javad sadidi

Taftan is a semi-active volcano located in southeast Iran with many craters. The main objective of this study is to investigate whether subsidence or uplift in Taftan Peak. 58 Sentinel 1-A images acquired from January 2015 to December 2020 in the ascending orbit mode, and 102 Sentinel 1-A Sentinel 1-B images acquired from October 2014 to June 2020 in the descending orbit mode were preprocessed for this purpose. The interferograms with the permanent scatterer interferometry (PSI) method were created using SARPROZ and StaMPS softwares, in which atmospheric corrections were made automatically, and then the surface displacement of Taftan volcano estimated.The Line of Sight (LOS) displacement corresponding to the uplift was 0.5 mm to 1 mm yr-1 for the ascending orbit and 1 mm yr-1for the descending orbit. Because no GPS station was close to Taftan volcano, the GPS measurements of one station located in the study area (Saravan station) was used to check the accuracy of PSI method. The GPS station of SARAVAN has situated inside the town ,and it is appropriate to use and analyze PSI technique in this station. As a result, the researchers found that the PSI method is in accordance with the GPS data.

Nowadays, deep learning as a branch of artificial intelligence acts as an alternative for human with hopeful outcomes. Open Street Map as the biggest open source data is used as a complementary data sources for spatial projects. It is notable that is some advanced counties the accuracy of VGI data is higher than governmental official data. This research aims to use artificial intelligence to produce and subsequently promote completeness of OSM data. Res_UNet architecture was utilized to train landuse categories to the network. The result shows that IoU metric is about 83 percent that implies a high accuracy paradigm. Then, united-based method was used to calculated completeness of OSM data. The unit-based results show that completeness of building blocks, forest, fruits garden and agriculture land are: 3.6, 9.7, 90.4 and 81.88 respectively. It shows the low volunteer  participation rate to produce OSM data. On the other side the high accuracy achieved by deep learning leads us to complete OSM data by artificial intelligence instead of human prepared data. The advantage of using machine rather than human may be utilized in undeveloped countries or low density population regions as well as inaccessible areas.

The pervasiveness of the concept of vulnerability in various dimensions has led to the emergence of the theory of vulnerability in the spatial sciences. According to the theory of vulnerability, in any given space, there is a coefficient of vulnerability, while the levels and amplitude of safety are not evenly distributed on the surface of that space. Residential use is one of the most important and main uses in the urban land use system, and safety management and attention to its defense requirements are very important due to the high population density in large cities. The present study is in the field of assessing the vulnerability of residential uses against external threats with a passive urban defense approach in District 10 of Tehran, which was conducted in the form of spatial studies and by implementing an analytical model in three steps. First, the principles and requirements of passive defense were identified and classified into three groups of structural, demographic and spatial parameters, and using the questionnaire and expert survey tools, the priorities of passive defense principles in relation to residential spaces were determined. Then, based on the network analysis process, the weight of each criterion was determined and the weight of the ANP model was applied to the spatial layers of the region in ArcGIS software. The results of the model showed that in terms of structural indicators, more than 78% of residential units in the region are in the group of structures with high vulnerability and in terms of demographic indicators, in 88% of residential units in case of external threats, the level of vulnerability is high. In terms of spatial indicators, more than 92% of residential spaces are adjacent to several incompatible uses and have the highest vulnerability. In general, the results of overlapping layers showed that more than 86% of residential units in the area are located in vulnerable zones and the vulnerability of residential units in these zones is very high.

Quality of life studies in order to identify the weaknesses of deprived areas, increase the level of satisfaction among citizens, ranking places and its role as an effective tool in urban management and planning. The purpose of this study is to create a fuzzy model including effective criteria for evaluating the quality of life in urban areas in areas 3, 4, 5 and 6 of Mashhad. In this regard, first, the effective criteria in quality of life that were obtained from distance measurement data, statistics and spatial data in 5 areas; Economic (employment rate, unemployment, home ownership and rent), social (literacy rate, divorce and migration), physical (type of materials and type of housing structure, slope and population density), access to services (educational, medical centers, Cultural, recreational and communication network) and environmental (greenery, air pollution and surface temperature) were extracted and then weighed using the network analysis process. Then, for spatial modeling of quality of life, the selected criteria were combined using the gamma fuzzy model. The results indicate that the quality of life in the central areas of the study area has a better quality of life, but by moving away from the central areas to the outskirts of the study area, the quality of life gradually decreases. Therefore, in order to improve the quality of life index of the citizens of neighborhoods that are not in a good condition in terms of quality of life, it is necessary to make changes to improve the economic, social, physical, spatial and environmental dimensions. In the following study, Moran spatial autocorrelation index (0.57) emphasizes the non-random nature of the distribution of urban quality of life characteristics and the existence of a cluster pattern in the study area.

Considering the travels of travelers in Tehran and identifying the most visited places according to the interests of tourists, the present study is in line with data mining the behavioral pattern of travelers' travel. In this regard, the purpose of this study is to identify the most visited places of travelers in Tehran based on their behavior for tourism purposes so that with the help of these models, their travel pattern can be studied and evaluated based on voluntary geographical information. For this purpose, data mining and RFM model have been used to analyze the relationships between passengers' characteristics and their desires. In this regard, in order to carry out the operational steps of the research, an application can be installed on the mobile phone and a data storage server sent by the tourist, which was prepared by the researcher and is available to tourists in Tehran in the next step. After storing voluntary geographical data by the tourist, according to the latest tourism statistics, about 85% of the most visited places in Tehran province, according to the tastes and visits of tourists, which includes religious, recreational, cultural-historical, scientific and social trips. According to the information obtained, it was found that the process of introducing the tourist area and directing tourists to the most visited places with the operational plans implemented in this section has been completed. In this situation, the recorded data of tourists is of great importance. In the RFM model, the types of trips, the number of places visited and tourist information are examined, and by identifying the existing data structure, the received data are matched with the variables of the date of visiting the place. Date of visiting the place) R and (duration of visiting the place) M and number of visits to the place (repeated visits to the place) F. Analysis of research data is not possible according to the three indicators of conventional statistical methods. The RFM method allows the researcher to cluster and analyze data to achieve the tourist travel pattern and is a suitable model for data analysis. The results of the study indicate that according to the interests of tourists in Tehran, recreational places are at the top of the pyramid, which are called based on the CLV calculated in the platinum study, and cultural, historical, religious and pilgrimage places are in the second place of silver and social places in the second degree. Third are the places called Messi, which indicate the importance of the places visited by tourists. It should be noted that in order to introduce the most visited places to other tourists, the tourism system of Tehran province can be used to attract tourists to this type of places.

Deep learning is a modern method of image processing and data analysis that has entered the field of urban management with promising results and high potential. The purpose of this study is to investigate data augmentation techniques in improving the results of segmentation of building using aerial images with high spatial resolution and deep learning method. For this purpose, MSB building data set and MapNet model were used. The model was trained and evaluated in three stages without data augmentation, with data augmentation of geometric transformations and with data augmentation of geometric and photometric transformations. The results of model evaluation showed that using geometric transformations as data enhancement techniques, F-1 and IoU score evaluation criteria have increased by 0.5 and 0.55%, respectively, and using data techniques Incremental geometric and photometric transformations increased by 1.41 and 1.57 percent. This increase was visually observed in the improvement of the segmentation of dense areas of the building and the discontinuity of large-scale buildings.

In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.

The advent of Web 2.0 enables the users to interact and prepare free unlimited real time data. This advantage leads us to exploit Volunteer Geographic Information (VGI) for real time crisis management. Traditional estimation methods for earthquake damages are expensive and time consuming. In contrast, volunteer and web-based service are near real time with almost no cost services. the lack of accessible real time data collection services causes delayed-emergency responses for disasters like an earthquake. This drawback is critical when we encounter a problem like buried people with valuable seconds for emergency rescue operation. The current research aims to design and implement a web-based volunteer data collection service for rapid estimation of earthquake damages and number of buried people.

To investigate the capacity of VGI in rapid estimation of earthquake, a technical frame work based on the web technologies has been programmed and implemented. The designed service is comprised of server and client sides.The client side is a two-side browser-based service includes volunteers (users) and managers pages. On the user page, volunteers have a web page to enter and fill in the blank forms and taking a photograph of the target building and compare it with pictures. They watch the sample pictures in different level of damages and compare their building with the samples and give a grade of the most similar picture with their building. This grading process leads the server to analysis and classify the incoming data and create the heatmaps for managers. On the managers page two online discrete heatmaps for the both earthquake damages and buried people are displayed. In fact, the heatmaps present the online and real time quantitative situation of the building damages and buried persons as hot spots. These hotspots have the first priority for giving emergency services. The manager page also exploits query tools to request different level of details and classes from the server side. The server side is responsible for receiving, saving, spatial analysis and transmission of the requested result to the client side. This task is carried out by the exchange side. As the citizens are entered to the browser-based service and fill in the blank forms for building damages based on the mentioned guideline and report the buried people, These forms are transmitted to the server side and a geo-server performs spatial analysis including Heatmap, distance and clustering analysis. Then, a real time damage and buried people map are prepared and delivered to the client side. The server updates the created maps whenever a new data is submitted. By this, a real time damage and buried people maps are accessible for official managers to conduct a goal-oriented emergency operation and a preliminary earth quake damages on city building blocks. After the technical frame work has been designed, it was tested in Oshanvieh city by 132 volunteers on the scene for an earthquake.

To investigate the capability of volunteer geographic information for earth quake afterwards, the designed service mentioned in the methodology was utilized on Oshnavieh city. It was assumed that an earthquake has occurred. 132 volunteers participated for the data collection process. According to the crisis management organization experts, 102 reports of the total 132 reports are correct that shows the accuracy of 76.52 percent. Besides the building damage level based on the defined guideline, the citizens also select their vital needs after the earthquake. the most requested vital needs are warm stuffs, medicine, water and foods respectively. Unfortunately, the participation rate is ranged from some seconds after the earthquake to three days. This means that some citizens have filled and transmitted their data three days after the earthquake. In the following, a comparison between the designed service and traditional earthquake damage estimation methods (in situ) was carried out. The result shows that field-based methods for a city like Oshnavieh need about 20 days. However, the designed volunteer-based service what is programmed and implemented in the current research does this job by 3 days.

As the results show, the proposed service designed in this research implements the damage estimation process 6.5 times faster than the governmental procedures. This proves the efficiency of the research achievements. Besides the velocity, traditional damage estimation methods are expensive compare to volunteer-based data collection and processing which are almost free, scalable and pervasive.

Methodology

The present studyinvestigates the effect of gradual reduction of noise from data collected by sensors, accelerometers, magnetometers, gyroscopes, and GPS technology in smartphones on improvement of vehicle positioning. The proposed method is based on using acceleration, azimuth, latitude, longitude and roll angle parameters as an input for the Kalman algorithm and investigates the effect of reducing noise produced by these parameters using the least-squares method onimprovement of the resulting position calculated by the Kalman algorithm. To reach this aim, the roll angle parameter is extracted from the angular Velocity() in y-direction and the azimuth parameter is extracted from the magnetic field() in both x and y directions. These parameters along with the acceleration(a) parameter in x and y directions and the geographic coordinates are selected for the Kalman filtering algorithm. In the proposed method, data received from sensors share common sources of noise produceddue to drift, random movements and bias errors.To reduce this noise independently and systematically, method of averaging with the least-squares is usedfor data produced by each sensor. Thus, noise in the received data is considered as a random parameter and noise reduction is performed based on the percentage of changes in the corrected and observed data in the range of 1 to 10%. Kalman algorithm is implemented for 10 levels of noise reduction and the results areinvestigated and compared.The filter calculates and improves an estimate of position vector x, denoted by  with minimum mean square error using a recursive model. The main objective is to derive an accurate estimate of   for the state of the observed system at time of k. Implementing Kalman filter consists of a prediction step and an updating step. The result is compared todata received from a more accurate reference using RMSE.  

The study area consists of lane no. 2 of the South-North (East-West) Azadegan Highway, Tehran, Iran with a total area of about 26km. Results show that compared to the reference data, using Kalman filter has decreased errorsin positioning the car from 0.8274 m to 0.6763 m with a 2%noise reduction. With a 10% noise reduction, the accuracy of this method has increases to 0.6771 m. This improved accuracy is due to noise reduction and consequently an increase in the correlation between the parameters. Accordingly, the threshold limit for noise reduction and improved positioning using Kalman filter is low and can be recognized by an investigation of a few lowlimits. According to the findings, although reducing the effect of noise can improve positioning with Kalman filter and smart phone sensors, irregular changes in the accuracy of noise reduction methods require determining an optimal percentage for noise reduction.

Sinkhole is a type of karst form that appear as pits on the surface of the earth. Identification of these sinkholes is crucial in the management of water resources, as contamination of these sites causes contamination of water resources in the region. Bistoon-Parav Karst Basin is important because it creates mirages in the cities of Bistoon and Kermanshah and provides part of the water to these cities. The purpose of this study was to identify sinkholes in this area as well as identify potential areas of sinkholes. In this research, using the analysis network process, different criteria for sinkhole formation such as precipitation, temperature, evaporation, lithology, soil type, slope, elevation, fault, stream and vegetation are ranked. The results of the analysis network process showed that lithology with 24.87% are the most important cause of the sinkhole. After combining the layers, a map of the possible sinkhole areas was identified. Furthermore, sinkholes in the area were detected using the interpretation of World Imagery and Google Earth imagery. The accuracy, completeness and quality indices were used to evaluate the results of the work, which were 98.42, 69.41 and 65.55, respectively. The high accuracy index indicates high performance in detecting existing sinkholes, but the low two indices do not indicate the weakness of the method, rather, the two indicators of completeness and quality indicate areas that are likely to form sinkholes. However, at the moment they are either not crashed or they are not in the reference data. In the end it can be said that this method is suitable for identifying sinkholes and susceptible areas of sinkholes.

Complexity of multi dimension developments and infrastructures intensifies the land related challenges to adopt legal laws, restrictions and responsibilities.  Although, multi dimension estates have been registering for many years, as the complexity of the estates are increased, disadvantages of the 2D cadaster is more appeared. Hence, the 3D cadaster has been a necessity for sustainable development. Visualizing is one of the important components of 3D cadaster. In the current research, for efficient and effective visualization of land ownerships and their related 3D information, firstly, the needed essentials of cadaster visualization systems have been classified into 3 groups including: cadaster, visualization and independent properties. Then, the trends of 3D visualization developments for older plugin and WebGL based technologies have been considered. Finally, a number of the most important systems according to the needed criteria for web-based 3D cadaster were evaluated and consequently, Cesium virtual environment has been selected as the best for the development purpose. To develop a system for 3D cadaster visualization, 2D building properties was converted to 3D using different software and then, land law properties were added and subsequently, Building Information Model (BIM) was provided. HTML5, JavaScript and CSS languages along with WebGL library and Cesium API were utilized. The implemented service is able to display WFS-based standard vector layers as well as WMS image of OGC standard. The system has the possibilities of 3D visualization like web-based 3D cadaster visualization and land law properties over the web. This enables the user to make the printed output of BIM along with descriptive information of the buildings.

Nowadays, due to the complexity of interior space of buildings, the need arises for indoor navigation inside such spaces. Indoor navigation systems may be helpful for emergency evacuation of the crowd in natural hazards such as earthquake as well as human-made disasters. These systems can also act as a decision support system for officials. Literature survey on indoor navigation services shows that a large number of researches have been conducted around designing and implementing such systems but automatic indoor spaces topology extraction of the current building information models remains as a challenge. This research aims to introduce, design and implement a web-based indoor navigation system using CityGML data model in LOD4 (level of detail) to overcome the mentioned problem. The architecture of the current research is a browser-based web application service such that the data model processing and graph creation is implemented on the server side, the client interface and calculated path are represented on the client side (browser). Through the CityGML data model processing, firstly, the building navigable spaces such as room floor, doors and stairs are extracted and then, each space as a node and the connections between the nodes are defined as edges, are imported to the navigation graph. Programming on the server side has been performed by Python language and web development languages including HTML (Hypertext Markup language), JavaScript, JQuery and AJAX are used on the client side. Cesium virtual globe has been exploited to display the data model and the calculated route. To evaluate the introduced methodology and designed service, a three floor house with CityGML format in LOD4 was used as the case study. Generally, a client can request a 3D calculated path by selecting the source and destination points on the client browser. The server receives the request and returns the response as a 3D line to the client browser on the Cesium environment. In addition, a descriptive graphical user interface for visual inception of the route is offered to the users on their browser. One of the advantages of the designed web application is that, the service is implemented on the browser. Hence, all devices equipped with a browser have possibility to run the 3D routing service. Besides the mentioned cross-platform capability, average expectation time of the graphical interface loading, data module processing and path finder module are 7.03 milliseconds, 12.42 seconds and 2.44 seconds respectively that visits a valuable criteria in emergency situations like an earthquake phenomenon. Regarding this fact that CityGML is a new data model and supported by a few software, the introduced architecture causes less implementation costs as well as automation of these systems.

Due to the complexity of air pollution action, artificial intelligence models specifically, neural networks are utilized to simulate air pollution. So far, numerous artificial neural network models have been used to estimate the concentration of atmospheric PMs. These models have had different accuracies that scholars are constantly exceed their efficiency using numerous parameters. The current research aims to compare Elman and Jordan recurrent networks for error distribution and validation to estimate atmospheric particular matters concentration in Ahvaz city. The used parameters are relative humidity, air pressure, temperature and aerosol optical depth. The latter one is extracted from MODIS sensor images and air pollution monitoring stations. The results show that Jordan model with RMSE of 219.9 milligram per cubic meter has more accuracy rather than Elman model with RMSE of 228.5. The value of R2 index that shows the linear relation between the estimated from the model and observed values for Jordan is equal to 0.5 that implies 50% estimation accuracy. The value is because of MODIS spatial resolution, inadequacy in numbers as well as spatial distribution of meteorological station inside the study area. According to the results of the current research, it seems that air pollution monitoring stations have to be increased in terms of numbers and suitable spatial distribution. Also, other ancillary data like volunteer geographic air pollution data entry using mobile connected cheap sensors as portable stations may be used to implement more accurate simulation for air pollution.

Evapotranspiration (ET), which includes water evaporation from soil surface and vegetation transpiration, represents a fundamental process of the hydrological cycle. For water resource management, especially in arid and semi-arid regions, this is a key element. A lot of empirical methods have been developed in order to estimate ET from meteorological data since 50 years ago. The major problem of this method is that it can be used for evaluation of uniform regions near the station. This could not be extracted for other regions. Nowadays, remote sensing based methods often used for calculation of different parameters of ET, are suitable to extract different parameters of ET at proper temporal and spatial scales.
One of the most famous algorithms for estimation of actual evapotranspiration is Surface Energy Balance Algorithm (SEBAL). This algorithm calculates all fluxes of the energy balance at the earth's surface including net radiation (Rn), soil heat flux (G), and sensible heat flux (H) from satellite images. Finally, actual ET is computed based on the energy balance at the earth surface. The aim of the current study is to evaluate the spatial and temporal variation of actual evapotranspiration of wheat in Agricultural Research Station of Haji Abad using SEBAL algorithm. The area has geographic coordinates 55° 54 'N, 28° 18' E, with an elevation of 900 m above mean sea level. For this purpose, 4 cloud free Landsat 7 / ETM images are used. Landsat 7 Enhanced Thematic Mapper Plus (ETM) in cloud free satellite images were downloaded from the USGS Earth Explorer site [(http://edcsns17.cr.usgs.gov/NewEarthExplorer/)]

During the recent years, advances in data collection and management technology, have led to create very large databases. In contrast to other data such as numbers and strings, raster data are considered as complicated and contain special characteristics so that they are classified as “big data”. Due to the nature of spatial analysis queries, the need arises to aggregate or Summarize a large portions of the data to be analyzed. The main issue in the database era is the efficient query processing so that users do not spend long time for retrieving the requests. Traditional query processes return exact answers, however, the answers take more time than what is needed in real time systems. It notable that sometimes the query running time is much more important rather than the accuracy, specially, in real time services.
AQP (Approximate Query Processing) is an alternative method for query processing in time – consuming environments that enables the system to provide fast approximated answers. One of the most significant applications of AQP is query optimization. AQP may play a valuable role to increase the speed of spatial queries facing robust and complicated data. It is also an efficient method for recognizing the needed data and subsequently, minimizing the cost of aggregation queries. Since 1980s, utilizing the approximation methods have been initiated for decision support systems. Also, AQP has been noticed to address some problems in database era during the past decade. The current technics in various research frontiers are only useful for relational database systems (Azevedo, et al., 2007(. The main idea behind in-database processing is elimination of big data sets transmission to disjointed programs. Since, in-database processing that all analysis are implemented into database, it offers fast implementation, scalability and security. Hence, In-Database processing improves the computer network productivity and participates in well-suited designing of fast response queries.
The current research aims to compare traditional and optimized Sum aggregation operation to decrease the running time of spatial queries into PostgreSQL database. To undertake the research, 60 precipitation rasters have been used. The study area is located in Lorestan province and precipitation gauging stations were used as primary data. Rasters data have been created from monthly precipitation data for the period of 2010-2014 using Krigging interpolation method and entered into PostgreSQL database using Raster2pgSQl extension. In the following, rasters pixels are stored into their related tables. In optimized aggregation method, firstly, raster data are clustered by the written similarity function. The used functions have been written by PL/pgSQL language in PostGIS. The execution steps of Sum function are as the following: creating the similarity function, performing the function, running the optimized query and consequently, resulting the approximated query respectively.
Subsequently, one raster is selected from each cluster and it is multiplied by the number of rasters belong to the given cluster. The resulted raster is entered to Sum function as the representative of the cluster. In each cluster, the number of implemented arithmetic operations is reduced as the following formula: (number of rasters in the cluster-1) *rows*columns of the given raster). Using the mentioned method, the number of arithmetic operations are significantly reduced and prepares the fast approximate answers. Finally, for accuracy assessment, the error of each method was approximated by calculating mean relative error, DI (difference indicator) error and relative error for each raster. Finally, the achieved results were analyzed.
It is mentionable that the user may make a decision whether the resulted accuracy is acceptable for a particular project or an exact query has to be executed.
In this research to compare the traditional and optimized Sum function, five scenarios have been implemented. The results show that the optimized Sum function is 27.2 times faster than the traditional function. The average difference of pixel values between the traditional and optimized one is 0.028. Consequently, the query running time for the optimized and traditional Sum is 7.754 and 211 seconds respectively, which implies the efficiency of the used method (optimized Sum).
It is notable that the accuracy of the optimized method depends on the nature and homogeneity or heterogeneity of the used rasters.
The valuable decreasing of the in-database spatial query running time may be used to offer real time web-based services such as meteorology, traffic and etc., which need to real time analysis and fast retrieving responses.

Landfilling is one of the important suffering issues for human being. Problems caused by landfilling have led some countries to gather urban waste in unprotected area. Because of the mentioned issue, landfilling is a significant discussion in environmental engineering. This research aimed at selecting suitable sites for garbage massing in Ahvaz urban area via integrated ELECTRE-AHP approach using GIS techniques in two main steps. At the first step, 23 effective criteria in landfilling site selection were collected from the in charge of organizations. The criteria were standardized and analyzed using Geographical Information Systems. After the standardization process, expertise opinion was applied and FAHP method was applied for criteria weighting process. Finally, each of the weighting criteria and 17 criteria were overlapping at this stage. The outcome of this method is 5 suitable sites for landfilling in the northern east and east region of Ahvaz. At the next step, 5 sites achieved from the previous step were ranked by ELECTRE method using the 6 remained criteria. The achievement of implementing this step is 3 options with the same worthiness for landfilling. The results show that the selected sites, compared to the current garbage mass place located on humid winds flows as well as high water level in the south and southeast region, have the better conditions in terms of health and sanitary problems. Also, integrated ELECTRE-AHP approach due to considering the relative nature of criteria values in weighting and ranking, has more efficiency compared to prior methods used to locate the current landfilling sites (Safireh and Brumi region).

Accessibility to precise spatial and real time data plays a valuable role in the velocity and quality of flood relief operation and subsequently, scales the human and financial losses down. Flood real time data collection and processing, for instance, precise location and situation of flood victims may be a big challenge in Iran regarding the hardware facilities (such as high resolution aerial imagery devices) owned by the correspond organizations. To overcome the mentioned inabilities as well as reducing the financial costs for real time monitoring purpose of a flood, the current research intended to use the capacity of the flood victims and other volunteers to collect and upload real time data to rescue themselves. By means of this, flood real time spatial and non-spatial data collection is applicable via public and per-person participation based on the needs of each victims. The current Open Source workflow has been so designed that by using a browser like Mozilla, Explorer, Chrome and etc., and without the need for installing any software, the victim transmits his/her exact geographic location (captured automatically by the designed web service) and other multimedia data such as video-photo. Also, the flood-affected person announces the type of the damage and consequently, demanded rescue operation to the managers as a text information. After data processing on the server, the information is represented as a real time rescue map for decision making. The rescue plan may be mapped based on the singular aid as well as plural plan in the cluster form specialized for a particular group of victims in each bounding box. To design the web service, a client architecture for victims, other volunteers and managers has been developed, for implementing the service, Open Source technologies, server-side and client-side programming languages, Geoserver and WFS (Web Feature Service) standard adopted by OGC for spatially-enabled representation of victims demands, have been exploited. The research result is a browser-based service in which the client service offers automatic zooming to the current location of the clients and sends the rescue request including personal identifications and the type of injury using PHP (stands for Hypertext Preprocessor) and SQL (Structured Query Language). In the other side, on the client side designed for managers, the requested rescue submitted by the victims and other volunteers are mapped and displayed real time by OpenLayers and WFS. The result introduces an efficient applicable method for gathering real time and high accuracy geographic-multimedia-text data collection and consequently, extremely reduces the relief operation costs. Finally, the proposed methodology causes better performance and spatially clustering of victims to decrease the aftermath of the flood in a region like Iran suffers from the lack of expensive hardware technologies for precise data collection and transmission.

Babol city has tourism function which during the peak seasons of the year the transportation network cannot cope with the high volume of vehicles. The present study aims to investigate the efficiency of the Babol city traffic network through the linear element analysis. In this paper by using network analysis in GIS and also by calculating connectivity and accessibility, the efficiency of the Babol city was investigated. Alfa and gamma indicators show the level of connectivity and accessibility of the urban networks. The figures of 21% and 18% were obtained for 92 join points and 50 main network lines for exist condition and 19% and 21% were obtained for 488 join points and 283 main network lines for suggesting plan. Finally the most important roads of the city were studied by the number of direct and indirect joins to approach to most remote point of the network. The results revealed that the urban network of the Babol has severe problems to connect and create accessibility and therefore there is a high level of time and capital waste.

The term "Game GIS” implies to real spatially enabled games in which a special part of the world is virtually simulated, represented and managed. In fact, game GIS is an integrated system consists of video games and geographical information systems, aimed to simulate and representing spatially enabled environment. The achieved result of implementing a game GIS service can be exploited before a crisis for wise designing of a city and diminution of the aftermath casualties. As the decision making process plays the key role to reduce the losses, the need arises for using the models as much as close to the reality. By this, it is possible to use the virtual world in in the form of a game rather than experiencing the real world with real wounded and killed persons in. This enables us to recognize and manage a test environment for promoting the managing the real environment of a city during and before a natural hazard disaster like an earthquake. The game GIS may be counted as a service for sharing and dissemination of spatial information as well as online GIS to have a visual and synoptic management of the earth plant facing various disasters. The current research is aimed to design and implement a software architecture for an earthquake game in Tabriz city (Iran).
The study area is district 10 of Tabriz located within a fault zone. According to field surveys, 82.1 percent of buildings in the study area may be vulnerable against earthquake in terms of the quality of building construction.
Methodology of the research to design, program and implement the game GIS service are undertaken as the following processes: data collection, database creation and software production.
The collected data includes master plan maps of the district 10, building quality, number of floors, building façade materials, age of building, street network (adopted from the master plan of Tabriz) and population of each parcel. Also, some regions are assumed as hospital, relief-rescue center and treasury money.
To design the software, 2D environment of MapControl and for implementing the game into the 2D environment, ArcEngine of ArcGIS have been exploited. The mentioned engine gives us possibility to use of analysis and modelling capabilities as much as closer to the ground reality which are compatible with available geometry of the terrain (Amirian, 2013, 17-19). The MapControl is a framework in which the map and game area are displayed. Symbology is used to show the persons as well as equipments. Briefly, the stages undertaken during the current research can be explained as the following:Data collection based on available sources via field surveying.
Data processing and creating a database from street networks and building owned the age, materials, floors fields.
Calculation of vulnerability rate for each building separately as well as the amount of deconstruction damage per Richter.
Drawing the street and alley network to prepare network analysis dataset.
Preparing special network analysis database and evaluation in various situations.
Using the gained layers and implementation of the scenario.
After that, the conceptual architecture of the software has been designed based on the scenario.
The game GIS services has been designed with 6 different classes offer numerous functionalities responsible for displaying program commands and different views of the game. Finally, the service is designed and implemented in a real schema for crisis management application. The resulted game is played in 4 stages. In the first stage, the player starts with a 5 Richter magnitude earthquake and ends while the player gets to 8 Richter. The designed software simulates the destruction rate of buildings based on the influential factors, wounded transfer routing and rescue operation. The game player gains credit according to his quickness and agility. The player would go to the next stage with one Richter magnitude higher, if gains enough credits during each stage. The result of the current research as a Game GIS service, can be used in earthquake simulation happens in various magnitudes for management of decreasing the effects of earthquake, quick reaction, maneuver and education. Considering the achieved results, designing and performing the game GIS service over the web based on open source technologies rather than being desktop and commercial service, can be suggested as a new research frontier for the future researchers.