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

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

ISBN-10: 3319227858

ISBN-13: 9783319227856

ISBN-10: 3319227866

ISBN-13: 9783319227863

This booklet includes refereed papers from the thirteenth foreign convention on GeoComputation held on the collage of Texas, Dallas, may perhaps 20-23, 2015. considering 1996, the individuals of the GeoComputation (the artwork and technological know-how of fixing complicated spatial issues of desktops) neighborhood have joined jointly to improve a sequence of meetings within the uk, New Zealand, Australia, eire and the us of the USA. The convention encourages assorted themes on the topic of novel methodologies and applied sciences to counterpoint the long run improvement of GeoComputation research.

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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. Given the preceding constraints, as well as prior probabilities qit for all pit , we determine the number of households wit = Npit that are like household i in target region t, as well as each of the errors eka , by solving the optimization problem MIN: n ∑ pit log it pit e2 + ∑ ka2 qit it 2σ ka ð1Þ subject to the relaxed pycnophylactic constraints: ∑ ∑ Npit ⋅ Ik ðwit Þ = pd opka ð2Þ i t⊆a + eka for each constraint k and sub−region a, where  Ik ðiÞ = 1 0 If household i has characteristic k .

References Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. Tech rep, Geological Survey Professional Paper 964 Bhaduri B, Bright E, Coleman P, Dobson J (2002) LandScan: locating people is what matters. Geoinformatics 5(2):34–37 Bhaduri B, Bright E, Coleman P, Urban ML (2007) LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal 69(1– 2):103–117 Chen K, McAneney J, Blong R, Leigh R, Hunter L, Magill C (2004) Defining area at risk and its effect in catastrophe loss estimation: a dasymetric mapping approach.

2002) develop a consumption model by training a neural network using detailed household billing data, whereas Parti and Parti (1980) applied linear regression to similar household electrical billing data and a detailed survey of appliance use. In addition, Kadian et al. (2007) utilized appliance A Hybrid Dasymetric and Machine Learning Approach … 49 distributions and microlevel data sources to develop an engineering-based energy consumption model for the residential sector of Delhi, while Saidur et al.

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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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