Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pi...

Many theories in political science predict the spatial clustering of similar behaviors among neighboring units of observation. This spatial autocorrelation poses implications for both inference and modeling that are distinct from the more familiar serial dependence in time series analysis. In this paper, I examine how political scientists can diagnose and model the spatial dependence that is pr...

Spatial autocorrelation in species abundances indicates a lack of independence between sample locations and causes problems in distribution modelling. Knowing the cause of such spatial autocorrelation is vital for selecting the best suited modelling methods. Most autocorrelation in distributions is caused by autocorrelation in the underlying environmental conditions. The aim of the study was to...

T he main objective of this study is to estimate the spatial spillover effects of the economic growth among the selected countries of MENA region. For this purpose, the Spatial Durbin Model in the framework of spatial dynamic panel data was estimated during the period of 1970-2010. The spatial Diagnostic tests affirmed the occurrence of spatial positive autocorrelation phenomenon for...