scholarly journals Tornado Occurrences in the United States: A Spatio-Temporal Point Process Approach

Econometrics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 25
Author(s):  
Fernanda Valente ◽  
Márcio Laurini
Keyword(s):  
United States ◽  
Point Process ◽  
Temporal Distribution ◽  
The United States ◽  
Cox Process ◽  
Spatial Effects ◽  
Dynamic Representation ◽  
Log Gaussian Cox Process ◽  
Spatio Temporal ◽  
Unites States

In this paper, we analyze the tornado occurrences in the Unites States. To perform inference procedures for the spatio-temporal point process we adopt a dynamic representation of Log-Gaussian Cox Process. This representation is based on the decomposition of intensity function in components of trend, cycles, and spatial effects. In this model, spatial effects are also represented by a dynamic functional structure, which allows analyzing the possible changes in the spatio-temporal distribution of the occurrence of tornadoes due to possible changes in climate patterns. The model was estimated using Bayesian inference through the Integrated Nested Laplace Approximations. We use data from the Storm Prediction Center’s Severe Weather Database between 1954 and 2018, and the results provided evidence, from new perspectives, that trends in annual tornado occurrences in the United States have remained relatively constant, supporting previously reported findings.

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

2020 ◽  
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).

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

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 99 ◽  
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.

scholarly journals Temporal and Spatial Characteristics of Snowstorms in the Contiguous United States

2006 ◽  
Vol 45 (8) ◽  
pp. 1141-1155 ◽  
Author(s):  
Stanley A. Changnon ◽  
David Changnon ◽  
Thomas R. Karl
Keyword(s):  
United States ◽  
Great Lakes ◽  
Temporal Distribution ◽  
The United States ◽  
High Elevation ◽  
High Plains ◽  
Deep South ◽  
Storm Activity ◽  
Lower Elevation ◽  
Temporal And Spatial Characteristics

Abstract A climatological analysis of snowstorms across the contiguous United States, based on data from 1222 weather stations with data during 1901–2001, defined the spatial and temporal features. The average annual incidence of events creating 15.2 cm or more in 1 or 2 days, which are termed as snowstorms, exhibits great spatial variability. The pattern is latitudinal across most of the eastern half of the United States, averaging 0.1 storm (1 storm per 10 years) in the Deep South, increasing to 2 storms along the Canadian border. This pattern is interrupted by higher averages downwind of the Great Lakes and in the Appalachian Mountains. In the western third of the United States where snow falls, lower-elevation sites average 0.1–2 storms per year, but averages are much higher in the Cascade Range and Rocky Mountains, where 5–30 storms occur per year. Most areas of the United States have had years without snowstorms, but the annual minima are 1 or more storms in high-elevation areas of the West and Northeast. The pattern of annual maxima of storms is similar to the average pattern. The temporal distribution of snowstorms exhibited wide fluctuations during 1901–2000, with downward 100-yr trends in the lower Midwest, South, and West Coast. Upward trends occurred in the upper Midwest, East, and Northeast, and the national trend for 1901–2000 was upward, corresponding to trends in strong cyclonic activity. The peak periods of storm activity in the United States occurred during 1911–20 and 1971–80, and the lowest frequency was in 1931–40. Snowstorms first occur in September in the Rockies, in October in the high plains, in November across most of the United States, and in December in the Deep South. The month with the season’s last storms is December in the South and then shifts northward, with April the last month of snowstorms across most of the United States. Storms occur as late as May and June in the Rockies and Cascades. Snowstorms are most frequent in December downwind of the Great Lakes, with the peak of activity in January for most other areas of the United States.

scholarly journals Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave in Europe and the United States

2021 ◽  
Author(s):  
Jessica T Davis ◽  
Matteo Chinazzi ◽  
Nicola Perra ◽  
Kunpeng Mu ◽  
Ana Pastore y Piontti ◽  
...  
Keyword(s):  
United States ◽  
Early Stage ◽  
The United States ◽  
International Travel ◽  
Surveillance Systems ◽  
Model Driven ◽  
Global Dynamic ◽  
The Us ◽  
Community Transmission ◽  
Spatio Temporal

Given the narrowness of the initial testing criteria, the SARS-CoV-2 virus spread through cryptic transmission in January and February, setting the stage for the epidemic wave experienced in March and April, 2020. We use a global metapopulation epidemic model to provide a mechanistic understanding of the global dynamic underlying the establishment of the COVID-19 pandemic in Europe and the United States (US). The model is calibrated on international case introductions at the early stage of the pandemic. We find that widespread community transmission of SARS-CoV-2 was likely in several areas of Europe and the US by January 2020, and estimate that by early March, only 1-3 in 100 SARS-CoV-2 infections were detected by surveillance systems. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 with possible importation and transmission events as early as December, 2019. We characterize the resulting heterogeneous spatio-temporal spread of SARS-CoV-2 and the burden of the first COVID-19 wave (February-July 2020). We estimate infection attack rates ranging from 0.78%-15.2% in the US and 0.19%-13.2% in Europe. The spatial modeling of SARS-CoV-2 introductions and spreading provides insights into the design of innovative, model-driven surveillance systems and preparedness plans that have a broader initial capacity and indication for testing.

scholarly journals On stay at home orders: Using the power of data science for spatial and temporal modeling and visualization of COVID-19

2021 ◽  
Author(s):  
Christopher R Prentice ◽  
Rachel Carroll
Keyword(s):  
United States ◽  
Data Science ◽  
The United States ◽  
Data Generation ◽  
Community Members ◽  
Temporal Modeling ◽  
Related Research ◽  
Mapping Model ◽  
Spatio Temporal ◽  
At Home

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.

scholarly journals Bibliometric assessment and implications for practice of sporotrichosis research (1945-2018)

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 654
Author(s):  
Priscila Costa Albuquerque ◽  
Bruna de Paula Fonseca e Fonseca ◽  
Fabio Zicker ◽  
Rosely Maria Zancopé-Oliveira ◽  
Rodrigo Almeida-Paes
Keyword(s):  
United States ◽  
United States Of America ◽  
Research Collaboration ◽  
Temporal Distribution ◽  
The United States ◽  
Etiologic Agent ◽  
Temporal Shift ◽  
Funding Sources ◽  
Research Themes ◽  
Research Findings

Background: Sporotrichosis has recently emerged as an important mycosis worldwide, with diverse transmission and epidemiologic profiles. For instance, in Brazil most cases are related to zoonotic transmission from naturally infected cats, and the majority of cases in China are due to external injury with environmental materials. Publications on sporotrichosis and on its etiologic agent may guide the direction of the research in this field. It can also define priorities for future studies. Methods: In this study, we evaluated the trends of global research in Sporothrix and sporotrichosis, based on publications records retrieved from Scopus and Web of Science databases for the period of 1945 to 2018. The overall productivity in the field, its geographical and temporal distribution, research themes, co-authorship networks, funding sources, and the implications of research findings for health practice were assessed using bibliometric approaches. Results: A total of 4,007 unique publications involving 99 countries were retrieved, most of them published after 2000. Authors based on institutions from the United States of America and Brazil accounted for 57.4% of the publications. Brazil was the leading country in terms of research collaboration and networking, with co-authorship with 45 countries. The thematic mapping revealed a temporal shift from clinical to applied research. Despite the large number of countries publishing in this field, most of funded studies came from Brazil, Mexico, China, South Africa, or the United States of America. The analysis of content identified few specific public health recommendations for prevention, case-management, or research. Moreover, most papers do not have a clearly defined intended audience. Conclusion: As the research in this field is emerging in several countries, with the generation of a large amount of data, it is necessary that scientists strengthen efforts to translate the research results into practice to curb this neglected infection.

scholarly journals CAMPAIGN FINANCE IN COMPARATIVE PERSPECTIVE: A NESTED ANALYSIS APPROACH

2018 ◽  
Vol 2 (2) ◽  
pp. 225-250
Author(s):  
Ranulfo Paranhos ◽  
Dalson Filho ◽  
Enivaldo Rocha ◽  
José Alexandre Júnior
Keyword(s):  
United States ◽  
Campaign Finance ◽  
The United States ◽  
Campaign Contributions ◽  
Comparative Perspective ◽  
Low Levels ◽  
Standardized Measure ◽  
Nested Analysis ◽  
Nested Approach ◽  
Unites States

This paper analyzes campaign finance in a comparative perspective, giving special attention to Brazil and the Unites States. The focus regards the level of regulation on the sources of campaign contributions. Methodologically, the research design adopts a nested approach, combining descriptive and multivariate statistics with deep case studies and documental analysis. Additionally, we replicate data from the Institute for Democracy and Electoral Assistance (IDEA) to estimate a standardized measure of regulation. The results suggest that most countries show low levels of control over the sources of campaign contributions. However, both Brazil and the United States display high levels of regulation on campaign finance, despite their widely different institutional designs. 

scholarly journals An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study (Preprint)

2018 ◽  
Author(s):  
Prathyush Sambaturu ◽  
Parantapa Bhattacharya ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
Madhav Marathe ◽  
...  
Keyword(s):  
United States ◽  
Compression Ratio ◽  
Seasonal Influenza ◽  
Minimum Description Length ◽  
Temporal Patterns ◽  
The United States ◽  
Disease Spread ◽  
Influenza Like Illness ◽  
Incidence Data ◽  
Spatio Temporal

BACKGROUND Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. OBJECTIVE Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. METHODS We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). RESULTS We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. CONCLUSIONS Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

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