Kriging Puns

Hello, so I recently stumbled upon the concept of "regression kriging", and I am wondering if this is actually the tool I have been looking for for months at this point, or if I really am just misunderstanding the concept here, and this is not what I am looking for. I am using QGIS. Within the SAGA tools, there is the tool "Regression kriging", which looks like this:

https://preview.redd.it/0lym8k8w81i71.png?width=2348&format=png&auto=webp&s=8c71564823c7fd99b5ddefa3dbf3e6b251654132

I have been trying to figure out how to predict the cooling intensity of green spaces in a city and am wondering if this might be the tool to do it, since I have been trying to develop a predictive regression model to predict the cooling influence of the green spaces.

I have the following layers to work with:

1. a green space polygons layer that contains the area of each polygon and its cooling intensity
2. a land surface temperature layer

displayed as such:

https://preview.redd.it/kt16sblna1i71.png?width=1436&format=png&auto=webp&s=2c4539d9397b7d9b8f834e02117254ba560b163b

3. Multi-ring buffers around each green space polygon showing the "cooling intensity" at 30 meter intervals (Note: "cooling intensity" here is defined as the absolute value difference between the average LST of any one ring and the ring where the cooling effect of the green space has eventually died out, such that the cooling intensity of the green space itself, contained in the green polygons shown above, is the absolute value difference of the average LST of the polygon itself and the average LST of the ring where the cooling effect has died out):

https://preview.redd.it/t1zh0g6pb1i71.png?width=1842&format=png&auto=webp&s=93fd4257aa2726867596d193a575b223f1ba1a7e

so rather than than just recreating an LST map, which I already have, I am trying to make a raster map of cooling intensity, which shows the "spread" of cooling in all directions around each green space. The idea would be to turn my green space polygons into centroids:

https://preview.redd.it/luxn82y5d1i71.png?width=1741&format=png&auto=webp&s=b7bc4b2a878a72d177f623bca7f9e72c6651d814

And then enter these points into the "Regression kriging tool, to create a map that is a raster layer of predicted cooling intensity values based on the "Predictors" entered into this tool, such as what is displayed in this map (visualizing the darker areas as having more cooling intensity, and yellow as less co

I just read about Kriging and how the Variogram is build. And i understand, that one can represent the kriging equations in the matrix form and solve them. But i am curious how to extract them (in)directly from the Variogram. Is it true, that you need to find the weights that minimize the overall variance with the lagrange method?

I am working on a project for some research and was told to do (run?) Kriging in ArcMaps.

Right now I have heat temperature data from New York and was told to download data from the national land cover database, crop it to fit with my existing map of New York, and run the Kriging spacial analysis tool.

I have no clue how to handle the data in the national land cover database and any help would be much appreciate. My research advisor just told me to google which has been less than successful.

Thank you!!

Hello,

I am trying to figure out how 2d kriging works and other methods like spline, idw from math side.

I am looking for simple example that contains all steps with all calculations required to calculate a point.

Most of the examples I have seen explain methods without formulas details or assume some background statistic knowledge.

I am new at learning about gis. When looking at kriging examples on ArcGIS and few python libraries, I found that all of them were using z score of the actual input to train and predict. But I found no article or paper that mentions what kind of value to use for kriging. So my question is, if I have temperature values of locations, what happens when I use z score and what happens with raw values?

When I use ordinary kriging to interpolate elevation data I sometime get these star-like features that clearly are not representative of real elevations (see photo below; example of star in red box). Any idea why this is happening? I'm happy to provide more info or edits on the map (e.g. adding input point data).

https://preview.redd.it/nifz4sgz6hg61.jpg?width=816&format=pjpg&auto=webp&s=98b284ef725fc0c038c2aa8f4dc73ee7b64f3ca0

Is it possible to perform co-kriging in QGIS? Looks like only kriging function is available. Thanks guys!

Hi! I have some points with air quality attributes - visibility, PM10 etc. - that I want to create a continuous surface from. I'm using the kriging tool but the outputs aren't smooth - any idea what I'm doing wrong? I'm guessing I need to change some of the semivariogram parameters but not sure I understand them enough to know how! Screenshot included, using ArcPro. Thanks!

https://preview.redd.it/qp8b2ohpty361.png?width=1366&format=png&auto=webp&s=d98d614b426bbf5277218abda386776ed157d20a

I've been working with some data and trying to teach myself how to krige in order to get a look at water level. I have converted the points I have data on into a SPDF and have set up my variogram. I've been working with Nabil A.'s tutorial on kriging with the meuse river data set. My issue I'm having is I'm unsure how to create a grid to krige on, like meuse.grid. I thought I had succeeded by creating a grid using the bbox information, but it just reports an average for the whole area rather than a heatmap showing the distribution. Any help would be appreciated

In regression setting and by assuming that one uses the same covariance function, the three methods mentioned above yield the same result. So my question is quite simple: Why? Are there methods equivalent?

I have read countless documents (articles/papers) but I have not found any satisfying answer. What I have understood so far is that KRR is a frequentist point of view while GPR is a bayesian one.

I currently work in environmental consulting, and there are times occasionally where I will be needed to create many contour maps (10 or more) of a parameter (groundwater elevation or groundwater contaminant concentration). The process for creating one is not very long, but it can become tedious when I have to create many. Which has better geostatistical functionality?

end note: I am currently learning python, but have some familiarity with R from previous study.

Hi everyone,

I’m doing some research and I breaking my head with some questions:

• I need some specific raster maps with climate data (temperature, relative humidity, max monthly temperature, etc) from a specific time frame, so pretty much I have to do them myself, I have the specific weather data from the weather stations in the analysis area and the DEM files of the area, and I was wondering what is the best way to interpolate and create the raster files considering the DEM files and the Points, is it the Universal Kriging? I understand that it is the best method to interpolate temperature, but is it okay to be used for other climatic variables?
• Does anybody know if there is a good tutorial on how to use the Universal Kriging function? Preferable in QGIS, I found one for saga gis but is really hard to understand what they are doing, and how they are doing it, I have tried to do it in QGIS but I am a bit puzzled because the interface to run the function (Toolbox->Saga->Raster creating tools->Universal Kriging ) is somehow different to the one in SAGA Gis and does not include the option to select the DEM file
• I calculated some climatic indexes (continentality, oceanity, and aridity) in python using the weather data from these weather files, and I would like to do a raster file, is it okay to use the Kriging function? Or is there a way to use more variables in the process such as the DEM files, the temperature in the points, and the indices results of each point for more accuracy?

I’m working on a project with a friend and we want to make a suitability map showing where it’s best to look for real estate to invest in based on various parameters. We want a continuous surface of population. We are using Census data down to the block level for a county. I made center points for all census blocks and used geo statistical wizard and used ordinary kriging. And it seemed to work pretty well. But when I try to convert the geostatistical layer into a raster the upper bound of the prediction drops by like half of what the total population of one of the census blocks is (39000 to 18000). I feel like the total population of that one census block is pretty important for the analysis I’m trying to do.

From what I’ve read online about GA to Raster tool is that the GA layer doesn’t actually contain data like a raster but instead does on the fly interpolation. When you convert the GA layer to raster, the tool pulls the on the fly interpolation for each cell calculation. But, depending on the cell size, if the min or max points don’t fall within the center of the cell, it weights it from the distance to the center of the cell and you get a lesser maximum value.

Anyway, I was just wondering if anybody here knew how to best preserve original input values while kriging.

By using R version 3.4.2 and the library "geoR", I made kriging interpolations for different variables (bellow I give an example of my process). I also made a matrix with the coordinates for 305 trees with distinct marks (species, DBH, Height) that are within the same space for the interpolations, as seen in the image attached (https://imgur.com/SLQBnZH). I've been looking for ways to extract the nearest value from each variable for each tree and save the corresponding values in a data.frame or matrix, but haven't been successful, and I can't find specific answers to this.

One thing I've been looking at is trying to convert the Kriging result into a Raster (.tif) and proceed from there. But Kriging interpolations are made out of vector data, so is it even posible?

I'd be glad to receive any sort of help, thank you in advance!

P.S. I'm doing this so that I can latter use the data for spatial point patern analysis.

#Kriging####: PG<-read.csv("PGF.csv", header=T, stringsAsFactors=FALSE) library("geoR") x<-(PG$x) y<-(PG$y)

#Grid loci<-expand.grid(x=seq(-5, 65, length=100), y=seq(-5, 85, length=100)) names(loci)<-c("x", "y")

mix<-cbind(rep(1,10000), loci$x, loci$y, loci$x*loci$y)

#Model pH1.mod<-lm(pH1~yx, data=PG, x=T) pH1.kg<-cbind(pH1.mod$x[,3], pH1.mod$x[,2], pH1.mod$residuals) #Transform to geographic data pH1.geo<-as.geodata(pH1.kg) #Variogram pH1.vario<-variog(pH1.geo, max.dist=35) pH1.vario.mod<-eyefit(pH1.vario) #Cross validation pH1.valcruz<-xvalid(pH1.geo, model=pH1.vario.mod) #Kriging pH1.krig<-krige.conv(pH1.geo, loc=loci, krige=krige.control(obj.model=pH1.vario.mod[[1]])) #Predictive model pH1a.yhat<-mix %% pH1.mod$coefficients + pH1.krig$predict #Exchange Kriging prediction values pH1.krig$predict<-pH1.yhat #Image image(pH1.krig2) contour(pH1.krig2, add=TRUE)

#Tree matrix####:

CoA<-read.csv("CoAr.csv", header=T) #Data xa<-(CoA$X) ya<-(CoA$Y) points(xa,ya, col=4)

TreeDF<-(cbind.data.frame(xa, ya, CoA$Species, CoA$DBH, CoA$Height, stringsAsFactors = TRUE)) m<-(cbind(xa, ya, 1:305)) as.matrix(m)

#I tried to find the value of a point in space (trees [1:305]) through the minimum distance to a predicted value, (I suggest not running this since it takes too long):

for(i in 1:2){print(c(2:10000)[as.matrix(dist(rbind(m[i,], as.matrix(pH1.krig2$predict))))[i,2:10000]==min(as.matrix(dist(rbind(m[i,],as.matrix(pH1.krig2$predict))))[i,2:10000])])}

Hi, Im a newbie with arcgis and Im sorry if this isnt the right subreddit. But I have an assignment on geostatistical analysis, where we are asked this question : map or represent the kriging variance .

I mean I know how to interpolate using the kriging method but Im not sure about this. some help please ?

I was given a map that has some weather data that was created using a Kriging technique(not my work), and now I'm being asked to find a way to take that contour map which is based on lat/long, and turn it into tabular information town and village level municipalities. 200-300 of 'em.

I'm not even sure what questions to ask right now.

Can someone help me understand co-kriging and universal kriging.

How come someone use this in R:

  A_surface = autokrige(A ~ B, data, grid) 

https://rstudio-pubs-static.s3.amazonaws.com/389738_5e956d8027244bd1842ea46fdc8c7afb.html And called it cokriging? Shouldn't it be universal kriging?

Ps.: My project requires to interpolate soil texture (6 pts) using soil conductivity (~1000 pts), assuming they are highly correlated, which method should I use?

The gstat package has the Meuse data set, which includes the meuse data frame (with x,y columns to be used as coordinates) and meuse.grid (prediction locations). I’m trying to use kriging to create a groundwater contour map, but the documentation doesn’t specify how create my own prediction locations.

Hi /r/python, I wanted to share a project I’ve been working on for a few months. It’s called pyKriging and it's a native Python Kriging toolbox. Kriging is a tool for interpolating n-dimension datasets (a much better introduction can be found on Wikipedia). I developed this code for aircraft optimization work, but it’s also been used in biomedical device optimization research, and for things like mapping rent prices and transit times across London. I thought it might be of interest to some users here.

Pretty pictures and high level info can be found here.

Code is on GitHub.

The code is closely related to this Matlab code and this book on the subject.

The package is on PyPi and can be installed with:

sudo pip install pyKriging

Edit: /u/giorgosR has been a great help building this code, his contributions and testing are very much appreciated.

So I've been looking into the prior filmography of The OA cast, because it's obvious at this point that many of them either play or associate with various characters in similar roles to ones they've played in the past.

This line from the Wikipedia article immediately jumped out at me. Alice played the role of Tully Sorenson.

"Henry is tracked down by Tully Sorenson, a wealthy female book publisher, who has been impressed with his writing and is interested in publishing some of his work. She finds him through the detective she has hired. Knowing Henry is destitute, Tully pays him an "advance" of $500.

According to Fola's description of the game in Part 2, Level 2 is $500.

I am actually a mechanical engineer. For a project, I need to use Kriging to predict some values dependent on 2 independent variables. Just a couple of questions:

1. Is it easy to do manually/using a MATLAB code if there are just around 25-30 values? Or does it have to be done on a software for ease?

2. What book could I refer for just the basic introduction to Kriging? I went through a lot of stuff by Googling it, but didn't understand too much.

Thanks!

Hi all,

Anyone knows if I can use Kriging to interpolate a surface from points, if there is no spatial autocorrelation in those points attributes?

Or if Kriging interpolation method needs some type of specific autocorrelation pattern?

For example, if I calculate Moran's I and I get a dispersed (or clustered or random) autocorrelation pattern, can I use Kriging?

Thanks!

Hello there,

I am looking to smooth out price timeseries for trading on betfair. The observed data that I generally have is spaced evenly, but in steps. That is, the intervals starts out at 30seconds (at market open), then gets smaller down to 200ms at the time the market goes into liveplay (ie. market is suspended, kick off, etc).

I have been researching the best way to smooth out the intervals of this data, so I can run other indicators on it.

I have come to the following preliminary conclusions all of which is still half thought out/sketchy;

- Linear Interpolation is going to be the fastest, easiest way. But not necessarily very accurate. (I am working on live data at 200ms at some points)

- Polynomial Interpolation is going to be better than Linear Interpolation. It is not so simple to calculate. (Live data not so easy )

- Gaussian Process (Kriging). I don't quiet understand this, but it seems to me that this might be quick to calculate on a continuous stream of live data (although I may be wrong)

- SMA. I could just do no interval smoothing and run a Simple moving average. Seems difficult to do this on inconsistant intervals.

I'd be super interested in any thoughts on how you guys would tackle such a thing.

Thank you kindly for reading

I have a project that involves ordinary kriging. I have heard from multiple colleagues in my university's geography department that publishing kriging interpolated maps from ArcMap can be problematic. I have have never come across any literature discussing why ArcMap might be inappropriate for publishing kriging interpolated maps. I am quite SAS savy so I can do this work in SAS if need be, but I would like to understand why ArcMap is potentially a no-go and I desperately need to find some literature on this topic. Also Any information on rank transformed kriging would be helpful as well. Thank you!

Hello!

I'm a Master's student who just recently collected a lot of field data. I am trying to use Kriging to interpolate river temperatures based on several data collection points. My dilemma is that I want to create multiple layers via kriging based on time (essentially make a time series for kriging based on time). Here is an example of my current attribute table:

http://i2.photobucket.com/albums/y49/Octoberr/sheet1_zpsba40c383.jpg

Do I need to separate my attribute table into multiple spreadsheets? IS there a way to do multiple krigings at the same time so I won't have to create 4000+ layers manually? I am trying to create a time series video of river temperature variations over the whole season.

Any help would be appreciated! :)

Hi r/gis,

I am using Ordinary Kriging with 450 points in ArcGIS10.3 to krige a residual layer.

When I first produced the layer it looked very smooth:

http://imgur.com/csnSGQe

However when I converted it to a raster (100m resolution) it produced a very odd layer:

http://imgur.com/TwwjkQU

I have used kriging numerous times before and this is the first time I have encountered this kind of output.

Is this normal? Or is there an issue I should be addressing,

Thanks!

Does anyone have a good explanation about the differences between ordinary, simple and universal kriging? I kind of know the differences, but am having a hard time explaining it to a friend of mine who was asking about what method they should use for interpolation (he is not a geographer or a statistian, but has to do some data processing for a biological study). Thanks.

I just read about Kriging and how the Variogram is build. And i understand, that one can represent the kriging equations in the matrix form and solve them. But i am curious how to extract them (in)directly from the Variogram. Is it true, that you need to find the weights that minimize the overall variance with the lagrange method?

I just read about Kriging and how the Variogram is build. And i understand, that one can represent the kriging equations in the matrix form and solve them. But i am curious how to extract them (in)directly from the Variogram. Is it true, that you need to find the weights that minimize the overall variance with the lagrange method?

In regression setting and by assuming that one uses the same covariance function, the three methods mentioned above yield the same result. So my question is quite simple: Why? Are there methods equivalent?

I have read countless documents (articles/papers) but I have not found any satisfying answer. What I have understood so far is that KRR is a frequentist point of view while GPR is a bayesian one.