{"title":"The Multiple Gradual Maximal Covering Location Problem","authors":"Ashleigh N. Price, Kevin M. Curtin","doi":"10.1111/gean.12410","DOIUrl":null,"url":null,"abstract":"This article describes a new spatial optimization model, the Multiple Gradual Maximal Covering Location Problem (MG‐MCLP). This model is useful when coverage from multiple facilities or sensors is necessary to consider a demand to be covered, and when the quality of that coverage varies with the number of located facilities within the service distance, and the distance from the demand itself. The motivating example for this model uses a coupled GIS and optimization framework to determine the optimal locations for acoustic sensors—typically used in police applications for gunshot detection—in Tuscaloosa, AL. The results identify the optimal facility locations for allocating multiple facilities, at different locations, to cover multiple demands and evaluate those optimal locations with distance‐decay. Solving the MG‐MCLP over a range of values allows for comparing the performance of varying numbers of available resources, which could be used by public safety operations to demonstrate the number of resources that would be required to meet policy goals. The results illustrate the flexibility in designing alternative spatial allocation strategies and provide a tractable covering model that is solved with standard linear programming and GIS software, which in turn can improve spatial data analysis across many operational contexts.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"14 1","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geographical Analysis","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1111/gean.12410","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 0
Abstract
This article describes a new spatial optimization model, the Multiple Gradual Maximal Covering Location Problem (MG‐MCLP). This model is useful when coverage from multiple facilities or sensors is necessary to consider a demand to be covered, and when the quality of that coverage varies with the number of located facilities within the service distance, and the distance from the demand itself. The motivating example for this model uses a coupled GIS and optimization framework to determine the optimal locations for acoustic sensors—typically used in police applications for gunshot detection—in Tuscaloosa, AL. The results identify the optimal facility locations for allocating multiple facilities, at different locations, to cover multiple demands and evaluate those optimal locations with distance‐decay. Solving the MG‐MCLP over a range of values allows for comparing the performance of varying numbers of available resources, which could be used by public safety operations to demonstrate the number of resources that would be required to meet policy goals. The results illustrate the flexibility in designing alternative spatial allocation strategies and provide a tractable covering model that is solved with standard linear programming and GIS software, which in turn can improve spatial data analysis across many operational contexts.
期刊介绍:
First in its specialty area and one of the most frequently cited publications in geography, Geographical Analysis has, since 1969, presented significant advances in geographical theory, model building, and quantitative methods to geographers and scholars in a wide spectrum of related fields. Traditionally, mathematical and nonmathematical articulations of geographical theory, and statements and discussions of the analytic paradigm are published in the journal. Spatial data analyses and spatial econometrics and statistics are strongly represented.