By Daniel A. Griffith, Yongwan Chun, Denis J. Dean
This e-book comprises refereed papers from the thirteenth overseas convention on GeoComputation held on the college of Texas, Dallas, may possibly 20-23, 2015. considering 1996, the contributors of the GeoComputation (the paintings and technology of fixing advanced spatial issues of pcs) group have joined jointly to advance a chain of meetings within the uk, New Zealand, Australia, eire and the us of the United States. The convention encourages different 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
8 but so is its solution. 6 Corners and Curves Okay, one ﬁnal challenge. Among the insular shapes that are shown in Fig. 14, note that some are more rectilinear than others. In fact, these shapes have been colored such that the more rectilinear ones appear in darker shades of green, while those that are less rectilinear appear in darker shades of red. So, how was rectilinearity measured? As always, we start by thinking like a worm. Each pixel is set to a value indicating the compass direction of the nearest edge of the shape containing that pixel, as illustrated in Fig.
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.
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. Utilizing land use ancillary data results in a proper delineation of uninhabited areas and the parks, and forested areas also are correctly shown as having low population density. Finally, the resolution in the downtown area is as good as, or better than, it is in the block-based map.