File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A minimum-volume oriented bounding box strategy for improving the performance of urban cellular automata based on vectorization and parallel computing technology

TitleA minimum-volume oriented bounding box strategy for improving the performance of urban cellular automata based on vectorization and parallel computing technology
Authors
KeywordsUrban cellular automata
bounding box
vectorization
parallel computing
geographical simulation
Issue Date2020
PublisherTaylor & Francis. The Journal's web site is located at https://www.tandfonline.com/toc/tgrs20/current
Citation
GIScience and Remote Sensing, 2020, v. 57 n. 1, p. 91-106 How to Cite?
AbstractAs an effective tool for simulating spatiotemporal urban processes in the real world, urban cellular automata (CA) models involve multiple data layers and complicated calibration algorithms, which make their computational capability become a bottleneck. Numerous approaches and techniques have been applied to the development of high-performance urban CA models, among which the integration of vectorization and parallel computing has broad application prospects due to its powerful computational ability and scalability. Unfortunately, this hybrid algorithm becomes inefficient when the axis-aligned bounding box (AABB) of study areas contains many unavailable cells. This paper presents a minimum-volume oriented bounding box (OBB) strategy to solve the above problem. Specifically, geometric transformation (i.e. translation and rotation) is applied to find the OBB of the study area before implementing the hybrid algorithm, and a set of functions are established to describe the spatial coordinate relationship between the AABB and OBB layers. Experiments conducted in this study demonstrate that the OBB strategy can further reduce the computational time of urban CA models after vectorization and parallelism. For example, when the cell size is 15 m and the neighborhood size is 3 × 3, an approximately 10-fold speedup in computational time can result from vectorization in the MATLAB environment, followed by an 18-fold speedup after implementing parallel computing in a quad-core processor and, finally, a speedup of 25-fold by further using an OBB strategy. We thus argue that OBB strategy can make the integration of vectorization and parallel computing more efficient and may provide scalable solutions for significantly improving the applicability of urban CA models.
Persistent Identifierhttp://hdl.handle.net/10722/288323
ISSN
2019 Impact Factor: 5.965
2015 SCImago Journal Rankings: 1.127

 

DC FieldValueLanguage
dc.contributor.authorXIA, C-
dc.contributor.authorZhang, B-
dc.contributor.authorWang, H-
dc.contributor.authorQIAO, S-
dc.contributor.authorZHANG, A-
dc.date.accessioned2020-10-05T12:11:10Z-
dc.date.available2020-10-05T12:11:10Z-
dc.date.issued2020-
dc.identifier.citationGIScience and Remote Sensing, 2020, v. 57 n. 1, p. 91-106-
dc.identifier.issn1548-1603-
dc.identifier.urihttp://hdl.handle.net/10722/288323-
dc.description.abstractAs an effective tool for simulating spatiotemporal urban processes in the real world, urban cellular automata (CA) models involve multiple data layers and complicated calibration algorithms, which make their computational capability become a bottleneck. Numerous approaches and techniques have been applied to the development of high-performance urban CA models, among which the integration of vectorization and parallel computing has broad application prospects due to its powerful computational ability and scalability. Unfortunately, this hybrid algorithm becomes inefficient when the axis-aligned bounding box (AABB) of study areas contains many unavailable cells. This paper presents a minimum-volume oriented bounding box (OBB) strategy to solve the above problem. Specifically, geometric transformation (i.e. translation and rotation) is applied to find the OBB of the study area before implementing the hybrid algorithm, and a set of functions are established to describe the spatial coordinate relationship between the AABB and OBB layers. Experiments conducted in this study demonstrate that the OBB strategy can further reduce the computational time of urban CA models after vectorization and parallelism. For example, when the cell size is 15 m and the neighborhood size is 3 × 3, an approximately 10-fold speedup in computational time can result from vectorization in the MATLAB environment, followed by an 18-fold speedup after implementing parallel computing in a quad-core processor and, finally, a speedup of 25-fold by further using an OBB strategy. We thus argue that OBB strategy can make the integration of vectorization and parallel computing more efficient and may provide scalable solutions for significantly improving the applicability of urban CA models.-
dc.languageeng-
dc.publisherTaylor & Francis. The Journal's web site is located at https://www.tandfonline.com/toc/tgrs20/current-
dc.relation.ispartofGIScience and Remote Sensing-
dc.rightsAOM/Preprint Before Accepted: his article has been accepted for publication in [JOURNAL TITLE], published by Taylor & Francis. AOM/Preprint After Accepted: This is an [original manuscript / preprint] of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. Accepted Manuscript (AM) i.e. Postprint This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI].-
dc.subjectUrban cellular automata-
dc.subjectbounding box-
dc.subjectvectorization-
dc.subjectparallel computing-
dc.subjectgeographical simulation-
dc.titleA minimum-volume oriented bounding box strategy for improving the performance of urban cellular automata based on vectorization and parallel computing technology-
dc.typeArticle-
dc.identifier.emailXIA, C: xia2016@whu.edu.cn-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/15481603.2019.1670974-
dc.identifier.scopuseid_2-s2.0-85073936166-
dc.identifier.hkuros314962-
dc.identifier.volume57-
dc.identifier.issue1-
dc.identifier.spage91-
dc.identifier.epage106-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats