February 22, 2017

Advances in Spatial Data Handling and GIS: 14th by Daniel A. Griffith, Gerard B. M. Heuvelink (auth.), Anthony

By Daniel A. Griffith, Gerard B. M. Heuvelink (auth.), Anthony G.O. Yeh, Wenzhong Shi, Yee Leung, Chenghu Zhou (eds.)

This e-book offers a cross-section of state-of-the-art examine components being pursued through researchers in spatial info dealing with and geographic info technological know-how (GIS). It provides chosen papers at the development of spatial facts dealing with and GIS in electronic cartography, geospatial info integration, geospatial database and information infrastructures, geospatial info modeling, GIS for sustainable improvement, the interoperability of heterogeneous spatial info structures, location-based prone, spatial wisdom discovery and knowledge mining, spatial selection aid platforms, spatial info constructions and algorithms, spatial statistics, spatial info caliber and uncertainty, the visualization of spatial information, and internet and instant purposes in GIS.

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The sampling mean variance is smaller than any other sampling models, but the simple random sampling model does not account for area heterogeneity and spatial dependency, so the efficiency is lower. The stratified sampling and spatial random sampling models account for only area heterogeneity or spatial dependency, hence their efficiency is between spatial stratified sampling models and simple random sampling models. For small sample sizes, the stratified sampling model is better than the spatial random sampling model because it shows that the dependence of samples is not strong and the samples are more representative.

Application of the AGA based CA model in Shanghai’s Jiading District, Eastern China demonstrates that the model was able to generate reasonable representation of urban growth even with limited input data in defining its transition rules. The research shows that AGA technique can be integrated within a conventional CA based urban simulation model to improve human understanding on urban dynamics. 1 Introduction This paper presents a method applying an adaptive genetic algorithm (AGA) to define and search for transition rules and parameters of a cellular automata (CA) model to simulate the spatio-temporal processes of urban growth.

J. Feng and Y. Liu Bies RR, Muldoon MF, Pollock BG, Manuck S, Smith G, Sale ME (2006) A genetic algorithmbased, hybrid machine learning approach to model selection. J Pharmacokinet Pharmacodyn 33(2):196–221 Couclelis H (1997) From cellular automata to urban models: new principles for model development and implementation. Environ PlannB Plann Des 24(2):165–174 Espinoza F, Minsker BS, Goldberg D (2001) A self-adaptive hybrid genetic algorithm. ) GECCO 2001: Proceedings of the Genetic and Evolutionary Computation Conference.

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