An Automated Multi-Criteria Cartographic Aid for Point Annotation

Huffman, F. Tyler; Cromley, Robert G.

doi:10.1179/caj.2002.39.1.51

Cited by 8 publications

(1 citation statement)

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“…Considering the MIVSP approach, Zoraster () presents a binary integer linear programming formulation and propose constraints of conflicts, utilizing fictional candidate positions, whose selection implies high cost in the objective function, thus, the fictional candidate positions are only chosen if there is no other real candidate position available. Huffman and Cromley () proposed a multi‐criteria model for the PFCLP, based on the linear programming formulation of the MIVSP. The objective function is expanded to consider criteria of position preferences, point size, and feature importance.…”

Section: Literature Reviewmentioning

confidence: 99%

A Constructive Genetic Algorithm for Discrete Dispersion on Point Feature Cartographic Label Placement Problems

Gomes

Lorena

Ribeiro

2015

Geographical Analysis

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This article concerns the point feature cartographic label placement (PFCLP) problem, which is a NP‐hard (Non‐deterministic Polynomial‐time hard) combinatorial problem. It is considered that when all points must be labeled and overlaps are inevitable, the map can be more readable if overlapping labels are placed in a dispersive way, that is, overlapping labels are distant from each other. This work presents a constructive genetic algorithm (CGA) for the discrete dispersion PFCLP that utilizes the notion of masking to preserve optimal subsequences in chromosomes. We also define the discrete dispersion PFCLP as a mixed integer linear programming model, considering the problem of the minimum number of labels in conflict as well. The computational results validate our CGA approach using instances up to 5,046 points.

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Section: Literature Reviewmentioning

confidence: 99%