Intersections as traffic junctions of inner-city thoroughfares are one of the most important geometric locations of pedestrian and vehicle interference, where a large number of pedestrian accidents occur every year. Therefore, the main purpose of this study is to analyze the effective factors in pedestrian accidents based on previous accident records and to investigate the impact of each factor on the number of pedestrian accidents at intersections using Poisson regression and Multivariate regression statistical analysis. Another purpose of this study is to present a (GIS)-based approach in order to identify and Spatial distribution of high-traffic intersections in Rasht metropolitan areas. In this study, fourteen independent factors obtained from police reports and fieldwork were divided into three general groups: human factors, weather conditions, and traffic-geometric factors. Accordingly, Poisson regression was used to analyze the direct effect of these factors on the number of pedestrian accidents at intersections, and multivariate regression was used to analyze the effect of three general categories of pedestrian crossings on pedestrian accidents at intersections. The results of the study showed that the factors of driver age 11%, rainy weather conditions 7%, and lack of traffic lights 40% had the greatest impact on increasing the number of pedestrian accidents at intersections. Also, the highest frequency of intersections with pedestrian accidents occurred in the second and fourth urban areas and the intersections of Toshiba Square, Heshmat Crossroads, and Gas Square were recognized as the most accident-prone intersections in Rasht.

Railway transportation system is composed of interactions among a set of equipment and operations determining the capability and capacity of the railway system in terms of cargo and passenger transportation. The structure of capacity in rail transport is different from that of other methods of transport and especially road transport. Parameters affecting the capacity of rail transport SYSTEMS are very extensive, depending on type of route in terms of the route geometry and the technologies used in operation. The ability and capacity of carrying freight and passenger in a rail transport network is a function of various parameters in the path of the fleet such as route characteristics (slope, arc, electric signs system, speed limitations, tunnels, etc.), features of fleet (locomotive speed, weight, type of pulling force, type of wagons, etc.), features and characteristics of operations in stations (loading and unloading system and equipment of stations), type of operation and schedule of departure in the rail network (speed and type of trains), construction or reopening of stations, and tracking. All these factors define and determine the capacity of a rail transport system and the nodes for utilization of the network. For this purpose, capacity calculation and analysis of changes in any factor affecting it are important as the corresponding knowledge imposes large contributions in enhancing operating level of existing railway networks. There are various methods for calculating the capacity, of which one may select one depending on the network type and application. As of now, in Iran, considering the variety of applications planned for railway network (cargo, passenger transportation, or a mixed of the two), so-called practical capacity method (Scott’s formula) is used to calculate line capacity. Accordingly, current procedures followed in data gathering and obtaining effective parameters on line capacity calculation, as well as actual capacity calculation and analysis are performed manually, resulting in some discrepancies in different calculations and results. Further, there are chances that, planning based on an inappropriate estimation of line capacity, a portion of actual line capacity remains unused. As such, the present research, which is the first on the topic, aims at using capabilities of geographic INFORMATION system ((GIS)) to design and implement a web-based (GIS) service to determine the state of railway network capacity via a novel approach which offers higher efficiency than conventional methods. For this purpose, a base (GIS) environment was designed and connected to different databases run by Islamic Republic Of Iran Railways Co., including the travel database, to observe current state of the network in terms of capacity and propose solutions to further exploit unused capacity of the network and address bottle-necks of railway network capacity usage. The use of this method not only resulted in enhanced pace and accuracy of network capacity calculations, but also brought about better managerial decision-making to use unused capacity of the network and sell this capacity to applicant companies and generate further revenue for Islamic Republic Of Iran Railways Co., to the extent that, prior to any new season or year, one could sign appropriate contracts to address the demand raised by cargo owners and passengers across the railway network.

Background: Malaria is one of the important communicable diseases in the world and it is recognized as one of the most important disease in south of Iran. This study was conducted to understand the ecology of Malaria in south of Iran, Bandar Abbas, by Geospatial INFORMATION System ((GIS)).Material and Method: Essential parameters for modeling the local Malaria were obtained from formal centers. Afterward, mapping of the related factors were done. AHP analytical procedure was used for weighting parameters ranking and predicting model. So the endangered area mapping were generated.Results: According to the local data and the results of this study, some predicting models could be used for predicting Malaria. Generated maps showed that endangered area and predicted models are presented for more accurate and effective malaria control programs.Conclusion: Prediction of Malaria and mapping endangered area is possible in south of Iran and Malaria early warning system may be established for the local health care system.

Phenomena in the real world are grouped into two categories: static and dynamic. The static phenomena don’t change with variation of the time but the dynamic phenomenon will do it so. These changes may be occurred in the phenomena property like spatial or geometry. Traffic is a Spatio-Temporal phenomenon because its properties which are referring to some locations on the earth will change in respect to the time variation. Today, due to the development of technology, the management capability of the spatial-temporal phenomenon has been provided in a Temporal Geospatial INFORMATION System (T(GIS)). The objective of this research is study of using T(GIS) for controlling and coordinating of traffic signals. For this purpose, first, parameters and components for determining traffic condition and coordinating traffic signals are collected. Then based on these data, the structure of the traffic data is determined for entering into a database. This structure determined in such a way that the traffic condition is introduced to the system based on the type of existing traffic behavior. In this study, the dynamic data along with spatial data of street path (static data) are stored and structured based on relational model and Time Stamping Spatial Objects model. With performing relation between these data the instant variations of the traffic condition is determined. Based on these variations the traffic parameters and offset between different intersections are computed and then based on this INFORMATION the schedule of traffic signals will be implemented with a singular or network method.

Landslide is one of these natural hazards which causes a great amount of financial and human damage annually allover the world. Accordingly, identification of areas with landslide threat for implementation of preventive measures in order to confront against the instability of hillsides for reduction of potential threats and related risks is very important. In this research a new method for classification of landslide risk according to geographical analysis and uncertainty modeling is presented which is based on data mining in previous events. In order to do so, adaptive neuro-fuzzy algorithm which is adjusted by means of sensitivity analysis is used in inferential basis of proposed model, which analyze landside risk efficiently. The selected region for this study is available lands in Alborz province. In proposed method factors like altitude, petrology, gradient, gradient direction, distance to fault and rainfall which are some of the most serious causes of hillside's instability had been inserted and their raster maps produced in (GIS) context and stored in georeference database. In the next step, areas prone to landslide had been identified according to findings of proposed model and finally in addition to model evaluation according to validation outputs, another round of validation is done by field monitoring of hih-risk regions and interpretation of provided 3D models. Results show that the proposed model with root mean square error of 0. 819 and correlation factor of 0. 934 has a relatively high accuracy in classification of landslide risk. In addition in landslide risk geographical distribution map inside studied region, the area of landslide-prone area is the highest with respect to total area of province which shows high-risk of Alborz province against landslides.

Background & Objective: Recommended illumination Engineering Society of North America (IESNA) methods were used to measure the illumination in regular geometric areas such as square or rectangular form areas. It seems that with using the (GIS) method which is used to save, manage and spatial data analysis and processing them by computer we would be able to examine the illumination in many situations and make new predictions.Materials & Methods: In this research the Testo 545 luxmeter sensor was put in height of 30 inches (76 cm) from the bottom of irregular geometric study saloon in libraries of Zanjan. Then total, natural and artificial illumination was measured in the center of each station in lux. These data were entered into Arcview software as input data for analyzing. Thus the illumination quantities, the area coverage percent and isolux curves were obtained for each station.Results: The results showed that illumination quantities in the libraries studied were not suitable because, total, natural and artificial illumination in 51%, 80% and 99% of the libraries were less than 300 lux (IESNA recommended) respectively. Therefore it is necessary to redesign the artificial lighting SYSTEMS or optimize them in order to achieve the standard illumination.Conclusion: It seems that (GIS) method could show the distribution of the illumination in the irregular geometric library halls. These data can also be showed as isolux curves with use of interpolation technique by ArcView software.

The study aimed to investigate the correlation between elevation and various environmental factors influencing Dirofilaria immitis (D. immitis) infection rate in stray dogs within the mountainous areas of Gilan province, Iran. This study was conducted from 2022 to 2023 and analyzed 392 stray dogs across 12 cities, categorizing them based on elevation and examining variables such as temperature, precipitation, and humidity. The diagnosis of D. immitis was performed using PCR methods, and data were processed with Arc(GIS) 10.8 software. The results revealed an overall infection rate of 44.28% ± 2.73, with Fuman-Masuleh reporting the highest at 60%, and Lahijan recorded the lowest at 25.5%. Linear regression analysis indicated a moderately positive correlation between D. immitis cases and elevation (β coefficient of 0.543). Maximum temperature showed a strong positive correlation with D. immitis cases (β coefficient of 0.907). Spatial analysis using Moran's I Index indicated no significant spatial autocorrelation between positive cases and elevation, suggesting that temperature and other environmental factors are critical in the distribution of D. immitis. The Inverse Distance Weighting (IDW) interpolation highlighted areas at higher risk for D. immitis infection, particularly along Gilan province's eastern and western borders. In conclusion, this study highlights the significant infection rate of D. immitis infection in stray dogs, even in the mountainous regions of Gilan province. It is crucial to recognize Gilan as a high-risk area for this infection. Therefore, implementing effective control and prevention programs is essential.

Normally, decision makers use number of pipe broken in specific sections of water distribution SYSTEMS to determine the pipes to be replaced. This index cannot solely evaluate hydraulic and quality parameters of the system and effects of pipe renovation on the system performance. In this study, a methodology is presented to manage the rehabilitation and replacement of water distribution network using hydraulic and geospatial INFORMATION SYSTEMS models. A preprocessor subroutine is developed to link geospatial INFORMATION SYSTEMS and hydraulic software. Hydraulic parameters together with the attribute data of pipes are used to determine the required renovation schemes based on several criteria. The proposed indices consist of pipe breaks and leakage analyses, hydraulic and quality performance and mechanical reliability of the network. A novel approach is also introduced to calculate leakage values throughout the network. Results of a real case study by the developed model introduced replacement of 4 km 40 mm galvanized pipes instead of 11 km 100 mm asbestos-cement pipes from conventional method. It is observed that the suggested geographic INFORMATION system based model produces more realistic results with less cost for renovation schemes in comparison with conventional method which just consider number of bursts as a key criterion.

Background: The municipal waste landfill sites have high a potential of contaminating the environment and having effects of the human health due to waste degradation, production of leachate, carbon dioxide and methane gases. The present study aims to evaluate the location of the solid waste landfill using (GIS) and based on the Oleckno index. Assessment parameters included average annual rainfall, soil type and ground water beneath and adjacent to the landfill site.Methods: This study was based on field- and library-based data collection, and surveys of relevant data. Then data were re(GIS)tered into the (GIS) environment and overlaying layers analysis method was used.Findings: In 2010 the total rainfall in the landfill region was less than 150 mm/year. The soil material was clay loam, and the average distance from the floor of the landfill to the groundwater level was 3-9 meters. As calculated results showed that, the Oleckno Index score in the study area was 40. This means that the conditions were good and there was low groundwater pollution potential.Conclusion: The new landfill site of the city and suburb of Isfahan had a good Oleckno index and the possibility of contamination of groundwater by leachate production based on this index was also low.

Background and Objectives: Geospatial INFORMATION SYSTEMS provide required science and tools for various transactions related to spatial data. Such an ability caused use of Geospatial INFORMATION SYSTEMS in several fields. One of these fields is mine exploration which can be known as highly dependent field to the geology. On the other hand, the traditional methods are not suitable for requirements of mine exploration. Thus, the new methods are utilized more and more for mining as well as mine exploration. Artificial intelligence is one of the new sciences transforming human life. Artificial Neural Networks as a long-standing method of artificial intelligence have found many applications in mine exploration. The goal of this research is producing mineral potential map using artificial neural networks.  Methods: Artificial neural network is a classifier method in essence. This method acts like a black box which is trained at first, then it is capable of classifying the new data. In this research the data captured from exploration studies of porphyry copper deposit located in Yazd, Iran is used for producing mineral potential map. The mineral potential map is essential and very important for mining activities. The data is entered to a feedforward back propagation artificial neural network. The artificial neural network is used in two manners. The first manner is the usual one: The artificial neural network is trained by the aid of boreholes data and then it is used to forecast the potential of copper at every cell of the study area. In the second manner after training the network, its inner weights are extracted. These weights show the amount of importance that the neurons of the network have been considered for the input criteria. These weights are entered into the index overlay method. Afterwards, the criteria maps are combined by index overlay method and the mineral potential map is produced.Findings: In this research more than testing the power of artificial neural networks in producing mineral potential map, testing the accuracy of its inner weights to be used in another method is aimed. At the training phase the values of criteria maps (essentially produced by exploration studies) at the positions of boreholes are entered into the artificial neural network and the network should forecast the potential of copper at that position qualitatively, while the true values are known from the data of boreholes. Then, the trained network forecast the potential of copper at every position of the deposit. The results revealed that the accuracy of artificial neural network when ignoring one of the non-efficient criteria can be reached up to 100 percent. However, the accuracy of index overlay method using the weights extracted from the artificial neural network is about 70 percent at maximum.Conclusion: The results of this study revealed again the power of artificial neural networks in classification and combination of spatial data. Despite, the unique result of this research is that the inner weights of artificial neural network have the maximum performance in their network and using them for weighting and combining data by another method would not be useful. However, these weights can illustrate the order of importance of data.