Improving the Basics of GIS Students’ Specialism by Means of Application of ESDA Method

University education should highlight the cultivation of students’ capability to make them qualified for meeting the social market requirements. University students, who major in GIS, in addition to abilities and qualities equipped generally, are supposed to have abilities in GIS basic theory and cutting-edge technologies, GIS software operation and data collection and processing, spatial data modeling and analysis, independent learning as well as GIS scientific research and innovation. The paper takes the spatial analysis of Fujian telecommunications consumption data as the example, attempts a new perspective that introduces ESDA, an analytic method, into the research of telecommunications consumption, thus combining perceptual and conceptual knowledge, qualitative and quantitative analysis, so as to not only improve the competency of GIS majors, but also cultivate their spatial analysis capability, develop favorable thinking mode, therefore laying a solid basis for their future study and work.

REFERENCES (14)

Anselin, L. (1998). Exploratory spatial data analysis in a geocomputational environment. In: P. A. Longley, S. M. Brooks, R. McDonnell, B. Macmillan (eds.) Geocomputation – A Primer, pp. 77–94. Wiley, Chichester.

Google Scholar

Anselin, L. (1999). Interactive techniques and exploratory spatial data analysis. In: Longley,P.A., Godchild, M.F., Maguire, D.J. (Eds.), Geographical Information Systems. John Wiley & Sons, New York, pp. 253–266.

Google Scholar

Anselin, L., Sridharan, S., & Gholston, S. (2007). Using exploratory spatial data analysis to leverage social indicator databases: the discovery of interesting patterns. Social Indicators Research, 82(2), 287e309.

Google Scholar

Cervero, R., & Kang, C. D. (2011). Bus rapid transit impacts onland uses and land values in Seoul, Korea. Transport Policy, 18, 102e116.

Google Scholar

Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5, 115- 145.

Google Scholar

Getis, A., & Ord, J. K. (1992). An analysis of spatial association by use of distance statistic. Geographical Analysis, 24(3), 189-206.

Google Scholar

Gimpel, A., Stelzenmüller, V., Grote, B., Buck, B. H., Floeter, J., Núñez-Riboni, I., … Temming, A. (2015). A GIS modelling framework to evaluate marine spatial planning scenarios: Co-location of offshore wind farms and aquaculture in the German EEZ. Marine Policy, 55, 102-115.

Google Scholar

Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17-33.

Google Scholar

Sánchez-García, S., Canga, E., Tolosana, E., & Majada, J. (2015). A spatial analysis of woodfuel based on WISDOM GIS methodology: Multiscale approach in Northern Spain. Applied Energy, 144, 193-203.

Google Scholar

10.

Su, S. L., Xiao, R., Jiang, Z. L., & Zhang, Y. (2012). Characterizing landscape pattern and ecosystem service value changes for urbanization impacts at an eco-regional scale. Appl. Geogr, 34, 295–305.

Google Scholar

11.

Tobler, W. (2014). On the first law of geography: A reply. Annals of the Association of American Geographers, 94(2), 304-310.

Google Scholar

12.

Wang, C.-H., & Chen, N. (2015). A GIS-based spatial statistical approach to modeling job accessibility by transportation mode: case study of Columbus, Ohio. Journal of Transport Geography, 45, 1-11.

Google Scholar

13.

Yang, Y., & Wong, K. K. (2013). Spatial distribution of tourist flows to China's cities. Tour. Geogr., 15(2), 338–363.

Google Scholar

14.

Zhang, Y., Xu, J. H., & Zhuang, P. J. (2011). The spatial relationship of tourist distribution in Chinese cities. Tour. Geogr, 13(1), 75–90.

Google Scholar

Submit your paper

For Authors

eISSN:	1305-8223
ISSN:	1305-8215