By Daniel A. Griffith, Yongwan Chun, Denis J. Dean
This publication includes refereed papers from the thirteenth foreign convention on GeoComputation held on the collage of Texas, Dallas, might 20-23, 2015. considering 1996, the individuals of the GeoComputation (the artwork and technology of fixing advanced spatial issues of pcs) neighborhood have joined jointly to strengthen a chain of meetings within the uk, New Zealand, Australia, eire and the USA of the USA. The convention encourages varied subject matters relating to novel methodologies and applied sciences to complement the longer term improvement of GeoComputation research.
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Extra resources for Advances in Geocomputation: Geocomputation 2015--The 13th International Conference
Comparing the Gen-1 grid with the block-based map, however, reveals that some industrial, uninhabited areas are shown as inhabited because some NLCD classes contain residential and non-residential buildings. Also, the resolution of our Gen-1 map in the downtown area is not as good as it is in the block-based map. Based on a comparison with a satellite image (Fig. 1a) and a land cover map (Fig. 1b), as well as our familiarity with the site, we conclude that our Gen-2 grid (Fig. 1g) oﬀers the best depiction of population density in this site.
The numerical data (people counts per cell) are too large to be distributed via SocScape, but are available upon request from the authors. Here we use three examples to demonstrate how our grids compare with other available population density resources. The ﬁrst example compares diﬀerent population density maps/grids in an urban setting (Cincinnati, Ohio). The second example compares diﬀerent population density maps/grids in a rural setting (Somerset, Ohio). Finally, the third example shows how our method can be used to produce high-resolution racial diversity grids.
Keywords Energy modeling ⋅ Dasymetric modeling ⋅ Machine learning A. Morton (✉) ⋅ N. Nagle ⋅ J. N. Stewart ⋅ R. gov N. gov J. N. gov R. A. Grifﬁth et al. 1007/978-3-319-22786-3_5 47 48 A. Morton et al. 1 Introduction As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for detailed information regarding energy consumption patterns has become increasingly important. Because the residential sector alone accounts for approximately 30 % of all energy consumption worldwide (Swan and Ugursal 2009), a detailed spatial understanding of residential energy consumption, in particular, is important for supporting efforts to promote conservation, technology implementation, and other necessary changes in the existing energy infrastructure.
Advances in Geocomputation: Geocomputation 2015--The 13th International Conference by Daniel A. Griffith, Yongwan Chun, Denis J. Dean