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Statistical and Spatial Analysis of Census Data for the Study of Family and Industrial Farming in Colombia

Statistical and Spatial Analysis of Census Data for the Study of Family and Industrial Farming in... This article presents a study of the Third National Agricultural and Livestock Census data to examine the characteristics of family and industrial farming in Colombia using together, statistical and spatial analysis techniques. From the available anonymized census microdata, which was added up to a level of rural relevance called vereda, a selection of variables was made according to properties found out in the state of art as related to both agriculture types and to the established theoretical framework. From there on, a Principal Component Analysis allowed distinguishing association between the attributes of family and industrial agriculture, while an Exploratory Spatial Data Analysis lets identifying polarized geographical distributions of these two phenomena in the country. In this way, this study seeks to become a starting point and a benchmark for analyzing the Colombian agricultural sector, as well as demonstrating the importance of census information, data reduction and statistical and spatial exploration methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Spatial Analysis and Policy Springer Journals

Statistical and Spatial Analysis of Census Data for the Study of Family and Industrial Farming in Colombia

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References (42)

Publisher
Springer Journals
Copyright
Copyright © Springer Nature B.V. 2019
Subject
Social Sciences; Human Geography; Landscape/Regional and Urban Planning; Regional/Spatial Science
ISSN
1874-463X
eISSN
1874-4621
DOI
10.1007/s12061-019-09324-4
Publisher site
See Article on Publisher Site

Abstract

This article presents a study of the Third National Agricultural and Livestock Census data to examine the characteristics of family and industrial farming in Colombia using together, statistical and spatial analysis techniques. From the available anonymized census microdata, which was added up to a level of rural relevance called vereda, a selection of variables was made according to properties found out in the state of art as related to both agriculture types and to the established theoretical framework. From there on, a Principal Component Analysis allowed distinguishing association between the attributes of family and industrial agriculture, while an Exploratory Spatial Data Analysis lets identifying polarized geographical distributions of these two phenomena in the country. In this way, this study seeks to become a starting point and a benchmark for analyzing the Colombian agricultural sector, as well as demonstrating the importance of census information, data reduction and statistical and spatial exploration methods.

Journal

Applied Spatial Analysis and Policy – Springer Journals

Published: Sep 24, 2020

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