Exploiting Parallelization in Spatial Statistics: An Applied Survey Using R

Exploiting Parallelization in Spatial Statistics: an Applied Survey using R

Computing tasks may be parallelized top-down by splitting into per-node chunks when the tasks permit this kind of division, and particularly when there is little or no need for communication between the nodes. Another approach is to parallelize bottom-up, by the substitution of multi-threaded low-level functions for single-threaded ones in otherwise unchanged user-level functions. This survey e...

Applied Statistics for Bioinformatics using R

Spatial Statistics Applied to Commercial Real Estate

Portfolio theory shows that diversification can enhance the riskreturn trade-off. This study uses the absolute location of commercial real estate property along with spatial statistics to address the inherent problem of determining geographical diversification based upon a set of economic and property-specific attributes, some of which are unobservable or must be proxied with noise. We find tha...

Analysis of Drought Spatial Statistics in Iran

Drought is one of the environmental events and an inseparable part of climatic fluctuations. This phenomenon is one of the main characteristics of the various climates. Awareness of spatiotemporal behavior is effective in land planning. The spatial statistical methods provide the means by which they analyze the spatial patterns of random variables such as precipitation. In this study, using the...

Using Spatial Statistics In GIS

A Geographical Information System (GIS) provides a powerful collection of tools for the management and visualization of spatial data. These tools can be even more powerful when they are integrated with methods for spatial data analysis. In this context, we provide several examples that show the power of exploratory spatial data analysis (ESDA) within a GIS and how this can provide the foundatio...