![]() |
![]() |
GI_Forum 2017, Volume 5, Issue 1Journal for Geographic Information Science
|
![]() |
Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
![]() |
|
DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
|
GI_Forum 2017, Volume 5, Issue 1, pp. 36-43, 2017/06/30
Journal for Geographic Information Science
Spatial correlation between variables may exist if the observed data exhibits spatial variation in a manner that is described by Tobler's first law of geography. Partial correlation is useful when considering multivariate data as it can highlight the effects of certain control variables on the correlation between any two other variables. Techniques for estimating spatial correlation have been developed based on a geographically weighted scheme. However, a partial correlation technique for spatial data has not yet been considered. Hence, we describe a technique for obtaining geographically weighted partial correlation coefficients between three variables. This approach is then applied, as an example, to global climate data in order to explore the relationship between terrestrial vegetation (by NDVI proxy), land surface temperature, and precipitation in the year 2014. Spatial variations of those variables are observed and the geographically weighted correlation and partial correlation coefficients (along with associated levels of statistical significance) are compared.
Keywords: partial correlation, spatial statistics, geographically weighted approach