SSSI is a professional spatial sampling and statistical inference tool. It can be used for sampling and statistical inference in environment, resources, land, ecological, social and economic sciences.
On a planned monitoring network (such as a planned sampling project in agriculture, demography, economy, environment, or epidemics, for example):
- Calculation of the optimum distribution and density of sample sites to form a highly efficient spatial sampling project or monitoring network;
On an existing monitoring network (such as an existing weather observation network, an existing epidemic surveillance network, an existing satellite monitoring scheme for example):
- Recommend the best overall valuation methods and recommendations to improve the monitoring network (based on monitoring the target characteristics and distribution of observations);
On published statistics (such as regional greenhouse gas (GHG) emissions; prevalence of a disease in a region, areas of contaminated soil in a region)
- Evaluation of the statistical errors (through the study of its sample distribution, density, statistical methods).
The software provides a total of six sampling and estimation methods: simple random sampling, systematic sampling, stratified random sampling, spatial random sampling, spatial stratified sampling, and sandwich estimation. In this software, we implement a new estimation method- the "sandwich" estimator, which is one of the major features of this software with a higher efficiency for sampling and statistical inference. On the basis of spatial stratified sampling, we develop a reporting layer, composed of the final reporting units that the user wishes to use, for example: county and/or provincial boundaries, watersheds, an artificial grid.
Methods:
1. Wang JF, Haining R, Liu TJ, Li LF, Jiang CS. 2013. Sandwich spatial estimation for multi-unit reporting on a stratified heterogeneous surface. Environment and Planning A, 45, 2515–2534. Download
2. Wang, JF, Jiang, CS, Hu, MG, Cao, ZD, Guo, YS, Li, LF, Liu, TJ, Meng, B. 2012. Design-based spatial sampling: Theory and implementation. Environmental Modelling & Software, 40, 280-288.Download
3. Wang JF, Haining R, Cao ZD. 2010. Sample surveying to estimate the mean of a heterogeneous surface: reducing the error variance through zoning. International Journal of Geographical Information Science, 24, 523-543. Download
4. Wang JF, Jiang CS, Li LF, Hu MG. 2009. Spatial Sampling and Statistical Inference (in Chinese). Beijing: Science Press. Browse
5. Wang JF, Liu JY, Zhuang DF, Li LF & Ge Y. 2002. Spatial sampling design for monitoring the area of cultivated land. International Journal of Remote Sensing, 13, 263-284.
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Acknowledgement: spatial sampling survey methods and the theory and its software (SSSI) are funded by the National Natural Science Foundation (No. 40471111) and National 863 hi-tech project (No. 2006AA12Z215).
