Errors and Their Scale Effect for Spatialization of Air Temperature Data

DOI

10.2991/rsete.2013.171How to use a DOI?

Keywords

Air temperature, data, spatialization, errors, impact factors

Abstract

Spatialization of attribute data is a way to output grid data products from vector data. It is beneficial to integrated analysis of geosciences data from various sources and in different formats. However it is also a process companied with errors, and the errors are closely related to density of data sources, spatializing models and resolution of grid cells. In this paper, seven levels of density of meteorological stations, five spatializing models and nineteen levels of resolutions of grid cells were used to analyze the relationships between the errors for spatialization of air temperature and these factors. It was found that reduction of density of meteorological stations led to increasing of errors. Of the five models, Adjusted IDW, Regression and ANUSPIN had higher accuracy than IDW and Kriging. And the accuracy generally decreases with increasing of size of grid cells. Of the three factors affecting accuracy of spatialization, the models had the greatest impact on the accuracy, the resolution of grid cells second and the density of meteorological stations the lowest.

Copyright

© 2013, 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/).

Download article (PDF)

TY  - CONF
AU  - Liao Shunbao
AU  - Zhang Sai
PY  - 2013/08
DA  - 2013/08
TI  - Errors and Their Scale Effect for Spatialization of Air Temperature Data
BT  - Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013)
PB  - Atlantis Press
SP  - 704
EP  - 707
SN  - 1951-6851
UR  - https://doi.org/10.2991/rsete.2013.171
DO  - 10.2991/rsete.2013.171
ID  - Shunbao2013/08
ER  -