A Majority Theorem for the Uncapacitated <i>p</i> = 2 Median Problem and Local Spatial Autocorrelation

The existing quantitative geography literature contains a dearth of articles that span spatial autocorrelation (SA), a fundamental property of georeferenced data, and spatial optimization, a popular form of geographic analysis. The well-known location–allocation problem illustrates this state of aff...

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Main Authors: Daniel A. Griffith, Yongwan Chun, Hyun Kim
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/13/2/249
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author Daniel A. Griffith
Yongwan Chun
Hyun Kim
author_facet Daniel A. Griffith
Yongwan Chun
Hyun Kim
author_sort Daniel A. Griffith
collection DOAJ
description The existing quantitative geography literature contains a dearth of articles that span spatial autocorrelation (SA), a fundamental property of georeferenced data, and spatial optimization, a popular form of geographic analysis. The well-known location–allocation problem illustrates this state of affairs, although its empirical geographic distribution of demand virtually always exhibits positive SA. This latent redundant attribute information alludes to other tools that may well help to solve such spatial optimization problems in an improved, if not better than, heuristic way. Within a proof-of-concept perspective, this paper articulates connections between extensions of the renowned Majority Theorem of the minisum problem and especially the local indices of SA (LISA). The relationship articulation outlined here extends to the <i>p</i> = 2 setting linkages already established for the <i>p</i> = 1 spatial median problem. In addition, this paper presents the foundation for a novel extremely efficient <i>p</i> = 2 algorithm whose formulation demonstratively exploits spatial autocorrelation.
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spelling doaj-art-0e6964a1f17c4981a1f2c35c23bb3d362025-01-24T13:39:53ZengMDPI AGMathematics2227-73902025-01-0113224910.3390/math13020249A Majority Theorem for the Uncapacitated <i>p</i> = 2 Median Problem and Local Spatial AutocorrelationDaniel A. Griffith0Yongwan Chun1Hyun Kim2School of Economic, Political, and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USASchool of Economic, Political, and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USADepartment of Geography and Sustainability, University of Tennessee, Knoxville, TN 37996, USAThe existing quantitative geography literature contains a dearth of articles that span spatial autocorrelation (SA), a fundamental property of georeferenced data, and spatial optimization, a popular form of geographic analysis. The well-known location–allocation problem illustrates this state of affairs, although its empirical geographic distribution of demand virtually always exhibits positive SA. This latent redundant attribute information alludes to other tools that may well help to solve such spatial optimization problems in an improved, if not better than, heuristic way. Within a proof-of-concept perspective, this paper articulates connections between extensions of the renowned Majority Theorem of the minisum problem and especially the local indices of SA (LISA). The relationship articulation outlined here extends to the <i>p</i> = 2 setting linkages already established for the <i>p</i> = 1 spatial median problem. In addition, this paper presents the foundation for a novel extremely efficient <i>p</i> = 2 algorithm whose formulation demonstratively exploits spatial autocorrelation.https://www.mdpi.com/2227-7390/13/2/249local spatial autocorrelationmajority theoremspatial autocorrelationspatial medianspatial optimization
spellingShingle Daniel A. Griffith
Yongwan Chun
Hyun Kim
A Majority Theorem for the Uncapacitated <i>p</i> = 2 Median Problem and Local Spatial Autocorrelation
Mathematics
local spatial autocorrelation
majority theorem
spatial autocorrelation
spatial median
spatial optimization
title A Majority Theorem for the Uncapacitated <i>p</i> = 2 Median Problem and Local Spatial Autocorrelation
title_full A Majority Theorem for the Uncapacitated <i>p</i> = 2 Median Problem and Local Spatial Autocorrelation
title_fullStr A Majority Theorem for the Uncapacitated <i>p</i> = 2 Median Problem and Local Spatial Autocorrelation
title_full_unstemmed A Majority Theorem for the Uncapacitated <i>p</i> = 2 Median Problem and Local Spatial Autocorrelation
title_short A Majority Theorem for the Uncapacitated <i>p</i> = 2 Median Problem and Local Spatial Autocorrelation
title_sort majority theorem for the uncapacitated i p i 2 median problem and local spatial autocorrelation
topic local spatial autocorrelation
majority theorem
spatial autocorrelation
spatial median
spatial optimization
url https://www.mdpi.com/2227-7390/13/2/249
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