the spatial distribution patterns of temperature, precipitation, and humidity using geostatistical exploratory analysis (case study: central area of iran)

spatial autocorrelation (sa) is the correlations of the observed data of an area in the form of spatial pattern. the criterion of sa phenomenon occurrence is when the distribution of one observed variable value follows a particular pattern systematically. the sa analysis is most useful and an important tool for investigating the spatial database, this analysis not only itself gives useful information about the relationship between the inner side, but the results for the most complex statistical analysis are given. the aim of this study is to investigate the pattern of the spatial distribution of temperature, rainfall and humidity model of spatial autocorrelation using moran's central local and global statistics. the main aim of this study is to investigate the spatial distribution patterns of temperature, precipitation, and humidity using geostatistical exploratory analysis in the central area of iran. for this purpose, data from 72 synoptic stations of iranian meteorological organization for the period from 1972 to 2012 were collected, reviewed, and analyzed. methods used include ordinary, sample and general kriging with circular, gaussian, spherical, and exponential variograms, which is done in arc gis 10.2. then, the errors criteria measures to assess their accuracy and precision have been used. kriging is a moderately quick interpolator that can be exact or smoothed depending on the measurement error model. kriging uses statistical models that allow a variety of map outputs including predictions, standard errors, and probabilities. kriging assigns weights according to a (moderately) data-driven weighting function, rather than an arbitrary function, but it is still an interpolation algorithm and will give very similar results to those of others methods in many cases. all kriging techniques are based on the simple linear models as:                                                                                                                      (1) whereis the estimator of the true value at any location, andare the weights allocated to each observation such that                                                                                                                               (2) the technique minimizes estimation variables by solving a set of kriging equations, which include covariance between the point or volume to be estimated and the sample points and covariance between each pair of sample points .in this investigation, we have used the simple, ordinary, and universal kriging for interpolation of temperature, precipitation, and humidity. various results are obtained with the use of different interpolation methods on similar data. with the wide and increasing applications of the spatial interpolation methods, there is also a growing concern about their accuracy and precision. several error measurements have been proposed. commonly used error measurements include: mean error (me) or mean bias error (mbe), mean absolute error (mae), mean squared error (mse) and root mean squared error (rmse). if me and mse are closer to zero, and rmse is smaller, the better is the model. ase and rsme should be the same or close. if ase>rsme, then the method overestimates the primary variable. if ase1, the method underestimates the primary variable, and if rmsse