Spatial geographic information variance analysis in Anhui Province
- https://doi.org/10.2991/icence-16.2016.105How to use a DOI?
- Geographic Information, Industrial Competitiveness, Principal Component Analysis
Regional industry competition is a momentous aspect of regional geographic information engineering. It is quite vital to investigate industrial competitiveness in both theoretical and practical fields. In this paper, the principle and procedure of principal component analysis (PCA) were respectively introduced in detail. The empirical study and comparative analysis of the regional economic competitiveness was conducted by setting some economic indicators. At the same time, SPSS software was applied to extract available geographic information elements which indirectly reflected the enhancement of industrial competitiveness. By analyzing and comparing the economic development of the 16 cities during 2013, we sought out the primary geographic elements affecting the growth trend, and then calculated the overall score of each city in Anhui Province according to comprehensive evaluation model. The results of spatial analysis indicated that the overall score was consistent with each city's economic strength. The analysis in this paper certainly makes sense to provide theoretical reference and practical value for overall economic strength and sustainable regional construction.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jie Zhu AU - Zhanqiang Chang AU - Xiaomeng Liu AU - Wen Yu AU - Wei Wang PY - 2016/09 DA - 2016/09 TI - Spatial geographic information variance analysis in Anhui Province BT - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016) PB - Atlantis Press SP - 552 EP - 557 SN - 2352-538X UR - https://doi.org/10.2991/icence-16.2016.105 DO - https://doi.org/10.2991/icence-16.2016.105 ID - Zhu2016/09 ER -