نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال هشتم شماره 2 (پیاپی 30، خرداد 1396)

Spatial relationships between objects, containing the fundamental information relating to the environment. Among these spatial relationships can be noted such as directional relationships, like: “left”,”right”,” above”, and “bottom” or distance based relations such as: “near” or “far”. The topology relationships used more in GIS desired location can be also noted such as: “inside”, “intersection”, “touch”, and “outside” and etc. Extraction of relationships between different features are done on the basis of data modeling techniques, since the traditional data models don’t cover capabilities of the ubiquitous computing tools used in ubiquitous GIS to extract 3D relationships, we discuss the creation of facilities for the extraction of spatial relationships in ubiquitous GIS. A new generation of GIS is 3D ubiquitous GIS, which in this generation service functionality to any user, at any given time and place. With the advancement of new technologies in capturing 3D point clouds of the environment, general changes are created in ubiquitous computing. The sensors used to obtain environmental information is provided in this generation are often inexpensive sensors. One of the important issues associated with this generation is the extraction of 3D topological relationships to interact with the environment better, which these sensors don’t have this ability to extract 3D topological relationships. The purpose of this paper is to extract topological relations by smart sensors used in ubiquitous computing. . The methodology used in this paper, is taking advantage of bounding box algorithm for mining the scope of any object in depth, length and the height. The results obtained from Kinect sensor shows the possibility of topological relationships extraction based on the smart sensors in ubiquitous GIS.

Managing the spread of epidemic diseases is vitally important, because of its major effect on economic and social development of growing modern societies. About 25% (>15 million) of annual deaths worldwide are directly related to such diseases. The outbreaks of epidemic diseases is a dynamic phenomenon that changes in place and time. With the advance of science and technology, human tools for studying health problems, have improved. Agent Based Modeling (ABM) can be used as a new technology to study the spread of epidemic diseases. Given that capabilities of agent based models and geospatial information systems (GIS) in spread of diseases, little attention has been aimed at such modelling. The object of this study is to review the applications of the models in simulating epidemic diseases. Therefore, in this article, some applications of GIS and ABM are introduced, then, after studying the theoretical foundations of ABM are given. Next, one of the applications; the spread of Cutaneous Leishmaniasis (CL) disease was simulated in an area of Golestan province and some of its results were presented. In this study, an agent-based model ABM is developed to simulate the dynamics of CL spread based on an improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model. Finally, suggestions for future works were presented.

The environmental protection through the control of pollution and particularly the emissions of gasses in the atmosphere still remains a major concern to the whole of the international community. So indicating proper alternative sources of energy is an essential affair. Renewable sources of energy are increasingly valued worldwide, because of energy shortages and environmental contamination. The global demand for energy and more specifically clean energy is growing rapidly. Since fossil energy is getting less and less, it is important to focus on usage and development of renewable energy. Current trends in energy policies are oriented increasingly to renewable energy using sustainable and natural sources. Due to the advantages of clean, widely distributed and with giant reserves, solar energy is recognized as one of the most competitive green energy and it draws the attention of the world. In the other hand, today one of the approaches to achieve comprehensive economic and social development in the world is having a source of sustainable energy, continuous and reliable.
Among the types of renewable energy, solar energy, due to many advantages like the ability to direct conversion to electricity and heat, the simplicity of use, storage capability and being endless, is being more considered. So that in recent decade a lot of researches in the world and Iran have been completed on solar energy systems.
One important aspect for achieving such highly ambitious plans to identify the promising geographical areas for establishing such solar plants since with the existing low conversion efficiencies, installation of solar power plant requires an enormous amount of investment in terms of land, money, and manpower. Selecting a proper site for a thermal power plant is important for its long-term efficiency and a lot of factors come into play when deciding where to install the plant. Of course it may not be possible to get everything which is desirable at a single place but still, the location should contain an optimum mix of the requirements for the settings to be feasible for long-term economic justification of the plant.
Due to the potential of solar energy and the country's need for renewable energy, it is necessary to identify the suitable location for using solar energy. Today, the project of locating industry and land use planning is principles and methods of ecological capability. This project involves the identification of existing technologies in terms of the balance of industrial and environmental requirements. In this article, with reviewing some published articles about a variety of locations for renewable energy, the main criteria applied to determine the optimal location for using solar energy and for Iran, localization and presented. This category has economic and environmental benefits. The results show that the main criteria for solar locating in Iran are, the sunshine, cloudiness, dust, humidity, altitude, solar potential map, land use and land cover types and distance from transmission lines and access roads. Due to the weakness and gap in locating and sizing the renewable energy resources in our country, it is suggested to focus on the sizing and locating the renewable energy power plants in order to minimizing the losses and optimizing the costs.

With the advance technology in discovery of data, data volume has increased. For this reason data mining methods, including Associative Classification for extracting knowledge from large data sources were used. At associative classification are used association rules for data classification. After classification rules generation, because large number of them, pruning methods are used to delete redundancy rules. In this research, the associative classification rules are used to determine relationship between the location of urban elements and optimality of location of bank branches and financial and credit institutions in Tehran. Because the location of the urban elements have a large impact on determining an optimal location for banks and optimal location of banks make greater profitability for them. After associative classification rules generation, four new method of pruning are being introduced to improve old ways that three methods reduce the number of rules and a method increases accuracy.

One of the error sources in GPS observations is tropospheric delay. Unlike the ionospheric delay, it is not possible to eliminate tropospheric delay from observations by using dual-frequency receivers and we have to estimate it or to use local or global tropospheric models such as Saastamoinen, Hopfield , etc. Some functions called Atmospheric Mapping Functions are used for these models. Different mapping functions for both hydrostatic and wet tropospheric components have been presented by researchers in this field. In the present study, by using GPS data of permanent station in Tabriz for 2012, we have tried to do a comparison different mapping functions of Saastamoinen, Hopfield, Davis, Chao, B&E and Neil. In this test, we estimated the coordinates of the station as well as the tropospheric delay including hydrostatic and wet components in different weather conditions and Zenith angles of 1̊-60̊ .
The results show that Niell mapping function may have the same behavior relative to other functions to estimate zenith tropospheric delay (ZTD) in different weather conditions and slant tropospheric delay (STD) under the zenith angles smaller than 30̊. But Niell mapping function provides a better estimation of zenith tropospheric delay and slant tropospheric delay under zenith angles between 30̊-60̊ with the lowest RMST. Moreover, according to the fixed coordinates of GPS station (Tabriz), the estimated coordinate components by Niell mapping function have totally the minimum difference.

During the disaster, rescue groups are responsible for tasks that require different types of information to optimize their activities. Preparation and distribution of information is very sensitive. The acquisition of new data and events such as degraded buildings and injured people in some cases using conventional methods is very difficult. With the advancement of technology, people familiar with social networks and smartphones, people produce and generate large amounts of data. This phenomenon is called Volunteered geographic information. Today, this data collected and shared with sites and social networks for free. This paper pays service that Volunteered geographic information provide to crisis management. Experience has shown that the system cannot work without regard to environmental considerations. The current system of data collection, updating and spatial data infrastructure, cannot consider emergency situations. In particular, companies such as Ushahidi and OSM's activities will be described. This article is based on articles and activities conducted in this field and provide category and in the end discussed and determine the factors affecting performance of them.

The physical world around us is three-dimensional (3D); yet traditional cameras and imaging sensors are able to acquire only two-dimensional (2D) images that lack the depth information. This fundamental restriction greatly limits our ability to perceive and to understand the complexity of real-world objects. The past several decades have marked tremendous advances in research, development, and commercialization of 3D surface imaging technologies, stimulated by application demands in a variety of market segments, advances in high-resolution and high-speed electronic imaging sensors, and ever-increasing computational power. The term “3D imaging” refers to techniques that are able to acquire true 3D data, i.e., values of some property of a 3D object, such as the distribution of density, as a function the 3D coordinates (x, y, z). A more general 3D surface imaging system is able to acquire a scalar value, such as surface reflectance, associated with each point on the nonplanar surface. One principal method of 3D surface imaging is based on the use of “structured light,” i.e., active illumination of the scene with specially designed 2D spatially varying intensity pattern. An imaging sensor (a video camera, for example) is used to acquire a 2D image of the scene under the structured-light illumination. If the scene is a planar surface without any 3D surface variation, the pattern shown in the acquired image is similar to that of the projected structured-light pattern. However, when the surface in the scene is nonplanar, the geometric shape of the surface distorts the projected structured-light pattern as seen from the camera. The principle of structured-light 3D surface imaging techniques is to extract the 3D surface shape based on the information from the distortion of the projected structured-light pattern. Accurate 3D surface profiles of objects in the scene can be computed by using various structured-light principles and algorithms.
The 3D modeling of object surface is one of the most important tasks of close-range photogrammetry when considering expanding range of different applications, considering focusing by a wide range of users and researchers. Numerous techniques for surface imaging by structured light are currently available. The various methods of 3D modeling depend on different factors such as the cost, distance and dispersion of measurement points, the amount of surface object deformation, characteristics of the work environment, time allowed for the measurement, object type, texture and color of the object, the stationary or moving of objects. In addition, in all these methods, automate of reconstruction and the automatic matching of corresponding points are the main challenges. Among these methods, the structured light method can be considered as a method to help automate of 3D reconstruction, when does not exist possibility of installation on the target object or is not possible to determine the high-density and automatic corresponding points (due to the same texture object). In this method, to produce 3D data of the object, a light pattern with known geometrical structure are projected on the surface of the object by means of projection tool. Then, the depth of the object is calculated using the distortion of the image taken by the camera. In the structured light method, as well as other methods, the main problem is determining corresponding points automatically, which is used to solve the coded light patterns. The aim of the research is design, construction and evaluation of modeling 3D objects that have the same texture and without the need to target investment, using structured light method. To this end, the Power shot G3 digital camera and Infocus X2 projector are used to take images and to project of structured light on the surface of the object, respectively. In this research, to achieve optimal accuracy and performance of 3D reconstruction process automatically, and create a unique identifier for automatic matching of each point measurement has been used 14 binary light pattern. To implement of 3D modeling, a statue 120´50 cm dimensions were chosen with the same texture. Then, to increase the density of 3D points extracted from the surface, in addition to binary coding, the phase transition was used. The results showed that the proposed method could be implemented with an average of ±100 micron accuracy in terms of the root mean square error (RMSE), to extract 3D information of surface objects.