جستجوی مقالات مرتبط با کلیدواژه "سیستم اطلاعات مکانی" در نشریات گروه "فنی و مهندسی"

Housing as a shelter is one of the basic rights and the most necessary basic needs of humans and all people of a society should have access to it regardless of race, gender, social class and moral background. In this regard, the purpose of the current research is to propose a method that every family living in cities can easily choose the right housing for buying a residential property, taking into account their desired parameters. In the presented method, multi-criteria decision making and spatial information system are used. Data analysis by multi-criteria decision-making and criteria weighting method by hierarchical analysis and pairwise comparison between these criteria, in the present research, showed that among the considered criteria (proximity to the workplace, density, age of the unit residential and proximity to educational centers), the criterion of age or year of construction of the residential unit has the highest weight and the criterion of proximity to educational centers (schools) has the least weight. Also, the introduced method can be a suitable guide for choosing a residential unit for city residents according to their desired criteria.

Geological risk analysis in an exploration process based on a set of traps is an example of a spatial multi-criteria decision-making process. Since the process of formation, gathering and exploration of hydrocarbons has many complex influencing parameters, and finding the potential of these hydrocarbon reserves is also time-consuming and costly, it is necessary to control and manage the risks in the process. Hydrocarbon exploration as much as possible. Most of these risks are caused by uncertainty in spatial data. This research is intended to design a framework for managing and reducing the geological risk of buildings in the sedimentary region of Fars by using the features of intuition theory and spatial information systems. In the case of this research, the main statement is «Which places of the play have less risk than other places in terms of the presence of a hydrocarbon system, and what is the uncertainty of this estimate». For this purpose, the theory of evidence or Dempster-Shafer has been used in this research. This theory has been used to calculate geological risk uncertainty in an interval form. This type of approach required that the data used in the formation of petroleum systems be collected in the form of a spatial database so that by combining spatial analysis with uncertainty models, geological risk maps can be created with the help of spatial information systems with proper accuracy. The results showed that geological risk can be monitored with 79.6 % accuracy. The integration of the methods used provides a framework that every point of the studied sedimentary area has a certain amount of geological risk, the uncertainty of which is controlled by the Dempster-Shafer theory. The proposed model enhanced the precision of the prospect classification by considering an uncertainty interval.

Iran recognized as one of the major fossil fuel producers in the world with a substantial portion of its energy basket reliant on fossil fuels, is situated in a region of the Earth with high solar radiation potential, making it suitable for solar energy production. The first step towards establishing solar power plants is the scientific and operational site selection for their construction. This study aims to identify suitable locations for solar power plants in Fars Province using a Geospatial Information System (GIS) and a multi-criteria decision-making approach. The proposed methodology involves the preparation of informational layers, spatial analyses, and visualizations using GIS. Additionally, criteria weighting and integration were performed using multi-criteria decision-making techniques. The study compares two methods for weighting the location criteria, Best-Worst Method (BWM) and Step-wise Weight Assessment Ratio Analysis (SWARA), which have been less utilized in previous research, and combines their results using geometric mean operator. According to the results, the criteria for solar power plant site selection, ranked by importance, are photovoltaic potential (0.241), direct normal irradiation (0.156), temperature (0.120), precipitation (0.092), distance from roads (0.089), distance from urban centers (0.085), distance from faults (0.066), and elevation (0.054). The land suitability maps indicate that over 50% of the area in Fars Province is highly or very highly suitable for the construction of solar power plants, with the northern, northeastern, and central regions, including the counties of Abadeh, Khorrambid, Pasargad, and Sarchehan, being prioritized.

According to the statistics of the World Health Organization, about 7.6 million people die from cancer every year, which is 70% of all deaths in low- and middle-income countries. It is also estimated that this number will reach more than 13 million people by 2030. Since the rate of cancer is increasing and the issues related to the spread of diseases, especially cancer, are directly related to geography and environment, this issue has been considered for a long time. The aim of this study is to develop statistical location models and use spatial information system to evaluate the role of spatial parameters and environmental factors in the prevalence of bladder cancer in the provinces of Iran. According to the statistical analysis of bladder cancer data in Iranian provinces, it was found that Yazd and Sistan-Baluchestan provinces have the highest and lowest incidence rates of bladder cancer in Iran, respectively. Also, by setting a minimum value and considering the global average of bladder cancer as a threshold, it was found that the rate of bladder cancer is low in 23% of the provinces, average in 30% of them, and higher than the global average in the remaining 47%. Bladder cancer, as one of the common public health problems in Iran, has a heterogeneous distribution in the country. Using random forest algorithm, this study examines the spatial distribution of bladder cancer in Iran and predicts its incidence in different regions. The results show that the random forest algorithm predicts the incidence of bladder cancer throughout Iran with considerable accuracy. Running the model on about 30% of the test data showed that the actual value of the data was predicted with an accuracy of 0.62. Finally, by implementing a statistical model, the risk factors of this disease are ranked and the rate of bladder cancer in the country is predicted. According to the results of this study, because the hot spots of bladder cancer are located in the central provinces of Iran, especially Isfahan, Chaharmahal and Bakhtiari provinces, it is suggested to conduct more detailed medical and statistical research in this field.

Unauthorized construction activities in forest lands have raised significant concerns. Environmental cadastre is regarded as a fundamental tool for providing precise spatial, legal, and environmental information, enabling close monitoring, land management, and sustainable development.

In this study, a combination of qualitative and quantitative research methods has been employed. The qualitative approach included the analysis of the legal and supervisory framework of the cadastre and the examination of institutional arrangements and stakeholder participation. On the other hand, the quantitative approach involved spatial data analysis and cadastre implementation to assess changes in land use and villa construction in forest lands. To identify villa constructions in the forest regions of Laloon village, data collection tools such as Google Earth images were utilized. Subsequently, using supervised maximum likelihood algorithms, classified images were generated and a land use map was prepared. Finally, a 1:5000 cadastral map of Laloon village was created and the implementation idea of an environmental cadastre in this area was examined.

Significant changes were observed in land use patterns in the village of Laloon between the years 2011 and 2024. The increase in residential areas on forest lands indicates undesirable environmental impacts. These changes have endangered the ecosystem and biodiversity of the region and led to population growth. In this study, the Kappa coefficient for the years 2011 at 0.902 and for 2024 at 0.945, has been evaluated as an indicator of land use differences in Laloon village. Furthermore, the overall accuracy of the research was calculated to be 92.703% for 2011 and 96.405% for 2024. The results indicate the high importance and accuracy in detecting land use changes in Laloon village. The assessment of the implementation of environmental cadaster for villa construction in forest areas of Laloon village shows the need for effective land management strategies, and the performance of the environmental cadaster system is capable of detecting land changes and conservation planning. This research emphasizes the importance of incorporating environmental considerations into cadastral mapping processes.

Based on the findings of this study, optimal use of environmental cadastre in Iran for the protection of natural resources and preservation of habitat sustainability is considered as a very vital action. Adhering to environmental laws and regulations during the execution of development processes and construction of structures is universal and undeniable for everyone. Implementing environmental cadastre requires collaboration among governmental organizations, technical expertise, utilization of advanced technologies, and community participation. This research indicates that implementing environmental cadastre plays a significant role in maintaining a balance between development and environmental protection. To enhance the efficiency of the environmental cadastre system in Iran, it is suggested that actions such as strengthening the legal framework, improving data management, upgrading technology, increasing stakeholder participation, and ensuring sustainable financial support be taken. These actions include drafting specific laws and regulations, establishing a centralized and powerful database, forming a multi-stakeholder advisory committee with the participation of various entities, and allocating sustainable financial resources for the advancement and development of the environmental cadastre.0.

The flood is the most important natural disaster that causes human and financial losses every year, then its management is very important. The most basic step in flood disaster management is the preparation of a flood susceptibility map, which integrating the geospatial information system and multi-criteria decision making is an efficient approach to handle it. In order to spatially model flood susceptibility, the present study has presented an approach of fusion the weights of the effective criteria on flood susceptibility using the Dempster-Shafer information fusion theory (DST). The purpose of the fusion of the weights of the criteria is to increase reliability, reduce the uncertainty of the weighting process, and increase the accuracy of flood susceptibility modeling. The present research has presented a hybrid weighting method by integrating the results of two weighting methods (Analytical Hierarchy Process (AHP) and Best-Worst Method (BWM)). The hybrid weighting methods of previous researches are mainly based on simple mathematical operators, and complex operators such as DST used in the present research are less used. The results of the fusion of the weights obtained from the two weighting methods of AHP and BWM indicate the very high weight of the flow accumulation criterion (0.437) and the very low weight of the vegetation index criterion (0.004). Comparing the results of the research with the facts of the studied area showed that the presented hybrid weighting approach with 96% accuracy Compared to each of the basic weighting methods used, is of higher performance. Also, the susceptibility modeling resulting from the new BWM weighting method has been more accurate compared to the common method of the AHP. According to the results of the research, more than 92% of the studied area has moderate to high flood susceptibility and less than 8% of the area has less than moderate susceptibility, which indicates that the studied area is prone to flooding.

One of the most contagious diseases of recent years is Covid 19 disease (Corona), which has spread from Wuhan, China to the rest of the world since late 2019, causing many crises and a profound impact on the world and our daily lives. In most people infected with the disease, it causes respiratory symptoms, the severity of which depends on the person's immune system. The main objectives of this study are to discover the clusters and predict the high risk areas of Covid 19 disease, compare the efficiency of the two proposed methods and determine the effective parameters by city. In this study, Moran index and hot spot analysis index were used to investigate the distribution pattern of disease incidence rates and clusters, respectively, and Pearson correlation coefficient was used to determine effective disease parameters. In this study, statistical data of Covid 19 disease of East Azerbaijan province in the city along with environmental and topographic, health, economic and urban facilities data in the period from February 22, 2020 to November 20, 2020, were collected weekly. According to the results, the incidence of Covid 19 disease during this period has passed two peaks and according to the maps obtained from the two models, in some weeks the GWR model and in some weeks the MLP model was the superior model; also, for the GWR model, the goodness of fit index value is 0.8985 and the normalized root mean square error is 0.0822 and for the MLP model is 0.8226 and 0.1340, respectively, which shows that the GWR method is more appropriate. Sensitivity analysis of different parameters showed that the parameters of the Covid 19 incidence rate of the previous week and wind speed are more important than other modeled parameters in this issue. In this study, effective parameters were extracted separately for each city and a local model was presented that compared to the general state of the model, the local model had better accuracy than the general model of the MLP method.

The importance of information has led researchers to look for the least cost and time ways to acquiring information. One of the newest mechanisms of acquiring and compiling geographic data is Volunteered Geographic Information (VGI). This way saves time and money and can provide regularly updated geographic information that is difficult to obtain through remote sensing but can easily be collected by citizens on the ground. VGI refers to geographic information created by citizen volunteers. VGI has proliferated in recent years, mainly due to the technological advancements enabling the public to contribute geospatial data. One of the fields that we can use VGI is agriculture. In this way, every farmer, as a citizen volunteers, records the information of his agricultural lands. Now, field visits and satellite image processing are the two main ways to collect this information. Both of these methods are costly and time-consuming.  In this article, we propose a VGI system to gathering the type of cultivation information and the spatial boundaries of agricultural lands. For evaluation, the proposed approach was implemented for a study area and thatchr(chr('39')39chr('39'))s results were compared with the remote sensing method. The results show that the accuracy of the information gathered with the proposed method, which obtained 91% by the confusion matrix. This accuracy is approximately equal to the accuracy obtained from the remote sensing method; While the time and cost of this method are much less than the remote sensing method. Also, the precision of the collected data according to the confusion matrix for the four crops of corn, rice, wheat, and barley in this study were 0.91, 0.89, 0.86, and 0.91, respectively, the results were reliable. This shows the reliability of the results obtained from the VGI for different products.

In recent years, rapid population growth has led to increase per capita water use in various sectors including agriculture and industry and a growing gap between water demand and water supply has emerged. Therefore, identifying and tracking changes in groundwater resources as an alternative and reliable source of surface water resources are so important to region located in the Middle East with dry weather and large volumes of drought and climate changes. In the current study, the potential and evaluation of water resources are investigated in the west and northwest formations of Shahroud in an area of about 480 square kilometres using remote sensing and spatial information techniques. In this region, due to the presence of carbonate rocks as well as the function of erosion and tectonic forces in parts of the region, relatively suitable aquifers have been formed. For this purpose, information layers including lineament, lithology, slope, aspect, waterways, precipitation and type of precipitation were evaluated by Analytical Hierarchy Process (AHP) method. In this study, information layers were prepared using the ability of RS and GIS in three main stages: extraction of geological map, extraction of area lineaments and extraction of other information layers. The proposed methodology applied two different types of remote sensing sensors including Landsat 8 as optical data and Sentinel-1 as radar data. In preparing lithology map with emphasis on calcareous formations, the proposed method applied four techniques including independent component analysis (ICA), minimum noise fraction (MNF), band ratio (BR) and color composition on Landsat 8. Using the pixel purity index, the endmemberes were extracted and the obtained maps were classified by Support Vector Machine (SVM) and maximum likelihood (ML). Among the classification methods, SVM has a higher ability to classify than ML and identified formations with higher accuracy in the region. Finally, tree decision making system was used to improve the classification of images. The geology of the area was extracted in four classes focusing on calcareous formations. The map was compared with the geological map prepared by the surveying organization and the following results were obtained: Kappa coefficient 0.83, Accuracy of Ku and Jd Formation (calcareous formations) with 99.2% accuracy, Shemshak Formation (Js) with 80.2% accuracy, Lalun Formation (Cl) with 81.2% accuracy and Alloviume (Q) with 78% accuracy were identified. Subsequently, lineaments area were extracted using integration of Landsat 8 and Sentinel 1 radar remote sensing data based on semiautomatic methods. Based on the results, the lineaments were in good agreement with the faults in terms of orientation and numbers at different lengths. Also, the densities of the lineaments extracted in different formations of the geological map of the region had 99% compliance with the densities of faults in the formations. Therefore, by combining band6 of Landsat8, VV and VH polarization Sentinel1, the area lineaments can be extracted with high accuracy. Other thematic layers were extracted by using remote sensing and GIS Techniques using 30 m SRTM. It was also observed that the groundwater potential map is mainly controlled by precipitation, lithology, and lineament density factors.

Coverage problem is one of the location/allocation problems that is used to minimize service point and provide more facilities. The solution for complete coverage is using short-range radio or electromagnetic transducers to improve coverage in areas where topography does not allow long-range transmitter coverage. The most important innovation of this research is the development of a three-dimensional spatial model to optimize the coverage of short-range transmitters in areas out of the coverage of long-range transmitters. The case study is full coverage of a UAV's route to disconnect its communication to the control center. In this model, by identifying the effective parameters of the UAV’s routing and the effective parameters of the transmitter function, a spatial model is developed to optimize the number of transmitters to achieve the highest coverage with the lowest number of transmitters. By using the proposed model, the UAV navigation system is disconnected and the result shows that the number of transmitters with a 90% efficiency decreased by about 30% compared to the conventional models.

Today, physical development and population growth in Iranian cities, like other developing countries, is on the rise. One of the main problems in the urban area is the lack of attention to the influential parameters in the sustainable urban development.  Various factors, such as natural phenomena, play a role in the urban development, and the effective parameters must be considered for locating the suitable areas for sustainable physical development. Otherwise, there will be a kind of heterogeneity and disorder in the physical development of the city and the sustainable development will not be achieved. The most important innovation of this research is the combination of spatial information system and fuzzy logic to determine the optimal range of the physical development of Hamedan city with a sustainable development approach. Since the determination of the optimal range of development has not yet taken place, and several factors have been involved, and uncertainty in development rang prediction, using fuzzy logic can be helpful.  In this study, the effective parameters are including faults, slopes, roads, rivers, and distance from the city are considered. Then fuzzy membership functions defined and applied spatial data using   ArcGIS software.  The Sugeno fuzzy system is used to determine best location of the urban physical development. In this system the fuzzy rule of a descriptive phrase with linguistic values, ​​becomes a simple relation and the output of the system is no fuzzy. Also, in this system, there is no need to apply the operator on the system and deactivate it. One of the advantages of Sugeno system is that its output can be used in most cases and there is no need to change it. In this system, the input of the system is applied to the rules and then multiplied by the weight of each rule and the result is divided by the sum of the values ​​of the rules. Due to population growth and the growing trend of physical development in developing countries, including Iran, it is necessary to rely on scientific tools and by examining various factors to advance this development towards sustainable physical development. Statistical studies have shown that the population of Hamadan has been increased over the past. It should be noted while planning for the sustainable physical development of cities, the factors of physical development must be examined and acted based them. For analyzing effective parameters of sustainable development Geo-Spatial Information System was used.  Due to uncertainty existence in urbanization, fuzzy logic was used as a suitable tool to model these parameters. Finally, the results were presented by combining different parameters. years. The results show that the best area for sustainable physical development of this city is the northern and northwestern parts of the city. Also, if the importance of the beauty parameter of nature decreases, the northeast of the city will be suitable for development. But in the none of the studied cases the south of the city is not suitable for development.

Urban communities have been developing and they are being industrialized. These developments have some benefits for these communities; however, they have created some significant problems. One of these problems in this area is traffic and road congestion and following that, noise pollution on urban areas. These days, noise pollution is one the considerable problems that the residents of crowded places in urban communities are dealing with. Noise pollution not only decreases the quality of urban life, but also its mental and physical side effects are lasting for years. Therefore, considering this issue, attentions must be paid to this topic and conducting research is necessary. The results of these researches can contribute to offering solutions and analyses for planning for controlling the noise pollution on urban communities. In this study we aim to present a 3D model of an urban area in which the amount of noise pollution caused by traffic on the surfaces of buildings is specified in the form of color maps with noise scales. For this, at first, a comprehensive analysis of the proposed models in the area of predicting noise was done. The models were CORTN،FHWA،CNR،RLS90،SCM1. Each of These models has calculating relations and characteristics unique to them. After analyzing these models from calculation and structural aspects, we chose the CORTN model for the predicting the noise. This model was chosen as the final model in predicting the noise due to its simplicity and calculability of all of its parameters and also its ability for calculating the noise scales in different heights. Then, with creating an arranged network of the areas in the 3D example in the level of region and doing three stages of refining on them, the amount of noise in all areas was calculated. These three steps of refinery were done for decreasing the extra and unnecessary points in a way that the remaining areas have logical long and wide distance from each other and be consistent of the outer surfaces of the buildings. Because for predicting the noise we just need those areas that are on the outer surfaces of the buildings and we do not need to predict the noise in other areas. With doing these three stages of refinery, the number of sample areas was decreased from 896000 to 11456. This reduction in the number of areas did not influence the final accuracy. However, because of the significant reduction, it increased the speed of calculation and the saving in time. It is worthy to note that all calculations related to predicting noise were conducted by through formulas in Microsoft Excel. Finally, after the calculating and predicting the noise in all sample areas, we use Kirging interpolation model to create interpolation maps of areas in the ArcGIS software and these maps were merged with the surfaces of the buildings by Sketch UP software. Then, the created model was moved to ArcGIS and finally, the built model was presented in it. The results of this study show the distribution of noise pollution on different surfaces. Knowing this awareness can help us in better planning for choosing the kind of usage of different floors in a building. Because we cannot decrease the number of cars in the urban communities these days and we are dealing with increasing number of cars, therefore, we must conduct such researches in order to deal with this kind of pollution an use them in designing and constructing the buildings.

In recent years, with the advancement of positioning systems, access to a large amount of movement data is provided. Among the methods of discovering knowledge from this type of data is to measure the similarity of trajectories resulting from the movement of objects. Similarity measurement has also been used in other data mining methods such as classification and clustering and is currently, an important and challenging topic for many researchers in the field of geospatial information systems. Although uncertainty is an inevitable issue in the field of geospatial information systems, so far little attention has been paid to this issue especially in the field of measuring the similarity of trajectories. One way to cope with the uncertainty in the observations and definitions of the problem, is to use fuzzy theory. In this study, two methods of sim1 and sim2 based on Longest Common Subsequence (LCSS) and Edit Distance on Real Sequence (EDR) methods, respectively, have been introduced to deal with uncertainty in measuring similarity of trajectories and improving their performance using fuzzy theory. The proposed methods use a fuzzy membership function based on the distance between the points of two trajectories to determine the degree of matching of every pair of points on two trajectories based on which the similarity of the two trajectories is calculated. In order to evaluate these two methods, two experiments have been performed on the real and synthetic trajectories of personal cars. Experimental results show that sim1 and sim2 are similar to LCSS and EDR in terms of sensitivity to noise, increasing and decreasing sampling rate and have better performance in terms of sensitivity to displacement. For example, the mean percentage change of similarity to distance variations for the four thresholds of 5, 10, 25, and 50 meters for LCSS is 0.02, 0.97, 0.66, and 0.23 but for sim1 and sim2 is 0.41 which is proportional to rate of changes in reference trajectory.

Leptospirosis is a common zoonosis disease with a high prevalence in the world and is recognized as an important public health drawback in both developing and developed countries owing to epidemics and increasing prevalence. Because of the high diversity of hosts that are capable of carrying the causative agent, this disease has an expansive geographical reach. Various environmental and social factors affect the spread and prevalence of the disease. The combination of epidemiology and Geospatial Information System plus using Ecological niche modeling provides the ability to identify areas at risk of disease, then predict the risk map of the disease for other regions by using relevant environment variables, and prevent and eventually eradicate the disease by conducting constructive activities such as increasing public awareness with education. In this study, using land use, environmental, and climate variables and taking advantage of the occurrences of the disease between 2009 and 2018, the risk level of Leptospirosis was modeled in three provinces of Gilan, Mazandaran, and Golestan based on ecological perspective. For modeling, highly correlated variables and also variables with high multicollinearity were identified and omitted. Because in ecological modeling regions to represent the absence of disease is required in addition to the presence and since these areas are not available, the second objective of this study was to evaluate the efficacy of different methods of generating pseudo-absence data in modeling leptospirosis. Finally, more accurate modeling of the prevalence of the disease in the northern provinces of the country can be obtained. Therefore, after selecting suitable variables for modeling, first, based on five methods (completely random generation of points in the study area, applying physical constraints with buffer at two radii of 5 and 10 km the generating points outside of designated buffer, applying environmental constraints by implementing two models of one-class support vector machine and BIOCLIM and generating points in unsuitable areas defined by these two models) pseudo-absence points representative of disease absence points in the study area were produced. Next, four models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine were deployed to produce the disease risk in the study area. BIOMOD2 package in the R programming language was applied for modeling. The results showed that applying physical constraints with buffers yields the most reliable performance in comparison to the other three methods. Finally, the constructed model that performed best in TSS Statistics (with values of 0.76, 0.87, 0.84, 0.82 for Models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine) was considered as the final output. Between all deployed models, Artificial Neural Network delivered the worst performance and had the most unstable results. Based on the risk-map of leptospirosis, central regions of Mazandaran and Gilan province, especially rural areas of Layl, Asalam, Eslam Abad, Chahar-deh, and Lafmejan have very high values of risk. Measures need to be made to reduce the high rate of Leptospirosis incidence in these regions. Furthermore, yearly precipitation was considered the most influential variable for the distribution of Leptospirosis.

The global burden of leptospirosis as a fatal zoonotic disease is increasing all over the world [1]. As there is not any significant decrease in yearly reported cases trend in Iran and potential spatial distribution of leptospirosis remain unknown in national level, we tried to figure out the geographic distribution pattern of leptospirosis in all parts of Iran. The aim of this study is producing leptospirosis risk map by analyzing relations between disease data reported by the Ministry of Health and nine environmental factors, for a period of 2009 to 2018, using Geospatial Information System (GIS) and Remote Sensing (RS) capabilities and Maximum Entropy (MAXENT) model. Altitude, precipitation, average temperature, maximum temperature, Normalized Difference Vegetation Index (NDVI), land cover, displacement (roads, railways and border entrance points), slope and water areas with 1km * 1km resolution were entered to the model as contributing factors, and patients home locations were used as disease incidence points. ArcGIS 10.6.1 and ENVI 5.3 were used to prepare the nine factors for analysis and interpretation of the results. To create the potential distribution, MAXENT as an ecological niche model was used which is a method that its performance in disease distribution modelling has been proved [2,3]. An advantage of this model is that variables can be either continuous or categorical and can be run for even less than 100 points as incidence data [2]. In this study, 60 percent of disease data was selected randomly for training and other 40 percent was applied as test data. Jackknife manipulation technique was performed to investigate the contribution of each variable in model. Our findings on spatial pattern of leptospirosis at least hint that except north parts of Iran that obviously are most vulnerable areas to the leptospirosis outbreaks, west parts of Iran specially Kermanshah are not safe from the spread of the disease, so health policy makers should consider these areas for monitor and control programs specially after severe rainfall or flood in spring and summer. Jackknife results showed that precipitation and altitude by 43.5 and 37 percent contribution, are the two major factors for risk prediction of leptospirosis. On other hand, maximum temperature, water areas and slope have not meaningful impact on incidence of leptospirosis. Land cover with 11.9%, NDVI with 4%, average temperature with 1.3% and displacement with 1.1% were participated in the model. Also, yearly models have been created for years between 2009 to 2018 to investigate that how parameters contributions change over years. Results showed that the incidence rate was related to altitude around 40% for all these ten years, but precipitation contribution percentage is fluctuating over years. Response curves showed a direct relation between incidence rate of disease and precipitation which means more rainfall causes more incidence. It also showed that altitudes around zero are the most suitable height condition on current distribution of leptospirosis. Also, the landcover output curve showed that Post-flooding or irrigated croplands, artificial surfaces and associated areas, mosaic forests or shrublands and grasslands are the most suitable landcovers for incidence of leptospirosis. To assess the model efficiency, Receiver Operating Characteristic (ROC) was employed. The Area under the Receiver Operating Characteristic Curve (AUC) for training data and test data was 0.956 and 0.955, respectively.

In recent years, Iran has faced serious water scarcity and excessive use of water resources. Therefore, exploring the pattern of urban water consumption and the relationships between geographic and demographic parameters and water usage is an important requirement for effective management of water resources. In this study, association rule mining has been used to analyze the data of municipal water consumption in Esfahan urban areas. Association rules mining have been used to discover the connections between geographic and demographic parameters including the number of family members, the number of apartment units, residential building types, areal coverage of the house and green spaces, distance from river and central of city, spatial location, distance from main road, population density and percentage of young population with water consumption patterns. A version of Appriori algorithm with less computational volume that can processes large amounts of data has been used for association rule mining. Each of the extracted rules that satisfy minimum support equal 30 and minimum confidence equal 60 reflects the relationship of each spatial and demographic parameters with the consumption water. Then, the obtained rules have been evaluated and water consumption hot spots were extracted. Results shows that distance from the main road, areal coverage of the residential and green spaces have direct relationship with the household water consumption. On the other hand, number of residential units, population density, distance from river, X and Y value and percentage of young population are inversely related to household water consumption. Also, if the distance from the city center increases, household water consumption decreases. Moreover, the southern areas of the city by minimum distance from the river have the highest water consumption and specified as hot spots. In these neighborhoods, building types are villa and have a lower population density with higher area of green space and yard than others.

Today, air pollution has become one of the serious problems in developing countries. In this paper, the eventful areas for Mashhad city has been represented and the polluted area has been predicted with interpolation technique. For this purpose, 6 air pollutant data (CO, NO2,PM10, PM2.5) in 11 Mashhad air-evaluating stations in for one year according to Iranian calendar has been used. Geographic information system and two ordinary and probability Kriging approach has been used for representing the hotspots areas in study area. The Kriging method has been used as a tool for interpolation. In this research, at first with using semivirogram map, the spatial dependence of air pollutant in air station has been evaluated. Then, spatial model has been created with average daily air pollutant concentration and the quantity of air pollution in various areas has been represented in study area.

Asthma is an untreatable but manageable disease that has increased dramatically during the last fifty years due to contamination of the surrounding environment with triggers such as air pollution and has become one of the common chronic diseases throughout the world. Researchers consider allergies and environmental stimuli to be one of the essential components of asthma management programs. Predicting the impact of these factors, identifying potential risk areas for asthma exacerbation and informing patients about preventing exposure to them in all places and times are an appropriate and effective approach in managing the disease in the long-run. For this reason, specialists consider that the design, development, and promotion of asthma management programs are vital to controlling asthmatic exacerbations. Today, with the development of information technology tools and influence web geography information system in health area, the design of managing programs for controlling asthma has undergone fundamental changes by removing spatial and temporal constraints. The tool is available at any location and time and provides appropriate information about the living environment of patients and useful information about the role of air pollution factors in allergic asthma exacerbations.
Study area: The study area is Tehran city in the article. We collected required data from patients with asthmatic exacerbations referring to one of the pulmonary hospitals (1702 asthmatic patients) in the city. The air pollutants data also were prepared from pollutant stations in Tehran. In two databases were merged in a unique geographical database.
Materials & In this study, we have developed an asthma management system based on air pollution using tree-based classification. Our research process includes four main steps. Firstly, required data is collected and prepared in a geographical database as spatial data. Then with the help of tree-based classification algorithm, predictive model of asthmatic exacerbations is made in Rstudio and its performance is assessed. Then with help of if-then rules of the model, it was prepared a toolbox in ArcGIS to reproduced maps of the prediction daily, because the conditions for air pollution factors change on a daily basis, and it is necessary for these maps to be updated continuously. In the final step, it was published these maps to the users in the Web, which will be realized with the help of ArcGIS Server tools. Patients can view these maps through ArcGIS Online (via the browsers of desktop and mobile devices).
Results & In this study, we have developed a tool based on decision tree algorithm integrated spatial data with accuracy 82% and the web-based geographic information system to provide daily maps of the occurrence of asthma exacerbations based on air pollution to alert patients about potentially dangerous areas and places which asthma exacerbates. This tool, which is available on browsers of both of web and smartphones platforms, improves patient adherence to asthma management programs and significantly raises their awareness about of an environmental-related disease. With the help of these maps, they will have a powerful self-management tool that can decide whether or not they will be present in these areas. Our asthma management system provides the useful and valuable map for users with asthma. Patients could use this tool in every place and every time without any limitation.  Our system improves adherence ‘s patients to asthma management plans Compared with traditional and paper-based tools. Patients inform about surrounding environment continually with the practical system to avoid places that are hazardous to their health. Therefore, we provide a suitable environment for users with help of various tools Arc GIS that is available for all users without extra cost. These maps are easy to understand and users can easily use it with a low level of literacy.

Epidemic diseases are a public health concern that has many economic costs and health problems. The geographical distribution of these diseases is a spatial and temporal process. By understanding the process and identifying the factors that affect it, we can take an effective step in preventing and treating these diseases. Brucellosis is one of the most important zoonotic diseases in Iran. To better understand the epidemiology of this disease in northern Iran, the main objectives of this study are to review the annual and monthly trends, to identify spatial and space-time clusters, and to determine the impact of ecological variables. The study was conducted on 6895 patients from April 2009 to March 2017. Data of disease incidence with environmental data including average temperature, maximum temperature, minimum temperature, number of freezing days, humidity, precipitation, evaporation, wind speed, vegetation, elevation, slope and aspect in Golestan, Mazandaran and Gilan provinces as Monthly collected. Global Moran's I was used to investigate spatial autocorrelation. Spatial scan, local moran’s I and space-time scan were used to identify clusters. Spearman correlation was used to study the effect of ecological parameters. The results showed that brucellosis was clustered in northern Iran. The incidence was maximum in the summer (34%) and at least in winter (16%). The months of May, June, July and August were the most susceptible months with a total of 3396 patients. In Local Moran's I, the cities of Minudasht, Kurdkuy, Bandar Ghaz and Galughah were identified as high-high, the cities of Lahijan, Astaneh Ashrafieh and Rasht as low-low and the shaft town was identified as high-low region. In the spatial scan, 10 classes were discovered, most of which were located in Golestan and Mazandaran, and only one was in Gilan. The first and most dangerous classes were in Golestan province. In a space-time scan, 4 classes were identified. The results obtained from the Moran index and spatial scan and space-time scan were confirmed by each other. Spearman showed a positive correlation between incidence of disease with evaporation, aspect, maximum temperature, height and mean temperature, and negative correlation with precipitation, moisture and vegetation. This study shows that in the north of Iran, we see an increase in the incidence of this disease from west to east. Brucellosis is higher in spring and summer in mountainous regions with warm and dry weather. This information can be used for the control strategy used by decision-makers in the field of health.