By Daniel A. Griffith, Yongwan Chun, Denis J. Dean
This e-book comprises refereed papers from the thirteenth foreign convention on GeoComputation held on the college of Texas, Dallas, might 20-23, 2015. seeing that 1996, the participants of the GeoComputation (the artwork and technology of fixing complicated spatial issues of desktops) neighborhood have joined jointly to enhance a chain of meetings within the uk, New Zealand, Australia, eire and the USA of the United States. The convention encourages diversified subject matters relating to novel methodologies and applied sciences to complement the longer term improvement of GeoComputation research.
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Extra info for Advances in Geocomputation: Geocomputation 2015--The 13th International Conference
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. , the number of ﬁve-bedroom housing units in a speciﬁc tract in Tennessee), where eka is the positive or negative error that is the difference between the estimated and true housing unit count, and σ 2ka is the variance of the error.
Therefore, it is more useful than the Mixed Metro map for assessing racial diversity of a neighborhood at the street scale. The same conclusion holds when comparing the two maps in the rural site (Fig. 3c and d). Because census tracts in more sparsely populated areas are larger, the resolution of the Mixed Metro map is worse than in the urban setting. The Mixed Metro map also can be misleading due to its lack of delineation of uninhabited areas and its lack of information about population density.
By narrowing the locations where people live within a block, we increase the spatial resolution of these variables, although we would not be able to account for High-Resolution Population Grids for the Entire Conterminous United States 45 variation of, for example, racial diversity within a block. Nevertheless, as illustrated by Fig. 3, using grids of demographic variables (like racial diversity) instead of maps based on census areal units results in a much better depiction of the actual spatial distribution of these variables.
Advances in Geocomputation: Geocomputation 2015--The 13th International Conference by Daniel A. Griffith, Yongwan Chun, Denis J. Dean
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