مکان‌یابی فضایی سبز شهری در شهرداری منطقه ١٥ کلان شهر تهران در محیط GIS

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استاد دانشکدة جغرافیا، دانشگاه تهران

2 کارشناس ارشد سنجش از دور و GIS، دانشگاه تهران

3 کارشناس ارشد مدیریت اجرایی، دانشگاه آزاد اسلامی واحد تهران‌مرکز

4 دکتری جغرافیای برنامه ریزی شهری، دانشگاه تربیت مدرس

چکیده

امروزه در کنار سیستم­های اطلاعات جغرافیایی، سیستم­های تصمیم­گیری چندمعیاره به­طور گسترده­ای برای حل مسائل فضایی به­کار گرفته می­شوند. در این مقاله با ترکیب دو روش FAHP و TOPSIS، روش جدیدی برای انتخاب مکان بهینه با استفاده از سیستم­های اطلاعات جغرافیایی به­کار گرفته شده است. در این زمینه، معیارهای انتخاب مکان بهینه، برای ایجاد فضای سبز شهری در مدل FAHP وارد و مقایسه شدند و سپس خروجی مدل FAHP به­عنوان وزن معیارها در مدل TOPSIS به­کار گرفته شد. طبقه­بندی فضاهای مکانی در سطح منطقة ۱٥ کلان‌شهر تهران به­منظور احداث یا پهنه­بندی فضاهای سبز با استفاده از تکنیک ترکیبی مدل­های  FAHPو TOPSIS به­صورت خیلی خوب، خوب، متوسط، ضعیف و خیلی ضعیف انجام گرفت. زمین­هایی که درجة تناسب خیلی خوب و خوب دارند، برای ایجاد فضای سبز شهری انتخاب می­شوند. نتایج نشان می­دهد که استفاده از مجموعه­های فازی در ترکیب با مدل سلسله­مراتبی، نه تنها سبب رفع ابهام از مدل ATP می­شود، بلکه میزان خطای تورش ناشی از مقایسة دوبه­دو لایه­ها را نیز کاهش می­دهد. درنهایت، ترکیب مدل FAHP با مدل TOPSIS، قابلیت استفادة همزمان از نظرهای مقیاسی کارشناسی را در کنار ایده­آل­های موجود همچون افزایش دقت و صحت طبقه­بندی در لایه­های اطلاعاتی، ممکن می­سازد.



 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Site Selection for Green Space in Region 15 of Tehran City by Geographic Information System (GIS)

نویسندگان [English]

  • Ahmad Poorahmad 1
  • Majid Shadmani Rudposhti 2
  • Siroos Hasanpour 2
  • Amir Shahrabi Farahani 3
  • khader Faraji Rad 4
1 Professor of geography, Faculty of Geography, University of Tehran, Tehran, Iran
2 MA student in RS and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
3 MA student in executive management, Department of MBA, Islamic Azad University, Central Tehran Branch, Iran
4 PhD candidate in geography and urban planning, Tarbiat Modares University, Tehran, Iran
چکیده [English]

Introduction
Nowadays integrated multi-criteria decision making (MCDM) and Geographic Information System (GIS) is commonly used in order to solve spatial problems. Different multi-criteria decision making techniques present different methodologies with certain limitations and advantages. Our proposed methodology focused on one of the vital problems of the cities shortcoming in the urban green areas, which can affect the human life from a variety of viewpoints. The levels of green areas of Iran cities not only are low in comparison to the standards of urbanism, but they also have been distributed in an improper way. Accordingly, the available spatial distribution caused so many  problems  namely,  difficulty  to  access  to  them  and inequality  in distribution of  the  green  areas. As a result, in order to answer the needs of urban residents in our country and overcome such problems, the urban managers and decision makers need to apply modern analytical tools. This article attempts to offer a new combinational method to propose suitability map for distribution green space in the region 15th of Tehran Municipality to the urban managers and decision makers using FAHP and TOPSIS techniques.
 
Methodology
Integrated Multi-Criteria Decision Making (MCDM) and Geographic Information System (GIS) are commonly used in order to solve spatial problems. Different multi-criteria decision making techniques present different methodologies with certain limitations and advantages. Our proposed methodology considered 8 green space related criteria to evaluate and priorities urban green space suitable sites. The proposed methodology has two steps: in step 1, AHP is improved by fuzzy set theory. In this regard, by using fuzzy set theory in AHP method, the qualitative judgment can be qualified to make comparison more intuitionistic and reduce or eliminate assessment bias in pairwise comparison. In step 2, obtained results have been used as input weights in TOPSIS algorithm. TOPSIS algorithm by considering ideal and non ideal solution helps decision maker evaluate ranking of locations to select the best one.
FAHP: To deal with ambiguity of human thought, Zadeh first introduced the fuzzy set theory, which was oriented to the rationality of uncertainty due to imprecision or vagueness. A major contribution of fuzzy set theory is its capability of representing vague data. The theory also allows mathematical operators and programming to apply to the fuzzy domain. A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function, which assigns to each object a grade of membership ranging between zero and one. Essentially, the uncertainty in the preference judgments gives rise to uncertainty in the ranking of alternatives as well as difficulty in determining consistency of the preferences. These applications are performed with many different perspectives and proposed methods for fuzzy AHP. In this study, Chang’s (1992) extent analysis on fuzzy AHP is formulated for a selection problem.
 
 
Fig. 1 A triangular fuzzy number
 
A triangular fuzzy number (TFN), M, is shown in Fig. 1. A TFN is denoted simply as (m1, m2, m3). The parameters m1, m2 and m3, respectively, denote the smallest possible value, the most promising value, and the largest possible value that describe a fuzzy event. The Analytical Hierarchy Process (AHP) is one of the extensively used multi-criteria decision-making methods. One of the main advantages of this method is the relative ease with which it handles multiple criteria. In addition to this, AHP is easier to understand and it can effectively handle both qualitative and quantitative data. The use of AHP does not involve cumbersome mathematics. AHP involves the principles of decomposition, pairwise comparisons, and priority vector generation and synthesis.
Though the purpose of AHP is to capture the expert’s knowledge, the conventional AHP still cannot reflect the human thinking style. Therefore, fuzzy AHP, a fuzzy extension of AHP, was developed to solve the hierarchical fuzzy problems. In the fuzzy-AHP procedure, the pairwise comparisons in the judgment matrix are fuzzy numbers that are modified by the designer’s emphasis.
TOPSIS: An extension of TOPSIS (technique for order performance by similarity to ideal solution), a multi-attribute decision making (MADM) technique, is applied to a set of investigated criteria. TOPSIS is a practical and useful technique for ranking and selection of a number of externally determined alternatives through distance measures. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution and the longest geometric distance from the negative ideal solution. It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalizing scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.
 
Results and discussion
In our study, we have categorized suitability value of geographic locations for urban green spaces in region 15th of Tehran Municipality. These are categorized into, very low suitability, low suitability, moderate suitability, high suitability and very high suitability. Comparison of categorized suitability classes in relation to final suitability map indicates that the two suitable categories (High and very high suitability) of the proposed suitability map together occupy about 20% of the study area.
 
Conclusion
AHP is an effective problem solving methodology. Decision problem may contain spatial social, economic, technical and politic factors that need to be evaluated by linguistic variables. Then, AHP is one of the most commonly used techniques for such situations. The Criteria set is determined at the beginning in many multi criteria decision making methods. It was also modeled depending upon these criteria. Multi criteria decision making techniques based on the linguistic evaluations like FAHP helps make the best decision selection by using a weighting process within the current alternatives via pair wise comparisons. In this methodology by utilizing improved Analytical Hierarchy Process by Fuzzy set theory, weight of each criterion has been calculated for urban green space site selection. Finally, this article introduces an approach that integrates improved AHP with TOPSIS algorithm to support related decisions of urban green space site selection.
FAHP is the AHP improved by fuzzy set theory which is a useful approach for evaluation of the complex multiple criteria alternatives involving subjective and uncertain judgment. By using fuzzy set theory in AHP method, the qualitative judgment can be quantified to make comparison more intuitionists and reduce or eliminate assessment bias in pairwise comparison process. In further step, TOPSIS algorithm considered ideal and non-ideal solution in combination with obtained expert results which have been deducted from FAHP algorithm.

کلیدواژه‌ها [English]

  • Fuzzy AHP
  • Geographic Information System
  • Green space
  • Multi-Criteria Decision Making (MCDM)
  • TOPSIS Technique
2. آرنوف، استن، 1375 ، سیستم اطلاعات جغرافیایی، ترجمة سازمان نقشهبرداری کشور، چاپخانة سازمان نقشهبرداری، تهران.
.3
آل شیخ، ع . ا .، سلطانی، م .ج. و هلالی. ح.، 1381 ، کاربرد GIS در مکانیابی عرصههای پخش سیلاب، فصلنامة تحقیقات
جغرافیایی، شمارة هفدهم، شمارة 4 ، صص 23 - 38 .
.4
اصغرپور، م. ج.، 1388 ، سامانة تصمیمگیری چندمعیاره، دانشگاه تهران، تهران.
.5
علایی طالقانی، م، 1381 ، ژئومرفولوژی ایران، شمارههای 1330 و 1337 ، چاپ اول، نشر قومس، تهران.
.6
قدسیپور، ح .، 1385 ، مباحثی در تصمیمگیری چندمعیاره، انتشارات دانشگاه صنعتی امیرکبیر )پلیتکنیک تهران(، تهران.
.7
مرکز آمار ایران، 1386 ، 1375 ، سالنامة آماری استان فارس.
مهدوی، محمد، 1382 ، هیدرولوژی کاربردی، جلد اول و دوم، انتشارات دانشگاه تهران، تهران.
9. Alsheikh, , 2001, Applications GIS in Flood Spreading Site Selection, Journal of Geographic Research, 551, P. 51. (In Persian)
10. Asgharpour, M. J., 2009, Multi-Criteria Decision-Making, University of Tehran Publications. (In Persian)
11.Bellman, R. E. and Zadeh, L. A., 1970, Decision Making in a Fuzzy Environment, Management Science, No. 17, PP. 141-164.
12.Boender, C. G. E., De Graan, J. G. and Lootsma, F. A., 1989, Multi Criteria-Decision Analysis With Fuzzy Pairwise Comparisons, Fuzzy Sets and Systems, No. 29, PP. 133-134.
13.Buckley, J. J., 1985, Fuzzy Hierarchical Analysis, Fuzzy Sets and Systems, 17, 233.
14.Chang, D. Y., 1996, Applications of the Extent Analysis Method on fuzzy AHP, European Journal of Operational Research, No. 95, PP. 649–655.
15.Chen, G. and Pham, T. T., 2001, Introduction to Fuzzy Sets Fuzzy, Logic and Fuzzy Control Systems, CRC Press, Florida.
16.Cheng, C. H., Yang, K. L. and Hwang, C. L., 1999, Evaluating Attack Helicopters by AHP Based on Linguistic Variable Weight, European Journal of Operational Research, No. 116, PP. 423-435.
17. Deng, H., 1999, Multi Criteria Analysis with Fuzzy Pairwise Comparisons, International Journal of Approximate Reasoning, No. 21, PP. 215-231.
18. Higgs, G., 2006, Integrating Multi-Criteria Techniques with Geographical Information Systems in Waste Facility Location to Enhance Public Participation, Journal of Waste Management & Research, Vol. 24, PP. 105-117.
Hwang, C. L. and Yoon, K., 1981, Multiple Attributes Decision-Making Methods and Applications, Springer, Berlin.
M., Soltani A. and Helali A. 8.

Jankowski, P., 1995, Integrating Geographical Information Systems and Multiple Criteria Decision-Making Methods, International Journal of Geographic Information Systems, Vol. 9, No. 3, PP. 251-273.
21. Jiang, H. and Eastman, R. R., 2000, Application of Fuzzy Measures in Multi-Criteria Evaluation in GIS, International Journal of Geographic Information Systems, Vol. 14, No. 2, PP. 173-184.
22.Malczewski, J., 1999, GIS and Multi-Criteria Decision Analysis, John Wiley and Sons, New York.
23. Mian, S. A. and Christine, N. D., 1999, Decision-Making Over the Project Life Cycle: An Analytical Hierarchy Approach, Project Management Journal, Vol. 30, No. 1, PP. 40–52.
24. Olson, D. L., , Comparison of Weights in TOPSIS Models, Mathematical and Computer Modelling, Vol. 40, No. 7, PP. 721-727.
25. Parhizkar, A. and Ghafari, A., 2006, Geographic Information System and Multi- Criteria Evaluation, ASMI Publication, Tehran. (In Persian)
26. Qodousi, M, 2002, Yesterday, Today and Tomorrow Urban Parks, Shahriha (Municipalities) Publications, 551, PP. 11. (In Persian)
27. Sanayinezhad, S. H., 2002, An Introduction to Geographic Information Systems, Jihad e Daneshgahi Mashhad Publication, Mashhad. (In Persian)
28. Teshnelab, M., 1999, Fuzzy Systems and Fuzzy Control, khajeh Nassir Toosi University of Technology (KNTV) Publication, Tehran. (In Persian)
Van Laarhoven, P. J. M. and Pedrycz, W., 1983, Fuzzy Extension for Saaty’s Priority Theory, Fuzzy Sets and Systems, No. 11, PP. 229-241.
2001