فصلنامه سنجش از دور و GIS ایران
پیاپی 4 (زمستان 1388)

Studying Quality of Life (QOL) is becoming an important tool for evaluating public policies, ranking places, as well as understanding and prioritizing the problems that communities face with, developing and monitoring management and urban planning strategies. In this study, quality of place has been considered as the resources, facilities and opportunities that are provided by the environment to meet human needs as subsets of quality of life. Integration of remote sensing, census and other spatial data in GISenvironment using Multicriteria Method is employed in order to access the quality of place forthe city of Tehran. Three main domains, including social, access and physical as a criterion to determining and using factor analysis from a total of 49 parameters components related to each domain were identified. Component weights associated with each domain using hierarchical analysis extracted and final index of each domain by weighted linear combination of components of the standard were created and finally, the three domains of social, access and physical in order to extract the ultimate indicator of quality of place through the multi-criteria evaluation in GIS environment were combined together. In addition, using the index spatial analysis Gi, optimal and critical clusters in each domain was determined. The results showed that in the social domain north and central northparts, in the access domain central parts and in the physical domain north parts of Tehran are desirable areas; ultimately, the spatial analysis of quality of place index shows that the north and central north of Tehran are desirable areas; but west, southwest and south parts of Tehran are critical ones. The results of correlation analysis between social, access and physical domains showed significant positive relationship between social domain with access and physical domains..

Major development activities in a region usually take place on the basis of a regional land use plan. The dynamic interactions of socio-economic and environmental factors in both space and time aspects are main reasons for complexity of regional land use planning. The main goal of this research is to develop a new GIS-based model for the allocation of various land use types at the regional level. The model is based on the comparison of landsuitability (calculated in large scale) and land demand (evaluated in small scale). In different parts of the model, computational intelligece, multi-criteria decision making methods, regression analysis and GIS functions are used. Important driving factors in land suitability model are ecological capability, neighborhood effect, accessibility and zoning regulations. In this research, proper factor maps for these driving factors were generatedand integrated using multiplicative weighted average. To generate ecological capability maps, the Makhdoums ecological capability model, which is widely applied in Iran, was implemented using a Fuzzy Inference System (FIS). The fuzzy maps of soil texture, soil depth, slope, climate, elevation, erosion and geology were considered as inputs to the FIS. Neighborhood effect maps were produced based on cellular automat concept. Theneighborhood effect for each cell was estimated based on the agglomerated spatial externalities of its neighboring cells, located in its influence region. The spatial externalities of land use types over distance were defined using land values, spatial metrics and expert knowledge. Accessibility was calculated using weighted average of theaccessibility to infrastructures and major activity centers. Accessibility to relevant infrastructures (road networks, electricity transmission lines, gas pipelines and water canals) was modeled on the basis of Euclidean distance.Accessibility to major activity centers (population and industry centers) was determined using the gravity model. To estimate the land demands in the years of 2016 and 2021, the land use changes occured during the past two decades were used, in a stocastic regression model. Beside land suitability and land demand, land use conversionrules and policies are also needed to be considered in land use allocation process. Finally, in an inventive procedure, the demanded land use types were allocated, shut that the arrangement of land use types in the entire area was optimised. The developed model was implemented using the data of Borkhar & Maymeh townships, inEsfahan province, Iran. Considering 2004 as the base year, the result of the model is the allocation of urban residential, rural residential, industry and agriculture land use types for the years of 2016 and 2021, based on both agglomerated policies, as well as non-agglomerated ones.The developed methodology can be used by plannersand decision makers when studying and proposing future land use plans. In this research, two conditions were assumed to limit the scope of the work. First, only the increases of land demands were modeled. Second, only the general land use types were considered. As the continuation of this research, both the decreases in the land demand and and the more detailed land use types can be considered.

Site selection for rural sports-cultural centers is a complicated process which is based on qualitative and quantitative criteria. There are several appropriate decision making methods which can be utilized for a spatial purpose. In decision making process, the qualitative and linguistic criteria are as important as quantitative ones. Moreover, stakeholders consideration should be taken into account.Therefore, it is necessary to use methods considering both factors including linguistic variables as well as stakeholders. AHP and Fuzzy screenings are two methods used for modeling qualitative and linguistic variables. However, since AHP contains a hierarchical organization, it can present more reliable results. On the other hand, the Ordered Weighted Average (OWA) is more flexible in taking the priorities of the decision makers into account. Hence better results can be achieved by the combination of OWA with other methods. In this paper, fuzzy screening, AHP, and a combined method of AHP and OWA are used in order to select the most appropriate site for rural sports-culturalcenters in Kani Bazar, located in the suburbs of Mahabad. The results show that the combined method presents more flexibility and higher performance. Furthermore, the result of sensitivity analysis carried out on the data reveals that the developed system demonstrates very high stability.

Availability and accessibility to reliable and up-to-date urban data are key components for successful planning and management of the city, as well as providing more efficient services to its citizens. In addition, the availability of reliable and up-to-date data can play a major role in reducing the costs of city management.Urban Services Data (USD) cover the main part of the data needed for efficient management of the city. Urban services data consist of all data about urban entities that must be created, maintained and enhanced by the municipality of Tehran. The USD consists of a large volumeof data and posses a high frequency of changes. Additionally, there are many users and decisionmakers from various disciplines that need data to conduct their daily business. As a result, there is a serious need for the implementation of services for accessing the USD for many distinct kinds of users and their respective preferences. Users preferences directly affect the design and development of services. In addition, because various kinds of users (municipality departments, public, private and academic sectors and citizens) utilize different computing platforms and diverse communication technologies to access these services, services should be interoperable. In this context, users comefrom two broad communities: the Geospatial Information (GI) and Information Technology (IT) communities. There are distinct technologies and concepts for providing interoperability for both communities. In order to provide highest level of interoperability, Open Geospatial Consortium services and Web Services technologies must be integrated. Since these two kinds of services are not fully compatible, various integration issues must be addressed efficiently. In this paper, each implementation issue is identified and evaluated. Then possible solutions are proposed and implemented using state of the art technologies.

By coming the new generation of SAR polarimetric satellites, such as TerraSAR-X, RADARSAT-2,ALOS, etc., the development of polarimetric synthetic aperture radar (PolSAR) applications in the field of natural hazard and environmental, such as subsidence, soil erosion, earthquake prediction, volcano activities and flood have been accelerated. The aim of this article is extraction of basic information from POLSAR images and determining the amount of the importance of each characteristics in feature vector spaces.The elements of the feature vector space are produced bymultiplication of HH, VV and HV bands that contained the scope of phase and amplitude information.Performed by making Fischer criterion for class separation, the significance of each features are verified and therefore, the features are ranked based upon the power of separability and correlations between the bands. In the next stage, by performing supervise Maximum likelihood classifier, theaccuracy of the different combination of the features has been analyzed. Finally, the best combination of the existing features was obtained. Extraction of the best mining consisting of at least features in the feature vector space, not only protects the most important information, but also leads to reduction in the volume of POLSAR image processing operations. In this regard, the proposed algorithm in thisarticle can be applied on any polarimetric data.

Precise classification and recognition of cloudy pixels are essential to study the earth surface bymeans of satellite imagery. Among the existing classification methods, neural network and decisiontree models in Data Mining can be exampled from which several algorithms have been developed. Inorder to evaluate classification accuracy of neural network and decision tree models, eleven algorithms from them were considered. To do this, 40,000 pixels from cloudy, clear, snowy and water bodies in four classes on the calibrated NOAA satellite images were chosen, spectral information of five-band were extracted and NDVI spectral ratio, first band to second band ratio and differencetemperature of fifth band from fourth band were calculated and by putting them as input variables,accuracy of each algorithm in classification of the pixels were compared. Elapsed times to running Decision tree models were more quick than Neural networks algorithms and Decisions had been obtained in Decision tree models were clear so that these are advantages of the mentioned category models. Finally, after forming of coincidence matrix that showing number of right and wrong in pixel classification, the results indicated among 11 compared algorithms, C5 algorithm is superior to diagnosis of cloudy pixel with 43 errors and roughly accuracy of 99.56%. By considering the mentioned advantage, C5 algorithm was introduced as the best algorithm for image classification anddiagnosis of cloudy pixels.

Location-based service is an informational service utilizing the ability to make use the location of userin a mobile network. One of the very important issues in such services is the informational privacy;means restricting persons that are unknown or unknowable to others. It is obvious that the core ofsuch problem is using users position unlike his/her opinion. The motivation behind this misuse isoften to observe and analyze the users behavior, attitudes, and social situation in order to tailorspecial offers or advertisements for him, but sometimes it may also be with criminal intentions.Therefore there is a concept called Privacy. In recent years, there has been much attention to usersanonymizing and privacy protection. Fuzzy logic method is proposed in this paper as a new way toprivacy protection. Different levels of accuracy to determine the user's location is defined based upontheir chronological context using Fuzzy inference engine. In order to use this method, it is necessaryto classify users and to define special fuzzy rule base for each group. The employees of a company areused as an experimental group to illustrate the efficiency of proposed method. The Fuzzy rule base isformed for them and final fuzzy inference engine for the users group was developed. Finally a way isrecommended to express different accuracies of positioning symbolically. The results of applicationshowed that this approach satisfies users to reveal their spatial information with favorite precision.Increase in number of fuzzy rules could cause complexity and reduce the efficiency of model forcommon users. The number of rules will decrease with some explanations to users. The proposedmodel could also be combined with other privacy protection methodls like anonymizing users forhigher security levels