Imputation of spatial air quality data using gis-spline and the index of agreement in sparse urban monitoring networks




spatial interpolation spline, index of agreement, spatial models, air pollution, small area estimation and handling missing spatial data


This paper presents a procedure to address the lack of spatial air quality data in urban areas, based on the use of Geographic Information Systems (GIS) and spatial interpolation techniques as an alternative to conventional methods of statistical imputation. Two spatial interpolation algorithms are compared: IDW and spline. The procedure considers the spatial interpolation process, the cross validation with the index of agreement (IOA), and the analysis of the effect of sampling density and the coeffi cient of variation (CVOi ), using different error statistics. The interpolation maps are complemented with gradient and directional gradient maps that may serve as complementary aides in the defi nition of critical sampling points. The procedure is applied to data imputation of three pollutants NO2 , PM10 (particulate matter of diameter 10 microns) and TSP (total suspended solids) from observed data samples in the city of Medellín (Colombia).

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Author Biographies

Libardo Antonio Londoño-Ciro, University of Antioquia

Research Group in Environmental Management and Modeling (GAIA), Faculty of Engineering.

Julio Eduardo Cañón-Barriga, University of Antioquia

Research Group in Environmental Management and Modeling (GAIA), Faculty of Engineering, professor.


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How to Cite

Londoño-Ciro, L. A., & Cañón-Barriga, J. E. (2015). Imputation of spatial air quality data using gis-spline and the index of agreement in sparse urban monitoring networks. Revista Facultad De Ingeniería Universidad De Antioquia, (76), 73–81.

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