Sandwich mapping of rodent density in Jilin Province, China

Abstract

Rodents are the main host animals that spread plague, and Spermophilus dauricus (S. dauricus) is the most common rodent in North China. In this study, a rodent density survey was carried out in China's Jilin Province from April to August 2005. Moran's I and semivariogram curves were used to investigate the spatial distribution characteristics of the sampling data. We found that the spatial auto-correlation index was low and failed to generate a meaningful semivariogram curve. In this case, commonly used interpolators, such as kriging, were not suitable for mapping density over the study area. However, the Sandwich model, which is based on spatial stratified heterogeneity, could be applied to our data. Our results showed that the type of soil and land use significantly influenced the distribution of rodent density, and the interactive effect of these variables was much stronger than that of each variable alone. The Sandwich-estimated rodent density map showed that rodent density increased from the southeast to the northwest in Jilin Province. Finally, a framework of a rodent density survey using the Sandwich model was introduced.

SBDM v1.0: A scaling-based discretization method for the Geographical Detector Model

“…Secondly, the results of discretization should be as suitable as possible for the subsequent models. If we arbitrarily discretize continuous data before modelling, the subsequent analysis and application may miss valuable information or lead to misspecification of models, e.g., Boolean networks (Kauffman, 1969), generalized logical networks (Song et al, 2009), or Sandwich Interpolation model (Wang et al, 2002(Wang et al, , 2013Liu et al, 2018). Therefore, the effectiveness of the discretization algorithm is not only related to discrete data distribution, but also related to the subsequent application models.…”

The spatial statistic trinity: A generic framework for spatial sampling and inference

“…Besides the above two case studies, Liu et al (2018) tested the SSH and SAC of rodent density in a study area, and they found that the former is significant and the latter is weak. Then, both the SSH-based Sandwich estimator (Wang et al, 2013a) and SAC-based kriging were applied to the same sample to map the population, respectively.…”

sandwichr: Spatial prediction in R based on spatial stratified heterogeneity

“…This study uses the Sandwich mapping model, which is based on the SSH property, to compute the values of the reporting units by propagating the means of the homogeneous strata to the intersecting reporting units (Wang et al, 2013). Even when the SAC is weak or absent, the Sandwich model allows for the creation of reliable predicted surfaces (Liao, Li, Zhang, Xia, et al, 2018; Liu et al, 2018; Yang et al, 2017). Another advantage of the Sandwich model is that it considers the stratification of heterogeneity that is independent of the reporting units; therefore, each stratum can contain samples that fall outside of its intersecting reporting units (Wang et al, 2013).…”