scholarly journals Spatial trend analysis of gridded temperature data at varying spatial scales

2020 ◽  
Vol 6 (1) ◽  
pp. 1-12
Author(s):  
Ola Haug ◽  
Thordis L. Thorarinsdottir ◽  
Sigrunn H. Sørbye ◽  
Christian L. E. Franzke
Keyword(s):  
Spatial Scales ◽  
Gaussian Random Field ◽  
Summer Season ◽  
Temperature Data ◽  
Hierarchical Bayesian Model ◽  
Spatial Smoothing ◽  
Spatial Correlations ◽  
Spatial Trend ◽  
Trend Coefficient

Abstract. Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as evaluation of the collective significance of individual tests is commonly neglected. In this article we build a space–time hierarchical Bayesian model for temperature anomalies where the trend coefficient is modelled by a latent Gaussian random field. This enables us to calculate simultaneous credible regions for joint significance assessments. In a case study, we assess summer season trends in 65 years of gridded temperature data over Europe. We find that while spatial smoothing generally results in larger regions where the null hypothesis of no trend is rejected, this is not the case for all subregions.

Quadratic Discriminant Analysis of Spatially Correlated Data

2001 ◽  
Vol 6 (2) ◽  
pp. 15-28 ◽  
Author(s):  
K. Dučinskas ◽  
J. Šaltytė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Gaussian Random Field ◽  
Correlated Data ◽  
Covariance Matrices ◽  
Classification Rule ◽  
Spatial Correlations ◽  
Linear Quadratic ◽  
Spatial Correlation Function ◽  
Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

scholarly journals Ensemble model for estimating continental-scale patterns of human movement: a case study of Australia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karen McCulloch ◽  
Nick Golding ◽  
Jodie McVernon ◽  
Sarah Goodwin ◽  
Martin Tomko
Keyword(s):  
Spatial Scales ◽  
Predictive Ability ◽  
Movement Patterns ◽  
Human Movement ◽  
Continental Scale ◽  
Movement Characteristics ◽  
Human Movements ◽  
Movement Models ◽  
Epidemiology Data

AbstractUnderstanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.

scholarly journals Investigating temperature breaks in the summer fruit export cold chain: A case study

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
Heinri W. Freiboth ◽  
Leila Goedhals-Gerber ◽  
F. Esbeth Van Dyk ◽  
Malcolm C. Dodd
Keyword(s):  
Temperature Variation ◽  
South African ◽  
Temperature Data ◽  
Added Value ◽  
Cold Chain ◽  
The South ◽  
Export Supply ◽  
Cold Store ◽  
Summer Fruit

There is concern in the South African fruit industry that a large amount of fruit and money is lost every season due to breaks in the fruit export cold chain. The possibility of a large percentage of losses in a significant sector of the economy warranted further investigation. This article attempted to highlight some of the possible problem areas in the cold chain, from the cold store to the port, by analysing historic temperature data from different fruit export supply chains of apples, pears and grapes. In addition, a trial shipment of apples was used to investigate temperature variation between different pallets in the same container. This research has added value to the South African fruit industry by identifying the need to improve operational procedures in the cold chain.

scholarly journals A multidisciplinary approach to the spatial dimension in ecosystem-based fisheries management

2018 ◽  
Vol 31 ◽  
pp. 23 ◽  
Author(s):  
Pascal Le Floc'h ◽  
Michel Bertignac ◽  
Olivier Curtil ◽  
Claire Macher ◽  
Emilie Mariat-Roy ◽  
...  
Keyword(s):  
Multidisciplinary Approach ◽  
Spatial Scales ◽  
Spatial Dimension ◽  
Common Denominator ◽  
Bay Of Biscay ◽  
First Case ◽  
Fishing Fleets ◽  
Coastal Space ◽  
Fish Habitats

This study considers how to reconcile different spatial scales to find the best common denominator to be used as an ecosystem-based management unit. For this, two fishery production zones differing ecologically, economically, legally and institutionally were investigated. The first case study is located within French territorial waters, in a MPA created in 2007- the Parc Naturel Marin d'Iroise (PNMI). The second case study, the Bay of Biscay, covers both territorial waters and the French exclusive economic zone. The paper adopts a multidisciplinary approach. Relevant questions concern how marine space is shared between exploited species and fishing fleets, especially the spatial mobility strategies they employ. An assessment of the institutional system established for the PNMI contributes to the discussion of changes in coastal space use. It is obvious that the area in need of protection, defined on the basis of essential fish habitats, does not solely concern the fisheries located within the coastal zone. Experiments conducted by scientists and professionals in the Bay of Biscay provide other key points for the discussion in terms of what institutional frameworks to promote.

scholarly journals Evaluation of Integrated Assessment Model hindcast experiments: A case study of the GCAM 3.0 land use module

2017 ◽  
Author(s):  
Abigail C. Snyder ◽  
Robert P. Link ◽  
Katherine V. Calvin
Keyword(s):  
Integrated Assessment ◽  
Structural Changes ◽  
Spatial Scales ◽  
Integrated Assessment Model ◽  
Assessment Model ◽  
Land Allocation ◽  
Time Period ◽  
Skill Scores ◽  
Performance Benchmarks

Abstract. Hindcasting experiments (conducting a model forecast for a time period in which observational data is available) are rarely undertaken in the Integrated Assessment Model (IAM) community. When they are undertaken, the results are often evaluated using global aggregates or otherwise highly aggregated skill scores that mask deficiencies. We select a set of deviation based measures that can be applied at different spatial scales (regional versus global) to make evaluating the large number of variable-region combinations in IAMs more tractable. We also identify performance benchmarks for these measures, based on the statistics of the observational dataset, that allow a model to be evaluated in absolute terms rather than relative to the performance of other models at similar tasks. This is key in the integrated assessment community, where there often are not multiple models conducting hindcast experiments to allow for model intercomparison. The performance benchmarks serve a second purpose, providing information about the reasons a model may perform poorly on a given measure and therefore identifying opportunities for improvement. As a case study, the measures are applied to the results of a past hindcast experiment focusing on land allocation in the Global Change Assessment Model (GCAM) version 3.0. We find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs, such as GCAM, that require global supply to equal global demand at each time period. Additionally, the deviation measures examined in this work successfully identity parametric and structural changes that may improve land allocation decisions in GCAM. Future work will involve implementing the suggested improvements to the GCAM land allocation system identified by the measures in this work, using the measures to quantify performance improvement due to these changes, and, ideally, applying these measures to other sectors of GCAM and other land allocation models.

scholarly journals Developing and Demonstrating Climate Indicators for Monitoring the Changing Water Cycle

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Xia Feng ◽  
Paul Houser
Keyword(s):  
Water Cycle ◽  
Spatial Scales ◽  
Atmospheric Moisture ◽  
Spatial Correlations ◽  
Climate Indicators ◽  
Terrestrial Water ◽  
The Mean ◽  
Atmospheric Moisture Content ◽  
Daily Total

In this study, we developed a suite of spatially and temporally scalable Water Cycle Indicators (WCI) to examine the long-term changes in water cycle variability and demonstrated their use over the contiguous US (CONUS) during 1979–2013 using the MERRA reanalysis product. The WCI indicators consist of six water balance variables monitoring the mean conditions and extreme aspects of the changing water cycle. The variables include precipitation (P), evaporation (E), runoff (R), terrestrial water storage (dS/dt), moisture convergence flux (C), and atmospheric moisture content (dW/dt). Means are determined as the daily total value, while extremes include wet and dry extremes, defined as the upper and lower 10th percentile of daily distribution. Trends are assessed for annual and seasonal indicators at several different spatial scales. Our results indicate that significant changes have occurred in most of the indicators, and these changes are geographically and seasonally dependent. There are more upward trends than downward trends in all eighteen annual indicators averaged over the CONUS. The spatial correlations between the annual trends in means and extremes are statistically significant across the country and are stronger forP,E,R, andCcompared todS/dtanddW/dt.

scholarly journals Spatial scales, patterns, and positivity trends of SARS-CoV-2 pandemics in mass rapid antigen testing in Slovakia

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256669
Author(s):  
Katarína Boďová ◽  
Richard Kollár
Keyword(s):  
Correlation Length ◽  
Spatial Scales ◽  
Mean Reversion ◽  
Spatial Trend ◽  
Geographical Data ◽  
Mean Reversion Process ◽  
Data Surveillance ◽  
Direct Guidance ◽  
Epidemic Response ◽  
Antigen Testing

We study geographical epidemic scales and patterns and positivity trends of SARS-CoV-2 pandemics in mass antigen testing in Slovakia in 2020. The observed test positivity was exponentially distributed with a long scale exponential spatial trend, and its characteristic correlation length was approximately 10 km. Spatial scales also play an important role in test positivity reduction between two consecutive testing rounds. While test positivity decreased in all counties, it increased in individual municipalities with low test positivity in the earlier testing round in a way statistically different from a mean-reversion process. Also, non-residents testing influences the mass testing results as test positivity of non-residents was higher than of residents when testing was offered only in municipalities with the highest positivity in previous rounds. Our results provide direct guidance for pandemic geographical data surveillance and epidemic response management.

scholarly journals Redundancy and reducibility in the formats of spatial representations

2022 ◽  
Author(s):  
Sami Ryan Yousif
Keyword(s):  
Mental Representations ◽  
Spatial Scales ◽  
Spatial Location ◽  
Spatial Behavior ◽  
Polar Coordinates ◽  
Levels Of Analysis ◽  
Spatial Representations ◽  
High Level ◽  
The Mind

Mental representations are the essence of cognition. Yet, to understand how the mind works, we must understand not just the content of mental representations (i.e., what information is stored), but also the format of those representations (i.e., how that information is stored). But what does it mean for representations to be formatted? How many formats are there? Is it possible that the mind represents some pieces of information in multiple formats at once? To address these questions, I discuss a ‘case study’ of representational format: the representation of spatial location. I review work (a) across species and across development, (b) across spatial scales, and (c) across levels of analysis (e.g., high-level cognitive format vs. low-level neural format). Along the way, I discuss the possibility that the same information may be organized in multiple formats simultaneously (e.g., that locations may be represented in both Cartesian and polar coordinates). Ultimately, I argue that seemingly ‘redundant’ formats may support the flexible spatial behavior observed in humans, and that we should approach the study of all mental representations with this possibility in mind.

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