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

作者：Pijanowski Bryan C^*; Tayyebi Amin; Doucette Jarrod; Pekin Burak K; Braun David; Plourde James

来源：Environmental Modelling & Software, 2014, 51: 250-268.

DOI：10.1016/j.envsoft.2013.09.015

• Land use land cover change
• Big data simulation
• Land Transformation Model
• High Performance Computing
• Extensible Markup Language
• Python environment
• Visual Studio 10 (C#)
• Continental scale

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.

• 出版日期2014-1