In many spatial data applications, the events at a location are highly influenced by the events at neighboring locations. In fact, this natural inclination of a variable to exhibit similar values as a function of distance between the spatial locations at which it is being measured is known as spatial dependence. Spatial autocorrelation is used to measure this spatial dependence. If the variable...

A hallmark of a plethora of intracellular signaling pathways is the spatial separation of activation and deactivation processes that potentially results in precipitous gradients of activated proteins. The classical metabolic control analysis (MCA), which quantifies the influence of an individual process on a system variable as the control coefficient, cannot be applied to spatially separated pr...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more diicult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial...

Nearshore circulation processes and local geomorphological patterns are thought to be important correlates of spatial variation in larval recruitment of benthic organisms. However, few studies have attempted to quantitatively separate their relative influences upon recruitment dynamics. Here we use 12 study sites spanning 250 km of the northern Chile upwelling ecosystem and the intertidal barna...

The main objective of this study is to evaluate the impact of maritime transportation instability on international trade instability based on wavelet smoothing and spatial panel data econometric method during the period 1990-2012. The results showed that the spatial effects have an impact on international trade instability. So that a sudden 100 percent increase in international trade in the nei...