this study aims at analyzing spatial pattern and associations of fagus oriantalis lipsky in different vertical classes in intact beech forests. data collection was done in 25 ha plot in gorazbon district of educational and experimental forests of university of tehran. species and dbh of all trees with dbh > 7.5 cm were recorded. the location of each tree was determined using azimuth and distanc...

A spatial time series dataset is a collection of time series, each referencing a location in a common spatial framework. Correlation analysis is often used to identify pairs of potentially interacting elements from the cross product of two spatial time series datasets. However, the computational cost of correlation analysis is very high when the dimension of the time series and the number of lo...

This paper derives several Lagrange Multiplier tests for the panel data regression model wih spatial error correlation. These tests draw upon two strands of earlier work. The Þrst is the LM tests for the spatial error correlation model discussed in Anselin (1988, 1999) and Anselin, Bera, Florax and Yoon (1996), and the second is the LM tests for the error component panel data model discussed in...

The time and the spatial correlations play important roles in non-equilibrium statistical physics [1-16]. The behaviors of the correlation functions in ordinary fluids are well understood. It is known that there exist long-time tails in the time correlation functions in fluids at equilibrium [1, 2, 3, 4]. In addition, long-range correlations exist in the spatial correlation functions in non-equ...

 Fitting a suitable covariance function for the correlation structure of spatial-temporal data requires de-trending the data. In this article, some potential models for spatial-temporal trend are presented. Eventually the best model will be announced for de-trending tropospheric ozone concentration data for the city of Tehran (Capital city of Iran). By using the selected trend model, some ...

Most machine learning, data mining and statistical methods rely on the assumption that the analyzed data points are independent and identically distributed (i.i.d.). More specifically, the individual examples included in the training data are assumed to be drawn independently from each other from the same probability distribution. However, cases where this assumption is violated can be easily f...

the complexity of urban systems in our recent world, make it difficult to evaluation and explanation of their changes with traditional and customarily methods. in the past, remote sensing has shown an ability to detect and describe urban growth patterns at different spatial scales. with new remote sensing devices (e.g. ikonos), with innovative image processing techniques and with further develo...