Background: This study was designed and conducted to investigate the Spatial distribution of permanent and temporary congenital hyperthyroidism (PCH and TCH) in Isfahan. Methods: This study was conducted on neonates who were born from March 21, 2006 to March 20, 2011 and had undergone the congenital hypothyroidism (CH) screening program in counties affiliated to the Isfahan University of Medical Sciences. CH was diagnosed in 958 patients who treated with levothyroxine. The incidence rates of permanent and temporary congenital hypothyroidism in Isfahan province were calculated and their distribution was shown on the map. The space maps were drawn using the ArcGIS software version 9. 3. Results: Based on the data obtained from the screening program, the average incidence of congenital hypothyroidism in the province during the period of 2006– 2011 was 2. 40 infants per 1000 live births (including both PCH and TCH). The most common occurrence was in Ardestan County (10: 1000) and the lowest overall incidence was observed in the Fereydounshahr county (1. 39: 1000). The incidence of PCH in the counties of Ardestan and Golpayegan had the highest rate in all years of study; and the greatest number of TCH cases in the five years were observed in Nain, Natanz, Khansar and Chadegan counties. Conclusion: Adding the time dimension and performing Spatial-temporal analysis is suggested because of the following items: high prevalence of CH in Isfahan province, the important role of this disease in mental retardation and neuropsychiatric disorder, the necessity of conducting future medical researches to find possible factors of CH etiology in Isfahan province, as well as necessity of performing Spatial analysis with advanced statistical methods.

ABSTRACTDuring the last century, ports of Persian Gulf become the most important storage, refining and transportation of crude oil, oil derivation, petrochemical products and also, business activities. Nowadays, due to functional diversity between ports, the impact of port activities on the development of urban spaces will be different, and this issue leads to structural differences of port cities. The purpose of this research is to identify the key variables affecting the relations and Spatial structure of port-city in Dubai as the most thriving and successful port-city in the Persian Gulf. This research is based on deductive reasoning and carried out by analysis of cross effects with secondary data (results of other prominent articles) and also primary data (questionnaire). sampling method is based on purposive sampling and Micmac software was used to analyze the data. Output of Micmac software shows the position of the instability of the system. Therefore, the position of each variables in the conceptual model can be categorized to driver, linkage, dependent, and autonomous factors. The research findings indicate 4 variables (like as “income, cost and investmnets in port-city”) can be introduced as a driver factor, 5 variables (like as “advanced multimodal transportation in the port”) can be introduced as a linkage factor, 6 variables (like as “quality of living environment and desirable city”) can be introduced as a dependent factor, 3 variables (like as “environmental protection and sustainable development of the city-port”) can be introduced as an autonomous factor. The rapid development of Dubai can be explained by the purposeful distribution of revenues from trade and tourismExtended AbstractIntroductionDuring the last century, ports of Persian Gulf become the most important storage, refining and transportation of crude oil, oil derivation, petrochemical products and also, business activities. The global increase in oil demand since the 1950s led to the creation of discovery of new oil wells and large industrial areas. With the construction of new oil or gas facilities in the port areas, increasing oil revenues contributed to the countries of the Persian Gulf region and form a new geographical relationship between ports and coastal cities which had an impact on the Spatial structure and relation of them. Nowadays, due to functional diversity between ports, the impact of port activities on the development of urban spaces will be different, and this issue leads to structural differences of port cities. In the 1950s, Containerization (container loading technology) was based in some of the world's ports and created a huge transformation in the shipping industry, which had a significant impact on the development of ports. Although the ports of the Persian Gulf have made a significant contribution to the economic growth and physical development of other ports in the world, but few researches have been done on the variables and factors affecting the development of the Spatial structure of the important ports of the Persian Gulf. This region, due to its unique geographical location, has always been considered as a most important corridor in the field of economic and transportation throughout history. Some of the most important energy and commercial ports in this region are: Dubai, Manama, Kuwait, Dammam, Doha, Bushehr, Bandar-Abbas and Assaluyeh. In this regard, the purpose of this research is to identify the key variables affecting the relations and Spatial structure of port-city in Dubai as the most thriving and successful port-city in the Persian Gulf. MethodologyThis research is based on deductive reasoning and carried out by analysis of cross effects with secondary data (results of other prominent articles) and also primary data (questionnaire). The method used in this research is quantitative and the sampling method is based on purposive sampling. the variables affecting the Spatial structure of port-city relations were identified in the form of a review of 31 prominent articles. 25 experts (who had comprehensive knowledge and information about the development process of the city-port of Dubai) participated to determine the effectiveness or influence of variables. Finally, the output of data analysis was done by the Micmac software. Results and discussionOutput of Micmac software shows the position of the variables in the diagram that it indicates their status in the system and their role in the dynamics and changes of the system. The method of distribution and dispersion of the variables in the Spatial structure of port-city relations indicates the instability of the system. Therefore, based on the output of the system, the position of each variables in the conceptual model can be identified in five categories (driver, linkage, dependent, autonomous and regulatory variables). According to the results, some variables such as “the changes in the strategic positions” and “geopolitics of port-city”, “the modification of the management method” and “regional planning of the ports”, “the promotion of local governance in the relations between port-city” and “the way of national and international management of ports have been introduced as driver variables. On the other hand, five variables consist of “advanced multimodal transportation in the port” and “income, cost and investment in the port-city”, “the contrast and physical integration of the port - city space”, “the development of various industrial and production activities in the ports and their local hinterland” and “their qualities Financial and commercial policies of ports” have been introduced as linkage variables. Findings of research also shows that some variables consist of “quality of living environment and desirable city”, “quantity and quality of transit corridors (rail, road and air) to hinterland”, “construction and launching the new port (sea port or dry port)”, “terminal facilities and infrastructure and warehousing”, “conflict or convergence between the city and the port” and “increase in population growth rate and migration to the port - city” categorized as dependent variables. Three variables including “cultural and historical background of ports”, “use of cheaper energy, Less-polluted, clean and renewable energies in the city-port” and “environmental protection and sustainable development of the city-port” categorized as autonomous variables. Finally, a variable with the title of “improvement in management and information technology and development in loading, unloading and storage of commodities” have been introduced as regulatory variables. The findings of this research have a remarkable similarity with other researches carried out in the field of city-port relation and clearly emphasize the direct impact of the "investment in port-city" in the development of them. For example, Grossmann (2008) emphasized that city-port of Hamburg has become one of the largest ports in Northern Europe during the last few decades due to huge domestic and foreign investment. ConclusionThis research carried out in order to introduce and explain the key variables affecting the development of the Spatial structure of the port-city of Dubai. On this basis, 21 variables have been extracted by systematic reviews of prominent articles. In the following, 25 experts were selected with the purposive sampling method. Among the 21 variables extracted, 4 variables (like as “geopolitics of port-city”) introduced as a driver factor, 5 variables (like as “advanced multimodal transportation in the port”) introduced as a linkage factor, 6 variables (like as “quality of living environment and desirable city”) introduced as a dependent factor, 3 variables (like as “environmental protection and sustainable development of the city-port”) introduced as an autonomous factor. The rapid development of Dubai can be explained by the purposeful distribution of revenues from trade and tourism. A part of these revenues has been directed towards investment in important transportation infrastructures such as airports and ports of this city. These strategies have a wide impact on improving the economic growth of the city and the development of the port in order to adapt with the global trade and advanced technologies. FundingThere is no funding support. Authors’ ContributionAuthors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of InterestAuthors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper.

Designing the flexible investment strategies for maximizing returns under Spatial variation and considering uncertainty in beef cattle investment decision-making are vital. Therefore, the objectives of the study were (i) To explore the real options and its values Spatially for beef cattle investments in Turkey, and (ii) To evaluate the adequacy of government support for beef cattle investment Spatially. Research data were collected from randomly selected 385 beef cattle farms by using questionnaires. The valuation of real options was assessed by using Binomial Valuation, Black-Scholes Method and Monte Carlo simulation. Tornado diagram was used for exploring sensitivity of decision variables for beef cattle investment. The results of the research showed that the classical Net Present Value (NPV) value was -200.82 thousand US $. The NPV values of the options of wait, expand, and input-output change for the beef cattle investment were US $102.37 thousand, 43.87 thousand, and 24.50 thousand, respectively. The research findings also showed that the value of real options and adequacy of government subsidies varied Spatially. Based on the resuts of the sensitivity analysis, the most important variables affecting the investor's decision are carcass meat price, yield rate, capacity utilization rate, and fattening feed price, respectively. The research suggests that policy makers should consider the Spatial distribution of investment subsidies and policies to the specific needs of different regions to increase efficiency of investment support policy.

One of the basic principles that should be considered in urban planning is that of Spatial distribution of population in relation to hazardous contexts for urban population. Earthquake is one of these risks which its occurrence is followed by irreparable damages. Since fault structure of a zone has a direct relationship with seismicity of that very zone, knowing the fault structure and tectonic structure could cause a proper distribution of population centers to these structures because zones with less than five kilometers away from fault zones are considered critical points, which have the highest risk of earthquake damage. Kerman city is always in danger of earthquake due to its seismic location. This study tries to investigate Spatial distribution of Kerman city population centers toward fault structure of Kerman city in a descriptive-analytical way. At first, data were prepared from census of population and housing in Kerman in year 2011. Then types of Kerman faults within 30 km distance from the city were identified. And at the end, distribution of population centers in relation with reverse faults, strike-slip faults, normal faults and minor faults were analyzed. The study results indicate that 669 centers of the population attraction centers are located within 20 kilometers and 407 centers are located within 30 kilometers from the reverse fault, 274 centers are located within 20 kilometers and 775 centers are located within 30 kilometers from the strike-slip fault.794 centers are located within 20 kilometers and 282 centers are located within 30 kilometers from the normal fault.699 centers are located within 20 kilometers and 377 centers are located within 30 kilometers from the main fault. At the other hand, 381 centers are located within 4 kilometers and 500 centers are located within 6 kilometers from the minor fault, and three commercial centers, a healthcare center and three educational centers are also located within 800 meters from this kind of fault. In general, commercial and educational centers in study area have the lowest distance to different types of faults, and sports centers have the lowest distribution in the minimum distance of different faults. Due to improper distribution of population attraction centers in the study area, efficient and proper planning for this area is necessary to prevent possible damages.

A B S T R A C TFor choosing suitable place multiple methods are introduced, which examine the qualities, methods and patterns of location selection. The current methods use global approach and do not pay attention to Spatial differences, environmental variation in variables, local changes modeled by local weighting indicators. It has been selected as a case study to evaluate the EMS center of Fars province and to evaluate the indicators of distance from the power plant, distance from logistics centers, industries, transportation terminals, hospitals, hotels and tourism, cities, rural areas and custom centers. has been used, using the AHP method to weight the qualities, and then for local weights, non-overlapping neighborhood filtering within a radius of 25 km are used and calculate the corresponding changes, quantity, ratio Presented in this article, the obtained weights, then the compute local weighted layers, and then local weight was multiplied in the normalized layers and the final layer were obtained. In the next step, it was combined and calculated to compare the data using the global method. The results of the two methods were transferred to the points of the EMS and the results were analyzedExtended AbstractIntroductionIn geographical studies and Spatial planning, there are different approaches to choosing the right place for all kinds of applications and uses, and a process is followed to choose the best places. This process includes stating the research problem (cognition), choosing the criteria and preferences of decision makers and how to determine the weight of indicators, combining criteria (design) and finally, choosing the best option. In the current methods of Spatial decision, the criteria are given a fixed weight, while in geographical locations, the distribution of geographical phenomena is not uniform. In this case, attention should be paid to the Spatial distribution pattern of geographical phenomena based on the principle of sensitivity of the range of changes, the size of the range of changes affects the outputs, and the data with a smaller range of changes also have a lesser effect on the output. The data with a larger range of changes will have a more significant effect on the output. Therefore, in Spatial analysis, indicators that have small changes are less important. Local models have been presented to model local changes in Spatial multi-criteria decision-making; in this research, the main issue is how to model the local changes that affect the expected outputs. MethodologyThe research process is based on multi-criteria Spatial decision-making methods (national and local). First, Spatial patterns of indicators were analyzed through exploratory analyzes of ESDA Spatial data. Then based on the review of the research criteria, the direct weighting method was used to calculate the weight of the indicators, and the experts were asked to specify the weight of the criteria in the range of 1 to 10. The opinions were combined, and the final weight was obtained. First, the data were Spatially modelled to model and calculate the local weights. Then Block Statistics Spatial filter without overlap was used to determine the neighborhood. The range of local changes, maximum and minimum values, were calculated, and local weights were obtained based on that. Finally, the information layers were descaled. After that, the unscaled criterion layers were multiplied by the national weights and the final national scores were obtained. In the next step, the data of the evaluated information layers (local and national) were transferred to the points of emergency databases. Based on the analysis, the fit was done. Results and discussionGeneral or global weights pay attention to the relative importance of indicators, which is the starting point for calculating local weights. Based on the research results, the most critical indicators are industries, cities and hospitals, which are more important in the location of road emergency stations. However, in order to achieve better results, it is necessary to build a local weight layer. The results of the research show that the indicators' weight and the difference in the criteria (general weight) are not equal in the entire geographical area, and the areas with a larger range of changes have a higher coefficient.Because, unlike general weights that are obtained based on different methods, local weights are estimated based on the principle of sensitivity of the range of changes and are changed Spatially, and a fixed value is not considered for all areas. According to the research findings, the highest level in the general and local evaluation method corresponds to 0.5 to 0.7. Based on the local (geographic) weight method, it is less than the national or general method in the range of 0.9 to 1. Furthermore, this is due to the more precise nature of this method, which models the effects of local changes. To evaluate the compliance of the emergency centers with the final maps of suitability assessment, the information on the suitability maps was transferred to the map of the emergency center points of Fars province. Based on the evaluation of 207 emergency stations, according to the general method, 14 percent are in the high group (0.9-1), and according to the local or geographical method (16.9), percent are in this group. ConclusionAt the level of space and geographical regions, phenomena and geographical complications are not uniformly distributed, some phenomena have a homogeneous distribution, and some have a heterogeneous distribution. On the one hand, the phenomena and decision issues are affected by the context in which they are formed and evolve; on the other hand, these phenomena are placed in the network of mutual relations. Furthermore, the results of the set of phenomena may differ from the results of the phenomena individually and separately; in this article, an attempt has been made to address the issue that in Spatial analysis and planning, it is necessary to pay attention to geographical differences in decision-making. Furthermore, it should not be evaluated in general terms of Spatial decision problems; one of the methods of modelling these differences and Spatial changes is to consider the local weight of the criteria. Based on the research findings, local (geographical) and national analysis results are different and can better show these changes. On the other hand, the pattern of Spatial differences also has its Spatial pattern, and paying attention to these differences in Spatial differences can be the subject of further studies. FundingThere is no funding support. Authors’ ContributionAuthors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of InterestAuthors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper.