The main objective in writing this text was to describe many of the developments in location science that have been proposed, modeled, and applied using a covering framework. Covering models are based upon the use of some standard of service, ranging from a maximal response time standard in locating ambulances to a minimal decibel standard used in placing warning sirens. Many covering problems can broadly be classified into two types: (1) cover each and every demand using the smallest number of facilities, the Location Set Covering Problem (LSCP); or (2) maximize the demand that is covered while locating a fixed number of facilities, the Maximal Covering Location Problem (MCLP). These two problems and related models emerged in the early 1970s and have formed the basis for a considerable portion of this book. Applications have involved the location of surveillance cameras, cell phone towers, fire stations, health clinics, ambulances, and sirens, just to name a few. They have even included the selection of biological reserve sites, designing teeth color shades, and laying out fabric cutting patterns. Altogether, the location science literature reflects a rich history and long term development of covering models, involving the fields of geography, engineering, business, computer science, health planning, environmental science, conservation biology, regional science and economics, and urban planning.
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Aly AA, White JA (1978) Probabilistic formulation of the emergency service location problem. J Oper Res Soc 29(2):1167–1179CrossRefGoogle Scholar
Brill ED Jr (1979) The use of optimization models in public-sector planning. Manag Sci 25(5):413–422CrossRefGoogle Scholar
Brill ED Jr, Chang SY, Hopkins LD (1982) Modeling to generate alternatives: the HSJ approach and an illustration using a problem in land use planning. Manag Sci 28(3):221–235CrossRefGoogle Scholar
Church RL (1974) Synthesis of a class of public facilities location models. PhD Dissertation, The Johns Hopkins University, Baltimore, MDGoogle Scholar
Church RL, Li W (2016) Estimating spatial efficiency using cyber search, GIS, and spatial optimization: a case study of fire service deployment in Los Angeles County. Int J Geogr Inf Sci 30(3):535–553CrossRefGoogle Scholar
Church RL, Weaver JR (1986) Theoretical links between median and coverage location problems. Ann Oper Res 6(1):1–19CrossRefGoogle Scholar
Current JR, Schilling DA (1990) Analysis of errors due to demand data aggregation in the set covering and maximal covering location problems. Geogr Anal 22(2):116–126CrossRefGoogle Scholar
Daskin MS, Haghani AE, Khanal M, Malandraki C (1989) Aggregation effects in maximum covering models. Ann Oper Res 18(1):113–139CrossRefGoogle Scholar
Hillsman EL (1984) The p-median structure as a unified linear model for location—allocation analysis. Environ Plan A 16(3):305–318CrossRefGoogle Scholar
Liebman JC (1976) Some simple-minded observations on the role of optimization in public systems decision-making. Interfaces 6(4):102–108CrossRefGoogle Scholar
Marr B (2016a) Big Data: mind-boggling facts everyone must read. Forbes (posted online Sept 2015)Google Scholar