kriging

Geographic Interpolate in Python [closed]

由 ﹥＞﹥吖頭↗ 提交于 2021-02-05 06:59:05
问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 months ago . Improve this question I have 500 points with longitude x, latitude y, altitude z, and the value at these points. On the other hand, I have other 200 points than I would like to interpolate, where the latitude, longitude, and altitude are known. I would like to interpolate considering

jk3d的使用方法详解——克里金插值的java实现

由 99封情书 提交于 2020-11-15 06:30:08
关于克里金的资料我这里就不罗嗦了，反正这种插值算法是非常优秀的，插值结果可靠度高，最近也是一个问题需要使用插值来解决，首选克里金，但是我大概了解克里金是怎么一回事儿，但要是自己去写算法实现，对我来说这成本还是高了些，网上搜索克里金插值的java实现，几乎搜不到，全是c、c++或是matlab的，本来想找个c的自己转成java，但是看了看c的源码，还是放弃了，因为那样只会把自己引入另一个无法预测深渊，功夫不负有心人，还是找到了一个java的实现，是一个jar包，也就是标题提到的jk3d，开始很开心，但过不多久，就发现自己被忽悠了，这个库没有例子，没有文档，google、baidu甚至bing都找不到有任何关于jk3d的例子，能找到的就只有一段话： jk3d is a java code for 3d interpolation of scattered data. jk3d uses a quality weighted ordinary Kriging method and optional an inverse distances weighting method. It has no graphical user interface and uses ASCII files to define the input. 然后就是一个下载链接，郁闷之极

GAM with “gp” smoother: predict at new locations

由 不羁的心 提交于 2020-01-30 08:04:40
问题 I am using the following geoadditive model library(gamair) library(mgcv) data(mack) mack$log.net.area <- log(mack$net.area) gm2 <- gam(egg.count ~ s(lon,lat,bs="gp",k=100,m=c(2,10,1)) + s(I(b.depth^.5)) + s(c.dist) + s(temp.20m) + offset(log.net.area), data = mack, family = tw, method = "REML") How can I use it to predict the value of egg.count at new locations (lon/lat) where I don't have covariate data, as in kriging ? For example say I want to predict egg.count at these new locations lon

Python - Kriging (Gaussian Process) in scikit_learn

由 别等时光非礼了梦想. 提交于 2019-12-30 06:20:50
问题 I am considering using this method to interpolate some 3D points I have. As an input I have atmospheric concentrations of a gas at various elevations over an area. The data I have appears as values every few feet of vertical elevation for several tens of feet, but horizontally separated by many hundreds of feet (so 'columns' of tightly packed values). The assumption is that values vary in the vertical direction significantly more than in the horizontal direction at any given point in time. I

Interpolation over regular grid in Python [closed]

由 喜欢而已 提交于 2019-12-29 10:16:09
问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 7 months ago . I have been struggling to inteprolate the data for "empty" pixels in my 2D matrix. Basically, I understand (but not deeply) interpolation techniques such as Inverse Distance Weighting, Kriging, Bicubic etc. I dont know the starting point exactly (either in the statement of the

Create variogram in R's gstat package

由 安稳与你 提交于 2019-12-24 07:37:09
问题 Suppose I have rainfall data taken at four weather stations over the span of 2004-2016. I fed the data into a database for retrieval in R. My goal is to take the data for every single day from that period, and krige using those values, repeatedly. So right now my data looks like this, each row corresponds to one of the points, and the columns in order are: lat, long, and rainfall_data. I followed this tutorial: https://rpubs.com/nabilabd/118172, to help me get started. So here's my code so

In R, kriging gives “system is exactly singular” error

由 一笑奈何 提交于 2019-12-24 01:33:09
问题 I'm having an issue where my specific inputs generate an error. library(kriging) x <- c(0.65,0.45,0.25,0.65,0.45,0.25,0.55,0.4,0.25,0.55,0.4,0.25,0.6,0.45,0.25,0.6,0.45,0.25,0.5,0.4,0.25,0.5,0.4,0.25,0.55,0.4,0.25,0.55,0.4,0.25,0.5,0.35,0.25,0.5,0.35,0.25) y <- c(0.25,0.45,0.65,0.2,0.4,0.6,0.25,0.4,0.55,0.2,0.35,0.5,0.25,0.4,0.6,0.2,0.35,0.55,0.25,0.35,0.5,0.2,0.3,0.45,0.25,0.4,0.55,0.2,0.35,0.5,0.2,0.35,0.45,0.15,0.3,0.4) r <- c(241.5,236.8333333,229.875,242,235.5,231.3333333,238,236.875,225

r heatmap on ggmap from kriging data

由 安稳与你 提交于 2019-12-06 12:29:05
问题 I want to plot a heatmap on a ggmap. library(ggmap) turku<-get_map('turku', zoom=13) turkumap<-ggmap(turku, extent="device", legend="topleft") turkumap turkumap+geom_density2d(mapping=aes(x = lon, y = lat),data = test, ) We have made a measurement campaign, so I have 4460 geo-referentiated points. To have a prediction map, I created a grid 400*400, for a total of 160000 points in which I calculate the prediction with kriging. To have a full picture of the phenomenon in my 3 km *3 km i think

Python - Kriging (Gaussian Process) in scikit_learn

由 回眸只為那壹抹淺笑 提交于 2019-11-30 19:30:18
I am considering using this method to interpolate some 3D points I have. As an input I have atmospheric concentrations of a gas at various elevations over an area. The data I have appears as values every few feet of vertical elevation for several tens of feet, but horizontally separated by many hundreds of feet (so 'columns' of tightly packed values). The assumption is that values vary in the vertical direction significantly more than in the horizontal direction at any given point in time. I want to perform 3D kriging with that assumption accounted for (as a parameter I can adjust or that is

Interpolation over regular grid in Python [closed]

由 一个人想着一个人 提交于 2019-11-29 19:54:39
I have been struggling to inteprolate the data for "empty" pixels in my 2D matrix. Basically, I understand (but not deeply) interpolation techniques such as Inverse Distance Weighting, Kriging, Bicubic etc. I dont know the starting point exactly (either in the statement of the problem or Python case). The problem definition: I have MxN matrix (regular grid) in which each pixel represents certain measurement value ( figure below and data used in this figure is here ). I wanted to interpolate the data for "question mark space" (white space which also consists of the same sized but empty pixels)
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