Modifiable Areal Unit Problem (MAUP): Analysis of Agriculture of the State of Paraná-Brazil

DOI 10.7160/aol.2021.130203
No 2/2021, June
pp. 35-50

Cima, E. G., Freire da Rocha-Jr., W., Uribe-Opazo, M. A. and Dalposso, G. H. (2021) "Modifiable Areal Unit Problem (MAUP): Analysis of Agricultural of the State of Paraná-Brazil", AGRIS on-line Papers in Economics and Informatics, Vol. 13, No. 2, pp. 35-50. ISSN 1804-1930. DOI 10.7160/aol.2021.130203.

Abstract

The way the researcher groups his research data will influence the result of his work. In the literature, this phenomenon is treated as a Problem of the Modifiable Areal Unit. The objective of this article was to analyze the three spatial levels by Municipalities, Regional Centers and Mesoregions using the following data: gross domestic product, effective agricultural production, grain production and gross value of agricultural production for the state of Paraná-Brazil in the period since 2012 until 2015. The methodological procedure studied data from the Paranaense Institute for Economic and Social Development of the above-named variables collected on the website of the Paranaense Institute for Economic and Social Development of the 399 municipalities, 23 regional centers and 10 mesoregions. The results found show the presence of the Modifiable Areal Unit Problem, presenting different results for each level of grouping. The study revealed the problem of the modifiable areal unit is a relevant occurrence and it should be disregarded by researchers who work with clusters of spatial data in their studies. The results found allow a better understanding of the scale effect and demonstrate the efficiency of spatial analysis in socioeconomic data.

Keywords

Aggregation, agribusiness, autocorrelation, scale effect, spatial process, decision making.

References

  1. Almeida, E. (2012) “Econometria Espacial Aplicada”, Alínea, p. 498, ISBN 8575166018.
  2. Anselin, L. (2018) “A Local Indicator of Multivariate Spatial Association: Extending Geary’s c”, Geographical Analysis, Vol. 51, pp 133-150. ISSN 1538-4632. DOI 10.1111/gean.12164.
  3. Anselin, L. and Bel, A. (2013) “Spatial fixed effects and spatial dependence in a single cross-section”, Papers Regional Science, Vol. 92, No. 1, pp. 3-17. E-ISSN 1435-5957. DOI 10.1111/j.1435-5957.2012.00480.x.
  4. Araújo, C. E., Uribe-Opazo, M. A. and Johann, J. A. (2014) “Modelo de regressão espacial para a estimativa da produtividade da soja associada a variáveis agrometeorológicas na região oeste do estado do Paraná”, Engenharia Agrícola, Vol. 34, No. 2, pp 286-299. ISSN 0100-6916. (in Spain). DOI 10.1590/S0100-69162014000200010.
  5. BANCO MUNDIAL (2020) “A Economia nos Tempos de COVID-19. Relatório Semestral sobre a América Latina e Caribe”, pp.1-66. (in Spain).
  6. Barbieri, R. S., Carvalho., J. B. and Sabbag, O. J. (2016) “Análise de viabilidade econômica de um confinamento de bovinos de corte”, Interações, Vol. 17, No. 3, pp. 357-369. ISSN 1984-042X. (in Spain). DOI 10.20435/1984-042X-2016-v.17-n.3(01).
  7. Burdziej, J. (2019) “Using hexagonal grids and network analysis for spatial accessibility assessmente in urban environments – a case study of public amemities in Torun´”, Miscellanea Geographica-Regional Studies on Development, Vol. 23, No. 2, pp. 99-110. ISSN 2084-6118. DOI 10.2478/mgrsd-2018-0037.
  8. Cabrera-Barona, P., Wei, C. and Hangenlocher, M. (2016b) “Multiscale evaluation of an urban deprivation index: implications for quality of life and healthcare accessibility planning”, Applied Geography, Vol. 70, pp. 1-10. ISSN 0143-6228. DOI 10.1016/j.apgeog.2016.02.009.
  9. Cabrera-Barona, P., Blaschke, T. and Gaona, G. (2018) “Deprivation, Healthcare Accessibility and Satisfaction: Geographical Context and Scale Implications”, Applied Spatial Analysis and Policy, Vol. 11, No. 2, pp. 313-332. ISSN 1874-463X. DOI 10.1007/s12061-017-9221-y.
  10. Chaves, E. M. D., Alves, M. C. and Oliveira, M. S. (2018) “A Geostatistical Approach for Modeling Soybean Crop Area and Yield Based on Census and Remote Sensing Data”, Remote Sensing, Vol. 10, No. 680, pp. 2-29. ISSN 1366-5901. DOI 10.3390/rs10050680.
  11. Chen, J. (2018) “Geographical scale, industrial diversity, and regional economic stability”, Journal of Urban and Regional Policy, Vol. 50, No. 2., pp. 609-663. ISSN 1468-2427. DOI 10.1111/grow.12287.
  12. Duque, J. C., Laniado, H. and Polo, A. (2018) “S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem”, Plos One, Vol.13, N. 11, pp. 1-25. ISSN 1177-3901. DOI 10.1371/journal.pone.0207377.
  13. Duan, P., Qin, L., Yeqiao, W. and Hongshi, H. (2015) “Spatiotemporal Correlations between Water Footprint and Agricultural Inputs: A Case Study of Maize Production in Northeast China”, Water, Vol.7, No. 8, pp. 4026-4040. ISSN 2073-4441. DOI 10.3390/w7084026.
  14. EMBRAPA, Empresa Brasileira de Pesquisa Agropecuária (2016) “Custos de produção de suínos e de frangos de corte sobem em maio e chegam a pontuação recorde”. [Online]. Avaiable: http://www.embrapa.br/busca-de-noticias/-/noticia/13594416/embrapa-custos-de-producao-de-suinos-e-de-frangos-de-corte-sobem-em-maio-e-chegam-a-pontuacao-recorde-style.htm [Accessed: 2 May 2019]. (in Spain).
  15. Fotheringham, A.S., Brunsdon, C. and Charlton, M. E. (2002) “Geographically Weighted Regression: The analysis of spatially varying relationship”, Wiley, pp. 284. ISBN 978-0-471-49616-8.
  16. IBGE, Instituto Brasileiro de Geografia e Estatísticas (2012) “Pesquisa Pecuária Municipal”. [Online]. Avaiable: https://biblioteca.ibge.gov.br/visualizacao/periodicos/84/ppm_2012_v40_br.pdf. [Accessed: 1 Feb. 2020]. (in Spain).
  17. IBGE, Instituto Brasileiro de Geografia e Estatísticas (2016) “Pesquisa Pecuária Municipal”. [Online]. Avaiable: https://biblioteca.ibge.gov.br/visualizacao/periodicos/84/ppm_2016_v44_br.pdf. [Accessed: 1 Feb. 2020]. (in Spain).
  18. IPARDES, Instituto Paranaense de Desenvolvimento Econômico e social (2015) “Índice Ipardes de Desempenho Municipal – IPDM”. [Online]. Avaiable: http://www.ipardes.gov.br/index.php?pg_conteudo=1&cod_conteudo=19-style.htm [Accessed: 22 Apr. 2019]. (in Spain).
  19. Janelle, D. G., Warf, B. and Hansen, K. (2004) “WorldMinds: Geographical Perspectives on 100 Problems”, Springer-Sc, p. 601. ISBN 978-1-4020-16l3-4. DOI 10.1007/978-1-4020-2352-1.
  20. Javi, S. T., Mokhtari, H., Rashidi, A. and Taghipour, H. (2015) “Analysis of spatiotemporal relationships between irrigation water quality and geo-environmental variables in the Khanmirza Agricultural Plain, Iran”, Journal of Biodiversity and Environmental Sciences, Vol. 6, No. 6, pp. 240-252. ISSN 2222-3045.
  21. Jiawei, Pan, J., Yiyun Ch., Yan, Z., Min Ch., Shailaja, F., Bo, L., Feng, W., Dan, M., Yaolin, L., Limin J., Jing, W. (2020) “Spatial- temporal dynamics of grain yield and the potential driving factors at the county level in China”, Journal of Cleaner Production, Vol. 255, pp. 120-312 . ISSN 0959-6526. DOI 10.1016/j.jclepro.2020.120312.
  22. Nelson, J. K. and Brewer, C. A. (2017) “Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem”, Cartography and Geographic Information Science, Vol. 44, N. 1, pp 35-50. ISSN 1523-0406. DOI 10.1080/15230406.2015.1093431.
  23. Didier, J. and Louvet, R. (2019) “Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?”, International Journal of Geo-information,V ol. 8, No. 156, pp. 1-20. ISSN 2220-9964. DOI 10.3390/ijgi8030156.
  24. Kupriyanova, M., Dronov, V. and Gordova, T. (2019) “Digital Divide of Rural Territories in Russia”, Agris on-line Papers in Economics and Informatics, Vol. 11, No. 3, pp 80-85. ISSN 1804-1930. DOI 10.7160/aol.2019.110308.
  25. Lee, G., Cho, D. and Kim, K. (2015) “The modifiable areal unit problem in hedonic house-price models”, Urban Geography, Vol. 37, No. 2, pp. 223-245. ISSN 0272-3638. DOI 10.1080/02723638.2015.1057397.
  26. Lee, S., Lee, M., Chun,Y., Griffth, D. A. (2018) “Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation: a simultation study”, International Journal of Geographical Information Science, Vol. 33, No. 6, pp. 1135-1154. DOI 10.1080/13658816.2018.1542699.
  27. Lesage, J. P. (2015) “The Theory and Practice of Spatial Econometrics”, Journal Spatial Economic Analysis, Vol.10, No. 2, pp. 400. ISSN 1742-1772. DOI 10.1080/17421772.2015.1062285.
  28. Lopes, B. S., Brondino, M. C. N. and Silva, R. N. A. (2014) “GIS – Based analytical tools for transport planning: spatial regression models for transportation demand forescast”, International Journal of Geo-Information, Vol. 3, No. 2, pp. 565-583. ISSN 2220-9964. DOI 10.3390/ijgi3020565.
  29. Meiyappan, P., Dalton, M., O’Neill, C. B. and Atulk, J. (2014) “Spatial modeling of agricultural land use change at global scale, Ecological Modeling”, Elsevier, Vol. 291, No. 1, pp. 152-174. ISSN 0304-3800. DOI 10.1016/j.ecolmodel.2014.07.027.
  30. Nelson, J. K. and Brewer, C. A. (2017) “Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem”, Cartography and Geographic Information Science, Vol. 44, No. 1, pp 35-50. ISSN 1523-0406. DOI 10.1080/15230406.2015.1093431.
  31. Pietrzak, M. B. (2019) “Modifiable Areal Unit Problem: the issue of determining the relationship between microparameters and a macroparameter”, Oeconomia Copernicana, Vol. 10, No. 3, pp. 393-417. ISSN 2083-1277. DOI 10.24136/oc.2019.019.
  32. R Core Team (2018) "R: A language and environment for statistical computing", Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-90005107-0. [Online]. Avaiable: http://www.R-project.org [Accessed: 5 May 2019].
  33. Roces-Díaz, J.V., Vayreda, J., Banqué-Casanovas, M., Díaz-Varela, E., Bonet, J.A., Brotons, L., de- Miguel, S., Herrando, S., Martínez-Vilalta, J. (2018) “The spatial level of analysis affects the patterns of forest ecosystem services supply and their relationships”, Science of the Total Environment, Vol. 626, pp. 1270-1283. ISSN 0048-9697. DOI 10.1016/j.scitotenv.2018.01.150.
  34. Salmivaara, A., Kummu, M., Porkka, M. and Keskinen, M. (2015) “Exploring the Modifiable Areal Unit Problem in Spatial Water Assessments: A Case of Water Shortage in Monsoon Asia”, Water, Vol. 7, No. 3, pp. 898-917. ISSN 2073-4441. DOI 10.3390/w7030898.
  35. Santos, A. H. A., Pitangueira, R. L.S., Ribeiro, G. O. and Caldas, R. B. (2015) “Estudo do efeito de escala utilizando correlação de imagem digital”, Revista IBRACON de Estruturas e Materiais, Vol. 8, No. 3, pp. 323-340. ISSN 1983-4195. (in Spain) DOI 0.1590/S1983-41952015000300005.
  36. SPRING (2003) “Statistic 333 Cp, AIC and BIC”. [Online]. Avaiable: http:// www.stat.wisc.edu/courses/st 333 larget/aic.pdf. [Accessed: 27 Apr. 2019].
  37. SEAB/DERAL - Secretaria da Agricultura e do Abastecimento do Paraná/Departamento de Economia Rural (2015) "Banco de Dados da Produção Agropecuária no Paraná. Situação mensal de plantio, colheita e comercialização de produtos agrícolas no Paraná". [Online]. Avaliable: http://www.agricultura.pr.gov.br. [Accessed: 15 Feb. 2019].
  38. Tunson, M., Yap, M. R., Kok, K., Murray, B., Turlach, B. and Whyatt, D. (2019) “Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem”, International Journal of Health Geographics, Vol. 18, No. 6, pp. 1-15. ISSN 1476-072X. DOI 10.1186/s12942-019-0170-3.
  39. Xu, P., Huang, H. and Dong, N. (2018) “The modifiable areal unit problem in traffic safety: Basic issue, potential solutions and future research”, Journal of Traffic and Transportation Engineering, Vol. 5, No. 1, pp. 73-82. ISSN 2095-7564. DOI 10.1016/j.jtte.2015.09.010.
  40. Zeffrin, R., Araújo, E. C. and Bazzi, C. L. (2018) “Análise espacial de área aplicada a produtividade de soja na região oeste do Paraná utilizando o software R”, Revista Brasileira de Geomática, Vol. 6, No. 1, pp. 23-43. ISSN 2317-4285. (in Spain). DOI 10.3895/rbgeo.v6n1.5912.
  41. Wei, C., Padgham, M., Barona, P. C. and Blaschke, T. (2017) “Scale-Free Relationships Between Social and Landscape Factors in Urban Systems”, Sustainability, Vol. 9, No. 1, pp. 1-19. ISSN 2071-1050. DOI 10.3390/su9010084.
  42. Zen, M., Candiago, S., Schirpke, U., Vigl, L. E. and Giupponi, C. (2019) “Upscaling ecosystem service maps to administrative levels: beyondscale mismatches’’, Science of the Total Environment, Vol. 660, pp. 1565-1575. ISSN 0048-9697. DOI 10.1016/j.scitotenv.2019.01.087.
  43. Zou, J. and Wu, Q. (2017) “Spatial Analysis of Chinese Grain Production for Sustainable Land Management in Plain, Hill, and Mountain Counties”, Sustainability, Vol. 9, No. 348, pp. 1-12. ISSN 2071-1050. DOI 10.3390/su9030348.

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