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The hidden traits of endemic illiteracy in cities

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  • Alves, Luiz G.A.
  • Andrade, José S.
  • Hanley, Quentin S.
  • Ribeiro, Haroldo V.

Abstract

In spite of the considerable progress towards reducing illiteracy rates, many countries, including developed ones, have encountered difficulty achieving further reduction in these rates. This is worrying because illiteracy has been related to numerous health, social, and economic problems. Here, we show that the spatial patterns of illiteracy in urban systems have several features analogous to the spread of diseases such as dengue and obesity. Our results reveal that illiteracy rates are spatially long-range correlated, displaying non-trivial clustering structures characterized by percolation-like transitions and fractality. These patterns can be described in the context of percolation theory of long-range correlated systems at criticality. Together, these results provide evidence that the illiteracy incidence can be related to a transmissible process, in which the lack of access to minimal education propagates in a population in a similar fashion to endemic diseases.

Suggested Citation

  • Alves, Luiz G.A. & Andrade, José S. & Hanley, Quentin S. & Ribeiro, Haroldo V., 2019. "The hidden traits of endemic illiteracy in cities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 566-574.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:566-574
    DOI: 10.1016/j.physa.2018.09.153
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    References listed on IDEAS

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    1. Baker, D.W. & Parker, R.M. & Williams, M.V. & Clark, W.S. & Nurss, J., 1997. "The relationship of patient reading ability to self-reported health and use of health services," American Journal of Public Health, American Public Health Association, vol. 87(6), pages 1027-1030.
    2. Luiz G A Alves & Haroldo V Ribeiro & Ervin K Lenzi & Renio S Mendes, 2013. "Distance to the Scaling Law: A Useful Approach for Unveiling Relationships between Crime and Urban Metrics," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    3. Elad Schneidman & Michael J. Berry & Ronen Segev & William Bialek, 2006. "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature, Nature, vol. 440(7087), pages 1007-1012, April.
    4. Messias, E., 2003. "Income Inequality, Illiteracy Rate, and Life Expectancy in Brazil," American Journal of Public Health, American Public Health Association, vol. 93(8), pages 1294-1296.
    5. Luiz G A Alves & Renio S Mendes & Ervin K Lenzi & Haroldo V Ribeiro, 2015. "Scale-Adjusted Metrics for Predicting the Evolution of Urban Indicators and Quantifying the Performance of Cities," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
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    Cited by:

    1. Jianxu Liu & Xiaoqing Li & Shutong Liu & Sanzidur Rahman & Songsak Sriboonchitta, 2022. "Addressing Rural–Urban Income Gap in China through Farmers’ Education and Agricultural Productivity Growth via Mediation and Interaction Effects," Agriculture, MDPI, vol. 12(11), pages 1-23, November.

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