Implementation of AHP-TAXONOMY model presented in GIS

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: m_salimi_3@yahoo.com
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