Extensión del Algoritmo ClustImpute para Variables Cualitativas y Mixtas: Una Aplicación al Capítulo de Cultivos de Bogotá D.C. del III Censo Nacional Agropecuario

Abstract

In the current Colombian context marked by demographic, economic, and environmental changes, coupled with the significant role of the agricultural sector in the economy, the implementation of the III National Agricultural Census in 2014 emerges as a crucial tool to comprehend the multifaceted variables impacting this sector. With an operational coverage of 98.9%, this census provided detailed and updated information on the agricultural sector nationwide, including municipalities, indigenous territories, lands of black communities, and national parks. However, data analysis presents challenges such as the existence of 4% of records with missing data, which are addressed through statistical approaches like missing value imputation.

Focusing on the Cultivation chapter in Bogotá, this work proposes the application of an extended version of the ClustImpute algorithm. By combining imputation techniques with the k-means method, this algorithm aims to address both quantitative and qualitative variables present in the census, offering an innovative alternative to conventional imputation methods. The ultimate goal is to provide a more comprehensive and reliable data analysis to contribute to the understanding and improvement of policies and efforts related to rural development and the quality of life in rural areas in Bogotá and, consequently, throughout the country.