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Research on the structure evaluation of regional tourism industry and information technology platform based on PCA method.

1. Introduction

Because of overseas tourism industry structure which adjustment and optimization was more affected by the market mechanism, without the specific research on the tourism industry structure, there are few analysis which were specific for tourism industry structure, the research also mainly concentrated on the structure of tourism industry division and other basic theory problems(Chen, 2002; Abreu, 2015). Although the domestic research on tourism industry structure had great development, which only involved part of the industrial structure, the analysis was not enough comprehensive and system, in terms of countermeasures was focusing on the macroscopic, lacked of practicality. In order to improve the structure of the tourism industry, we must research what the factors influence the structure of tourism industry change, clearly differentiate the pros and cons of tourism industrial structure. Experts and scholars have different views about the structure of tourism industry (Gossling, 2004; Hu, 2008). Due to the structure of tourism industry research started late, the research of tourism industry structure and division haven't unified standard at present, moreover the range of tourism industry structure is relatively complex, and the factors affecting the tourism industry structure optimization is also more complicated. From what has been discussed about the research results to see, the author thinks that the factors affecting the tourism industry structure optimization include two aspects, external factors and internal factors (Shi, 1999; Meng, 2007).

The internal factors that affect the tourism industry structure optimization are diverse. The author thinks that the internal factors include the supply and demand factors. There are specific factors for tourism resources and geographical factors, human resources, capital supply factors, science and technology progress and tourism market factors and consumer demand factors (Wang, 2007; Zhou, 2008). Factors of tourism resources is the basis of defining the structure of tourism industry, tourism resources are the material basis for the regional tourism survival and development, which determine the supply capacity of regional tourism industry. It is important for tourism resources quantity and quality of local tourism industry development and the development level of scale. Impacting factor of tourism resources is mainly in the tourism resource endowment, the composition of tourism resources, tourism resources development condition, etc. Tourism market factors such as tourism market attraction and market competitiveness. Tourism resources through the planning and development of tourist market appeal of resident, when the developing of tourism industry can produce competitiveness. This is also the process of the tourism industry structure optimization. In addition, the investment and financing of tourism, tourism human resources supply, tourism, scientific and technological progress and other factors will be the more important impact on regional tourism industry structure optimization.

2. Evaluation system of tourism industrial structure

2.1. The principle and basis of the evaluation system

After the factors that affect the structure of the tourism industry have cleared, we need to establish a complete evaluation index system, in order to analyze and evaluate the degree of optimization of regional tourism industry structure. Since the content of the evaluation system is not readily available, and at the same time, the relevant data is not easy to get, therefore, the study should follow the relevant principles. Besides, index should focus on effectiveness and have some certain guidance function to the formulation of tourism related policies. This article mainly refers to the research results of the industrial structure optimization and tourism industry structure by the relevant experts and scholars when building evaluation index system, on the basis of analyzing the influence factors of optimization of tourism industry system, according to the available official authority data to analyze and research, such as, statistical yearbook, tourism yearbook and social economic yearbook.

2.2. Tourist system establishment

Since the tourism industry structure research is still in the initial start-up, only few experts from home and abroad research tourism industry structure to construct the evaluation index system model. Existing research on evaluation of the industrial structure of tourism mainly uses tourism industrial structure of productivity analysis, deviation--share analysis method, grey correlation analysis method, the input-output analysis method, or calculates tourism industrial structure of the system entropy, diversity index, the index of industrial structure change, concentration, in order to evaluate the tourism industry structure or industrial structure optimization analysis. Based on the idea of these methods and ideas, but does not directly use these methods to study, but based on impact factor in optimizing the structure of the tourism industry, tourism industrial structure of evaluation index system is built to make, to study and analysis from another view.

On the choice of index factor, from the tourist industry economics and regional economics research perspective, based on the economics of the industry structure optimization theory, the evaluation research on industrial structure optimization and selection evaluation index factor. At present, the research on industrial structure mainly involves the index factors mainly include the overall Labor productivity, fixed assets, profits, profit, profit margins and taxes per capital, per capital taxes, operating income, etc., these indicators can reflect the efficiency of industrial structure, and is accurate statistical yearbook data can be checked, objective evaluation on the measure to optimize industrial structure. Selection based on the tourism industrial structure of evaluation index factor with reference to some indicators, starting from the factors of affect the tourism industry structure optimization, on the basis of consulting a large number of literature material, collect opinions of relevant experts and scholars, and established the evaluation index system of tourism industrial structure in the table 1.

3. Evaluation of the industrial structure of tourism model building

3.1. Evaluation principle and model

Principal component analysis is a mathematical thought and principle of dimension reduction, Find a few variables in numerous variables instead of the original variables, and these variables can be as much as possible the amount of information on behalf of the original variables, and variables are unrelated to each other. This will be more than one variable into a few of them had nothing to do with each other, the statistical analysis of comprehensive variables is known as principal component analysis and principal component analysis.

For a sample data, observation p a variable [x.sub.1], [x.sub.2], ... [x.sub.p], n data array for the sample:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

Principal component analysis is to p observation variable integrated into p a new variable (variables), namely:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)

The model was expressed by matrix: F = AX. Among them: A is called the coefficient matrix of main component.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)

3.2. Evaluation methods and steps

Which has been widely applied principal component analysis is a kind of research methods, especially in the field of medical and industrial, agriculture use more, but at present in the study of tourism industrial structure, using principal component analysis modeling analysis of the literature is not a lot. When evaluate the tourism industrial structure in Liaoning province, uses Principal Component Analysis modeling analysis, for the convenience, the expression of principal component analysis method and steps are easy to use. Mainly reflects in: articles in calculating the variance contribution rates of the principal component, the principal component factor score, on the basis of building comprehensive evaluation model of the structure of the tourism industry, calculate the comprehensive factor score.

Assumes that the matrix of the sample data:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)

Step 1: standardizing the original data:

[x.sup.*.sub.ij] = [[[x.sub.ij] - [[bar.x].sub.j]]/[square root of var([x.sub.j])]] (i = 1,2, ..., n; j = 1,2, ..., p) (7)

Among them:

[bar.x] = [1/n] [n.summation over (i=1)] [x.sub.ij]; var([x.sub.j]) = [1/[n - 1]] [n.summation over (i=1)] ([x.sub.ij] = 1,2, ..., p) (8)

Step 2: calculate the sample correlation coefficient matrix and characteristic value. After the standardization of data correlation coefficient matrix of the matrix R:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9)

In order to study the convenient assumption is that after the standardization of original data is expressed with x, after standardized treatment of the data correlation coefficient is:

[r.sub.ij] = [1/[n - 1]] [n.summation over (t=1)] [x.sub.ti][x.sub.ij](i,j j - 1,2, ..., p) (10)

Step 3: calculating the variance contribution rates of the principal component and factor loading matrix. In general, main component is mainly based on the contribution rate of each principal component cumulative size, which is the size of a certain proportion of total eigenvalue, generally require the cumulative contribution rate of 85% or more, namely: [m.summation over (i=1)][lambda][).sub.i]([p.summation over (i=1)][[lambda].sub.i.sup.-1] [greater than or equal to] 85%.

Contribution: [b.sub.k] = [lambda][).sub.i]([p.summation over (i=1)][[lambda].sub.i.sup.-1], bk represents the first k of the variance contribution rates of the principal component;

The cumulative contribution rate: b = [m.summation over (i=1)][lambda][).sub.i]([p.summation over (i=1)][[lambda].sub.i.sup.-1] On behalf of former m b of the cumulated variance contribution ratio of the principal components.

Step 4: calculate principal component factor score. By using SPSS software to do rotation to get great variance factor regression coefficient [W.sub.j], formula is as follows:

[W.sub.j] = [lambda][).sub.j]([p.summation over (i=1)][[lambda].sub.i.sup.-1] (11)

According to factor regression coefficient w construction evaluation factor model, principal component factor score is calculated respectively and the [Z.sub.ik], evaluation factor model is as follows:

[Z.sub.ik] = [n.summation] over (j) [W.sup.j][X.sup.ij] (12)

Step 5: building a comprehensive evaluation model, calculate the comprehensive factor score.With each principal component factor variance contribution as the weight, build a comprehensive evaluation model, calculate the comprehensive factor score Fi, make Vk factor variance contribution rate of main component, the comprehensive factor score formula is:

[F.sub.i] = [SIGMA] [Z.sub.ik][V.sub.k] (13)

In the evaluation of regional tourism industry structure, the comprehensive evaluation model was constructed according to the index factor, regional comprehensive factor score calculation.

4. Comprehensive evaluation

In establishing the evaluation index system and through simple calculation processing and finishing after, on the basis of the original index data, using spsssi7.0 software to carry on the principal component analysis. Liaoning province tourism industry structure around the city calculated by using SPSS17.0; 19 index factor correlation coefficient matrix, the correlation coefficient matrix can be seen that many direct correlation between variables. Based on the data of boundless toughened factor rotation, to the fourth power solution (Quartimax) as a factor to rotate, calculates the characteristic value of the principal component, the contribution rate and the cumulative contribution rate, and to determine the number of principal components factor.

By the above-mentioned table 2, the cumulative variance contribution ratio of the first three principal components was 84.796%, the first four principal components of the cumulative variance contribution rate has reached 90.259%, because when the principal component analysis of generally take the cumulative contribution rate above 85%, which might explain the most variable information, choose the first four principal components. Among them, the first principal component characteristic value was 12.179, explained 64.1% of the total variance of 19 original variables. At the same time, the factor loading matrix can be calculated,in order to explain the factor of the principal component. In order to more clearly see the meaning of the main factors, can be controlled by the fourth powersolution out of the rotating factor loading matrix (table 3).

According to the relevant data, factor loading matrix generation into the formula, can calculate weight of the principal component factor, specific calculation is as follows:

[W.sub.1] = [[lambda].sub.1])([4.summation over (i=1)][[lambda].sub.i.sup.-1] = 12.179/(12.179+2.13+1.802+1.038)=0.71

[W.sub.2] = [lambda][).sub.2]([4.summation over (i=1)][[lambda].sub.i.sup.-1] = 2.13/(12.179+2.13+1.802+1.038)=0.12

[W.sub.3] = [lambda][).sub.3]([4.summation over (i=1)][[lambda].sub.i.sup.-1] = 1.802/(12.179+2.13+1.802+1.038)=0.11

[W.sub.4] = [lambda][).sub.4]([4.summation over (i=1)][[lambda].sub.i.sup.-1] =1.038/(12.179+2.13+1.802+1.038)=0.06

After finishing can be calculated, the eigenvalues of the first four principal components lambda, variance contribution rate, cumulative contribution rate, the factor weights W correlation value shown in the following table 4.

Then, 14 cities of liaoning province indicators to evaluate the principal component scores of value, the first thing you need to use SPSS17.0 to calculate the factor score correlation coefficient matrix, based on principal component score coefficient factor model function of expression can write the following:

Y1=0.08X1+0.049X2+0.041X3+ ... +0.085X18-0.042X19

Y2=-0.072X1+0.13X2+0.028X3+ ... -0.053X18-0.226X19

Y3=-0.001X1-0.055X2-0.14X3+ ... +0.014X18+0.133X19

Y4=-0.107X1+0.428X2+0.502X3+ ... -0.173X18+0.395X19

Among them, Yn values of main component, Xn matrix for standardization. 14 cities of liaoning province principal component score value of index factor which is a standardized evaluation index system of the product of matrix and principal component score coefficient matrix, mathematical expressions for the:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (14)

The evaluation index system of matrix after for standardization:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (15)

Main component score coefficient matrix; The data in the study of the p value is 19, n value of 14 m value is 4.Using the above data and formula calculation, evaluation factor principal component score values around the city in liaoning province, and is verified by SPSS17.0 obtained data. In 14 cities of liaoning province evaluation Factor, principal component analysis can be seen, dalian and shenyang city is on the first principal component Factor (1) the scores of 2.3745 and 2.1042, respectively, the top two, namely, dalian and shenyang in "regional GDP, the proportion of urban population, urban residents per capita disposable income of urban residents, the per capita consumer spending, more than three a-level scenic area ownership, hotel number, domestic tourism, inbound tourism market share, market share, the original value of fixed assets of star-rated hotels, hotel workers, the annual average number, regional tourism revenue and receives the total number of tourism, tourist foreign exchange income, total hotel revenue" on indicators such has the absolute advantage.

In addition to the shenyang and dalian, dandong and benxi city is on the first principal component Factor (1) scores also is positive, that is higher than the entire province average level, the Factor of the load Factor l has a good advantage, the rest of the ten cities on the Factor l value is negative, in tourism resource structure, market share, tourism income lagged behind the four cities, especially in fuxin, the Factor scores on the l value is 0.9769, the tourism industry economic structure also there is a big problem. Dandong file, fushun, huludao, benxi, liaoyang, six city on the second principal component Factor (2) the value greater than 0, suggests six city "tourism revenues accounted for the proportion of the tertiary industry, the tourism industry location 0entropy" index data is higher than the entire province average level, especially the value of 2.1113 dandong in Factor 2, show that the tourism industry status is higher. Tieling, anshan, panjin, benxi, dandong and dalian city on the third principal component Factor (3) the value is positive, that six city "the overall Labour productivity, per capita taxes" index data is higher than the entire province average level, indicates that the tourism industry per capita contribution to local economic development. Panjin, benxi, anshan, yingkou, jinzhou city, on the fourth principal component Factor (4) value is positive, that five "average guest room occupancy rate index data is higher than the entire province average level, pertaining to the five tourist resources utilization level is higher.

Comprehensive evaluation of the tourism industry structure, also need the multi-target weighted comprehensive evaluation model is set up. On the basis of above research, the establishment of the tourism industrial structure of principal component comprehensive evaluation model is as follows:

F = [W.sub.1]*[Y.sub.1]+[W.sub.2]*[Y.sub.2] + ... +[W.sub.m]*[Y.sub.m]

Among them, the weight of factors of main component, main component score values. According to establishes a comprehensive evaluation model of principal component, the 14 cities of liaoning province tourism industry structure can be calculated value, and the sorting according to the comprehensive evaluation value, can see clear 14 cities of liaoning province tourism degree of level and optimize the industrial structure, the specific data results as shown in table 6.

5. Conclusion

According to establishes a comprehensive evaluation model of principal component, the 14 cities of Liaoning province tourism industry structure can be calculated value, and the sorting according to the comprehensive evaluation value, can see clear 14 cities of Liaoning province tourism degree of level and optimize the industrial structure, the specific data results as shown in table 6. Specific view, Dalian city tourism industrial structure comprehensive score values were 1.6392, ranked first, which, in the first principal component Factor (l) and the third principal component scores on the Factor (3) is positive, 2.37 and 0.37, respectively, in the second principal component Factor (2) and the third principal component Factor (4) the score on the negative, 0.49 and 0.48, respectively, showed that the local tourism industry has good economic structure, the tourism industry to the local economy contribution rate is bigger, but the tourism industry status needs to be improved.

Tourism industrial structure in Shenyang comprehensive score values were 1.3883, in the second place, tourism industry economic level is higher, but the tourism industry in the region's economic status is not high, location entropy is low, the tourism industry accounted for the proportion of the tertiary industry is lesser, tourism resources utilization and industrial structure benefit remains to be improved, it is necessary to further optimize the tourism industry. Dandong city tourism industry as a whole structure score in third place, the tourism industry accounted for the proportion of the tertiary industry is larger, the highest location entropy of provincial tourism industry, tourism industry in the region's economic status is very high. Benxi on four principal component factor method, numerical and the comprehensive score of the positive and show that the local tourism industry has good economic structure, the tourism industry status is higher, the tourism industry to the local economy contribution rate is bigger, tourism resources utilization and industrial structure benefit is higher than the entire province average level, the tourism industry structure is good, high degree of optimization. Chaoyang and Fuxin city in four principal component method, numerical and comprehensive scores of the factors on the negative, indicates that the two cities of the tourism industry economic structure remains to be improved, the two cities of the tourism industry status is not high, small contribution to the local economy, the tourism industry tourism resources utilization and industrial structure benefit are lower than the entire province average level, the tourism industrial structure problems, need to optimize industrial structure.

Acknowledgments

This study was financially supported by Humanities and social sciences research key project of Anhui College "Evaluation and utilization of wetland tourism resources in Northern Anhui" (SK2015A565); Anhui Province Department of education "teacher studio"(2013jxms083); Key project of Humanities and social sciences of Anhui Provincial Department of Education "The study area tourism development under the background of urbanization in Wanbei Coal Mining subsidence" (No: SK2016A1003).

Recebido/Submission: 06/04/2016

Aceitacao/Acceptance: 08/05/2016

References

Abreu, A., Rocha, A., Cota, M. P., & Carvalho, J. V. (2015). Caderneta Eletronica no Processo Ensino-Aprendizagem: Visao de Professores e Pais de alunos do ensino Basico e Secundario. RISTI--Revista Iberica de Sistemas e Tecnologias de Informacao, (16), 108-128.

Chen, J. and Yang, Z. (2002). The Discussion about optimization and adjustment of tourism industrial structure in our country. Yunnan Institute of Nationality Journal, 19, 4-11.

Gossling, S. (2004). Sustainable tourism competitiveness clusters: application to world heritage sites network development in Indonesia. Asia Pacific Journal of Tourism Research, 9, 293-307.

Hu, Y. (2008). The current situation and the optimization of International Tourism industrial structure about Tianjin province. Harbin University of Commerce journal, 9, 111-115.

Meng, T. (2007). Quantitative analysis and optimum research of Tourism industrial structure's adjustment in Fujian province. Fujian Normal University's master's thesis, 5, 50-55.

Shi, P. (1999). Initial exploration on evaluation methods about tourism industrial structure. Northwest University Journal, 29, 85-89.

Wang, H. and Zhang, J. (2007). The evaluation and optimizing analysis about Tourism industrial structure of Huangshan province. East China Economic Management, 1, 12-15.

Zhou, M. (2008). Tourism industrial structure's adjustment and optimization thinking in Hunan. Xiangtan normal college journal, 5, 43-44.

Long Li, Ling Wu *

* wuling2021@163.com

Suzhou University, Suzhou city, Anhui province, 234000, China
Table 1--The evaluation index system of tourism industrial structure

Index type        code      Selection index

Regional          Index 1   Regional GDP
economy           Index2    The urban population proportion
                  Index3    In urban residents per capita disposable
                               income
                  Index4    In urban residents per capita consumer
                               spending

tourist           Index5    More than three a-level scenic area
resources                      ownership
structure         Index6    Number of hotel

tourism           Index7    Domestic tourism market share
market            Index8    Inbound tourism market share
structure

Tourism           Index9    The original value of fixed assets of the
investment                     star hotels
structure         Index10   Annual average number of workers in hotel

The tourism       Index11   Regional tourism revenue
industry          Index12   Hospitality tourism total number
scale             Index13   Tourism revenues accounted for the
                               proportion of the tertiary industry
                  Index14   International Tourism Receipts

specialization    Index15   Tourism industry location entropy
level             Index16   overall labor productivity

Tourism           Index17   Per capita taxes
industrial        Index18   Total hotel revenue
structure         Index19   Average room occupancy
benefit

Index type        code      Units

Regional          Index 1   100 million yuan
economy           Index2    %
                  Index3    RMB
                  Index4    RMB

tourist           Index5    individual
resources         Index6    individual
structure

tourism           Index7    %
market            Index8    %
structure

Tourism           Index9    Thousand yuan
investment        Index10   individual
structure

The tourism       Index11   100 million yuan
industry          Index12   10thousand persontime
scale             Index13   %
                  Index14   10thousand dollars

specialization    Index15
level             Index16   Thousand yuan/one person

Tourism           Index17   Thousand yuan/one person
industrial        Index18   Thousand yuan
structure         Index19   %
benefit

Table 2--Total Variance Explained

          The initial eigenvalue

element   total    variance%   accumulation %

1         12.179   64.100      64.100
2         2.130    11.213      75.313
3         1.802    9.484       84.796
4         1.038    5.462       90.259
5         .609     3.204       93.462
6         .530     2.787       96.249
7         .370     1.947       98.197
8         .166     .873        99.069
9         .078     .412        99.482
10        .047     .249        99.730
11        .041     .215        99.945
12        .007     .038        99.983
13        .003     .017        100.00
14        .000     .000        100.00
15        .000     .000        100.00
16        .000     .000        100.00
17        .000     .000        100.00
18        .000     .000        100.00
19        .000     .000        100.00

          Extraction of sum of squares loaded

element   total    variance%   accumulation %

1         12.179   64.100      64.100
2         2.130    11.213      75.313
3         1.802    9.484       84.796
4         1.038    5.462       90.259
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

          Rotate the of sum squares loaded

element   total    variance %   accumulation %

1         11.867   62.460       62.460
2         2.272    11.957       74.417
3         1.819    9.571        83.988
4         1.191    6.271        90.259
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

Table 3--Rotated Component Matrixa

                                                           Component

Factor                                                    1        2

Reginal GDP                                             .961     -.241
The proportion of urban population                      .683     .147
In urban residents per capita diaposable income         .674     -.083
In urban residents per capita consumer spending         .916     -.154
More than three a-level scenic area ownership           .902     .123
Number of star hotel                                    .939     -.063
Domestic tourism market share                           .933     .089
Inbound tourism market share                            .904     .037
The original value of fixed assets of the star hotels   .959     -.198
Annual average number of workers in hotel               .913     -.251
Regional tourism revenue                                .993     .057
Hospitality tourism total number                        .937     .088
Tourism revenues accounted for the proportion of        -.362    .884
   the tertiary industry
Tourist foreign exchange income                         .924     .006
Tourism industry location entropy                       -.209    .945
Member of the labor productivity                        .263     -.036
Per capita taxes                                        .353     .042
Total hotel revenue                                     .957     -.190
Average room occupancy                                  .205     -.553

                                                           Component

Factor                                                     3        4

Reginal GDP                                             .049     -.054
The proportion of urban population                      .075     .502
In urban residents per capita diaposable income         -.061    .578
In urban residents per capita consumer spending         .072     .329
More than three a-level scenic area ownership           .032     -.223
Number of star hotel                                    .072     -.154
Domestic tourism market share                           .030     .029
Inbound tourism market share                            .117     .173
The original value of fixed assets of the star hotels   .028     -.133
Annual average number of workers in hotel               -.220    -.099
Regional tourism revenue                                .050     -.042
Hospitality tourism total number                        .032     .032
Tourism revenues accounted for the proportion of        .103     .051
   the tertiary industry
Tourist foreign exchange income                         .113     .139
Tourism industry location entropy                       -.017    -.030
Member of the labor productivity                        .908     -.091
Per capita taxes                                        .874     .182
Total hotel revenue                                     .060     -.129
Average room occupancy                                  .335     .528

Table 4--Principal component weight

The principal     Characteristic   The variane
components(F)     root value(X)    contribution rate(%)

The first         12.18            64.10
The second        2.13             11.21
The third         1.80             9.48
The fourth        1.04             5-46

The principal     Accumulation           The factor
components(F)     contribution rate(%)   weights(W)

The first         64.10                  0.71
The second        75.31                  0.12
The third         84.80                  0.11
The fourth        90.26                  0.06

Table 5--Component Score Coefficient Matrix

                                             Component

Factor                              1       2       3       4

Regional GDP                        .080    -.072   -.001   -.107

The proportion of urban             .049    .130    -.055   .428
population

In urban residents per capita       .041    .028    -.140   .502
diaposable income

In urban residents per capita       .064    -.011   -.038   .245
consumer spending

More than three a-level scenic      .099    .087    .006    -.234
area ownership

Number of star hotel                .089    .004    .023    -.190

Domestic tourism market share       .091    .087    -.029   -.008

Inbound tourism market share        .076    .068    .006    .109

The original value of fixed         .086    -.056   -.005   -.173
assets of the star hotels

Annual average number of workers    .089    -.076   -.154   -.111
in hotel

Regional tourism revenue            .097    .070    -.010   -.080

Hospitality tourism total number    .091    .087    -.028   -.006

Tourism revenues accounted for      .001    .400    .053    .098
the proportion of the tertiary
industry

Tourist foreign exchange income     .078    .052    .008    .076

Tourism industry location           .027    .434    -.016   .038
entropy

Member of the labor productivity    -.014   -.028   .543    -.214

Per capita taxes                    -.010   .030    .481    .039

Total hotel revenue                 .085    -.053   .014    -.173

Average room occupancy              -.042   -.226   .133    .395

Table 6--Comprehensive evaluation value

Region       Factor l   Factor 2   Factor 3

Shenyang      2.10      -0.46      -0.32
Dalian        2-37      -0.49       0.37
Anshan       -0.06      -0.85       0.79
Fushun       -0.10       1.27      -0.79
Benxi         0.01       0.87       0.56
Dandong       0.07       2.11       0.58
Jinzhou      -0.20      -0.67      -2.25
Yinkou       -0.51      -0.87      -0.54
Fuxin        -0.98      -1.33      -0.42
Liaoyang     -0.48       0.35      -0.27
Panmian      -0.28       0.07       0.68
Tieling      -0.76      -0.93       2.13
Chaoyang     -0.77      -0.10      -0.02
Hu ludao     -0.42       1.01      -0.50

Region       Factor 4   Comprehensive   sorting
                        index

Shenyang     -0.26       1.3883          2
Dalian       -0.48       1.6392          1
Anshan        0.91       0.0002          6
Fushun       -0.59      -0.0426          7
Benxi         1.57       0.2679          4
Dandong      -0.41       0.3440          3
Jinzhou       0.21      -0.4570         10
Yinkou        0.61      -0.4919         12
Fuxin        -0.23      -0.9136         14
Liaoyang     -0.12      -0.3339          9
Panmian       2.03       0.0064          5
Tieling      -1.26      -0.4910         11
Chaoyang     -1.58      -0.6589         13
Hu ludao     -0.40      -0.2571          8
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Date:Aug 1, 2016
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