Spatial point process models for location-allocation problems

DOI10.1016/j.csda.2008.10.016zbMath1453.62049OpenAlexW1999537251MaRDI QIDQ961747

Florent Bonneu, Christine Thomas-Agnan

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.10.016

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Non-Markovian processes: estimation (62M09) Continuous location (90B85) Discrete location and assignment (90B80) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (2)

Mass transportation and the consistency of the empirical optimal conditional locations ⋮ Editorial. Spatial statistics: methods, models \& computation

Uses Software

• spatstat
• bootlib

Cites Work

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• Resampling a coverage pattern
• Location analysis: a synthesis and survey
• Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems
• Mass transportation and the consistency of the empirical optimal conditional locations
• Solution of the Multisource Weber and Conditional Weber Problems by D.-C. Programming
• A Thinned Block Bootstrap Variance Estimation Procedure for Inhomogeneous Spatial Point Patterns
• Sensitivity analysis of the optimal location of a facility
• Locating Emergency Vehicle Bases When Service Time Depends on Call Location
• Bootstrap Confidence Regions for the Intensity of a Poisson Point Process
• Heuristic Methods for Location-Allocation Problems
• Multicriteria Optimization
• Modelling Spatial Point Patterns in R
• Is bootstrap really helpful in point process statistics?

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