Redistricting Using Heuristic-Based Polygonal Clustering

Joshi, Deepti; Soh, Leen-Kiat; Samal, Ashok

doi:10.1109/icdm.2009.126

Cited by 14 publications

(11 citation statements)

References 8 publications

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“…The main idea of our districting algorithm is similar to the Constraint-Based Polygonal Spatial Clustering (CPSC) algorithm developed by [13]. The two main steps of the algorithm are 1) select seed polygons (one atomic geographical unit) and 2) select the polygon in neighborhood to be added to the existing cluster until all units are assigned to districts.…”

Section: Generation Of Alternative Districting Plansmentioning

confidence: 99%

Police patrol districting method and simulation evaluation using agent-based model & GIS

Zhang

Brown

2013

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Police patrols play an important role in public safety. The patrol district design is an important factor affecting the patrol performances, such as average response time and workload variation. The redistricting or redrawing police command boundaries can be described as partitioning a police jurisdiction into command districts with the constraints such as contiguity and compactness. The size of the possible sample space is large and the corresponding graph-partitioning problem is NP-complete. In our approach, the patrol districting plans generated by a parameterized redistricting procedure are evaluated using an agent-based simulation model we implemented in Java Repast in a geographic information system (GIS) environment. The relationship between districting parameters and response variables is studied and better districting plans can be generated. After in-depth evaluations of these plans, we perform a Pareto analysis of the outputs from the simulation to find the non-dominated set of plans on each of the objectives. This paper also includes a case study for the police department of Charlottesville, VA, USA. Simulation results show that patrol performance can be improved compared with the current districting solution.

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Section: Generation Of Alternative Districting Plansmentioning

confidence: 99%

Police patrol districting method and simulation evaluation using agent-based model & GIS

Zhang

Brown

2013

Secur Inform

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“…Note that this work is an extension of [14]. While in [14] we present a version of the algorithm CPSC and its application to the congressional redistricting problem, here we refine and extend our comparison of CPSC to the genetic algorithms (GAs) for redistricting.…”

Section: Use Of Heuristic Functions To Apply the Constraints Duringmentioning

confidence: 94%

“…While in [14] we present a version of the algorithm CPSC and its application to the congressional redistricting problem, here we refine and extend our comparison of CPSC to the genetic algorithms (GAs) for redistricting. Also, we present two additional versions of CPSC and apply our algorithm to the school district formation problem to more comprehensively evaluate our algorithms.…”

Section: Use Of Heuristic Functions To Apply the Constraints Duringmentioning

confidence: 98%

Redistricting Using Constrained Polygonal Clustering

Joshi

Soh

Samal

2012

IEEE Trans. Knowl. Data Eng.

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Abstract-Redistricting is the process of dividing a geographic area consisting of spatial units-often represented as spatial polygons-into smaller districts that satisfy some properties. It can therefore be formulated as a set partitioning problem where the objective is to cluster the set of spatial polygons into groups such that a value function is maximized [1]. Widely used algorithms developed for point-based data sets are not readily applicable because polygons introduce the concepts of spatial contiguity and other topological properties that cannot be captured by representing polygons as points. Furthermore, when clustering polygons, constraints such as spatial contiguity and unit distributedness should be strategically addressed. Toward this, we have developed the Constrained Polygonal Spatial Clustering (CPSC) algorithm based on the A Ã search algorithm that integrates cluster-level and instance-level constraints as heuristic functions. Using these heuristics, CPSC identifies the initial seeds, determines the best cluster to grow, and selects the best polygon to be added to the best cluster. We have devised two extensions of CPSC-CPSC* and CPSC*-PS-for problems where constraints can be soft or relaxed. Finally, we compare our algorithm with graph partitioning, simulated annealing, and genetic algorithm-based approaches in two applications-congressional redistricting and school districting.

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“…An applied police districting model for the Buffalo Police Department is presented in Sarac et al (1999). The Constraint-Based Polygonal Spatial Clustering (CPSC) method is another heuristic solution method used in police districting (Joshi et al 2009). The optimization approach was abandoned because the multiple objectives (with regards to demand balance, contiguity and compactness of the districts) made the problem computationally challenging for problems of real size.…”

Section: Literature Reviewmentioning

confidence: 99%