20-23 June 2011, University of Cantabria, Santander, Spain
During the past decades the main problem in geographical analysis was the lack of spatial data availability. Nowadays the wide diffusion of electronic devices containing geo-referenced information generates a great production of spatial data. Volunteered geographic information activities (e.g. Wikimapia, OpenStreetMap), public initiatives (e.g. Spatial Data Infrastructures, Geo-portals) and private projects (e.g. Google Earth, Microsoft Virtual Earth, etc.) produced an overabundance of spatial data, which, in many cases, does not help the efficiency of decision processes. The increase of geographical data availability has not been fully coupled by an increase of knowledge to support spatial decisions. The inclusion of spatial simulation techniques in recent GIS software favoured the diffusion of these methods, but in several cases led to the mechanism based on which buttons have to pressed without having geography or processes in mind. Spatial modelling, analytical techniques and geographical analyses are therefore required in order to analyse data and to facilitate the decision process at all levels, with a clear identification of the geographical information needed and reference scale to adopt. Old geographical issues can find an answer thanks to new methods and instruments, while new issues are developing, challenging the researchers for new solutions. This workshop aims at contributing to the development of new techniques and methods to improve the process of knowledge acquisition.
The programme committee especially requests high quality submissions on the following Conference Themes:
- Geostatistics and spatial simulation;
- Agent-based spatial modelling;
- Cellular automata spatial modelling;
- Spatial statistical models;
- Space-temporal modelling;
- Space-temporal modelling;
- Environmental Modelling;
- Geovisual analytics, geovisualisation, visual exploratory data analysis;
- Visualisation and modelling of track data;
- Spatial Optimization;
- Interaction Simulation Models;
- Data mining, spatial data mining;
- Spatial Data Warehouse and Spatial OLAP;
- Integration of Spatial OLAP and Spatial data mining;
- Spatial Decision Support Systems;
- Spatial Multicriteria Decision Analysis;
- Spatial Rough Set;
- Spatial extension of Fuzzy Set theory;
- Ontologies for Spatial Analysis;
- Urban modeling;
- Applied geography;
- Spatial data analysis;
- Dynamic modelling;
- Simulation, space-time dynamics, visualization and virtual reality.
Each paper will be independently reviewed by 3 programme committee members. Their individual scores will be evaluated by a small sub-committee and result in one of the following final decisions: accepted, or accepted on the condition that suggestions for improvement will be incorporated, or rejected. Notification of this decision will take place on February 2011.
Individuals and groups should submit complete papers (10 to 16 pages).
Accepted contributions will be published in the Springer-Verlag Lecture Notes in Computer Science (LNCS) volumes.
Applied Geography, In Press, Corrected Proof, Available online 3 September 2010
“Significant relationships between land use and water quality have been found in watersheds around the world. The relationships are commonly examined by conventional statistical methods, such as ordinary least squares regression (OLS) and Spearman’s rank correlation analysis, which assume the relationships are constant across space. However, the relationships often might vary over space because watershed characteristics and pollution sources are not the same in different places. This study applies an exploratory spatial data analysis (ESDA) technique, geographically weighted regression (GWR), to analyze the spatially varying relationships between six land use and fourteen water quality indicators across watersheds with different levels of urbanization in eastern Massachusetts, USA. The study finds that the relationships between water quality and land use and the abilities of land use indicators to explain water quality vary across the urbanization gradient in the studied watersheds. Percentages of commercial and industrial lands have stronger positive relationships with the concentrations of water pollutants in less-urbanized areas than in highly-urbanized areas. Percentages of agricultural land, residential land, and recreation use show significant positive relationships with the concentrations of water pollutants at some sampling sites within less-urbanized areas, whereas they have significant negative relationships at some sampling sites within highly-urbanized areas. Thus, the adverse impact of land use changes on water quality is more substantial in less-urbanized suburban areas than that in highly-urbanized central cities. Pollution control policies should be adjusted in different areas based on the spatially varying pollution sources and good predictors of water quality.
- This study extends the application of geographically weighted regression (GWR) to water resources research. This study applies GWR to analyze the spatially varying relationships between land use and water quality across watersheds with different levels of urbanization in eastern Massachusetts, USA.
- The study has several novel findings: 1) It finds that the relationships between water quality and land use and the abilities of land use indicators to explain water quality vary across the urbanization gradient in the studied watersheds. 2) Percentages of commercial and industrial lands have stronger positive relationships with the concentrations of water pollutants in less-urbanized areas than in highly-urbanized areas. Percentages of agricultural land, residential land, and recreation use show significant positive relationships with the concentrations of water pollutants at some sampling sites within less-urbanized areas, whereas they have significant negative relationships at some sampling sites within highly-urbanized areas. Thus, the adverse impact of land use changes on water quality is more substantial in less-urbanized suburban areas than that in highly-urbanized central cities.
- This study has important policy implications for choosing urbanization pattern. The findings of this study suggest that smart growth that promotes high-density development in the center of a city has less adverse impact on water quality than urban sprawl that encourages low-density suburban.
- The study suggests that GWR can serve as a useful tool for policy makers, regional and local agencies, and researchers to unveil the local pollution causes, to improve the understanding of local pollution status, and to adopt appropriate environmental and land use planning policies suitable to the local watershed conservation and management.”
Geophysics, October 2010; v. 75; no. 5; p. 75A165-75A176
Miguel Bosch, Tapan Mukerji, and Ezequiel F. Gonzalez
“There are various approaches for quantitative estimation of reservoir properties from seismic inversion. A general Bayesian formulation for the inverse problem can be implemented in two different work flows. In the sequential approach, first seismic data are inverted, deterministically or stochastically, into elastic properties; then rock-physics models transform those elastic properties to the reservoir property of interest. The joint or simultaneous work flow accounts for the elastic parameters and the reservoir properties, often in a Bayesian formulation, guaranteeing consistency between the elastic and reservoir properties. Rock physics plays the important role of linking elastic parameters such as impedances and velocities to reservoir properties of interest such as lithologies, porosity, and pore fluids. Geostatistical methods help add constraints of spatial correlation, conditioning to different kinds of data and incorporating subseismic scales of heterogeneities.”
KSCE Journal of Civil Engineering, Volume 14, Number 6, 897-904
Geun Sang Lee and In Ho Choi
“Accurate estimation of soil loss/deposition forced by rainfall events plays a major role in water resources management, which directly affects the quality of agricultural land and water storage capacity in reservoirs. In this paper, the soil loss model, Geographic Information System (GIS) based Universal Soil Loss Equation (USLE) was used to quantify soil loss in a small basin located in the southern part of Korea. The surface characteristics, such as soil texture, elevation and vegetation type, are needed to run the USLE model. Geospatial data has been successfully used to derive suitable model factors for this purpose. However, it is difficult to select the grid size of elements for the best fit, which is often decided in a subjective and intuitive way. A GIS spatial analysis was performed to investigate the scaling effect to estimate the soil loss in the USLE model using remotely sensed geospatial data. The results showed that the slope length factor (L) and slope steepness factor (S) were sensitive to the grid size; the optimal resolution for quantifying soil loss in the USLE model for the study site was 125 m. This approach presents a method for the selection of a suitable scale for estimating soil loss using remotely sensed geospatial data, which eventually improves the prediction of soil loss on a basin scale.”