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TopSpatial Statistics Modeling
Spatial modelling is analysing various data of spatial process: 1. geostatistical data, 2. lattice data, and 3. point patterns (Figure 1).
The notations that will be used are based on (Cressie, 1993; Diggle, 1983; 2003; Ripley, 1981). The study region (domain) is given by D. Usually D is a subset of 2-dimensional space, but it could be 1-dimensional or 3-dimensional or even beyond so 
, where d is the dimension we choose. The vector s denotes the data location. Locations in D are denoted by the vector s. For example, in 2-dimensional space, s will have 2 components containing the coordinates (x, y), such as latitude and longitude. At location s, we obtain some value z. So z(s) is the values for each value z that correspond location s. Finally, we will assume that Z(s) is a random variable at each location. The general spatial model could be defined as 
. Basic models in spatial statistic are: 1. Geostatistical data where D is a continuous fixed subset of 
; Z(s) is a random vector at location 
. 2. Lattice data where in this case D is a fixed countable subset of 
such as a grid some representation with nodes; Z(s) is a random vector at location 
. 3. Point Patterns where D is a random subset of 
denoted as a point process; if Z(s) is a random vector at location 
then it is a marked spatial point process; if Z(s) ≡ 1 so that it is a degenerate random variable, then only D is random and it is called a spatial point process.