By Daniel A. Griffith, Gerard B. M. Heuvelink (auth.), Anthony G.O. Yeh, Wenzhong Shi, Yee Leung, Chenghu Zhou (eds.)
This e-book presents a cross-section of state-of-the-art study components being pursued through researchers in spatial info dealing with and geographic details technological know-how (GIS). It offers chosen papers at the development of spatial facts dealing with and GIS in electronic cartography, geospatial information integration, geospatial database and knowledge infrastructures, geospatial info modeling, GIS for sustainable improvement, the interoperability of heterogeneous spatial information platforms, location-based providers, spatial wisdom discovery and information mining, spatial choice help platforms, spatial info buildings and algorithms, spatial information, spatial information caliber and uncertainty, the visualization of spatial info, and internet and instant purposes in GIS.
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Extra resources for Advances in Spatial Data Handling and GIS: 14th International Symposium on Spatial Data Handling
Rapid urban expansion had occurred in the 1990s due to the fast economic development and population growth. Urban growth of this area from 1989 to 2006 was mapped out using data from various sources, including two Landsat-5 Thematic Mapper (TM) images acquired on August 6, 1989 and April 30, 2006 respectively to obtain spectrum information of the study area. In addition, other essential ancillary data include a 1:50,000 topographic map, some cadastre and transportation maps collected from relevant local government agencies.
J. Feng and Y. Liu Conventionally, CA based urban models require strict definition of various spatial and non-spatial factors driving the development of urban growth (Batty and Xie 1994; Batty et al. 1999; Couclelis 1997; He et al. 2006; Li and Yeh 2002a, b; Liu 2008; Liu and Phinn 2003; Stevens and Dragic´evic´ 2007; Stevens et al. 2007; White and Engelen 1993; Wu 1998, 2002). Many CA models have been developed using a diverse range of methods to define such factors and configure the relevant parameters; these methods include multi-criteria evaluation, logistic regression, principal component analysis, and partial least squares regression, to name a few.
3 show the ratio of mean variance for different sampling models to the mean variance from the simple random sampling model. The values in the tables are all less than one, indicating that the simple random sampling model (Srs) is the least efficient and the spatial stratified sampling model (SStrs) is the most efficient. Spatial autocorrelation slightly impacted the sampling accuracy. The spatial stratified sampling model produces accuracy, higher than any other sampling models. 101 knowledge layer may be very far apart and even spatially separated from the other layers.
Advances in Spatial Data Handling and GIS: 14th International Symposium on Spatial Data Handling by Daniel A. Griffith, Gerard B. M. Heuvelink (auth.), Anthony G.O. Yeh, Wenzhong Shi, Yee Leung, Chenghu Zhou (eds.)