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Covering models have been used in a wide range of modeling and geospatial analysis applications ranging from planning emergency services to natural reserve design. One topic in coverage modeling that has received considerable research attention is addressing uncertainty due to facility unavailability and service disruptions. In this article, we propose a covering model that maximizes the expected coverage of demand by considering the possibility of facility failures. Unlike existing models that assume a uniform failure probability across all sites in an area, the proposed model can account for spatially varying failure probabilities and describes better the underlying geographic processes that cause facility failures. The model is posed as a spatial optimization problem using integer linear programming. We compare two different formulations of the covering model and discuss their properties. The proposed model formulations have been tested computationally using a warning sirens data set that has been widely used in assessing covering models. We conclude with a summary of findings as well as possible directions of future research. [ABSTRACT FROM PUBLISHER]

Many government agencies and corporations face locational decisions, such as where to locate fire stations, postal facilities, nature reserves, computer centers, bank branches, and so on. To reach such location-related decisions, geographical information systems (GIS) are essential for providing access to spatial data and analysis tools. Moreover, geographic insights can be gained from GIS as they enable capabilities for better reflecting problems of interest in location modeling. The resulting models can be complex, however, and hence computationally challenging to solve. This article examines an important model for regional service coverage maximization. This model is solved heuristically using a genetic algorithm. The new heuristic innovatively incorporates problem-specific knowledge by exploring the geographical structure of the problem under study. Comparative application results demonstrate important nuances of the new genetic algorithm, enhancing overall performance. [ABSTRACT FROM AUTHOR]

How to represent geographic space has long been an issue in location modelling. Facilities, demand and/or the region of interest are often abstracted using aggregated points. However, substantial errors can be introduced, and obtained solutions could be dependent on the degree of aggregation. To address this, geographic representation of space is undergoing renewed research interest in spatial analysis and modelling. In this article the maximal coverage problem is studied, with a particular focus on demand coverage representation. Due to the limitations of existing modelling approaches for examining the coverage of space, there exist significant discrepancies between what is modelled and actual geographic coverage. In order to accurately reflect the mechanism of maximal coverage for spatial objects (points, lines or polygons), we introduce a new model explicitly accounting for joint service provided by multiple facilities. The new model can be viewed as an extension of existing approaches, but also a generalisation. An application to warning siren siting is carried out to demonstrate the merits of this new approach. [ABSTRACT FROM AUTHOR]