Small area estimation for spatially correlated populations - a comparison of direct and indirect model-based methods

Linear mixed models underpin many small area estimation (SAE) methods. In this paper we investigate SAE based on linear models with spatially correlated small area effects where the neighbourhood structure is described by a contiguity matrix. Such models allow efficient use of spatial auxiliary information in SAE. In particular, we use simulation studies to compare the performances of model-based direct estimation (MBDE) and empirical best linear unbiased prediction (EBLUP) under such models. These simulations are based on theoretically generated populations as well as data obtained from two real populations (the ISTAT farm structure survey in Tuscany and the US Environmental Monitoring and Assessment Program survey). Our empirical results show only marginal gains when spatial dependence between areas is incorporated into the SAE model

Applications of remote sensing to stream discharge predictions

A feasibility study has been initiated on the use of remote earth observations for augmenting stream discharge prediction for the design and/or operation of major reservoir systems, pumping systems and irrigation systems. The near-term objectives are the interpolation of sparsely instrumented precipitation surveillance networks and the direct measurement of water loss by evaporation. The first steps of the study covered a survey of existing reservoir systems, stream discharge prediction methods, gage networks and the development of a self-adaptive variation of the Kentucky Watershed model, SNOPSET, that includes snowmelt. As a result of these studies, a special three channel scanner is being built for a small aircraft, which should provide snow, temperature and water vapor maps for the spatial and temporal interpolation of stream gages

Inferring Population Preferences via Mixtures of Spatial Voting Models

Understanding political phenomena requires measuring the political preferences of society. We introduce a model based on mixtures of spatial voting models that infers the underlying distribution of political preferences of voters with only voting records of the population and political positions of candidates in an election. Beyond offering a cost-effective alternative to surveys, this method projects the political preferences of voters and candidates into a shared latent preference space. This projection allows us to directly compare the preferences of the two groups, which is desirable for political science but difficult with traditional survey methods. After validating the aggregated-level inferences of this model against results of related work and on simple prediction tasks, we apply the model to better understand the phenomenon of political polarization in the Texas, New York, and Ohio electorates. Taken at face value, inferences drawn from our model indicate that the electorates in these states may be less bimodal than the distribution of candidates, but that the electorates are comparatively more extreme in their variance. We conclude with a discussion of limitations of our method and potential future directions for research.Comment: To be published in the 8th International Conference on Social Informatics (SocInfo) 201

APPLICATION AND COMPARISON OF THREE SPATIAL STATISTICAL METHODS FOR MAPPING AND ANALYZING SOIL ERODIBILITY

The Revised Universal Soil Loss Equation (RUSLE) is a model to predict longtime average annual soil loss, related to rainfall-runoff, soil erodibility, slope length and steepness, cover management, and support practice. The soil erodibility factor K accounts for the influence of soil properties on soil loss during storm events in upland areas. In this paper, ordinary kriging, sequential Gaussian and indicator simulation methods were used and compared for spatial prediction and uncertainty analysis of soil erodibility based on a data set from a very intensive soil survey (524 observations, 10 m by 10 m grid). Half the data was used for calibration, the other half used for validation. The results show that the three methods produce similar spatial distributions for predicted values. The method yielding the smallest mean square error was Gaussian simulation, followed by ordinary kriging and indicator simulation. However, the variance estimates obtained using indicator simulation consistent with the spatial variation, while those obtained by Gaussian simulation and ordinary kriging were overly smoothed

Coherence methods in mapping AVO anomalies and predicting P-wave and S-wave impedances

Filters for migrated offset substacks are designed by partial coherence analysis to predict ‘normal’ amplitude variation with offset (AVO) in an anomaly free area. The same prediction filters generate localized prediction errors when applied in an AVO-anomalous interval. These prediction errors are quantitatively related to the AVO gradient anomalies in a background that is related to the minimum AVO anomaly detectable from the data. The prediction-error section is thus used to define a reliability threshold for the identification of AVO anomalies. Coherence analysis also enables quality control of AVO analysis and inversion. For example, predictions that are non-localized and/or do not show structural conformity may indicate spatial variations in amplitude–offset scaling, seismic wavelet or signal-to-noise (S/N) ratio content. Scaling and waveform variations can be identified from inspection of the prediction filters and their frequency responses. S/N ratios can be estimated via multiple coherence analysis. AVO inversion of seismic data is unstable if not constrained. However, the use of a constraint on the estimated parameters has the undesirable effect of introducing biases into the inverted results: an additional bias-correction step is then needed to retrieve unbiased results. An alternative form of AVO inversion that avoids additional corrections is proposed. This inversion is also fast as it inverts only AVO anomalies. A spectral coherence matching technique is employed to transform a zero-offset extrapolation or near-offset substack into P-wave impedance. The same technique is applied to the prediction-error section obtained by means of partial coherence, in order to estimate S-wave velocity to P-wave velocity (VS/VP) ratios. Both techniques assume that accurate well ties, reliable density measurements and P-wave and S-wave velocity logs are available, and that impedance contrasts are not too strong. A full Zoeppritz inversion is required when impedance contrasts that are too high are encountered. An added assumption is made for the inversion to the VS/VP ratio, i.e. the Gassmann fluid-substitution theory is valid within the reservoir area. One synthetic example and one real North Sea in-line survey illustrate the application of the two coherence methods

inlabru: an R package for Bayesian spatial modelling from ecological survey data

1. Spatial processes are central to many ecological processes, but fitting models that incorporate spatial correlation to data from ecological surveys is computationally challenging. This is particularly true of point pattern data (in which the primary data are the locations at which target species are found), but also true of gridded data, and of georeferenced samples from continuous spatial fields. 2. We describe here the R package inlabru that builds on the widely used RINLA package to provide easier access to Bayesian inference from spatial point process, spatial count, gridded, and georeferenced data, using integrated nested Laplace approximation (INLA, Rue et al., 2009). 3. The package provides methods for fitting spatial density surfaces and estimating abundance, as well as for plotting and prediction. It accommodates data that are points, counts, georeferenced samples, or distance sampling data. 4. This paper describes the main features of the package, illustrated by fitting models to the gorilla nest data contained in the package spatstat (Baddeley, & Turner, 2005), a line transect survey dataset contained in the package dsm (Miller, Rexstad, Burt, Bravington, & Hedley, 2018), and to a georeferenced sample from a simulated continuous spatial field