نشریه علوم و فنون نقشه برداری
سال نهم شماره 4 (پیاپی 36، فروردین 1399)

Address matching (also called geocoding) is an applied spatial analysis which is frequently used in everyday life. Almost all desktop and web-based GIS environments are equipped with a module to match the addresses expressed in pre-defined standard formats on the map. It is an essential prerequisite for many of the functionalities provided by location-based services (e.g. car navigation). Several methods have been proposed for address matching which assume a standard for-mat for the components of the address, and propose solutions for matching the known address components to map components. The situation is, however, more difficult when there is no addressing standard, and addresses are expressed in descriptive languages. An interesting example is Iran, where people express ad-dresses as a sequence of spatial elements (e.g. streets, squares, landmarks, etc.), starting from a known element. Although this method of addressing may seem very unpleasant at first, it is very efficient, because (1) it not only specifies the destination, but it also tells how to reach it. In other words, you do not need any map, navigation system, etc. to find the destination. Instead, you can reach the known starting point and then look for the next components, step-by-step; and (2) this way, you will inevitably be exposed to the environment and its spatial elements, which helps you in building up your cognitive map. This article proposes a solution for automatic matching of descriptive addresses. The solution has two main steps. In the first step, the address is decomposed to its addressing components by defining a formal grammar. For this, we assume that a descriptive address consists of two types of components: Geo-names (GN) Constant geo-names (cGN): avenue, street, alley, etc. Variable geo-names (vGN): names of the constant geo-names Spatial relations (SR): after, before, etc. In the second step, a cognitively-enriched data model is developed for interpretation of the addressing components resulted from the previous step. We use "Paths”, “Edges”, “Landmarks”, “Nodes”, and “Districts“ as the basic types of the components of a descriptive address. To perform the matching, each component of the address is assigned one of the above basic types and a search is carried out to match the each component to a single location.

Having a 3-Dimensional model of high-rise buildings can be used in disaster management such as fire cases to reduce casualties. The fundamental dilemma in 3D building modeling is the unavailability of suitable data sources. However, available cadastral 2D maps could be used as low-cost and attainable resources for 3D building modeling. Smoke will be a great threat to people's health during a fire in a building and its movement and diffusion in different parts of the building are affected by the architectural design of the building. Computer simulation can be utilized to investigate smoke movement and its emanated toxic gases under various scenarios which can play a significant role in decision making during a fires incident event to reduce human casualties. In this research, at first, 2D cadastral maps of a high-rise building located at Tehran were utilized to produce a 3D model of building with adequate details in CityEngine. Next, smoke emission in the building was simulated under seven scenarios, using PyroSim software. The two-dimensional data of apartment separation is readily available and at the lowest cost. Using some tools and techniques in spatial information systems, making it possible to utilize this data to implement a three-dimensional model of a building with details to simulate the spread of smoke from fires. The process adopted in this study made it possible to use a two-dimensional floor plan to produce a three-dimensional model of the building at the LoD4. The results confirm the appropriateness of the constructed 3D model to simulate smoke emission under various scenarios in the high-rise building. This result shows the effect and importance of smoke transfer routes, both inside and outside the building, on the emission of toxic gases. The results show that ensuring that the ducts are closed is one of the factors controlling the spread of smoke to other floors. Flue gutters and ducts are among the most important routes for emitting smoke in high-rise buildings. The simulation results of Scenario 4 show that 40 seconds after the start of the fire, the toxic fumes spread through the stairs at all top floors of the building. Although data from 2D cadastral maps can be used to create a three-dimensional model of a building, these data have shortcomings. The part related to the texture of the map features is faced with a lack of information such as the thickness of the walls, the type of used materials, the exact location of the components (doors and windows) on the walls, and so on. These factors affect the smoke emission simulation results. As a result, development of a three-dimensional model of the building containing more detailed information about the specifications and materials used in the walls and various parts of the building needs to be considered in future research. The smoke emission simulation results can also be used in a variety of applications, including emergency evacuation modeling in the event of a fire in a building, along with the inclusion of other information such as number and physical characteristics of residents in different parts of the building.

Nowadays, due to the widespread use of social networks, they can be used as a convenient, low-cost, and affordable tool for disseminating all kinds of information and data among the massive users of these networks. Issues such as marketing for new products, informing the public in critical situations, and disseminating medical and technological innovations are topics that have been considered by the owners of companies as well as government and private organizations. In order to achieve this goal, the methods of maximizing the speed and quality of information dissemination in social networks have been studied by researchers in various fields of science. One of the best ways to increase the speed of information diffusion is to take advantage of the social status of influential node. The role of some people within the networks is more prominent than the others, who are known by titles such as leader, important, influential, central, and vital. These people having the centrality in the social networks can have the greatest impact on the network. Identifying these people allows us to control the way of information dissemination, the spread of diseases, the more effective advertising of commercial products, or the identification of the head of different social groups. Different measures have been proposed to identify these people and to measure the centrality of nodes in a network. Identifying influential people in a network is not easy task, and the criteria for being important are varied. Therefore, there is no general index that best determines the importance of people in a network, and this index varies from one network to another and from one situation to another. In addition, whether these indexes are local or global can be a point of contention and it can produce different concepts of centrality. This paper aims to provide a new measure so-called “socio-spatial centrality” to find the central nodes in the location-based social networks, which is a linear combination of social centrality and spatial centrality. In the new proposed measure, the central node is the one that, while having more direct neighbors than the other nodes, its neighboring nodes will have a uniform and proper spatial dispersion within the given area. Degree centrality has been used to determine the social centrality and the standard distance is used to calculate the spatial centrality. By simultaneously assessing the nodes' degree and the spatial dispersion of their neighbors, the top-k nodes are identified as influential nodes. The proposed measure, along with the existing methods for measuring centralities such as degree centrality, betweenness centrality, and closeness centrality, has been applied to two real location-based social networks. The results show that the geographic coverage of the selected nodes by the socio-spatial centrality was the highest in comparison to other centrality measures, and the correlation between the output of the new centrality measure as well as the conventional centrality measures is decreasing with the increase in the number of selected nodes. Overall, evaluations show the positive impact of the proposed method on the geographic coverage maximization in location-based social networks.

Dust is a phenomenon in arid and semi-arid areas due to high wind speeds on the un-polluted soil surface and prone to erosion. The occurrence of dust has always been a problem for residents in the western and southwestern regions of the country, has gradually spread to other provinces, including the capital. The study area is southwest of Iran, Khuzestan province. The latitude and longitude of the study area is, 47° 50´ E and 30° 33´ N respectively and is considered the centerm for Iranian oil and gas production. Khuzestan is the fifth most populous city in Iran. Ahvaz is the capital of Khuzestan province. Negative consequences of this phenomenon include damage caused by pests and diseases, increased road accidents due to reduced visibility and failure of this risk in human life. And retinal detachment, skin allergies, and so on. In the environment, water pollution is affected by the process of plant photosynthesis and reduced yield. The phenomenon of dust usually occurs in arid and semi-arid regions. In order to investigate the dust phenomenon in Khuzestan province using synoptic data and remote sensing data, the dust days of 20 synoptic stations were analyzed on a daily basis and from 2010 to 2017. MOD021KM product material data were obtained in accordance with the date of the dust days announced at the station, to identify dust phenomena, visual interpretation method, TDI dust parameters and D parameter. After detection of dust, the correlation between the data with horizontal visibility less than 1000 m and the meteorological parameters of relative temperature and relative humidity were evaluated by regression method. Finally, it shows the correlation and relationship between dust characteristics and meteorological parameters (temperature and humidity). According to Figures 9-10-11-12, it is possible to determine which dust index (TDI, D) has the highest and lowest correlation with meteorological parameters (temperature, humidity). Due to the fact that the data are related to the warm seasons of the year (spring and summer) And temperature changes are less volatile. The above figures show that there is the least correlation between dust indicators and synoptic parameters. The highest correlation in the TDI index with relative humidity is about 0.22. Also, the correlation coefficient of the other indicators with TDI meteorological parameters with temperature is 6.9, parameter D with temperature is 0.9, parameter D with humidity is 11.9. Therefore, there is a weak relationship between dust and temperature and humidity parameters. Correlation and regression analysis showed that meteorological parameters had the least correlation (R²) with dust indices. The results also show that TDI index compared to parameter D has the best performance in detecting dust phenomenon. And by examining the temperature, humidity and correlation with the TDI and D indices, the dust was not of domestic origin and was transient. By examining the temperature and humidity parameters and their correlation with TDI and D indicators, we came to the conclusion that dust did not have an internal origin and was transient, and also there is no correlation between dust index and meteorological parameter. According to previous studies, Iraq, Saudi Arabia, and Syria have the most dust, despite the hot, dry climate, with most of the dust coming to Iran coming from neighboring countries.

of this Research is the study and evaluation of the relationship between vegetation area and water area with the number of birds in Band-e-Alikhan wetland between 2011 and 2015. Due to the importance of Band-e-Alikhan wetland as a seasonal wetland with fresh water, which is located 35 km south of Varamin city, it has been used as a study area. In the present study, first, the necessary pre-processing, ie radiometric corrections, which include atmospheric corrections, banding errors, etc., were performed on Landsat 7 and 8 images, and then the normalized differential plant index maps (NDVI) and water index. Normalized difference (NDWI) was generated using pre-processed images. Then, by examining the histogram of plant and water indicators produced for each year, the appropriate threshold for each index was selected and by limiting the threshold on plant and water indicators produced, vegetation and water area maps were obtained for the year, respectively. . Each year, the maps obtained by the reference data selected manually in this study by satellite imagery were evaluated, the results of which show that the average overall accuracy and capability coefficient of vegetation surface extraction and of water over five years, with89/37% and 81/89%, which demonstrates the successful extraction of these parameters are. By obtaining the effective parameters, ie the level of vegetation and water, between the number of birds and the effective parameters of linear regression, two variables were formed and its coefficients were estimated by the least squares method. The results of studies and evaluations show that the model and regression formed between the number of birds and the parameters of vegetation surface and water area have a coefficient of determination of 0.861 and an adjusted coefficient of determination of 0.582 and indicate the appropriate fit of regression. Then, by the obtained coefficients and regression, the number of birds in 1394 was estimated using the level of vegetation and water area of the same year and was compared with the actual amount of birds in 1394, the results of which showed that the bird population is 93 . 1. It is predicted. The results also show that changes in the environmental conditions of the wetland provide a clear response to changes in bird populations and bird migration.

Qualitative spatial representation and reasoning are among the important capabilities in intelligent geospatial information system development. Although a large contribution to the study of moving objects has been attributed to the quantitative use and analysis of data, such calculations are ineffective when there is little inaccurate data on position and geometry or when explicitly explaining events rather than presenting numerical results is needed. In some cases, the computational complexity of quantitative methods is a factor in their weakness. In such a situation, the ideas of expressing and presenting moving objects relationships in qualitative and explicit forms and using logical and rational methods instead of computational and analytical ones have been proposed. This paper presents the application and extension of the remarkable Qualitative Trajectory Calculus (QTC) framework to represent and reason about moving point objects in GIS network paths by considering the concept of accessibility. QTC extension and reasoning paradigm were theoretically defined based on basic relationships such as moving one object toward another, moving one object away from another, moving one object following on another, and so on, allowing one object to access another along a specified path. The important properties of the developed methodology were discussed by examining the conceptual neighborhood of relationships and introducing and proving a number of inference rules. The obtained rules, which are mainly presented in the form of a composition table, formed a knowledge base for logical inference. Also, some of the extracted relationships showed features such as being inverse, symmetric, and transitive. For the purpose of practical implementation, examples of inference and query were tested by employing Prolog language to develop a deduction system using logical rules and facts. In this implementation, eight moving agents were considered. The existing facts of the spatial and movement situations complementing the knowledge base were expressed in terms of seven logical sentences. In addition to these facts, five items from the whole derivation rules, defined in the theoretical part, were selected and rewritten in the form of first-order logic. According to these facts and derivation rules, some example queries were proposed including 1. Which of the following agents does agent A have access to? 2. To which agents do agent A move? 3. What agents does agent A move away from? 4. Does agent A reach agent D? 5. Does agent A move away from agent H? The SWI Prolog system was then used to answer the queries. The output of the queries from the logical inference system was compared with the output of the queries from a conventional relational database. Due to the inference capability, the logical inference system provided results that were not recoverable from the initial information in the database. For example, a regular survey of a database found that only agent B was available for agent A, but the results of the inference on the knowledge base showed that in addition to B, the factors E, C, and D were also available for A. Comparing the results of queries from the above framework with the output of the contemporary relational databases enhanced the findings of this research eventually.

Spatial data, which is one of the main needs of human societies from business organizations to the general users today, cannot meet the needs of a wide range of users without changing the structure of conventional methods of data registration and updating on a metropolitan scale. Open Street Map, as one of the most successful implementations of the crowdsourcing approach to spatial data with the participation of the public users as the millions of scattered sensors in the environment, has provided a solution to achieve several goals such as improvement in rapid data collection and coverage of areas. Apart from the numerous benefits of volunteered geographic information, the reliance of this information solely on the participation and activity of users challenges the use of this data in various applications. However, this problem is exacerbated by the lack of the necessary frameworks for entering incorrect data, different levels of user knowledge, and different methods of data entry. Among the quality parameters, the completeness parameter, which provides the presence of one data set compared to another data set and shows the degree of importance of the features from the users' point of view, was discussed in the present study. It is also important to address this aspect of the quality of VGI in terms of its impact on the other quality parameters. In this study, after introducing the common approaches in evaluating this element of data quality and comparing the results of methods, the strengths and limitations of each method are also explained. In the general classification, the completeness parameter is examined by two region-based and the object-based approaches. After comparing results, the necessary pre-processing, and the analysis time of each method, the findings of this study demonstrate the high speed of evaluation of the region-based approach and the higher accuracy of object-based methods. Moreover, the mentioned advantage of region-based approach is achieved if there is no topological error in the VGI and there are correspondence relationships between the uses of both data sets. On the contrary, the object-based methods despite the high calculation due to the matching process; If they use the appropriate matching algorithm and guarantee the positional accuracy, they will provide more accurate results for the completeness parameter. Moreover, the object-based methods, with the possibility of calculating the omission and commission of the VGI data set compared to the official data set, provide a more accurate understanding of the completeness parameter. In the region-based approach, it is not possible to evaluate the completeness parameter separately due to the combination of these two sub-sectors. In the region-based approach, evaluation of the completeness parameter was implemented by parameters such as the ratio of the length of the two data sets, the ratio of the number of constituent points of features, and the ratio of the number of features. From the above three criteria, the length ratio of the two data sets was selected as a suitable evaluation criterion by less pre-processing analysis such as creating the graph structure in the number of features criteria and simplifying the lines in the constituent points of features. However, the length criterion in topologically erroneous datasets strongly affects the evaluation results. At the end, after introducing and using the vector matching algorithm based on determining the geometric similarity of the features, the completeness parameter was calculated as 66.2. High rate of two quantities of omission and commission information of the VGI data set compared to the official data set of 33.8 and 47.8 in the current study due to the weakness of the matching algorithm in the two data sets, it is expected that with the improvement of this algorithm, the amount of identified omission and commission information will be reduced.

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.

Our country has experienced flood and earthquakes in most cities, due to its location in the Alpine-Himalayan mountain belt, with varying climates which they are so destructive. The earthquake is considered as a harmful natural hazard that affect human society around the world, and is extremely dangerous because it cannot be foreseen and its energy can be freed at any time, and is classified in the catastrophe. This disaster is quite probable in Tehran since it is surrounded by powerful faults. So, by considering the fact that it is the most crowded city in Iran with heavily populated areas, and its importance in terms of political and economic issues, there is a vital need for devising appropriate plans for the period of after earthquake. Waste landfill after a destructive earthquake is one of the most important environmental, health and humanitarian problems in the crisis response process. And because it is not predictable and its energy may be freed at any moment, it is very dangerous and placed in the classification of catastrophe.. Given that, after evacuating people from residential areas, the damaged people usually live in temporary accommodation, so there should be more places than the pre-crisis situation to land the waste. Given the temporary nature of these sites and the environmental impacts of these sites, it is necessary to introduce an optimal location. Given the temporary nature of these sites and the environmental impacts of these sites, it is necessary to introduce an optimal location. The purpose of this paper in the first step is finding suitable areas for the temporary accommodation after earthquake for the damaged people, and in the second step, Optimization of suitable places for waste landfill. The main innovation of this paper is the application of particle mass optimization (pso) algorithm in introducing temporary landfill sites. For this purpose, firstly, suitable places for temporary accommodation were determined by determining the relevant criteria, weighing them by the Fuzzy-OWA and the combining layers using weighted overlap method. Suitable landfill sites were selected according to the urban extension process. The results show that according to the three modes, OR, AND and WLC, on average, sites of 5 and 10 are suitable places for landfill. Also, the most suitable landfill sites are located west of Tehran. But due to the physical expansion of Tehran to the west and southwest in recent decades, the winds from the west and the presence of high and mountainous areas in the north of this city and the probability of further rainfall in these areas, in general, the areas in the south and Southeast of Tehran, will be more suitable for landfill, because by creating burial places in the west of Tehran, due to urban physical expansion, there are many problems, including environmental and economic problems. Therefore, sites 9, 10 and 11 located west of Tehran have been eliminated from the options. And among other sites, the most suitable site for landfill site is 5. And sites 1, 5 and 4, respectively, are prioritized for landfill.

Air pollution is one of the most problems that people are facing today in metropolitan areas. Suspended particulates, carbon monoxide, sulfur dioxide, ozone and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. The goal of this study is to propose a spatial approach for estimation and analyzing the spatial and temporal distribution of ozone based on GIS analysis and multi perceptron neural network. In the first step, by considering the accuracy of different interpolation methods, IDW method was selected as the best interpolation method for mapping the concentration of ozone in Tehran. according to the daily data of these pollutants, the daily, monthly, and annual mean concentrations maps were prepared for years 2015, 2016 and 2017. According to the results, it can be said that the highest concentrations of ozone are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters. According to the data of ozone pollutants in year 2018, the accuracy of neural network for hot and cold days of year were about 68% and 77% respectively. Therefore, it can be said that the meteorological parameters of temperature, wind speed and direction, and precipitation are significantly related to the concentration of O3 pollutant.

Interferometric Synthetic Aperture Radar (InSAR) technology provides a useful tool for quantitatively measuring the deformation of the earth, influenced by natural factors (earthquake, subsidence, and landslide) and human factors (construction of structures, drilling, and the overexploitation of underground water aquifers). In this context, time-series analysis of radar images allows the monitoring of long-term deformations and analysis of geodynamic phenomena. However, the radar interferometric technique is only capable of measuring the displacement along the satellite's line of sight (LOS), and one interferometric LOS observation is not capable of extracting a 3D displacement field. Thus, this will limit the potential of the InSAR technique to the study of many tectonic phenomena that require a comprehensive understanding of their three-dimensional displacement components. The purpose of this paper is to provide a comprehensive overview of the main methods of retrieval of the earth's 3D surface field using radar interferometric observations, including; recent advances in this field and the advantages and weaknesses of each of these methods. In fact, in this paper, the existing methods for recovering 3D surface displacement fields using radar interferometry measurements developed in recent decades are reviewed in detail. Several methods are used to exploit the potential of InSAR for 3D surface displacement determination. In general, these methods can be divided into three general categories. The first is the use of homogeneous data, including radar images of other satellites or the use of independent radar imaging geometries. In this category, we can mention ideas such as 1) Using the observation combination along the satellite LOS in at least three independent geometries (DInSAR) 2) Combining the observations along the LOS with the azimuth observations (azimuth offset, MAI) and 3) Overlapping between the burst (burst overlap interferometry) in the Sentinel satellite data. In the first batch methods, the accuracy of estimating 3D field components for different geometries is estimated. However, it has been observed that the least accuracy will be related to the north-south component retrieval. The second batch method is the use of heterogeneous data (independent geodetic observations such as GPS, leveling, gravity data, etc.), which through combining GPS displacement vectors or leveling with observations from radar interferometry (the so-called data fusion) tries to recover the real 3D dimensional field, And finally, the third set of methods includes previous studies and experiences of how to relocate the area or use assumptions about the relocation of the area, in which there are two methods; 1) Ignoring one or two displacement component (if the displacement mechanism is known), which provides an analytical form for measuring the error in retrieving the other components and evaluates with similar data. 2) Considering the hypothetical models for reshaping or combining geophysical models with radar interferometry data. Each of the methods mentioned above is used for retrieving the 3D displacement field and have their strengths and weaknesses, Which are discussed in detail in this article. Finally, we hope to provide useful guidance for choosing a suitable method to resolve the challenging issues of extracting a 3D surface displacement field.

The expansion of location-based services (LBS) and their applications has led to a growing interest in localization, which can be done on the smartphone platform. Various positioning techniques can be used for indoor or outdoor positioning. Indoor positioning systems have been one of the most challenging technologies in location-based services over the past decade. Considering the increase of people activities inside buildings such as offices, hospitals, and large stores, determining the position and guidance of people inside these buildings is one of the most urgent and important issues to be discussed and challenged in the area of Location-based Services (LBS). There are various ways to determine the position inside a building. The method(s) used to determine the position in an indoor environment depends on several factors such as cost, accuracy, independence of, or dependence on the infrastructure, security, and system scalability. This study focuses on the infrastructure requirements necessary to determine the position of individuals thorough a comprehensive study of previous studies. Moreover, focusing on the Pedestrian Dead Reckoning positioning method using smartphones as an infrastructure-free method, several effective aspects of the accuracy and positioning process are examined. The effective measures examined include the use of a variety of noise filtering, combined filters (Particle filter, Kalman filter), the criterion of the of sensor data classification algorithm, the criterion of the initial point determination, the use of landmarks as checkpoints and plot maps for setting the estimated position, the detection criteria and estimation of the length of the step, and the user direction estimation criteria. The particle filter has good accuracy in small-scale areas, but in large-scale areas, it is out of date and has problems due to the limited source of the smartphone. In studies, Kalman filter has been used to integrate the information of different sensors, some of which have reached the desired accuracy according to the state model and the measurement model. Given that the generalized Kalman filter has a simple formula for nonlinear estimation, the linearization of the positioning problem causes an error in the Jacobi Matrix model and reduces the accuracy of the estimate, which negatively affects the cost of calculations and system timeliness. Step length varies from person to person. In fact, there should be a variable associated with pedestrians in the step estimation model. Also, a person's walking rate during a walk is not constant. Accordingly, assuming a constant value of step length for users causes an error during positioning and a large drift at the end of the path. Determining the heading is one of the most challenging parts of the PDR system because the heading error leads to a quick increase in the positioning error. It is difficult to determine the reliable heading in the environments with high magnetic disturbances. Another problem is that the heading of the smartphone may vary with the heading of the pedestrian movement. Therefore, two main tasks must be performed before implementing indoor positioning. One of them is to determine the heading of the smartphone. Another is to infer the offset heading between the smartphone and the pedestrian movement. Therefore, determining the state of the smartphone is necessary for specifying the heading of the pedestrian movement.  Finally, the advantages and disadvantages of each of the infrastructure-based and infrastructure-free methods are compared and evaluated. Also, the research uses algorithms such as Naive Baye, MLP, SVM, DT and KNN to classify the type of user movement and phone holding mode.

The rapid growth of mobile phone technology and its combination with various technologies like GPS has added location context to social networks and has led to the formation of location-based social networks. In social networking sites, recommender systems are used to recommend points of interest (POIs) to users. Traditional recommender systems, such as film and book recommendations, have a long history. However, due to the existence of the location component and the physical connection of users with the outside world in social networking sites, several special features such as spatial, temporal, and social factors are considered to improve recommendations. Among the specific features of location-based social network data, spatial factor plays an important role in improving recommendations. Because people's desire to visit places is greatly influenced by the distance between the person and the place. Also, the distribution of POIs in the region changes the pattern of user visits. In the first part of this study, we discuss challenges which social networking sites may face by comparing location-based recommender systems with traditional recommender systems. In the following, we mention some important contexts and factors in POI recommendation. Spatial factor, social relations, different types of contents, different categories, sequential pattern, and time factor are contexts which are commonly used in POI recommendation. Next, we mention different types of location-based recommender systems: the fused model and the joint model. In the fused model we model user’s preferences and other additional contexts individually and after that, we combine their results with collaborative filtering. In a joint model, all contexts are learned Simultaneously.  In the next part, we discuss methods for extracting spatial context in location-based recommender systems. There are three major ways of modeling spatial data: independent, dependent, and restrictive models.  In independent modeling, we model spatial factor independently without considering the user’s preferences and other contexts. Here we discuss four basic independent models in detail: power law, Gaussian distribution, Kernel Density Estimation, and distance-based models. The power law is a relationship between two quantities in which a relative change in one quantity causes a change in another quantity, and this change is independent of the initial values ​​of the two quantities. This rule is used for modeling spatial data in recommender systems. Changes in many natural quantities around a constant value follow the Gaussian distribution, and this has led to its use to model spatial factors. Kernel density estimation is a non-parametric method for estimating the probability density function of a random variable. To recommend personalized items this method can be very useful because we could model spatial data of every user individually. distance-based methods model spatial factor by considering the distance between users and items or items with each other.  At dependent modeling spatial context is learned with other contexts Simultaneously. For this, we determine four popular methods matrix factorization, probability-based models, artificial intelligence, and combined models. These methods are general algorithms for recommending items in recommender systems and spatial factor is just one of their components. Restricted models filter recommendations by considering spatial constraints.  At the end of the article, we summarize the various features of the proposed methods and mention their advantages and disadvantages.rapid growth of mobile manufacturing technologies and its combination with various technologies have led to the addition of location dimension to social networks and the formation of location-based social networks. Recommender systems are used on location-based social networks to recommend points of interest to users. Traditional recommender systems such as movies and book recommendations have a long history. However, due to the locational component and physical connection of users with the outside world in location-based social networks, several specific features such as spatial, temporal, and social factors are considered to improve recommendations. Among the specific features of the location-based social network’s data, location factor plays an important role in improving recommendations. Because people's desire to visit places is largely influenced by the distance between the person and the place. The distribution of attractive places in the area also changes the pattern of user visits. In this study, we first discuss the challenges that location-based social networks face by comparing them with traditional recommender systems. Next, the factors that influence location recommendation in location-based recommender systems are discussed in detail. Finally, a variety of location modeling methods, which is one of the most important factors in recommending attractive locations to users using location-based social network data, are discussed.

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.