Re: [R-sig-Geo] WPS client implementation in R

[Vijay Lulla]

Is WPS like Open Sound Control (OSC)?

On Tue, Feb 20, 2018 at 11:30 AM, Barry Rowlingson <
b.rowling...@lancaster.ac.uk> wrote:

> On Tue, Feb 20, 2018 at 3:51 PM, Javier Martínez-López <
> javi.martinez.lo...@gmail.com> wrote:
>
> > You can try OpenCPU (https://www.opencpu.org/). It works great!
> >
>
> I don't see anything WPS there. The advantage of implementing a WPS is that
> it is then available to anything supporting the WPS standard, so you could
> access your analysis from QGIS, for example.
>
>  If you implemented an analysis via some generic REST API you'd still have
> to write some client code to interpret the outputs.
>
> Barry
>
> [[alternative HTML version deleted]]
>
> ___
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>



-- 
Vijay Lulla, Ph.D.

Assistant Professor,
Dept. of Geography, IUPUI
425 University Blvd, CA-207C.
Indianapolis, IN-46202
vlu...@iupui.edu


http://vijaylulla.com

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Re: [R-sig-Geo] [DKIM] Re: Interpolating snowfall values on a Digital Elevation Model [SEC=UNCLASSIFIED]

[Li Jin]

The statement ‘the kriging functions in R still don't accept lat/long’ is 
incorrect. Please check the gstat and spm packages for details. When your data 
is collected from one utm, it is a good idea to project the data using utm. If 
the data is from two or more utms, you need to use different projection 
systems. The references provided demonstrated that the commonly used WGS84 is 
as good as relevant projection systems.

From: Dominik Schneider [mailto:dominik.schnei...@colorado.edu]
Sent: Wednesday, 21 February 2018 5:02 AM
To: Li Jin
Cc: Stefano Sofia; r-sig-geo@r-project.org
Subject: Re: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a Digital 
Elevation Model [SEC=UNCLASSIFIED]

The effects of spatial reference systems on interpolations and accuracy are 
minimal, and lat and long can be used.
Fair enough, thanks for sending the references. But, as far as I know, the 
kriging functions in R still don't accept lat/long.



On Mon, Feb 19, 2018 at 8:54 PM, Li Jin 
> wrote:
The effects of spatial reference systems on interpolations and accuracy are 
minimal, and lat and long can be used. Please see the following studies for 
details.

Jiang, W., Li, J., 2013. Are Spatial Modelling Methods Sensitive to Spatial 
Reference Systems for Predicting Marine Environmental Variables, 20th 
International Congress on Modelling and Simulation: Adelaide, Australia, pp. 
387-393.
Jiang, W., Li, J., 2014. The effects of spatial reference systems on the 
predictive accuracy of spatial interpolation methods. Record 2014/01. 
Geoscience Australia: Canberra, pp 33. 
http://dx.doi.org/10.11636/Record.2014.001.
Turner, A.J., Li, J., Jiang, W., 2017. Effects of Spatial Reference Systems on 
the Accuracy of Spatial Predictive Modelling along a Latitudinal Gradient, 22nd 
International Congress on Modelling and Simulation: Hobart, Tasmania, 
Australia, pp. 106-112.


-Original Message-
From: R-sig-Geo 
[mailto:r-sig-geo-boun...@r-project.org]
 On Behalf Of Dominik Schneider
Sent: Wednesday, 14 February 2018 3:21 AM
To: Stefano Sofia
Cc: r-sig-geo@r-project.org
Subject: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a Digital 
Elevation Model

You can't use a lat/long coordinate system when kriging because the concept of 
distance is ambiguous. Convert all your data a UTM grid like you had in your 
first post and it should work.

Another note, It looks like you are working at 0.01 deg which is on the order 
of 1km resolution so you may find  other covariates such as aspect, slope, and 
wind sheltering/exposure, terrain roughness for estimating snow on the ground 
useful. see some of the earliest papers by Carroll, Cressie, and Elder.

Carroll, S. S., and N. Cressie (1996), A comparison of geostatistical 
methodologies used to estimate snow water equivalent, *JAWRA Journal of the 
American Water Resources Association*, *32*(2), 267–278, 
doi:10./j.1752-1688.1996.tb03450.x.

Carroll, S. S., and N. Cressie (1997), Spatial modeling of snow water 
equivalent using covariances estimated from spatial and geomorphic attributes, 
*Journal of Hydrology*, *190*(1-2), 42–59.

Balk, B., and K. Elder (2000), Combining binary decision tree and 
geostatistical methods to estimate snow distribution in a mountain watershed, 
*Water Resources Research*, *36*(1), 13–26, doi:10.1029/1999WR900251.

Erxleben, J., K. Elder, and R. Davis (2002), Comparison of spatial 
interpolation methods for estimating snow distribution in the Colorado Rocky 
Mountains, *Hydrological Processes*, *16*(18), 3627–3649, doi:10.1002/hyp.1239.

Erickson, T. A., M. W. Williams, and A. Winstral (2005), Persistence of 
topographic controls on the spatial distribution of snow in rugged mountain 
terrain, Colorado, United States, *Water Resour. Res.*, *41*(4), W04014, 
doi:10.1029/2003WR002973.


On Tue, Feb 13, 2018 at 3:45 AM, Stefano Sofia < 
stefano.so...@regione.marche.it> wrote:

> Dear Daniel and list users,
> I tried to follow the instructions but I encountered two kinds of errors.
> This is a reproducibile code:
>
> 
> ---
> library(automap)
> library(ggplot2)
> library(gstat)
> library(raster)
> library(rasterVis)
> library(rgdal)
> library(maptools)
>
> ## LOADING DEM
> ita_DEM <- getData('alt', country='ITA', mask=TRUE)
> crs(ita_DEM) <- "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs
> +ellps=WGS84 +towgs84=0,0,0"
> #ita_DEM <- as(ita_DEM, "SpatialGridDataFrame")
> str(ita_DEM)
>
> ## LOADING RAINFALL DATA
> rain_data <- data.frame(Cumulata=c(11.8, 9.0, 8.0, 36.6, 9.4),
> Long_Cent=c(12.61874, 12.78690, 12.96756, 13.15599, 13.28157),
> Lat_Cent=c(43.79447, 43.85185, 43.76267, 43.03470, 43.08003),
> Altitude=c(112.20, 42.93, 36.14, 747, 465))
>
> stations <- 

Re: [R-sig-Geo] [DKIM] Re: Interpolating snowfall values on a Digital Elevation Model [SEC=UNCLASSIFIED]

[Dominik Schneider]

>
> The effects of spatial reference systems on interpolations and accuracy
> are minimal, and lat and long can be used.

Fair enough, thanks for sending the references. But, as far as I know, the
kriging functions in R still don't accept lat/long.



On Mon, Feb 19, 2018 at 8:54 PM, Li Jin  wrote:

> The effects of spatial reference systems on interpolations and accuracy
> are minimal, and lat and long can be used. Please see the following studies
> for details.
>
> Jiang, W., Li, J., 2013. Are Spatial Modelling Methods Sensitive to
> Spatial Reference Systems for Predicting Marine Environmental Variables,
> 20th International Congress on Modelling and Simulation: Adelaide,
> Australia, pp. 387-393.
> Jiang, W., Li, J., 2014. The effects of spatial reference systems on the
> predictive accuracy of spatial interpolation methods. Record 2014/01.
> Geoscience Australia: Canberra, pp 33. http://dx.doi.org/10.11636/
> Record.2014.001.
> Turner, A.J., Li, J., Jiang, W., 2017. Effects of Spatial Reference
> Systems on the Accuracy of Spatial Predictive Modelling along a Latitudinal
> Gradient, 22nd International Congress on Modelling and Simulation: Hobart,
> Tasmania, Australia, pp. 106-112.
>
>
> -Original Message-
> From: R-sig-Geo [mailto:r-sig-geo-boun...@r-project.org] On Behalf Of
> Dominik Schneider
> Sent: Wednesday, 14 February 2018 3:21 AM
> To: Stefano Sofia
> Cc: r-sig-geo@r-project.org
> Subject: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a Digital
> Elevation Model
>
> You can't use a lat/long coordinate system when kriging because the
> concept of distance is ambiguous. Convert all your data a UTM grid like you
> had in your first post and it should work.
>
> Another note, It looks like you are working at 0.01 deg which is on the
> order of 1km resolution so you may find  other covariates such as aspect,
> slope, and wind sheltering/exposure, terrain roughness for estimating snow
> on the ground useful. see some of the earliest papers by Carroll, Cressie,
> and Elder.
>
> Carroll, S. S., and N. Cressie (1996), A comparison of geostatistical
> methodologies used to estimate snow water equivalent, *JAWRA Journal of the
> American Water Resources Association*, *32*(2), 267–278,
> doi:10./j.1752-1688.1996.tb03450.x.
>
> Carroll, S. S., and N. Cressie (1997), Spatial modeling of snow water
> equivalent using covariances estimated from spatial and geomorphic
> attributes, *Journal of Hydrology*, *190*(1-2), 42–59.
>
> Balk, B., and K. Elder (2000), Combining binary decision tree and
> geostatistical methods to estimate snow distribution in a mountain
> watershed, *Water Resources Research*, *36*(1), 13–26,
> doi:10.1029/1999WR900251.
>
> Erxleben, J., K. Elder, and R. Davis (2002), Comparison of spatial
> interpolation methods for estimating snow distribution in the Colorado
> Rocky Mountains, *Hydrological Processes*, *16*(18), 3627–3649,
> doi:10.1002/hyp.1239.
>
> Erickson, T. A., M. W. Williams, and A. Winstral (2005), Persistence of
> topographic controls on the spatial distribution of snow in rugged mountain
> terrain, Colorado, United States, *Water Resour. Res.*, *41*(4), W04014,
> doi:10.1029/2003WR002973.
>
>
> On Tue, Feb 13, 2018 at 3:45 AM, Stefano Sofia <
> stefano.so...@regione.marche.it> wrote:
>
> > Dear Daniel and list users,
> > I tried to follow the instructions but I encountered two kinds of errors.
> > This is a reproducibile code:
> >
> > 
> > ---
> > library(automap)
> > library(ggplot2)
> > library(gstat)
> > library(raster)
> > library(rasterVis)
> > library(rgdal)
> > library(maptools)
> >
> > ## LOADING DEM
> > ita_DEM <- getData('alt', country='ITA', mask=TRUE)
> > crs(ita_DEM) <- "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs
> > +ellps=WGS84 +towgs84=0,0,0"
> > #ita_DEM <- as(ita_DEM, "SpatialGridDataFrame")
> > str(ita_DEM)
> >
> > ## LOADING RAINFALL DATA
> > rain_data <- data.frame(Cumulata=c(11.8, 9.0, 8.0, 36.6, 9.4),
> > Long_Cent=c(12.61874, 12.78690, 12.96756, 13.15599, 13.28157),
> > Lat_Cent=c(43.79447, 43.85185, 43.76267, 43.03470, 43.08003),
> > Altitude=c(112.20, 42.93, 36.14, 747, 465))
> >
> > stations <- data.frame(rain_data$Long_Cent, rain_data$Lat_Cent)
> > rain_data <- SpatialPointsDataFrame(stations, rain_data,
> > proj4string=CRS("+init=epsg:4326"))
> > stations <- SpatialPoints(stations,
> > proj4string=CRS("+init=epsg:4326"))
> >
> > ## EXTRACT THE ELEVATION VALUES TO MY POINTS
> > rain_data$ExtractedElevationValues <- extract(x=ita_DEM, y=stations)
> >
> > ## CREATE GRID FOR KRIGING OUTPUT
> > minx <-  rain_data@bbox[1,1]
> > maxx <- rain_data@bbox[1,2]
> > miny <- rain_data@bbox[2,1]
> > maxy <- rain_data@bbox[2,2]
> > pixel <- 0.01
> > grd <- expand.grid(x=seq(minx, maxx, by=pixel), y=seq(miny, maxy,
> > by=pixel))
> > coordinates(grd) <- ~x+y
> > gridded(grd) <- TRUE
> > proj4string(grd) <- 

Re: [R-sig-Geo] WPS client implementation in R

[Barry Rowlingson]

On Tue, Feb 20, 2018 at 3:51 PM, Javier Martínez-López <
javi.martinez.lo...@gmail.com> wrote:

> You can try OpenCPU (https://www.opencpu.org/). It works great!
>

I don't see anything WPS there. The advantage of implementing a WPS is that
it is then available to anything supporting the WPS standard, so you could
access your analysis from QGIS, for example.

 If you implemented an analysis via some generic REST API you'd still have
to write some client code to interpret the outputs.

Barry

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Re: [R-sig-Geo] WPS client implementation in R

[Javier Martínez-López]

You can try OpenCPU (https://www.opencpu.org/). It works great!

Best,

Javier

On Tue, Feb 20, 2018 at 4:31 PM, Barry Rowlingson <
b.rowling...@lancaster.ac.uk> wrote:

> On Tue, Feb 20, 2018 at 3:13 PM, Dr. Benedikt Gräler <
> b.grae...@52north.org>
> wrote:
>
> > Dear Barry,
> >
> > I am not quite sure what you are targetting at:
> > i) a solution to expose (geo) R functionality as a WPS on the web or
> > ii) a R package to ease the access to an exposed WPS.
> >
> > Regarding i), there is WPS4R (as a follow-up on the UseR! abstract;
> > available as part of the 52°North WPS bundle [1]) where annotated R
> scripts
> > can automatically be wrapped in a WPS process and exposed on the web (a
> > tutorial can be found here [2]).
> >
> > In terms of ii), I am not aware of any existing solution, but would be
> > interested to contribute; possibly also as a GSoC project (I'll sketch
> the
> > idea on the 52N's GSoC 2018 project page [3]).
> >
> >
>
> I'm currently more interested in a WPS client for R, to call WPS services.
>
> I think if I wanted to implement a WPS service in R I'd probably hook into
> pywps, but that's just my python bias against a java solution! I admit the
> 52N solution looks pretty neat (annotate an R script and there you have
> it).
>
> thanks
>
> Barry
>
> [[alternative HTML version deleted]]
>
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>

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Re: [R-sig-Geo] WPS client implementation in R

[Barry Rowlingson]

On Tue, Feb 20, 2018 at 3:13 PM, Dr. Benedikt Gräler 
wrote:

> Dear Barry,
>
> I am not quite sure what you are targetting at:
> i) a solution to expose (geo) R functionality as a WPS on the web or
> ii) a R package to ease the access to an exposed WPS.
>
> Regarding i), there is WPS4R (as a follow-up on the UseR! abstract;
> available as part of the 52°North WPS bundle [1]) where annotated R scripts
> can automatically be wrapped in a WPS process and exposed on the web (a
> tutorial can be found here [2]).
>
> In terms of ii), I am not aware of any existing solution, but would be
> interested to contribute; possibly also as a GSoC project (I'll sketch the
> idea on the 52N's GSoC 2018 project page [3]).
>
>

I'm currently more interested in a WPS client for R, to call WPS services.

I think if I wanted to implement a WPS service in R I'd probably hook into
pywps, but that's just my python bias against a java solution! I admit the
52N solution looks pretty neat (annotate an R script and there you have it).

thanks

Barry

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Re: [R-sig-Geo] WPS client implementation in R

[Dr . Benedikt Gräler]

Dear Barry,

I am not quite sure what you are targetting at:
i) a solution to expose (geo) R functionality as a WPS on the web or
ii) a R package to ease the access to an exposed WPS.

Regarding i), there is WPS4R (as a follow-up on the UseR! abstract; 
available as part of the 52°North WPS bundle [1]) where annotated R 
scripts can automatically be wrapped in a WPS process and exposed on the 
web (a tutorial can be found here [2]).


In terms of ii), I am not aware of any existing solution, but would be 
interested to contribute; possibly also as a GSoC project (I'll sketch 
the idea on the 52N's GSoC 2018 project page [3]).


Cheers,

 Ben

[1] https://wiki.52north.org/Geostatistics/WPS4R
[2] 
https://wiki.52north.org/Geoprocessing/GeoprocessingTutorials#WPS4R_tutorials

[3] https://wiki.52north.org/Projects/GSoC2018ProjectIdeas


On 20/02/2018 11:26, Barry Rowlingson wrote:

I'm wondering what's the state of WPS (Web Processing Service) client
implementations in R? All I can find are the abstract of a 2008 UseR! talk:

https://www.r-project.org/conferences/useR-2008/abstracts/Henneboehl+Pebesma.pdf

and what looks like code designed to call a custom WPS server in the GSIF
package:

https://github.com/cran/GSIF/blob/master/R/WPSworldgrids.R

for a server that doesn't seem to work at the moment (wps.worldgrids.org).

Any advance on those?

Barry

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--
Dr. Benedikt Gräler
52°North Initiative for Geospatial Open Source Software GmbH
Martin-Luther-King-Weg 24
48155 Muenster, Germany

E-Mail: b.grae...@52north.org
Fon: +49-(0)-251/396371-39
Fax: +49-(0)-251/396371-11

http://52north.org/
Twitter: @FiveTwoN

General Managers: Dr. Albert Remke, Dr. Andreas Wytzisk
Local Court Muenster HRB 10849
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Re: [R-sig-Geo] How to fit Spatial logistic regression models to DHS data

[Thierry Onkelinx]

Have a look at Zuur et al (2017) Beginner's Guide to Spatial, Temporal
and Spatial-Temporal Ecological Data Analysis with R-INLA

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkel...@inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///
To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may be
able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data. ~ John Tukey
///




2018-02-20 12:26 GMT+01:00 Bedilu Ejigu :
> I am analyzing geospatial data come from malaria intervention survey,
> to compare standard multilevel models with spatial models.   Some of
> the variables in my dataset are the following:
>
>
>
> 1.  malaria-malaria test result(1-presence, 0-absence) which is
> our outcome variable
>
> 2.  LATNUM-coordinates of the survey cluster
>
> 3.  LONGNUM- coordinates of the survey cluster
>
> 4.  hv024-region (categorical variable)
>
> 5.  hv025-residence (urban/rural)
>
> 6.  hv227 -net use (yes/no)
>
> 7.  hv270 -wealth index(poorest, poorer, middle, richer, richest)
>
> 8.  hc1 – age in days
>
> 9.  hc27- sex (male/female)
>
> 10.hc68-educational level (no education, primary, secondary)
>
> 11.anebin- Anemia level(1-anemic,0-nonanemic)
>
>
>
>
>
>  What I want to fit is a spatial logistic regression model by using
> the aforementioned variables using any of the packages in R which can
> handle the task (i.e. prevMap, geoRglm).  Can anyone help me on how to
> fit such a spatial logistic regression model? If possible, and someone
> did similar tasks before, could you share me your R code?
>
>
>
>  Sample dataset, which shows the structure of my dataset:
>
>
>
> hv024
>
> hv025
>
> hv227
>
> hv270
>
> hc1
>
> hc27
>
> hc68
>
> LATNUM
>
> LONGNUM
>
> anebin
>
> malaria
>
> western
>
> rural
>
> yes
>
> middle
>
> 18
>
> female
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 42
>
> female
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 15
>
> male
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 30
>
> male
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 39
>
> male
>
> primary
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 19
>
> male
>
> primary
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> no
>
> poorer
>
> 28
>
> male
>
> no education
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> no
>
> poorer
>
> 8
>
> male
>
> primary
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 32
>
> male
>
> no education
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 59
>
> male
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 40
>
> male
>
> NA
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 36
>
> male
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 19
>
> male
>
> no education
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 19
>
> female
>
> NA
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> urban
>
> yes
>
> richer
>
> 9
>
> female
>
> middle/jss/jhs
>
> 5.286215
>
> -2.76342
>
> 0
>
> 0
>
> western
>
> urban
>
> no
>
> richest
>
> 48
>
> female
>
> primary
>
> 5.286215
>
> -2.76342
>
> 0
>
> 0
>
>
>
>
>
> With best regards,
>
>
>
>  Bedilu
>
>
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>
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Re: [R-sig-Geo] How to fit Spatial logistic regression models to DHS data

[Anthony Damico]

hi, https://github.com/davidbrae/swmap  might help some but probably not
everything you need

On Feb 20, 2018 11:26 AM, "Bedilu Ejigu"  wrote:

> I am analyzing geospatial data come from malaria intervention survey,
> to compare standard multilevel models with spatial models.   Some of
> the variables in my dataset are the following:
>
>
>
> 1.  malaria-malaria test result(1-presence, 0-absence) which is
> our outcome variable
>
> 2.  LATNUM-coordinates of the survey cluster
>
> 3.  LONGNUM- coordinates of the survey cluster
>
> 4.  hv024-region (categorical variable)
>
> 5.  hv025-residence (urban/rural)
>
> 6.  hv227 -net use (yes/no)
>
> 7.  hv270 -wealth index(poorest, poorer, middle, richer, richest)
>
> 8.  hc1 – age in days
>
> 9.  hc27- sex (male/female)
>
> 10.hc68-educational level (no education, primary, secondary)
>
> 11.anebin- Anemia level(1-anemic,0-nonanemic)
>
>
>
>
>
>  What I want to fit is a spatial logistic regression model by using
> the aforementioned variables using any of the packages in R which can
> handle the task (i.e. prevMap, geoRglm).  Can anyone help me on how to
> fit such a spatial logistic regression model? If possible, and someone
> did similar tasks before, could you share me your R code?
>
>
>
>  Sample dataset, which shows the structure of my dataset:
>
>
>
> hv024
>
> hv025
>
> hv227
>
> hv270
>
> hc1
>
> hc27
>
> hc68
>
> LATNUM
>
> LONGNUM
>
> anebin
>
> malaria
>
> western
>
> rural
>
> yes
>
> middle
>
> 18
>
> female
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 42
>
> female
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 15
>
> male
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 30
>
> male
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 39
>
> male
>
> primary
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 19
>
> male
>
> primary
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> no
>
> poorer
>
> 28
>
> male
>
> no education
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> no
>
> poorer
>
> 8
>
> male
>
> primary
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 32
>
> male
>
> no education
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 59
>
> male
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> middle
>
> 40
>
> male
>
> NA
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 36
>
> male
>
> middle/jss/jhs
>
> 5.076585
>
> -2.88716
>
> 0
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 19
>
> male
>
> no education
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> rural
>
> yes
>
> poorer
>
> 19
>
> female
>
> NA
>
> 5.076585
>
> -2.88716
>
> 1
>
> 0
>
> western
>
> urban
>
> yes
>
> richer
>
> 9
>
> female
>
> middle/jss/jhs
>
> 5.286215
>
> -2.76342
>
> 0
>
> 0
>
> western
>
> urban
>
> no
>
> richest
>
> 48
>
> female
>
> primary
>
> 5.286215
>
> -2.76342
>
> 0
>
> 0
>
>
>
>
>
> With best regards,
>
>
>
>  Bedilu
>
>
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>
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[R-sig-Geo] How to fit Spatial logistic regression models to DHS data

[Bedilu Ejigu]

I am analyzing geospatial data come from malaria intervention survey,
to compare standard multilevel models with spatial models.   Some of
the variables in my dataset are the following:



1.  malaria-malaria test result(1-presence, 0-absence) which is
our outcome variable

2.  LATNUM-coordinates of the survey cluster

3.  LONGNUM- coordinates of the survey cluster

4.  hv024-region (categorical variable)

5.  hv025-residence (urban/rural)

6.  hv227 -net use (yes/no)

7.  hv270 -wealth index(poorest, poorer, middle, richer, richest)

8.  hc1 – age in days

9.  hc27- sex (male/female)

10.hc68-educational level (no education, primary, secondary)

11.anebin- Anemia level(1-anemic,0-nonanemic)





 What I want to fit is a spatial logistic regression model by using
the aforementioned variables using any of the packages in R which can
handle the task (i.e. prevMap, geoRglm).  Can anyone help me on how to
fit such a spatial logistic regression model? If possible, and someone
did similar tasks before, could you share me your R code?



 Sample dataset, which shows the structure of my dataset:



hv024

hv025

hv227

hv270

hc1

hc27

hc68

LATNUM

LONGNUM

anebin

malaria

western

rural

yes

middle

18

female

middle/jss/jhs

5.076585

-2.88716

0

0

western

rural

yes

poorer

42

female

middle/jss/jhs

5.076585

-2.88716

0

0

western

rural

yes

poorer

15

male

middle/jss/jhs

5.076585

-2.88716

1

0

western

rural

yes

poorer

30

male

middle/jss/jhs

5.076585

-2.88716

1

0

western

rural

yes

middle

39

male

primary

5.076585

-2.88716

0

0

western

rural

yes

middle

19

male

primary

5.076585

-2.88716

1

0

western

rural

no

poorer

28

male

no education

5.076585

-2.88716

1

0

western

rural

no

poorer

8

male

primary

5.076585

-2.88716

1

0

western

rural

yes

middle

32

male

no education

5.076585

-2.88716

1

0

western

rural

yes

middle

59

male

middle/jss/jhs

5.076585

-2.88716

0

0

western

rural

yes

middle

40

male

NA

5.076585

-2.88716

1

0

western

rural

yes

poorer

36

male

middle/jss/jhs

5.076585

-2.88716

0

0

western

rural

yes

poorer

19

male

no education

5.076585

-2.88716

1

0

western

rural

yes

poorer

19

female

NA

5.076585

-2.88716

1

0

western

urban

yes

richer

9

female

middle/jss/jhs

5.286215

-2.76342

0

0

western

urban

no

richest

48

female

primary

5.286215

-2.76342

0

0





With best regards,



 Bedilu


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[R-sig-Geo] WPS client implementation in R

[Barry Rowlingson]

I'm wondering what's the state of WPS (Web Processing Service) client
implementations in R? All I can find are the abstract of a 2008 UseR! talk:

https://www.r-project.org/conferences/useR-2008/abstracts/Henneboehl+Pebesma.pdf

and what looks like code designed to call a custom WPS server in the GSIF
package:

https://github.com/cran/GSIF/blob/master/R/WPSworldgrids.R

for a server that doesn't seem to work at the moment (wps.worldgrids.org).

Any advance on those?

Barry

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