نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال هفتم شماره 1 (پیاپی 25، اسفند 1394)

Nowadays different kind of earth and satellite data are used in Earth Gravitational Model (EGM) production and therefore the precision of this model became better and can be used in orthometric height determination. Geoid as an equipotential surface of Earth gravity field and datum of orthometric height system has an important role in civil, exploration and oceanography studies and projects. In this study the accuracy of two Earth gravitational models EGM96 and EGM08 are evaluated in Iran region. Spherical harmonics coefficients of these two Earth gravitational models are used to compute the geoid heights in Iran region. The geodetic coordinate and height of some precise levelling stations in north-east, west and south of Iran were available, therefore the GPS/Levelling method are used to compute the orthometric height of these stations. Root mean square of discrepancies between precise levelling orthometric height differences and modelled orthometric height differences show that the EGM96 is more precise than EGM08 in north-east of Iran and EGM08 is more precise than EGM96 in west and south of Iran.

One of the most difficult issues in cartography is generalization. It is sometimes required to simplify the data in order to produce a map which visually adapts the purpose of the study also the statistics on this map should be developed in a way representing the main features. One of the most important steps in generalization is to aggregate similar phenomena to be represented as a whole phenomenon. In recent years, rapid development of multimedia technology has provided the opportunity to create and visualize the accurate 3D models of cities. This paper focuses on aggregation operator and tries to present and implement a method to aggregate 3D buildings in the process of generalization.

For many people, it is a biggest challenge how to purchase a home. Sometimes, choosing and buying a house can be a complex and expensive process. The complexity of the process of buying property depends on the circumstances, needs, and desires of the individual. Despite economic conditions, purchasing a house also affect the mental and emotional aspects of buyers. In this paper, after the classification of the available real estate services, there is discussed about the pivotal role of Volunteered Information System in the real estate services. Applications Volunteered Geographic Information in the field described. The proposed services are classified into two offline and online categories. Finally, the implementation of a Volunteered Geographic Information environment related to the area around the property was conducted.

LiDAR has been recently known as a powerful technology for 3D information acquisition from objects, and researchers are working on developing reliable techniques such as clustering for automatic object extraction from LiDAR data. Finding optimal clustering method, number of clusters and descriptors are the most challenging problems in clustering of LiDAR data. In this research, a GA based solution is presented for finding optimal descriptors of in LiDAR data clustering.
A vast number of features were considered in two categories of single operator and composite descriptors. Single operator features are extracted from first and last pulses of intensity and range data (FR, LR, FI and LI) in which, each operator are executed on this four data to extract four operator features. Besides, two other features of NDIR and NDII are also extracted from the resulted features. Here, ten groups of single operator features were extracted: Raw data (6 features), Scene Ratio(12 features), fractal (12 features), Gabor (12 features), Haralick (24 features), Moments (24 features), SemiVariogram (12 features), Normal Vector (36 features) and Roughness (6 features). Composite features are extracted using a combination of the operators in the first category which nine features were generated. Finally, 171 features are extracted from LiDAR data.
To simplify the computations, in the first level, unsuitable features are deleted from the list based on manual observation. Minimizing the NCE measure in clustering of LiDAR data using k-means algorithm, optimal descriptors in the remained list are assessed through Genetic Algorithm.
To evaluate the proposed method a sample area from the city of Stuttgart, Germany, were used. From the 171 extracted features 100 unsuitable features were deleted manually and the optimization process were executed on 71 features. In the sample area three clusters of Buildings, Trees and Land were considered for clustering. After executing GA process 5 features were selected as optimal features. Obtained results proved the ability of the GA algorithm as an optimization tool as well as the optimality of the selected descriptors in clustering.

Movement is part of all the ecosystems and most of the spatio-temporal processes. Studying movement and movement patterns aid in knowledge extraction and discovering meaningful relationships of dynamic events in our surroundings. Movement of mobile point objects, in the course of time, generates trajectories which analyzing them is highly imperative in different sciences. Among the similarity analysis approaches, detection and similarity measurement has been at focal attentions. In this regard, this research firstly investigates the concept of movement and the way a trajectory is forming. Afterward, moving spaces are presented and the pros and cons of each are outlined. In the following, similarity measurement approaches are introduced and their positive and negative features are concisely addressed. In the end, related studies which have used diverse similarity measurement approaches for various applications are surveyed comprehensively. It is hope that this research provides researchers a better understanding and perception of the process of point objects’ movement and helping them in choosing and implementing the proper similarity measurement approach.

In the recent years, the high cost for purchasing the software, inability to access the source code and some other reasons, encouraged many experts to develop open source software. The main goal of this study was to develop a urban land use allocation on a open source software. This study with review the theoretical foundations, a variety of strengths and weaknesses open source software, compared four GIS desktop open source software GRASS, UDig, QGIS and MapWindow in order to develop a decision support system for land allocation and Finally the MapWindow software was chosen and a system that called OSLAS was developed for the purpose. OSLAS has four potential, land demand, land allocation and evaluate modules and the basic ability of a Geographic Information System. The developed system is implemented for the allocation of urban land use in district one of Tehran and predicted land use map for the year 2021 is created.

Groundwater compared to surface water has several advantages such as higher quality and less pollution. Quantity and quality of the resource and its specific relationship with the characteristics of the surface, and at least try to protect this valuable resource would be the case.Quality evaluation on all stations in the study area that includes 11 sites sampled that is available Including springs, wells, semi-deep waters reviews Hydrochemistry sample using data obtained from the analysis of water quality in the form of diagrams Piper, Schuler, Will Cox, Gibbs, Stiff, and Durov AqQA software has been analyzed. And statistically analyzed using ANOVA test and LSD test was performed and the method by GIS land Mapping Kriging was used. The results obtained have shown that Type of Piper diagram of water stations in the study is Ca-SO4 and Cl-Mg. Schuler for drinking water in the study area by the diagram at the appropriate diagram type Wilcox, C2S1, which is suitable for irrigation and drinking. Gibbs has shown that geological formations are affected by erosion. Durov diagram shows that TDS is less than 450 milligrams per liter. The magnesium hazard is low and the risk of salinity in the study area is too low. Responsible of good quality water are studied minerals dolomite prairiea and As well as the lack of factories and sewage treatment plants and optimum use of urban and rural area of ​​water resources.