Evaluating the association between climatic events and sheep condemnations in the United States using cluster analysis and spatio-temporal modeling

Abstract Coronavirus disease 2019 dominated and augmented many aspects of life beginning in early 2020. Related research and data generation developed alongside its spread. We developed a Bayesian spatio-temporal Poisson disease mapping model for estimating real-time characteristics of the coronavirus disease in the United States. We also created several dashboards for visualization of the statistical model for fellow researchers and simpler spatial and temporal representations of the disease for consumption by community members in our region. Findings suggest that the risk of confirmed cases is higher for health regions under partial stay at home orders and lower in health regions under full stay at home orders, when compared to before stay at home orders were declared. These results confirm the benefit of state-issued stay at home orders as well as suggest compliance to the directives towards the older population for adhering to social distancing guidelines.

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Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19

Scientific Reports ◽

10.1038/s41598-021-93433-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rachel Carroll ◽

Christopher R. Prentice

Keyword(s):

United States ◽

Statistical Model ◽

Real Time ◽

The United States ◽

Data Generation ◽

Temporal Modeling ◽

Related Research ◽

Mapping Model ◽

Spatio Temporal ◽

At Home

AbstractCoronavirus disease 2019 dominated and augmented many aspects of life beginning in early 2020. Related research and data generation developed alongside its spread. We developed a Bayesian spatio-temporal Poisson disease mapping model for estimating real-time characteristics of the coronavirus disease in the United States. We also created several dashboards for visualization of the statistical model for fellow researchers and simpler spatial and temporal representations of the disease for consumption by analysts and data scientists in the policymaking community in our region. Findings suggest that the risk of confirmed cases is higher for health regions under partial stay at home orders and lower in health regions under full stay at home orders, when compared to before stay at home orders were declared. These results confirm the benefit of state-issued stay at home orders as well as suggest compliance to the directives towards the older population for adhering to social distancing guidelines.

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Cluster Analysis: Air Pollution in the United States

A Handbook of Statistical Analyses using SAS ◽

10.1201/9781584887850-20 ◽

2008 ◽

pp. 335-354

Keyword(s):

United States ◽

Air Pollution ◽

Cluster Analysis ◽

The United States

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Fine-grained, spatio-temporal datasets measuring 200 years of land development in the United States

10.5194/essd-2020-217 ◽

2020 ◽

Cited By ~ 2

Author(s):

Johannes H. Uhl ◽

Stefan Leyk ◽

Caitlin M. McShane ◽

Anna E. Braswell ◽

Dylan S. Connor ◽

...

Keyword(s):

United States ◽

Temporal Resolution ◽

Data Extraction ◽

Historical Analysis ◽

Land Development ◽

Remote Sensing Data ◽

The United States ◽

Building Stock ◽

Fine Grained ◽

Spatio Temporal

Abstract. The collection, processing and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth’s surface. While satellite-based earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially-explicit settlement data for the United States that extend back to the early nineteenth century, and is consistently enumerated at fine spatial and temporal granularity (i.e., 250 m spatial, and 5 a temporal resolution). We create these time series using a large, novel building stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at unprecedented spatial and temporal resolution. The datasets are available at https://dataverse.harvard.edu/dataverse/hisdacus (Uhl and Leyk, 2020a, b, c, d).

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Measurement of Genetic Diversity of Chinese Seashore Paspalum Resources through Morphological and Sequence-related Amplified Polymorphism Analysis

Journal of the American Society for Horticultural Science ◽

10.21273/jashs04700-19 ◽

2019 ◽

Vol 144 (6) ◽

pp. 379-386

Author(s):

Yan Liu ◽

Hailin Guo ◽

Yi Wang ◽

Jingang Shi ◽

Dandan Li ◽

...

Keyword(s):

United States ◽

Genetic Diversity ◽

Cluster Analysis ◽

Genetic Variation ◽

Genetic Distance ◽

Morphological Characteristics ◽

The United States ◽

Germplasm Resources ◽

Seashore Paspalum ◽

Chinese Germplasm

Seashore paspalum (Paspalum vaginatum) is a notable warm-season turfgrass. Certain germplasm resources are distributed in the southern regions of China. The objectives of this study were to investigate the genetic diversity and genetic variation of Chinese seashore paspalum resources. Morphological characteristics and sequence-related amplified polymorphism (SRAP) markers were used to assess genetic relationships and genetic variation among 36 germplasm resources from China and six cultivars from the United States. The results showed significant variation for 13 morphological characteristics among 42 tested seashore paspalum accessions, and that the phenotypic cv was, in turn, turf height > turf density > internode length > inflorescence density > leaf width > reproductive branch height > spikelet width > leaf length > spikelet number > inflorescence length > internode diameter > inflorescence width > spikelet length. According to the morphological characteristics and cluster analysis, 42 seashore paspalum accessions were divided into six morphological types. In total, 374 clear bands were amplified using 30 SRAP primer combinations; among these bands, 321 were polymorphic with 85.83% polymorphism. SRAP marker cluster analysis showed that 42 seashore paspalum accessions were grouped into seven major groups, with a genetic similarity coefficient ranging from 0.4385 to 0.9893 and genetic distance values ranging from 0.0108 to 0.8244. The high level of genetic diversity occurred among Chinese germplasm, and the genetic distance was relatively high between Chinese germplasm and cultivars introduced from the United States. The patterns in morphological trait variations and genetic diversity will be useful for the further exploitation and use of Chinese seashore paspalum resources.

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Profiles in workplace giving: a cluster analysis of ‘types’ of givers within a public university

Voluntary Sector Review ◽

10.1332/204080521x16359450188693 ◽

2022 ◽

Author(s):

Jorge Ruiz-Menjivar ◽

Tracy Johns ◽

Tara Counts ◽

Yong Liu ◽

Jennifer Amanda Jones

Keyword(s):

United States ◽

Cluster Analysis ◽

The United States ◽

Ordinary Least Squares ◽

Public University ◽

Strategic Marketing ◽

Public Employees ◽

Least Squares Regression ◽

Job Classification ◽

Employment Duration

This study examines public employees’ donations to a workplace giving campaign at a large public university in the south-east of the United States. First, we employed logistic regression to predict the likelihood of donating through workplace giving programmes using a sample of employees at a large public university (N = 11,726). Second, we estimated an ordinary least squares regression to identify the significant predictors of donation value with a subsample of employee donors (n=1,832). Third, we developed donor profiles (for example, clusters) of employee benefactors using K-medoids clustering. Factors such as sex, age, education and salary were significant predictors of both being a donor and the donation amount. Additionally, employment duration was significantly related to being a donor and the donation amount, while job classification only predicted being a donor. Employee donors fell into five distinct clusters. These findings contribute to our knowledge of workplace giving campaigns and can be used to develop strategic marketing campaigns.

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Spatio-Temporality and Tribal Water Quality Governance in the United States

Water ◽

10.3390/w11010099 ◽

2019 ◽

Vol 11 (1) ◽

pp. 99 ◽

Cited By ~ 5

Author(s):

Teresa Cavazos Cohn ◽

Kate Berry ◽

Kyle Powys Whyte ◽

Emma Norman

Keyword(s):

United States ◽

Water Quality ◽

Water Governance ◽

Dynamic Network ◽

The United States ◽

Space Time ◽

Cultural Bias ◽

Ontological Pluralism ◽

Political Relationships ◽

Spatio Temporal

Hydrosocial spatio-temporalities—aspects of water belonging to space, time, or space-time—are central to water governance, providing a framework upon which overall hydrosocial relations are constructed, and are fundamental to the establishment of values and central to socio-cultural-political relationships. Moreover, spatio-temporal conceptions may differ among diverse governing entities and across scales, creating “variability” through ontological pluralism, as well as power asymmetries embedded in cultural bias. This paper explores spatio-temporal conceptions related to water quality governance, an aspect of water governance often biased toward technical and scientific space-time conceptions. We offer examples of different aspects of spatio-temporality in water quality issues among Tribes in the United States, highlighting several themes, including spatiotemporal cycles, technological mediation, and interrelationship and fluidity. Finally, we suggest that because water is part of a dynamic network of space-times, water quality may be best governed through more holistic practices that recognize tribal sovereignty and hydrosocial variability.

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