Journal of Spatial Analysis

Spatial analysis as the main approach of geography was reviewed and searched through its historical development. The results of this exploratory research showed that this approach was born after the Second World War due to the overall interest of geographers to develop universal theories and laws. The advocators of this field believed that the old regional geography was not able to develop a scientific and applied knowledge. The main motivation of the development of the spatial analysis was the quantitative revolution of the 1960’s which was triggered by the article published by Shaeffer in 1953. This was followed by some prominent geographers such as Bungeh, Ulman, Barry, Hagget, Chorley and others. Bungeh and Harvey strengthened the philosophical foundation of spatial analysis and others such as Hagget , Chorley and Hajestrand published important books in the field of quantitative geography. The main objective of spatial analysis is to analyze the distributions through the identification of their global and local structures and reasoning these structures by their spatial relationship with other distributions. In this regard it uses quantitative data and mathematical language to achieve the spatial theories and laws.

     The spatial analysis studies the spatial distributions and structures. These are the entities that are not subject to the human interpretation and thinking. This approach is true in the both physical and human geography. The knowledge it tries to achieve is the theories and laws about the spatial distributions. The methodology of spatial analysis is the quantitative methods such as experiment and survey. Thus in terms of ontology the entities of spatial analysis are independent of human mind and objective. The spatial characteristics of distributions are not constructed but discovered. The methodology used in spatial analysis is quantitative and objective including some methods such as experiment and survey. In 1980 and onward, human geography tried to move toward qualitative methods such hermeneutics but during 21^st century all branches of geography are using quantitative methods more frequently than qualitative ones; but the use of the combined version of quantitative and qualitative methods is becoming more frequent day by day.

  The introduction of Geographic Information System as the operational environment for spatial analysis works the approach has become more widespread and dominant. Geographers are now able to analyze more spatial data and discover more spatial theories to solve the spatial problems. GIS is the main tool for spatial analysis and by introducing the science of geostatistics has improved the scientific and applied power of spatial analysis. The application of quantitative geography including geostatistics and GIS requires improved knowledge of mathematics, geometry and statistics; the main language of today geography. The spatial analysis covers the important topics of geography including spatial distributions, regions, spatial relations especially the relation between human and environment, spatial structures, spatial reasoning, interpolation, and the most important topic of spatial planning. The spatial analysis is the only scientific field to define and develop spatial planning. With correct and logic spatial planning there won’t be any environmental hazards. Because in any region all human settlements and activities are planned according the potentials of the region.

Drought is the most important natural disaster, due to its widespread and comprehensive short and long term consequences. Several meteorological drought indices have been offered to determine the features. These indices are generally calculated based on one or more climatic elements. Due to ease of calculation and use of available precipitation data, SPI index usually was calculated for any desired time scale and it’s known as one of the most appropriate indices for drought analysis, especially analysis of location. In connection time changes, most studies were largely based on an analysis of trends and changes in environment but today special attention is to the variability and spatial autocorrelation. In this study we tried to analyze drought zones in the North West of Iran, using the approach spatial analysis functions of spatial statistics and detecting spatial autocorrelation relationship, due to repeated droughts in North West of Iran and the involvement of this area in the natural disaster.

In this study, the study area is North West of Iran which includes the provinces of Ardebil, West Azerbaijan and East Azerbaijan. In this study, the 20-year average total monthly precipitation data (1995-2014) was used for 23 stations in the North West of Iran. In this study, to study SPI drought index, the annual precipitation data of considered stations were used. According to the statistical gaps in some studied meteorological stations, first considered statistics were completed. The correlation between the stations and linear regression model were used to reconstruct the statistical errors. Stations annual precipitation data for each month, were entered into Excel file for the under consideration separately and then these files were entered into Minitab software environment and the correlation between them was obtained to rebuild the statistical gaps. Using SPI values drought and wet period’s region were identified and zoning drought was done using ordinary kriging interpolation method with a variogram Gaussian model with the lowest RMS error. Using appropriate variogram, cells with dimensions of 5_×5km were extended to perform spatial analysis on the study area. With the establishment of spatial data in ARC GIS10.3 environment, Geostatistic Analyze redundant was used to Interpolation analysis Space and Global Moran's autocorrelation in GIS software and GeoDa was used to reveal the spatial relationships of variables.

The results showed that most studied stations are relatively well wet and this shows the accuracy of the results of the SPI index. Validation results of the various models revealed that Ordinary Kriging interpolation method with a variogram Gaussian model best explains the spatial distribution of drought in North West of Iran. So, using the above method the stations data interpolation related to SPI index in North West of Iran was done. The results showed that Moran index values for the analysis of results of standardized precipitation index (SPI) in all studied years, is more than 0.95. Since Moran’s obtained values are positive close to 1, it can be concluded that drought, in the North West of Iran during the statistical period has high spatial autocorrelation cluster pattern of 90, 95 and 99 percent. Results also showed that in all the years of study, Moran's global index is more than 0.95 percent. This type of distributed data suggests that spatial distribution patterns of drought in North West of Iran changes in multiple scales and distances from one distance to another and from scale to another and this result shows special space differences in different distances and scales in this region of the country. Results also showed that drought in North West of Iran in 2008 is composed of two parts: Moderate drought in parts of West and North West region (stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad and Piranshahr) and severe drought in the southeastern part of the study area (stations: Sarab, Khalkhal, Takab, Tabriz and Mianeh). So the pattern of cluster drought in the North West of Iran in 2008 is on the first and fourth quarter. The results of this index showed that drought and rain periods are similar in the studied stations. The results of the application of Moran's index about identifying spatial distribution of drought patterns showed that The values of the different years during the period,  have a positive a positive coefficient close to 1 (Moran's I> 0.959344) and this shows that the spatial distribution of drought is clustered. The results of the standard score Z values and the P-Value proved the clustering of spatial distribution of drought.

The results of the analysis of G public value, In order to ensure the existence of areas with clusters of high and low values showed that The stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad, Piranshahr and Parsabad follow the moderate drought pattern in the region and are significant at the 0.99 level. Jolfa station also has a mild drought of 0.95 percent confidence level and for Sardasht station is significant in 0.90 percent. High drought pattern in Sarab, Khalkhal, Takab, Tabriz and Mianeh stations was significant in 0.99 percent level and also for Ardabil, Sahand and Maragheh stations very high drought pattern was significant in 0.95 percent level and for Meshkinshahr and Ahar high drought pattern is significant in 0.90 percent. By detection of clusters of drought and rain in the North West of Iran using Moran’s spatial analysis technique and G general statistics a full recognition of the drought affected areas in this region can be obtained and take the necessary measures in its management