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Advances in Geocomputation: Geocomputation 2015--The 13th by Daniel A. Griffith, Yongwan Chun, Denis J. Dean

By Daniel A. Griffith, Yongwan Chun, Denis J. Dean

This ebook comprises refereed papers from the thirteenth overseas convention on GeoComputation held on the collage of Texas, Dallas, might 20-23, 2015. seeing that 1996, the participants of the GeoComputation (the artwork and technological know-how of fixing advanced spatial issues of pcs) group have joined jointly to increase a chain of meetings within the uk, New Zealand, Australia, eire and the U.S. of the USA. The convention encourages diversified themes concerning novel methodologies and applied sciences to complement the long run improvement of GeoComputation research.

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Visualizing all these water data in both Euclidean space and spatial network space is nontrivial. Second, previous visualization approaches focus on known information, but the FEW nexus requires more sophisticated techniques to visualize the uncertainty about location, value, recency, and quality of spatiotemporal information. For instance, the lack of site-specific data and the limitations of estimation models result in uncertainty when estimating water resource consumption. To visualize a map of water consumption with uncertainty or to compare two temporal snapshots with uncertain inferred change is nontrivial.

Four population density maps are compared: census block-based (Fig. 2c), SEDAC 1 km grid (Fig. 2d), Gen-1 90 m grid (Fig. 2e), and Gen-2 30 m grid (Fig. 2f). All grids have the same legend (see Fig. 1 for the legend). Note that, unlike the Cincinnati site, the SEDAC 250 m grid is not available for this area. 42 A. F. Stepinski (a) (b) (c) (d) 0 (e) 2 4 6 8km (f) Fig. 2 A comparison of population grids for the Somerset (Ohio) site. a Satellite image (Google Maps), b land cover map (NLCD 2011), c census blocks-based map of population density, d SEDAC 1 km grid, e Gen-1 90 m grid, f Gen-2 30 m grid.

We use land use data (NLUD 2010) to make this differentiation. In the ancillary data preprocessing step, we combine information from NLCD 2011 and NLUD 2010 to define six land cover/use classes: developed open space, developed low intensity, developed medium intensity, developed high intensity, vegetation, and uninhabited. Following Mennis and Hultgren (2006), representative population density for each of the six land cover/use classes is established using a set of blocks (selected from the entire United States) having relatively homogeneous land cover (90 % for developed classes, and 95 % for vegetation classes).

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