Sara Beheshtifar; Abbas Alimohammadi
Volume 19, Issue 53 , September 2015, , Pages 49-68
Abstract
The ever-increasing population leads to establishing new educational centers. One of the important stages in school establishing, is selecting the optimal locations for them according to different objectives and criteria. The objectives are defined to determine well-distributed schools with balanced ...
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The ever-increasing population leads to establishing new educational centers. One of the important stages in school establishing, is selecting the optimal locations for them according to different objectives and criteria. The objectives are defined to determine well-distributed schools with balanced capacity and minimize the incompatibility of land-uses in the area. Increasing the related factors and criteria makes the location-allocation problem more complicated. Therefore, it is necessary to use efficient methods to solve it. In this paper, GIS, Multi-criteria decision making methods and multi-objective evolutionary algorithm were used to location-allocation of multiple girls’ primary schools in region 17 of Tehran urban area. Equity in geographic access and balance of schools capacity were considered in optimization process. Furthermore, suitability of the selected sites was determined considering the distance from the compatible and incompatible land-uses of the area. Because of using a multi-objective evolutionary algorithm, multiple solutions are presented in results instead of only one solution. In this research, five solutions were selected and investigated. In the best solution according to the first objective function, although the suitability of sites is adequate, the capacities of schools are imbalanced. In the best solution according to the second objective function (equity in geographic access), the chosen sites are well-distributed, but the compatibility of the land-uses and suitability of the sites are not satisfactory. Similarly, in the best solution according to the third objective function (balance of schools capacity), compatibility of the land-uses and suitability of the sites are inappropriate. Anyway, decision-makers can compare different optimal solutions and choose one of them to implementation according to different relative importance of objective functions.
Abolfasl Ranjbar; Mohammad Saadi Mesgari
Volume 16, Issue 42 , March 2013, , Pages 155-171
Abstract
The population growth, industrial development, bio-climate changes and scarcity of land resources are the main reasons and causes of forest degradation in developing countries. To control and decrease forest degradation, the governments need to know where, when, how fast, and why (with what causes) such ...
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The population growth, industrial development, bio-climate changes and scarcity of land resources are the main reasons and causes of forest degradation in developing countries. To control and decrease forest degradation, the governments need to know where, when, how fast, and why (with what causes) such degradations happen. On the basis of such knowledge, a general and sustainable management of these resources will be possible.
The science and technologies of GIS and remote sensing could be a perfect tool for answering the above questions. Remote sensing can be the basis of fast and inexpensive data collection and the analytical capabilities of a GIS can be used for analyzing the types, location and rates of changes.
In this research, the Landsat TM and ETM+ images of years 1987 and 2001 are used for land use classification and analysis of changes at the forest area of Arasbaran in north-west of Iran. The classification is mainly aimed at the separation of forest from non-forest areas.
A few methods have been studied to calculate and show the occurred changes. These include methods that only describe the change areas (such as subtraction and division methods) and those that describe the area, amount and type of the changes (such as comparison after classification).
By classifying the forest and non-forest areas of years 1987 and 2001 and overlaying them, a map was extracted representing the stable forest area and deforested area. From the topographic data of the study area, some other raster maps were created showing elevation, slope, aspect and distance from population areas.
Information of these maps were entered into a statistical model (a logistic regression model) having the above-mentioned classified map as the dependent parameter and all other maps as the independent parameters. It was resulted that the parameters of distance from populated areas, elevation and aspect have a meaningful relation with the deforestation phenomenon. From such an analysis, the importance of each factor in the phenomenon was defined and the areas that are in higher risk of deforestation and need an urgent protection were defined.
