Key Factors Analysis of Port Logistics Development Based on Principal Component Analysis

2012 ◽  
Vol 253-255 ◽  
pp. 1522-1526
Author(s):  
Shou Wen Ji ◽  
Ling Shan Zhao
Keyword(s):  
Principal Component Analysis ◽  
Condition Factor ◽  
Principal Component ◽  
Component Analysis ◽  
Key Factors ◽  
Factors Affecting ◽  
Factors Analysis ◽  
Port Logistics ◽  
Spss Software

The factors affecting the modern port logistics development have become more numerous and complex, which is hard to verify the key factors. Principal component analysis which is short for PCA is used to analyze these factors, aiming at finding out the key factors of them. Lianyungang Port was chosen for case study, and nine factors were selected to analyze. By applying PCA, combined with expert scoring and SPSS software, the cumulative contribution rate could be calculated and the principal component was determined. At last the infrastructure factor, collecting and distributing condition factor, information factor and policy factor are found out as key factors. It provides a basis for Lianyungang Port’s planning and policy-making.

scholarly journals Low-carbon Development of Urban Logistics Based on Principal Component Analysis —A Case Study of Lanzhou City

2016 ◽  
Vol 8 (1) ◽  
pp. 73 ◽  
Author(s):  
Huifang Bai ◽  
Kedong Tan ◽  
Kaishun Huang ◽  
Qian Guo
Keyword(s):  
Principal Component Analysis ◽  
Carbon Emission ◽  
Principal Component ◽  
Component Analysis ◽  
Low Carbon ◽  
Key Factors ◽  
Low Carbon Economy ◽  
Lanzhou City ◽  
The Impact

The Low-carbon economy is a sustainable economic model advocated by many countries all over the world. Urban logistics, as an important source of urban environmental pollution, is in an urgent demand for the development. This paper takes Lanzhou City as the research object to find out the key factors of urban logistics carbon emissions through the construction of carbon emission index system, the principal component analysis of the impact indicators from 2005 to 2014. And by evaluating the carbon emission level of logistics in Lanzhou City, it provides the corresponding solutions for the low carbon logistics development in Lanzhou City.

Application of the principal component analysis on geochemical data: a case study in the basement complex of Southern Ilesa area, Nigeria

2010 ◽  
Vol 4 (1-2) ◽  
pp. 239-247 ◽  
Author(s):  
Emmanuel A. Ariyibi ◽  
Samuel L. Folami ◽  
Bankole D. Ako ◽  
Taye R. Ajayi ◽  
Adebowale O. Adelusi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Geochemical Data ◽  
Basement Complex

scholarly journals Research on key influence factors of creativity development of applied undergraduates based on PCA

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Xiangmin Ren ◽  
Jingwei Yu
Keyword(s):  
Principal Component Analysis ◽  
Influence Factors ◽  
Principal Component ◽  
Heilongjiang Province ◽  
Key Factors ◽  
Factors Affecting ◽  
The Core ◽  
Creativity Development ◽  
Teachers And Students ◽  
Core Characteristics

Abstract Creativity is one of the core characteristics of talent; for this reason, the creativity development of applied undergraduates should be one of the basic components of their education. This article gives an overview of the meaning of the creativity of applied undergraduates and makes a literature knowledge-mining and expert investigation on the factors affecting the creativity development. We obtained more than 100 influencing factors, filtered out the duplicative factors, and formed the remaining factors into a questionnaire. A survey was conducted among 1460 teachers and students of some applied undergraduates in Heilongjiang Province. By using principal component analysis (PCA) to analyse the questionnaire, the key factors that affect the creativity development of applied undergraduates are obtained, and the key factors are systematically analysed. According to the results of the analysis, the specific ways and methods of the creativity development of applied undergraduates are put forward.

scholarly journals Groundwater Quality Assessment: An Improved Approach to K-Means Clustering, Principal Component Analysis and Spatial Analysis: A Case Study

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 437 ◽  
Author(s):  
Ana Marín Celestino ◽  
Diego Martínez Cruz ◽  
Elena Otazo Sánchez ◽  
Francisco Gavi Reyes ◽  
David Vásquez Soto
Keyword(s):  
Principal Component Analysis ◽  
Spatial Analysis ◽  
Groundwater Quality ◽  
Quality Assessment ◽  
Principal Component ◽  
Component Analysis ◽  
Groundwater Quality Assessment
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