scholarly journals Star: an efficient snow point-sampling method

2010 ◽  
Vol 51 (54) ◽  
pp. 64-72 ◽  
Author(s):  
Cora Shea ◽  
Bruce Jamieson
Keyword(s):  
Spatial Correlation ◽  
Sampling Methods ◽  
Model Fit ◽  
Spatial Studies ◽  
Spatial Continuity ◽  
Point Sampling ◽  
Spatially Correlated ◽  
Random Methods ◽  
Variogram Model ◽  
Elevation Data

AbstractThe changeable, variable and fragile nature of snow creates unique sampling challenges. In this paper, we present Star: an efficient, field-usable method for use in point-sampling spatial studies. We validate the accuracy of the Star method using a comparative Monte Carlo simulation of 1024 detailed samples of elevation data. As spatial snow studies generally attempt to find spatial continuity in layers and other properties, we use variogram ranges to compare the ability of four sampling methods to accurately reveal such spatial correlation. The three methods compared to Star represent gridded, gridded-random and pure-random methods; Star can be described as a linear-random method. The simulation shows Star’s accuracy to be comparable to both gridded and gridded-random methods. Following this comparative process we introduce a new measure of appropriateness for sampling methods: the correct convergence on a variogram model, which we call correct spatial correlation detection. This directly measures how many sampled areas become correctly classified with either spatially correlated or non-correlated variance for a given variogram model fit. In this measure, Star performs equivalently to the other methods, and in correct convergence it performs as well as pure-random sampling.

Comparison of seismic responses of a reticulated dome under stochastic uniform and spatially correlated and coherent multiple-support excitation for a scenario seismic event

2020 ◽  
Vol 35 (4) ◽  
pp. 113-125
Author(s):  
YG Li ◽  
TJ Liu ◽  
F Fan ◽  
HP Hong
Keyword(s):  
Spatial Correlation ◽  
Seismic Event ◽  
Amplitude Spectrum ◽  
Spatial Coherence ◽  
Single Layer ◽  
Seismic Load ◽  
Seismic Responses ◽  
Spatially Correlated ◽  
Multiple Support ◽  
Support Excitation

Structures with multiple supports can be sensitive to spatial coherence and spatial correlation. Since the historical recordings are insufficient for selecting records that match predefined inter-support distances of a structure, desired seismic magnitude (or intensity) and site to seismic source distance for structural analysis, such records need to be simulated. In this study, we use a procedure that is extended based on the stochastic point-source method to simulate records for scenario events. The application of the simulated records to a single-layer reticulated dome with multiple supports is presented. The application is aimed at investigating the differences between the responses subjected to spatially uniform excitation and to spatially correlated and coherent multiple-support excitation for a scenario seismic event, assessing the relative importance of the spatial coherence and spatial correlation on the responses, and evaluating the effect of the uncertainty in the spatially correlated and coherent records for a scenario event on the statistics of the seismic responses. The analysis results indicate that the spatial correlation of the Fourier amplitude spectrum has a predominant influence on the linear/nonlinear responses, and the consideration of spatially correlated and coherent excitation at multiple supports is very important. The consideration of uniform excitation severely underestimates the seismic load effects as compared to those obtained under spatially correlated and coherent multiple-support excitation.

Pixel-level Signal Modelling with Spatial Correlation for Two-Colour Microarrays

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Claus T Ekstrøm ◽  
Søren Bak ◽  
Mats Rudemo
Keyword(s):  
Spatial Correlation ◽  
Substantial Improvement ◽  
Model Fit ◽  
Oligonucleotide Microarrays ◽  
Data Set ◽  
Pixel Intensity ◽  
Spherical Models ◽  
Correlation Structures ◽  
Signal Modelling ◽  
Better Than

Statistical models for spot shapes and signal intensities are used in image analysis of laser scans of microarrays. Most models have essentially been based on the assumption of independent pixel intensity values, but models that allow for spatial correlation among neighbouring pixels can accommodate errors in the microarray slide and should improve the model fit. Five spatial correlation structures, exponential, Gaussian, linear, rational quadratic and spherical, are compared for a dataset with 50-mer two-colour oligonucleotide microarrays and 452 probes for selected Arabidopsis genes. Substantial improvement in model fit is obtained for all five correlation structures compared to the model with independent pixel values, and the Gaussian and the spherical models seem to be slightly better than the other three models. We also conclude that for the data set analysed the correlation seems negligible for non-neighbouring pixels.

scholarly journals Fluid Flow and Heat Transport Computation for Power-Law Scaling Poroperm Media

Geofluids ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Peter Leary ◽  
Peter Malin ◽  
Rami Niemi
Keyword(s):  
Fluid Flow ◽  
Spatial Correlation ◽  
Fluid Velocity ◽  
Flow Property ◽  
Well Log ◽  
Advective Flow ◽  
Spatially Correlated ◽  
Scale Temperature ◽  
Flow Transport ◽  
Neutron Porosity

In applying Darcy’s law to fluid flow in geologic formations, it is generally assumed that flow variations average to an effectively constant formation flow property. This assumption is, however, fundamentally inaccurate for the ambient crust. Well-log, well-core, and well-flow empirics show that crustal flow spatial variations are systematically correlated from mm to km. Translating crustal flow spatial correlation empirics into numerical form for fluid flow/transport simulation requires computations to be performed on a single global mesh that supports long-range spatial correlation flow structures. Global meshes populated by spatially correlated stochastic poroperm distributions can be processed by 3D finite-element solvers. We model wellbore-logged Dm-scale temperature data due to heat advective flow into a well transecting small faults in a Hm-scale sandstone volume. Wellbore-centric thermal transport is described by Peclet number Pe ≡ a0φv0/D (a0 = wellbore radius, v0 = fluid velocity at a0, φ = mean crustal porosity, and D = rock-water thermal diffusivity). The modelling schema is (i) 3D global mesh for spatially correlated stochastic poropermeability; (ii) ambient percolation flow calibrated by well-core porosity-controlled permeability; (iii) advection via fault-like structures calibrated by well-log neutron porosity; (iv) flow Pe ~ 0.5 in ambient crust and Pe ~ 5 for fault-borne advection.

scholarly journals An ego-centred approach for the evaluation of health inequalities in urban areas

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
O Sauzet ◽  
K A Zolitschka ◽  
J Spallek ◽  
J Breckenkamp ◽  
O Razum
Keyword(s):  
Health Outcomes ◽  
Spatial Correlation ◽  
Health Inequalities ◽  
Urban Areas ◽  
Complex Mixture ◽  
Urban Setting ◽  
Neighbourhood Effects ◽  
Spatially Correlated ◽  
Using Data ◽  
Definition Of

Abstract Background Neighbourhood possesses attributes, structural, physical and social, for which pathways to health inequalities could be hypothesized. Hence, neighbourhood is a complex mixture of factors which cannot be simply defined by a delineation on a map, making common definitions of neighbourhood (e.g. administrative borders) problematic. We present a new concept for the evaluation of contextual health inequalities in an urban setting. Methods An ego-centred approach to neighbourhood effects on health allows to establish to what degree the health outcomes of a person are on average correlated to the health outcomes of his/her neighbours. This approach does not necessitate the definition of what a neighbourhood is, or of its boundaries. Using data from the BaBi birth cohort following up 958 mother-child pairs in Bielefeld/Germany we illustrate how the method provides information about the spatial structure of a possible association between unmeasured neighbourhood factors and birthweight. Spatially correlated birthweight indicates a neighbourhood effect on maternal health. Results A parametric model of the correlation structure gives two indicators: a distance after which health outcomes are no longer correlated (practical range), and the strength of correlation (RSV). We modelled birthweight directly and residuals after controlling for (spatially correlated) covariates. After adjusting for the mother’s demographics and neighbourhood characteristics, birthweights remained spatially correlated with RSV of 11% and a practical range of 128 m. Conclusions Modelling the spatial correlation of a health outcome provides a measure of the degree of health correlation, thus offering new evidence on the production of health inequalities while incorporating current modelling approaches. Moreover, it measures heterogeneity in a city. This could be used as an indicator for policy makers or town planners to identify areas in need of socioeconomic investment. Key messages Modelling the spatial correlation of health outcomes is an approach which enable to assess unmeasured neighbourhood effects. The health correlation neighbourhood approach helps to investigate the production of health inequalities and to identify urban areas in need of socioeconomic investment.

Spatial Interpolation for Periodic Surfaces in Manufacturing Using a Bessel Additive Variogram Model

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Yuhang Yang ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Interpolation Method ◽  
Spatial Models ◽  
High Resolution Data ◽  
Periodic Surfaces ◽  
Variogram Model ◽  
Geostatistical Interpolation ◽  
Proposed Model

High-resolution spatial data are essential for characterizing and monitoring surface quality in manufacturing. However, the measurement of high-resolution spatial data is generally expensive and time-consuming. Interpolation based on spatial models is a typical approach to cost-effectively acquire high-resolution data. Conventional modeling methods fail to adequately model the spatial correlation induced by periodicity, and thus their interpolation precision is limited. In this paper, we propose using a Bessel additive periodic variogram model to capture such spatial correlation. When combined with kriging, a geostatistical interpolation method, accurate interpolation performance can be achieved for common periodic surfaces. In addition, parameters of the proposed model provide valuable insights for the characterization and monitoring of spatial processes in manufacturing. Both simulated and real-world case studies are presented to demonstrate the effectiveness of the proposed method.

New and composite point sampling estimates

2010 ◽  
Vol 40 (11) ◽  
pp. 2234-2242 ◽  
Author(s):  
John Paul McTague
Keyword(s):  
Sampling Method ◽  
Sampling Methods ◽  
Basal Area ◽  
Population Parameters ◽  
Critical Height ◽  
Sampling Methodology ◽  
Stand Volume ◽  
Point Sampling ◽  
Repeated Sampling ◽  
Angle Count Sampling

A new estimator for basal area is introduced that is based on the concepts of angle count and angle summation sampling. Using the ratio of the angle count basal area factor and the angle summation (borderline) factor, it is possible to estimate stand volume without measuring the diameters and distances of the trees included in the sample. Employing simulation of repeated sampling in a 40 ha forest of known population parameters, it is demonstrated that the new sampling methodology is unbiased and weakly correlated with conventional angle count sampling. Hence, considerable gains in efficiency are made by combining the two sampling methods with composite estimators. Two applications are explored with the new composite point sampling estimates, including the use of the big basal area factor sampling method and critical height sampling using a Max and Burkhart taper formulation.

scholarly journals Spatial clustering of TB-infected cattle herds prior to and following proactive badger removal

2010 ◽  
Vol 139 (8) ◽  
pp. 1220-1229 ◽  
Author(s):  
G. E. KELLY ◽  
S. J. MORE
Keyword(s):  
Spatial Correlation ◽  
Large Scale ◽  
Intervention Study ◽  
Spatial Clustering ◽  
Meles Meles ◽  
Spatially Correlated ◽  
Badger Culling ◽  
Cattle Herds ◽  
The Uk ◽  
Wildlife Reservoir

SUMMARYBovine tuberculosis (TB) is primarily a disease of cattle. In both Ireland and the UK, badgers (Meles meles) are an important wildlife reservoir of infection. This paper examined the hypothesis that TB is spatially correlated in cattle herds, established the range of correlation and the effect, if any, of proactive badger removal on this. We also re-analysed data from the Four Area Project in Ireland, a large-scale intervention study aimed at assessing the effect of proactive badger culling on bovine TB incidence in cattle herds, taking possible spatial correlation into account. We established that infected herds are spatially correlated (the scale of spatial correlation is presented), but at a scale that varies with time and in different areas. Spatial correlation persists following proactive badger removal.

Comparing the Efficiency of Three Inventory Sampling Methods To Determine Timber Volumes in Pinyon-Juniper Woodlands

1992 ◽  
Vol 7 (4) ◽  
pp. 110-113 ◽  
Author(s):  
Bruce E. Fox ◽  
Pamela E. Raskob
Keyword(s):  
Standard Error ◽  
Sampling Methods ◽  
Field Measurements ◽  
Measurement Time ◽  
Point Sampling ◽  
Plot Sampling ◽  
Transect Sampling ◽  
The Mean ◽  
Time Required ◽  
Line Intercept

Abstract We compared three sampling methods to determine timber volumes in pinyon-juniper (Pinus edulis-Juniperus spp.) woodlands, fixed radius plots, variable radius "point" sampling, and line intercept transects. Based on the criterion of the square of the standard error of the mean volume per acre multiplied by total time required to take the field measurements, line intercept transect sampling was the most efficient method. Fixed plot sampling had the highest standard error of the mean and highest measurement time, while point sampling fell between the other two methods in measurement time and standard error of the mean. Based on this analysis, transect sampling is an efficient approach for inventorying pinyon-juniper woodlands, in terms of required field time and in the variance estimates of volume per acre. West. J. Appl. For. 7(4):110-113.

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