Self-Organizing Map approach to cluster Brazilian agricultural spatiotemporal diversity
Abstract
This work aims to cluster Brazilian municipalities according to their spatiotemporal agricultural diversity pattern. The diversity index has been defined for eight categories and calculated by Shannon’s entropy index from annual (1999-2018) IBGE’s estimates for agricultural production. The proposed clustering method is based on the Self-Organizing Map, an unsupervised artificial neural network, and comprises visual and automatic steps. The method partitioned the municipalities into eight groups spatially organized in three regions showing different spatiotemporal patterns.References
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Chen, I.-T., Chang, L.-C., and Chang, F.-J. (2018). Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps. Journal of Hydrology, 556:131–142.
Dessie, A., Abate, T., Mekie, T., and Liyew, Y. (2019). Crop diversification analysis on farming system: evidence from northwest red pepper dominated smallholder ethiopia. Ecological Processes, 8(50).
Genolini, C., Alacoque, X., Sentenac, M., and Arnaud, C. (2015). Kml and kml3d: R packages to cluster longitudinal data. Journal of Statistical Software, 65(4):1–34.
Hagenauer, J. and Helbich, M. (2013). Hierarchical self-organizing maps for clustering spatiotemporal data. International Journal of Geographical Information Science, 27(10):2026–2042.
IBGE. Sistema ibge de recuperação automática. Available at https://sidra.ibge.gov.br (2021/06/15).
Ling, C. and Delmelle, E. (2016). Classifying multidimensional trajectories of neighbourhood change: a self-organizing map and k-means approach. Annals of GIS, 22(3):173–186.
Luo, Z. T., Sang, H., and Mallick, B. (2021). A bayesian contiguous partitioning learning clustered latent variables. Journal of Machine Learning method for Research, 22:1–52.
Qi, J., Liu, H., Liu, X., and Zhang, Y. (2019). Spatiotemporal evolution analysis of timeseries land use change using self-organizing map to examine the zoning and scale effects. Computers, Environment and Urban Systems, 76:11–23.
Sales, C. and Rodrigues, R. (2019). Espaço rural brasileiro: diversificação e peculiaridades. Revista Espinhaço, 8(1):54–65.
Sambuichi, R., Galindo, E., Pereira, R., Constantino, M., and Rabetti, M. (2016). Diversidade da produção nos estabelecimentos da agricultura familiar no brasil: uma análise econométrica baseada no cadastro da declaração de aptidão ao pronaf (dap). Technical report, Brasília: Rio de Janeiro.
Schneider, S. and Cassol, A. (2014). Diversidade e heterogeneidade da agricultura familiar no brasil e algumas implicações para políticas públicas. Cadernos de Ciência & Tecnologia, 31(2):227–263.
Shannon, E. (1948). Mathematical theory of communication. The Bell System Technical Journal, 28(4):656–715.
Silva, M., Siqueira, E., and Teixeira, O. (2010). Abordagem conexionista para análise espacial exploratória de dados socioeconômicos de territórios rurais. Revista de Economia e Sociologia Rural, 48:429–446.
Skupin, A. and Hagelman, R. (2005). Visualizing demographic trajectories with selforganizingmaps. GeoInformatica, 9(2):159–179.
Teixeira, L. V., o, R. M. A., and Loschi, R. H. (2019). Bayesian space-time partitioning by sampling and pruning spanning trees. Journal of Machine Learning Research, 20:1–35.
Teixeira, M. and Ribeiro, S. (2020). Agricultura e paisagens sustentáveis: a diversidade produtiva do setor agrícola de minas gerais, brasil. Sustainability in Debate, 11(2):29–41
Wang, N., Biggs, T., and Skupin, A. (2013). Visualizing gridded time series data with self organizing maps: An application to multi-year snow dynamics in the northern hemisphere. Computers, Environment and Urban Systems, 39:107–120.
Chen, I.-T., Chang, L.-C., and Chang, F.-J. (2018). Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps. Journal of Hydrology, 556:131–142.
Dessie, A., Abate, T., Mekie, T., and Liyew, Y. (2019). Crop diversification analysis on farming system: evidence from northwest red pepper dominated smallholder ethiopia. Ecological Processes, 8(50).
Genolini, C., Alacoque, X., Sentenac, M., and Arnaud, C. (2015). Kml and kml3d: R packages to cluster longitudinal data. Journal of Statistical Software, 65(4):1–34.
Hagenauer, J. and Helbich, M. (2013). Hierarchical self-organizing maps for clustering spatiotemporal data. International Journal of Geographical Information Science, 27(10):2026–2042.
IBGE. Sistema ibge de recuperação automática. Available at https://sidra.ibge.gov.br (2021/06/15).
Ling, C. and Delmelle, E. (2016). Classifying multidimensional trajectories of neighbourhood change: a self-organizing map and k-means approach. Annals of GIS, 22(3):173–186.
Luo, Z. T., Sang, H., and Mallick, B. (2021). A bayesian contiguous partitioning learning clustered latent variables. Journal of Machine Learning method for Research, 22:1–52.
Qi, J., Liu, H., Liu, X., and Zhang, Y. (2019). Spatiotemporal evolution analysis of timeseries land use change using self-organizing map to examine the zoning and scale effects. Computers, Environment and Urban Systems, 76:11–23.
Sales, C. and Rodrigues, R. (2019). Espaço rural brasileiro: diversificação e peculiaridades. Revista Espinhaço, 8(1):54–65.
Sambuichi, R., Galindo, E., Pereira, R., Constantino, M., and Rabetti, M. (2016). Diversidade da produção nos estabelecimentos da agricultura familiar no brasil: uma análise econométrica baseada no cadastro da declaração de aptidão ao pronaf (dap). Technical report, Brasília: Rio de Janeiro.
Schneider, S. and Cassol, A. (2014). Diversidade e heterogeneidade da agricultura familiar no brasil e algumas implicações para políticas públicas. Cadernos de Ciência & Tecnologia, 31(2):227–263.
Shannon, E. (1948). Mathematical theory of communication. The Bell System Technical Journal, 28(4):656–715.
Silva, M., Siqueira, E., and Teixeira, O. (2010). Abordagem conexionista para análise espacial exploratória de dados socioeconômicos de territórios rurais. Revista de Economia e Sociologia Rural, 48:429–446.
Skupin, A. and Hagelman, R. (2005). Visualizing demographic trajectories with selforganizingmaps. GeoInformatica, 9(2):159–179.
Teixeira, L. V., o, R. M. A., and Loschi, R. H. (2019). Bayesian space-time partitioning by sampling and pruning spanning trees. Journal of Machine Learning Research, 20:1–35.
Teixeira, M. and Ribeiro, S. (2020). Agricultura e paisagens sustentáveis: a diversidade produtiva do setor agrícola de minas gerais, brasil. Sustainability in Debate, 11(2):29–41
Wang, N., Biggs, T., and Skupin, A. (2013). Visualizing gridded time series data with self organizing maps: An application to multi-year snow dynamics in the northern hemisphere. Computers, Environment and Urban Systems, 39:107–120.
Published
2021-10-25
How to Cite
SANTOS, Flávio E. de O.; SILVA, Marcos A. S. da; MATOS, Leonardo N.; MOURA, Fábio R. de; DOMPIERI, Márcia H. G..
Self-Organizing Map approach to cluster Brazilian agricultural spatiotemporal diversity. In: REGIONAL SCHOOL ON COMPUTING OF BAHIA, ALAGOAS, AND SERGIPE (ERBASE), 21. , 2021, Maceió.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 65-73.
DOI: https://doi.org/10.5753/erbase.2021.20058.
