Skip to main content
Log in

Give-And-Take heuristic model to political redistricting problems

  • Published:
Spatial Information Research Aims and scope Submit manuscript

Abstract

Political redistricting is a process used to redraw political boundaries based on a number of criteria that include demographic criteria (e.g. population equality and minority representation) and geographic criteria (e.g. contiguity and compactness). Redistricting can be highly controversial because it is possible that the drafters can be involved in the decision making where to draw political boundaries. The use of computers in political redistricting can remove several factors such as intentions of the decision makers or the majority in political views or race from the redistricting process. The main purpose of this paper is to develop a heuristic approach called Give-And-Take Greedy model in order to solve large scale political redistricting problem with respect to a strict population equality and contiguity. The heuristic follows the basic greedy concept to accept a best solution every iteration. The distinctive concept of the heuristic is to exchange or swapping population units within specified districts as well as to use the more efficient contiguity checking method. The computational results from Give-And-Take greedy heuristic can successfully be applied to real redistricting plans, especially for a congressional redistricting in the United States in satisfying with a strict equal population.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://www.spatialdatamining.org/iRedistrict.

References

  1. Morrill, R. L. (1981). Political redistricting and geographic theory. Washington, DC: Association of American Geographers.

    Google Scholar 

  2. Altman, M., & McDonald, M. P. (2010). The promise and perils of computers in redistricting. Duke Journal of Constitutional Law and Public Policy, 5, 69–112.

    Google Scholar 

  3. Altman, M. (1998). Modeling the effect of mandatory district compactness on partisan gerrymandering. Political Geography, 17(8), 989–1012.

    Google Scholar 

  4. Altman, M., McDonald, K., & McDonald, M. P. (2005). From crayons to computers: The evolution of computer use in redistricting. Social Science Computer Review, 23(3), 334–346.

    Google Scholar 

  5. Cooper, L. (1964). Heuristic methods for location-allocation problems. SIAM Review, 6, 37–54.

    Google Scholar 

  6. Xiao, N. (2008). A unified conceptual framework for geographical optimization using evolutionary algorithms. Annals of the Association of American Geographers, 98(4), 795–817.

    Google Scholar 

  7. Garfinkel, R. S., & Nemhauser, G. L. (1970). Optimal political districting by implicit enumeration techniques. Management Science, 16(8), B495–B508.

    Google Scholar 

  8. Shirabe, T. (2009). Districting modeling with exact contiguity constraints. Environment and Planning B: Planning and Design, 36, 1053–1066.

    Google Scholar 

  9. Kim, M. J., & Xiao, N. (2017). Contiguity-based optimization models for political redistricting problems. International Journal of Applied Geospatial Research, 8(4), 1–18.

    Google Scholar 

  10. Kim, M. J. (2018). Multiobjective spanning tree based optimization model to political redistricting. Spatial Information Research, 26(3), 317–325.

    Google Scholar 

  11. Weaver, J. B., & Hess, S. W. (1963). A procedure for nonpartisan districting: Development of computer techniques. The Yale Law Journal, 72, 288–308.

    Google Scholar 

  12. Hess, S. W., Weaver, J. B., Siegfeldt, H. J., Whelan, J. N., & Zitlau, P. A. (1965). Non-partisan political redistricting by computer. Operations Research, 13, 998–1006.

    Google Scholar 

  13. Mills, G. (1967). The determination of local government electoral boundaries. Operations Research Quarterly, 18, 243–255.

    Google Scholar 

  14. Morrill, R. L. (1973). Ideal and reality in reapportionment. Annals of the Association of American Geographers, 63, 463–477.

    Google Scholar 

  15. Helbig, R. E., Orr, P. K., & Roediger, R. R. (1972). Political Redistricting by computer. Communications of the ACM, 15(8), 735–741.

    Google Scholar 

  16. Plane, D. A. (1982). Redistricting reformulated—A maximum interaction minimum separation objective. Socio-Economic Planning Sciences, 16(6), 241–244.

    Google Scholar 

  17. Robertson, I. M. L. (1982). The delimitation of local government electoral areas in Scotland: A semi-automated approach. The Journal of the Operational Research Society, 33, 517–525.

    Google Scholar 

  18. Hojati, M. (1996). Optimal political districting. Computers & Operations Research, 23(12), 1147–1161.

    Google Scholar 

  19. George, J. A., Lamar, B. W., & Wallace, C. A. (1997). Political district determination using large-scale network optimization. Socio-Economic Planning Sciences, 31(1), 11–28.

    Google Scholar 

  20. Barkan, J. D., Densham, P. J., & Rushton, G. (2006). Space matters: Designing better electoral systems for emerging democracies. American Journal of Political Science, 50(4), 926–939.

    Google Scholar 

  21. Openshaw, S. (1977). Optimal zoning systems for spatial interaction models. Environment and Planning A, 9, 169–184.

    Google Scholar 

  22. Openshaw, S., & Rao, L. (1995). Algorithms for reengineering 1991 Census geography. Environment and Planning A, 27(3), 425–446.

    Google Scholar 

  23. Alvanides, S., Openshaw, S., & Duke-Williams, O. (2000). Designing zoning systems for flow data. In P. Atkinson & D. Martin (Eds.), GIS and geocomputation: Innovations in GIS (pp. 115–134). London: Taylor and Francis LTD.

    Google Scholar 

  24. Marsten, R. E. (1974). An algorithm for large set partitioning problems. Management Science, 20(5), 774–787.

    Google Scholar 

  25. Nygreen, B. (1988). European assembly constituencies for Wales-comparing of methods for solving a political districting problem. Mathematical Programming, 42, 159–169.

    Google Scholar 

  26. Mehrotra, A., Johnson, E. L., & Nemhauser, G. L. (1998). An optimization based heuristic for political districting. Management Science, 44(8), 1100–1114.

    Google Scholar 

  27. Vickrey, W. (1961). On the prevention of gerrymandering. Political Science Quarterly, 76(1), 105–110.

    Google Scholar 

  28. Thoreson, J. D., & Liittschwager, J. M. (1967). Computers in behavioral science. Legislative districting by computer simulation. Behavioral Science, 12(3), 237–347.

    Google Scholar 

  29. Gearhart, B. C., & Liittschwager, J. M. (1969). Legislative districting by computer. Behavioral Science, 14(5), 404–417.

    Google Scholar 

  30. Liittschwager, J. M. (1973). The Iowa redistricting system. Annals of the New York Academy of Sciences, 219, 221–235.

    Google Scholar 

  31. Harris, C. C., Jr. (1964). A scientific method of districting. Behavioral Science, 9, 219–225.

    Google Scholar 

  32. Nagel, S. S. (1965). Simplified bipartisan computer redistricting. Stanford Law Review, 17, 863–899.

    Google Scholar 

  33. Kaiser, H. (1966). An objective method for establishing legislative districts. Midwest Journal of Political Science, 10, 200–213.

    Google Scholar 

  34. Browdy, M. (1990). Simulated annealing: An improved computer model for political redistricting. Yale Law and Policy Review, 8, 163–179.

    Google Scholar 

  35. Macmillan, W. D., & Pierce, T. (1994). Spatial analysis and GIS. London Bristol, PA: Taylor & Francis.

    Google Scholar 

  36. Alvanides, S. (2000). Zone design methods for application in human geography. Ph.D. thesis, School of Geography, University of Leeds.

  37. Macmillan, W. D. (2001). Redistricting in a GIS environment: An optimization algorithm using switching-points. Journal of Geographical Systems, 3(2), 167–180.

    Google Scholar 

  38. Bozkaya, B., Erkut, E., & Laporte, G. (2003). A tabu search heuristic and adaptive memory procedure for political districting. European Journal of Operational Research, 144(1), 12–26.

    Google Scholar 

  39. Horn, M. E. T. (1995). Solution techniques for large regional partitioning problems. Geographical Analysis, 27(3), 230–248.

    Google Scholar 

  40. Bação, F., Lobo, V., & Painho, M. (2005). Applying genetic algorithms to zone design. Soft computing–A fusion of foundations. Methodologies and Applications, 9(5), 341–348.

    Google Scholar 

  41. Cheshmidari, M. N., Ardakani, A. H. H., Alipor, H., & Shojaei, S. (2017). Applying Delphi method in prioritizing intensity of flooding in Ivar watershed in Iran. Spatial Information Research, 25(2), 173–179.

    Google Scholar 

  42. Hamidy, N., Alipur, H., Nasab, S. N. H., Yazdani, A., & Shojaei, S. (2016). Spatial evaluation of appropriate areas to collect runoff using Analytic Hierarchy Process (AHP) and Geographical Information System (GIS) (case study: the catchment “Kasef” in Bardaskan. Modeling Earth Systems and Environment, 2(4), 172.

    Google Scholar 

  43. Openshaw, S. (1996). Developing GIS-relevant zone-based spatial analysis methods. In P. Longley & M. Batty (Eds.), Spatial analysis: Modelling in a GIS environment (pp. 55–73). Cambridge: GeoInformation International.

    Google Scholar 

  44. Ardakani, A. H. H., Shojaei, S., Ekhtesasi, M. R., & Ardakani, A. P. (2018). Evaluation of the quantitative and qualitative relationship between springs and geological formations using geostatistics as well as Boolean logic in Iran. Arabian Journal of Geosciences, 11(14), 377.

    Google Scholar 

  45. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to algorithms. Cambridge: The MIT Press.

    Google Scholar 

  46. Guo, D., & Jin, H. (2011). iRedistrict: Geovisual analytics for redistricting optimization. Journal of Visual Languages & Computing, 22(4), 279–289.

    Google Scholar 

  47. Williams, J. C., & ReVelle, C. S. (1996). A 0–1 programming approach to delineating protected reserves. Environment and Planning B, 23, 607–624.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Myung Jin Kim.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: The process of Give-And-Take greedy algorithm

Appendix: The process of Give-And-Take greedy algorithm

figure a
figure b

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, M.J. Give-And-Take heuristic model to political redistricting problems. Spat. Inf. Res. 27, 539–552 (2019). https://doi.org/10.1007/s41324-019-00254-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41324-019-00254-4

Keywords

Navigation