376 reputation
19
bio website
location Mexico
age
visits member for 1 year, 10 months
seen yesterday

Mar
27
asked Extract information of a raster overlayed on another raster with a lower resolution directly in python
Feb
26
accepted Extract points from regular data.frame with coordinates based on SpatialPolygonsDataFrame in R
Jan
17
revised Extract points from regular data.frame with coordinates based on SpatialPolygonsDataFrame in R
edited tags
Jan
17
asked Extract points from regular data.frame with coordinates based on SpatialPolygonsDataFrame in R
Oct
17
asked Python script to open many small images in QGIS
Jul
19
awarded  Citizen Patrol
Jun
27
comment Novel ways of filling in missing values in a raster
How would I include spatial correlation in the process? could I perhaps just include the coordinates as variables?
Jun
26
revised Novel ways of filling in missing values in a raster
deleted 8 characters in body
Jun
26
comment Novel ways of filling in missing values in a raster
Done, tell me if it helps you helping me :)
Jun
26
revised Novel ways of filling in missing values in a raster
Added a detailed explanation of my procedure.
Jun
26
comment Novel ways of filling in missing values in a raster
They all overlap perfectly. What do you mean by including spatial correlation terms in the model? One thing that worries me is that originally the rasters are in a finer resolutions than my objective variable. My variable of interest is at 1km but I have rasters that are at 30m. To train and then predict I first resampled the rasters to 1km. Would this come into play here?
Jun
26
comment Novel ways of filling in missing values in a raster
Depending on the variable, the missigness is caused by sensor faultiness or measurement error replaced by a missing value.
Jun
26
comment Novel ways of filling in missing values in a raster
I am using the layers as covariates for a predictive model. The model I am using does not handle missing values, so It simply does not calculate the pixels with a missing value in any of the rasters, leaving holes in my "predicted layer". Maybe the word robust was poorly used, I apologize. What I would be looking for is that the imputation conserves the underlying relation between my covariates and my objective variable. I'm not sure how to call this, the manifold assumption?
Jun
26
asked Novel ways of filling in missing values in a raster
Jun
16
awarded  Teacher
Jun
16
answered R gridding and contouring options
Jun
16
answered spatial and spatiotemporal analyses
Jun
16
comment Validation of Regression Kriging
Ok. I'm really just learning about this so don't expect that much. My main problem is that, for example, random forests tend to perform very well on the training set so kriging those "residuals" doesn't make much sense. These are where my doubts begin. I tried separating the data into 80% training and 20% testing. I did 10-fold CV (RF) on the 80%. Obtained prediction errors. I did kriging on those errors and then I added the krigings prediction on the 20% to the rf predcition (using all 80% as training) and the MSE seems to deacrease nicely. But bviously this is wrong on so many levels. Help.
Jun
15
comment Validation of Regression Kriging
I recently took a workshop where the professor (Gerard Heuvelink) and he mentioned that regression kriging could be based on any predictive model, he specifically mentioned random forests. I was just curious about this comment and wanted to try it out but I am still confused on to how to validate this type of exercise. @Whuber I am not sure what regression kriging you are thinking about, I am talking about the one defined in wikipedia: Regression-kriging is implementation of the best unbiased linear predictor for spatial data. At the same conference I saw it being used extensively.
Jun
14
comment Validation of Regression Kriging
I have been reading this document: Investigating the potentiality of Regression Kriging in the Estimation of Soil Prganic Carbon Versus the Extracted Result from the Existing Soil Map. By Navneet Kumar. There he explains the difference between Regression kriging, Kriging tiwh external drift and Universal kriging: UK and KED compute trend along with residuals simultaneously in one system and gives a combined kriging variance whereas in regression kriging the trend is first subtracted from the residuals, krige the residuals separately and finally add trend back...