# envisim_samplr Provides design-based sampling methods, with a focus on spatially balanced and balanced sampling designs. > "everything is related to everything else, but near things are more related than distant things" > > — Tobler's first law of geography, Waldo Tobler **Balanced sampling** utilizes auxilliary information in order to obtain a sample where the Horvitz-Thompson (HT) estimator of the total of the auxilliary information equals the population total of the auxilliaries. This may be very efficient (yield relatively low variance) if there is a linear relationship between the auxilliaries and the variable of interest.[^1] **Spatially balanced sampling** uses auxilliary information in order to obtain a sample that is well-spread in auxilliary space, as well as being balanced. The samples can then be seen as a miniature version of the population. This generally yields low variances for the variable of interest, if there is a general relationship between the auxilliaries and the variables of interest.[^2] [^1]: Grafström, A., & Tillé, Y. (2013). Doubly balanced spatial sampling with spreading and restitution of auxiliary totals. *Environmetrics*, 24(2), 120-131. [^2]: Grafström, A., & Schelin, L. (2014). How to select representative samples. *Scandinavian Journal of Statistics*, 41(2), 277-290. ## Links - [Envisim](https://envisim.se)