International Journal of Management and Humanity Sciences. Vol., 4 (1), 4421-4429, 2015 Available online at http://www.ijmhsjournal.com ISSN 2322-424X©2015 Implementation of Ahp-Taxonomy Model in Gis Environment for Sport Facilities Site Selection 1 2 3 4 Mahmood Gudarzi, Mehdi Salimi*, Majid Jalali Fararhani, and Masood Taghvaie 1- Professor, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran. 2- PhD Student, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran. E3- Associated Professor, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran 4- Professor, Faculty of Geography and Urban Planning, University of Isfahan, Isafahan, Iran. *Corresponding author E-mail: [email protected] Abstract The aim of this study was to present AHP-TAXONOMY model in GIS environment for Sport facilities site selection in urban texture. The study is of descriptive - analytic and applied in terms of type. Data was gathered in survey method. Software Arc\View, Arc\GIS, Auto\Cad, Excel and some hardware to input and output data are of tools used for the study. Three southern regions in Isfahan were selected as areas to study and the process of site selection was performed there. The research was divided into four stages: A) Establishing a comprehensive database; B) Determining appropriate areas using Analytic hierarchy process (AHP); C) Field observation determined areas and selection of best lands, considering the real existing conditions to prepare the inputs for taxonomy method; and D) Prioritizing inputs and selecting the best ones to build desired sport facilities, using taxonomy method. Results showed in incessant space, 1.28% of total area was in very proper status that was in center of district frequently and in cessation space among 5 alternatives, was selected one alternative. Keywords: AHP Method, TOPSIS Method, Site Selection, Sport Places, Urban Texture. Introduction Clinical and epidemiological studies conducted over the past few years show that regular physical activity protects peoples against cardiovascular diseases, obesity, hypertension, type 2 diabetes and pulmonary diseases. Physical activity enhances individuals’ life quality and satisfaction and results in reduced smoking and proper diet. For this reason, various efforts are underway in local and national level by governmental and nongovernmental organizations to promote physical activity in the community. These efforts include programs to encourage exercise, projects to create, develop, and complete sport facilities and spaces. Studies show that the more sport facilities are available, the higher peoples’ physical activity will be (Mohseni, 2006). Creating new facilities necessitates accurate detailed scientific studies on site selection and ignoring this issue, in addition to non-optimal performance of created sport spaces, made a lot of money to waste. Today, there are many sport facilities in Iran which have deviated from the path of productivity because of such reason. Regulating indices and factors affecting decision making and providing rational approaches, site selection process is intended to help decision maskers and planners to select right and proper sites for activities (Salehi and Reza Ali, 2005). Regardless spatial relationships and geometry, such process can lead to improper consequences (Haining, 2004). There are various methods for site selection, which are divided in general into two categories: Discrete spatial Patterns In discrete spatial patterns, the options are identified and one or more options are selected from the available ones. Thus, a set of indices and criteria are selected and then they are rated and combined by particular methods, finally the best options are determined among all options (Minciardi, 2008). Continuous spatial patterns In this model, there are no previous options and the whole space is considered as a single unit. To determine proper sites, a set of criteria are specified, and using Multi Criteria Decision Making (MCDM), Intl. J. Manag. Human. Sci. Vol., 4 (1), 4421-4429, 2015 spatial data is mathematically formulated and combined with each other and accordingly spatial decisions are made (Faraji Sabokbar and Reza Ali, 2009). Multi criteria decision making methods involve a wide range of mathematical techniques which are used in the different ways depending on the objectives of the study. Generally, they consist of two main groups of planning. The first is Multi Objective Decision Making (MODM) planning which in general is applied in designing. The second is Multi Attribute Decision Making (MADM) in which the purpose of planning is to rank and select the best options (Kohansal and Rafiee, 2008). Analytic hierarchy process (AHP): In fact, AHP is a structured comprehensive technique for solving multi criteria problems and is applied in real world or in theory to solve strategic problems (Tolga, 2005). It was developed by Thomas L. Saaty in the 1970s and was introduced as a comprehensive analytic instrument for modeling the issues such a political, economical, social problems, as well as problems in educational sciences. It is based on pairwise comparisons of values of a set of problems (Sun Yu, 2002). This is one of the most comprehensive systems designed for multi criteria decision making, because it allows formulating the problem in a hierarchal way, as well as considering a variety of quantitative and qualitative criteria in the problem. The process lets various options to be involved in making decisions and provides a sensitivity analysis on the criteria and sub criteria (Ghodsy Poor, 2010). AHP process as an effective technology is used to determine the optimal site for facilities from multi criteria indices and define thematic coefficient and values of parameters (Yang and Ping, 2002). Taxonomy method: Taxonomy method isn’t considered as one of MCDM subsets does, however it is the best ranking method for regions in terms of development, so that it is applied in geography extensively (Soltan Panah, Farughi , and Golabi, 2010). One specific type of taxonomy is called Numerical Taxonomy. It is used to evaluation the similarity and closeness between the taxonomic units and to grade such elements into taxonomic groups. This method was introduced first by Adenson in 1763, then developed and expanded in 1950 by a group of Polish mathematicians, and proposed by Helving Professor from UNESCO College of Economy as a tool for grading and ranking the development in different nations (Salimi and et al, 2012). This technique is an excellent method for ranking, classifying and comparing various options (alternatives) with regards to their degree of development. Also, it is a method dividing a set into more or less homogenous subsets, thereby introducing an acceptable scale to investigate the rate of options (alternatives)’ development to the planners (Azar, and Rajabzade, 2009; Soltan Panah, Farughi , and Golabi, 2010). Accordingly, the present study intends to separate very suitable lands from others by using the AHP method, then to specify the best place to build new sports facilities by using the taxonomy method for designated areas, and finally based on above stages to introduce a model. Materials and Methods The study is of descriptive - analytic and applied in terms of type. Data was gathered in survey method. Tools: Software Arc\View, Arc\GIS, Auto\Cad, Excel and some hardware to input and output data are of tools used for the study. Research area Three southern regions in Isfahan were selected as areas to study and the process of site selection was performed there. Statistical population and sample In this study, of the various sport facilities in the areas, it was selected indoor pools as the sample and the process of site selection was done for them. Also to create matrices of pairwise comparisons, it was used the opinions of 36 experts in the field of sports facilities. Research method The research was divided into four stages: Establishing a comprehensive database. Determining appropriate areas using Analytic hierarchy process (AHP). Field observation determined areas and selection of best lands, considering the real existing conditions to prepare the inputs for taxonomy method. Prioritizing inputs and selecting the best ones to build desired sport facilities, using taxonomy method. Results In all issues resolve based on the AHP process, graphic representation of the problem in a hierarchy tree can manifest the roadmap in a general and comprehensive manner. Hierarchical classification in site 4422 Intl. J. Manag. Human. Sci. Vol., 4 (1), 4421-4429, 2015 selection is arbitrary because the problem can be divided into sections, subsections, and many smaller subordinate divisions. Structural hierarchy tree is shown schematically in fig. (1) and Table (1) present some interpretations for the symbols. Figure 1. A Structural hierarchy tree for site selection In fig (1), the final goal is marked in purple color (site selection), factors in green, indices in orange, and options in red color. Table (1) indicates the interpretations of the symbols used in fig. (1). 4423 Intl. J. Manag. Human. Sci. Vol., 4 (1), 4421-4429, 2015 Title Educational Centers GardenRestaurant Fire Departments Medical care Centers Role Table 1. Interpretations for symbols used in hierarchy tree in fig (1) Symbol Title Role Symbol Index B11 Site Selection Goal F-O Index B12 Accessibility Criterion A Index C1 Integration Criterion B Index C2 Safety Criterion C Police Stations Index C3 Distance Observation Criterion D 0-200 m. Option Fair Distribution Criterion E 200-400 m. Option Green Spaces Index B1 400-600 m. Option River Index B2 800-600 m. Option Cemetery Index B3 > 800 m. Option Gas Stations Index B4 206-296 m. 137-206 m. Option Option A1, B11, B21, B31, B41, B51, B61, B71, B81, B91, B101, B111, B121, C11, C21, C31, D1 A2, B12, B22, B32, B42, B52, B62, B72, B82, B92, B102, B112, B122, C12, C22, C32, D2, A3, B13, B23, B33, B43, B53, B63, B73, B83, B93, B103, B113, B123, C13, C23, C33, D3, A4, B14, B24, B34, B44, B54, B64, B74, B84, B94, B104, B114, B124, C14, C24, C34, D4, A5, B15, B25, B35, B45, B55, B65, B75, B85, B95, B105, B115, B125, C15, C25, C35, D5, E1 E2 Index Index B5 B6 87-137 m. Option E3 Index B7 41-87 m. 5-41 m. - Option Option - E4 E5 - Parking Lots Industrial Centers Administrative Centers Historical Places Cultural Centers Religious Places Index Index Index B8 B9 B10 As stated above, the AHP process is a method of continuous spatial model, and to perform the process, it is used the criteria which are possible to implement in continuous mode. Elements in any level are evaluated from right to left levels versus to all relevant elements at higher levels. Thus, decision options are evaluated based on the last level of decision indices. After drawing an analytic hierarchy tree and relying thereon, it is formed for all factors, indices and options, respectively, the matrices of pairwise comparisons whose values are scored from 1 to 9 in terms of importance of factors. It is worth to mention that it was benefited from experts’ viewpoints to create of pairwise comparison matrices. Accordingly, Table (2) shows the relative weights of the options in AHP model which are calculated based on the eigenvectors. 4424 Intl. J. Manag. Human. Sci. Vol., 4 (1), 4421-4429, 2015 Relative Weight 0.0004 0.0004 0.0008 0.0015 0.0024 0.0019 0.0019 0.0019 0.0037 0.0140 0.0019 0.0098 0.0068 0.0035 0.0014 0.0046 0.0031 0.0015 0.0008 0.0008 0.0099 0.0282 0.0325 0.0521 0.0155 0.0031 0.0065 0.0161 0.0096 0.0031 0.0197 0.0591 0.0338 0.0183 0.0099 0.0030 0.0060 0.0130 0.0270 0.0510 0.0612 0.0324 0.0156 0.0072 0.0036 Option 0-200 200-400 400-600 600-800 >800 0-200 200-400 400-600 600-800 >800 0-200 200-400 400-600 600-800 >800 0-200 200-400 400-600 600-800 >800 0-200 200-400 400-600 600-800 >800 0-200 200-400 400-600 600-800 >800 0-200 200-400 400-600 600-800 >800 0-200 200-400 400-600 600-800 >800 206-296 137-206 87-137 41-87 5-41 Table 2. Relative weights of the options in AHP model Relative Index Criterion Option Index Criterion Weight 0.1428 0-200 0.0756 200-400 Cultural 0.0346 400-600 Accessibility Centers 0.0168 600-800 0.0084 >800 0.0103 0-200 0.0068 200-400 Religious 0.0030 400-600 Green Spaces places 0.0016 600-800 0.0016 >800 Integration 0.0045 0-200 0.0045 200-400 Educational 0.0018 400-600 River centers 0.0009 600-800 0.0009 >800 0.0004 0-200 0.0004 200-400 Garden 0.0004 400-600 Cemetery Restaurant 0.0016 600-800 0.0026 >800 0.0002 0-200 0.0002 200-400 Fire 0.0005 400-600 Gas station Department 0.0008 600-800 0.0018 >800 Integration 0.0086 0-200 Medical 0.0086 200-400 Care Safety 0.0059 400-600 Parking Lots Centers 0.0019 600-800 0.0019 >800 0.0029 0-200 0.0029 200-400 Police Industrial 0.0029 400-600 Station Centers 0.0058 600-800 0.0216 >800 0.0004 0-200 0.0009 200-400 Distance Administrative 0.0015 400-600 Observation Centers 0.0022 600-800 0.0022 >800 0.0001 0-200 0.0001 200-400 Fair Historical 0.0001 400-600 Distribution Places 0.0005 600-800 0.0009 >800 The final map of the AHP process which is indicating the divisions of the studied area in 5 ranges of very unsuitable, unsuitable, moderate, suitable, and very suitable, and also representing the final map of site selection for indoor pools in continuous mode was achieved by collective overlapping of 18 primary maps prepared in GIS environment. Thus, figure 2 Represents the final map of analytic hierarchy process in the studied area. Also, points marked on the map are indicating the inputs for taxonomy method. Figure 2. Final map of analytic hierarchy process in the studied area and input options Taxonomy method In the next step of the site selection process for indoor pools, which is performed in discrete mode, options are the same lands determined based on the research team’s field observations of the very suitable areas of final maps of analytic hierarchy process. 4425 Intl. J. Manag. Human. Sci. Vol., 4 (1), 4421-4429, 2015 Table 3 shows the normalized data matrix for numerical taxonomy method with the aim of site selection in discrete mode. These numbers were obtained using the field observations by the research team. Also, the criteria used in this matrix are the ones which are feasible and practical in discrete mode. In this table, the elements in rows indicate the studied options and the ones in columns represent the assessed criteria. Row 1 2 3 4 5 6 7 Table 3. Normalized data matrix for numerical taxonomy method Criteria Geomorphologic Difficulty in Price of Worn out Option conditions possession land texture A 0.794 -1.107 -1.087 -0.642 B 0 0.227 -0.362 -0.642 C 0.794 -1.107 -0.362 1.290 D -0.794 1.560 -0.362 -0.642 E 0.794 0.227 0.362 -0.642 F -1.587 0.227 1.812 1.290 DOj 0.794 1.560 1.812 1.290 According to data from Table 3, Table 4 indicates the grading or ranking of development of studied options based on Fi resulted from each option. Table 4. Ranking the studied options in discrete mode Index Cio Fi Rank Option A 4.387 0.969 6 B 3.296 0.728 3 C 3.441 0.760 5 D 3.314 0.732 4 E 2.759 0.609 2 F 2.729 0.603 1 As it is obvious from the results in Table 4, of options studied in the discrete mode, the option F was defined as the best place to construct an indoor swimming pool. Implementing the stages of site selection process in continuous and discrete modes using the methods of AHP and Taxonomy, respectively, it is introduced the proposed model of AHP-TAXONOMY based on Figure 3 To select sites for sport facilities. 4426 Intl. J. Manag. Human. Sci. Vol., 4 (1), 4421-4429, 2015 Figure 3. Proposed model of AHP-TAXONOMY to select sites for sport facilities Discussion One of the basic requirements to establish sport facilities with high productivity is to select optimum sites to build and it is one of the most important duties of sport managers. 4427 Intl. J. Manag. Human. Sci. Vol., 4 (1), 4421-4429, 2015 Several factors can affect the optimal site selection of which 5 cases are mentioned as follows: Using a powerful and comprehensive database for the studied area. Considering the type of sport facilities and selecting the specific site for each of them (sport facilities have different properties and the results from site selection for certain sport facilities can not be generalized to others and every sport facility needs separate site selection process. The reason for failure in generalizing the results can attributed to various factors such as different weighting the indices, sub-indices, or selection of indices). Using various indices coinciding with the real conditions of the studied area (criteria can differ according to different conditions of the area). Weighting scientifically and properly each of indices and sub-indices. Using the proper models in order to integrate accurately prepared layers of data. Slight negligence in any of the abovementioned cases by the researcher can cause major changes in the output maps and lead the results of site selection process to become completely invalid. Conducting the site selection process and considering the final output map, it was found out that of the whole lands examined in the study, a few pieces of lands (<1.28%) is suitable to build new samples of sport facilities (indoor swimming pools), and they are often located in the central and northern parts of the studied area, with no trace in the southern and eastern parts. The reason can be attributed to more relative cogeneration of factors incompatible with sport facilities, the existence of the same sport facilities, as well as lower accessibility and population in these areas. Salimi (2010) dealt with the spatial analysis and the site selection for various sport facilities in 5th and 6th districts of Isfahan the city in GIS environment. Criteria he considered for this operation consisted of consistency, fair distribution, accessibility and safety. Finally, the studied area was divided into 5 discrete classes and the lands suitable to build indoor swimming pool shared 13% of the whole lands. He structured his innovative method based on weighting the interval classes and the criteria, using the experts’ viewpoints, and didn’t use any models in the sit selection process. The results from the first part of the current study (segmentation based on AHP model) can be consistent with the results from above study, because in both cases the studied area was divided into 5 different classes based on the same criteria and they are overlapped one on another with a slight difference and the same degree. Fazel Nia and et. al. (2010) conducted a spatial analysis and site selection for sport facilities in the city of Zanjan, using AHP model. They considered in their study the criteria of the range of usage, consistent usage with nearby usages, price, and finally evaluated the studied area as the relatively suitable (43%) and completely suitable lands (6%) to construct the sport facilities. Considering spatial and temporal analyses of existing places, the range of usage, and distance observance, using AHP model and demographic criteria, Taji (2010) dealt in his M.S. thesis with the optimal site selection for new sport facilities in the city of Rasht. In his M.S. thesis, Soleimani Amiri (2010) dealt with the site selection for sport facilities in the city of Babel, using analytic hierarchy model and relying on the criteria such as the range of usage, and distance observance. Reviewing the abovementioned studies and considering the differences between the comprehensiveness of databases, and the differences between the quality of criteria and the methods of integration of layers, their results can not regarded consistent with the present study’s. The point should be noted is that the rate of lands scored as suitable and very suitable is different between various studies, indicating the accuracy and comprehensiveness of the process of site selection. The more comprehensive database, the more number of criteria and the more accurate the models used in the study, the fewer lands are introduced as suitable and very suitable to construct new sport facilities. As it is shown in the final map, the lands with suitable and very suitable conditions form a small fraction of the studied area, while only a piece of land is introduced in the next stage, indicating the major difference between this study and others mentioned above. References rd Azar A, Rajabzade A, 2009. Applied Decision Making (MCDM Approach), 3 Ed, Knowledge Vision Press, Tehran. Faraji H, Reza Ali M, 2009. Comparing discrete and continuous spatial models, Journal of Research in Human Geography, No. 67, 2009 spring, pp. 69-83. Fazel Nia G, Kiani A, Rastegar M, 2010. Optimal site selection for sport spaces in the city of Zanjan, using AHP and GIS, Journal of Research and Urban Planning, No. 1, pp. 1 - 20. th Ghodsy Poor H, 2010. Analytic hierarchy process (AHP), 8 ed., Amirkabir University of Technology, Tehran, p. 5, 14, 16. 4428 Intl. J. Manag. Human. Sci. Vol., 4 (1), 4421-4429, 2015 Haining R, 2004. Spatial Data Analysis, Cambridge University Press. Kohansal M, Rafiee H, 2008. Selecting and ranking irrigation systems and traditional ones in the province of Khorasan Razavi, Journal of Agricultural Science and Technology specified for Economics and Agricultural Development, Vol. 22, No. 1, pp. 93-95. Minciardi R, 2008. Multi-Objective Optimization of Solid Waste Flows: Environmentally Sustainable Strategies for municipalities, Waste Management, 28. Mohseni MJ, 2006. Studying sports spaces in the area covered by the Health Promotion and Social Development Research Center, Tehran University of Medical Sciences, 2003, Science and Research Journal of Arak University of Medical Sciences, Demographic Research Special Edition., 2006 summer, p. 62. Salehi R, Reza Ali M, 2005. Spatial organization of educational facilities in the city of Zanjan using GID, Journal of Geographical Researches, No. 52. Salimi M, 2010. Spatial analysis and site selection for sport facilities using GIS, M.S. Thesis, Faculty of Physical Education and Sports Science, University of Isfahan. Salimi M, Soltanhosseini M, Taghvaie M, 2012. Optimal Site Selection to Construct Outdoor Sport Facilities using GIS, journal of research in sport sciences, No. 16, pp. 37-62. Soleimani Amiri G, 2010. Site selection for sport spaces in the city of Babel, using GIS, and determining their usage rate, M.S. Thesis, Faculty of Physical Education and Sports Science, Islamic Azad University, Science and Research Branch. Soltan Panah H, Farughi H, Golabi M, 2010. Utilization and Comparison of Multi Attribute Decision Techniques to Rank Countries in Terms of Human Development Index, Journal of Knowledge and technology, No. 2, pp. 1-28. Son Yu C, 2002. A GP-AHP Method for solving Group Decision-Making Fuzzy AHP Problems, Computer and Operations Research, (29) 1970. Taji A, 2010. Site selection for sport facilities in the city of Rasht, using AHP in GIS environment, M.S. Thesis, Faculty of Physical Education and Sports Science, University of Guilan. Tolga E, 2005. Operating System Selection Using Fuzzy Replacement Analysis and Analytic Hierarchy Process, Production Economics, NO 97. Yang J, Ping S, 2002. Applying Analytic Hierarchy Process in Firms Overall Performance Evaluation: Case Study in China, International of Business 7(1), 33. 4429
© Copyright 2024