كليدواژه :
تجميع شاخصها , توسعه منطقهاي , روشهاي تحليل چند شاخصه , شاخصهاي توسعه , شاخص تركيبي توسعه منطقهاي
چكيده لاتين :
  Extended
abstract Introduction A composite indicator refers to an index
derived from some specific individual indicators for measuring the aggregated
performance of a multi-dimensional issue. A major problem for constructing the composite
indicator of development is the determination of an appropriate aggregating
method to combine multi-dimensional development indicators into an overall
index. Therefore, strongly, the usefulness of a composite indicator depends
heavily on the underlying construction scheme. Â Construction on a composite indicator involves
the definition of study scope, selection of underlying variables, data
collection and preprocessing, data weighting and aggregation, and post analysis
of the composite indicator derived, among which data weighting and aggregation
has been an interesting but controversial topic (Esty et al. 2005). At the
stage of data (weighting and) aggregation, there are two major families of
operations research methods, namely data envelopment analysis (DEA) and
multiple criteria decision analysis (MCDA), which have recently received much
attention in composite indicator construction (Zhou and Ang 2008). While there are a large number of alternative
methods for constructing composite indicator,
none could be regarded as a âsuper methodâ
suitable for all cases. As a result, researchers
have developed a large number of criteria, such as theoretical foundation, understandability, ease of use and validity, which are helpful to
analysts for the selection of an
appropriate method for constructing composite indicator. Zhou et al. (2006)
introduced a novel criterion
ââinformation lossââ and developed an objective measure called the
Shannon-Spearman measure (SSM) for comparing aggregation methods in
constructing composite indicator. This research investigates the effectiveness of the Shannon-Spearman
measure (Zhou and Ang, 2009) and compares several popular methods in
constructing composite indicator.  Methodology This research aimed to
compare aggregating methods for constructing the composite index of regional
development. There are many criteria for comparing aggregating methods of composite indicator. Based on the information loss concept this research
was used the Shannon-Spearman measure (SSM), Spearman correlation coefficient
and Coefficient of Variation (CV) to compare alternative aggregation methods in
constructing composite
indicator. Using the proposed measure,
five popular methods in constructing composite indicator (SAW, TOPSIS, revised PCA, Taxonomy and revised
Optimum Deviation) are evaluated and compared through an empirical study based
on information from 17 indicators of economic development for 31 Iranian
provinces. To collect data, census of 20011 and yearbook in different time was
used.  Discussion Since
different sub-indicators are likely to have different measurement units,
normalization is usually done before data aggregation. Weight of individual
indicators exploited from Antropy-Shanon and PCA techniques. The five aggregations methods are applied to
the 31 Iranian provinces based on 17 individual economic indicators. Results demonstrated that different
aggregation methods tend to give different composite indicator and even different ranking orders.
However Tehran, Khozestan, Esfahan and Razavi Khorasan are considered by almost
most the aggregation methods as better performers in economic development.
Moreover North and South Khorasan, Sistan and Baluchestan, Lorestan, Ardabil
and Chaharmahal Bakhtiari are ranked in least position by almost most the
aggregation methods. We shall apply the Shannon-Spearman measure to evaluate
the five alternative methods in constructing composite indicator in a more comprehensive way. Results
of calculated the Shannon-Spearman measure revealed that TOPSIS method has the
smallest value while the Taxonomy and Optimum Deviation method have the largest
the Shannon-Spearman measure value. This indicates that the TOPSIS method is
the most appropriate with regards to the information loss criterion. In
contrast, the Taxonomy and Optimum Deviation method may not be a good choice
because it often results in the maximum loss of information. It is also found
that except TOPSIS, other four methods are highly correlated with each other.
This indicates that different methods are complementary to each other. Based on
information from five composite indicator development level of province were
classified through cluster analysis. The spatial pattern is divided into six
clusters: Tehran (cluster 1-1), Khozestan (cluster 1-2), Esfahan (cluster 2-1)
and other province (cluster 2-2). Recent cluster include two sub-clusters sub-cluster
(2-2-1) contain 10 provinces and sub-cluster (2-2-2) contain 18 provinces which
were in low development level. These results indicate that the gap between the
economic development of provinces and has been emphasized in several studies. Conclusion In
recent years, different methods have been widely explored in constructing composite
indicators. A problem faced by researchers is to determine the most suitable
method to apply. Zhou et al. (2006) developed an empirical criterion called the
Shannon-Spearman measure for comparing alternative aggregation methods in
constructing composite indicators based on the concept of information loss.
This paper applies the Shannon-Spearman measure, spearman correlation
coefficient and coefficient of variation (CV) to assess the effectiveness of
the different method of constructing composite indicators in practice. Results demonstrates
that the Shannon-Spearman measure could be an effective measure for comparing
aggregation methods in constructing of composite indicators. The case studies presented show that the TOPSIS
method is the most appropriate with regards to the information loss criterion.
In contrast, the Taxonomy and revised Optimum Deviation method may not be a
good choice because it often results in the maximum loss of information. It is
also found that the different methods are complementary to each other. Â Some
recommendations are provided include: Decentralization policy and make more
attention to less developed province such Sistan and Baluchestan, Lorestan, Ardabil, Chaharmahal
Bakhtiari, South Khorasn and North Khorasan. Appling Shannon-Spearman measure in different
indicator and for other composite indicators methods in the future research
should be considered. The application
of quantitative methods more cautious should be considered and fitted with the regional
facts. Future
research is needed especially its sensitivity should be evaluated when the numbers
of compared units are changing. Â Moreover
it should be noted that different ranking caused from nature of individual
indicator, weighing method, normalization and aggregating methods. The results
of each method is complementary to other methods and simultaneously use and
compare them often a better guide for the assessment of regional development. As
well as other aspects of development through the use of socio-economic indicators
and measures proposed in this research is recommended in future works. Finally, this research is not able to judge
which method is the most appropriate since these results are obtained based on
a specific decision matrix. Key
words: Aggregating of
indicator, regional development, multi attribute decision making, indicators of
development
