m. He deep dives into the philosophy behind both probability axioms of finite (Kolgomorov) and countable (de Finetti) perspectives. His take on Bayes vs frequentist is a gem! He also clarifies the most severe shortcoming on the frequentist approach: that mixed modal priors cannot produce mixed modal posteriors … Regards, Rudy Banerjee

On Apr 24, 2024, at 8:53 AM, Christopher W. Ryan via R-sig-Geo <r-sig-geo using r-project.org> wrote:

Josiah--

I've found the following very helpful over the years:

Geographic Information Analysis, by David O'Sullivan and David Unwin

Spatial Point Patterns, by Adrian Baddeley, Ege Rubak, and Rolf Turner

Applied Spatial Data Analysis with R, by Roger Bivand, Edzer Pebesma,
and Virgilio Gomez-Rubio

Statistical Analysis of Spatial and Spatio-Temporal Point Patterns

The last 3 are, as the titles imply, focused specifically on spatial
point patterns. The first is a bit more general, including methods for
areal data.

I listed them in increasing order (in my opinion) of mathemtical complexity.

--Chris Ryan

In
Josiah Parry wrote:
Hey folks,

I'm hoping to build up my knowledge around spatial regression techniques
from the ground up—e.g. I'm not interested in R-INLA or other exceptionally
complex techniques.

I'm hoping this listserv has some recommendations for what readings /
models I should prioritize learning about in, possibly, an opinionated
order.

At the moment I've purchased "Modern Spatial Econometrics in Practice" by
Luc Anselin and Sergio Rey and will try to work through that. But if there
are additional resources that folks recommend that are friendly for the
not-so-math-inclined, I'd love to have a look at them!

The Spatial Regression section of the R-spatial book (
https://r-spatial.org/book/16-SpatialRegression.html) is good but with less
handholding than I might need.

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