Scarc diansheng guo_2013


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Scarc diansheng guo_2013

  1. 1. http://www.SpatialDataMining.orgAutomated Zoning andRedistricting OptimizationDr. Diansheng GuoAssociate ProfessorDepartment of GeographyUniversity of South Carolina
  2. 2. http://www.SpatialDataMining.orgOutline Introduction of the problem◦ Redistricting problem and challenges◦ Redistricting criteria and methods Our innovative solution (NSF SBIR Grant)◦ Automated and powerful optimizationalgorithm◦ User sketching to constrain optimizationsearch◦ Visual inspection to balance different criteria◦ Easy-to-use and time efficient for generalusers Demonstration
  3. 3. http://www.SpatialDataMining.orgIntroduction Zoning or Redistricting is the process of redrawingboundaries of districts such as:◦ Precincts◦ School districts◦ Congressional districts◦ County council districts◦ Emergence response zone◦ Business service zone (or sales territory)◦ Health service planning◦ Delivery/dispatching zones (e.g., UPS)◦ … The need of re-zoning is a response to new censusdata, demographic changes, service improvement, etc.,
  4. 4. http://www.SpatialDataMining.orgIntroduction Zoning or Redistricting is the process of redrawingboundaries of districts in response to new census data,demographic changes, service improvement, etc.,
  5. 5. http://www.SpatialDataMining.orgChallenges and Problems It is a combinatorial optimization problem withmultiple constraints and criteria, some of which arevaguely defined: Geographic Contiguity Equal Population Majority-Minority District(Voting Rights Act of 1965) Respecting Existing Boundaries Preserving Communities of Interest Compactness of shape Competitiveness Incumbency protection …
  6. 6. http://www.SpatialDataMining.orgChallenges and ProblemsCalifornias 23rd congressionaldistrict. The Republican Party has fought to avoidhaving any part of this highly packed Democraticdistrict combined with any nearby counties to protectthe Republican majorities in those counties.The 4th Congressional District ofIllinois, containing two Hispanic partsof Chicago.(Source: Gerrymandering—manipulation of district boundaries to:◦ Favor a political party (a.k.a. political gerrymanders)◦ Dilute the power of minorities (a.k.a. racial gerrymanders).◦ Politicians can choose their voters instead of voters choosethem.
  7. 7. http://www.SpatialDataMining.orgSchool Zoning CriteriaZoning/Redistricting Optimization Geographic Contiguity Number of Students School Capacity Shortest Distance Balance the Number of Households Respecting Existing Boundaries Preserving Subdivision Boundaries Preserving Communities of Interest Compactness of shape …
  8. 8. http://www.SpatialDataMining.orgExisting redistricting software
  9. 9. http://www.SpatialDataMining.orgManual ApproachZoning/Redistricting Optimization• A trial-and-error approach• Extremely time consuming• Inadequate quality• Individuals generally cannot afford the time and thecostExisting Redistricting Software:• ESRI• Caliper• CityGate• Others …
  10. 10. http://www.SpatialDataMining.orgAutomated ApproachZoning/Redistricting Optimization• clustering (Forrest 1964)• location-allocation (Hess et al. 1965, Kalcsics et al.2005)• space partitioning (Ricca et al. 2008, Novaes et al.2009)• integer programming (Caro et al. 2004)• graph partitioning (Mehrotra et al. 1998)• genetic algorithms (Forman 2002),• Tabu search (Bozkaya 2003, Ricca 2008)• simulate annealing (Browdy 1990, DAmico et al.2002)• …
  11. 11. http://www.SpatialDataMining.orgChallenges and Problems Difficult for an individual to participate inredistricting◦ Existing approaches to redistricting are mainlymanual and extremely time consuming andchallenging to use. On the other hand, automated methods aloneare not sufficiently useful in practice for severalreasons:◦ Redistricting optimization is a computationallyintractable;◦ Difficult consider vague criteria and individualpreferences;
  12. 12. http://www.SpatialDataMining.orgOur Approach More efficient and powerful optimization algorithm◦ Achieve both efficiency and high optimization quality Visual analytical approach for human-computer collaboration◦ Flexibly define various criteria/constraints;◦ Automatically construct plans satisfying user criteria andconstraints;◦ Visually compare alternatives and balance different criteria;◦ Iteratively accumulate a collection of high-quality plans Easy-to-use and time efficient for general users◦ One can incorporate her/his preference through: sketching, algorithm configuration, and interactive selection of alternatives.
  13. 13. http://www.SpatialDataMining.orgEvaluation and Comparison Iowa 2000 Census data (99 counties) to construct 5congressional districts. Population equality is measured by the total absolutionpopulation deviation: Compared with four existingmethods: Genetic algorithm local greedy search (hill climbing) Kernighan–Lin (K-L) algorithm Traditional Tabu search Each method was repeated 1000times (each time with a random start).
  14. 14. http://www.SpatialDataMining.orgEvaluation and Comparison
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  16. 16. http://www.SpatialDataMining.orgUser Inputs and Visual Control
  17. 17. http://www.SpatialDataMining.orgNSF SBIR GrantZoning/Redistricting OptimizationThe broader impact/commercial value of thisproduct is that it could fundamentally change theway redistricting is performed in practice.For the first time, it offers the potential to meetend user requirements in an automated or semi-automated manner.Patent Pending
  18. 18. http://www.SpatialDataMining.orgDemonstration of iRedistrict
  19. 19. http://www.SpatialDataMining.orgConclusion We presented an interactive and computing-assistedGIS software to redistricting optimization.◦ The computational optimization algorithm is a keycomponent in the process that can relieve the user fromthe tedious and time-consuming work.◦ The user can focus on defining criteria and constraints,evaluate outcome plans, and address complexrequirements. Such a human-computer collaboration allows a userto quickly derive high-quality redistricting plans thatsatisfy both individual preferences and mandatoryrequirements. It can be used for a variety of projects andapplications.
  20. 20. http://www.SpatialDataMining.orgAcknowledgementThe research is in part supported by the NationalScience Foundation (NSF) under Grant No. 0748813.The development of the product (iRedistrict) is nowsupported by an NSF SBIR grant.