Translator Disclaimer
3 November 2008 An improved cellular automata forecasting model for urban land use spatial structure changes
Author Affiliations +
Proceedings Volume 7143, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments; 714310 (2008) https://doi.org/10.1117/12.812560
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
Though the urban land use spatial dynamic simulation and forecasting based on cellular automata (CA) model have achieved remarkable progress, the CA model still has some problems and drawbacks in forecasting urban land use changes. In view of the deficiencies of traditional urban CA, an improved CA model based on spatial dynamic data mining and random forecast is proposed in this paper, which establishes an operable CA method to forecast and simulate the discrete status attribute. This improved CA model is examined in analyzing the urban land use structure changes in Jinan 2002-2006 and testified both feasible and effective. Based on the remote sensing images in Jinan 2002 and 2006, the urban land use spatial structures are classified into five types, commercial land, residential land, education facility, industrial land and the other. With the improved CA model, the urban land use framework in Jinan in 2010 was calculated, the result of which can be used as a reliable reference information for the following urban land use planning.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yan Wang, Peilin Wu, Zhenbai Song, and Junru Cao "An improved cellular automata forecasting model for urban land use spatial structure changes", Proc. SPIE 7143, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 714310 (3 November 2008); https://doi.org/10.1117/12.812560
PROCEEDINGS
8 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Grid-based earthquake data sharing
Proceedings of SPIE (October 09 2009)
Mining textural association rules in RS image
Proceedings of SPIE (November 13 2007)
Automated geodata analysis and metadata generation
Proceedings of SPIE (March 11 2002)
The study on rough set in GIS and remote sensing
Proceedings of SPIE (December 02 2005)
Scan patterns for association rule mining of image data
Proceedings of SPIE (June 10 2003)
Analysis and summarization of correlations in data cubes
Proceedings of SPIE (March 11 2002)

Back to Top