Daoqin Tong

Coverage optimization in continuous space facility siting

Facility placement and associated service coverage are major concerns in urban and regional planning. In this paper an approach is detailed for the problem of covering spatial demand for service, where potential facilities are located in the continuous plane. It is shown that weighted demand, represented as points, lines or polygons, can be optimally served by a finite number of potential facility locations, called the polygon intersection point set (PIPS). The developed approach is an extension of a point‐based abstraction of demand to more general representations (e.g. points, lines or polygons). An empirical analysis of warning siren siting in Ohio is carried out, highlighting the applicability of this approach.

Spatial Optimization in Geography

This article discusses spatial optimization in geography, focusing on contributions of geographers in explicit geographical contexts. An overview of spatial optimization is given, as well as illustrative examples. Many of the individuals contributing to this area of the discipline are identified, demonstrating the breadth of academic institutions spanning the globe where spatial optimization is represented in the research and curriculum of geographers. The article provides a characterization of what a spatial optimization problem is, but also properties, relationships, and challenges behind this. The ultimate purpose of this article is to highlight the spatial optimization subspecialty within geography and in doing so, highlight the need for continued spatial model development and application in the discipline. Further, there is also a need for research focused on techniques to solve spatial optimization problems, particularly in the context of geographic information systems.

Enhancing Classic Coverage Location Models

An important area of regional science has long been location analysis and modeling. Its significance continues, now more formally known as location science, and has evolved because of the need to address complex facility siting problems and issues. This article focuses on classic coverage location problems, and how advances along theoretical and methodological fronts have enabled such problems to be viewed in new ways. Specifically, notions of implicit and explicit coverage, along with geographic information systems (GIS), provide the capacity to reconceptualize as well as better model intended planning goals and objectives. This article reviews covering problems and presents a comparative framework for both linkage and assessment. This research is significant because evolving models enable issues of frame independence, and the modifiable area unit problem, to be addressed, making planning and analysis more reliable and valuable.

Heuristics in Spatial Analysis: A Genetic Algorithm for Coverage Maximization

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.

Maximizing Wireless Mesh Network Coverage

As an emerging technology, wireless communication revolutionizes the way data are shared and transferred. In particular, wireless mesh network (WMN) technology allows data transmission from one node to another without extensive cabling. In this article, spatial characteristics of maximal covering problems are explored, and a novel spatial optimization model is proposed for WMN topology planning. The model selects the optimal locations for network infrastructure to achieve the maximal coverage of spatial demand. Additionally, important WMN design requirements have been accounted for, including network topology and throughput capacity. The validity of the model is tested through a WMN deployment developed for an emergency medical service application in Tucson, Arizona.

A Geocomputational Heuristic for Coverage Maximization in Service Facility Siting

Siting service facilities in order to maximize regional coverage is important when budget resources are limited. Various approaches exist for addressing this particular planning problem for discrete or continuous representations of potential facility sites and demand to be served. In cases where both candidate facility sites and service demand are continuous, approaches for maximizing regional coverage have only examined the siting of a single facility. In this article, a geocomputational approach is proposed for addressing multiple facility siting when demand is continuously distributed and facilities may be located anywhere in the region. Emergency warning siren location is used to highlight the developed approach.

Aggregation in continuous space coverage modeling

Uncertainties and errors associated with aggregation have long been recognized in the study of spatial problems. In facility location modeling, while much has been done to examine the aggregation of large datasets of discrete points, errors and uncertainties involved in aggregating continuous spatial units are not well understood. This study focuses on the effects of aggregating continuous spatial units into discrete points within the context of the location set covering problem. We propose new measures to understand and quantify errors associated with a continuous aggregation scheme. In a real-world application, the proposed methods can be used to suggest an appropriate aggregation scheme before the application of the location model. We demonstrate the concepts developed here with an empirical study of siting emergency warning sirens in the city of Dublin, OH.

Hedging against service disruptions: an expected median location problem with site-dependent failure probabilities

The vector assignment p-median problem (VAPMP) (Weaver and Church in Transp Sci 19(1):58–74, 1985) was one of the first location-allocation models developed to handle split assignment of a demand to multiple facilities. The underlying construct of the VAPMP has been subsequently used in a number of reliable facility location and backup location models. Although in many applications the chance that a facility fails may vary substantially with locations, many existing models have assumed a uniform failure probability across all sites. As an improvement, this paper proposes a new model, the expected p-median problem as a generalization of existing approaches by explicitly considering site-dependent failure probabilities. Multi-level closest assignment constraints and two efficient integer linear programming (ILP) formulations are introduced. While prior research generally concludes that similar problems are not integer-friendly and cannot be solved by ILP software, computational results show that our model can be used to solve medium-sized location problems optimally using existing ILP software. Moreover, the new model can be used to formulate other reliable or expected location problems with consideration of site-dependent failure probabilities.

Predicting potential distributions of geographic events using one-class data: concepts and methods

One common problem with geographic data is that, for a specific geographic event, only occurrence information is available; information about the absence of the event is not available. We refer to these specific types of geospatial data as geographic one-class data (GOCD). Predicting the potential spatial distributions that a particular geographic event may occur from GOCD is difficult because traditional binary classification methods that require availability of both positive and negative training samples cannot be used. The objective of this research is to define GOCD and propose novel approaches for modelling potential spatial distributions of geographic events using GOCD. We investigate the effectiveness of one-class support vector machine (OCSVM), maximum entropy (MAXENT) and the newly proposed positive and unlabelled learning (PUL) algorithm for solving GOCD problems using a case study: species distribution modelling from synthetic data. Our experimental results indicate that generally OCSVM, MAXENT and PUL are effective in modelling the GOCD. Each method has advantages and disadvantages, but PUL seems to be the most promising method.

New Perspectives on the Use of GPS and GIS to Support a Highway Performance Study

In recent decades, rapid growth of travel volume has resulted in a significant increase in traffic congestion, accidents, environmental pollution and energy consumption. Accurate traffic data are drastically needed for effective evaluation of traffic systems in order to alleviate the impacts of increasing travel volume of the quality of life and economic development of urban areas. This article provides a discussion on a data acquisition methodology for highway traffic pattern recognition and congestion analysis by integrating data from the global positioning system (GPS) with a geographic information system (GIS). The GPS technology is a powerful tool in capturing continuous positioning and timing information, whereas the GIS is capable of storing, managing, manipulating, analyzing and displaying the acquired spatial information. Compared to previous studies, the effective integration of the two technologies allows for traffic analysis to be conducted at a finer resolution. The proposed method is illustrated with a case study on multiple major highway segments in Columbus, Ohio.