Results 241 to 250 of about 1,186,895 (272)

Bayesian spatial prediction

Environmental and Ecological Statistics, 1999
This paper presents a complete Bayesian methodology for analyzing spatial data, one which employs proper priors and features diagnostic methods in the Bayesian spatial setting. The spatial covariance structure is modeled using a rich class of covariance functions for Gaussian random fields.
Marie Gaudard, Marvin Karson, Ernst Linder, Debajyoti Sinha   +3 more
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Spatial Predictive Queries

2015 16th IEEE International Conference on Mobile Data Management, 2015
In this seminar, we address spatial predictive queries both in Euclidian spaces and over road networks. We provide a definition for various types of spatial predictive queries, describe current research trends, and envision future directions. We present practical application scenarios and emphasize the roadblocks that are holding industry back from the
Abdeltawab M. Hendawi, Mohamed Ali
openaire   +1 more source

Predicting Cognitive Styles from Spatial Abilities

The American Journal of Psychology, 2006
Abstract Previous studies on spatial memory reveal that people represent spatial information in 3 different forms: landmark, route, and survey. The aim of this work was to assess spatial abilities in order to predict a person’s cognitive style. In order to do this we used 9 different spatial tasks, which were linked with these 3 forms of
NORI, RAFFAELLA, GIUSBERTI, FIORELLA
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Spatial Prediction Models for Mining Spatial Data

2007 IEEE International Conference on Integration Technology, 2007
The multivariate linear regression (MLS) model is a very good technique for non-spatial prediction. But spatial prediction needs to account for spatial information, which makes the MLS model inappropriate, for it assume that the learning samples are independently and identically distributed(i.i.d).
Caiping Hu, Xiaolin Qin, Jun Zhang
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Spatial prediction from networks

Chemometrics and Intelligent Laboratory Systems, 1990
Abstract Cressie, N., Gotway, C.A. and Grondona, M.O., 1990. Spatial prediction from networks. Chemometrics and Intelligent Laboratory Systems , 7: 251–271. This article defines a random-field model that can be used for the prediction of pollutants at locations where no data are available, based on data taken from a spatial network of monitoring ...
Cressie, Noel A, Gotway, C, Grondona, M
openaire   +2 more sources

Spatial Prediction Fundamentals

2020
The analysis of spatial data involves different procedures that typically include model estimation, spatial prediction, and simulation. Model estimation or model inference refers to determining a suitable spatial model and the “best” values for the parameters of the model. Parameter estimation is not necessary for certain simple deterministic models (e.
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Spatial prediction of counts and rates

Statistics in Medicine, 2003
AbstractIn this paper we provide both theoretical and empirical comparisons of marginal and conditional methods for analysing spatial count data. We focus on methods for spatial prediction developed from a generalized linear mixed model framework and compare them with the traditional linear (kriging) predictor.
Carol A, Gotway, Russell D, Wolfinger
openaire   +2 more sources

PREDICTING SPATIAL DATA WITH RBF NETWORKS

International Journal of Neural Systems, 2004
Spatial prediction needs to account for spatial information, which makes conventional radial basis function (RBF) networks inappropriate, for they assume independent and identical distribution. In this paper, we fuse spatial information at different layers of RBF.
Hu, T., Sung, S.Y.
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Predicting spatial hotspot regions

2023
The identification of spatially extreme observations or "hotspots" figures prominently in the application of various forms of statistical analyses. Such analyses do not necessarily capture all suspect or interesting observations. A spatial modeling approach can incorporate possible dependencies in the data.
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Prediction diagnostics for spatial linear models

Biometrika, 1992
Summary: Case deletion diagnostics are developed for detecting observations that are influential for prediction in linear models with a general covariance matrix. A primary application of such results is in universal kriging and the related methodologies of ordinary kriging and intrinsic random function kriging.
Christensen, Ronald, Johnson, Wesley, Pearson, Larry M.   +2 more
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