GEOGRAPHICAL DATA
Year:2020 | Volume:29 | Issue:115|Papers Count:12

Introduction: Due to the importance of precipitation in various aspects of human life, precipitation data are largely applicable in different fields of study. Therefore, accurate measurement of precipitation is considered to be crucialin various fields such as agriculture, water resources, and industrymanagement. Due to the problems related to generalization of point precipitation to regional precipitation, alternative methods have been proposed forthe measurement of this variable. In many cases, short reference period, inadequate density of stations and poor quality of data collected from precipitation measurement networks have challenged the analysis of this climate variable. In order to overcome these problems, it is necessary to identify alternative sources, evaluate and use them to estimate the amount of precipitation. The present study primarily seeks to evaluate precipitation data from the TMPA and provide calibration data for arid, semi-arid, Mediterranean, humid, and very humid regions of Iran on a monthly scale. Materials and Methods: In the present study, monthly precipitation data of 15 synoptic stations in 5 regions of Iran (arid, semi-arid, Mediterranean, humid and very humid) were selected as reference data and monthly precipitation data from the TMPA (3B43-v7) were corrected based on them. To ensure reliability of results and reduce errors, stations were selectedrandomly from 15 separate provinces with different topographic conditions. A 20-year reference period (1998-2017) was selected for the study. Collected satellite data have a monthly temporal resolution and a spatial resolution of 0. 25 degrees covering 50th parallel south to 50th parallel north. Table 1 shows features of the selected stations and their corresponding pixels. Pre-processing included quality control, homogeneity test, and data accuracy test. Usinga long-term reference period of 20 years, different statistical criteria to evaluate satellite data and a correction relationindependent from ground data are among the advantages of this research. In this study, a more efficient method is used to determine errors and one of the most modern methods of calibration is also used. Followingthe application of log transformation and multiplicative model, monthly C parameter was calculated to rectify satellite data collected from different climates. Results were evaluated using R2 (Coefficient of Determination), MBE, MAE and RMSE. Results and Discussion: Findings indicated that the distribution of initial data obtained from TMPA satellite in a monthly scale is similar to the distribution of pattern obtained from ground data (due to a correlation of above 75% (R2>0. 6)). Satellite data collected from arid areas are usually overestimated, while data collected from humid areas are generally underestimated. However, determination coefficients (R2) of different climates show a strong correlation between these two sources of data. The initial TMPA data have estimated the monthly precipitation of Bam, Piranshahr and Abali stations with the least amount of error. The highest level of errors were obtained from Marivan, Bandar Anzali, and Koohrang stations. In other words, the highest level of errors have occurred in the very humid region. Calibration of TMPA data collected from the 5 different climates indicated that correction of TMPA monthly data would improve valuesestimated from satellite images. Mean bias error (MBE) was reduced by 88. 7, 95. 3, 68. 4, 38. 4 and 63. 9 percentin arid, semi-arid, Mediterranean, humid and very humid climates, respectively. Values of the correction parameter (C) in the arid climate indicate that a reduction factor has been applied to rectify satellite data collected in each month of the year. In the semi-arid climate, reduction factorswere obtained for each months of the year. A reduction factor is also required to rectify data collected in the warmest months of the year (June, July, and August) in the Mediterranean climate. Due to the low precipitation of these months, overestimation seems reasonable in these areas. A reduction factor should also be applied in the humid climate for 6 months of spring and summer. Considering the precipitation rate in these areas, decreasing precipitation rate in these seasonsresults in overestimation and error. Due to the significant precipitationrate in the cold months of the year (autumn and winter), decreasing factorand underestimation are expected to occur. In the very humid climate, a reduction factor should be appliedin the warmest months of the year (June, July, and August). Due to the low precipitation rate of these months and higherfrequency of cloudy days, overestimation will be reasonablein these areas. Due to underestimationin the coldest months of the year (autumn and winter), coefficients higher than one must be corrected. Conclusion: Based on the results, the model used to correct precipitation in all 5 climates have reduced errors in precipitation measurement. However, this improvement was more obvious in arid and semi-arid climates. Sincea large part of Iran havean arid and semiarid climate, this calibration model is highly recommended. In addition, the final correction model does not depend on ground data and thus, applying the calibration modelto areas other than the specified stations will also be useful.

Introduction: With their dynamic nature, water resources are essential fortheenvironment and play a vital role in human life, development of communities, and climate change. Water bodies have been declining over time due tothe rapid growth of urbanization, excessive abstraction of water, damming, increasing demand for agricultural products, pollution anddegradationofthe environment. Therefore, monitoring water bodies and retrievingrelated information are essential for management of environmental issues and decision making in this field. Accurate recognitionof water bodiesiscrucialin many applied fields, such as environmental monitoring, production of land cover and land use maps, flood risk assessing and monitoring, and drought monitoring. Modern methods such as object-oriented processing take advantage of remote sensing capabilities to make accurate and precise recognition of water bodies possible. Classical methods on the other hand, cannot accurately classify satellite imagery with similar spectral information merging into each other. This reduces the accuracy of pixel-based classification methods. Therefore, object-oriented processing of satellite images is used in the present study to obtain precise maps for the identification of waterbodies. Materials and Methods: A part of Aji Chai River, near the city of Khajeh in Harris County, has been selected as the study area. The total study area included 28 square kilometers. Based on the aim of the present study, the study area was selected in a way to contain linear features, arable lands, and other topographical and human-madefeatures (shading factor) which interfere with the extraction of water bodies and reduce the classification accuracy. Object oriented methods (the closest neighbor and fuzzy object-oriented methods) were used in the present study to identify and extract water bodies from high resolution images (Sentinel 2A imagery). Discussion and results: Different functions used in OBIA techniques, such as GLCMtextual features, average number of bands in the image, geometric information (shape, compression and asymmetry), and normalized difference vegetation index(NDVI) were used in the present studyto precisely extract land cover. Moreover, algorithms with the highest membership degree in the class of water bodies were considered as effective factors in classification. Usual methods of extracting and monitoring water bodies use spectral information of pixels, and therefore, have limited ability in distinguishing water bodies from linear features, such as roads, clouds, shaded regions, and residential areas. These methods also have limited capabilities in mountainous areas, especially when they are required to separate water from snow. In other words, these methods cannot separate water bodies from regions with lower albedo. Therefore, the present study takes advantage of object-oriented methods (the nearest neighbor and fuzzy methods) and evaluate their effectiveness in the extraction of water bodies. Conclusion: In this study, the nearest neighbor and fuzzy object-oriented methods were used to extract water bodies and their efficiencies were compared. To improve the results in the nearest neighbor method, the separation space between the samples was optimized using the FSO algorithm, then the water bodies were extracted with 95% accuracy and a Kappa coefficient of 93%. Findings of the present studyindicated that this method cannot distinguish water bodies from shaded regions, and linear featuressuch as roads, and residential areas, and categorizes these features as water bodies, which reduces the accuracy of the final results. In the next step, water bodies were once more extracted using object-oriented fuzzy model. In this method, membership degrees were first calculated for each sampleand then applied in the classification procedure. High accuracy of the results of this method (overall accuracy of 98% and a kappa coefficient of 96%) indicated the superiority of this method over the previous one (nearest neighbor). In this method, water bodies are completely distinguished from linear features such as roads, as well as shaded regions, clouds and residential areas. The results of this study can be generalized to other rivers and water bodies. Compared to classical methods, object-oriented methods are more time efficient and accurate.

Introduction: : Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such as natural resource management, engineering, and infrastructure projects, crisis management and risk analysis, archaeology, security, aviation industry, forestry, energy management, surveying and topography, landslide monitoring, subsidence analysis, and spatial information system (Makineci&Karabö rk, 2016). Satellite images are one of the main sources used to produce DEM. In satellite remote sensing, optical and radar imagery are often used to generate DEM. Compared to optical satellite images, the main advantage of using radar satellite images for DEM production is that they are available in different weather conditions and even at nights. Two strategies used to produce DEM from radar satellite images include radar interferometry and radargrammetry(Saadatseresht&Ghannadi, 2018). Phase information of the images is used in radar interferometry, whereas domain information of the images is used in radargrammetry (Ghannadi, Saadatseresht, &Eftekhary, 2014). Moreover, short baseline image pairs are used in radar interferometry, while long baseline image pairs are useful in radargrammetry. These technologies both have their own advantages and disadvantages, which were investigated in previous studies (Capaldo et al., 2015). With radar interferometry, it is possible to produce DEM forlarge areas. Sentinel is one of the recent projects in satellite remote sensing. Sentinel constellation collects multi-spectral imagery, radar imagery and thermal imagery from the earth. Sentinel-1 is the radar satellite of the constellation. Recent studies have investigated the precision of radar interferometry using Sentinel-1 imagery (Yagü e-Martí nez et al., 2016) and the precision of DEM produced using these images(Letsios, Faraslis, &Stathakis; Nikolakopoulos &Kyriou, 2015). Generally, DEMs generated through radar interferometry needs to be improved, mainly due tothe phase errors which in many cases turn into outlier points (Zhang, Wang, Huang, Zhou, & Wu, 2012). Various methods have been used to improve DEM generated from SAR imagery, one of which use the information obtained from SRTM DEM. For instance, a previous study used SRTM DEM to improve DEM generated from ESRI/2. Using the information obtained from SRTM, the interferometric phase of areas with lower coherency were improved (Zhang et al., 2012). The present study proposed a method to improve the accuracy of DEMs generated by Sentinel-1 imagery. In this method, using ascending and descending Sentinel-1 image pairs from the study area, DEM is generated using radar interferometry process. Then, precision is improved using SRTM DEM and a method based on 2D wavelet transform.

Introduction: Geomorphologists have always considered geomorphological processes such as weathering, erosion, and sedimentation, and tectonic processes as the main factor creatingdifferent landforms in the ecosystem. Moreover, a large part of the earth’ s surface is affected by the presence and existence of organisms, thus these biological species play a major role in environmental changes and consequently in the creation of landforms. In fact, geomorphology is one of the important factors affecting vegetation heterogeneity in the scope of landscape. Alluvial fans are among the important and majorgeomorphologicalforms in which two natural parameters of landform and vegetation coexist. Various methods are used to study vegetation density. Vegetation variables are commonly estimated using land surveying, but satellite images have made more accurate methods of rangeland management and alsoestimationof plant quantities in inaccessible areas possible. However, usingdata obtained from satellite imageries for partial measurements has some limitations due to unavailabilityof high spatial resolution images such as QuickBird satellite images or high expenses of retrieving such imagery. In the present study, plant variables were investigated using large-scale aerial imagery and field sampling. Plant density and percent canopy cover were also determined in the study area using the same methods.: Study area The area under study is located in the northeastern regions of Semnan province, northernShahroud city. The study area includes three alluvial fans including Saran, Moghatelan and Hot-Sokhteh. Methods: Based on field observations, Google Earth images, and drainage pattern, alluvial fans were divided into active (young surfaces) and inactive (old surfaces) parts. Six sites (P1 to P6) including upstream, downstream, active and inactive parts of the alluvial fans under study were selected in order to determine the density and percent of canopy cover in channels, interfluves (in old surfaces), bars and swales (in young surfaces). The aerial image was acquired using a Dji Phantom 4 Pro Drone with a relative flying height of 100 m, and a 20 megapixel, FC6210 digital camerain December 2018 (Table 1 and Fig. 3). The canopy covers in alluvial fan landforms (including channels, interfluves, bars and swales) were measured using large-scale images (1: 500) acquiredby drone. In the next stage, 50 rectangle and squareshaped plots were selected to determine the density and percent canopy cover of the aforementioned landforms in the upstream and downstream of the three alluvial fans; 5 squareshaped plots with a dimension of 10*10 m were selected from the interfluves, 45 rectangularshaped plots with a dimension of 3*10 m were selected from the channels, swales and bars. Then, percent canopy cover was calculated in each plot and the average percent canopy cover was finally calculated for the 50 plots of each site. Experimental studies: In order to investigate physical and chemical characteristics of soil and its effects on the density and vegetation type across alluvial fans, 48 soil samples were collected from a depth of 0-20 cm in the three alluvial fanseach including active and inactive parts, bars, swales, channels, and interfluves. PH, EC, phosphorus (P), absorbable potassium (K) and sodium (Na), calcium carbonate (CaCO3), Saturation percentage (Sp), water retention capacity of soil (WHC), soil texture, and total organic carbon (OCT) were also measured in the samples. Sampling vegetation and identifying plant species In order to identify plant species, field work was carried out in June 2019. Plant species of the study area were identified and a sample was collected, dried and pressed. Systematic random sampling was used in the specified types. In fact, a 200-meter transect was selected in each site, and 8 plots with a dimension of 8 * 8 m were identified along each transect including channels, interfluves, swales, and bars of the upstream and downstream alluvial fans. Therefore, 43 vegetation sampling plots were selected along the 200-meter transect. Results & Discussion: In the active surfaces of both upstream and downstream alluvial fans, density and percent canopy cover of bars arehigher than those of swales, because of the higher amount of silt and clay in bars. Larger plant species such as shrubs and sub-shrubs requiringfine-textured soil grow in these bars. On the other hand, swales have a higher amount of organic materials and calcium carbonate. EC and PH are lower in the bars as compared to the swales. Water-holding capacity (WHC) and Saturation percentage (Sp) of the soil are higher in the swales as compared to the bars. There are more absorbable potassium and phosphorus in the bars. However, vegetation density and percent canopy cover in swales are lower than those of bars despite their high soil fertility and moisture. This is probably due to the lower stability of the swales whichresults in their higher exposure to unstable currents during occasional storms and floods. Overall, plant species adapted to the specific environmental conditions are settled in each landform. PerovskiaAbrotanoides is the dominant plant species in active surfaces ofbars. The vegetation type is more limited in the swales of active surfaces including species likePoabulbosa and Bromusdanthoniae. In inactive surfaces of alluvial fans, elementsrequired for soil fertility (organic materials, calcium carbonate, absorbable potassium and sodium, phosphorus, pH, saturated moisture of the soil, and soil retention) are higher in the interfluves as compared to channels. The relative higher fertility of interfluves can be attributed to their gentle slopes, higher stability and hence higher possibility of soil formation. Long-term exposure of sediments or alluviums to weathering elements on relatively flat surfaces of interfluves has resulted in the formation of more clay and silt, and thereby denser vegetation in interfluves compared to channels. Herbaceous and shrub species, which require fine-textured soils, settle in interfluves. On the other hand, vegetation density of channels with higher amounts of sand and pebbles is lower likely due to their steep slopes as well as their higher level of erosion. However, percent of canopy cover is higher in channels as compared to interfluves. Channels have a relatively higher level of moisturesince they are in the shade and in vicinity of groundwater. Hence, shrubsare settled in these landforms. These species havea denser canopy cover, and deeper roots and require coarser soil texture. Artemisia sieberi is the dominant plant species in inactive surfacesofinterfluves. This species is a sun-loving plant requiring lots ofsunshine to grow. Apart from Artemisia sieberi, other plants such as Astragalus sp., Acanthophyllum sp., Peganumharmala, AmygdalusScoparia and convolvulus acanthocladus have also settled in the interfluves. Conclusion: Analyzing vegetation density and percent canopy cover of alluvial fans and their related landforms indicated that bushes are more frequent in the interfluves of old surfaces as compared to other parts of these fans. Despitelower vegetation densityin bars of young fans and channels of old fans, they have a larger type of vegetation (mainly shrubs) and thus, a higherpercent canopy cover. Generally, this study has revealed that bushes are more frequent in the old alluvial fans, especially upstream parts of the fans as compared to other areas. Overall, the results indicate that geomorphological processes such as aggradation and degradation affect the texture and fertility of soil as well as type and density of vegetation.

Introduction: Applying GPS/IMU data in aerial triangulation has increased the strength of photogrammetric block and reduced the number of ground control pointsneededfor block adjustment. Systematic errors in data used fortriangulation reduce the accuracy of the process and make ground control pointsnecessarydespitetheexistenceof GPS/IMU data. Therefore, reducing systematic errorsin data naturally increases the accuracy of triangulation and reduces the number of ground control points required forblock adjustment andthe number of crossstrips used to eliminate systematic errorsin GPS data. Materials: Digital images captured by the National Cartographic Centerof Iran from an area in Fars province usingUltraCam-Xpcamera in2010 were used in the present study to investigate the roleof self-calibration parameters in the reduction of ground control points and cross strips requiredfor block adjustmentin aerial triangulation. The intended block consists of 58 images and four strips; two of which are cross strips. Control points in this block include eight horizontal control points, eight vertical control points and eight full control points. Each image has a dimension of 11310 by 17310 pixels, a pixel dimensionof 6 microns, afocal length of 10500 microns, an end lap of 70%, and a side lap of 30%. Theregion has an average elevation of 760 m. Given the focal length, flight height and pixel dimensions, ground resolution is around 12 centimeters. Each image covers anarea of 2077. 2 mlength and 1357. 2 mwidth on the ground. Methodology: The present study investigates theroleof self-calibration parameters, such as elimination of systematic error in GPS/IMU data and image sensor, in increased accuracy oftriangulation, and reduced number of ground control points and cross strips required for block adjustment. To reach this aim, optimal self-calibration parameters are determined using a genetic algorithm and the identified parameters are used in the bundle block adjustment. Variance components estimation method was used to solve the problem of equationsinstability. This method not only stabilizes the equation, but also determines the optimal weight matrix during the adjustment process. Results and Discussion: Since images at a scale of 1: 2000 were used in the present study, maximum RMSE equals 60 cm and maximum residual errorsequal 1. 2 m. Using additional parameters to eliminate systematic errors results in an acceptable maximum error at the control points, but absence of additional parameters results in an unacceptable maximum error at the horizontal and vertical control points even in the presence of crossstrips. In addition to the evaluation of horizontal and vertical errors at the ground control points, horizontal and vertical RMSE of the checkpointsare also used to evaluate the geometric accuracy of aerial triangulation. Again, applying additional parameters keeps the RMSE at a much lower level than the accepted limit, while absence of additional parameters results in a horizontal and verticalRMSE higher than the accepted limit even in the presence of cross strips. It should be noted that using cross strips reduces RMSE at the vertical component. Conclusion: Results indicated that using self-calibration parameters and reducing errorsin data used for the adjustment process decreases the number of control points and cross strips required for block adjustment. Using optimal self-calibration parameters(even in the absence of control points) resultsin a maximum RMSE of 0. 143 m at the checkpoints, while absence of these parameters results in a maximum RMSE error of around one meter with or without cross strips. Genetic algorithm is capable of determining optimal self-calibration parameters. It is also capable of optimizing nonlinear functions. Therefore, it is not necessary to linearize the equations before determination of self-calibration parameters, which reduces the amount of necessary calculations. Variance components estimation can also be used along with the bundle block adjustment method to stabilize the equations and determine the optimal weight matrix. As a result, it is suggested to take advantage of these three methods, i. e. block adjustment, stabilization and optimal weight matrixdetermination, simultaneously.

Introduction: The Caspian Seaclassed as the world’ s largest lake, lies between Europe and South Western Asia (between 45. 43° to 54. 20° longitude east and 36. 33° to 47. 07° latitude north). The Caspian Sea level has changed widely over time. These changes have occurred gradually and incrementally leading to landward and seaward migration of the coastline. Therefore, it is very important to study and predict futurechanges of the Caspian Seacoastline. Today, experts in atmospheric and marine physics from all around the world consider the Caspian Sea as a natural dynamic model of oscillatory processes in watersurface. High annual rate of water level changeshas made oscillatory processes of this lake different from those of oceans. With the advent of satellite altimetry in 1973, highly accuratemonitoring of sea level has been made possible. The present study seeks to investigate the trend of dynamic topography changes in the Caspian Sea and determine the effects of changes in thesea level on the southern coastline. Methodology: Various sets of satellite data have been used in the present study. Long-term average ofglobal sea level data was obtained from MSS_CNES. CLS15. Covering a period of 20 years (1993 to 2012), these datasets are produced based on information received from different satellitealtimeters. Mean sea level is calculated foreach point of the network created atthe Caspian Sea (with a distance of 0. 25° ). The correlation between altimetry data and sea level changes is calculated using gravity changes. Investigating these changes leads us to equipotentialgeomagnetic surfaces called geoid. Geoid is an equilibrium surface of the Earth’ s gravitational field showingapproximately the average leveloffree water. Mean sea level does not coincide with geoid and theirdifference at any given point is called absolute dynamic topography. In this study, GOCE model was used to calculate geoid value at every point of the network created at 1′ distance from the Caspian Sea. Aviso Altimetry dataset was used to obtain sea level anomaly data. Mean sea level was obtained by adding dynamic topography mean to geoid height. In order to obtain average dynamic sea topography, MDT values were calculated for all the points created in the Caspian Sea. Afterwards, sea level anomaly was added to the mean dynamic sea topography to obtain absolute dynamic topography. Daily SLA data of the Caspian Sea were extracted with a resolution of 0. 25° from AVISO and CNES. CLS15 SLA ultrasound satellites and interpolated at the specific location created on the Caspian Seanetwork with a resolutionof 1′ . Aabsolute dynamic topography were calculated on a daily basis. These calculations were repeated for a 20 year period (7305 days) from 1993 to 2012 using MATLAB and in this way, a complete database including the Caspian Sea surface topographic datawas obtained for this period. Result: Following the calculation of the mean ADT data obtained fromall over the Caspian Sea, time series of daily Sea Level Fluctuations were extracted. These time series indicated that despite the positive trend of the Caspian Sea water level changes in both 1993-1995 and 2000-2005 periods, the overall trend of water level changes over the 20-year period is negative. Moreover, examining sea level changes over this 20-year period shows thatthe highest altitude (-25. 914m) has occurred on June 1st, 1995, while the lowest altitude (-27. 20) has occurred on November 26th, 2012. In addition, March 20th, 2002 and June 29th, 2005 have experienced two abrupt changes of-26. 843m and-26. 26m in the time series. In this time series, an upward trend is observed until June 1st, 1995, while a decreasing trend of 93 cmis observed from March 20th, 2002 over a period of approximately 7 years. Between March 20th, 2002 to June 29th, 2005 (a period of approximately 3 years), we observe a decreasing trend of 61 cm. Over a 7-year period (until late 2012), we also observe a 97cm decreasing trend. Altimetry data received from three stations located in the Caspian Sea are used to verify the results obtained from the above mentioned method. Examination of these values and comparing them with the values obtained from the method used in the study confirms the resulting trend. In orderto investigate the shoreline changes caused by changesin the Caspian Sea water level, the southern shoreline of the Sea is mapped based on the obtained trend. Days with the highest and lowest sea level over the 20-year study period were extracted from satellite images. Mapping and overlayingthe coastlines based on the information related to these two time series, changes have been observedthroughthe Caspian coastlines. However, these changes are more significant in the South Eastern Gorgan Bay (Miankale) due to the smaller slope of the South Eastern Caspian Sea compared to other areas of the Sea. Conclusion: Investigating changes of the Caspian Sea level shows anegativetrend of changes, with a-1. 287 m difference between thehighest and lowest altitudes. Of course, the trend has not always been negative over these years. For an instance, a positive trend was observed from 1993 to1995 and from 2000 to 2005. Results indicate that the Caspian Sea dynamics of water level fluctuations changes rapidly and long-term prediction of the Caspian Sea water level cannot be very accurate. However, it can be concluded that the Caspian water level changes will continue its decreasing trend in the future. This negative trend of sea level changes has resulted in the seaward migration of the Caspian coastline, which has began in 1995 and still is present today. This has resulted in drying up of more than 12850 hectares of the GorganGulf.

Introduction: Since urban bus networkis considered to be the most important part of transportation system in developing countries, optimal design of this networkis crucial for improving the status of public transportation. To reach this aim, it is necessary to locate these stations in areas which increase users of this system in different parts of the city. The present study seeks to identify suitable places for the construction ofproposedbus stations in the 6th district of Tehran municipality using GIS functions, Analytic Network Process and Allen’ s temporal model. Proposedstationswere then optimized. Materials & Methods: Based on necessary investigations about the 6th district of Tehran, 17 indicators were identified: access criterion (sub criteria: business, administrative, medical, religious, educational and sports centers, and urban facilities, subway, roads), demographic criterion (sub criteria: population and employeesdensity) and traffic status (sub criteria: BRT lines, one way and two way streets, street width, traffic load, slop of the area and kind of road). At the first phase, questionnaires were distributed among 35 experts of transportation and traffic. Based on the results of DEMATEL questionnaires and their analysis in MATLAB, the severity of relationship between the criteria were calculated and pairwise comparison questionnaires were designed. Using DEMATEL technique, the presence or absence of a relationship between the aforementioned criteria and sub criteria was investigated. As a decision makingtechnique based on pairwise comparison, DEMATEL uses experts’ judgments to extractelements of a system and find a systematic structure for them using the principles of graph theory. This technique provides a hierarchical structure of the factors of the system along with their corresponding relationship, and determines the effect of these relations in the format of numerical scores. DEMATEL technique is used to identify and investigate the mutual relationships between criteria and to produce a map of network relations. The ANP model not only calculates the relationship between the criteria, but also the relative weight of each criterion. The result of these calculations make a supermatrix, from which it is possible to derive dependency between each criterion and selection and their weights. An increase in this weight shows higher priority, so it is possible to choose the best option. (Saa’ ti, 2003)It is possible to calculate ANP process in both Super Decision and and ANP-solver software. After calculating weight of the criteria, spatial layers are created in GIS software and finally suitable digital layer is created through integration of the criteria. The obtained digital layer shows the best spatial zones for the construction of bus stations in the study area. Results & Discussion: Time and place are inseparable parts of each phenomenon in our world. Since, the first step of processing and analyzing a phenomenon in spatial information systemsismodeling, creating a model with necessary capabilities to include temporal dimension is inevitable. One of the main requirements of spatio-temporal modelling is the ability to investigate the topological temporal-spatial relations betweendifferent phenomena. The present study used Allen’ s Interval Algebra to extract all relations between different dimensions of time. These include 3 relations between two temporal events, 6 relations between one event and a time mode, and 13 relations between two time modes. Based on Allen’ s model, the rush hours were investigated and common temporal – spatial features of each station were obtained. New stations were proposed based on existing stations and the desirable layer, and a desirable time was determined for the buses to pass stations based on land uses around the stations, the rush hours of each land useand common temporal – spatial features of each station (based on Allen’ s model). Conclusion: Results indicate that the ANP and Allen model can only search a very small number of possible answers and reach the required answer. 6thdistrict of Tehran municipality covers an area of 1557. 65 hectares, from which 18. 10% are in a suitable condition, 21. 41% are relatively suitable, 30. 45% are moderate, 23. 88% are relatively improper and 6. 17% are completely improper. 281. 923 hectares of the district has no problem regarding the access criterion and donot need a station. This district has 185 bus stations and 61 new stations are proposed (a total number of 246). From the aforementioned 246 stations, 17 stations do not have a common schedule, 87 stations have a common point in their schedule, 89 stations have 2, 42 have 3, 10 stations have 4 and one station have 5 common points in their schedule. In terms of time, 42. 28% stations are in a suitable condition, 36. 18% are relatively suitable, 17. 07% are moderate, 4. 07% are relatively improper and 0. 41% are completely improper. Accordingly it is recommended that a bus should pass every 5 minutesfrom stations with 5 and 4 common points in their schedule. For stations with 4 common points in their schedule, this time reaches 10 minutes. Stations with two common points in their schedule need a bus every 15 minutes and stations with 1 common point in their schedule need a bus every 20 minutes.

Introduction: Earthquake is one of the most frequent natural hazardsannually leading to numerous human and economic losses. Both in the planning stage and after the earthquake occurrence in the relief phase, findingappropriate sites for temporary housing is considered to be one of the most important issues in reduction of damages caused by earthquake. Temporary housing, especially in a crisis situation is always accompanied by elements of uncertainty. Hence, definitive and classical approaches normally do not lead to acceptable results without involving elements of uncertainty. Although using methods based on fuzzy logic and fuzzy set theory are conventional and appropriate for uncertainty modeling, these methods also have their own disadvantages. For an instance, they require a certain and definitive membership function for each parameter. Moreover, fuzzy theory cannot describe verbal variables related to doubt and hesitation. Temporary housing is always accompanied byuncertainty. Thus, fuzzy theory cannot lead to reliable results in this regard. However, in case sufficient information is not obtained using fuzzy theory, intuitionistic fuzzy logic isconsidered to be an appropriate solution for this problem and uncertaintymodeling. Despite various applications of intuitionistic fuzzy logic in uncertainty modeling, few researches have focused on this method. Materials & Methods: Designing a qualitative model based on human knowledge requires a rule-based inference system, which is called an Expert System. This system consists of several parts. In the knowledge-based part, data and a set of rules, which are based on expert knowledgearesavedin the form of logical sentences. The input of this system is a set of numbers fuzzified in the inference engine by a set of fuzzy rules. Then, defuzzification is performed to map the fuzzy set and reach a certain point. In other words, the outputs must be readable and easy for the users. The present study takes advantage of fuzzy and intuitionistic fuzzy approaches todetermine optimal sites for temporary housing. Furthermore, determinant factors of danger and safety followinganearthquakeare used to identify safe places for sheltering in such situations. The present study has applied layers of faults, hospitals, emergency and medical centers, fire stations, parks and green spaces, and roads as determinant factors. New spatial layers were produced for each ofthe aforementioned layers using distance and other similar functions. Then, trapezoidal functionwas used to determine membership and non-membership function of each layer in both fuzzy and intuitionistic fuzzy methods. Membership functions obtained from these methods are different in that they assign different membership values to the pixels surrounding the layer. Following the definition of membership and non-membership functions for each layer in both methods, temporary accommodation maps were obtained using the classical fuzzy as well as intuitionistic fuzzy methods. Results and Discussion: The results obtained from these two methods were not identical. The main reason for this difference is that they treat data uncertainty differently. Furthermore, the results of membership and non-membership functions inintuitionistic fuzzy are not complementary. This provides us with a powerful tool for interpretation and, of course, decision making about the study area. As the first case, membership and non-membership degreesequal zero and one implying that the membership degree equals one and the non-membership degree equalszero. This occurs when the method identifies the area as quite appropriate for temporary housing after the earthquake. In this case, results are determinative, and data can be used in the area. In the second case, membership and non-membership degreesare low, which occurs in areas lacking enough information. It implies that more information is neededin such areas for decision making. The third condition takes place when both membership and non-membership degrees equal 0. 5. In such a case, it can be conclude that either the stated variable belongs to the area with a membership degree of 0. 5, or the variable doesn’ t belong to the area with a non-membership degree of 0. 5. In the fourth condition, the membership degree is high and the non-membership degree is low. In this case, the results can be trusted and used in decision-making. The fifth condition is in contrast with the fourth case. It occurs when the non-membership degree is high and membership degree is low. Under this condition, it can be concluded that the results are not reliable. Conclusion: The proposed method and model were implemented in the second district of Tehran. According to the results, it can be concluded that the proposed approachperforms better than theclassical fuzzy approach, especially in the presence ofuncertainvariablesand lack of adequate data.

Introduction: Increased density of Co2 in the atmosphere during the Anthropocene epoch has resulted in pervasive concerns for the global environment. Global warming has resulted in sea level rise and coastal flooding. Forecasting has indicated that a vast area of coastal countries and their economic and social infrastructureswill be damaged due to 200 cm sea level rise by2100. Sea level rise in oceans has caused coastal erosion and flooding. Thus, it is considered as a real threat to coastal environment. The Caspian Sea environment has reacted differently to climate changesduring the last 70 years and vast areas of its coastal lagoons have dried. Therefore, the present study primarily seeks toinvestigate ecological variations of coastal habitats in the Gorgan Bay during the period of 1995 to 2019. Materials and Methods: Gorgan Bay and Miankaleh Lagoon are considered to be among global biosphere reservesand the most important protected areasalong the southern coasts of the Caspian Sea. The present study has evaluated coastal variations, such as shoreline displacement, changes in the depth of sea bed, land cover and coastal habitats using satellite images and GIS processing. Shorelines of Gorgan Bay are determined usingremote sensing software Envi 5. 3, while land cover and coastal habitats are evaluated through GIS processing in Arc – Map 10. 5. The shoreline is determined through the calculationsperformedonthe proportion of green and blue bands in reflected electromagnetic waves and histogram thresholding of near infrared (NIR) spectrum in Envi 5. 3. The total area of Gorgan Bay was determinedusingthe Normalized Distance Water Index (NDWI). The most important land covers and coastal habitats are classified using Support Vector Machine (SVM). Finally, variations of coastal habitats are calculated using Change Detection Workflow index and the final maps areproduced in Arc Map 10. 5. Results and Discussion: Results indicate that due to about 150 cm decrease in the Caspian Sea level from 1995 to 2019, the total area ofGorgan Bay has faced about 176 km decrease. Bathymetric maps shows that the depth of Gorgan Bay has decreased dramatically along the East to west side. The depth of the Ashouradeh and Chopoghlei inlets have also decreased and vast areas of these water bodies haveturned into arid islands. The Gorgan Bay is connected to the Caspian Sea through some narrow channels. The most important land covers and coastal habitats of the Gorgan Bay in 2019 include sandy beach (2%), salt marsh (7%), brackish marsh (14%), wetland (15%), mudflat (7%), coastal forest (10%) and coastal lagoons (45%). The total area of coastal lagoons, vegetation covering and sandy beaches have decreased from 1995 to 2019 and the area of the brackish marsh, salt marsh, mud flat and pit wetlands have increased at the same time. The total area of sandy beaches have decreased about 52 Km2 since 1995. Instead, the area covered by salt marshes and brackish marshes have increased by about 87 and 60 Km2 during the same period. 62 Km2 of mud flat have been created during the same time, and thus, the area of Miankaleh Lagoon and Gorgan Bay have decreased by about 176 Km2. The environment of Gorgan Bay and Miankaleh Lagoon is directly related to the fluctuations in theCaspian Sea level. Survival of these coastal lagoons depends on permanent water exchange between the Caspian Sea and Gorgan Bay. Rapid fluctuations of the Caspian Sea level and high levelof deposition are considered to be among the most important factorsof coastal habitats destruction and ecosystems displacement. These natural phenomena happened twice during the Anthropocene period (1945-1978 and 1995-2019). Conclusion: Results have confirmed that arid ecosystems have replaced aquatic ecosystems in study area. The main results of the study have confirmed that the fluctuation in the Caspian Sea level has direct impact on coastal habitats of the study area and decreasing sea level could change marginal ecosystems. Due to the decrease in water exchange volume rate between the Caspian Sea and Gorgan Bay during the 1995-2019 period, a 32 percent decrease has happened in the area ofGorgan Bay and salt marshes have dominated along the Gorgan Bay coastal area. Unfortunately, the continual decrease inthe Caspian Sea level can destroy biodiversity and coastal habitats in the future. Therefore, integrated coastal zone management (ICZM (is influential insaving and preserving of the Gorgan Bay.

Introduction: The growth and expansion of urbanization in the contemporary era and the emergence of metropolitan areas as places in which large number of people live together and capital and assets are accumulated have recently attracted the attention of many planners, governments and nations topotential natural hazards and the importance of crisis management in these areas. In this regard, land use planning is considered to be critical due to its importance for sustainable development, optimal configuration, and crisis management. Materials & Methods: The present study primarily seeks to design a spatial model for spatial evaluation of urban land use in district 19 of Tehran Municipalityusing a crisis management approach. In this descriptive-analytical study, necessary information were collected through library research methods and the analysis of quantitative and qualitative indicators. In quantitative index analysis, per capita land use of the district was compared to the country’ s standard level, and in qualitative index analysis, three criteria of compatibility, capacity and desirability have been evaluated. One of the goals of urban land use planning is proper site selection for different land uses and separation of incompatible land uses from each other which is achieved through collection of necessary information about the current situation and evaluation of the collected information. For example, attempts are made to find a proper site forland uses producing pollutants such as smoke, odors, and noise away from residential, cultural, and social areas. In contrast, activities that complement each other are located in vicinity of each other. ArcGIS was used to evaluate and model the compatibility level of neighboring land uses. The proposed model inthe present study aims to evaluate the proximity of activities in order to determine their level of compatibility from the perspective of crisis management. Results of this model can be used for land use planning. This model is based on two principles: the logic behindland uses’ compatibility, and spatial neighborhood relationship and models of this relationship in GIS environment. Model Builder, a visual programming language at the Arc Info Editor Level which is undoubtedly one of the most important features offered in this program has been used to achieve the desired goal in this study. This modeler is actually an interface which forms the input-output parameters and processing functions in the processor. In this interface, the user can call several functions in a sequence and the processes will be performed one after the other. The input parameters includeeverysupported format in ArcGIS. Processing functions include all functionsused in the spatial analysis network. Outputs can be stored and used in later steps. Results & Discussion: Qualitative analysis of land uses in District 19 of Tehran Municipality Evaluating the compatibility of land uses in District 19 In order to analyze the compatibility levelofneighboring land uses, the number of neighbors in each parcel is determined. Then, a binary compatibility relationship is stipulated for each pair and finally a fixed number is reached in the process of comparing land uses. As required by the land use compatibility matrix, urban uses are hypothetically classified into 5 groupsbased on their current situation: fully compatible, relatively compatible, indifferent, completely incompatible, and relatively incompatible. Following the production of compatibility matrix, details oflandusescollected during the field study have been analyzed and presented as maps. Then, the model is run in Arc GIS and the level of each land use’ s compatibility is presented with anespecialcolor. Results indicate that except for Velayat park which is not compatible with the surrounding land uses, most of the incompatible land uses are located in the western and southwestern parts of the district. These incompatible land uses are presented with 5 different color ranges. Investigating the capacity of different land uses in district 19 of Tehran Municipality Qualitative analysis of accessibility zone in the capacity matrix In this matrix, the performance of each major land use in service provision and performance coverage is determined based on the urban population and its area of influence, and the results are presented in the relevant tables and on a map produced using Euclidean analysis in Arc GIS. The basic level of performance for each land use at the regional level and its area of direct influenceis identified based on the population required for standard performance of that specific land use in this area. This identified level of performance was used as the basis for further calculations. According to the proposed model and considering the accessibility zone, a special buffer zone is identified for each land use and its census blocks are determined. Finally, population within the blocks is determined separately for each land use (in this section, a few maps of buffer zones are provided for some land usesas an example). This model determines whether the rules of accessibility have been complied in different land uses. Based on the accessibility zone and censusblocks, decision making about the necessity of different land uses is made possible. Investigating the desirability of land uses in district 19 To reach the desired goal, a land use layerto which a new field of desirability has been added will be consideredaccording to the model (Figures 5 and 7) and the existing rules for specific land uses. Results are exhibited in two different classes (desirable, undesirable). According to the existing rules and maps, the desired parts of the area are marked in green and the undesirable parts of the area are marked in red. Conclusion: As one of the most important tools and a major goal of urban planning, land use planning has a vital role in risk mitigation duringurban development. Thus, improving methods and processes of realizing this goal is of great importance and priority. Physical and functional characteristicsof land use have a significant effect on the number of casualties in different urban crisis. Therefore, land use planning is considered to be an important principle of urban planning and an urban planner is primarily expected to make the right decisions and to properly monitor land uses. On the other hand, crisis management and resilience approach have become a pervasive topic of debate in the present decade. Many researchers consider resilience to be the internal ability of a system, community or element to withstand the effects of a natural or social event. In order to determine the level of resilience, land uses in this area are investigated based on different aspects of crisis management. A major difference is observed between the findings of the present study and that of other researchers: most of dimensions, criteria or the main indicator in the present study are related to each other. The dimensions proposed in this model cover all physical and non-physical aspects and the proposed criteria or indicators also show important factors in each dimension. Moreover, the vulnerability of each dimension affects other dimensionsdirectly or indirectly. In fact, a district of Tehran municipality may be more resilient than other districts in some dimensions, but this does not suffice by its own and vulnerability in one dimension reduces the resilience of the whole district. Therefore, a resilient city or district needs to reduce its vulnerability in all dimensions and achieve resilience and crisis management in their real sense. One of the main concerns in the study of urban issues and urban planning is the issue of quantitative and qualitative analysis of the city and urban land uses. In the present study, an attempt has been made to perform quantitative index analysis of urban land uses under the topic of fair distribution of land uses per capita. And for the analysis of quality index, urban land uses were separately investigated based on their compatibility, capacity, and desirability level. This is another innovation of the present study which makes it different from other researches. In the discussion of compatibility, a model has been developed in ArcGIS environmentbased on the rules of urban planning to determine the compatibility of neighboring land uses. The highest level of incompatibility between neighboring land uses was observed in the western and southwestern part of the district. In terms of capacity, a model has been designed for existing uses in the area based on the standard accessibility zone. Regarding desirability, appropriateness of spatial conditions (slope, pollution, odor, location. . . ) and the land uses in a particular place have been considered as important criterion used to distinguish desirable and undesirable areas. The present studyinvestigates appropriateness of land uses based on standard slope. As previously mentioned, a model has been developed for each case in the GIS environment and the results are provided as a map at the end of each section. Following qualitative analysis, the most incompatible land uses were identified. Finally, the following solutions and suggestions are provided to improve and manage land use and for the managementof possible future crisis: Using empty spaces in the district and worthless lands in the southern and southwestern areas of the district to prevent the physical expansion and encroachment of the district on Tehran and Islamshahr. Considering a special zone around high-risk land uses such as gas stations and applying additional rules and regulations to prohibit increased density in these zones. Collecting physical and environmental data as much as possible, combining these data with each other, and using them statistically. Assessing earthquake risk and including the results in land use planning to mitigate risk and manage possiblecrisis. Distributingthe population in this districtbased on identified hazards in the area. Properly distributing parks and green spaces in the district, maintaining them for the times of possible crisis, ensuring easier access and equitable distribution among citizens. Increasing the quality of buildings in accordance with construction and urban planning standards, planning for the reconstruction and repairing of worn out buildings.

Introduction: Evaluating the ecological capability is so important that if the selected land lacks the appropriate ecological potential for the implementation of a specific land use, implementing the plan (even if there is a socio-economic need for that specific land use) not only does not improve the environmental status of the region, but also causes more environmental damages. As an economic activity that somehowsells the natural and cultural heritage of different regions, and depends on the natural environment and its exploitation, tourism is one of the most important environmental potentials. Therefore, tourism is considered to be a path to sustainable development, which through its multidimensional nature not only meets the needs of tourists, but also creates major changes in the systemof the host society. Consequently, in order to achieve sustainability, tourismshould be planned in a way that it does not negatively affect the environment, economy and culture of the host societyand meets the needs of the current generation without overusing what also belongs to the next generations. Materials & Methods: The present studywas applied in nature and took advantage of a descriptive-analytical method to study the parameters in two main sub-sections. The first part included a library research performed with the aim of investigating related theoretical literature and the research background. The second part included some interviews and a field research performed for data collection. To evaluate the regional environmental capability and overlayingmaps in ArcGIS environment, Weighted Linear Combination (WLC) methodand Fuzzy operatorswere used. First, the final map of ecological capability for the development of sustainable rural tourism was analyzed and evaluated using WLC method based on highly appropriate, appropriate, limited appropriateness, inappropriate, and highly inappropriate classes. Then, fuzzy maps were produced with a gamma value of 0. 7, 0. 8 and 0. 9 to obtain the tourism capacity of the region. And finally, the Kappa coefficient was used to compare the accuracy of classifications obtained from the WLC and fuzzy methods. Results & Discussion: Findings indicate that with a weight of 0. 33, tourism resources are the most important factor or capability in the development of sustainable rural tourism in Neyshabur County. The topography, with a weight of 0. 192 is considered to be the second most important factor according to the experts and specialists. The third most important factor is the land cover with a weight of 0. 138 and then, climate criteria with a weight of 0. 117, hazards with a weight of 0. 088, socioeconomic factors with a weight of 0. 084 and water resources with a weight of 0. 051 had the highest scores. Finally, the scores were applied to the GIS environmentusing the WLC method, and the final map of land capability for sustainable rural tourism in Neyshabur County was obtained. Also, the statistical information obtained from the final map of land capability shows that 27. 27% of the area is located in the very appropriate class, and31. 76%is located in the appropriate class, while 22. 23% and 4. 28% of the region belongs to the highly inappropriate and inappropriate classes respectively. In the next step, tourism capacity maps of the region were prepared using a Fuzzy model with 0. 7, 0. 8 and 0. 9 operators. The study area was divided into five categories: very high, high, medium, low and very low in terms of tourism capability. The last and the most important step was to find the most accuratemap from those produced using AHP and fuzzy methods with different gamma values of 0. 7, 0. 8 and 0. 9. To reach this aim, field observations and interviews with experts and specialists ofthe field were performed. Therefore, results obtained from the maps were compared with the experts’ opinions. Findings indicates that the operator with a gamma value of0. 7 and a kappa coefficient of 0. 84 is considered to bemore reliable than the operators with a gamma value of0. 8, and 0. 9 and AHP model. Thus, the 0. 7 gamma operator is considered to bethe most suitable model for environmental capabilityassessmentin the region regarding tourism. Conclusion: Using natural capabilities and potentials is the most cost-effective and lucrative way to achieve sustainable development. Findings of the present studyindicated that the operator with a gamma value of 0. 7 and a Kappa coefficient of 0. 84 is considered to be the most suitable model for the assessmentof the region’ senvironmental capability for the development of sustainable rural tourism and it is more reliable and appropriate than the AHP model and operators with a gamma value of 0. 9 and 0. 8. Finally, considering the capabilities and potentials of the Neyshabur County for the development of sustainable rural tourism, it is recommended to consider development of tourism in this county as the priority of rural development plans and to use the natural resources of the area especially in the appropriate and highly appropriate classesas a way to achieve sustainable tourism development of the county in the most cost-effective way. It is also suggested that with appropriate management, planning and using the ideas of academic researchers to improve the capabilities of theaverage class, we can make the most out of the potentials of this area to develop sustainable regional tourism.

Introduction: Along with other environmental factors, climatic conditions are among the most important factors affecting social, moral and cultural problems. People behave differently in different climates. Quetelet and Gurreydeveloped crime statistics in Franceandinvestigatedits relationship with physical environment. Thus, they studied the effects of geography and climatic conditions on human behavior, including criminal behavior. In Climate and Crime, Ellen J. Cohen argues that situational approaches, selected rationaltheories and routine activity theory all suggest that climate has a major impact on the rate of crimes and criminal behaviors. Based on their observations, Quetelet and Gurreyformulatedthethermic law of delinquencyin criminology. Based on statistical studies, they concluded that violent crimes are more frequent in hot seasons and hot regions, while in cold regions and cold seasons, more deceptive crimes such as crimes against property requiring thinking and imaginationoccurmore often. It should be noted that crime is a social phenomenon affected by various factors. Environmental conditions can also intensify the threat of human behaviors. The present study seeks to investigate the relationship between the climatic element of temperature and the occurrence of crime in Shiraz, Abadeh and Larestancounties of Fars province? Materials & Methods: The present study is applied in nature and purpose, while taking advantage of an analytical-descriptive method. 3 meteorological stations of Shiraz, Abadeh and Larestan were studied here. Investigated data included the seasonal average temperature and seasonal rate of crimes for the2008-2013 period. Seasonal rate of crimes including social corruption, theft, forgery, strife, mischief, intimidation and coercion, smuggling, drug-related crimes, murder, and suspicious death were investigated in Shiraz, Abadeh and Larestan, which have a meteorological station. Crime statistics were collected from the Prevention Police Department of Fars province Law Enforcement Force and statistics related to the climatic elements of temperature were obtained from Fars Meteorological Department. Different descriptive and inferential statistical methods were used to analyze the data and Pearson correlation coefficient test was used in inferential statistics. Data analysis in the present study included two stages. First, the seasonal and annual percentage of various crimes were studied in each of the mentioned cities. In the second stage, the correlation coefficient between the average temperature and the total (seasonal) number of crime occurrence were investigated. Discussion: Investigation of various crime occurrence in Shiraz, Abadeh and Larestancounties of Fars province revealed that in spring, strife and affray (47. 11), theft (23. 16) and social corruption (19. 16) were the most frequently committed crimes in Shiraz. However, intimidation and coercion (0. 32), smuggling (0. 24), forgery (0. 20) and murder (0. 05) had the lowest frequency in Shiraz during spring. In summer, strife and affray (47. 71), theft (24. 64) and social corruption (20. 95)are considered to be the most frequent crimes, while intimidation and reluctance (0. 33), smuggling (0. 23), forgery (0. 20) and murder (0. 03) arethe least frequent crimes, respectively. In autumn, strife and affray (44. 36), theft (27. 71) and social corruption (18. 24) were more common, whileintimidation and coercion (0. 33), smuggling (0. 27), forgery (0. 26) and murder (0. 04) had the lowest frequency. In winter, strife and affray (43. 92), theft (29. 99) and social corruption (16. 84) were the most frequently reported crimes, whileintimidation and coercion (0. 35), smuggling (1. 4), forgery (0. 24) and murder (0. 02) were the least frequently reported crimes. Findings indicate that during the 2008-2013 period, strife and affray (45. 86), theft (28/28) and social corruption (18. 84) were the most common crimesin Shiraz city, while smuggling (0. 43), intimidation and coercion (0. 33), forgery (0. 22) and murder (0. 03) were the least common crimes. Generally in the three counties, crimes against the person such as strife and affray, murder, mischief, intimidation and coercion were more frequently reported in warm seasons (spring and summer). However, crimes against property, such as theft, were more frequent in cold seasons (autumn and winter). Strife and affray(0. 95) in Shiraz have the highest correlation with the seasonal average temperature. There is a negative correlation between the crime of strife and affray and the seasonal average precipitation in Shiraz. The same relationship existsbetweenstrife and affray and the seasonal average relative humidity in Shiraz. In Larestan, drug-related crimes (-0. 97) have the highest negative correlation with the seasonal average temperature. In Abadeh city, social corruptions (0. 99) have the highest correlation with the seasonal average temperature. Conclusion: In total, crimes against the person, such as strife, murder, mischief, intimidation and coercion were more commonly reported in the warm seasons of the year (spring and summer) in the three counties on the whole and separately. However, crimes against property such as theft had a higher rate of occurrencein the cold seasons (autumn and winter). Therefore, as crimes against the personare more common in warm seasons and crimes against property are more frequent in cold seasons, it can be concluded that QueteletandGurrey’ s thermic law of delinquencyis in force in all the three specified counties. However, this law is not generalizable and it cannot be concluded that crimes against property occur more in cold regions and crimes against the person occurs more in warm regions of Fars province. In this respect, this law only applies to Larestan which is located in the warm region of the province.