Evaluation and classification of residential greenbelt quality based on factor analysis &amp; clustering analysis: An example of Xinxiang City, China

Qiao, Lifang; Zhang, Yichuan; Qi, Anguo

doi:10.1007/s11676-008-0056-x

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Factor analysis method has the characteristics of finding a few comprehensive factors from multiple observation variables to explain the original data, which is helpful to objectively and effectively determine the weight of comprehensive indicators, and has good objectivity. It can fundamentally remove the influence of the correlation between indicators, and is suitable for the comprehensive evaluation of the object system with correlation between evaluation indicators [ 42 ]. Factor analysis also has shortcomings.…”

Section: Research Methods and Evaluation Indicatorsmentioning

confidence: 99%

Study on the influence of COVID-19 on the growth of China’s small and medium-sized construction enterprises

et al. 2022

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The outbreak of COVID-19 at the beginning of 2020 had a significant impact on China’s economy, society, and citizens; it also had a negative impact on the development of the construction industry. In particular, small and medium-sized construction enterprises with low ability to withstand risk have been strongly impacted, aggravating a crisis of survival among these firms. The focus of this study is to analyze the impact of COVID-19 on the growth of small and medium-sized construction companies. Based on the characteristics of small and medium-sized construction enterprises, this paper establishes a growth evaluation index and builds a growth evaluation model based on factor analysis. Twenty-three construction enterprises listed on small and medium-sized enterprises board are selected as samples, and the quarterly data of 2019 and 2020 are used for empirical analysis. The results show that the epidemic has had a high short-term impact on construction enterprises, and the total output value of the construction industry in the first quarter of 2020 was 16% lower than that in the same period of last year. In the long run, the impact of the epidemic on the growth of small and medium-sized construction enterprises has been limited. In the first quarter of 2020, the growth score of enterprises decreased by only 1.95% year-over-year, and it was essentially flat in the second and third quarters. The epidemic has had little influence on solvency, tangible resources or intangible resources but a high short term influence on profitability, capital expansion and market expectations. The long-term impact is small; It is conducive to the improvement of enterprise operation ability. Finally, to both address the influence of the COVID-19 on small and medium-sized construction enterprises and to realize their transformation and upgrading, targeted suggestions are offered at the policy and enterprise levels. The results will help to understand the impact of the epidemic on the growth of construction enterprises, and provide decision support for the healthy and orderly development of the follow-up construction industry.

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Section: Research Methods and Evaluation Indicatorsmentioning

confidence: 99%

Study on the influence of COVID-19 on the growth of China’s small and medium-sized construction enterprises

et al. 2022

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“…However, the selection of the clustering centers for this algorithm is still random. Qiao et al [3] first ranked the affinities of the variables, and then aggregated the observations one by one to classify the standardized variables by a fast clustering method. However, it is difficult to extend the clustering algorithm to high dimensions in the validation of functional and qualitative clustering.…”

Section: Related Workmentioning

confidence: 99%

A novel high-dimensional clustering algorithm and its application in financial data forecasting

Qian,

Du,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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The purpose of this paper is to study the nonlinear characteristics of stock market price rise and fall, and optimize the characteristics of high-dimensional data for the challenges brought by the sparsity and complexity of high-dimensional data to the traditional clustering algorithms. Explore the impact of the multidimensional characteristics and sparsity of stock data on clustering analysis, and propose a high-dimensional clustering algorithm based on Pareto-optimal Steiner points. Traditional clustering algorithms are deficient in dealing with nonlinear data and spatial clustering, while randomly selected clustering centers are easily interfered by market noise, which affects the accuracy of clustering results. In this paper, more accurate clustering results are achieved by selecting Steiner points with Pareto optimality as clustering centers. And the algorithm is applied to the analysis of the stock market, which can effectively help investors choose the appropriate investment portfolio and has important practical application value. Meanwhile, the distance matrix is constructed as the basis of clustering analysis through the preprocessing of stock data and distance metric. The innovation of this paper is that the Steiner points can better represent the center of the clusters thus effectively reducing the computational complexity and storage overhead and improving the efficiency of the algorithm. And the principle of Pareto optimality is applied to the high-dimensional clustering algorithm, which improves the accuracy of stock market analysis.

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“…The purpose is to reduce multiple variables to a lesser number of underlying factors that are measured by the variables. Factors are formed by grouping the variables that have a correlation with each other [5][6][7] . Factor analysis is a statistical method based on the correlation analysis of multi-variables, which is used to simplify the mass indexes into fewer factors so as to express the relationship between original variables and the factors briefly.…”

Section: Factor Analysismentioning

confidence: 99%

Evaluation and Classification of Black Soil Structure Degradation in Jilin by Factor Analysis and Dynamic Clustering Analysis

Niu

Wang

Zhou

et al. 2011

AMR

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The black soil in Jilin Province, famous for providing significant amount of commodity grain to entire China, had already begun to regress to disappear on different degrees. To study the structure degradation of black soil in Jilin Province, We used SPSS and matlab programming on data information processing, thirty indexes were chosen, factor analysis on twenty-three samples from different sites and depths in Jilin Province was carried out, and dynamic clustering analysis taking the score of main factor as the variable was performed in turn. Combining them together, as a result, then the degradation mechanism was evaluated and classified. The results showed that eight factors from thirty indexes were found as the principal factors by factor analysis. By dynamic clustering analysis, the areas where twenty-three samples came from could be classified into three types: high degradation, medium degradation and light degradation. Finally, the differences among index scores were analyzed respectively, and reasonable suggested measures to prevent the black soil degradation were stated.

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Evaluation and classification of residential greenbelt quality based on factor analysis & clustering analysis: An example of Xinxiang City, China

Cited by 7 publications

References 6 publications

Study on the influence of COVID-19 on the growth of China’s small and medium-sized construction enterprises

Study on the influence of COVID-19 on the growth of China’s small and medium-sized construction enterprises

A novel high-dimensional clustering algorithm and its application in financial data forecasting

Evaluation and Classification of Black Soil Structure Degradation in Jilin by Factor Analysis and Dynamic Clustering Analysis

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