sandwichr: Spatial prediction based on spatial stratified heterogeneity

Lifecycle: stable

sandwichr is an R package for spatial prediction based on the spatial stratified heterogeneity (SSH) theory, which enables users to:

You may also find this package on CRAN:

Getting setup with R

Using the sandwichr package requires a recent version of R to be installed on your computer. The easiest way is to install it through CRAN , which stands for the Comprehensive R Archive Network.

Once R is installed, you can proceed to install the RStudio Integrated Development Environment (IDE) to have a much improved environment to work with R. Here is detailed description of RStudio. It is free to download on

Installing the package

Now you have the base R and a nice IDE installed on your computer, you can navigate to the console window (in RStudio, the bottom left panel) and install the sandwichr package by executing the following lines of code:



Once you install the package, we strongly recommend you going through a tutorial of this package to explore it in different scenarios of use. You can find it using the argument:



Please cite the following reference if you use the code. We are also working on an article about this particular software.

    author = {Jin-Feng Wang and Robert Haining and Tie-Jun Liu and Lian-Fa Li and Cheng-Sheng Jiang},
    title ={Sandwich Estimation for Multi-Unit Reporting on a Stratified Heterogeneous Surface},
    journal = {Environment and Planning A: Economy and Space},
    volume = {45},
    number = {10},
    pages = {2515-2534},
    year = {2013},
    doi = {10.1068/a44710}


If there are any questions or suggestions (or anything else you want to talk about concerning this project), please feel free to let us know! If you have found a bug, you can also file an issue.

Email: (Ms Yue Lin), (Dr Chengdong Xu), (Dr Jinfeng Wang*)

State Key Laboratory of Resources and Environmental Information System
Institute of Geographic Sciences and Natural Resources Research
Chinese Academy of Sciences
Beijing, 100101, China