Special Issue "Spatial Optimization and GIS"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 October 2020).

Special Issue Editors

Prof. Kevin M. Curtin
E-Mail Website
Guest Editor
Department of Geography, Laboratory for Location Science, College of Arts & Sciences, University of Alabama, Box 870322, Tuscaloosa, AL, 35401, USA
Interests: spatial optimization; geographic information science; logistics; network analysis; transportation geography; team spatial behavior
Prof. Thomas J. Cova
E-Mail Website
Guest Editor
Geography Department, College of Social & Behavioral Science, University of Utah, 260 S. Central Campus Dr., Orson Spencer Hall, Salt Lake City, UT 84112-9155, USA
Interests: environmental hazards; emergency management; transportation; geographic information science; spatial optimization
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The ongoing revolutions in high-performance computing and in the collection and analysis of spatial data, combined with continued development and refinement of optimization solution procedures, are fundamentally changing the landscape for performing spatial optimization in the context of geographic information systems (GIS). The number and types of optimization problems that are envisioned and formulated is continually growing. The demand for spatial optimization regularly extends to new domain areas. The size of problems that can be approached with optimization grows as the bounds of tractability are pushed outward with increased computing power and sophisticated solution procedures. Taken together, these developments have altered the ways in which spatial optimization in GIS can be performed and how those analyses can contribute to both theory and practice. This Special Issue is dedicated to examining the changing practice of spatial optimization in the context of GIS, identifying the current frontier of the possible in this field, and identifying challenges and opportunities for continued development in the future. All papers—whether theoretical or applied—that address issues of spatial optimization in the context of GIS are welcomed.

Prof. Kevin M. Curtin
Prof. Thomas J. Cova
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spatial optimization
  • geographic information systems
  • geographic information science
  • location-allocation
  • algorithms
  • heuristics

Published Papers (5 papers)

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Research

Article
Bus Service Level and Horizontal Equity Analysis in the Context of the Modifiable Areal Unit Problem
ISPRS Int. J. Geo-Inf. 2021, 10(3), 111; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030111 - 26 Feb 2021
Viewed by 393
Abstract
The modifiable areal unit problem is of great importance in geographic science. The use of a specific zoning impacts the social and economic imbalances that can be generated in the deployment of services, facilities, and infrastructure. In this article, GIS is used together [...] Read more.
The modifiable areal unit problem is of great importance in geographic science. The use of a specific zoning impacts the social and economic imbalances that can be generated in the deployment of services, facilities, and infrastructure. In this article, GIS is used together with simulation and optimization tools to analyse the effects of bus frequency changes in the levels of service and horizontal equity derived from different types of territorial zoning. The city of Palma (Balearic Islands, Spain) was chosen as a case study for the method, for which different geographical areas are used: neighbourhoods, census sections, cadastral blocks, and a 400 × 400 m mesh. The results show significant variations of the optimal frequencies obtained, depending on the type of zoning used. In general, smaller zonings show much higher sensitivity for the detection of imbalances between the population and bus service level. Likewise, orthogonal zonings also prove useful for identifying service and population concentration over other zonings. The use of large spatial units could lead to the misdiagnosis of needs and the implementation of actions that do not actually improve the level of service or the equity of the transport service. It is recommended to consider combining zonings of different sizes simultaneously, in order to accurately highlight imbalances and to argue for transport service improvements. Full article
(This article belongs to the Special Issue Spatial Optimization and GIS)
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Article
A Unified Methodology for the Generalisation of the Geometry of Features
ISPRS Int. J. Geo-Inf. 2021, 10(3), 107; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030107 - 25 Feb 2021
Viewed by 530
Abstract
The development of generalisation (simplification) methods for the geometry of features in digital cartography in most cases involves the improvement of existing algorithms without their validation with respect to the similarity of feature geometry before and after the process. It also consists of [...] Read more.
The development of generalisation (simplification) methods for the geometry of features in digital cartography in most cases involves the improvement of existing algorithms without their validation with respect to the similarity of feature geometry before and after the process. It also consists of the assessment of results from the algorithms, i.e., characteristics that are indispensable for automatic generalisation. The preparation of a fully automatic generalisation for spatial data requires certain standards, as well as unique and verifiable algorithms for particular groups of features. This enables cartographers to draw features from these databases to be used directly on the maps. As a result, collected data and their generalised unique counterparts at various scales should constitute standardised sets, as well as their updating procedures. This paper proposes a solution which consists in contractive self-mapping (contractor for scale s = 1) that fulfils the assumptions of the Banach fixed-point theorem. The method of generalisation of feature geometry that uses the contractive self-mapping approach is well justified due to the fact that a single update of source data can be applied to all scales simultaneously. Feature data at every scale s < 1 are generalised through contractive mapping, which leads to a unique solution. Further generalisation of the feature is carried out on larger scale spatial data (not necessarily source data), which reduces the time and cost of the new elaboration. The main part of this article is the theoretical presentation of objectifying the complex process of the generalisation of the geometry of a feature. The use of the inherent characteristics of metric spaces, narrowing mappings, Lipschitz and Cauchy conditions, Salishchev measures, and Banach theorems ensure the uniqueness of the generalisation process. Their application to generalisation makes this process objective, as it ensures that there is a single solution for portraying the generalised features at each scale. The present study is dedicated to researchers concerned with the theory of cartography. Full article
(This article belongs to the Special Issue Spatial Optimization and GIS)
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Article
Continuous k Nearest Neighbor Queries over Large-Scale Spatial–Textual Data Streams
ISPRS Int. J. Geo-Inf. 2020, 9(11), 694; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110694 - 20 Nov 2020
Viewed by 467
Abstract
Continuous k nearest neighbor queries over spatial–textual data streams (abbreviated as CkQST) are the core operations of numerous location-based publish/subscribe systems. Such a system is usually subscribed with millions of CkQST and evaluated simultaneously whenever new objects arrive and old objects expire. To [...] Read more.
Continuous k nearest neighbor queries over spatial–textual data streams (abbreviated as CkQST) are the core operations of numerous location-based publish/subscribe systems. Such a system is usually subscribed with millions of CkQST and evaluated simultaneously whenever new objects arrive and old objects expire. To efficiently evaluate CkQST, we extend a quadtree with an ordered, inverted index as the spatial–textual index for subscribed queries to match the incoming objects, and exploit it with three key techniques. (1) A memory-based cost model is proposed to find the optimal quadtree nodes covering the spatial search range of CkQST, which minimize the cost for searching and updating the index. (2) An adaptive block-based ordered, inverted index is proposed to organize the keywords of CkQST, which adaptively arranges queries in spatial nodes and allows the objects containing common keywords to be processed in a batch with a shared scan, and hence a significant performance gain. (3) A cost-based k-skyband technique is proposed to judiciously determine an optimal search range for CkQST according to the workload of objects, to reduce the re-evaluation cost due to the expiration of objects. The experiments on real-world and synthetic datasets demonstrate that our proposed techniques can efficiently evaluate CkQST. Full article
(This article belongs to the Special Issue Spatial Optimization and GIS)
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Article
Station-Free Bike Rebalancing Analysis: Scale, Modeling, and Computational Challenges
ISPRS Int. J. Geo-Inf. 2020, 9(11), 691; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110691 - 19 Nov 2020
Cited by 2 | Viewed by 422
Abstract
In the past few years, station-free bike sharing systems (SFBSSs) have been adopted in many cities worldwide. Different from conventional station-based bike sharing systems (SBBSSs) that rely upon fixed bike stations, SFBSSs allow users the flexibility to locate a bike nearby and park [...] Read more.
In the past few years, station-free bike sharing systems (SFBSSs) have been adopted in many cities worldwide. Different from conventional station-based bike sharing systems (SBBSSs) that rely upon fixed bike stations, SFBSSs allow users the flexibility to locate a bike nearby and park it at any appropriate site after use. With no fixed bike stations, the spatial extent/scale used to evaluate bike shortage/surplus in an SFBSS has been rather arbitrary in existing studies. On the one hand, a balanced status using large areas may contain multiple local bike shortage/surplus sites, leading to a less effective rebalancing design. On the other hand, an imbalance evaluation conducted in small areas may not be meaningful or necessary, while significantly increasing the computational complexity. In this study, we examine the impacts of analysis scale on the SFBSS imbalance evaluation and the associated rebalancing design. In particular, we develop a spatial optimization model to strategically optimize bike rebalancing in an SFBSS. We also propose a region decomposition method to solve large-sized bike rebalancing problems that are constructed based on fine analysis scales. We apply the approach to study the SFBSS in downtown Beijing. The empirical study shows that imbalance evaluation results and optimal rebalancing design can vary substantially with analysis scale. According to the optimal rebalancing results, bike repositioning tends to take place among neighboring areas. Based on the empirical study, we would recommend 800 m and 100/200 m as the suitable scale for designing operator-based and user-based rebalancing plans, respectively. Computational results show that the region decomposition method can be used to solve problems that cannot be handled by existing commercial optimization software. This study provides important insights into effective bike-share rebalancing strategies and urban bike transportation planning. Full article
(This article belongs to the Special Issue Spatial Optimization and GIS)
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Article
A Spatial Optimization Approach for Simultaneously Districting Precincts and Locating Polling Places
ISPRS Int. J. Geo-Inf. 2020, 9(5), 301; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9050301 - 06 May 2020
Cited by 1 | Viewed by 800
Abstract
Voting is the most basic form of political participation. The agencies that are responsible for voting must delineate precincts and designate a polling place for each precinct. This spatial decision-making requires a strategic approach for several reasons. First, changes in the location of [...] Read more.
Voting is the most basic form of political participation. The agencies that are responsible for voting must delineate precincts and designate a polling place for each precinct. This spatial decision-making requires a strategic approach for several reasons. First, changes in the location of polling places induce transportation and search costs from the perspective of voters. Second, improving accessibility to polling places can increase turnout. Third, differences in the population sizes of precincts may produce biased voting results. Spatial optimization approaches can be a strategic method for delimiting precincts and siting polling places. The purpose of this paper is to develop a spatial optimization model, namely, the capacitated double p-median problem with preference (CDPMP-P), which simultaneously delimits boundaries of precincts and selects potential facilities in terms of mixed integer programming (MIP). The CDPMP-P explicitly includes realistic requirements, such as population balance, the spatial continuity of precincts, the preferences of potential facilities where polling places can be installed, and the possibility of allocating multiple polling places in one facility. Full article
(This article belongs to the Special Issue Spatial Optimization and GIS)
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