Re: [R-sig-Geo] Error when saving an sf (data) object to file as a shapefile
Hello.
The "Write error" seems a permissions issue. Do you have write
permission to the folder you are trying to save the file? You can see
the default path by typing getwd() in the console.
Regarding the error message you got while trying to write the shape
using the shapefile function, I guess you mean the function belonging
to the raster package, which you may don't have installed in your
computer. That may be the reason why R can't find the function after
searching in your libraries.
If you type ?raster::shapefile in the console, with the raster package
installed, you will find the documentation of the function, where the
argument x (the source) is defined as "character (a file name, when
reading a shapefile) or Spatial* object (when writing a shapefile)".
So if you provide an object (which you did), it must be an object of
class Spatial*, but the one you provided seems to be an sf object. You
can try to coerce the Spatial to an sf object, but I advise against
that, because it is an unnecessary workaround when using the sf
workflow.
BTW, try to avoid using shapefile as the default format.
Best regards.
El sáb., 20 jun. 2020 a las 4:44, Lom Navanyo () escribió:
>
> Hello,
>
> I have had to merge a shapefile that I read into R as an sf object with a
> .csv data containing some variables. Now I want to save the merged data to
> a file (a folder on my pc). I am however getting following error:
>
> Error in CPL_write_ogr(obj, dsn, layer, driver,
> as.character(dataset_options), :
> Write error
>
> Below is a snippet of code used:
> library(sf)
> library(dplyr)
> library(ggplot2)
> library(stringr)
> library(rgdal)
> library(sp)
>
> parcel1 <- st_read("parcels_all.shp")
> balance5 <- read.csv("Balanced_5.csv")
>
> mergedparcel <- merge(parcel1, balance5, by=c('PARCEL_ID','CAL_YEAR'),
> all.x = FALSE, all.y=TRUE)
>
> st_write(mergedparcel,"mergedparcel.shp")
>
> I also used the shapefile function thus:
>
> shapefile(mergedparcel , "D:/Documents/mergedparcel.shp")
> This also gives me:
> Error in shapefile(mergedparcel, "D:/Documents/
> Documents/mergedparcel.shp") :
> could not find function "shapefile"
>
> Am I doing this right?
> Any suggestion to resolve this issue would be appreciated.
>
> -
> Lom
>
> [[alternative HTML version deleted]]
>
> ___
> R-sig-Geo mailing list
> R-sig-Geo@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
--
José Ramón Martínez Batlle
Investigador/Profesor Universidad Autónoma de Santo Domingo (UASD)
Correo electrónico: jmartine...@uasd.edu.do
Página web: http://geografiafisica.org
___
R-sig-Geo mailing list
R-sig-Geo@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Re: [R-sig-Geo] Inference of local Gi*
Thanks Roger for your feedback and clarification. Best regards. El lun., 27 abr. 2020 a las 5:04, Roger Bivand () escribió: > On Sat, 25 Apr 2020, Jose Ramon Martinez Batlle wrote: > > > Dear Anaïs. > > > > I am sure more experienced members will give you a better answer, but > until > > that I will try to help. > > > > 1) If I understood correctly, the spatial objects have 15 000 and 30 000 > > points in each case study, respectively. If this is the case, I am afraid > > that nb objects of such large datasets surely would have an impact on the > > system performance when used in subsequent tasks. The best I can suggest > is > > to try some sort of spatial binning if possible (e.g. hexbins), but at > the > > same time accounting for the modifiable areal unit problem. > > > > 2) The spdep:localG help page states that "For inference, a > Bonferroni-type > > test is suggested in the references, where tables of critical values may > be > > found". The source mentioned is free access, and can be found here: > > > > Ord, J. K. and Getis, A. 1995 Local spatial autocorrelation statistics: > > distributional issues and an application. Geographical Analysis, 27, > 286–306 > > > https://onlinelibrary.wiley.com/doi/pdf/10./j.1538-4632.1995.tb00912.x > > > > Standard measures (critical values) for selected percentiles and number > of > > entities, are included in Table 3 of the cited reference. Since the > values > > returned from localG are Z-values, you can use them to determine whether > > the critical value chosen is exceeded and thus infer significant local > > spatial association for each entity. > > Thanks, José, you are quite correct that false discovery rate problems are > among the main reasons why so-called "hot-spot" analyses may be very > misleading, in appearing to give an inferential basis for apparent map > pattern. > > In our survey paper with David Wong referenced on ?localG, > https://doi.org/10.1007/s11749-018-0599-x, we show that the analytical > and > bootstrap-based inferences are similar - the normality is related not to > the underlying variable seen globally, but the the local behaviour of the > statistic. For this reason, bootstrap permutation implementations are not > included in spdep, though the code is available if need be. Please > indicate whether users would like this code included for comparative > purposes here or in a github issue on > https://github.com/r-spatial/spdep/issues/. > > Further, the LOSH statistic, which is a measure of local spatial > heteroscedasticity building on local G, provides a little insight into the > problems raised for so-called "hot-spot" analyses by variability across > the study area in the behaviour of the variable of interest. If, for > example, the variable of interest is influenced by a background variable > with a spatial pattern, we will probably find "hot-spots" which look like > the omitted background variable on a map. > > While local G cannot take residuals of a linear model, local Moran's I can > do so. For local G, we do not have exact case-by-case standard deviates; > we do have these for local Moran's I as discussed in the article with > David Wong, and they very typically reduce strongly the counts of > apparently significant local statistcs even before adjusting p-values for > FDR. Finally, only some local measures can adjust for global > autocorrelation - unadjusted local measures also respond to the presence > of global autocorrelation. > > On balance, judicious choice of class intervals in mapping a variable of > interest may prove more helpful than trying to present wobbly inferences > from ESDA. > > Hope this isn't too discouraging, > > Roger > > > > > > Kind regards. > > José > > > > El vie., 24 abr. 2020 a las 14:00, Anaïs Ladoy () > > escribió: > > > >> Dear list members, > >> > >> I'm currently working on a point dataset, from which I want to conduct > >> a Hot Spot Analysis with local Gi* statistics (Getis-Ord). > >> > >> I'm trying to find a way of computing its significance. I see two ways > >> of computing significance in this case: > >> > >> 1) Compare the obtained local Gi from spdep::localG to a normal > >> distribution. But here I have several questions : > >> a) In my first case study (BMI value of 15 000 participants in a cohort > >> study), the distribution of local Gi is far from normal (it is bimodal > >> with a mode around very negative values and a mode around 0). However, &
Re: [R-sig-Geo] Inference of local Gi*
Dear Anaïs. I am sure more experienced members will give you a better answer, but until that I will try to help. 1) If I understood correctly, the spatial objects have 15 000 and 30 000 points in each case study, respectively. If this is the case, I am afraid that nb objects of such large datasets surely would have an impact on the system performance when used in subsequent tasks. The best I can suggest is to try some sort of spatial binning if possible (e.g. hexbins), but at the same time accounting for the modifiable areal unit problem. 2) The spdep:localG help page states that "For inference, a Bonferroni-type test is suggested in the references, where tables of critical values may be found". The source mentioned is free access, and can be found here: Ord, J. K. and Getis, A. 1995 Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27, 286–306 https://onlinelibrary.wiley.com/doi/pdf/10./j.1538-4632.1995.tb00912.x Standard measures (critical values) for selected percentiles and number of entities, are included in Table 3 of the cited reference. Since the values returned from localG are Z-values, you can use them to determine whether the critical value chosen is exceeded and thus infer significant local spatial association for each entity. Kind regards. José El vie., 24 abr. 2020 a las 14:00, Anaïs Ladoy () escribió: > Dear list members, > > I'm currently working on a point dataset, from which I want to conduct > a Hot Spot Analysis with local Gi* statistics (Getis-Ord). > > I'm trying to find a way of computing its significance. I see two ways > of computing significance in this case: > > 1) Compare the obtained local Gi from spdep::localG to a normal > distribution. But here I have several questions : > a) In my first case study (BMI value of 15 000 participants in a cohort > study), the distribution of local Gi is far from normal (it is bimodal > with a mode around very negative values and a mode around 0). However, > I do need a normal distribution of Gi in order to compare it with a > normal distribution, right? Or am I missing something here? What should > I do in this case? > b) In my second case study (Years of life lost for 30 000 individuals), > the distribution of Gi returned by spdep::localG is approximately > normal but the standard deviation is far from 1. In fact, in > spdep::localG, the Gi values are supposedly standardized (from what I > understood using an analytical mean and variance). Should I use these > to compare to a normal distribution, or should I use raw G values > (using return_internals=TRUE) and standardize them with the observed > mean and variance of Gi? Does it cause a problem that my observed > variance does not match the analytical variance? > > 2) Compute permutations > However this is not implemented in R for localG. I tried using PySAL > but the initial file is big and the weight file is huge, and my > computer crashes. Any thoughts to solve this issue? > > Thank you for any feedback. > Kind regards, > Anaïs > > -- > Anaïs Ladoy > PhD student, Laboratory of Geographic Information Systems, Swiss > Federal Institute of Technology in Lausanne (EPFL), Switzerland. > > ___ > R-sig-Geo mailing list > R-sig-Geo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > -- *José Ramón Martínez Batlle* *Investigador/Profesor Universidad Autónoma de Santo Domingo (UASD)* Correo electrónico: jmartine...@uasd.edu.do Página web: http://geografiafisica.org [[alternative HTML version deleted]] ___ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
