Skip to main content

A System for Political Districting in the State of Mexico

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9413))

Abstract

Districting is the redrawing of the boundaries of legislative districts for electoral purposes in such a way that the Federal or state requirements, such as contiguity, population equality, and compactness, are fulfilled. The resulting optimization problem involves the former requirement as a hard constraint while the other two are considered as conflicting objective functions. The solution technique used for many years by the Mexican Federal Electoral Institute was an algorithm based on Simulated Annealing. In this article, we present the system proposed for the electoral districting process in the state of Mexico. This system included, a geographic tool to visualize and edit districting plans, and for first time in Mexico, the use of an Artificial Bee Colony based algorithm that automatically creates redistricting plans.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    The National Electoral Institute, INE, since April 4, 2014.

References

  1. Ardanaz, M., Leiras, M., Tommasi, M.: The politics of federalism in Argentina and its implications for governance and accountability. World Dev. 53, 26–45 (2014)

    Article  Google Scholar 

  2. Baçao, F., Lobo, V., Painho, M.: Applying genetic algorithms to zone design. Soft Comput. 9, 341–348 (2005)

    Article  Google Scholar 

  3. Birattari, M.: Tuning Metaheuristics: A Machine Learning Perspective. Studies in Computational Intelligence, vol. 197. Springer, Heidelberg (2009)

    Google Scholar 

  4. Bozkaya, B., Erkut, E., Laporte, G.: A tabu search heuristic and adaptive memory procedure for political districting. Eur. J. Oper. Res. 144, 12–26 (2003)

    Article  MATH  Google Scholar 

  5. Chung-I, C.: A knowledge-based evolution algorithm approach to political districting problem. Comput. Phys. Commun. 182(1), 209–212 (2011)

    Article  Google Scholar 

  6. Caro, F., Shirabe, T., Guignard, M., Weintraub, A.: School redistricting: embedding GIS tools with integer programming. J. Oper. Res. Soc. 55, 836–849 (2004)

    Article  MATH  Google Scholar 

  7. Dell’Amico, S., Wang, S., Batta, R., Rump, C.: A simulated annealing approach to police district design. Comput. Oper. Res. 29, 667–684 (2002)

    Article  Google Scholar 

  8. DesJardins, M., Bulka, B., Carr, R., Jordan, E., Rheingans, P.: Heuristic search and information visualization methods for school redistricting. AI Mag. 28(3), 59–72 (2006)

    Google Scholar 

  9. Duque, J.C., Ramos, R., Suriach, J.: Supervised regionalization methods: a survey. Int. Reg. Sci. Rev. 30(3), 195–220 (2007)

    Article  Google Scholar 

  10. Forest, B.: Redistricting and the elusive ideals of representation. Polit. Geogr. 32, 15–17 (2013)

    Article  Google Scholar 

  11. Garfinkel, R.S., Nemhauser, G.L.: Optimal political districting by implicit enumeration techniques. Manag. Sci. 16, 495–508 (1970)

    Article  Google Scholar 

  12. Gilbert, K.C., Holmes, D.D., Rosenthal, R.E.: A multiobjective discrete optimization model for land allocation. Manag. Sci. 31, 1509–1522 (1985)

    Article  Google Scholar 

  13. Andrade, M.A.G., García, E.A.R.: Redistricting by square cells. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds.) MICAI 2009. LNCS, vol. 5845, pp. 669–679. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Hess, S.W., Weaver, J.B., Siegfeldt, H.J., Whelan, J.N., Zitlau, P.A.: Nonpartisan political redistricting by computer. Oper. Res. 13(6), 998–1006 (1965)

    Article  Google Scholar 

  15. Kalcsics, J., Nickel, S., Schrder, M.: Towards a unified territorial design approach: applications, algorithms and GIS integration. Top 13(1), 1–74 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  16. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  17. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)

    Article  Google Scholar 

  18. Kirkpatrick, S., Gellat, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  19. Macmillan, W.: Redistricting in a GIS environment: an optimisation algorithm using switching-points. J. Geogr. Syst. 3, 167–180 (2001)

    Article  Google Scholar 

  20. Ricca, F., Simeone, B.: Local search algorithms for political districting. Eur. J. Oper. Res. 189(3), 1409–1426 (2008)

    Article  MATH  Google Scholar 

  21. Ricca, F., Scozzari, A., Simeone, B.: Political districting: from classical models to recent approaches. J. Oper. Res. 204(1), 271–299 (2011)

    Google Scholar 

  22. Rincón-García, E.A., Gutiérrez-Andrade, M.A., de-los-Cobos-Silva, S.G., Lara-Velázquez, P., Mora-Gutiérrez, R.A., Ponsich, A.: A discrete particle swarm optimization algorithm for designing electoral zones. In: Methods for decision making in an uncertain environment, pp. 174–197. World Scientific Proceedings Series on Computer Engineering and Information Science, Reus (2012)

    Google Scholar 

  23. Shirabe, T.: A model of contiguity for spatial unit allocation. Geogr. Anal. 37, 2–16 (2005)

    Article  Google Scholar 

  24. Shirabe, T.: Prescriptive modeling with map algebra for multi-zone allocation with size constraints. Comput. Environ. Urban Syst. 36, 456–469 (2012)

    Article  Google Scholar 

  25. Shortt, N.K., Moore, A., Coombes, M., Wymer, C.: Defining regions for locality health care planning: a multidimensional approach. Soc. Sci. Med. 60, 2715–2727 (2005)

    Article  Google Scholar 

  26. Tavares-Pereira, F., Rui, J., Mousseau, V., Roy, B.: Multiple criteria districting problems: the public transportation network pricing system of the Paris region. Ann. Oper. Res. 154, 69–92 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  27. Vickrey, W.: On the prevention of gerrymandering. Polit. Sci. Q. 76(1), 105–110 (1961)

    Article  Google Scholar 

  28. Zoltners, A.A., Sinha, P.: Sales territory design: thirty years of modeling and implementation. Mark. Sci. 24(3), 313–331 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eric Alfredo Rincón García .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

García, E.A.R., Andrade, M.Á.G., de-los-Cobos-Silva, S.G., Ponsich, A., Mora-Gutiérrez, R.A., Lara-Velázquez, P. (2015). A System for Political Districting in the State of Mexico. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27060-9_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27059-3

  • Online ISBN: 978-3-319-27060-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics