With the advancement of technology, expansion of research areas, deployment of different commercial and open source GIS systems has led to massive collection of data stored in different basics. Nowadays, we generate about several billion bytes of data every day, characterized by high dimensionality and large sample size and called Big Data or massive volumes of data. However, in the current situation, the data is mysterious, we have rich data but little information. MD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable data patterns. Fayyad et al. (1996)Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay Spatial data mining, by other means, is a distinct type of data mining. The main distinction between data mining and spatial data mining is that in spatial data mining tasks, we use not only non-spatial attributes but also spatial attributes. It has been said that spatial data is special and therefore needs to be processed or analyzed. special methods and techniques. This concept has appeared in various papers and review articles, although few of them argue against it. Most of these review papers suggest that extracting interesting patterns in geographic datasets is extremely difficult compared to extracting them in traditional data. because geographic or spatial data. In order to decide whether spatial data is special or not, I suggest spending our little time taking a brief overview to describe the term spatial analysis and then describing just two characteristics of spatial data. Spatial analysis is a special type of methods with the aim of identifying or describing the pattern to identifying and understanding the process associated with that particular pattern. Spatial analysis results change when the locations of the analyzed objects change. This is well explained by Tobler (1979) in his First Law of Geography: “everything is linked to everything else, but close things are more linked than distant things”. The first law of geography places more emphasis on spatial dependence or spatial autocorrelation, which implies that the phenomenon at one location is more likely to be repeated in a nearby location than a distant one. To deal with this type of situation, very special techniques are required. First, compare the pattern observed in the data (e.g., locations in point pattern analysis, values ​​at locations in spatial autocorrelation) to one in which space is irrelevant (Anselin, 1989 ). These datasets are scale dependent, the associated queries to extract information for this dataset are more advanced and much more complex, as explained in . This is in contrast to traditional statistical techniques which assume that observations are independent and therefore, in this sense, these techniques cannot be critically implemented for data showing spatial dependence behavior. Spatial data has another unique characteristic called spatial heterogeneity, which means that the behavior of relationships in space is not stable and varies across different areas of the map. A realistic perspective on most spatial data must assume that in general most spatial processes are non-stationary and