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

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

ISBN-10: 3319227858

ISBN-13: 9783319227856

ISBN-10: 3319227866

ISBN-13: 9783319227863

This publication comprises refereed papers from the thirteenth overseas convention on GeoComputation held on the college of Texas, Dallas, may perhaps 20-23, 2015. seeing that 1996, the individuals of the GeoComputation (the artwork and technology of fixing complicated spatial issues of pcs) neighborhood have joined jointly to boost a chain of meetings within the uk, New Zealand, Australia, eire and the us of the US. The convention encourages different themes with regards to novel methodologies and applied sciences to complement the longer term improvement of GeoComputation research.

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Geological Survey Open-File Report 2008–1379 Gallego FJ (2010) A population density grid of the European Union. Popul Environ 31(6):460–473 Gallego FJ, Batista F, Rocha C, Mubareka S (2011) Disaggregating population density of the European Union with CORINE land cover. Int J Geogr Inf Sci 25(12):2051–2069 Gaughan AE, Stevens FR, Linard C, Jia P, Tatem AJ (2013) High resolution population distribution maps for Southeast Asia in 2010 and 2015. PLoS One 8(2):e55,882 Goodchild M, Anselin L, Deichmann U (1993) A framework for the areal interpolation of socioeconomic data.

In addition, assume each survey response contains a variable or vector of variable values ci that can be used by some machine learning function f(ci) to estimate household electricity consumption. Our first goal is to estimate the expected number of households wit = Npit that are like household i in target region t by using complementary ancillary data and a dasymetric model. Suppose we are given this ancillary information in the form of household counts with specific characteristics for nested geographies within s.

2014) with a complementary machine learning algorithm. Rather than basing the model on sparsely available high-resolution data sources, we choose to disaggregate publicly available datasets into higher resolution target regions. The flexible data requirement, along with the model’s statistical framework, ensures that the model both is applicable to a wide range of regions and considers errors in input data sources. 2 Related Work The residential energy modeling and dasymetric modeling fields have been widely studied by several researchers.

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


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