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

Wed Apr 24 18:51:59 CEST 2024

This is short and well-written: https://us.sagepub.com/en-us/nam/spatial-regression-models/book262155,  Spatial Regression Models, Second Edition,  Michael D. Ward - Duke University, USA, Kristian Skrede Gleditsch - University of Essex, UK.

Roger

--
Roger Bivand
Emeritus Professor
Norwegian School of Economics
Postboks 3490 Ytre Sandviken, 5045 Bergen, Norway
Roger.Bivand using nhh.no

________________________________________
From: Josiah Parry <josiah.parry using gmail.com>
Sent: 24 April 2024 18:35
To: Roger Bivand
Cc: Christopher W. Ryan; r-sig-geo using r-project.org
Subject: Re: [R-sig-Geo] Learning Resources Spatial Regression Models from the ground up

Thank you, Roger!

On Wed, Apr 24, 2024 at 12:25 PM Roger Bivand <Roger.Bivand using nhh.no<mailto:Roger.Bivand using nhh.no>> wrote:
Please also consider:

@book{lesage+pace:09,
   author={James P. {LeSage} and R. Kelley Pace},
   title={Introduction to Spatial Econometrics},
   year={2009},
   publisher={Chapman and Hall/CRC},
   address={Boca Raton FL}
}

which provides the underpinnings to Golgher & Voss just suggested by Dexter. A good deal has been going on recently, both about spillovers, and very recent work by Bera & Koley on Rao score tests (aka Lagrange multiplier tests). I have some notes but no recording from recent lectures, so the notes are skeletal at best: https://rsbivand.github.io/PG_AGII_2sem/<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Frsbivand.github.io%2FPG_AGII_2sem%2F&data=05%7C02%7CRoger.Bivand%40nhh.no%7C479829832beb4d92138808dc647c9587%7C33a15b2f849941998d56f20b5aa91af2%7C0%7C0%7C638495733461093640%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=d88qeVfPDYgBhrdwhx0RZtQSXX5SELGjYXex3tUA6Js%3D&reserved=0>. In SDSr, my focus was on pointing up the topics areas where spatial econometrics could very well benefit from the much larger community in disease mapping and in ecology. In both of these broad communities, the dependent variable is often discrete, and both of these draw lots of maps. I haven't yet got Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach by Haining & Li, and expect it to be useful.

Hope this helps,

Roger

--
Roger Bivand
Emeritus Professor
Norwegian School of Economics
Postboks 3490 Ytre Sandviken, 5045 Bergen, Norway
Roger.Bivand using nhh.no<mailto:Roger.Bivand using nhh.no>

________________________________________
From: R-sig-Geo <r-sig-geo-bounces using r-project.org<mailto:r-sig-geo-bounces using r-project.org>> on behalf of Josiah Parry <josiah.parry using gmail.com<mailto:josiah.parry using gmail.com>>
Sent: 24 April 2024 17:58
To: Christopher W. Ryan
Cc: r-sig-geo using r-project.org<mailto:r-sig-geo using r-project.org>
Subject: Re: [R-sig-Geo]  Learning Resources Spatial Regression Models from the ground up

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Thank you, Chris! I can take a look at the first resource. At the moment my
interest is specifically in spatial econometric models and less so about
point patterns (for the time being).

On Wed, Apr 24, 2024 at 11:51 AM Christopher W. Ryan <cryan using binghamton.edu<mailto:cryan using binghamton.edu>>
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<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fr-spatial.org%2Fbook%2F16-SpatialRegression.html&data=05%7C02%7CRoger.Bivand%40nhh.no%7C479829832beb4d92138808dc647c9587%7C33a15b2f849941998d56f20b5aa91af2%7C0%7C0%7C638495733461105290%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=3A0FGWazHD8Y2ZkGipNU1kmdHUZXcFac7teNY3%2FaryI%3D&reserved=0>) is good but with
> less
> > handholding than I might need.
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
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>

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