A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment.
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| Title: | A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment. |
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| Authors: | Pijanowski, Bryan C.1 bpijanow@purdue.edu, Tayyebi, Amin1,2, Doucette, Jarrod1, Pekin, Burak K.1,3, Braun, David4,5, Plourde, James1,6 |
| Source: | Environmental Modelling & Software. Jan2014, Vol. 51, p250-268. 19p. |
| Subject Terms: | *Urban growth, *Land use, Simulation methods & models, Geographic information systems, Artificial neural networks, High performance computing |
| Abstract: | Abstract: The Land Transformation Model (LTM) is a Land Use Land Cover Change (LUCC) model which was originally developed to simulate local scale LUCC patterns. The model uses a commercial windows-based GIS program to process and manage spatial data and an artificial neural network (ANN) program within a series of batch routines to learn about spatial patterns in data. In this paper, we provide an overview of a redesigned LTM capable of running at continental scales and at a fine (30m) resolution using a new architecture that employs a windows-based High Performance Computing (HPC) cluster. This paper provides an overview of the new architecture which we discuss within the context of modeling LUCC that requires: (1) using an HPC to run a modified version of our LTM; (2) managing large datasets in terms of size and quantity of files; (3) integration of tools that are executed using different scripting languages; and (4) a large number of steps necessitating several aspects of job management. [Copyright &y& Elsevier] |
| Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 91974381 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pijanowski%2C+Bryan+C%2E%22">Pijanowski, Bryan C.</searchLink><relatesTo>1</relatesTo><i> bpijanow@purdue.edu</i><br /><searchLink fieldCode="AR" term="%22Tayyebi%2C+Amin%22">Tayyebi, Amin</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Doucette%2C+Jarrod%22">Doucette, Jarrod</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pekin%2C+Burak+K%2E%22">Pekin, Burak K.</searchLink><relatesTo>1,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Braun%2C+David%22">Braun, David</searchLink><relatesTo>4,5</relatesTo><br /><searchLink fieldCode="AR" term="%22Plourde%2C+James%22">Plourde, James</searchLink><relatesTo>1,6</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Modelling+%26+Software%22">Environmental Modelling & Software</searchLink>. Jan2014, Vol. 51, p250-268. 19p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Urban+growth%22">Urban growth</searchLink><br />*<searchLink fieldCode="DE" term="%22Land+use%22">Land use</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+information+systems%22">Geographic information systems</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22High+performance+computing%22">High performance computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: The Land Transformation Model (LTM) is a Land Use Land Cover Change (LUCC) model which was originally developed to simulate local scale LUCC patterns. The model uses a commercial windows-based GIS program to process and manage spatial data and an artificial neural network (ANN) program within a series of batch routines to learn about spatial patterns in data. In this paper, we provide an overview of a redesigned LTM capable of running at continental scales and at a fine (30m) resolution using a new architecture that employs a windows-based High Performance Computing (HPC) cluster. This paper provides an overview of the new architecture which we discuss within the context of modeling LUCC that requires: (1) using an HPC to run a modified version of our LTM; (2) managing large datasets in terms of size and quantity of files; (3) integration of tools that are executed using different scripting languages; and (4) a large number of steps necessitating several aspects of job management. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.envsoft.2013.09.015 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 250 Subjects: – SubjectFull: Urban growth Type: general – SubjectFull: Land use Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Geographic information systems Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: High performance computing Type: general Titles: – TitleFull: A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pijanowski, Bryan C. – PersonEntity: Name: NameFull: Tayyebi, Amin – PersonEntity: Name: NameFull: Doucette, Jarrod – PersonEntity: Name: NameFull: Pekin, Burak K. – PersonEntity: Name: NameFull: Braun, David – PersonEntity: Name: NameFull: Plourde, James IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 13648152 Numbering: – Type: volume Value: 51 Titles: – TitleFull: Environmental Modelling & Software Type: main |
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